State of Charge Dual Estimation of a Li-ion Battery Based on Variable Forgetting Factor Recursive Least Square and Multi-Innovation Unscented Kalman Filter Algorithm

Hongyuan Yuan, Youjun Han, Yu Zhou, Zongke Chen, Juan Du and Hailong Pei
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Hongyuan Yuan: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Youjun Han: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Yu Zhou: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Zongke Chen: School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
Juan Du: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Hailong Pei: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2022, vol. 15, issue 4, 1-22

Abstract: Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and discharge, which all depend on the real-time and accurate estimation of the battery’s state of charge (SOC). However, the battery’s SOC has highly complex nonlinear time-varying characteristics related to the complex chemical and physical state and dynamic environmental conditions, which are difficult to measure directly, and this has become a difficulty in design and research. According to the characteristics of ternary lithium-ion batteries of electric vehicles, a battery SOC dual estimation algorithm based on the Variable Forgetting Factor Recursive Least Square (VFFRLS) and Multi-Innovation Unscented Kalman Filter (MIUKF) is proposed in this paper. The VFFRLS algorithm is used to estimate battery model parameters, and the MIUKF algorithm is used to estimate the battery’s SOC in real time. The two algorithms are coupled to update battery model parameters and estimate the SOC. The experiment results show that the algorithm has high accuracy and stability.

Keywords: equivalent circuit model; multi-innovation; Unscented Kalman Filter; variable forgetting factor recursive least square; SOC online estimation; battery management system (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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