State-of-charge estimation of lithium ion batteries based on adaptive iterative extended Kalman filter

Abstract

Lithium-ion batteries are the power source of electric vehicles. Accurate SOC estimation is essential to prevent safety problems caused by the overcharge and over-discharge, then to extend battery life. Based on the Thevenin equivalent circuit model, an adaptive iterative extended Kalman filter method to estimate SOC is proposed in the paper. Firstly, based on the iterative extended Kalman filter, the AIEKF method introduces the terminal voltage difference as the threshold. When the voltage difference exceeds the threshold, the method starts iteration to improve the estimation accuracy. When the voltage difference is lower than the threshold, the method degenerates to extended Kalman filter algorithm to reduce the calculated pressure. Then an improved Sage-Husa estimator is used to correct the process noise and measurement noise. At the same time, the influence of temperatures on battery capacity and SOC-OCV relationship is considered to further improve the estimation accuracy. Finally, experiments and simulations are carried out to prove the AIEKF method has good performance in terms of accuracy and computational complexity.