State-of-charge Estimation of Lithium-ion Batteries Based on Multiple Filters Method

Abstract Energy crises and environmental issues have promoted research into development of various types of electric vehicles (EVs). Since the control strategy of EVs is essentially dependent on the state-of-charge (SOC) estimation of the batteries, one of the most critical issues of battery management system (BMS) is to accurately estimate the SOC in real-time. This paper proposes a multiple filters method for SOC estimation by combining extended Kalman filter (EKF) and particle filter (PF) method. Compared with EKF and PF method, this approach has higher SOC estimation accuracy. In the new method, each particle has its own filter, thus provides better tracking and prediction of SOC. Validation experiments are carried out based on IFP1865140-type batteries developed by Hefei Guoxuan High-Tech Power Energy CO. LTD. of China. Experiments under dynamic current condition are performed to verify the robustness of the proposed method. The experimental results have indicated that accurate and robust SOC estimation results can be obtained by the proposed method.