SOC Estimation of Lithium Battery Based on Improved Multi-Innovation Cubature Kalman Considering Temperature Effect

Measuring State of Charge (SOC) accurately plays an important role in Battery Management System (BMS) in different temperature. The parameters of the battery model are identified by using the recursive least square method with forgetting factor (FFRLS). In order to fully consider the current innovation and historical information, the exponential weighted multiple innovation Cubature Kalman Filter (EWMI-CKF) with stronger robustness is obtained by combining the exponential weighted multiple innovation theory with the Cubature Kalman Filter (CKF). The battery model and algorithm are demonstrated by using the DST, FUDS and US06 experimental data. The results show that compared with the traditional EKF, UKF and CKF, the Exponential Weighted Multi Innovation Cubature Kalman Filter (EWMI-CKF) proposed in this paper has better stability at different temperatures and working conditions, and has certain robustness under the sensor error.