Research on Estimation of State of Charge of Li-ion Battery based on Cubature Kalman Filter

Abstract

Li-ion batteries are widely used in the new energy vehicle industry. To make the battery pack work safely and reliably, it is necessary to estimate the state of charge of battery (SOC) accurately. In this paper, the second-order Thevenin is used as the equivalent circuit model, and the parameters of the model are identified based on the experimental data of Hybrid Pulse Power Characterization (HPPC). In view of the shortcomings of the Extended Kalman Filter (EKF) algorithm and the Unscented Kalman Filter (UKF) algorithm, the Cubature Kalman Filter (CKF) algorithm is chosen to estimate the SOC in this paper. Three algorithms are estimated and verified under different operating conditions. This paper also compare the estimated results with the simulation data. The results showed that the accuracy of SOC-estimation is significantly improved by using the optimized CKF algorithm method. The maximum error of estimated SOC is kept within 4% and the average error is kept within 1%. Compared with other algorithms, it is greatly improved and satisfies the accuracy and real-time requirements of Li-ion battery SOC estimation, which provides a reference value for practical applications.