Estimation of model parameters and state-of-charge for battery management system of Li-ion battery in EVs

The Battery Management System (BMS) is responsible for accurate monitoring of the status of the battery (State-of-Charge (SOC)) for maintaining optimal battery performance in Battery Electric Vehicles (BEVs). Ambient temperature is a significant factor that influences the accuracy of SOC estimation, hence electrochemical combined model dependents of temperature was utilized for simulating the dynamic behavior of battery in BMS. Unknown parameters of the battery model are identified using the least square algorithm for Dynamic Stress Test (DST), validation of estimation is conducted for Federal Urban Driving Schedule (FUDS) and concluded that the error between predicated terminal voltage form model and voltage from DST profile was less than 0.08V for defined conditions. Then, for SOC estimation, recursive Bayesian estimation method based Extended Kalman Filtering (EKF), and Sigma-Point Kalman Filtering (SPKF) approaches were adopted. To quantify the performance of the estimators, Root Mean Square Error (RMSE) and execution time at different temperature were evaluated. The evaluation results indicate that maximum error in case of EKF is 2.43% whereas for SPKF is 1.2% and maximum execution time taken by EKF is 3.57 sec whereas for SPKF is 4.53 sec. The results reported that SPKF provides accurate and robust SOC estimation in compared EKF and could be efficiently applied in BMS for BEVS.