State of Charge Estimation for Lithium-Ion Batteries Based on Extended Kalman Particle Filter and Orthogonal Optimized Battery Model

State of charge (SOC) is a key state variable in lithium-ion battery management system. The battery model and estimation algorithm are important factors that affect the accuracy of SOC estimation. In this paper, the optimization battery model is created by optimizing the hybrid power pulse characteristic (HPPC) parameter combination through orthogonal analysis. The simulation results demonstrate that optimizing the HPPC parameter combinations can improve battery modeling accuracy. Then, an extended Kalman particle filter (EKPF) algorithm is proposed by using the extended Kalman filter (EKF) algorithm as the density function of the particle filter (PF) algorithm. The EKPF algorithm is verified under the dynamic stress test and Beijing bus dynamic stress test conditions, the root mean absolute errors and root mean square errors in all cases are less than 1.5%. The experimental results show that the EKPF algorithm can combine the advantages of EKF and PF to estimate lithium-ion battery SOC accurately. This work employs an orthogonal experimental method to optimize the HPPC parameter identification experiment of the Li-ion battery model, and it improves the accuracy of the battery model. The EKPF algorithm is proposed for estimating the battery SOC, and the DST and BBDST conditions are utilized to verify the accuracy of the proposed method. image