Lithium-ion Battery State of Charge Estimation Model Based on Kalman Filtering Algorithm and Equivalent Circuit

In recent years, electric vehicles have garnered significant attention, with lithium-ion batteries (LIBs) being central to their operation. Researchers and scholars have prioritized the accurate estimation of the state of charge (SOC) within the battery management system (BMS) as a key area of study. In this paper, by analyzing different equivalent circuit models, we choose to use the second-order RC model, elaborate the Kalman filter (KF) principle, and propose the adaptive extended Kalman filter (AEKF) to construct the estimation model of SOC. MATLAB validates the AEKF estimation model under two different operating conditions, UDDS and LA92, and the results show that the designed model can efficiently and accurately estimate the battery charge state with high competitiveness and accurately predict the real SOC direction regardless of the initial state, AEKF is more competitive than KF in terms of SOC prediction accuracy, Despite the different initial values of SOC, the root-mean-square error of prediction was able to be controlled around one percent