An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries.

Zhu, Chenyu; Wang, Shunli; Yu, Chunmei; Zhou, Heng; Fernandez, Carlos

Abstract

For lithium-ion batteries, the state of charge (SOC) of batteries plays an important role in the battery management system, and the accuracy of the battery model and parameter identification is the basis of SOC estimation. Considering that the system has inevitable steady-state errors and the influence of random noise on SOC estimation results under dynamic conditions, this paper proposed an improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering (PCFFRLS-MCAEKF) algorithm for high-precision state-of-charge estimation of lithium-ion batteries. The experimental results show that the proportional control forgetting factor recursive least square algorithm has higher parameter identification accuracy under HPPC and BBDST conditions. Under HPPC working conditions, the root mean square error of PCFFRLS-MCAEKF algorithm is reduced by 1.275%, 0.687%, and 0.549% compared with FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithm, and the average absolute error is reduced by 0.71%, 0.537%, and 0.11%. Under BBDST working conditions, the SOC estimation result of PCFFRLS-MCAEKF algorithm is closer to the real SOC, which is consistent with the result obtained under HPPC working conditions. The experimental results show that under HPPC and BBDST working conditions, the PCFFRLS-MCAEKF algorithm can better improve the accuracy and robustness of SOC estimation than FFRLS-EKF, PCFFRLS-EKF, and PCFFRLS-AEKF algorithms.

Citation

ZHU, C., WANG, S., YU, C., ZHOU, H. and FERNANDEZ, C. 2023. An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries. Journal of solid state electrochemistry [online], 27(9), pages 2277-2287. Available from: https://doi.org/10.1007/s10008-023-05514-w

• Journal Article Type: Review
• Acceptance Date: Apr 14, 2023
• Online Publication Date: May 2, 2023
• Publication Date: Sep 30, 2023
• Deposit Date: May 23, 2023
• Publicly Available Date: May 3, 2024
• Journal: Journal of solid state electrochemistry
• Print ISSN: 1432-8488
• Electronic ISSN: 1433-0768
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 9
• Pages: 2277-2287
• DOI: https://doi.org/10.1007/s10008-023-05514-w
• Keywords: Lithium-ion battery; State of charge; Monte Carlo; Adaptive extended Kalman filtering; Proportional control
• Public URL: https://rgu-repository.worktribe.com/output/1966230