A Fusion-Based Method of State-of-Charge Online Estimation for Lithium-Ion Batteries Under Low Capacity Conditions

Abstract

The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. Based on the analysis of battery charge and discharge data under actual vehicle driving cycles, this paper presents an online estimation method of battery SOC based on the extended Kalman filter (EKF) and neural network (NN). A battery model is established to identify and calibrate battery parameters. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. In the fusion online estimation method, the NN is carried out to propose the estimation as the global mainstream trend providing a high precision feasible region; the EKF algorithm is used to provide the initial assessment and the local fluctuation boundary revision. Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. It has achieved good adaptability to the estimation accuracy of low battery capacity SOC in different cycle conditions.