State of charge estimation for LiMn2O4 power battery based on strong tracking sigma point Kalman filter

Abstract The State of Charge ( SOC ) estimation is important since it has a crucial role in the operation of Electrical Vehicle (EV) power battery. This paper built an Equivalent Circuit Model ( ECM ) of the LiMn 2 O 4 power battery, and vast characteristics experiments were undertaken to make the model identification and thus the battery SOC estimation was realized. The SOC estimation was based on the Strong Tracking Sigma Point Kalman Filter (STSPKF) algorithm. The comparison of experimental and simulated results indicates that the STSPKF algorithm performs well in estimating the battery SOC , which has the advantages of tracking the variables in real-time and adjusting the error covariance by taking the Strong Tracking Factor (STF) into account. The results also show that the STSPKF algorithm estimated the SOC more accurately than the Extended Kalman Filter (EKF) algorithm.