Lithium-ion Battery SOC Estimation Based on Weighted Adaptive Recursive Extended Kalman Filter Joint Algorithm

Abstract

Accurate estimation of the lithium-ion battery SOC is critical to the battery management system (BMS). In order to accurately estimate the lithium-ion battery SOC, a second-order equivalent model of the lithium-ion battery is firstly established in this paper, and the lithium-ion battery's nonlinear relationship of SOC-OCV is obtained through the experiment. Then the online parameter identification method based on the least square method is used to estimate the parameters of the lithium-ion battery's online model, and the accurate estimation of lithium-ion battery SOC is achieved by combining weighted adaptive recursive least square method with extended Kalman filter. This paper compares estimation accuracy of the battery SOC based on the extended Kalman filter algorithm (EKF), the recursive least square method based on the forgetting factor (FRLS), and the weighted adaptive recursive extended Kalman filter joint algorithm (WAREKF) in the experiment. The experiment result shows that the estimation accuracy of the battery SOC based on WAREKF which is proposed in this paper is higher than that of EKF and FRLS, and its root mean square error (RMSE) is less than 1%.