Applications of Electrochemical Impedance Spectroscopy to in-situ Dynamic
Characterization of Energy Conversion and Storage Systems
by
Ying Zhu
A dissertation submitted to the Graduate Faculty of
Auburn University
in partial fulfillment of the
requirements for the Degree of
Doctor of Philosophy
Auburn, Alabama
December 14th, 2013
Keywords: impedance spectroscopy, equivalent circuit simulation,
proton exchange membrane fuel cell, solid oxide fuel cell, Ni-MH rechargeable battery
Copyright 2013 by Ying Zhu
Approved by
Bruce J. Tatarchuk, Chair, Charles E. Gavin III Professor of Chemical Engineering
Mario R. Eden, Joe T. & Billie Carole McMillan Professor of Chemical Engineering
R. Mark Nelms, Professor of Electrical and Computer Engineering
Jin Wang, B. Redd Associate Professor of Chemical Engineering
Abstract
Electrochemical impedance spectroscopy (EIS) is considered as a powerful and valid
technique for non-destructive in-situ dynamic measurement of electrochemical power
systems. Together with equivalent circuit (EC) simulation, EIS is competent to perform
impedance simulation, system characterization, mechanism validation, performance
evaluation, and system diagnostics. The impedance measurement and simulation of both
energy conversion systems and energy storage systems are presented in this dissertation.
The performance of a commercial high temperature proton exchange membrane
(PEM) fuel cell tack module is studied by measuring its impedance at various current
loads and different operating temperatures above 100?C. The high temperature operation
is achieved by its novel phosphoric acid (PA) doped polybenzimidazole (PBI)
membranes. The performance of a traditional PEM stack module operated at a
temperature below 65?C is also studied by impedance measurement. A generalized EC
model is proposed and validated to simulate both commercial PEM fuel cell stacks. The
performance comparison between two stacks and the performance degradation of HT-
PEM fuel cell stack are analyzed qualitatively and quantitatively based on EC simulation
and impedance interpretation. The impedance study on PEM stacks at commercial level
reveals a more realistic status of current fuel cell development.
The single cells of tubular solid oxide fuel cell (T-SOFC) fueled directly with
ii
reformate mixture are studied by impedance measurement and EC simulation. Due to the
complexity and uncertainty of SOFC mechanisms and the difficulty of SOFC operation at
varying conditions, the EC simulation lacks validation for impedance interpretation.
However, the impedance spectra measured under limited operation conditions still
provide the preliminary validation for the physical interpretation of the proposed EC
model. The measurements and simulation performed on the T-SOFC single cell tubes
provide experimental data for studies on reformate fueled SOFC systems.
Impedance measurement and EC simulation are also applied to two commercial Ni-
MH D-size rechargeable cells at different state-of-health (SoH). Their impedance spectra
are measured and simulated at varying state-of-charge (SoC) levels. A validated EC
model can be utilized to find out the correlation between battery impedance and SoC for
power prediction and battery diagnostics. The prediction of battery SoC is useful to
further develop a powerful and efficient smart-charging system for portable electronics
and even electric vehicle development.
iii
Acknowledgments
The author, Ying Zhu, would like to express the gratitude to her advisor, Dr. Bruce J.
Tatarchuk, for his motivated guidance, insightful advices, and generous supports. The
great appreciations also go to Ying's committee, Dr Mario Richard Eden, Dr. Robert
Mark Nelms, Dr. Jin Wang, and the university reader Dr Curtis Shannon for their
discerning suggestions and comments.
The author would like to thank Dr. Wenhua Zhu for his general expertise; Dr. Robert
U. Payne, Zenda Davis, Dr. Donald Cahela, and Dr. Hongyun Yang for their suggestions
help on experiments and data analysis; Mr. Dwight Cahela, Mrs. Kimberly Dennis, and
all CM3 group members for their contributions to the research life of the author.
The author would like to give her special appreciation to her father Mr. Jialin Zhu,
her mother Mrs. Fengsen Huang, and her husband Dr. Yixian Yang for their enduring
dedications and supports.
This work was performed under a U.S. Army contract (W56HZV-05-C0686) at
Auburn University administered through TARDEC.
iv
Table of Contents
Abstract ............................................................................................................................ ii
Acknowledgement .......................................................................................................... iv
List of Tables ....................................................................................................................x
List of Figures ................................................................................................................. xi
Chapter 1 Backgrounds and Introduction .........................................................................1
1.1 Motivation........................................................................... .....................................1
1.2 Energy conversion systems ......................................................................................4
1.2.1 Fuel Cells ...........................................................................................................4
1.2.2 Classifications ....................................................................................................6
1.2.3 Proton exchange membrane (PEM) fuel cells ...................................................8
1.2.3.1 Basic mechanisms ........................................................................................8
1.2.3.2 Current challenges to applications ...............................................................9
1.2.4 High Temperature (HT) PEM Fuel Cells ........................................................ 11
1.2.4.1 PA-PBI membranes ...................................................................................11
1.2.4.2 Proton conduction mechanisms .................................................................12
1.2.5 Solid oxide fuel cells (SOFCs) ........................................................................14
1.2.5.1 Current development ..................................................................................14
1.2.5.2 Basic mechanisms ......................................................................................15
1.3 Energy storage systems ..........................................................................................17
v
vi
1.3.1 Rechargeable batteries .....................................................................................17
1.3.2 Nickel-Metal Hydride rechargeable batteries ..................................................18
1.3.2.1 Development and advantages ....................................................................18
1.3.2.2 Basic mechanisms ......................................................................................20
1.4 Conclusion .............................................................................................................22
References.......... ..........................................................................................................23
Chapter 2 Electrochemical Impedance Spectroscopy .....................................................27
2.1 Electrochemical system evaluation and diagnostics ..............................................27
2.1.1 Purposes and procedures ..................................................................................27
2.1.2 Non-destructive in-situ diagnostic tools ..........................................................29
2.2 Electrochemical impedance spectroscopy (EIS) ....................................................31
2.2.1 Measuring techniques ......................................................................................32
2.2.2 Data presentation .............................................................................................37
2.2.3 Equivalent circuit (EC) simulation and data interpretation ............................ 39
2.2.3.1 Ideal EC elements ......................................................................................41
2.2.3.2 Non-ideal EC elements ..............................................................................41
2.2.3.3 Typical EC circuits ....................................................................................48
References.......... ..........................................................................................................53
Chapter 3 EIS Application to Proton Exchange Membrane Fuel Cells I .......................56
3.1 Introduction ............................................................................................................56
3.1.1 Impedance measurement of HT-PEM fuel cells ..............................................57
3.1.2 Current discussions on impedance analysis .....................................................58
3.1.2.1 Ohmic resistance ...........................................................................................59
3.1.2.2 High frequency impedance arc ..................................................................59
3.1.2.3 Middle frequency impedance arc ...............................................................61
3.1.2.4 Low frequency impedance arc ...................................................................61
3.2 Experimental details...............................................................................................62
3.2.1 HT-PEM fuel cell stack ...................................................................................62
3.2.2 Traditional PEM fuel cell stack .......................................................................64
3.3 Results and discussion ...........................................................................................65
3.3.1 Impedance spectra of HT-PEM fuel cell stack ................................................65
3.3.2 EC simulation of HT-PEM fuel cell stack .......................................................65
3.3.2.1 EC model for simulation ............................................................................65
3.3.2.2 EC element interpretation ..........................................................................71
3.3.3 EC simulation of traditional PEM....................................................................80
3.3.4 Comparison between HT-PEM and traditional PEM ......................................86
3.4 Conclusion.... .........................................................................................................88
References.......... ..........................................................................................................90
Chapter 4 EIS Application to Proton Exchange Membrane Fuel Cells II ......................93
4.1 Introduction ............................................................................................................93
4.2 Experimental details...............................................................................................94
4.2.1 EIS measurement .............................................................................................94
4.2.2 Polarization curves ...........................................................................................94
4.3 Results and discussion ...........................................................................................95
4.3.1 EC simulation of the first set of data ...............................................................95
4.3.1.1 Impedance dependence on current density ................................................95
vii
4.3.1.2 Impedance dependence on temperature .....................................................98
4.3.2 Stack degradation ...........................................................................................102
4.3.2.1 Comparison between two sets data of impedance spectra .......................102
4.3.2.2 Comparison between polarization curves ................................................105
4.4 Conclusion ...........................................................................................................109
Reference ...................................................................................................................111
Chapter 5 EIS Application to Tubular Solid Oxide Fuel Cells .....................................112
5.1 Introduction.. ........................................................................................................112
5.2 Cell descriptions and experimental details ..........................................................112
5.2.1 Tubular solid oxide fuel cells .........................................................................112
5.2.2 Experiments ...................................................................................................113
5.3 Results and discussion .........................................................................................114
5.3.1 Comparison of impedance spectra between cells ..........................................114
5.3.2 EC simulation.................................................................................................116
5.3.3 Impedance interpretation ...............................................................................120
5.3.3.1 Dependence of temperature .....................................................................120
5.3.3.2 Dependence of current density .................................................................123
5.3.3.3 Dependence of fuel utilization .................................................................126
5.4 Conclusion... ........................................................................................................126
Reference.............. .....................................................................................................129
Chapter 6 EIS Application to Ni-MH Rechargeable Batteries .....................................130
6.1 Introduction ..........................................................................................................130
6.2 Experimental details.............................................................................................130
viii
ix
6.3 Results and discussion .........................................................................................133
6.3.1 Electrode compositions ..................................................................................133
6.3.2 Impedance spectra and EC simulation ...........................................................133
6.3.3 EC element interpretation ..............................................................................142
6.3.3.1 Mechanism on negative electrodes ..........................................................143
6.3.3.2 Mechanism on positive electrodes ...........................................................144
6.3.3.3 Full battery impedance .............................................................................145
6.3.4 Correlation between impedance and SoR/SoC ..............................................146
6.4 Conclusion.... .......................................................................................................153
References.......... ........................................................................................................155
Chapter 7 Conclusion and Future Work .......................................................................157
7.1 General conclusion...............................................................................................157
7.2 Challenges to the future .......................................................................................158
7.2.1 Energy conversion systems ............................................................................158
7.2.2 Energy storage systems ..................................................................................159
Publications............... ....................................................................................................162
List of Tables
Table 2.1 The mathematical expressions and physical meanings of ideal
EC elements ...............................................................................................42
Table 4.1 The fitting data of ohmic and polarization resistance calculated
from the EC simulation ........................................................................... 104
x
List of Figures
Figure 1.1 U.S. primary energy consumption estimated by major source,
1949 - 2011 ..................................................................................................2
Figure 1.2 Illustration of Ni-MH rechargeable battery mechanism and its
over-charge protection mechanism ............................................................21
Figure 2.1 A typical connection diagram for ac impedance measurement
conducted on PEM fuel cell stacks ............................................................35
Figure 2.2 Examples of (a) Nyquist plot and (b) Bode Plot ........................................38
Figure 2.3 Illustration of impedance spectra of ideal EC elements in Nyquist
plot .............................................................................................................43
Figure 2.4 Illustration of impedance spectra of constant phase element in
Nyquist plot................................................................................................45
Figure 2.5 Illustration of impedance spectra of finite diffusion element in
Nyquist plot................................................................................................47
Figure 2.6 Illustration of impedance spectrum of (CR) and (QR) sub-circuit
in Nyquist plot ...........................................................................................49
Figure 2.7 Nyquist plot of Randle's circuit .................................................................51
Figure 2.8 Nyquist plot of a typical EC model for batteries and fuel cells .................52
Figure 3.1 Illustration of the electrical configuration for obtaining
impedance data from the HT-PEM stack ...................................................63
Figure 3.2 Impedance spectra of the HT-PEM fuel cell stack measured at a
current density of (?) 100 mA cm
-2
, (?) 200 mA cm
-2
, (?) 267
mA cm
-2
, (?) 300 mA cm
-2
, and (?) 333 mA cm
-2
. ...................................66
Figure 3.3 The non-ideal EC model proposed to simulate the HT-PEM fuel
cell stack ....................................................................................................68
Figure 3.4 The impedance spectrum of the HT-PEM fuel cell stack
measured under a current load of 9 A (200 mA cm
-2
). The
xi
operating temperature is set at 160?C ........................................................69
Figure 3.5 The impedance spectra of the HT-PEM fuel cell stack measured
under various current loads. The operating temperature is set at
160?C .........................................................................................................70
Figure 3.6 The dependence of stack ohmic resistance (R
?
) and anode
activation resistance of HOR process (R
a
), and cathode activation
resistance of ORR process (R
c
) on current density ....................................73
Figure 3.7 The dependence of stack cathode activation resistance of ORR
process (R
c
) and the equivalent diffusion resistance (R
O
) on
current density............................................................................................74
Figure 3.8 The values of the time constant parameter of FDE (B, in the unit
of sec
1/2
) simulated from the impedance spectra of HT-PEM fuel
cell stack at an operating temperature of 160?C and changing
current density............................................................................................78
Figure 3.9 The ideal EC model with three time constants ..........................................81
Figure 3.10 The impedance spectrum of the traditional PEM fuel cell stack
(?) measured under a dc current load of 24.4 A (200 mA cm
-2
).
The solid fitting curve is simulated from the non-ideal EC model
(Figure 3.3). Pure H
2
and ambient air are supplied to the stack ................82
Figure 3.11 Stack impedance of the traditional PEM fuel cell stack measured
at a current density of (?) 100 mA cm
-2
, (?) 200 mA cm
-2
, and
(?) 267 mA cm
-2
, along with their fitting curves simulated from
the non-ideal EC model (Figure 3.3) .........................................................84
Figure 3.12 The values of the equivalent diffusion resistance R
O
simulated
from the impedance spectra of traditional PEM fuel cell stack
under changing current density ..................................................................85
Figure 3.13 Impedance comparison between the traditional PEM fuel cell
stack module and the HT-PEM fuel cell stack based on a
normalized impedance in the unit of ??cm
2
per cell. Ohmic
resistances are not included in the comparison ..........................................87
Figure 4.1 The impedance spectra of the HT-PEM fuel cell stack module
collected at an operating temperature set at 120?C under varying
current load density and their fitting curves simulated from the
stack EC model ..........................................................................................96
Figure 4.2 The impedance spectra of the HT-PEM fuel cell stack module
collected at an operating temperature set at 140?C under varying
xii
xiii
current load density and their fitting curves simulated from the
stack EC model ..........................................................................................97
Figure 4.3 The impedance spectra of the HT-PEM fuel cell stack module
measured under a current load of 9 A (200 mA cm
-2
) at varying
operating temperatures ...............................................................................99
Figure 4.4 The dependence of stack module ohmic resistance and anode
HOR charge transfer resistance on temperature ......................................100
Figure 4.5 The dependence of stack cathode ORR resistance on temperature .........101
Figure 4.6 The (a) first and (b) second set of impedance data collected at
160?C and varying current density ..........................................................103
Figure 4.7 Two sets of impedance data collected from the high temperature
PEM fuel cell stack under the same setting of operating
conditions .................................................................................................106
Figure 4.8 The voltage of each single cell in the stack when conducting the
first set and the second set of impedance measurement ..........................107
Figure 4.9 Comparison between the polarization curves collected from the
HT-PEM stack module at its BOL and during the impedance
measurement ............................................................................................108
Figure 5.1 Impedance spectra of Cell 1 (?), Cell 3 (?), and Cell 5 (?)
measured under a current density of 120 mA cm
-2
at 800?C with
a fuel utilization of 50% ...........................................................................115
Figure 5.2 Impedance spectra of Cell 3 (?) measured under a current
density of 120 mA cm
-2
at 800?C with a fuel utilization of 50%. ............117
Figure 5.3 Non-ideal EC model for T-SOFC single cell simulation .........................118
Figure 5.4 Impedance spectra of Cell 3 measured when the operating
temperature is set at (?) 750?C and (?) 800?C, along with their fit
curves simulated from the non-ideal EC model (Figure 5.3). The
current density is 120 mA cm
-2
with a fuel utilization of 50% ................121
Figure 5.5 Values of resistance elements in the proposed non-ideal EC
model (Figure 5.3) calculated from the simulation of Cell 3,
operating at different temperature of 750?C and 800?C under a
current density of 120 mA cm
-2
with a fuel utilization of 50%.
The impedance spectra for simulation are shown in Figure 5.4 ..............122
xiv
Figure 5.6 Impedance spectra of Cell 3 measured under a current density of
(?) 120 mA cm
-2
and (?) 150 mA cm
-2
, along with their fit
curves simulated from the non-ideal EC model (Figure 5.3). The
operating temperature is set at 750?C, with a fuel utilization of
50% ..........................................................................................................124
Figure 5.7 Values of resistance elements in the proposed non-ideal EC
model (Figure 5.3) calculated from the simulation of Cell 3,
operating at different current density of 120 mA cm
-2
and 150
mA cm
-2
at a temperature of 750?C with a fuel utilization of
50%. The impedance spectra for simulation are shown in Figure
5.6 ............................................................................................................125
Figure 5.8 Impedance spectra of Cell 3 measured when operating with a
fuel utilization (FU) of (?) 29%, (?) 50%, and (?) 75% along
with their fit curves simulated from the non-ideal EC model
(Figure 5.3). The operating temperature is set at 750?C, with a
current density load of 120 mA cm
-2
.......................................................127
Figure 6.1 Impedance spectra of NiMH Cell A and Cell B measured after
charging to the SoR level at 40%, 60%, and 100% .................................134
Figure 6.2 EC diagram employed to simulate the impedance spectra
measured from NiMH rechargeable cells ................................................136
Figure 6.3 The typical EC model for NiMH cells based on Randles circuit.............137
Figure 6.4 Impedance spectra measured from Cell A at 30% SoR level and
simulated by the EC model proposed in Figure 5.3 .................................139
Figure 6.5 Impedance spectra measured from Ni-MH Cell B at different
SoR level and their fitting curves simulated from the EC diagram
shown in Figure 5.3 .................................................................................140
Figure 6.6 Impedance spectra measured from Ni-MH Cell B at different
SoR level and their fitting curves simulated from the EC diagram
shown in Figure 5.3 .................................................................................141
Figure 6.7 Correlation between SoC and SoR ..........................................................147
Figure 6.8 Ohmic resistance of Cell A and Cell B at different SoR levels ...............149
Figure 6.9 Charge transfer resistance contributed by HOR process on
negative electrodes of Cell A and Cell B at different SoR level .............150
xv
Figure 6.10 Charge transfer resistance contributed by NiOOH reduction
process on positive electrodes of Cell A and Cell B at different
SoR level ..................................................................................................151
Chapter 1
Backgrounds and Introduction
1.1. Motivation
After being recognized as one of the most important energy sources in the late
eighteenth century, fossil fuels arose to predominate the commercial implementation of
energy conversion and power generation along with the development of combustion
engines. However, there are two severe challenges to combustion engines and fossil fuel
systems. One is the environmental crisis brought by the consumption of fossil fuels.
According to the statistic data of U.S. Energy Information Administration (EIA), over
80% of U.S. primary energy consumption comes from fossil fuels in 2011 (Figure 1.1)
[1]. It includes 35% of consumption from petroleum, 25% from natural gas, and 20%
from coal. Although the emergence and development of renewable and nuclear energy
save the consumption of fossil fuels in some applications, the dramatically growing
energy demands could not be satisfied without the increasing consumption of fossil fuels.
The indisputable dependence upon the non-renewable combustion sources inevitably
brings excessive emission of greenhouse gases, generation of other water and air
pollutions (sulfuric, carbonic, and nitric acid, heavy metals, and organic compounds), and
depletion of natural resources. U.S. Environmental Protection Agency (EPA) reported
that 89% of greenhouse gas emissions in 2010 [2] were produced from combustion
sources. The application of alternative energy sources can provide a feasible solution to
1
Figu
re 1.1.
U.
S.
prim
ary
en
erg
y
consum
pti
on
esti
mate
d
by
major
sour
ce,
1949
- 2011.
The
fossil
fue
ls
include
s the
sourc
es
of
pe
trole
um (35%
), n
atura
l g
as
(25%
), a
nd c
oa
l (20
%). (
Da
ta sour
ce: US
EIA
Annua
l Ene
rgy
R
evie
w 2011
[1]
)
2
the issues of combustion sources. This idea inspires the development of Fischer-Tropsch
synthetic fuels [3-5], biofuels [6, 7], and hydrogen fuel [8, 9].
The other severe issue of combustion system is its low energy conversion efficiency.
Based on the current technology, the traditional automobile engines generally perform at
an energy efficiency between 17% to 23% [10]. Even with further improvement, the
expected thermal efficiency of a well designed combustion engine is still restricted by the
intrinsic characteristics of the Carnot cycle according to the Second Law of
Thermodynamics. A large portion of input chemical energy has to be consumed to
overcome the energy losses due to irreversible processes and temperature gradient. The
low efficiency not only decreases the economical efficiency, but also increases the
pollution due to incomplete combustion of fuels. The more fuels consumed, the more
economic losses will cost and the more pollution will produced. Other than developing
alternative fuels for combustion systems, it improves the energy sustainability and power
economics in essence to replace the combustion systems with other high energy efficient
systems.
Electrochemical cell is a promising alternative to traditional combustion engine for
commercial applications. Its maximum conversion efficiency is no longer vulnerable to
the criteria of the Carnot cycle. The electrochemical work is a type of non-expansion
work. A reversible electrochemical process at constant temperature and pressure
theoretically converts all of the Gibbs energy change to electrochemical work. A
theoretical comparison between a hydrogen-oxygen (H2-O2) electrochemical cell and a
reversible heat engine shown that the reversible work of the electrochemical cell was
3
much larger than the heat engine at the temperatures below 950 K and even larger than
twice below 500 K [11]. The development and improvement of electrochemical cells,
including fuel cells and rechargeable batteries, provide the practical and effective solution
for energy sustainability and efficiency.
1.2. Energy conversion systems
1.2.1. Fuel cells
Fuel cells are capable of continuously converting chemical energy to electrical
energy at a high energy efficiency level without combustion. Similar to combustion
engines, fuel cells produce energy in real time without energy storage. A continuous
supplement of fuel is required for non-interrupted cell operation. A proper system design
is important to establish a reliable and durable fuel cell system that can be used as an
enduring, high efficient, and environmentally benign power source for many applications,
such as global transportations, portable devices, and residential backups. Along with fuel
processors, power electronics, and thermal management, fuel cells can consist a
integrated system called the combined heat and power (CHP) [12]. It is able to produce
both electricity and heat simultaneously from the same power source.
