|dc.description.abstract||Accurate estimations of the state of charge (SOC), capacity fade (SOHQ) and power fade (SOHP) are critical for ensuring safe and reliable operations of Lithium-ion batteries. Traditional estimation methods using complex models and look-up tables do not satisfy either the required accuracy or computational time necessary for real-time applications. In this paper, we propose a method that simultaneously estimates both SOC and SOH over different temperature ranges under aging conditions. The battery is modeled with a second-order equivalent circuit (ECM) and then its states and parameters are estimated by implementing a combination of a Variable model framework (VM) based Adaptive Extended Kalman Filter along with a forgetting factor based Recursive Least Square (RLS) filter algorithm in a closed-loop framework.
The VM-AEKF is employed to efficiently estimate the fast varying SOC and model parameters where the VM framework is designed specifically to improve the stability and accuracy of the estimator under conditions when the system is not sufficiently excited by the input signal. Simultaneously, the RLS estimates the slowly varying maximum capacity and updates the value based on a delayed approach. The parameters estimated by the proposed estimator are then used to calculate the SOHP and SOHQ.
The proposed algorithms are validated with a large format NMC/Carbon pouch type power cell with a nominal capacity of 58.4 Ah at multiple charge-discharge cycles considering aging and temperature effects. The experimental results have shown less than 5% SOC estimation error and less than 3% capacity estimation error for the typical SOC range of 10% to 90%.||en_US