Electrochemical-Model-Based Estimation of Li-ion Battery SoC: Performance and Limitations of EKF Method in an Electromobility Use Case

This paper presents an approach for state of charge (SoC) estimation in Li-ion batteries using the Extended Kalman Filter (EKF) within the Single Particle Model (SPM) describing battery electrochemical phenomena. Accurate SoC estimation is crucial for battery management, enhancing safety, lifespan, and reliability. Whereas traditional methods struggle with complex dynamics, this paper investigates the opportunity of combining EKF’s estimation performance with SPM accuracy, with model parameters being assumed as properly identified. It is shown that SoC estimation is robust to noise and model simplifications. The EKF was validated in an electromobility use case against a previously proposed two-level SoC observer. MATLAB®/Simulink® simulations confirm the EKF’s robustness and estimation accuracy, while its tuning complexity appears as a slight drawback against the two-level observer, more simply to adjust.