The concept of fuel cell was firstly demonstrated by Humphry Davy in 1801. It was
then further developed into principle by Christian Friedrich Sch?nbein in 1838. Shortly
after that, the first functional fuel cell was successfully constructed by William Grove in
1839 [13], called "gaseous voltaic battery" [14]. With Grove's demonstration model, it
was successfully proved that the reaction of hydrogen (H2) and oxygen (O2) could
produce electricity. Based on others' experience and attempts at improving the ?gas
4
battery?, Ludwig Mond and Carl Langer developed the first practical system to produce
electricity. Their ?new gas battery? [15] published in 1889 was considered as the
prototype of current fuel cells. However, it was not until 1960s when the commercial
application of fuel cell was initially accomplished in NASA's Gemini program [11]. Later
in 1967, the hydrogen powered fuel cell vehicle produced by General Motors [11]
inspired the research and development of fuel cell application to commercial
automotives. The increasing demand for novel energy systems to replace the combustion
engines accelerates the improvement of fuel cells. It is possible to attain an energy
efficiency up to 45% in a commercial proton exchange membrane (PEM) fuel cell system
[11], and as high as 60% by a natural gas powered solid oxide fuel cell (SOFC) device
(named BlueGen, announced by Ceramic Fuel Cells Limited in 2009 [16]).
The attraction of fuel cell lies in its simple chemistry but complicated mechanisms.
For general hydrogen fueled cells, the chemical reactions of half cells are hydrogen
oxidation reaction (HOR) at anodes and oxygen reduction reaction (ORR) at cathodes.
The detailed cell mechanisms of fuel cells change one from another with different
electrode catalyst, electrolyte materials, electrode / electrolyte interface structure, fuel gas
compositions, and fuel cell operating conditions. For example, the charge carrier through
electrolytes of H2-O2 fuel cells could be protons (H+), hydroxyl ions (OH-), carbonate
ions (CO32-), and oxygen ions (O2-) depending on different electrolyte compositions,
which further changes the half cell reactions and electrode mechanisms.
5
1.2.2. Classifications
According to current manufacture technologies, fuel cells can be classified into five
main types based on the characteristics of their electrolytes [17]. They are alkaline fuel
cells (AFC), proton exchange membrane (PEM) fuel cells, phosphoric acid fuel cells
(PAFC), molten carbonate fuel cells (MCFC), and solid oxide fuel cells (SOFC).
AFC utilizes aqueous alkaline solutions as electrolyte, generally potassium
hydroxide (KOH) solution. The hydroxyl ions (OH?) in the electrolyte solution are the
charge carrier. This type of fuel cell is able to be operated at high temperature around
250?C with high concentrated KOH solution (85wt%) or at a temperature lower than
120?C with lower concentrated KOH solution (35wt% to 50wt%) [18]. Although the
aqueous status and sensitivity of its electrolyte brings high requirements on handling,
transportation, and fuel purity, AFC is one of the most preferred choices for the Space
Shuttle Program because of its low manufacture cost, high energy efficiency, and
moderate operating conditions.
PEM fuel cell is equipped with solid polymer electrolyte that well overcomes the
drawbacks of AFC. The aqueous status and sensitivity of electrolyte are no long the
restrictions of fuel cell handling. The charge carrier of this type of fuel cell is proton.
After its birth, PEM fuel cell successfully replaced the applications of AFC in many
areas, especially as stationary, portable, and transport fuel cells. It performs a highest
power density of about 350 mA/cm2 among all five fuel cell types [19], and becomes one
of the most promising solutions to combustion engines.
6
PAFC was the first type of fuel cells to be commercialized and widely employed for
stationary applications [14]. It uses concentrated or liquid phosphoric acid (H3PO4,
abbreviated to PA) as electrolyte. Proton transport is responsible for the electrolyte
conductivity. Its optimal operating temperature ranges from 150?C to 220?C [18].
However, the development of PAFC was slowed and gradually replaced by PEM fuel cell
because of the high cost of fuel processing and stack materials [18].
A molten mixture of alkali metal carbonates is employed as the electrolyte of MCFC.
Unique to other types, carbonate ions (CO32-) provide ionic conductivity of the electrolyte
[14]. Its high operating temperature up to around 650?C solves several issues existing in
low temperature fuel cells, such as fuel purification, catalyst activity, and heat
management. However, the unique electrolyte and high temperature brings new problems
of corrosion and stability. Its low power density comparing to other types of fuel cells
also limits the practical applications of MCFC.
SOFC employ solid and nonporous metal oxide [18] as its electrolytes. The charge
carriers could be oxygen ions (O2-) and protons (H+) depending on different electrolyte
compositions. Due to restrictions on materials and fuel compositions, the development of
current SOFC technology generally focuses on O2- conducting electrolytes. The
extremely high temperature, ranging between 600?C to 1000?C, overcomes the poisoning
issues of low temperature fuel cells and improves cell kinetics and heat management. An
electrical efficiency of 60% were announced to be achieved in 2009 by a natural gas
powered SOFC device (BlueGen by Ceramic Fuel Cells Limited, [16]). On the other
hand, the high operating temperature also becomes the cause of disadvantages. SOFCs
7
operating at lower temperature ranges are under development to overcome the issues of
material fabrication, condition stability, and system compatibility.
In some other cases, fuel cells can also be classified according to fuel types [18].
Direct methanol fuel cell (DMFC) has a supplement of methanol without reforming. It
employs the same cell structure as PEM fuel cell, but performs higher energy density.
The issue hindering the commercial development of DMFC lies in its slower oxidation
kinetics and sever fuel crossover [20]. Solid carbon can be supplied directly to AFC,
MCFC, and SOFC without gasification. It is called direct carbon fuel cell (DCFC). A
great improvement of coal-based power generation with high efficient energy conversion
is achievable if DCFCs can be developed into practical systems [18].
1.2.3. Proton exchange membrane (PEM) fuel cells
1.2.3.1.Basic mechanisms
PEM fuel cell is one of the most developed fuel cell types that have large prospect in
commercial applications. The employment of polymer electrolyte membranes well
overcomes the limitations of aqueous electrolyte. The solid and thin electrolyte improves
the durability, portability, and safety of PEM fuel cells. The half-cell reactions of PEM
fuel cells are:
Anode: (HOR) (1.1)
Cathode: (ORR) (1.2)
Based on the current research results, platinum (Pt) is considered as the best catalyst for
reactions at both electrodes [21]. Although the detailed mechanisms are still under
discussion, HOR process has proven to have very fast kinetics. The activation losses of
8
PEM fuel cells are usually contributed by ORR process, either controlled by the
adsorption of oxygen onto the electrode or by the transport of oxygen ions to the
electrode/electrolyte interfaces. The polymer electrolytes of fuel cell allow the
penetration of protons only but insulate electrons, which ensures the correct direction of
electron migration to produce electricity. The migration of protons though electrolytes is
fulfilled by the proton-hopping mechanism along the chains of water molecules and
hydrophilic acid groups bonded to the hydrophobic polymer backbones [22, 23]. The
membrane conductivity significantly depends on the water uptake of the electrolyte
polymers, which is sensitive to relative humidity and temperature.
1.2.3.2.Current challenges to applications
Most of traditional PEM fuel cells for commercial application are featured by
perfluorosulfonic acid (PFSA) based membranes. The sulfonic acid groups are the
hydrophilic groups that provide proton conductivity. And the perfluorinated polymers are
the hydrophobic backbones to provide mechanical supports. Commercial products of
PFSA membranes were first produced by DuPont in1970s [19], called Nafion?. This
became a standard prototype and quickly followed by several different modified types,
such as Flemion? by Asahi Glass, Aciplex-S? by Asahi Chemical, and Dow? by Dow
Chemical. PFSA was found to be one of the most promising membranes for PEM fuel
cell commercial applications. It exhibits great proton conductivity, high hydrogen and
oxygen solubility, fast electrode reaction kinetics, excellent mechanical properties, but
low gas permeability [22]. The commercial Nafion was reported to achieve a lifetime of
over 60,000 hours under fuel cell operating conditions [20, 22].
9
It is critical to maintain the level of water uptake in PFSA membranes, because the
proton conductivity relies on the membrane water content. Membrane dehydration occurs
when the operating temperature exceeds the boiling point of water. It not only degrades
the proton conductivity but also causes severe membrane shrinkage [22]. To ensure a
functional and long-term operation, the temperature of PEM fuel cells has to be restricted
below 100?C under ambient pressure. On the other hand, higher temperature provides
better cell performance. A temperature of around 80?C has been considered as the
optimal condition for PEM fuel cell operation under ambient pressure. However, this
operating condition is close to the boiling point of water. Attentions have to be paid to
gas-liquid dual phase water management.
Carbon monoxide (CO) poisoning of the electrode catalyst become one of the most
critical concerns of PEM fuel cell applications. For carbon-supported Pt electrode
catalyst, a CO content of only 10 ppm to 20 ppm [24] in the fuel supplement causes
severe degradation in the PEM fuel cell operated at 80?C. The HOR process at anode
electrodes on Pt catalyst follows the reaction path of dissociative chemisorption of
hydrogen and electrochemical oxidation of adsorbed hydrogen atoms. The catalyst
poisoning is resulted from the adsorption of CO molecules to the Pt sites, competing with
the chemisorption of hydrogen. The consumption of Pt sites by CO molecules impedes
the following electrochemical oxidation of hydrogen. The adsorption of CO on Pt
exhibits a large negative standard entropy [24]. An elevated temperature can theoretically
enhance the selectivity of hydrogen adsorption on Pt. The experimental results show that
the CO tolerance of PEM fuel cells can significantly increases up to 30,000 ppm at 200?C
[25].
10
Other challenges to the traditional PEM fuel cells include the development of
hydrogen infrastructure for direct hydrogen supply, the potential applications of methanol
reformate and direct methanol power systems, and thermal management and heat
recovery due to low operating temperature [22].
1.2.4. High temperature (HT) PEM fuel cells
1.2.4.1.Phosphoric acid (PA) doped polybenzimidazole (PBI) membranes
Most concerns of PFSA membrane based traditional PEM fuel cells can be meliorate
by elevating the operating temperature. Thus, it is desired to develop alternative
membranes to overcome the temperature limitation of PFSA membranes. Three different
types of alternative membranes have been found to be realistic. They are classified by its
fabrication methods. The first type is fabricated by attaching charged units to a
conventional polymer [23]. Most attentions to this type of alternative membranes are paid
to sulfonated polymer membranes and their composites [22]. The second type is named
inorganic-organic composites or hybrid, which is fabricated by incorporating a polymer
matrix with inorganic compounds [23]. Modified PFSA membranes [22], especially
modified Nafion membranes [19, 26], are highly recommended due to competitive
advantages of PFSA membranes in PEM fuel cell applications over others. Modifications
of PFSA membrane are mainly focused on the proton conductivity at higher temperature,
water uptake and retain at higher temperature, low humidification operations, and
mechanical stability at higher temperature. The third type of alternative membranes is
acid-base polymer membranes. This type of membranes is complexes fabricated by
doping strong acids or polymeric acids in conventional polymers [23]. So far, phosphoric
acid (H3PO4, abbreviated to PA) doped polybenzimidazole (PBI) has been found to be
11
one of the most commercially promising materials for HT-PEM fuel cell under ambient
pressure [19].
The advantages of PBI over other polymers, including low cost [27], high glass
transition temperature [28], excellent textile fiber properties [29], and great thermal
stability [30], promise itself to be an excellent polymer for membrane fabrications. One
of the most significant advantages of a PA doped PBI membrane over a PFSA based
membrane is that its conductivity no longer relies on the water content due to its unique
proton conduction mechanism [23, 31-33], but strongly depends on the PA doping level
[23, 34-36] and the operating temperature [23, 36, 37].
1.2.4.2.Proton conduction mechanisms of PA-PBI membranes
The conductivity of PA doped PBI membrane is reported to be strongly depend on
PA doping level and temperature [22, 23, 34, 35, 37]. PA is doped onto the PBI backbone
in two different manners. One is named as bond acids. As far as the doping level is lower
than two molecules of PA per repeat unit of PBI [22, 23, 35], corresponding to two N
sites for H bonding in a PBI monomer unit, the acids are stably linked to the PBI
structure by H bonding. This can also be explained by the fact that the maximum degree
of protonation for PA is reached at two moles PA per repeat unit of PBI [34, 37]. The
conductivity at low PA doping level comes from a cooperative movement of two protons
along the polymer-PA anion chain [34], that is one proton hopping away from an acid
anion to form a N-H bond with the polymer and this anion accepting the proton hopping
from another N-H bond at the same time. This type of proton migration provides great
contribution to membrane conductivity but is not enough for fuel cell applications.
12
Experimental data [22, 23, 35] supported that the conductivity of PA doped PBI
significantly increases with an increasing doping level of PA when more than two
molecules of PA per repeat unit of PBI are doped. The bonded PA remains at a level of
two molecules per repeat unit. The rest doped acid can be easily washed away, and are
called the unbonded free acids. The unique proton conduction mechanism of the
unbonded free PA by self-ionization and self-dehydration provides the main attribution to
the conductivity [23]:
(1.3)
The proton conduction mechanism is described as a proton hopping mechanism along the
anionic chains of H2PO4?/HPO42?. This mechanism significantly increase the
conductivity of PA-PBI membranes to meet the requirement of fuel cell applications.
Arrhenius law is employed to explain the activation behavior of both proton hopping
conduction processes [37] but with different activation energy:
(1.4)
where AP are pre-exponential factors, Ea,P is the activation energy, and Rig is the ideal gas
constant. The subscriptions, P and T, refer to the temperature and pressure conditions
under the measurement.
For PA doped PBI membranes, water is no longer the essential contributor to the
conductivity. And the water drag coefficient of PBI membranes is reported close to zero
[38, 39]. However, the presence of water still significantly promotes the proton
conductivity. The acid further dissociate with the existence of water and provides more
13
charge carriers (Eq. 1.3) [23]. The addition of water also decreases the viscosity within
the membrane and enhances the mobility and conductivity.
(1.5)
The situation becomes more complicated when the content of water increases. An
increase of activation energy was reported with the increasing relative humidity (RH) at
low doping level of PA [40]. This causes a decrease of conductivity with the increasing
RH. Another explanation pointed out that the negative effect of reducing charge carriers
due to excessive water content is more significant than the enhancement by decreasing
viscosity [23], and the conductivity decreases at the presence of further addition water.
1.2.5. Solid oxide fuel cells (SOFCs)
1.2.5.1.Current development
Practical SOFC was developed in the early 1960s. However, the first solid oxygen-
ion conductor, Nernst mass, was invented by Nernst much more earlier in 1899,
composed of zirconium dioxide (ZrO2) with 15wt% yttrium (III) oxide (yttria, Y2O3)
[41]. Nernst mass was initially operated in electrolysis mode. The first prototype of
ceramic fuel cell was demonstrated in 1937, shortly after Schottky suggesting the
application of Nernst mass as fuel cell electrolyte in 1935 [41]. As the development of
ceramic materials for SOFC electrodes and electrolytes, studies of SOFC also include
[42] electrode catalysts, mechanisms and kinetics of reactions, cell optimal designs,
system stability, degradations mechanisms, cell diagnostics, and scale up. Up till now, the
most commonly used materials for SOFCs are yttria-stabilized zirconia (YSZ)
electrolyte, nickel-zirconia cermet (Ni-YSZ) anode and lanthanum manganite (LaMnO3)
14
based cathode. The prevailing ceramic materials provide good stability, electrical
conductivity, catalytic activity, and compatibility with each others.
1.2.5.2.Basic mechanisms
The mechanisms of electrode reactions of SOFCs are much more complicated than
PEM fuel cells. The rate determining steps changes with the materials of electrodes, types
of catalysts, fuel compositions, and cell operating conditions. The current understandings
of these mechanisms are limited. Generally, oxygen-ion conducting electrolytes are more
preferred than proton conducting electrolytes for applications at commercial levels due to
the feasibility of fuel compositions. CO and hydrocarbons cannot used as fuels for
SOFCs with proton conducting electrolytes.
For oxygen-ion conducting SOFCs, the reaction occurring on the cathode electrodes
are the oxygen reduction reaction (ORR), coupling with adsorption of oxygen and
diffusion of O2 and oxygen species. After the produced oxygen-ions transferring through
the ceramic electrolyte and reaching the anode electrodes, it oxidizes the fuel
composition on the anode.
Cathode: (ORR) (1.6)
Anode: (HOR) (1.7)
The fuels supplied to anode can be pure H2, H2 mixed with CO and / or H2O, and
even hydrocarbons. CO is no longer a poisoning composition under the operating
temperature of SOFCs. The shift reaction is the favorable path for CO oxidation reaction
when H2O exists [41]:
Anode: (the shift reaction) (1.8)
15
When directly fueled with hydrocarbons, the reforming process occurs internally that
simplifies the fuel pretreatment. The reforming reaction produces a mixture of H2-H2O-
CO-CO2, which is further oxidized to H2O and CO2 to produce electricity. One of the
important factors for the reforming process is the steam ratio in the fuel supplement.
Insufficient steam may lead to the formation of carbon [41]:
(1.9)
(1.10)
The formation of carbon may bring anode degradation due to its impedance to fuel
supplement and its deposition on the active sites of electrode surface.
The requirements on the fuel composition for SOFCs are no longer as strict as for
PEM fuel cells and other fuel cell systems operated at lower temperatures. The attention
has to be paid to sulfur contaminant. A mixture of few parts per million (ppm) sulfur
compounds can cause severe irreversible performance degradation in SOFCs. Up to date,
the general accepted explanations for sulfur poisoning includes the formation of nickel
sulfide (NiS) at anode and the adsorption of hydrogen sulfide (H2S) on anode surface The
adsorbed H2S not only competes for active sites on anode surface with hydrogen
adsorption process, but also impedes the shift reaction (Eq. 1.8). The study on sulfur
poisoning mechanisms and the improvement of desulfurization technology provide
significant supports to the development of SOFC systems.
16
1.3. Energy storage systems
1.3.1. Rechargeable batteries
Unlike traditional combustion engines and fuel cells, the operation of rechargeable
batteries does not require an in-time fuel supplement. It stores energy in chemicals and
converts it into electrical energy at needs. However, it takes time to recharge between two
discharge processes, during which it demands an input of electrical energy.
The phrase "electric battery" was innovatively re-defined in late 1740's by Benjamin
Franklin [43] when he described a series of his experiments with electricity. However,
Franklin's "electric battery" referred to the pile of glass plate capacitors set up in 1748.
The production and storage of energy were not achieved until Alessandro Volta designed
and built his ?crown of cups? and the columnar pile in late 1790s [43]. The Volta pile
published in 1800 is generally acknowledged as the first battery because it fulfilled one of
the most important functions of batteries: energy storage in chemicals and energy
conversion to electrical energy by chemical reactions.
The phenomena of a current in opposite direction was observed by Nicholas
Gautherot in 1801 from a voltaic battery consisting of two copper plates and sulfuric
acid. Shortly after that, the first reversible system was successfully developed by Johann
Ritter in 1802 [10]. However, the real rechargeable batteries (also known as secondary
batteries) did not come out until Raymond Gaston Plant? invented the first lead (Pb)-acid
battery in 1859 [10]. The original concerns to the emergence and development of
rechargeable batteries focused on the environmental problems caused by the toxic
substances used in batteries. The pollutant substance, generally mercury, used in primary
17
batteries was greatly reduced by decreasing the use of mercury batteries. And the reuse
and recycle of rechargeable batteries also moderated the pollutions introduced by itself.
The greater contribution of rechargeable batteries to human beings was explored and
well developed with the advancing requirements of grid energy storage. Since
rechargeable batteries are feathered by reversible electrochemical reactions, they are able
to adapt the energy supplement to the energy demand, that is to store energy in chemicals
during valley period and convert energy into electricity during peak period. Having
developed for over 150 years, rechargeable batteries have been matured in the
commercial applications to automotive starters, electronic products, and all other portable
electrical devices daily used. Even EVs requiring light power consumption, such as
bicycles and wheelchairs, have been well commercialized. The development and
commercialization of pure electric automobiles powered by a build-in rechargeable
battery makes it possible to save human lives out from energy crisis and environmental
pollutions caused by fossil fuels and combustion engines. The development of electric
rail network began from the 19th century and prevailed in UK in most of the 20th century
[10]. In the past few decades, electric bicycles also predominated in city transportations
in China. One of the most challenge issues is the range limitation that an EV can travel
with unit battery charge time.
1.3.2. Nickel-Metal Hydride rechargeable batteries
1.3.2.1.Development and advantages
Beginning from 1980s, nickel metal hydride (Ni-MH) batteries were rapidly
developed and commercialized based on the mature manufacture of nickel-cadmium (Ni-
18
Cd) batteries. It gradually replaced the predominance of lead-acid batteries in the market
for rechargeable storage systems in early 1990s. Although Li-ion battery quickly became
the dominant in the market of portable electronics, the important role of Ni-MH system in
the commercial market is still irreplaceable.
Ni-MH rechargeable storage systems are able to overcome several critical concerns
of other prevailing types of storage batteries. The overall performance of nickel-iron (Ni-
Fe) batteries is limited by the self-discharge of iron-electrodes caused by corrosion
reactions [44]. Pb-acid batteries and Ni-Cd batteries present great energy performance,
but their widespread applications are disputed due to environmental contaminations. The
sodium-nickel chloride systems have complicated thermal management due to the high
operating temperature [45]. Nickel-hydrogen (Ni-H2) batteries suffer from safety
problems, high self-discharge rate, and low volumetric energy density despite its high
gravimetric energy density. Li-ion batteries have better economical efficiency for unit
energy storage on the cell level than Ni-MH batteries; however, Li-ion batteries are still
more expensive at the pack level considering the safety and life-time of the systems [46].
Ni-MH rechargeable battery has identical electrolyte and cathode materials to Ni-Cd
systems [17]. The electrolyte is usually a concentrated potassium hydroxide (KOH)
solution, and the active material for cathode (nickel positive plate) is nickel oxyhydroxide
(NiOOH) [47]. But unlike the traditional Ni-Cd systems, the hydrogen (H2) reacted at the
anode (negative plate) of commercial Ni-MH batteries is absorbed in a metal alloy [47].
Following Ovshinsky?s pioneering metal alloy structure for battery electrodes [48], the
19
disordered AB5 type mischmetal (Mm) alloy has been developed to the most commercial
level performing a better cycle ability than other types [17].
1.3.2.2.Basic mechanisms
The basic principles and electrochemical reactions occurring during discharge
process in the Ni-MH battery are described as [45] (Figure 1.2):
At the positive electrode,
(1.11)
At the negative electrode,
(1.12)
The overall cell reaction is written as
(1.13)
The nickel electrode is thermodynamically unstable in the sealed cell. Oxygen evolution
reaction (OER) occurs at the electrode as a parallel and competing reaction when the
battery is in an overcharge process (Figure 1.2). The parasitic reaction is expressed as:
(1.14)
This reaction happens during the charge and overcharge processes. Reaction (1.14) starts
as a parallel side-reaction, competing with the primary charging Reaction (1.11) at a
certain state-of-recharge (SoR, i.e. the actual charge input as percent of the battery-rated
capacity). At a higher charging rate, the difference between SoR and SoC may even start
earlier due to higher potential and mass transfer limitation of the electrolyte. Hence, the
HEV storage application preferably uses the 70%~80% SoC level as the higher hybrid
operation limit. However, the nickel-based battery is normally designed that the cell
capacity is limited by the positive electrode. The negative to positive capacity ratio varies
20
Figu
re 1.2.
