Research on SOC Estimation of Lithium Battery Based on Robust Extended Kalman Filter

Abstract

In this paper, a robust extended Kalman filter method (REKF) is proposed to accurately estimate the state of charge (SOC) of electric vehicle lithium-ion battery. Firstly, the state space equation of the lithium ion battery is established by Thevenin equivalent circuit model, and then the recursive formula is used to realize the identification model parameters. The REKF- based method is used to compensate for battery modeling uncertainty and linearization errors involved in the Extended Kalman Filter Method (EKF), and to some extent to improve the robustness to battery system noise. The REKF algorithm was simulated by using the charge and discharge experimental data of 20Ah lithium ion battery to verify the effectiveness of the REKF algorithm. The evaluation results show that during the whole simulation process, the REKF method converges near the reference value, and the curve can well predict the value of the battery SOC. Since the average modeling error is small, REKF has good average performance given the appropriate noise covariance, which ensures that the estimation error of the lithium ion battery SOC is controlled within 5%.