The Scheme for SOC Estimation of Lithium-ion Batteries based on EQ-OCV-Ah-EKF

Abstract

The state of charge (SOC) of a battery is one of the most important indicators to monitor in a Battery Management System (BMS). It is very important to accurately and quickly predict the state of battery charge for the safe driving of electric vehicles and the efficient utilization of battery equipment. However, several factors, such as the working environment and the aging degree of the battery, can have a coupling effect on the internal state of the battery. This leads it difficult to accurately predict the SOC of a battery. For this purpose, this paper proposes an online estimation scheme for the SOC of Li-ion batteries with an equivalent circuit model and an extended Kalman filtering algorithm. Firstly, a batch of ternary Li-ion batteries are charged and discharged at constant current with 1C current condition under constant temperature at 25°C using the hybrid power pulse (HPPC) test method, and the collected data are pre-processed. Then, the parameters of the second-order Thevenin circuit model were fitted by combining the Genetic Algorithm (GA) and Levenberg-Marquardt algorithm (LM) and verified by the MATLAB program. Finally, the Extended Kalman algorithm (EKF) is leveraged to further improve the accuracy of prediction results. The proposed scheme also has some reference significance for the identification of equivalent circuit model parameters of lithium-ion batteries and the rapid estimation of battery charge states.