Ill
ust
rati
on of
Ni
-M
H r
echa
rge
abl
e ba
tte
ry me
cha
nism
s and
its
ove
r-c
ha
rge
prote
cti
on mec
ha
nism
.
21
from 1.5 to 2.0. The evolved oxygen from the positive electrode diffuses to the MH
electrode and recombines to form water. Typically, the discharge reserve is
approximately 20% of the positive capacity [45]. The range from 0 to 20% SoC level is
called as deep discharge region. In order to ensure proper power output capability, the
HEV energy storage considers the 20~30% SoC level as the lower hybrid operating limit
[49].
1.4. Conclusion
Although the applications of energy conversion and storage systems at commercial
level are still under development, the high energy efficiency and the low emissions of
electrochemical cells continuously attract attentions and efforts for researches on fuel
cells and rechargeable batteries. Their application to portable devices, vehicles, and large
scale stationary power systems is feasible and has been realized to some extent. However,
the unclear mechanisms and restrictions of operating conditions keep challenging the
further improvement and commercialization. In the following chapters, the focuses are
placed on in-situ dynamic characterizations of the HT-PEM fuel cell stack (Chapter 3),
tubular SOFC cells (Chapter 5), and Ni-MH rechargeable batteries (Chapter 6). The
analysis of stack degradation is also detailed for the study of HT-PEM fuel cell stack
(Chapter 4). Electrochemical impedance spectroscopy (EIS) and equivalent circuit (EC)
simulation are employed in this work for the in-situ dynamic measurement and
simulation. The fundamental of EIS is presented in the next chapter (Chapter 2).
22
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26
Chapter 2
Electrochemical Impedance Spectroscopy
2.1. Electrochemical system evaluation and diagnostics
2.1.1. Purposes and procedures
It is significantly important to obtain deep understanding of energy conversion and
storage systems to approach both technical and commercial breakthrough in sustainable
energy development. The purpose of system characterization is to find out how and to
what degree the properties, kinetics, and other effects of a system influence its
performance. The understanding of performance also provides information to assess and
optimize the systems. For fuel cells, attentions are mainly paid to electrode structures,
electrolyte fabrications, conductivity mechanisms, reaction limitations, catalytic
poisoning, and cell degradations. And for rechargeable batteries, the important system
parameters include actual capacity, rate performance, charge/discharge curves, depth-of-
discharge (DoD), state-of-charge (SoC), and state-of-health (SoH).
The evaluation and diagnostics of power systems refers to the process that uses
logical judgment, analytical methods, and empirical knowledge to determine the nature
and causes of the conditions, situations, and problems of power systems. An integrated
procedure uses various diagnostic tools to measure and monitor the parameters and
behaviors of power systems under loads and off load in a certain sequence. It can be
applied to system characterization, design validation, component and technique
27
evaluation, and operation optimization [1]. The techniques of evaluation and diagnostics
are especially important for commercial products of power systems. An in-situ real-time
technique facilitates regular system maintenance and ensures a long-term stable system
performance.
Several organizations are focusing on setting up testing procedures or protocols for
fuel cell . They are standard methodology focusing on system operating parameters.
Martin [1] combined and summarized the procedures proposed by US Fuel Cell Council
(USFCC) and Joint Research Council (JRC) into an integrated testing prototype for PEM
fuel cell diagnostics. He listed each step of pretest evaluation, testing, and posttest
evaluation procedures in sequence, and described the suitable techniques and their
measuring parameters for each step in details. The testing procedures of JHQTF (by
USFCC) and the Fuel Cell Testing & Standardization NETwork (FCTestNet) / Fuel Cell
Systems Testing, Safety, and Quality Assurance (FCTesQA) (by JRC) were also carried
out for SOFC tests, both on single cells and cell stacks. The requirements on the supplied
and generated data put forward in Joint Hydrogen Quality Task Force (JHQTF) by
USFCC are [1]:
? applicable: industrial relevant
? repeatable: duplicable under the same conditions with the same type of equipment
? reproducible: duplicable under the same conditions with a different type of equipment
? scalable and serviceable: sufficient information for scale-up
These criteria are not only important for fuel cell diagnostics but also applicable to the
procedures of battery tests. Only meeting the requirements, the testing procedure has
practical value for studies of general power systems.
28
2.1.2. Non-destructive in-situ diagnostic tools
Generally, there are three stages in a whole set of testing procedures. They are
pretest evaluation, testing, and posttest evaluation. Non-destructive diagnostic tools are
required for the stages of pretest evaluation and testing. And for the testing procedure, an
in-situ diagnostic tools is preferred for real-time measurement. For electrochemical
power systems, there are three types of diagnostic tools classified as non-destructive in-
situ techniques. They are polarization curve (i-V curve), current interruption (CI), and
electrochemical impedance spectroscopy (EIS).
Polarization curve, also called "i-V" curve, explicitly illustrate the relationships of
current and cell potential. In some cases, the dependence of the cell output power on
current is also plotted. It can be conducted either by a steady-state or a transient state
technique [1]. In steady-state mode, the polarization curve is simply recorded by
measuring the cell potential at each current step. And a slow current sweep rate can be
utilized to obtain a transient polarization curve.
CI is capable to obtain the ohmic resistance of an electrochemical system directly
from measurement. Since the response of physical processes to the current change is
much faster than electrochemical processes, ohmic response can be distinguished from
electrochemical responses by their different voltage transient behaviors when the current
applied to the electrochemical system is interrupted. CI technique has been applied to the
measurement, evaluation, and diagnostics of alkaline dry cells [2], rotating disk
electrodes (RDE) [2], cathodes of molten carbonate fuel cells (MCFC) [3], single direct
methanol fuel cells (DMFC) [4], a small scale DMFC stack [5], and PEM fuel cells [6-8].
29
Other than polarization curve and CI techniques, EIS does not obtain data directly
from measurement. It generates the system impedance over a spectrum of frequencies
from the measuring data of amplitudes and phase shifts. However, the great prospect of
EIS lies in characterizing chemical reactors in terms of electronics. Chemical,
electrochemical, and physical processes occurring in electrochemical systems are
simulated in chemical process simulators (ASPEN for example) to study the mass
balance, thermodynamics, and kinetics of the systems. The moles are able to be converted
to electrons; however, this is where chemical engineering typically ends. On the other
side, electrical circuits are simulated in analog circuit simulators (PSpice for example) to
predict circuit behaviors and provide industrial standard solutions, such as the non-linear
transient analysis for voltage and current versus time. It is EIS that generates equivalent
circuits (EC) and establishes the connection between the world of chemical engineering
(chemical processes) and electrical engineering (power electronics). Depending on the
power applications, the unique load requirements are described in circuit simulators. The
minimum weigh and volume of the power supplies can be designed to meet the load
requirements. In this way, the in-situ real-time measuring technique as well as EC
simulation systematically provides a competent methodology for integrated system
design, operation, and control [9].
Since EIS has the ability to establish the connection between the load outputs and the
power supply, it can be applied to smart batteries for power prediction. A proper charge-
discharge process is significantly important for maintaining rechargeable battery at a
long-term well-performing status. The phrase of "smart battery" was proposed in 1990s
[10] and gradually developed to a technically defined concept. It enables communications
30
between a rechargeable battery and its host system. The requirement of electric vehicle
(EV) commercialization further developed the concept of "smart" into EV charging
infrastructure. A real smart charging system should be able to determine SoC of
rechargeable batteries and protect them from over-charging process. However, SoC
cannot be measured directly. One of the generalized method is to monitor the voltage of
the battery and estimate its SoC level according to its discharge curve. Accuracy is the
upmost concern of this method, because the voltage difference of a unit battery is in small
magnitude and sensitive to current, temperature, and battery chemistry. Compensation
variables are usually required for accuracy corrections. Impedance is sensitive to current,
temperature, and battery chemistry. It can be accurately measured by EIS as well as its
change with SoC. Thus, EIS is expected to be applied to SoC determination and power
detection for battery control and testing.
2.2. Electrochemical impedance spectroscopy (EIS)
The history of impedance spectroscopy (IS) can be dated back to 1880s, when
Heaviside initially introduced the concept of ?impedance? [11, 12] to his research on
electromagnetic induction. It was almost a century lag behind the first measurement of
direct current (dc) resistance (the so called "pure resistance") conducted by Georg Simon
Ohm in early 1800?s and published later in 1825 [13]. Kennelly [14] quickly followed up
Heaviside's idea and extended the concept of ?impedance? to generalized conductors in
1883. He also mathematically defined the total impedance, in a complex plane, as the
vector sum of its resistance, its inductance-speed1, and the reciprocal of its capacity-
1 Inductance-speed: The product of angular frequency and inductance, ?L.
31
speed2 [14]. However, the technique of IS itself did not come out until Nernst [15]
employed Wheatstone bridge to measure dielectric constants in 1894.
During the past century, impedance measurement contributed to the characterization
of materials and devices, study of electrochemical reaction systems, corrosion of
materials, and investigations of power sources. According to different materials and
systems it applied to, IS can be classified into two branches [16]. The one following
Nernst?s initial achievement is called non-electrochemical IS. It applies to dielectric
materials, electronically conducting materials, and other complicated materials with
combining features [16]. The other branch, newly coming out based on the development
of non-electrochemical IS, is named electrochemical impedance spectroscopy (EIS). It
focuses on IS applications to ionically conducting materials and electrochemical power
sources [16]. The popularity of IS keeps improving especially after the prevailing of
electronics and computers. Not only is it capable of dealing with complex processes,
reactions, and variables through simple electrical elements, it is also a valid technique for
power source diagnosis and system quality controls.
2.2.1. Measuring techniques
EIS measurement is conducted by superimposing an electrical stimulus on the output
of the tested electrochemical system and measuring the resulting signal. The impedance
of the measuring system is then calculated from the stimulus and its resulting signal by
transform functions and Ohm?s Law. In this way, the performance of the system under
2 Capacity-speed: The product of angular frequency and capacitance, ?C.
32
measurement can be studied as a black box, which is described as ?feeling an elephant
that we cannot see? in Mark Orazem and Tribollet?s book [15]. Thus, for an
electrochemical system, it is possible to study the properties of its interfaces and
materials without taking the system apart. The word ?in-situ? generally refers to this type
of techniques that characterizes electrochemical systems under operation with the help of
voltage, current, and time, distinguishing from ?ex-situ? techniques which studies
individual components departed from electrochemical systems in a non-assembled and
non-functional form [17].
Different kinds of electrical stimulus can be used in EIS measurement, including a
step function of voltage, a random noise, a single frequency signal, and any other types of
stimuli generated by combining the foregoing three ones [18]. With the increasing
commercial availability of measuring instruments, the characterization of electrochemical
systems generally employs an ac signal (either a small ac voltage or ac current). In most
cases, it is also called ?ac impedance?.
Several impedance measuring instruments are commercially available, including
products from EG&G Inc., Gamry Instruments, Scribner Associate Inc., and AMETEK
(Solartron Analytical and Princeton Applied Research). An instrument set basically
consists of a potentiostat, or known as an electrochemical interface, connecting to a
frequency response analyzer (FRA). Besides, a complete circuit connection for
measurement requires an electronic load. Specifications of different instruments are
designed for different scales of measurements. The feasibility and accuracy of
measurements are determined by the frequency resolution, frequency accuracy, and
bandwidth of instruments and electronic loads. For example, batteries and fuel cells are
33
low impedance systems, usually much lower than 1 ? (sometimes even down to
milliohms) [18]. Their impedance measurement requires a high current and a low
frequency bandwidth. A typical connection diagram for ac impedance measurement of a
traditional PEM fuel cell stack is presented in Figure 2.1 [19]. The commercial
instrument connected in this measuring system is Gamry FC350TM Fuel Cell Monitor. It
is featured by its capability to measure impedance at high current levels. The four-
terminal connection is commonly used for low impedance measurement (such as batteries
and fuel cells) to avoid measurement errors led by the impedance of cables and
connections [18].
It is worth to noting that the choice of ac signal value have great effect on impedance
measurement. The reliability of EIS analysis is based on the assumption that the
measured system is linear. It is also important to keep the system in a relatively steady
state throughout the measurement to ensure the accuracy of the measured data. The
measured impedance data may appear scattered and unregulated if the signal is too weak
to excite a measurable perturbation. On the other hand, the signal has to be small enough
to keep the measuring system within the range of pseudo-linearity. A larger signal also
brings more extra heat that breaks the steady state of the system. The data measured at
low frequencies behave more sensitive to the strength of the applied ac signal due to
larger impedance at lower frequency [20]. Correspondingly, an appropriate value of the
signal should be well picked to ensure the accuracy of the impedance data measured. It is
not necessary to determine an exact value for the input signal, however, it has to be
controlled in a certain range which is neither so small that the output signals are too weak
34
Figu
re 2.1.
A
typica
l c
onne
cti
on
dia
gra
m
for
ac
im
pe
da
nc
e mea
sure
ment
conduc
ted
on
PEM
fue
l c
ell
stac
ks:
sing
le
stac
k,
two
stac
ks i
n pa
rall
el a
rra
nge
ment, a
nd two st
acks i
n ser
ies a
rra
nge
ment
[19]
.
35
to be measured, nor so large that a great distortion is introduced into measurements.
Different signal values can be trailed before measurements to find out a suitable one.
There are two traditional modes for impedance measurements according to different
regulating variables: potentiostatic mode and galvanostatic mode. Potentiostatic mode
employs a small ac voltage signal with fixed amplitude and measures the resulting ac
current. In this mode, the dc voltage is controlled at a certain value that facilitate the
control of system linearity. Reversely, a small ac current signal with fixed amplitude is
superimposed on the regulated dc current in galvanostatic mode. The resulting ac voltage
is measured. Galvanostatic mode provides higher accuracy than potentiostatic mode
when measuring low impedance systems because the voltage can be measured more
accurately than controlled. This mode is more welcomed where a steady dc current is
required during the entire measurement. However, it is difficult to control the resulting ac
voltage strictly within the linear range especially at low frequencies.
A novel measuring mode, hybrid mode, is put forward to overcome the drawback of
galvanostatic mode. It was firstly known as variable-amplitude galvanostatic mode when
studying H-cells with copper/sea water system [20]. The validation and accuracy of
impedance measurement rely on the pseudo-linear behavior of electrochemical systems.
The current signal is regulated before measurement of each data point in hybrid mode to
ensure that its resulting voltage stays within the linear region. A desired voltage
perturbation and an estimated impedance magnitude are set before the measurement. The
initial current perturbation is calculated by [9]:
(2.1)
36
?I1 is the amplitude of the current perturbation superimposed on the dc load for
measurement at the first frequency point. ?V is the amplitude of the desired voltage
perturbation. |Zest| is the magnitude of the system impedance estimated before the
measurement. The measured impedance value at this frequency point is then recorded as
Z1. The current perturbation employed for each following frequency point is calculated
by [9, 20]:
(2.2)
?In is the amplitude of the current perturbation used at the nth measuring point, and |Zn-1|
is the magnitude of the system impedance measured at the (n-1)th point. Similar to
galvanostatic mode, the power systems are studied under constant current density.
However, the current signal applied to the systems at each measuring point is different.
By the step of signal regulation, it is able to yield the ac voltage at the desired magnitude.
The behaviors of tested power systems are ensured in the pseudo-linear region.
2.2.2. Data presentation
The two most preferred methods to present impedance data are the Nyquist plot and
the Bode plot. Nyquist plot, named after Harry Nyquist, is a polar plot of the transfer
function [21]. In some publications, it is also called the complex-plane plot or the Argand
plot [18]. An example of Nyquist plot is shown in Figure 2.2a (plotting method adapted
from [21]). This impedance spectrum is a set of continuous complex impedance points
produced by the circuit diagram [11] over a certain range of frequency (from zero to
positive infinite in Figure 2.2a). The x-axis presents the real component of the impedance
points and the y-axis presents their negative imaginary component. In this way, the
capacitive impedance arcs are flipped to the first quadrant in the plot. As the frequency
37
Figure 2.2. Examples of (a) Nyquist plot and (b) Bode plot generated by the EC
diagram [11].
38
goes from zero to positive infinite, the impedance points move toward the origin of the
plane. The impedance value and phase angle of the points can be calculated from the
Nyquist plot:
(2.3)
(2.4)
Nyquist plot can provide plenty information of impedance variables but failed to
present their frequency dependence. Bode plot, named after Hendrik W. Bode,
overcomes this inadequacy. It displays the frequency dependence of a linear, time-
invariant system, usually shown in logarithmic axis. Figure 2.2b is an example of Bode
plot generated by the diagram [11]. It includes a magnitude plot presenting the magnitude
of impedance (|Z|) versus the logarithmic angular frequency (log ?), and a phase plot
presenting the phase shifts of ac current to ac voltage (?) against the logarithmic angular
frequency (log ?).
2.2.3. Equivalent circuit (EC) simulation and data interpretation
There are mainly two methods to derive models for impedance simulation, visually
summarized and classified in a flow chart by Macdonald [16]. One mathematically
establishes models based on the theory, which puts forward a hypothesis for physical and
chemical processes contributed to the impedance. The other one utilizes empirical
models, called equivalent circuits (EC). Some researchers also presented a combining
method with both of them [22]. The values of certain variables, such as ohmic resistance,
were acquired directly from the empirical EC simulation and used as known variables to
establish the mathematical model. Whichever employed, the validations of data
39
themselves are essential before simulation. The relations originally published by Kramers
(1929) and Kronig (1926) (K-K Transforms) became a simple but effective method for
data validation from 1980s, in order to ensure the causality, linearity, and stability of the
measured systems [23, 24]. Both the mathematical models and the empirical ECs also
have to be validated before data interpretation and system characterization. The fitting
programs, generally following the procedures of complex nonlinear least squares (CNLS)
fit algorithm (such as LEVM [25] and EQUIVCRT [26]), are employed to validate the
proposed EC models by estimating the parameter standard deviation and the goodness of
fit [25].
Comparing to mathematical models, deriving an EC model is easier, faster, and more
intuitive. An EC diagram is a physical electrical circuit which produces a similar load
response to the measured system, derived based on both experiences and theories. The
overpotential losses of the testing cell (electrochemical systems) are introduced by the
impedances contributed by different physical and chemical processes occurring in the cell
(electrochemical system). The impedances of different processes predominate different
frequency regions. Thus, they can be identified and mechanistically discriminated by EC
simulation according to different process relaxation times. However, the physical
interpretation of circuit elements is not straightforward due to the uncertainty of EC
diagrams. Different arrangements or combinations of EC elements can produce the same
dynamic response when three or more elements are employed in one EC diagram. The
only solution to overcome this difficulty is acquiring sets of impedance data with
different variables and changing conditions.
40
2.2.3.1. Ideal EC elements
Resistor R, capacitor C, and inductor L are three fundamental elements reflecting
ideal processes. Their mathematical expressions and physical meanings [27] are
summarized in Table 2.1. Z is the impedance of the elements. Y is the reciprocal of Z,
called admittance. The impedance behaviors of an ideal resistor, capacitor, and inductor
are sketched out individually in Figure 2.3 (plotting method adapted from [27]).
The impedance of an resistor is calculated at R = 10 ?. The real part of its
impedance equals to its resistance. The impedance of an ideal resistor does not have
imaginary part. As frequency changes, the impedance of an ideal resistor keeps constant.
The impedance of an capacitor is calculated at C = 0.01 F (at frequency from 0.04
Hz to 100 kHz). It only has imaginary part. And this imaginary part is in negative value.
The magnitude of an ideal capacitor's impedance decreases with increasing frequency.
The impedance of an ideal inductor is calculated at L = 0.01 H (at frequency from
0.1 mHz to 7 kHz). It also has imaginary part only, but in positive value. The magnitude
of an ideal inductor's impedance equals to the value of its imaginary part. It increases
with increasing frequency.
2.2.3.2. Non-ideal EC elements
The three ideal EC elements are not able to reflect non-ideal factors in practical
cases. A generalized element, constant phase element (CPE) Q, was developed to
simulate non-ideal processes. Q reflects the exponential distribution of time constants.
This non-ideal distribution may be caused by the surface roughness and vary thickness of
41
Table 2.1. The mathematical expressions and physical meanings [27] of ideal EC elements: R, C, and L.
Element Symbol Element Name Impedance Expression Physical Interpretation
R Resistor Contributed by energy losses, dissipation of energy, and potential barrier
C Capacitor Contributed by accumulations of electrostatic energy or charge carriers
L Inductor
Contributed by accumulations of magnetic
energy, self-inductance of current flow, or
charge carrier?s movement
42
Figu
re 2.3.
I
llustra
tion of
im
pe
da
nc
e spec
tra
of
id
eal EC
elem
ents
in N
yquist
plot
.
43
electrodes, unevenly distributed current, and non-homogeneous reaction rate. In the
impedance expression of Q [18]:
(2.5)
the exponent ? reflects the degree of non-ideality. When the value of ? in the expression
of Q equals to 1, 0, and -1, it can be found that Eq. 2.5 becomes the same as the
expression of capacitor C, resistor R, and inductor L, respectively. Figure 2.4 (plotting
method adapted from [27]) shows two sets of impedance data produced by the expression
of Q with A = 100 F-1. They behaves as straight lines across the origin in Nyquist plot.
When the value of ? decreases from 0.8 to 0.5, the slope decreases. The angle of the
incline equals to (??90)?.
Emil Warburg developed the expression of the impedance response for diffusion
processes in 1899 [15]. This is now called Warburg diffusion element (W). It is used to
simulate an one-dimensional unrestricted diffusion process to a large planar electrode.
The impedance expression of ZW is [28]:
(2.6)
Thus, the magnitude of W (|ZW|) can be calculated by:
(2.7)
In Eq 2.6 and 2.7, ? is the angular frequency, the same as in Eq 2.5. And ? is called the
Warburg coefficient, given by [28]:
(2.8)
where, Rig is ideal gas constant; F is the Faraday constant; n is the stoichiometric number
of electrons involved in the standard chemical reaction system (Eq. 2.9); and DO and DR
44
Figu
re 2.4.
I
llustra
tion of
im
pe
da
nc
e spec
tra
of
const
ant phas
e e
leme
nt (
Q)
and W
arbu
rg
eleme
nt (
W)
in
N
yquist
plot
.
45
are diffusion coefficient of the oxidized and reduced form of the reaction species; and
CO* and CR* are bulk concentration of species, respectively.
(2.9)
For EC simulation, a more simplified and straight way is to employ the magnitude of
admittance at ? = 1 rad s-1 as model parameter [29]:
(2.10)
The impedance spectrum of Warburg element behaves as a straight line with unit slope in
Nyquist plot, exactly the same as a CPE (Q) with the exponent ? = 0.5 (Figure 2.4).
