|dc.description.abstract||This thesis introduces a data-driven approach for lithium-ion concentration estimation in
battery cells, leveraging a deep learning model based on Long Short-Term Memory (LSTM)
networks. As lithium-ion batteries gain widespread adoption, particularly in electric vehicles
and renewable energy storage, accurate and efficient estimation of lithium-ion concentration
is pivotal. Existing methods like the polynomial Reduced Order Models (ROMs), although
popular, face trade-offs between computational efficiency and accuracy.
In this work, we propose an LSTM-based estimator designed to achieve high accuracy
without compromising computational speed. The LSTM architecture’s depth allows it to
capture intricate relationships within the cell. The estimator’s performance was rigorously
evaluated and compared against a polynomial ROM model. Our estimator exhibited re markable accuracy in short term estimation, achieving a Mean Absolute Percentage Error
(MAPE) of 1.09% at 1000s and 3.27% at 2000s for estimation of surface concentration for
single particle anode. For estimation of average concentration for single particle anode, the
MAPE was 1.27% at 1000s and 2.55% at 2000s. The estimator’s swift computation time
provides potential for real-time applications.
Overall, this thesis provides a comprehensive exploration of LSTM-based lithium-ion
concentration estimation, from model design and implementation to performance evaluation.
This work underscores the potential of data-driven approaches in battery state estimation,
opening up new possibilities for real-time applications and improved battery management