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

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