Finite diffusion element (FDE) O, or sometimes called Porous Bounded Warburg,
was established based on Warburg element (W). Its application is extended to a rotating
disk electrode (RDE), where diffusion occurs over the Nernst Diffusion Layer (NDL),
that is a diffusion layer with finite thickness. The expressions for ZO [30] is:
(2.11)
where, Y0 is the same parameter as the one employed in the model of Warburg element
with the same physical meaning and the same expression equation (Eq. 2.10); and
(2.12)
which is called time constant. In Eq. 2.12, ? is the thickness of the diffusion layer and D
is the diffusion coefficient. Figure 2.5 [27] shows a typical Nyquist plot of an O element.
The intercept of the spectrum to the real axis is the impedance of FDE [30] calculated at
? = 0:
(2.13)
46
Figu
re 2.5.
I
llustra
tion of
im
pe
da
nc
e spec
trum of f
ini
te dif
fusi
on e
leme
nt (
O) in N
yquis
t pl
ot
[27
].
R
O
47
The impedance spectrum of FDE (O) presents the same behavior as Warburg element
(W) at higher frequency region. As frequency decreases, it changes to an arc similar to
(CR), detailed in the following section.
2.2.3.3. Typical EC circuits
The (CR) circuit is one of the basic combinations commonly used in EC simulations.
The elements enclosed in parentheses are connected parallel to each other. And the
square brackets indicate that the elements enclosed are connected in series. For example,
(CR) means a pure capacitor C and a pure resistor R are parallel in the connection; while
[31] means a pure capacitor and a pure resistor are connected in series. Figure 2.6
presents the Nyquist plot of (CR). It is a semi-circle centering at (R/2, 0) with a radius of
R/2. As the angular frequency ? increases from 0 to ?, the summit of the semi-circle is
reached at:
(2.14)
The (CR) circuit can be employed to simulate an ideal double-layer process. The non-
ideal one requires the circuit of (QR). The element Q replaces C to reflect the non-ideality
of a double-layer process. (QR) behaves as a depressed semi-circle having its center
dropped down below the Zreal axis (Figure 2.6). The degree of the depression is presented
by the exponent ? of the Q element.
The EC diagram [11] mentioned previously in Figure 2.2 is another fundamental
combination. Based on the spectrum of (CR), [11] shifts horizontally along the positive
Zreal axis in Nyquist plot (Figure 2.2a). The smaller intercept with the Zreal axis equals to
48
Figu
re 2.6.
I
llustra
tion of
im
pe
da
nc
e spec
trum of (
CR
) sub
-cir
cuit
in N
yquist
plot
, a
long
with t
he
spe
ctrum o
f (
QR
) sub
-cir
cuit
.
49
the resistor R1. And the larger intercept equals to the sum of R1 and R2. The center of the
semi-circle is (R1+R2/2, 0) and its radius is R2/2.
Randles circuit [R?(Cdl[RctW])] considers the contribution from a diffusion process.
It consists of one ohmic resistance R?, one parallel (CdlRct) sub-circuit behaving as a
semi-circle in Nyquist plot, and one infinite diffusion element W behaving as a unit slope
line at the lowest frequency region. Figure 2.7 [32] sketches the impedance spectrum of a
Randles circuit. The dash lines illustrate the overlap region of (CdlRct) and W.
The impedance spectra of a typical EC diagram commonly used to simulate
electrochemical cells is sketched in Figure 2.8 [33]. This diagram consists of three time
constants. Its total impedance is the sum of the ohmic losses Z?, the anode polarization
losses Za, the cathode polarization losses Zc, and the mass transfer losses Zm. The ohmic
losses are simulated by one pure resistor R?. Its value is represented as the smaller
intercept on the real axis. The anode and cathode polarization losses are simulated by a
parallel (CR) sub-circuit respectively. Their impedance spectra look like two overlapping
semi-circle in Nyquist plot. Some electrochemical systems, like Ni-MH batteries, have
strong Warburg behavior in the low frequency region for the mass transfer processes. The
diagram can be expressed as [R?(CaRa)(Cc[RcW])]. However, some do not always show
this behavior obviously, such as PEM fuel cells. The low frequency arc can be simulated
by a parallel (QR) sub-circuit instead of a pure Warburg element. The diagram is
expressed as [R?(CaRa)(Cc[Rc(QmRm)])]. The Warburg element and sub-circuit (QmRm)
are connected in series with Rc because the mass transfer processes are considered as
cathodic processes.
50
Figu
re 2.7.
N
yquist
plot
of Ra
ndle
's c
irc
uit
([
32]
C
ourte
sy o
f S
olar
tron A
na
lyti
cal).
51
Figu
re 2.8.
N
yquist
plot
of a
ty
pica
l EC m
ode
l for
ba
tte
rie
s a
nd fu
el ce
lls
[33]
.
52
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[32] C. Gabrielli, Identification of Electrochemical Processes by Frequency Response
Analysis, in: Solatron Analytical AMETEK, Inc. 1998; Technical Report No. 004/83.
[33] W.H. Zhu, R.U. Payne, B.J. Tatarchuk, Journal of Power Sources, 168 (2007) 211-
217.
55
Chapter 3
EIS Application to Proton Exchange Membrane Fuel Cells
Stack Characterization and Performance Comparison between High
Temperature and Traditional Proton Exchange Membrane Fuel Cell Stacks
3.1. Introduction
This chapter is submitted to Journal of Power Sources as a journal article. In order to
keep the consistency of this dissertation and avoid content duplication to Chapter 1, the
introduction and backgrounds of the journal article are not included here. Instead, a brief
review on EIS applications to HT-PEM fuel cells and PA-PBI membranes will be
presented in this section.
The work in this chapter highlights the advantages of EIS technique and EC
simulation in their application to dynamic characterization and evaluation of proton
exchange membrane (PEM) fuel cell systems. Both stacks are manufactured based on
current PEM technologies at commercial level. The comparison of stack characterization
reveals the advantages of the HT-PEM fuel cell stack over the traditional one as well as
its immaturity in development. This work provides references for improvement of PEM
fuel cells aiming at commercial applications. Based on the current research development,
the insufficient experimental impedance data of HT-PEM fuel cell systems challenges the
validation of proposed theories and mechanisms. The results of impedance spectra and
EC simulation in this work also provide further experimental data for mechanism
validation and stack diagnostics.
56
3.1.1. Impedance measurement of HT-PEM fuel cells
Compared to the impedance study of traditional PEM fuel cell systems, there have
been only limited studies of EIS applications to high temperature PEM fuel cells.
Impedance measurement was pioneered to study the electrical conductivity of PBI-based
films at the end of 1990s. Fontanella and his co-workers [1] utilized impedance
measurement to study the conductivity of PA doped PBI films at the temperatures below
100?C at a pressure up to 0.25 GPa. Soon after that, Bouchet and Siebert [2] published
their work of measuring the conductivity of acid doped PBI films with the help of
impedance measurement. However, in these works, impedance measurement was utilized
only as an auxiliary method.
It had not been applied to a membrane electrode assembly (MEA) or a fully
constructed PEM fuel cell based on a high temperature membrane until 2005. Xu [3]
employed impedance analysis to study the effect of relative humidity (RH) on oxygen
reduction reaction (ORR) kinetics for a high temperature PEM fuel cell manufactured
from Nafion-Teflon-phosphotungstic acid (NTPA) membranes. Almost at the same time,
Ramani [4] published their impedance measurement to a PEM fuel cell based on PA
doped Nafion membrane at 120?C and 35% RH.
Several EIS studies on PA-PBI based HT-PEM fuel cells emerged from 2006 [5-9].
Jalani and his co-workers [5] published their impedance analysis of a single cell
assembly, named Celtec?-P series 1000 MEA (BASF Fuel Cell). Qi and his group [9]
applied EIS to study the performance and degradation of a PA-PBI based PEM fuel cell
at 180?C with a load of 0.2 A cm-2. But both of the group did not perform EC simulations
of the cell impedance. At the same time, a more completed EIS application was published
57
by Jingwei Hu and his co-workers [6-8, 10], which included a series work of impedance
measurement and analysis, EC simulations, and degradation tests of cell performance.
3.1.2. Current discussions on impedance analysis
The published applications of EIS on high temperature PEM fuel cells, although
limited, present different measurement results one from another. Several different EC
models have also been proposed to interpret measured impedance data. Ohmic
resistances, introduced by cell components (electrodes, membranes, gas diffusion layers,
and other supporting plates) and connections, and wiring inductions are involved in
impedance analysis of all published works. Main differences exist in the analysis and
interpretation of polarization impedance. Cells tested with different configurations and
operating conditions may perform differently from each other, however, they should
behave certain characteristics in common, especially, the cells configured the same type
of membrane. Below, the EIS applications to high temperature PEM fuel cells based on
PA doped PBI membranes will be summarized and discussed. The emphases are placed
in the EC simulation and data interpretation.
So far, up to three arcs have been reported in Nyquist plots of measured impedance
spectra. But these three arcs may not be well separated in all cases. One arc may overlap
with its neighboring ones when they shift to a similar frequency range with the change of
cell operating conditions. Or one arc may decrease to be negligible under some
conditions. The processes involved in data interpretation mainly include charge transfer
process, mass transfer process, and gas diffusion process. While, some groups observing
two impedance arcs in Nyquist plot prefer to classify the impedance arcs as anodic and
cathodic processes [11-16].
58
3.1.2.1.Ohmic resistance
Ohmic resistance (R?) was reported as a set of scattered numbers with increasing
current density in Jalani?s work [5]; however, a trend of decline was still observable from
those published data. Later, it was confirmed in many other experiments that R?
decreases with increasing current density [15, 17-20]. Following Zhang?s theory [17], it
has been accepted that the decrease of R? is resulted from an increase in the proton
conductivity of the membrane due to higher water productivity at higher current density.
However, Zhang [17] also expected a constant level of R? at a current density larger than
1.0 A cm-2 because of a constant water uptake of the membrane balanced between
produced and purged water. This constant level was observed in Andreasen?s
experiments both on a 1 kW cell stack [16] and a single cell MEA [21] even at a current
density lower than 1.0 A cm-2.
The effect of operating temperature on R? is more complicated than current density.
And lots of attentions have been paid to it since the activation behavior closely relates to
the proton conductivity mechanism of membranes. Some reported that R? decreases with
increasing temperature [15, 17, 21]. The proton hopping mechanism proposed by
Bouchet [2, 22] was applied to explain this thermal effect [17]. But, inconsistent with
these results, many other researchers reported an increase of R? with increasing
temperature when the temperature went higher than around 140?C [11, 18, 20].
3.1.2.2.High frequency impedance arc
High frequency (HF) impedance arc appears right after the wiring inductive loop as
frequency decreases in Nyquist plot. And generally it is quite a small semi-circle
comparing to the following impedance arcs. It dominates the region of frequency from
59
above 100 Hz up to 1000 Hz [5, 16, 17, 19-21, 23], or even higher. The effects of
temperature [17, 20] and current density [19, 20] on this impedance arc are observable
but not as significant as on other impedance arcs dominating lower frequency regions.
The resistance of this HF impedance arc decreases with increasing current densities, and
its time constant decreases with increasing temperature. The latter can be observed in
Nyquist plot as the impedance arc shifts toward higher frequency, or ?shrinks?.
Kinetically, this phenomenon can be explained as the process occurring faster at higher
temperature.
It has been validated and discussed in many published works that this HF impedance
arc is contributed by charge transfer processes. Its impedance is generally simulated by
the (RctCdl) sub-circuit in EC models. The Rct refers to charge transfer resistance and the
Cdl refers to double-layer capacitance introduced by the charge accumulation and
separation in the interface of electrode-electrolyte. In some cases, the double layer
capacitor may be substituted by a CPE to reflect the non-ideal characteristics of the
interface.
Many researchers preferred to ascribe this HF arc to the charge transfer process
occurring on the anode, that is the charge transfer process involved in hydrogen oxidation
reaction (HOR) [11, 15, 16, 18]. While some other groups stated that the charge transfer
processes of both HOR and ORR contributed to this HF impedance arc but the
contribution of HOR was negligible at low current density [17, 20].
60
3.1.2.3.Middle frequency impedance arc
Middle frequency (MF) impedance arc is contributed by the most dominating
process occurring in the cell. It usually appears as the largest semi-circle in Nyquist plot
and spans from 100 Hz to 1 Hz [5, 16, 17, 19-21, 23]. A consistent interpretation
proposed for this arc has been almost accepted in all published cases that an activation
process related to ORR contributes to this polarization loss.
In some cases, only one impedance arc was observed in Nyquist plot [6-8, 10, 24,
25]. This only arc should be explained as mass transfer impedance. Firstly, the LF arc
does not perform significant contribution to the total impedance all the time (discussed
later). Secondly, as mentioned in the discussion of HF arc, sometimes the HF arc may
shrink to be hardly observable. In this case, the HF arc actually is merged by the MF arc
and the total impedance appears as one arc.
3.1.2.4.Low frequency impedance arc
Low frequency (LF) impedance arc only appears in some certain cases when the
contributions of concentration processes to cell impedance are comparably significant.
Generally, it dominates frequency region below 1 Hz to around 0.1 Hz [5, 16, 17, 19-21,
23]. The impedance of this arc strongly depends on the composition of cathode gas,
which is generally air, oxygen, or a mixture of them. Besides, the LF arc enlarges with
the increase of current loads and may dominate the total impedance at high current loads
instead of MF arc. Studies on oxygen stoichiometry can provided further information for
the study of diffusion processes.
61
3.2. Experimental details
3.2.1. HT-PEM fuel cell stack
The commercial HT-PEM fuel cell stack module (Serenus 166Air C Fuel Cell
Evaluation Kit, Serenergy Inc.) under research is featured by PA-PBI membranes. The
active area of the membrane electrode assembly (MEA) is about 45 cm2. The whole stack
consists of 65 planar single cells, that is 66 electrode plates. The nominal power at its
beginning of life (BOL) is about 1 kW. The stack is capable to be operated in the
temperature range between 100?C and 175?C, with an optimum at 160?C. It can be fueled
not only with pure hydrogen (H2) but reformate gases.
Pure H2 was supplied to the anode during impedance measurement. Ambient air was
supplied through a blower to the cathode. A purge valve (B?rkert 6011, 2/2-way
Miniature Solenoid Valve) was installed at the fuel outlet to provide a dead-end anode
configuration. The pressure of the stack system was stable at 45 mbar (0.6527 psig) by a
proportional valve (B?rkert 2835, 2/2-way proportional valve) installed at the H2 inlet.
The valve was controlled by a digital controller (B?rkert 8611, eControl) and a pressure
sensor (B?rkert 8314, pressure transmitter) by feedback mechanism. An illustration of the
experimental configuration is shown in Figure 3.1. The bold black solid lines indicate the
electric circuit connections. And the thin black solid lines refers to signal channels for
data communication.
EIS measurement was conducted by Gamry FC350TM fuel cell monitor (Gamry
Instruments), in conjunction with TDI-Dynaload? RBL488 programmable load. A
LabVIEW (National Instruments) based program, called Embedded Fuel Cell Control
Unit (EFCU) (Serenergy Inc.) was installed to monitor the module status during its
62
Figu
re 3.1.
I
llustra
tion
of
the
ele
ctri
cal
confi
gur
ati
on
for
obtaining
im
pe
da
nc
e da
ta
from
the
HT
-PEM
fue
l c
ell
stac
k.
A
fue
l c
ell
moni
tor
(G
amr
y I
nstrum
ent
s)
and
a c
urre
nt
load
(TD
I-D
yn
aloa
d)
are
con
ne
cted
to
thi
s mea
suring
circ
uit
. S
oli
d a
rrow
s
refe
r to
ga
s inl
et
and
outl
et
of
the
anod
e.
Da
she
d a
rrow
refe
rs
to
wa
ter
outl
et
of
the
cathode.
Bold
line
s re
fer
to
ele
ctric
al
conne
cti
ons, a
nd thi
n li
ne
s re
fer to s
ignal c
ha
nn
els
.
63
operation. A set of impedance data of the whole stack module (including the blower,
heater, bleeder resistors, sensors, and other electronic devices embedded in the system)
was measured during cell operation. The operating temperature was set at 160?C. A dc
current of 4.5 A, 9 A, 12 A, 13.5 A, and 15 A, corresponding to the current density of 100
mA cm-2, 200 mA cm-2, 267 mA cm-2, 300 mA cm-2, and 333 mA cm-2, was loaded to the
stack from low to high. The frequency swept from 10 kHz to 0.1 Hz at a rate of 10 points
per decade under each current load setting.
3.2.2. Traditional PEM fuel cell stack
The traditional PEM fuel cell stack module, named NexaTM fuel cell system (Ballard
Power Systems Inc.), was studied by EIS measurement [26, 27]. Nexa system is a 47-cell
stack module with an unregulated dc power of 1.2 kW. Its output current can reach 44 A.
The stack voltage normally rises up to 41 V at open circuit and 26 V at full load. The fuel
cell geometric working area is estimated as ca. 122 cm2 due to the inexistence of
manufacturing data [26]. The automated operation is maintained by an embedded
controller board. H2 was supplied to the stack anode at 5.0 psig, and air was supplied
through a blower to the stack cathode at 2.2 psig.
An experimental configuration similar to the one used for HT-PEM fuel cell stack
(Figure 3.1) was employed to obtain the impedance data of the Nexa stack. Gamry
FC350TM fuel cell monitor in conjunction with TDI-Dynaload? RBL488 programmable
load was utilized to measure the stack impedance. The dc current load was set at 12.2 A,
24.4 A, and 32.5 A, from low to high, corresponding to a current density of 100 mA cm-2,
200 mA cm-2, and 267 mA cm-2 that is consistent with the load condition of the HT-PEM
system. The measuring frequency swept from 10 kHz to 0.01 Hz. The operating
64
temperature of the Nexa stack module was automatically stabilized between 40?C and
65?C.
3.3. Results and discussion
3.3.1. Impedance spectra of HT-PEM fuel cell stack
Five impedance spectra of the HT-PEM fuel cell stack module measured under
different current loads are plotted together in one Nyquist plot (Figure 3.2). The operating
temperature is set at 160?C. As the current increases from 100 mA cm-2 to 333 mA cm-2,
a decrease of total stack impedance is observed. It is mainly contributed by the decrease
of the impedance arcs dominating the frequency range lower than 100 Hz. The smaller
intercept of the spectra and the real axis (the one closer to the origin of Nyquist plot,
measured at the frequency above 1000 Hz) almost keeps constant at around 15 ? cm2
despite the change of the stack load. But the larger intercept of the spectra and the real
axis (the one further from the origin of Nyquist plot, measured at the frequency point
lower than 1 Hz) decreases significantly from approximately 70 ? cm2 to 40 ? cm2 with
increasing current density. No significant change of the impedance arc dominating high
frequency range can be observed from Figure 3.2. High frequency inductive impedance
also keeps at a steady status with the change of the stack load.
3.3.2. EC simulation of HT-PEM fuel cell stack
3.3.2.1.EC model for simulation
Due to limited researches on HT-PEM fuel cells, no general consensus has been
achieved on its EC simulation, neither the EC models nor the physical explanations of EC
elements. The impedance arcs contributed by different activation and concentration
processes overlap with each other to a different extent under different operating
65
Figu
re 3.2.
I
mped
anc
e spec
tra
of
the
HT
-PEM
fue
l c
ell
stac
k mea
sure
d a
t a
cu
rre
nt
de
nsit
y of
(?
) 100
mA
cm
-2 ,
(?)
200
mA
cm
-2 ,
(?
) 267
mA
cm
-2 ,
(?)
30
0 mA
cm
-2 ,
and
(?
) 333
mA
cm
-2 .
The
im
pe
da
nc
e da
ta
at
fre
qu
enc
y of
100
0 Hz
, 100
Hz
, 10
Hz
,
and 1
Hz
of
eac
h spe
ctra
are
mar
ke
d with s
oli
d do
ts. The
op
era
tin
g temp
era
ture
is
se
t a
t 160?C
. P
ure
H
2 is suppl
ied to the
stac
k a
node
at ar
ound 45
mbar
and a
mbi
ent t
empe
rature
. Ambient
air
is tak
en int
o the stac
k c
atho
de
by
the blow
er.
66
conditions. The generated impedance spectra significantly differ from one another. In this
work, a three time constant non-ideal EC model (Figure 3.3) is proposed to simulate the
HT-PEM fuel cell stack. It consists of one pure resistor R?, one paralleled (CaRa) sub-
circuit, one paralleled (QcRc) sub-circuit, one finite diffusion element (FDE) O, and one
wiring inductance L. In this EC model, one constant phase element (CPE) Qc is used to
replace the ideal capacitor and reflect the non-ideality of cathode processes caused by the
inhomogeneous characterization of the electrode surface. The detail explanations are
presented in the following section of EC interpretation.
The impedance spectrum measured at a dc current load of 9 A (200 mA cm-2) is
simulated by the non-ideal EC model (Figure 3.3). The measured data and its fitting
curve are shown in Figure 3.4. The smaller intercept on the real axis refers to the ohmic
resistance (R?) of the stack module. The difference between two intercepts on the real
axis refers to the polarization resistance (Rp) of the stack, which is a combined
contribution of activation and concentration processes. The small capacitive arc
dominating the high frequency region is simulated by the paralleled (CaRa) sub-circuit.
This arc has a summit frequency at about 680 Hz. The depressed large capacitive
impedance arc spanning over the rest frequency region is simulated as two overlapped
capacitive arcs. The middle frequency arc, which has a summit frequency around 20 Hz,
is simulated by the paralleled (QcRc) sub-circuit. This impedance arc is much larger than
others and dominates the stack impedance. The low frequency arc is simulated by the
diffusion element and gives very limited contribution to the stack impedance under its
operating condition. The proposed non-ideal EC model is then validated by simulating
the impedance spectra measured under other load conditions (Figure 3.5). The simulating
67
Figu
re 3.3.
The
non-
idea
l EC
model
propose
d to s
im
ulate
the H
T-
PEM f
ue
l c
ell
stac
k.
68
Figu
re 3.4.
The
im
pe
da
nc
e spec
tru
m
of
the
HT
-PEM
fue
l c
ell
stac
k me
asure
d und
er
a c
urr
ent
load
of
9 A
(200
mA
cm
-2 ).
The
im
pe
da
nc
e �G
�D
�W�D�
�P
�H
�D
�V�X�U
�H
�G�
�D
�W�
�I�U
�H
�T�X�H
�Q
�F
�\
�
�R�I
�
�����
�+�]
��
����
�+�]
��
���
�+�]
��
�D
�Q
�G�
��
�+�]
�
�D
�U
�H
�
�P�D�U
�N
�H
�G�
�Z�L�W�K
�
�V�R�O�L
�G�
�G�R�W�V�
� ?
