SOC Estimation of Lithium Iron Phosphate Battery Based on Kalman Filtering Algorithm

Abstract

In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle, a second-order RC equivalent circuit model considering the battery capacity time-variation is proposed. Combining the nonlinear characteristics of lithium iron phosphate battery and the second- order RC equivalent circuit model, the state space equation of lithium iron phosphate battery is established. Based on the limited estimation accuracy of the extended Kalman filtering algorithm for nonlinear state equations, a central difference Kalman filtering algorithm is proposed. The simulation results show that the central difference Kalman filtering algorithm is better than the extended Kalman filter algorithm in the same condition for the estimation accuracy of SOC.