State-of-Charge Estimation for Li-Ion Battery using Extended Kalman Filter (EKF) and Central Difference Kalman Filter (CDKF)

A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.