� ��
�7�K�H
�
ope
rati
ng
tempe
ratu
re
is
set
at
160?C
. The
fitti
ng
cur
ve
is
sim
ulate
d b
y th
e non
-idea
l EC
model
(Fig
ure
3.3)
. P
ure
H
2 i
s
suppl
ied
to
the
stac
k a
node
at
around
45
mbar
and
ambi
ent
tempe
rature
. Ambient
air
wa
s take
n int
o the
stac
k c
athode
by
the blowe
r.
69
Figu
re 3.5.
The
im
pe
da
nc
e sp
ectr
a o
f the
HT
-PEM
fue
l c
ell
stac
k me
asure
d unde
r va
rious
cur
rent
loads.
Th
e o
pe
rati
ng
tempe
ratur
e
is
set
at
160?C
. The
fitt
ing
cur
ve
s a
re
sim
ulate
d by
the
non-
ide
al
EC
model
(F
igu
re
3.3)
. P
ure
H
2 i
s suppl
ied
to
the
stac
k
anode
at a
round
45 mbar
and a
mbi
ent t
emper
ature
. Ambient a
ir is t
ake
n int
o the stac
k c
athod
e b
y the
blowe
r.
70
curves show great goodness of fit with the changing current density. The fitting values of
each EC elements and their changes with current loads are analyzed below to perform EC
element interpretation.
3.3.2.2.EC element interpretation
3.3.2.2.1. Ohmic resistance
The ohmic loss refers to pure resistive loss and can be simulated by an ideal resistor
R?. It consists of resistances contributed by membranes, electrodes, catalyst layers, gas
diffusion layers, component connections, and any other hardware connected to the
measuring system, such as wires, heaters, blowers, and controller boards. However, it is
difficult to discriminate their impedance one from another. The variation of ohmic loss
with changing current load reflects the change of proton conductivity of the membranes.
a. Proton conductivity mechanism
The mechanism of proton conductivity changes with PA doping level. Generally, PA
is doped onto the PBI backbone in two different manners. As far as the doping level is
lower than two molecules of PA per repeat unit of PBI [28] the acids are stably linked to
the PBI structure by H bonding. The conductivity at low PA doping level comes from a
cooperative movement of two protons along the polymer-PA anion chain [2], that is one
proton hopping away from an acid anion to form a N-H bond with the polymer and this
anion accepting the proton hopping from another N-H bond at the same time. This type of
proton migration provides great contribution to membrane conductivity but is not enough
for fuel cell applications. Experimental data [29] supported that the conductivity of PA
doped PBI significantly increases with an increasing doping level of PA when more than
two molecules of PA per repeat unit of PBI are doped. Other than the PA molecular
71
bounded to PBI by H bonding, the rest of doped PA molecular form H2PO4? by self-
ionization and self-dehydration [30]:
(3.1)
The proton conduction is described as a proton hopping mechanism along the anionic
chains of H2PO4? / HPO42? [30]. This mechanism provides the main attributions to
proton conductivity and significantly increase the conductivity of PA-PBI membranes to
met the requirement of EC application.
In PA-PBI system, water is no longer the essential contributor to proton
conductivity. This feature enables fuel cell operations above 100?C. However, the
presence of water still have non-negligible effects on proton conductivity of PA-PBI
membranes. Additional hydron carriers can be formed by dissociating acid molecular in
water [30] and increase the proton conductivity.
(3.2)
The situation changes when the content of water continuously increases. The reducing
concentration of charge carriers due to excessive water content leads to a decrease of
conductivity.
b. Dependence on current density
Our experimental data (Figure 3.5) and simulation results (Figure 3.6 and 3.7)
present a relevant stable value of ohmic resistance (R?) when the current density is lower
than 267 mA cm-2. However, it slightly decreases when the current density increases
from 267 mA cm-2 to 333 mA cm-2. A decrease of ohmic resistance with increasing
current density was reported [15, 18, 19], but Zhang [17] also reported a stable ohmic
resistance with the change of current when the current density went over 1.0 A cm-2. The
72
Figu
re 3.6.
The
de
pe
nde
nc
e o
f stac
k oh
mi
c re
sis
tan
ce
(R
?)
and
anode
ac
tiva
tion
resis
tanc
e o
f HO
R
proc
ess
(R
a)
on
cur
rent
de
nsit
y.
The
va
lues
are
sim
ulate
d b
y the
non
-idea
l EC
mo
de
l (F
igur
e 3.3)
from
the
im
pe
da
nc
e sp
ectra
of
H
T-
PEM
fue
l c
ell
sta
ck
mea
sure
d a
t an ope
rati
ng tempe
ratur
e of 16
0?C
an
d c
ha
ng
ing
cur
rent densit
y.
73
Figu
re 3.7.
The
de
pe
nde
nc
e o
f stac
k c
athode
ac
tiva
tion
resis
tanc
e of
ORR
pr
oc
ess
(R
c)
and
the
equivale
nt
dif
fusion
resis
tanc
e R
O
calcula
ted
from
Eq.
3.5.
The
va
lues
are
sim
ulate
d fr
om
the
im
pe
da
nc
e spe
ctra
of
HT
-PEM
fue
l c
ell
stac
k a
t a
n op
era
tin
g
tempe
ratur
e of 16
0?C
an
d c
ha
ng
ing
cur
rent densit
y.
74
conductivity following the hopping mechanism changes with temperature and membrane
water content. It is theoretically independent on current load. The stable value of R? at
low current densities represents a stable conduction process throughout the current
density range below 267 mA cm-2. More water is produced under higher current loads.
When the current density increases to over 267 mA cm-2, water content of MEAs slightly
increases due to the disequilibrium between produced water and purged water. Thus, the
slight decrease of R? at higher current loads is observed because the small amount of
water increases the proton conductivity.
3.3.2.2.2. Anode electrode
The electrode process occurring on the anode of a hydrogen-oxygen (H2-O2) fuel cell
is hydrogen oxidation reaction (HOR). It has been widely accepted that the rate determine
step (RDS) of HOR is the transfer process of electrons from the absorbed hydrogen atoms
to the electrode [31]:
(3.3)
HOR kinetics is much faster than oxygen reduction reaction (ORR) kinetics and
other cell processes. When the frequency sweeps from high to low values, the impedance
arc of HOR charge transfer process appears right after the wiring inductive loop. It
usually behaves as a semi-circle much smaller than other impedance arcs. A dominating
frequency range from about 100 Hz up to 1000 Hz or even higher [5, 11, 15, 17-19, 23]
was reported for this process. Following this theory, the (CaRa) sub-circuit in the
proposed EC model (Figure 3.3) is employed to simulate the charge transfer process
occurring over anode / electrolyte interface. Ra refers to the resistance of transfer process
of electrons (Eq. 3.3) occurring on anode electrode. Ca refers to the double-layer
75
capacitance caused by the charge accumulation and separation in the interface of anode
and electrolyte. The kinetics of HOR process is so fast that no significant effect of current
density is expected on HOR, especially when the cell is operated under low current
densities. As shown in Figure 3.6, the charge transfer resistance of HOR (Ra) keeps at a
stable value with the change of current load.
3.3.2.2.3. Cathode electrode
The electrode process occurring on the cathode of H2-O2 fuel cells is ORR. Its
kinetics is very sluggish comparing to HOR process. The impedance of ORR process
usually dominates the performance of total cell systems. No consensus has been reached
after decades of study on ORR mechanism. The main controversy lies between whether
the charge transfer process of ORR is the RDS [32] or the adsorption process of oxygen
molecular on catalyst surface is the RDS [33]. The generally accepted mechanism for
PEM fuel cell application considers the charge transfer process as the RDS of ORR
process but with a change of Tafel slope at higher overpotential [32].
The large capacitive impedance arcs spanning over hundreds of hertz down to
several hertz (Figure 3.2) is ascribed to the activation process of ORR. Its behavior and
dominating frequency range obtained in this work keep consistency with the results of
other published cases of HT-PEM impedance study [5, 17, 19, 23]. In this work, the sub-
circuit (QcRc) in the proposed EC model (Figure 3.3) is used to simulate the activation
process of ORR. The ORR activation resistance Rc drops about 25 ? cm2 when the
current density increases from 100 mA cm-2 to 300 mA cm-2 (Figure 3.7). The electrons
accelerate at higher current density, which enhance the charge transfer process and lower
the impedance of ORR process. However, the dependence of Rc on current density
76
changes when the current density increases to more than 300 mA cm-2. It keeps at a stable
value when the current density increase from 300 mA cm-2 to 333 mA cm-2 (Figure 3.7).
The RDS of ORR process changes in this current density range when the adsorption of
oxygen molecular on catalyst surface gradually lags behind the charge transfer process.
This explanation could be further validated by continuously measuring cell impedance at
even higher current densities.
3.3.2.2.4. Finite diffusion process
The impedance arc dominating the frequency lower than 1 Hz mostly merges with
the ORR activation impedance arc. Only a small tail following the ORR impedance arc
can be observed from the Nyquist plot (Figure 3.2). The low frequency impedance arc is
contributed by the diffusion process of O2 to the cathode electrode. The finite diffusion
element (FDE) O is employed in the non-ideal EC model to simulate the low frequency
diffusion process. The two simulating parameters of FDE are B and Yo,0.
B is the time constant parameter in the unit of sec1/2. It reflects the rate of diffusion
process. The value of B slightly decreases with the increase of current density, but
approaches to a stable level at high current density (Figure 3.8). The increase of current
load does not fasten the diffusion process to a significant degree.
Yo,0 is the magnitude of FDE admittance at ? = 1 rad s-1, in the unit of S sec1/2, which
is the reciprocal of ? sec-1/2. Its magnitude is defined based on the model of Warburg
element, as [34]:
(3.4)
77
Figu
re 3.8.
The
va
lues
of
the
time
const
ant
pa
ramete
r o
f F
DE
(B
, in
the
unit
of
se
c1
/2 )
sim
ulate
d fr
om
the
im
pe
da
nc
e spec
tra
of
HT
-
PEM f
ue
l ce
ll stac
k a
t a
n ope
rati
ng
tempe
rature
of
160?C
and c
ha
ng
ing
cur
rent densit
y.
78
where, n is the stoichiometric number of electrons involved in the reduction reaction; F is
the Faraday constant; Rig is the ideal gas constant; DO and DR are diffusivity of oxidized
species and reduced species, respectively; and CO* and CR* are their bulk concentrations,
respectively. RO is the impedance of FDE [35] calculated at ? = 0, equivalent to the value
of R in an paralleled (CR) circuit:
(3.5)
Ambient air is fed to the cathode as the source of O2. As the ORR process is enhanced at
higher current density, the concentration of O2 decreases and fails to keep at the constant
level due to a faster consumption by the ORR process. The diffusion process of O2 to the
cathode lags behind the electrode reaction at higher current densities and gives large
impedance to the stack performance. When the current density increases to higher than
300 mA cm-2, the RDS of ORR process on the cathode changes. The adsorption of
oxygen molecular dominates the electrode process, which relieves the lag of O2 diffusion
to the electrode. The explanation can be further validated by operating the HT-PEM fuel
cell stack with pure O2 supplement at varying oxygen stoichiometry.
The impedance arc of diffusion process in the HT-PEM fuel cell system is
overlapped by the ORR impedance arc to a large extent. And the diffusion resistance is
much smaller than the ORR resistance (Figure 3.7). Thus, the diffusion process does not
have dominating limitation to the stack performance of HT-PEM fuel cell under the
operating condition during impedance measurement.
79
3.3.3. EC simulation of traditional PEM
One widely accepted interpretation of PEM fuel cell impedance is to separate the
polarization impedance according to anodic and cathodic processes. Following this idea,
an ideal EC model (Figure 3.9) consisting with one ohmic resistor, three (CR) sub-
circuits, and one wiring inductor was used to simulate the NexaTM PEM fuel cell stacks
in our previous work [27]. Three parallel (CR) sub-circuits were ascribed to anode HOR
process (CaRa), cathode ORR process (CcRc), and cathode finite diffusion process
(CdiffRdiff), respectively in sequence from high frequency to low frequency. The EC model
with only ideal circuit elements was used to facilitate PSpice simulation.
The newly proposed non-ideal EC model (Figure 3.3) shows great fitness to the
impedance spectra measured from the HT-PEM fuel cell stack. It inspires the idea that
the non-ideal EC model can be used to simulate the traditional PEM fuel cell stack. As
expected, its simulation result shows great consistency to the impedance spectra
measured at 200 mA cm-2 (Figure 3.10). The small impedance arc simulated by the
parallel (CaRa) appears in the frequency range larger than 100 Hz. It refers to the
activation process of HOR on anode electrodes. The large impedance arcs spanning over
from several Hertz up to 100 Hz is contributed by the activation process of ORR on
cathode electrodes. For the HT-PEM fuel cell stack, ORR process is the RDS of the total
stack performance. But for the traditional PEM fuel cell stack, a large impedance arc
comes out in the frequency range below 1 Hz, giving equivalent contribution to the total
stack impedance as ORR process. This arc is simulated as a finite diffusion process. The
non-ideal EC model reflects the non-ideal characteristics of the electrodes and interfaces,
and provide realistic interpretation to each impedance arc.
80
Figu
re 3.9.
The
idea
l EC
model with t
hre
e ti
me c
onst
ants.
81
Figu
re 3.10.
The
im
pe
da
nc
e s
pe
ctrum
of
the
tra
dit
ional
PEM
fue
l c
ell
stac
k (?
) me
asur
ed
at
a dc
cur
rent
of
24.
4 A
(200
mA
cm
-2 ).
The
soli
d fitti
ng
cur
ve
is
sim
ulate
d fr
om
the
non
-idea
l EC
model
(Fi
gur
e 3
.3).
Pur
e H
2 a
nd
ambi
ent
air
are
suppl
ied
to
the stac
k.
82
The impedance spectra of Nexa stack #751 measured under the current density of
100 mA cm-2, 200 mA cm-2, and 267 mA cm-2 are compared in Figure 3.11, along with
their fitting curves simulated from the non-ideal EC model (Figure 3.3). The high
frequency HOR impedance arc almost keeps stable with the increase of current density
since the facile HOR does not have strong dependence on cell loads. The ORR
impedance arc dominating middle frequency range significantly shrinks when the current
density increases from 100 mA cm-2 to 200 mA cm-2, but keeps almost unchanged when
the current density continuously increases from 200 mA/cm2 to 267 mA cm-2. This
similar behavior to the HT-PEM fuel cell stack can also be explained as a change of
RDS. When the current density is low, the charge transfer process of ORR limits the
cathode performance. An increase of current density promotes ORR process and
decreases its impedance. When the current density increases to a certain level, the
adsorption process of ORR lags behind the charge transfer process and limits the cathode
behavior.
The significant change of diffusion impedance with increasing current density shows
its great dependence on the stack load. According to Eq. 3.4 and Eq. 3.5, the diffusion
resistance decreases when the current density changes from 100 mA cm-2 to 200 mA cm-2
(Figure 3.12). However, more reduced species is produced at higher current density. Due
to the operating temperature of the traditional PEM fuel cell stack, the accumulation of
liquid water inside the fuel cell at higher current densities brings challenge to the
diffusion of O2 to the cathode electrode. The diffusion of liquid water itself away from
the electrode also contributes to the diffusion process. On the other side, the increased
rate of O2 consumption at higher current densities decreases the O2 concentration in the
83
Figu
re 3.11.
Stac
k im
pe
da
nc
e of
the
tra
dit
ional
PEM
fue
l c
ell
mea
sure
d a
t a
cur
rent
de
nsit
y o
f (
?)
100
mA
cm
-2 ,
(?
) 200
mA
cm
-2 ,
and (?
) 267
mA
cm
-2 , a
long
with t
he
ir f
itti
ng
cur
ve
s si
mul
ated f
rom the no
n-idea
l EC
model (
Fig
ure
3.3
).
84
Figu
re 3.12.
T
he
va
lues
of
the
equiva
lent
diff
usion
re
sis
tanc
e R
O sim
ulate
d fr
om
the
im
pe
da
nc
e sp
ectra
of
tra
dit
ional
PEM
fue
l c
ell
stac
k unde
r c
ha
ng
ing
cur
rent densit
y.
85
air supplement, which also gives more impedance to the diffusion process (Eq. 3.4 and
Eq. 3.5).
3.3.4. Comparison between HT-PEM and traditional PEM
To facilitate the comparison between two types of PEM fuel cell, their impedance
spectra are normalized to a comparable base, which is ? cm2 per single planar cell (that is
? cm2 cell-1). This unit is based on the average output power density of each single cells
inside the stack. Since the impedance spectra are measured from two commercial stack
modules. It is difficult to separate the effects of stack control hardware from the effects of
membranes and electrodes on ohmic resistances. The comparison between ohmic
resistances of two modules cannot provide exact information for membrane comparison.
Thus, the impedance spectra of two stacks are compared in the same Nyquist plot (Figure
3.13) after subtracting ohmic resistances. The values of ohmic resistances calculated from
EC simulation are used here for subtraction.
The impedance difference between two PEM fuel cell stacks at high frequency range
is small, especially under low current density. The HOR activation process on anode
occurs so fast that the temperature dependence on its impedance is small. Its impedance
slightly decreases when the operating temperature of the PEM fuel cell stack is elevated
over 100?C. But the difference of cathode activation impedance between two stacks is
significant. The ORR activation process presents much better performance at higher
operating temperature. Since the cell performance is mainly dominated by ORR process,
the enhancement of its activation provides great improvement of cell performance. The
finite diffusion process at low frequency range also shows large differences under
different operating temperatures. The diffusion impedance arc of the HT-PEM fuel cell
86
Figu
re 3.13.
Imped
anc
e c
ompa
rison
be
twe
en
the
tra
dit
ional
PEM
fue
l c
ell
stac
k modu
le
and
the
HT
-PEM
fue
l c
ell
stac
k b
ase
d on
a
norma
liz
ed im
pe
da
nc
e in
the unit of ?
?cm
2 pe
r c
ell. Ohmi
c r
esis
tanc
es
are
not i
nc
luded in the c
ompar
ison.
87
stack is very small and mostly overlapped by the ORR impedance arc. But the traditional
PEM fuel cell stack has a large diffusion impedance arc. At low current region, the
diffusion impedance decreases with increasing current density. With the further increase
of current density, the dependence of diffusion impedance changes. It increases with
increasing current density due to the accumulation of liquid water inside the stack. This
problem can be solved in the HT-PEM fuel cell systems with the single phase water
environment. The higher operating temperature facilitates the water management inside
the PEM fuel cells.
3.4. Conclusion
In this work, both a novel high temperature (HT) proton exchange membrane (PEM)
fuel cell stack module operated at elevated temperatures around 160?C and a traditional
one operated at temperature between 40?C and 65 ?C were studied by impedance
measurement and equivalent circuit (EC) simulation. Both PEM fuel cell stacks were
manufactured at commercial level. One non-ideal EC model was proposed to simulate the
HT-PEM fuel cell stack. The excellent goodness of fit inspired the idea to simulate the
traditional PEM fuel cell stack by this non-ideal EC model. Comparing to our previous
simulation with ideal EC model [27], the use of one constant phase element (CPE) Qc and
one finite diffusion element (FDE) O enhanced the simulation of the oxygen reduction
reaction (ORR) process and the diffusion process at middle and low frequency range.
Based on the EC simulation and impedance interpretation, the chemical and physical
processes occurring in the HT and traditional PEM fuel cell stacks were similar. The
hydrogen oxidation reaction (HOR) on anode had fast kinetics no matter the operating
temperature is above or below 100?C. It contributed a small part to stack impedance. The
88
ORR process on cathode had sluggish kinetics and dominated the total stack impedance.
However, the magnitude of ORR impedance arc in the HT-PEM fuel cell stack system
was much smaller than in the traditional system due to the faster kinetics at higher
temperature. The elevated operating temperature of HT-PEM fuel cell stack also
facilitated the O2 diffusion process and the water management inside the stack. Thus, the
diffusion impedance was decreased to a magnificent degree.
The performance of two commercial PEM fuel cell stacks were compared by EIS
technique and EC simulation at a normalized impedance level. Although the application
of HT-PEM fuel cells is still in an immaturity stage, the significantly smaller polarization
impedance of HT-PEM fuel cell stack gives a promising possibility to future
development. This work highlights the ability of EIS and EC simulation in stack
characterization and performance evaluation, and experimentally provides references for
further study and diagnostics of commercial fuel cell stacks.
89
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92
Chapter 4
EIS Application to Proton Exchange Membrane Fuel Cells
Stack Degradation and Performance Diagnostics
4.1. Introduction
In Chapter 3, a non-ideal equivalent circuit (EC) model was proposed to simulate
both high temperature (HT) and traditional proton exchange membrane (PEM) fuel cell
stacks (Figure 3.3). The interpretation of impedance was performed based on the widely
accepted cell mechanisms. A performance comparison between two commercial PEM
fuel cell stacks manufactured based on current PEM technologies was also presented in
the previous chapter. In this chapter, the rationality of this EC model will be further
validated by simulating the HT-PEM fuel cell stack modules under other operating
conditions. The emphasis will be placed on the performance comparison to HT stack
itself under various operating conditions. The competency of electrochemical impedance
spectroscopy (EIS) and EC simulation on stack diagnostics will be shown in this chapter.
Certain degradations is found in the HT-PEM fuel cell stack. It brings uncertainty
and unexpected behaviors to the stack performance. The experimental data also present a
more complicated behavior. However, the study on this stack still provides important
experience and references for practical applications of HT-PEM fuel cells and EIS
diagnostics.
93
4.2. Experimental details
4.2.1. EIS measurement
Following the HT-PEM fuel cell stack specifications, impedance equipment, electric
circuit connection, and measurement procedures described in Chapter 3, the impedance
of the commercial HT-PEM fuel cell stack was collected under various operating
temperatures and stack loads. All measurements was carried out with pure H2 as fuel
supplement and ambient air as both reactant and cooling. The operating temperature was
set at 120?C, 140?C, and 160?C from low to high. A dc current load of 4.5 A, 9 A, 12 A,
13.5 A, and 15 A was loaded to the stack at each setting temperature, which is a current
density of 100 mA cm-2, 200 mA cm-2, 267 mA cm-2, 300 mA cm-2, and 333 mA cm-2,
correspondingly. A bleeder resistor of 24 ? is connected directly across the stack
terminals. It is enabled and disabled by the Embedded Fuel Cell Control Unit (EFCU)
(Serenergy Inc.) to provide a minimum load of 2 A for the stack module. Under the
setting of the EFCU control system, the resistor is not disabled until the stack current
goes over 4 A. Thus, the current load is chosen above 4 A to avoid the effect of the
bleeder resistor. For each current load setting, the frequency swept from 10 kHz to 0.1 Hz
at a rate of 10 points per decade, and the stack impedance was generated at each
frequency point. This whole set of measurement procedures was repeated to collect the
second sets of impedance spectra of the HT-PEM module for degradation study.
