An improved second-order electrical equivalent modeling method for the online high power Li-ion battery state of charge estimation

Abstract

The estimation of power battery state of charge and the selection of an effective equivalent model have always been the focus and difficulty of current research. This article has improved and optimized the traditional Thevenin model, and the performance of the Li-ion-ion battery is tested through Hybrid Pulse Power Characterization (HPPC) experiments. The off-line curve fitting identification method is used to identify the relevant parameters in the model to obtain the intrinsic relationship with the state of charge (SOC). An equivalent circuit model is established on the simulation platform to compare and verify actual data. The error of the verification result is controlled within 3.05%, which proves the effectiveness of the improved Thevenin model. On the basis that the relevant parameters of the equivalent model have been identified, the recursive principle of the extended Kalman filter algorithm (EKF) is studied, and the algorithm is applied to the Li-ion battery SOC estimation to obtain an accurate state estimation of the Li-ion battery. The algorithm is simulated and verified under working conditions, and the verification showed that its accuracy reached more than 98.376%, which ensured the reliability of the battery during use.