An extended Kalman filter based SOC estimation method for Li-ion battery. (August 2022)

• Record Type: Journal Article
• Authors: Cui, Zhenjie; Hu, Weihao; Zhang, Guozhou; Zhang, Zhenyuan; Chen, Zhe
• Is Part Of: Energy reports. Volume 8(2022)Supplement 5
• Page Start: 81
• Page End: 87
• Publication Date: 2022-08
• Subjects: Li-ion battery -- State of charge -- Estimation -- Extended Kalman filtering algorithm; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.02.116
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: In recent years, the global environmental pollution and energy crisis are becoming more and more serious. The Li-ion battery is widely used in vehicles due to long cycle life and high energy density. The state of charge (SOC) of Li-ion battery is an important indicator. The accurate estimation of SOC can ensure the safe operation of Li-ion battery. However, the traditional estimation method, the ampere-hour integration method, has a cumulative error and cannot maintain good results for a long time in an operating environment with the Gaussian noise. To this end, this paper firstly applies Thevenin equivalent circuit model of a battery to establish estimation model, and it can reflect the working state of the battery. Then, the extended Kalman filtering algorithm is employed to solve the estimation error caused by Gaussian noise. Finally, the test system is built in MATALAB/Simulink to investigate the performance of the proposed method. Simulation results show that the proposed method achieves better performance, and it has higher estimation accuracy in comparison with traditional methods under different working conditions.