4.2.2. Polarization curves
After finishing the measurement of the first set of impedance data, the method of
current sweep was conducted to measure the polarization curve (i-V curve) of the stack.
The operating temperature of the stack was set at 160?C. And the stack was running at a
94
current load of 9 A (200 mA cm-2) to reach a stable condition. The current loaded to the
stack was then sweep from 0.5 A to 32 A by a step of 0.5 A at a rate of 1 A/min. After
reaching 32 A, the current go back to 0.5 A at the same step magnitude and sweep rate.
The output voltage at each current point was measured. Along with the increase of
current load, the temperature of the stack was continuously increasing. Thus, the voltage
measured during the back cycle was a little bit higher than the forth cycle. An average
value was used to plot the polarization curve.
4.3. Results and discussion
Since 160?C is the theoretical optimal operating temperature, the impedance
spectrum measured at this temperature setting in the first data set is analyzed to evaluate
the performance comparison to the traditional PEM fuel cell stack in Chapter 3.
Impedance spectra measured at other operating temperature in the first data set is
analyzed to illustrate temperature dependence of impedance and further validate the
current dependence of impedance. The comparison between the first and the second set of
stack impedance data is analyzed to perform the degradation study of this stack.
4.3.1. EC simulation of the first set of data
4.3.1.1.Impedance dependence on current density.
The impedance spectra measured at operating temperatures 120?C (Figure 4.1) and
140?C (Figure 4.2) under changing stack loads are plotted in Nyquist plots with their
fitting curves simulated from the stack EC model. The points indicate the experimental
data and the solid and dashed lines represent the simulation results. As the impedance
analysis on spectra obtained at 160?C in Chapter 3, the impedance spectra obtained at
lower operating temperatures also shows similar dependence of stack impedance on
95
Figu
re 4.1.
The
im
pe
da
nc
e sp
ectr
a of
the
HT
-PEM
stac
k modul
e c
oll
ected
at
an
ope
rati
ng
tempe
rature
se
t a
t 120?C
unde
r va
ryin
g
cur
rent
load
de
nsit
y a
nd
their
fitti
ng
cu
rve
s sim
ulate
d fr
om
the
sta
ck
EC
model
. P
ure
H
2 wa
s supp
lie
d to
the
stac
k a
nod
e
at ar
ound 45 mbar
and a
mbi
ent t
emper
ature
. Ambient a
ir wa
s tak
en int
o the stac
k c
athode b
y the b
lowe
r.
96
Figu
re 4.2.
The
im
pe
da
nc
e sp
ectr
a of
the
HT
-PEM
stac
k modul
e c
oll
ected
at
an
ope
rati
ng
tempe
rature
se
t a
t 140?C
unde
r va
ryin
g
cur
rent
loa
d de
nsit
y a
nd
their
fitti
ng
cur
ve
s c
alc
ulate
d fr
om
the
de
riv
ed
EC
model
shown
in
Figure
3.3.
Pur
e H
2 wa
s
suppl
ied
to
the
stac
k a
node
at
around
45
mbar
and
ambi
ent
tempe
rature
. Ambient
air
wa
s take
n int
o the
stac
k c
athode
by
the blowe
r.
97
current density. The ohmic resistance (R?) and charge transfer resistance (Ra) do not
show significant dependence on current density. But the large capacitive impedance arc,
spanning over the middle and low frequency region, shrinks significantly with the
increasing current density. This is because that smaller energy is required to surmount the
activation barrier at larger overpotential, which means the activation process overcomes
smaller impedance at higher temperature.
4.3.1.2.Impedance dependence on temperature.
The impedance spectra measured under a current load of 200 mA cm-2 at three
different operating temperature is illustrated in Figure 4.3. The points are impedance data
measured from the stack, and the lines are fitting curves simulated from the EC model
derived in Chapter 3 (Figure 3.3). Generally, smaller impedance is expected at higher
operating temperature due to faster kinetics. However, the stack impedance shifts to the
right on Nyquist plot when the operating temperature increases from 120?C to 160?C
(Figure 4.3). This means the ohmic resistance (R?) of the stack increases with increasing
temperature. Comparing the fitting values of the EC elements, the resistance of ORR
process on cathode (Rc) (Figure 4.5) also presents the same behavior as the ohmic
resistance (R?) (Figure 4.4). While the charge transfer resistance of HOR process on
anode (Ra) (Figure 4.4) slightly increases with increasing temperature but almost keeps at
a constant level.
Several other groups also reported similar unexpected temperature dependence of the
ohmic resistance (R?) [1-3] and ORR resistance (Rc) [1, 2, 4]. The most possible
explanations for this phenomenon are the dehydration of the membranes [1, 2] and the
decrease of gas concentration in the diffusion layer [1, 4]. Although water is no longer
98
Figu
re 4.3.
The
im
pe
da
nc
e spec
tra
of
the
HT
-PEM
fue
l c
ell
stac
k modul
e mea
sur
ed
unde
r a
cur
rent
load
of
9 A
(200
mA
cm
-2 )
with
the
ope
rati
ng
tempe
ratur
e se
t a
t 120?C
, 140?C
, a
nd
160?C
. T
he
ir
fitti
ng
cur
ve
s a
re
calcula
ted
fro
m
the
stac
k E
C
mode
l
de
rive
d in
Cha
pte
r 3
(F
igur
e 3.3)
. P
ure
H
2 wa
s s
uppli
ed
to
the
stac
k a
nod
e a
t a
round
45
mbar
and
ambi
ent
tempe
rature
.
Ambient a
ir w
as tak
en int
o the stac
k c
athode b
y th
e blowe
r.
?
120?C
Da
ta
?
140?C
Da
ta
?
160?C
Da
ta
?????
120?C
Fit
ting
- -
- 140?C
Fit
ting
?
160?C
Fit
ting
99
Figu
re 4.4.
The
de
pe
nd
enc
e of
sta
ck
modul
e ohmi
c re
sis
tanc
e R
? a
nd
anode
HO
R
ch
arg
e tr
ansfe
r re
sis
tan
ce
R
a on
tempe
rature
. Th
e
va
lues
of
resis
tors
we
re
calcula
ted
by
the
sta
ck
EC
model
. The
e
xpe
rimen
tal
da
ta
used
for
si
mul
ati
on
we
re
thre
e
im
pe
da
nc
e spe
ctra
of the
fir
st data se
t m
easu
red un
de
r a
cu
rre
nt l
oa
d d
ensit
y of 20
0 mA c
m
-2 (F
igur
e 4.2)
.
100
Figu
re 4.5.
The
de
pe
nd
enc
e of
sta
ck
modul
e c
athode
ORR
re
sis
tanc
e R
c on
tempe
ratu
re.
The
va
lues
of
resis
tor
s we
re
cal
culate
d b
y
the
stac
k
EC
model.
The
ex
pe
rimen
tal
da
ta
use
d
for
sim
ulation
we
re
thr
ee
im
pe
da
nc
e spe
ctra
of
the
fir
st
da
ta
set
mea
sure
d und
er a
cur
rent l
oa
d de
nsit
y of 20
0 mA
cm
-2 (F
igu
re 4.2).
101
essential for proton conductivity in PA doped PBI membranes, the humidity of
membranes can change its conduction mechanism [1], which changes the impedance
dependence on temperature.
4.3.2. Stack degradation
4.3.2.1.Comparison between two sets data of impedance spectra
The measurement of the second set of impedance spectra repeats the procedure of the
first set. Two measurements are carried out under the identical settings of operating
conditions. However, the measured impedance spectra have different shapes and different
magnitudes (Figure 4.6). The derived stack EC model still present great fitness for data
fitting. The fitting values of ohmic resistances and polarization resistances are listed in
Table 4.1.
The ohmic resistance (R?) here includes not only the resistances of cell membranes
and electrodes, but also electronic devices and controllers connected in the stack
modules. Generally, it is expected to be independent on current density. The ohmic
resistance (R?) of the first data set presents a slight decrease with increasing current
density; however, the change is almost unnoticeable in the Nyquist plots (Figure 4.6). But
it is quite significant that the ohmic resistance (R?) of the second data set decreases with
increasing current density. This means that the stack underwent a more unstable
operation during the second measurement than the first one.
The polarization resistance (Rp) is the sum of the activation resistances and the
concentration resistance. In a Nyquist plot, Rp is indicated as the difference between two
interceptions in the real axis. Comparing two sets of impedance data, the polarization
102
Figure 4.6. The (a) first and (b) second set of impedance spectra measured at 160?C.
The current density was loaded at (?) 100 mA cm-2, (?) 200 mA cm-2, and
(?) 300 mA cm-2, and their fittings (dashed and solid lines) calculated from
the stack EC model.
? 100 mA/cm2 Data
? 200 mA/cm2 Data
? 300 mA/cm2 Data
????? 100 mA/cm2 Fitting
- - - 200 mA/cm2 Fitting
? 300 mA/cm2 Fitting
? 100 mA/cm2 Data
? 200 mA/cm2 Data
? 300 mA/cm2 Data
????? 100 mA/cm2 Fitting
- - - 200 mA/cm2 Fitting
? 300 mA/cm2 Fitting
(a)
(b)
103
Table 4.1. The fitting data of R? and Rp calculated from the EC simulation.
Current Density The First Set The Second Set
i (mA/cm2) R? (?*cm2) Rp (?*cm2) R? (?*cm2) Rp (?*cm2)
100 16.19 52.01 23.22 68.46
200 16.08 32.89 22.68 47.94
300 15.89 25.80 20.93 38.71
104
resistance (Rp) of the stack increased significantly when conducting the second set of
measurement.
To compare in a more intuitive way, two sets of impedance data collected at 160?C
and different current densities are plotted together in one Nyquist plot, shown in Figure
4.7. The second set of data, although repeating the procedures and operating conditions of
the first set, presents significant differences from the first set of data. Both the ohmic loss
and the polarization loss of the second set of impedance data increase, as concluded from
the simulation data listed in Table 4.1.
4.3.2.2.Comparison between polarization curves
The performance degradation can also be implied from observing the decrease in cell
voltages. When measuring the impedance, the stack voltage was recorded at each current
load. The average voltage of single planar cell in the stack was calculated from the stack
voltage and plotted in Figure 4.8. The cell voltage decreased when conducting the second
set of measurement. Also, the polarization curve measured after the first set of impedance
measurement is compared to the one measured by Serenergy at the beginning of life
(BOL) of the stack module (Figure 4.9). A significant performance degradation is
observed and about one third of the maximum output power measured at its BOL is
dropped.
To find out the reasons for the change of the stack impedance, the status of the
blower and heater during two sets of measurement were studied. It was shown that the
blower worked in the same speed range when the stack was operated at the same
conditions. Also, the heater was disabled when performing impedance measurement.
105
Figu
re 4.7.
Two
sets
of
im
pe
da
nc
e spec
tra
of
the
hi
gh
tempe
ratur
e P
EM
stac
k
mea
sure
d
unde
r the
sam
e ope
rati
on
sett
ing
. The
ope
rati
ng
tempe
ratur
e w
as
set
at
160?C
and
the
cur
rent
de
nsit
y c
ha
nge
s f
rom
100
mA
cm
-2 to
300
mA
cm
-2 for
ea
ch
da
ta
set.
?
100 mA/cm
2 Da
ta Set 1
?
200 mA/cm
2 Da
ta Set 1
?
300 mA/cm
2 Da
ta Set 1
?
100 mA/cm
2 Da
ta Set 2
?
200 mA/cm
2 Da
ta Set 2
?
300 mA/cm
2 Da
ta Set 2
106
Figu
re 4.8.
The
vol
tag
e
of
eac
h
sing
le
cell
in
the
stac
k
whe
n
condu
cti
ng
th
e
(?
) fir
st
and
the
(?
) se
co
nd
set
of
im
pe
da
nc
e
mea
sure
me
nt. A
cur
rent
de
nsit
y of 10
0 mA
cm
-2 , 200 mA
cm
-2 , a
nd 300 mA
cm
-2 wa
s loade
d to t
he
stac
k.
?
1st
Se
t of Me
asure
ment
?
2
nd
Se
t of Me
asure
ment
107
Figu
re 4.9.
C
ompar
ison
be
twe
en
the
polar
iza
tion
cur
ve
s c
oll
ected
from
the
HT
-PEM
stac
k modul
e (?
) a
t it
s B
OL
and
(x)
aft
er
the
fir
st
set
of
im
pe
da
nc
e me
asure
ment
. B
oth
cur
ve
s we
re
obt
ained
aft
er
the
sta
ck
wa
s st
able
at
an
op
era
ting
tempe
ratur
e se
t
at 160?C
.
108
Thus, the energy consumptions of the blower and heater did not contribute to the
inconsistency of stack impedance.
The temperature profile of the stack is monitored by three temperature sensors
during impedance measurement. For both sets of experiments, the variations of the stack
temperature with the operating time is more significant when the stack was operated at
the current density of 100 mA cm-2 than at higher loads. And at this current load, all three
temperatures are lower than 160?C. When the current density increases to 200 mA cm-2,
the middle and rear temperatures of the stack were able to be stabled at 160?C, and the
temperature variations with the operating time is negligible. However, the front
temperature still stay at a value lower than 160?C. At a current density of 300 mA cm-2,
the front temperature is able to elevate up to about 150?C. The decrease of the
temperature gradient over the whole stack provided a more stable condition for the stack
operating. Although the temperature profiles behaved similar in both sets of
measurements, the temperature variations with the operating time during the second
measurement were more observable than during the first one. Also, the front temperature
of the stack when conducting the second set of measurement was generally lower than the
first one. It amplified the temperature gradient over the whole stack and induced more
cell impedance.
4.4. Conclusion
Two sets of impedance measurement were carried out following the same procedure.
The operating conditions of the HT-PEM fuel cell stack module is set at the same values.
However, the measured impedance spectra failed to present identical or similar
performance behavior as expected. The EC model derived in Chapter 3 for stack
109
simulation is used to simulate these two sets of impedance spectra. The stack model gives
great fitness to the experimental data. The performance degradation of the HT-PEM fuel
cell stack is quantitatively analyzed by the impedance values simulated from the stack EC
model. Polarization curves are also used to illustrate the conditions of the stack module.
The impedance data measured at lower operating temperatures also shows similar
current dependence as at 160?C. Stack impedance decreases with increasing current
loads. The difference is most significant when the load increases from 100 mA cm-2 to
higher current density. The stack impedance increases with decreasing temperature at all
loads. Although this trend is not expected according to activation theory, similar
phenomena also discovered by other research groups. The complicate proton conduction
mechanism of PA-PBI membranes introduces more effect factors to the dependence of
stack impedance on operating temperature.
110
Reference
[1] C.Y. Chen, W.H. Lai, Journal of Power Sources, 195 (2010) 7152-7159.
[2] J. Lobato, P. Canizares, M.A. Rodrigo, J.J. Linares, Electrochimica Acta, 52 (2007)
3910-3920.
[3] J.L. Jespersen, E. Schaltz, S.K. Kaer, Journal of Power Sources, 191 (2009) 289-296.
[4] J.L. Zhang, Y.H. Tang, C.J. Song, J.J. Zhang, Journal of Power Sources, 172 (2007)
163-171.
111
Chapter 5
EIS Application to Tubular Solid Oxide Fuel Cells
Single Cell Characterization and EC Simulation
5.1. Introduction
Electrochemical impedance spectroscopy (EIS) is applied to the tubular solid oxide
fuel cells (T-SOFCs) in this chapter. The cell tubes are manufactured at commercial
level. The complex compositions and structures of the electrodes, the oxygen-ion (O2-)
conducting electrolytes, and the high operating temperature up to 1000?C make the
kinetic mechanisms of SOFC processes very different from proton exchange membrane
(PEM) fuel cells. The practical cell operation with reformate fuel takes the impedance
study into a more complicated situation. The mechanisms of electrode reactions in SOFC
systems are not well understood. Even less attention has been paid to SOFCs with tubular
geometry. No impedance data of SOFCs directly fueled with reformate has been
published. The measurements and results described and analyzed in this chapter provide a
preliminary study on reformate fueled T-SOFC systems.
5.2. Cell descriptions and experimental details
5.2.1. Tubular solid oxide fuel cells (T-SOFCs)
The impedance spectra of tubular solid oxide fuel cells (T-SOFC, patented by
Acumentrics Corp.) were small tubes with a horizontal 22 mm inner diameter by 450 mm
long. Five single tubes were horizontally placed in the same Cell Test Stand, parallel to
112
each other. They were numbered from Cell 1 to Cell 5 in sequence. Cell 1 and Cell 5
were two tubes placed on the sides, and Cell 3 was the one placed in the middle. The
tubes were anode-supported SOFCs, with the anode as the inside wall of the tube and the
cathode as the outside wall. The O2- conducting electrolyte was stacked between the
anode and the cathode. Ambient air was circulated around the tubes, supplying oxygen to
the cathodes of the T-SOFCs. A mixture of hydrocarbon with a general structure of
CnH2n+2 was fed into the tube. It was reformed inside the tube into hydrogen (H2) and
carbon monoxide (CO) along the flow of the fuel mixture. The generated H2 and CO
were continuously oxidized to water (H2O) and carbon dioxide (CO2) along the fuel flow
and produced electricity. This tubular geometry of SOFC is able to integrate the reformer
into each cell. An approximate area of 215 cm2 was measured (data provided by
Acumentrics) as the nominal area of each single T-SOFC tube. This number is only about
one third of the inner area of each tube, because part of the tube was served as the
internal fuel reformer for the mixture of hydrocarbons.
5.2.2. Experiments
The testing tubes were manufactured at commercial level. The measurements were
conducted during cell operations under different temperatures, current densities, and fuel
utilizations. These parameters were set before cell operations at a desired number.
However, it was usually difficult to stable the cells exactly at the operating conditions as
set. The parameters described below refers to the desired number set before the
operations. The status of cell tubes were stabled as close to the set parameters as possible
during the practical operations.
113
The Cell Test Stand was preheated to 800?C. The impedance spectrum of Cell 1 was
measurement under a current density around 120 mA cm-2 at a fuel utilization around
50%. The measurement was repeated on Cell 3 and Cell 5 in sequence under the same
operation setting. The impedance was then measured from Cell 3. The Cell Test Stand
was preheated to about 750?C. A current density of 120 mA cm-2 was loaded to Cell 3. Its
impedance spectra were obtained after the tube was stabilized at a fuel utilization of 50%,
75%, and 29% in sequence. For validation of EC simulation, the impedance spectra of
Cell 3 were then measured under a current density of 150 mA cm-2 with a fuel utilization
stable at around 50% and 75%.
The impedance measurement was performed by Gamry FC350TM fuel cell monitor
(Gamry Instruments) in mode of low noise Galvanostatic EIS. For each measurement of
impedance spectra, the frequency swept from 10 kHz down to 0.01 Hz at the rate of 10
points per decade. The amplitude of ac signal applied to the single T-SOFC tube during
the measurement was 0.25 A. The external electric currents were loaded to the testing
cells by connecting to TDI-Dynaload? RBL488 programmable load.
5.3. Results and discussion
5.3.1. Comparison of impedance spectra between cells
The impedance spectra measured from Cell 1, Cell 3, and Cell 5 under a current
density of 120 mA cm-2 at 800?C with a fuel utilization of 50% is plotted in the same
Nyquist plot for comparison (Figure 5.1). The configuration of three cell tubes are
identical to each other, however significant differences can be observed from their
impedance under the same operation setting. Cell 3 performs smaller total impedance
than the other two cells. It is placed in the middle between Cell 1 and Cell 5. This
114
Figu
re 5.1.
I
mped
anc
e spec
tra
of
C
ell
1
(?
), C
ell
3
(?),
and
Ce
ll 5
(?
) mea
sure
d unde
r a
cur
rent
de
nsit
y of
120
mA
cm
-2 a
t 800?
C
with a f
ue
l ut
iliz
ati
on of
50%.
115
position minimize the effect of ambient environment on the cell operation during
measurement. Both the operating temperature and fuel supplement are able to be
maintained at a more stable status closer to the setting of operation. The impedance
spectra measured from Cell 3 are used for simulation analysis below.
5.3.2. EC simulation
The impedance spectrum of Cell 3 measured under a current density of 120 mA cm-2
at 800?C with a fuel utilization of 50% is shown in Figure 5.2, along with its fitting curve
simulated from the non-ideal EC model shown in Figure 5.3. Two large capacitive
impedance arcs are clearly separated in the Nyquist plot. The one in the higher frequency
region is considered as three overlapped impedance arcs contributed from different
processes. The paralleled (RaCa) sub-circuit simulates the impedance arc above 100 Hz,
ascribing to the oxidation reaction process at anode. The paralleled (RcQc) sub-circuit
simulates the impedance arc dominating the frequency range from 100 Hz down to
around 10 Hz, ascribing to the oxygen reduction reaction (ORR) process at cathode.
SOFC systems have more complicated ORR mechanism on cathode than PEM fuel cell
systems. The rate determining step (RDS) changes with different cell operating
conditions, electrode materials, and other cell configurations. Measurements at different
O2 concentration can be used to validate the simulation and interpretation of ORR
impedance. The constant phase element (CPE) Qc is used to replace an ideal capacitor for
non-ideal characteristics of the electrode. The finite diffusion element (FDE) O simulates
the impedance arc dominating the frequency range from 10 Hz to 1 Hz. This arc overlaps
the arc of (RcQc) to a significant extent and bridges it to the low frequency capacitive
impedance arc. It is considered as the contribution from the diffusion process on cathode.
116
Figu
re 5.2.
I
mped
anc
e spec
tra
of
C
ell
3
(?
) mea
sure
d und
er
a c
urr
ent
de
nsit
y of
120
mA
cm
-2
at
800?C
with
a fue
l uti
liz
ati
on
of
50%.
The
soli
d
line
is
its
fitti
ng
cur
ve
sim
ulate
d
from
the
non
-idea
l EC
model
shown
in
Fig
ure
5.3.
The
da
rk
dots
mar
ke
d out t
he
im
pe
da
nc
e da
ta me
asure
d a
t the
fr
eque
nc
y of 10
00 Hz
, 100 Hz
, 10 Hz
, 1 Hz
, a
nd 0.1
Hz
.
117
Figu
re 5.3.
Non
-idea
l EC
model f
or
T-
SOF
C sing
le
cell
sim
ulation
118
The total resistance of the large capacitive impedance arc at higher frequency region is
about 0.4 ? cm2, illustrated directly by the Nyquist plot (Figure 5.2). The impedance arc
dominating the frequency range below 0.1 Hz is simulated by the paralleled (RgcQgc) sub-
circuit of the proposed EC model. It also shows a resistance of about 0.4 ? cm2 in Figure
5.2, equivalent to the total resistance of the capacitive arc at higher frequency. The
capacitive impedance arc with such magnitude at frequency below 0.1 Hz is not related to
charge transfer processes or adsorption processes. The term of "gas conversion" was put
forward for the impedance arcs with similar characters in Primdahl's work [1] on their
three electrode SOFC pellets. It refers to a bulk process over the electrode, with
dependence on gas flow rate. The fit curve simulated from the non-ideal EC model has
good consistency with the impedance spectra measured from Cell 3.
