On-Line Parameter Identification and SOC Estimation for Lithium-Ion Batteries Based on Improved Sage–Husa Adaptive EKF

Xuan Tang, Hai Huang, Xiongwu Zhong, Kunjun Wang, Fang Li, Youhang Zhou and Haifeng Dai ()
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Xuan Tang: School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China
Hai Huang: School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China
Xiongwu Zhong: CRRC Times Electric Vehicle Co., Ltd., Zhuzhou 412007, China
Kunjun Wang: CRRC Times Electric Vehicle Co., Ltd., Zhuzhou 412007, China
Fang Li: School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China
Youhang Zhou: School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, China
Haifeng Dai: Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China

Energies, 2024, vol. 17, issue 22, 1-13

Abstract: For the Battery Management System (BMS) to manage and control the battery, State of Charge (SOC) is an important battery performance indicator. In order to identify the parameters of the LiFePO 4 battery, this paper employs the forgetting factor recursive least squares (FFRLS) method, which considers the computational volume and model correctness, to determine the parameters of the LiFePO 4 battery. On this basis, the two resistor-capacitor equivalent circuit model is selected for estimating the SOC of the Li-ion battery by combining the extended Kalman filter (EKF) with the Sage–Husa adaptive algorithm. The positivity is improved by modifying the system noise estimation matrix. The paper concludes with a MATLAB 2016B simulation, which serves to validate the SOC estimation algorithm. The results demonstrate that, in comparison to the conventional EKF, the enhanced EKF exhibits superior estimation precision and resilience to interference, along with enhanced convergence during the estimation process.

Keywords: state of charge; the Sage–Husa adaptive method; extended Kalman filter; equivalent circuit model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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