Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model

Electric vehicles (EVs) have developed rapidly in the face of critical problems of climate change, resource scarcity and environmental pollution, while lithium-ion batteries (LIBs) have been widely used as the onboard power source of EVs. As a key state in the battery management system (BMS), state of charge (SOC) not only defines the safety margin of battery to avoid over- charge/discharge, but also underlies the system-level energy management. This paper proposes an online adaptive model-based SOC estimator. This method combines the Thevenin battery model, the recursive least squares (RLS) algorithm and the extended Kalman filter (EKF) algorithm to accomplish parameter identification and SOC estimation in a cascaded manner. Simulations and experiments are performed to evaluate the proposed method. Results suggest that the proposed method can effectively track the change of model parameters, and thus estimate the SOC accurately in real time.