Comparing to the impedance spectra measured from the disc-like SOFC button cells
fueled with a gas mixture of pure hydrogen (H2) and water (H2O) [2], the capacitive
impedance arc does not come out until the frequency sweeps down to the magnitude of
100 Hz. However, the cathode activation process simulated by (RcQc), the diffusion
process simulated by O, and the gas conversion process simulated by (RgcQgc) still
dominate the frequency region similar to other studies [1-3]. This behavior indicates that
the anode electrode process simulated by (RaCa) occurs at a slower rate in this SOFC
system than in other SOFC systems. The SOFC tubes studied in this work are fueled
directly with a mixture of hydrocarbons. The anode processes start with the reforming
reaction of hydrocarbon, followed by the oxidation reaction of H2 and CO. The
mechanism of hydrocarbon reforming at anode has not been well understood. Its kinetics
119
was reported to have dependence on the steam ratio of the fuel supplement [4]. The
mechanism of CO oxidation also slows the activation process on anode electrode.
5.3.3. Impedance interpretation
5.3.3.1.Dependence of temperature
Impedance spectra of Cell 3 measured when the operating temperature is set at
750?C and 800?C are plotted in Figure 5.4. Their fit curves are simulated by the proposed
non-ideal EC model. The current density during measurement is 120 mA cm-2, and the
fuel utilization is stable at around 50%. The most significant changes introduced by the
operating temperature is the decreased ohmic resistance R? at elevated temperature. The
measurements at only two different temperatures failed to give any quantitative
dependence. However, the decrease of ohmic resistance with temperature is expected as
the conductivity of O2- through the ceramic electrolyte follows Arrhenius behavior [4].
The resistances of activation processes on both electrodes (Ra and Rc) decrease with
increasing temperature due to improved kinetics (Figure 5.5).
As stated in Chapter 1, the diffusion resistance (RO) is the equivalent resistance of
FDE calculated from the simulation values of its time parameter B and admittance
parameter YO,0:
(5.1)
The dependence of diffusion process on temperature is complicated. The kinetics of
reduction reaction and the diffusivity of gas mixtures both have strong dependence on
temperature, and their changes give different effects to the diffusion resistance.
Moreover, the cathode diffusion process is not only contributed by the process of O2 gas
diffusion to the electrode. The surface diffusion process of adsorbed oxygen also limits
120
Figu
re 5.4.
I
mped
anc
e spec
tra
of
C
ell
3 mea
sure
d whe
n the
ope
rati
ng
tempe
ratur
e is
set
at
(?
) 750?C
and
(?)
800?C
, a
long
with
their
fit
cur
ve
s sim
ulate
d fr
om
the
non
-idea
l EC
model
(Fi
gur
e 5.3
). The
cur
rent
de
nsit
y is
12
0 mA
cm
-2 with
a fue
l uti
liz
ati
on
of 50%
.
121
Figu
re 5.5.
Va
lues
of
resis
tanc
e e
lements
in
the
propose
d no
n-idea
l EC
model
(Fig
ure
5.3)
calcula
ted
from
the
sim
ulation
of
Ce
ll 3,
ope
rati
ng
at
diff
ere
nt
tempe
rature
of
750?C
and
800?C
unde
r a
cur
rent
de
ns
ity
of
120
mA
cm
-2 wi
th
a fue
l uti
liz
ati
on
of
50%.
The
im
pe
da
nc
e spe
ctra
for
sim
ulation ar
e sh
own in F
igu
re 5.4.
122
the cell performance, which is a thermally activated process [4]. Its impedance can be
decreased at higher operating temperature.
The change of gas conversion resistance (Rgc) show reverse dependence on
temperature. It increases with the increasing temperature (Figure 5.5). According to
Primdahl's CSTR model established for the conversion of H2-H2O gas mixture [1], the
value of Rgc is first order with respect to the operating temperature and first order
reciprocal of the gas mixture flow rate. Their idea of the CSTR model can be explained
as a convection process in the volume over the anode. With the increase of temperature,
the volume of gas mixture expands, which decreases the gas flow rate and increases the
gas conversion resistance.
5.3.3.2.Dependence of current density
Impedance spectra of Cell 3 measured under a current density of 120 mA cm-2 and
150 mA cm-2 are illustrated in Figure 5.6, along with their fit curves. The operating
temperature is set at 750?C, with a fuel utilization stabilized at about 50%. The changes
of impedance spectra with the increase of cell load are mainly contributed by the
improved performance of diffusion process and gas conversion process under the cell
operation at higher current density (Figure 5.7). The increased current density facilitates
the diffusion process of O2 by accelerating the consumption rate of O2. And it also
decreases the gas conversion resistance by increasing the flow rate of fuel supplement.
The effects of current density on activation processes of both electrodes are not as
significant as expected for charge transfer processes, because the RDS of anode oxidation
reaction and cathode reduction reaction change to adsorption processes especially at high
operating temperatures.
123
Figu
re 5.6.
I
mped
anc
e spec
tra
of
C
ell
3 mea
sure
d unde
r a
cur
rent
de
nsit
y of
(?
) 120
mA
cm
-2 a
nd
(?)
150
mA
cm
-2 ,
along
with
their
fit
cur
ve
s sim
ulate
d fr
om
the
non
-idea
l EC
mod
el
(Fi
gur
e 5.3).
Th
e ope
rati
ng
tempe
rature
is
set
at
750?C
, with
a fue
l
uti
liz
ation of 50%
.
124
Figu
re 5.7.
Va
lues
of
resis
tanc
e e
lements
in
the
propose
d no
n-idea
l EC
model
(Fig
ure
5.3)
calcula
ted
from
the
sim
ulation
of
Ce
ll 3,
ope
rati
ng
at
diff
ere
nt
cur
rent
de
nsit
y of
120
mA
cm
-2 a
nd
150
mA
cm
-2 a
t a
tempe
ratur
e of
750?C
with
a fue
l uti
liz
ati
on
of 50%
. The
im
pe
da
nc
e s
pe
ctra
for
sim
ulation ar
e shown in F
igu
re 5.6.
125
5.3.3.3.Dependence of fuel utilization
Impedance spectra of Cell 3 measured with different fuel utilization are shown in
Figure 5.8. The operating temperature is set at 750?C. A current density of 120 mA cm-2
is loaded to the cell tube during measurement. A smaller fuel utilization means that the
larger flow rate of fuel supplement is required when the rate of fuel consumption keeps
constant under the same setting of operating temperature and cell load. The impedance of
electrode kinetic processes and cathode diffusion process slightly decreases with
decreasing fuel utilization and almost keeps at a constant level. The impedance arc of gas
conversion process significantly enlarges when the fuel utilization increases from 50% to
75%. The decrease of gas flow rate limits the cell performance. This behavior also
validates that the impedance arc dominating the frequency below 0.1 Hz is contributed by
the concentration process of gas conversion over the anode.
5.4. Conclusion
The single cells of tubular solid oxide fuel cell (T-SOFC) fueled directly with
reformate mixture are studied by impedance measurement and equivalent circuit (EC)
simulation in this chapter. Based on the mechanisms put forward for SOFC button cells
and pellets with pure H2 fuel supplement, a non-ideal EC model with four time constants
was proposed for impedance simulation. Due to the complexity of SOFC mechanisms
and the difficulty of SOFC operation at varying conditions, the proposed EC model lacks
validation for impedance interpretation. However, the impedance spectra measured under
several different operation conditions, though limited, provide a preliminary validation
for the physical interpretation of EC elements. The conduction of oxygen-ion through
electrolyte is the main contribution to the ohmic resistance. The adsorption process of
126
Figu
re 5.8.
I
mped
anc
e spec
tra
of
C
ell
3
mea
sure
d whe
n ope
rati
ng
with
a fu
el
uti
liz
ati
on
(FU)
of
(?
) 29
%,
(?
) 50%,
and
(?)
75
%
along
with
their
fit
cu
rve
s sim
ulate
d f
rom
the
n
on
-idea
l EC
mod
el
(Figure
5.3).
The
ope
rati
ng
tempe
ratur
e is
set
at
750?C
, with a c
urre
nt den
sit
y lo
ad of 1
20 mA c
m
-2 .
127
hydrogen and the adsorption process of oxygen are considered as rate determining steps
of the chemical reaction processes on both electrodes. The contribution to the diffusion
process is ascribed but not limited to the gas diffusion process of O2 to cathode electrode.
The impedance contribution from surface diffusion process of adsorbed oxygen can also
be considered for the diffusion impedance arc. The impedance contribution of gas
conversion process is confirmed by studying the effect of fuel utilization on cell
performance.
128
Reference
[1] S. Primdahl, M. Mogensen, Journal of the Electrochemical Society, 145 (1998)
2431-2438.
[2] R.U. Payne, Y. Zhu, W.H. Zhu, M.S. Timper, S. Elangovan, B.J. Tatarchuk,
International Journal of Electrochemistry, 2011 (2011).
[3] M.J. Jorgensen, M. Mogensen, Journal of the Electrochemical Society, 148 (2001)
A433-A442.
[4] N.Q. Minh, T. Takahashi, Science and Technology of Ceramic Fuel Cells, Elsevier
Science, 1995.
129
Chapter 6
EIS Application to Ni-MH Rechargeable Batteries
EC Simulation and Characterization of Batteries
and Study of Correlation between State-of-Charge and Impedance
6.1. Introduction
Two commercial nickel metal-hydride (Ni-MH) rechargeable battery D cells are
studied by impedance measurement and EC simulation to perform cell characterization,
degradation diagnostics, and SoC study. The content included in this chapter is in the
process of submission as a journal article to Applied Energy. To avoid duplicate content
in this dissertation, the introduction and part of the experimental details are not included
in this chapter.
The purpose of this work is not limited to characterize two Ni-MH rechargeable
batteries through impedance measurement and EC simulation. It also analyzes and
explains the interpretation of battery impedance and the validity of EC model based on
battery chemistry. The change of impedance at different SoC level studied in this work
provide important experimental data for power prediction and improvement of smart
charging systems.
6.2. Experimental details
Two sealed Ni-MH rechargeable batteries were studied by AC impedance in this
work, numbered Cell A and Cell B respectively. They are D-size cylindrical cells with a
130
height of 60 mm and a diameter of 32 mm. These two commercial Ni-MH cells were
originally purchased from a Radioshack? store (a division of Tandy Corp., Fort Worth,
TX) in a brand new condition. The rated voltage is labeled at 1.2 V and the original cell
capacity is rated at 4500 mAh (Radioshack? #23-519).
Performance degradation was gradually occurring in the Ni-MH Cell A and Cell B
due to years of charge-discharge cycles and storages. The degradation can be reflected
quantitatively by an decrease of cell capacity or an increase of cell impedance. Any
irreversible changes of active electrode materials, electrode structures, and electrolyte
compositions can cause cell degradation. Based on the current knowledge of rechargeable
battery technology, a gradual reduction of cell capacity is inevitable, but a cycle life of
about 500 charge-discharge cycles [1] can be generally achieved by sealed Ni-MH
batteries with regular operation. After aging, the two commercial Ni-MH rechargeable
batteries Cell A and Cell B were measured at 3702 mAh and 4362 mAh [2], less than
4500 mAh as specified by the manufacturer.
For impedance measurement at a certain capacity level, the specified amount of
charge was input to the fully-discharged cells at a rate of 0.2C. The percentage of the
input capacity to the cell capacity is defined as state-of-recharge (SoR), i.e. the ratio of
charge input to the rated cell capacity:
(6.1)
As described in our published work [2], SoR is different from SoC but has certain
correlation to it. When the specified SoR level was reached, the impedance measurement
was applied to the cells under a dc load current of 0.1C (discharge process). Thus, the
131
loaded dc current is 370 mA for Cell A, and 436 mA for Cell B. Gamry FC350TM fuel
cell monitor, connecting to TDI-Dynaload? RBL488 programmable load, was employed
to obtain impedance spectra when the frequency swept from 10 kHz down to 0.01 Hz.
The impedance data were recorded at a rate of 5 points per decade frequency. When
finishing the impedance measurement, the cells were continuously discharged to the cut-
off voltage of 1.0 V at a rate of 0.2C. In other words, the cells were discharged to a SoC
level of 0% after each impedance test. The impedance measurement at a higher SoR level
was then began by repeating this procedure. The proposed EC model was then used to
simulate the measured impedance spectra in Gamry Echem Analyst. The mode of hybrid
EIS was selected in this work for Ni-MH cell measurement. The amplitude of the desired
voltage perturbation ?V was set at 5 mV.
After conducting all the impedance measurements and other performance tests [2],
the cells were broken down to get samples of the negative electrodes. The electrode
composition was then analyzed by energy dispersive X-ray spectroscopy (EDX or EDS).
EDS is a bulk analytical technique used to qualitatively and quantitatively detect element
composition. It is able to probe about 3-4 ?m below the surface of the powder and has a
sensitivity of about 1000 ppm for elements from Beryllium (Be) with atomic number 4 to
Uranium (U) with atomic number 92. The electrode samples were powdered and pressed
into double-sided C tape on the EDS specimen mount. The specimen should be thick
enough to completely cover the tape. The composition of metal alloy used for the
negative electrodes were then detected by identifying the metal elements and their
percentage.
132
6.3. Results and discussion
6.3.1. Electrode compositions
The formulas of metal hydride alloys used for commercial Ni-MH batteries are quite
different one from the other, but they are basically follow the disordered AB5 type, A2B7
type, or disordered AB2 type [3]. Each type has its typical components. However, in all
cases, A refers to rare earth mixture and B refers to transition metals. Following
Ovshinsky?s pioneering metal alloy structure for battery electrodes [4], the disordered
AB5 type mischmetal (Mm) alloy has been developed to the most commercial level
performing better cycle ability than other alloys [5].
In this work, EDS reports that the electrode samples roughly follow the atomic ratio
of AB5. A type elements include Lanthanum (La) and Cerium (Ce). B type elements
include Nickel (Ni), Cobalt (Co), Aluminum (Al), and Manganese (Mn) with the primary
component of Ni. Small amount of Potassium (K) is also detected. It is introduced by
contacting with the electrolyte. Mn is able to adjust the metal-hydrogen bond strength [6].
And the existence of Al and Co significantly promotes electrode kinetics [3, 6]. The
formula of this AB5 type hydride alloy can be expressed as
(La9.70Ce4.39)(Ni60.75Co6.66Al3.59Mn4.45), following Ovshinsky's expression [3].
6.3.2. Impedance spectra and EC simulation
Two batteries are expected to have similar performance due to identical
specifications. In Nyquist plots (Figure 6.1), the impedance spectra of both batteries
consist with two overlapped semi-circles at higher frequency region and a straight tail at
lower frequency region. However, Cell B has a significantly larger impedance than Cell
A. The impedance of Cell B also shows greater changes with increasing SoR level than
133
Figu
re 6.1.
I
mped
anc
e spec
tra
of
Ni
-MH
ba
tte
ry
Ce
ll
A
and
Ce
ll
B
mea
sure
d a
fte
r c
ha
rgin
g to
the
SoR
lev
el
at
40%,
60%,
and
100%
. The
im
pe
da
nc
e po
int
s mea
sure
d a
t the
fre
que
nc
y of 1
Hz
ar
e hi
ghli
ghted in the plot (
?)
.
134
Cell A. The different magnitude and behavior of impedance spectra represent different
SoH of two batteries.
According to the shape of impedance spectra, one parallel (QR) sub-circuit in series
with one modified Randles circuit was proposed to simulate the measured impedance
spectra (Figure 6.2). A typical Randles circuit (Figure 6.3) consists of one ohmic
resistance R?, one parallel (CdlRct) sub-circuit behaving as a semi-circle in Nyquist plot,
and one Warburg element (symbol in W) behaving as a unit slope line at the lowest
frequency region [7]. This typical model considers faradaic impedance contributed by
both kinetic reaction processes and diffusion processes. Since two overlapped semi-
circles are observed from the impedance spectra of Ni-MH cells (Figure 6.1), one more
parallel (CR) sub-circuit is added in series with Randles circuit to derive the ideal EC
model for Ni-MH cells (Figure 6.3). Three constant phase elements (CPEs, symbol in Q)
are used to replace two ideal capacitors and one Warburg element (W) in the case of non-
ideal processes. The non-ideal EC model shown in Figure 6.2 is finally derived after
these modifications.
Kuriyama [8] proposed a four time constant EC model for their metal hydride
electrodes. The model consisted of three (CR) parallel circuits and one Warburg element.
The fitting values of model elements were used to study the deterioration mechanism of
electrodes without showing the fitting curves. However, EC models having less time
constants were preferred to avoid redundancy. Cheng [9] and Buller [10] chose EC
models with less time constants to simulate the impedance spectra of their battery
systems. In this work, a three time constant EC model is proposed for battery simulation
based on the behavior of our impedance spectra.
135
Figu
re 6.2.
The
non-
idea
l EC
model
empl
oy
ed to s
im
ulate
the
im
pe
da
nc
e spe
ctra
me
asur
ed fr
om bot
h Ni
-MH
rec
ha
rge
able ba
tte
rie
s.
136
Figu
re 6.3.
The
idea
l EC
model f
or t
he
Ni
-MH r
echa
rge
abl
e ba
tte
rie
s, st
ruc
tur
ed ba
sed on R
andles c
irc
uit
.
137
Figure 6.2 illustrates the EC model employed for the simulation of both Ni-MH
rechargeable batteries. The fitting curve shown in Figure 6.4 simulates the impedance
spectra of Cell A measured at 30% SoR level. The pure resistor R? refers to the smaller
intercept of impedance spectra in real axis. Two parallel sub-circuits (Qdl,1Rct,1) and
(Qdl,2Rct,2) are used to simulate two overlapping impedance arcs in higher frequency
range. For non-ideal capacitive behavior, the exponential numbers of Qdl,1 and Qdl,2 are
expected to stay in the range between 0.8 and 1. The element Qdiff simulates the straight
lines in the lowest frequency range for the non-ideal Warburg behavior with an
exponential number less than 0.5.
The fitting curves shown in Figure 6.5 and Figure 6.6 simulate impedance spectra of
Cell A and Cell B at different SoR levels by the proposed EC model. In higher frequency
range, the two parallel (QR) circuits simulate the measured impedance spectra quite well.
The difficulty remains in low frequency region. The fitting curves gradually deviate from
the impedance data when the frequency decreases to lower than 1 Hz. This deviation
becomes more significant at higher SoR levels, especially at 100% SoR level. Similar
fitting deviation can be observed in Ruiz?s simulation of the MH electrode with a
mathematical model [11]. Li?s fitting curves [12] for their nickel electrodes cannot
provide references for diffusion impedance simulation because they only published fitting
results for charge transfer impedance at high frequency region. Other impedance studies
of Ni-MH systems [8, 9, 13, 14] rarely revealed fitting curves comparing to measured
impedance spectra. The attentions were usually paid to faradaic impedance at higher
frequency region contributed by hydrogen oxidation reaction (HOR) and NiOOH
reduction reaction.
138
Figu
re 6.4.
I
mped
anc
e spec
tra
mea
sur
ed
from
Ce
ll A
at
the
SoR
leve
l of
30%
and
simul
ated
by
the
EC
model
propose
d in
Fig
ure
6.2.
139
Figu
re 6.5.
I
mped
anc
e spec
tra
me
asur
ed
from
Ce
ll
A
at
diff
ere
nt
SoR
leve
l a
nd
thei
r fitti
ng
cur
ve
s sim
ulate
d fr
om
the
EC
mode
l
shown in
Fig
ure
6.2.
140
Figu
re 6.6.
I
mped
anc
e spec
tra
mea
sur
ed
from
Ce
ll
B
at
dif
fere
nt
SoR
leve
l a
nd
thei
r fitti
ng
cur
ve
s sim
ulat
ed
from
the
EC
model
shown in F
igu
re
6.2.
141
6.3.3. EC element interpretation
The pure resistor R? in series with all other EC elements is contributed by ohmic
resistance. It comprises the resistance of cell components (electrodes, electrolyte, and
conducting substrates) and resistance introduced by contact and connection. The parallel
sub-circuits (Q1,dlR1,ct) and (Q2,dlR2,ct) refer to charge transfer processes related to kinetic
reactions occurring on each electrode. The diffusion element Qdiff is connected with
(Q2,dlR2,ct) in the EC model. The EC arrangement ascribes the major contribution of the
diffusion impedance to the electrode where the kinetic process of (Q2,dlR2,ct) occurs.
Cell mechanisms are the basis of EC simulation and impedance interpretation. The
electrolyte of commercial Ni-MH rechargeable batteries is usually a concentrated
potassium hydroxide (KOH) solution. The mechanism of discharge process can be
expressed by the overall electrochemical reaction [1] as:
(6.2)
The reactant nickel oxyhydroxide (NiOOH) refers to the active material of positive
electrodes. The reactant MH refers to the hydrided metal alloy of negative electrodes, that
is hydrogen (H2) absorbed into metal alloys. The overall mechanism of charge process is
simply the reversing reaction of Eq. 6.2. However, it was proved that the electrode
kinetics was asymmetric. Batteries usually undertake larger impedance during charge
process than discharge process under the same measuring conditions [14]. The
mechanisms discussed below are typically for discharge process.
142
6.3.3.1.Mechanism on negative electrodes
The half-cell process on the negative electrode is called the dehydriding reaction
[13]. It can be expressed step by step as:
a) Hydrogen atoms adsorbed in metal alloy transfer from bulk sites to surface sites, that
is metal alloy sites at the interface of electrode and electrolyte:
(6.3)
where Had refers to the hydrogen atom adsorbed in the metal alloy; MS and MB refer to
the empty sites of metal alloy on surface and in bulk, respectively. Viitanen [15]
explained this process as hydrogen released from the hydride phase to form adsorbed
hydrogen atoms.
b) HOR at the electrode/electrolyte interface, that is a charge transfer step coupled by
the diffusion process of hydroxide ions from bulk electrolyte to the
electrode/electrolyte interface:
(6.4)
(6.5)
where OHB- and OHS- refer to the hydroxide ions in the bulk of electrolyte and at the
interface of electrode/electrolyte, respectively; H2OS is water atoms produced by the
HOR process at the electrode/electrolyte interface.
c) Transport of water molecules away from electrode/electrolyte interface to bulk
electrolyte
(6.6)
where H2OB is water molecules in bulk electrolyte.
