A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy

The accurate state-of-charge (SOC) estimation and real-time performance are critical evaluation indexes for Li-ion battery management systems (BMS). High accuracy algorithms often take long program execution time (PET) in the resource-constrained embedded application systems, which will undoubtedly lead to the decrease of the time slots of other processes, thereby reduce the overall performance of BMS. Considering the resource optimization and the computational load balance, this paper proposes a multi-model switching SOC estimation method for Li-ion batteries. Four typical battery models are employed to build a close-loop SOC estimation system. The extended Kalman filter (EKF) method is employed to eliminate the effect of the current noise and improve the accuracy of SOC. The experiments under dynamic current conditions are conducted to verify the accuracy and real-time performance of the proposed method. The experimental results indicate that accurate estimation results and reasonable PET can be obtained by the proposed method.