![]() Verified results show that it can improve the SOC estimation in low-battery capacity accuracy. 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. SOC estimation is conducted in the low-SOC area by exploring the relationship between battery parameters and SOC through many experimental results. A battery model is established to identify and calibrate battery parameters. 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). The accurate estimation of the battery state of charge (SOC) is crucial for providing information on the performance and remaining range of electric vehicles. 3Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province, Chengdu, China.2Key Laboratory of Vibration and Control of Aero-Propulsion System, Northeastern University, Shenyang, China. ![]() 1School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China.Nan Zhou 1,2,3, Hong Liang 1, Jing Cui 1, Zeyu Chen 1,2* and Zhiyuan Fang 1
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