143
It is accepted that the rate limiting step on the negative electrode is the one-electron
transfer kinetic process (Eq. 6.5) when there is sufficient hydrogen atoms adsorbed in the
metal alloy sites on electrode surface. Diffusion of hydrogen atoms from bulk electrode
to surface sites (Eq. 6.3) is a semi-infinite diffusion process featured by Warburg
behavior at low frequency. This process does not have significant effect on cell
impedance under general conditions [13, 16], but will become the rate limiting process at
low temperature [17] due to decrease of diffusion coefficient. The mass transfer process
of hydroxide ions (Eq. 6.4) from bulk electrolyte to electrolyte/electrode interface is a
porous bounded diffusion process. This process is generally negligible unless batteries
are operated under a large dc current load. The effect of hydroxide ion diffusion process
become significant because its transfer rate lags behind the electrode kinetics. The
transfer process of water molecules (Eq. 6.6) can be neglected when compared to other
processes, because its concentration in electrolyte are assumed to be large enough [13]
and globally constant during battery operation [18]..
6.3.3.2.Mechanism on positive electrodes
Zimmerman [19] proposed a mechanism for the discharge process on the positive
electrodes. Following his idea, the step-by-step reaction can be explained as
a) Transport process of water molecules from bulk electrolyte to the interface of
electrolyte and electrode, followed by the formation of proton at catalytic site at the
interface:
(6.7)
(6.8)
144
b) Transport process of hydroxide ions from the interface back to the electrolyte bulk:
(6.9)
c) Reduction reaction of electrode active material NiOOH, that is a charge transfer
process coupled by the diffusion process of proton from the surface site to the charge
transfer site in the electrode bulk:
(6.10)
(6.11)
The situation on positive electrodes is different from what discussed above for
negative electrodes. Diffusion processes are considered as the rate limiting step [19],
because the effect of diffusion process on nickel hydroxide electrode is normally
observed under general battery operating conditions [19]. This means the charge transfer
process expressed as Eq. 6.11 is not the rate limiting process. And the diffusion process
of proton in positive electrode (Eq. 6.10) is regarded as the rate limiting step under
normal discharge process. The impedance of proton diffusion process presents the
Warburg behavior.
6.3.3.3.Full battery impedance
The impedance arcs of semi-infinite diffusion processes can be observed from
impedance spectra of both negative electrode studies [8, 16, 20] and positive electrode
studies [21, 22]. However, only one Warburg behavior arc is observed at low frequency
region from the impedance spectra of Ni-MH cells (Figure 6.1). Thus the difficulty of
interpretation lies in the diffusion process. Karden [14] measured the half-cell impedance
separately and compared the sum of them to the total cell impedance. The comparison
145
clearly illustrated that the negative electrode impedance dominated higher frequency
region and the diffusion impedance in lower frequency region was contributed by the
positive electrode. Hammouche [18] also found that the nickel hydroxide electrode
contributed the diffusion impedance to the full cell under normal discharge operation.
Based on the discussion of half-cell mechanism in the previous section, the diffusion
element Qdiff in this simulation work is ascribed to the rate limiting diffusion process of
positive electrode. It is explained as the solid-state diffusion process of proton in nickel
hydroxide electrode shown as Eq. 6.10. The (Q2,dlR2,ct) circuit connected with the
diffusion element is applied to simulate the charge transfer process occurring on the
electrolyte/positive electrode interface coupling with the reduction reaction of the active
material NiOOH (Eq. 6.11). And the sub-circuit (Q1,dlR1,ct) is ascribed to the one electron
transfer process of HOR occurring on the surface of the negative electrode.
6.3.4. Correlation between impedance and SoR
The correlation between SoR and SoC was studied on Cell B [2]. When certain
amount of charge was input to Cell B, its SoR was calculated. Then, its SoC was
measured by discharging Cell B to cut-off voltage at 0.2C. The comparison of SoR to
SoC is plotted versus the amount of input charge in Figure 6.7. The value of SoR is
identical to the value of SoC even when the SoC level increases up to over 80%. As SoR
continue to linearly increase with the amount of input charge, the increase of SoC slows
down. The value of SoC deviates from SoR at high SoC level, but within a small
magnitude of difference. The reason is that the side reactions of oxygen reduction
reaction (ORR) on positive electrodes (Eq. 6.12) compete for the input charges. To
protect Ni-MH batteries under this overcharge condition, the capacity of negative
146
Figu
re 6.7.
C
orre
lation betwe
en S
oC and S
oR. Da
ta a
re mea
sur
ed fr
om C
ell
B
[2]
.
147
electrodes are usually designed as 1.5 to 2 times of positive electrodes [1]. The oxygen
evolved on positive electrodes then diffuses through electrolyte and reach negative
electrodes to recombine with water molecular (Eq. 6.13). The side reactions on both
electrodes under overcharge condition can be expressed as [1]:
Positive electrodes: (6.12)
Negative electrodes: (6.13)
Side reactions also competes with cell reactions when the operation falls into
overdischarge conditions at low SoC levels [1]:
Positive electrodes: (6.14)
Negative electrodes: (6.15)
Hydrogen transfers from positive electrode to negative electrode through electrolyte. Side
reactions occurring under both overcharge and overdischarge conditions consume
charges without changing electrolyte and electrodes. In this way, Ni-MH cells are
balanced but cell efficiency drops to an unsatisfactory level. Nelson pointed out that the
realistic operation window for hybrid driving duty cycle of HEVs is from 30% SoC to
70% SoC [23]. As observed in Figure 6.7, within this charge-discharge window, the
values of SoR are equivalent to SoC. When studying the correlation between cell
impedance and SoC, SoR can be used as SoC during normal operation levels for
simplification.
The change of ohmic resistance and charge transfer resistance with SoR level is
shown in Figure 6.8 to 6.10. The resistances of Cell A and Cell B contributed by the same
processes are plotted on the same figure for comparison. The ohmic resistance R? of Cell
148
Figu
re 6.8.
Ohmic r
esis
tanc
e (
R
?) o
f (
?)
Ce
ll A a
nd
(?
) C
ell
B
at di
ffe
rent S
oR l
eve
ls.
Va
lues a
re c
alcula
ted b
y the E
C model shown
in F
igu
re 6.2
.
149
Figu
re 6.9.
C
ha
rge
tra
nsf
er
resis
tan
ce
contribut
ed
by
HO
R
proc
ess
on
the
ne
ga
tive
elec
trode
s of
(?
) C
ell
A
and
(?
) C
ell
B
at
diff
ere
nt S
oR
leve
ls. Va
lue
s ar
e c
alcul
ated b
y the
EC m
ode
l shown i
n F
igu
re 6.2
.
150
Figu
re 6.10.
C
ha
rge
tra
nsfe
r re
sis
tan
ce
contribut
ed
by
NiOO
H
reduc
tion
pro
cess
on
the
posi
tive
elec
trod
es
of
(?
) C
ell
A
and
(?
) C
ell
B
at di
ffe
rent S
oR l
eve
ls.
Va
lues a
re
calcula
ted b
y the E
C model shown in F
igu
re 6.2
.
151
A stays at a lower and more stable level than Cell B (Figure 6.8). Between 30% SoR and
70% SoR, R? of Cell B increases with increasing SoR due to the reduction of NiOOH
and the formation of Ni(OH)2 on the positive electrodes. However, this expected
variation is not significant in Cell A. Cell A has lower cell capacity after aging. The
smaller change of electrode composition before and after the reduction of NiOOH
presents a more stable R? of Cell A than Cell B. The cell electrodes are not the only
contribution to the ohmic resistance of Ni-MH cells. The lower R? of Cell A does not
determine the condition of its cell capacity. The status of electrolyte and any other
auxiliary components also changes the value of R?. The values of R? at SoR levels lower
than 30% and higher than 70% are slightly higher than R? at SoR levels between 30%
and 70% due to dissolution of H2 and O2 evolved by the side reactions.
The charge transfer resistances contributed by the HOR process on negative
electrodes (Rct,1) are plotted against SoR levels in Figure 6.9. Rct,1 of Cell A decreases
with increasing SoR level. This trend can also be observed from Cell B except the point
at 20% SoR level. At higher SoR level, more active materials on negative electrodes
facilitate the kinetic reactions and decrease the charge transfer resistance. The Rct,1 of
both cells stay at an equivalent value at each SoR level. This reflects an equivalent
electrode kinetics of HOR process on negative electrodes of both cells. Although the cell
capacity of two cells are different after aging, the capacity of negative electrodes is
designed at a sufficient level for cell protection. The reduction of cell capacity does not
have significant effect on HOR kinetics.
The cell capacity is dominated by the capacity of positive electrodes. Significant
differences exist between the charge transfer resistances contributed by positive
152
electrodes (Rct,2) of two cells (Figure 6.10). Cell B has a much smaller and more stable
Rct,2 comparing to Cell A. This means that more active materials are remained at the
positive electrode of Cell B, corresponding to a faster kinetics and higher electrode
capacity. The better cell capacity of Cell B can be reflected by the smaller charge transfer
resistance of its positive electrode (Rct,2). As a result of experimental measurement, the
cell capacity of Cell A after aging is measured at about 3702 mAh, much smaller than the
4362 mAh of Cell B. Since the positive electrode of Cell A is more degraded comparing
to Cell B due to insufficient amount of active materials, the production of Ni(OH)2 at low
SoR levels is expected to have more significant effects on electrode kinetic process (Eq.
6.11). Thus, the Rct,2 of Cell A presents a more significant increase with decreasing SoR
level as expected.
6.4. Conclusion
In this work, electrochemical impedance spectroscopy (EIS) and equivalent circuit
(EC) simulation was applied to characterize two aged commercial Ni-MH rechargeable
batteries. The proposed EC model is able to simulated the impedance spectra measured
from both cells at varying SoR levels. The two cells should perform similar or even
identical behaviors at brand new conditions; however, significant performance
differences could be observed from their impedance spectra after aging. The EC
simulation broke the total cell impedance down into several parts. According to the
widely accepted battery chemistry, these parts are ascribed to the ohmic conduction, the
electron transfer process of hydrogen oxidation reaction (HOR) on the negative electrode,
the charge transfer process of NiOOH reduction process on the positive electrode, and the
solid-state diffusion process on the positive electrode. The discrimination and
153
interpretation of impedance spectra illustrated that the difference between cell impedance
is mainly contributed from ohmic resistance, and secondly contributed from the NiOOH
reduction process on the positive electrode.
The two aged cells studied in this work were at different state-of-health (SoH). Their
impedance were measured at different levels of state-of-recharge (SoR) during
discharging process. The correlation of impedance contributed by different cell processes
to SoR level was analyzed, along with its correlation to cell capacity level. The ohmic
resistance increases with increasing SoR level, while the activation resistances of both
electrodes decrease with increasing SoR level. The degradation of cell capacity can be
reflected by the change of activation resistance of positive electrode.
So far, there has been only a few research focusing on impedance of Ni-MH
batteries. More experimental data of commercial Ni-MH batteries measured under
various SoH and SoC are required to establish a quantitative model between SoC and
impedance. This work not only demonstrates the ability of EIS and EC simulation to
perform battery diagnostics on commercial cells, but also provides a probability to find
another method for determining the value of SoC, which can help to improve the smart-
charging systems strongly required by hybrid electric vehicles (HEVs), electric vehicles
(EVs), and other electronic products.
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Chapter 7
Conclusions and Future Challenges
7.1. General conclusion
This dissertation systematically integrates the projects done in the past few research
years, including the system characterization and performance evaluation of the proton
exchange membrane (PEM) fuel cell stacks, the single cells of tubular solid oxide fuel
cell (T-SOFC), and nickel-metal hydride (Ni-MH) rechargeable batteries. The
competence of electrochemical impedance spectroscopy (EIS) is highlighted to perform
dynamic in-situ characterization of energy conversion and storage systems.
Basic equivalent circuit (EC) elements and models are established according to the
generalized mathematic models for electrochemical processes. However, the
mechanisms, materials, and geometric structures differ from one power system to
another. The behaviors and variation tendencies of impedance spectra are sensitive to the
specific system processes and operating conditions of the power systems. Both having
kinetic processes and diffusion processes, the fuel cell systems and Ni-MH rechargeable
battery systems have significantly different characteristic impedance shapes. Even for
fuel cell systems, the magnitudes and changing tendency of impedance arcs are different
among the high temperature (HT) PEM fuel cell stack, the traditional PEM fuel cell
stack, and the T-SOFC single cell tubes, because different electrolyte membranes provide
different proton conduction mechanisms. On the other side, the non-uniqueness of EC
157
models brings great challenges to model validation. Large amount of data sets measuring
under different operating conditions and system configurations are required to validate
the simulation. The uniqueness of power systems are employed to validate and interpret
EC models; while the non-uniqueness of EC models is utilized to generalize power
systems into a comparable level and facilitate the establishment of standard procedures
for performance evaluation and diagnostics of power systems.
7.2. Challenges to the Future
7.2.1. Energy conversion systems
Fuel cells are famous by its high energy efficiency and zero emission. However, the
realization of commercial fuel cell systems subjects to several restrictions, not only of
fuel cell itself but also of required up and down processes and auxiliaries. Hydrogen
infrastructure is one of the most challenge issues closely related to fuel cell
commercialization. The development of hydrogen storage and transportation is currently
grinding to a standstill. Economic concerns and safety uncertainty bring additional
difficulty to technological issues.
The alternative solution is to develop the fuel cell systems powered by methanol
reformate. CO tolerance becomes the upmost concern to the direct methanol
technologies. As analyzed in previous chapters, the novel phosphoric acid (PA) doped
polybenzimidazole (PBI) successfully achieved the PEM fuel cell operation at elevated
temperatures. However, current technology is still not sufficient to support a stable
performance. The HT-PEM fuel cell stack is able to provide a power output at
commercial level, but the novel MEAs are vulnerable to ambient and operating
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conditions. Unclear mechanisms of proton conductivity and lack of experimental data
bring further uncertainty to diagnose the degradation of the stack.
SOFC operated at an temperature up to hundred Celsius degree facilitates the
pretreatment of fuels. It overcomes the CO poisoning issue for PEM fuel cell systems;
however, the irreversible sulfur poisoning is still one of the most challengeable issue for
SOFC operation. Although the operating temperature up to 1000?C enhanced the kinetic
processes, it also places strict requirements on the characteristics of materials of
electrodes and electrolytes. Considering the recovery of heat, a lower operating
temperature is desired but without sacrificing the energy efficiency. SOFC systems for
operation around 400?C to 600?C are still under research. Without validated mechanisms,
the feasibility and improvement of direct reformate operation is another big issue of
SOFC research due to the complicated fuel compositions with reactants and reaction
intermediates sensitive to electrochemical reaction and surface processes.
7.2.2. Energy storage systems
The Ni-MH battery has natural protection against overcharging and over-discharging
with oxygen and hydrogen recombination inside the cell to form water. The overcharge
process, overdischarge process, capacity retention, and the in-situ state-of-charge (SoC)
parameter are quite important to the battery cycle life and calendar life time. The
improvement of the battery energy storage and conversion efficiencies will significantly
increase the overall energy efficiency and prolong the HEV?s traveling miles per unit fuel
use.
159
Impedance-based battery models are essential to the simulation of the complexity of
modern power electronics. Battery behaviors are highly non-linear and their dynamic
performance depends on different parameters such as temperature, service life-time, and
state-of-charge. Future batteries for vehicle applications of electric vehicles (EVs) and
hybrid-electric vehicles (HEVs) require the development of deep-cycle long-life EV
batteries and high-rate long-life HEV batteries. Flat thin-plate structures have been made
progress for deep-cycle requirement and cranking power needs. The related technology
greatly reduces lead used in the system. The further work on newly structured batteries is
necessary for examination of basic battery chemistry and improved electrode processes,
exploration of mass transfer limitations, and investigation of the failure mode or
mechanisms. Active material, utilization of active material, support structure, current
collector, separator, electrolyte, and battery system maintenance (gas recombination and
thermal management) are important factors for battery performance, state-of-healthy
(SoH), and operating life-cycle time. Further advanced battery design concepts and
structure improvements potentially give birth to the novel battery performance and
promote the technical improvements for the high-rate and deep-cycle applications in
future power reserve and energy storage applications.
The advanced vehicle systems or processes with higher energy efficiency are
preferred to use for saving fuels and improving the mileage of per unit fuel use. Hybrid
power-trains reduce undesirable emissions and also have their potential to improve fuel
economy significantly. A highly efficient engine can charge the battery pack and propel
the vehicle at the same time. The battery pack is also returned with some energy from the
electric motor, which is served as another generator in the regenerative braking or
160
coasting mode. Therefore, the battery burst charge acceptance during frequent braking
and power output capability during heavy acceleration are significantly important for the
HEV fuel efficiency and engine emissions.
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Publications
Refereed Journal Articles
[1] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?Dynamic Analysis and Diagnostics of a
High Temperature PEM Fuel Cell Stack?, ECS Transactions 2013 50(2): 745-751.
[2] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?In-situ Performance Analysis of a High
Temperature Proton Exchange Membrane Fuel Cell Stack at Loads?, ECS
Transactions 2013 45(13): 67-72.
[3] Wenhua H. Zhu, Ying Zhu, Zenda Davis, and B. J. Tatarchuk, "Energy Efficiency
and Capacity Retention of Ni-MH Batteries for Storage Applications", Applied
Energy, 106 (2013) 307-313.
[4] Wenhua H. Zhu, Ying Zhu, and B. J. Tatarchuk, ?A Simplified Equivalent Circuit
Model for Simulation of Pb-Acid Batteries at Load for Energy Storage Application?,
Energy Conversion and Management, 52 (2011) 2794-2799.
[5] Robert U. Payne, Ying Zhu, Wenhua Zhu, Mark S. Timper, S. Elangovan and B. J.
Tatarchuk, ?Diffusion and Gas Conversion Analysis of Solid Oxide Fuel Cells at
Loads via AC Impedance?, International Journal of Electrochemistry, vol. 2011,
Article ID 465452, doi:10.4061/2011/465452.
Book Chapter
[6] Ying Zhu, Wenhua H. Zhu and Bruce J. Tatarchuk (2013). "In-Situ Dynamic
Characterization of Energy Storage and Conversion Systems", in: Energy Storage -
Technologies and Applications, Ahmed Zobaa (Ed.), Chapter 10, pp. 239-270,
InTech, Croatia, 2013. ISBN: 978-953-51-0951-8.
162
Conference Proceedings / Abstracts
[7] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?An In-situ Dynamic Performance Study
on an HT-PEM Stack and its Comparison to a Traditional PEM Stack?, in:
Proceedings of the 45th Power Sources Conference, pp.139-142, Las Vegas, Nevada,
June 11-14, 2012.
[8] W. H. Zhu, Ying Zhu, and B. J. Tatarchuk, ?Rate Performance and Energy
Efficiency of Lithium-Ion Batteries for Storage Applications?, in: Proceedings of the
45th Power Sources Conference, pp.13-16, Las Vegas, Nevada, June 11-14, 2012.
[9] W. H. Zhu, Ying Zhu, and Bruce J. Tatarchuk, ?Energy Efficiency of Ni-MH Battery
for Rapid Storage Application?, in: Pittcon 2012 Conference & Expo - Energy &
Fuels: Advanced Materials and Characterization Methods, #1690-6, Orlando, FL,
March 10-15, 2012.
[10] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?In-situ Electrical Characterization of a
High Temperature PEM Fuel Cell Stack at Loads?, in: AIChE Annual Meeting:
Fuels and Petrochemicals Division - Alternate Fuels & New Technology - Fuel Cell
Technology II, #373b, Minneapolis, MN, October 16-21, 2011.
[11] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?Validation of the Equivalent Circuit
Diagram for SOFC Modeling?, in: AIChE Annual Meeting: Fuels and
Petrochemicals Division - Alternate Fuels & New Technology - Fuel Cell
Technology II, #373c, Minneapolis, MN, October 16-21, 2011.
[12] W. H. Zhu, Ying Zhu, and B. J. Tatarchuk, ?Comparison of On-Board Hydrogen
Production from Several Non-Fossil Fuel Feedstocks?, in: Proceedings of the AIChE
Annual Meeting: Environmental Division - Renewable Hydrogen Production I,
#259b, Minneapolis, MN, October 16-21, 2011.
[13] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?Breakdown of Polarization Losses in
Button Sized SOFCs and a Prismatic Stack via Impedance Spectroscopy?, in:
Proceedings of the AIChE Annual Meeting: Fuels and Petrochemicals Division -
Fuel Cell Technology and Alternate Fuels & New Technology, #641a, pp.1-7, Salt
Lake City, UT, November 7-12, 2010.
163
[14] W. H. Zhu, Ying Zhu, and B. J. Tatarchuk, ?Self-Discharge Evaluation of Ni-MH
Battery Using Metal Hydride Alloy for Energy Storage Applications?, in:
Proceedings of the AIChE Annual Meeting: Materials Engineering and Sciences
Division - Composites for Energy Applications, #139c, pp.1-3, Salt Lake City, UT,
November 7-12, 2010.
[15] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?AC Impedance Study of Mass Transfer
Processes and Hydrogen Oxidation Reaction in Solid Oxide Fuel Cells?, in:
Proceedings of the 44th Power Sources Conference, pp.401- 404, Las Vegas,
Nevada, June 14-17, 2010.
[16] W. H. Zhu, Ying Zhu, and B. J. Tatarchuk, ?Advanced Pb-Acid Batteries for
Potential High-Rate Power Applications?, in: Proceedings of the 44th Power Sources
Conference, pp.75-78, Las Vegas, Nevada, June 14-17, 2010.
[17] Ying Zhu, W. H. Zhu, and B. J. Tatarchuk, ?AC Impedance in Characterization of
SOFC and Interpretation of a Low Frequency Inductive Loop?, in: Proceedings of
the AIChE Annual Meeting: Fuels and Petrochemicals Division ? Fuel Cell
Technology, #89e, Nashville, TN, November 8-13, 2009.
[18] W. H. Zhu, Ying Zhu, and B. J. Tatarchuk, ?Massive Deep-Cycle Pb-Acid Batteries
for Energy Storage Applications?, in: Proceedings of the AIChE Annual Meeting:
Sustainable Electricity - Generation and Storage, #676d, pp.1-4, Nashville, TN,
November 8-13, 2009.
[19] R.U. Payne, Ying Zhu, W.H. Zhu, and B. J. Tatarchuk, ?Determining Kinetic and
Mass Transfer Limiting Behavior of a SOFC via AC Impedance?, in: Proceedings of
the AIChE Annual Meeting: Novel Electrochemistry and Materials for Fuel Cells II,
#768d, Philadelphia, PA, November 16-21, 2008.
[20] W. H. Zhu, R.U. Payne, Ying Zhu, and B. J. Tatarchuk, ?Electrical Characterization
of Lead-Acid Battery at Load for HEV Applications?, in: Proceedings of the AIChE
Annual Meeting: Battery and Fuel Cell Energy on Vehicles, #457a, pp.1-3,
Philadelphia, PA, November 16-21, 2008.
164