A novel multi–input–multi–output–fuzzy uncoupling model parameter identification strategy for the online state of charge estimation of high-power lithium-ion batteries.

Liu, Donglei; Li, Xiaoxia; Wang, Shunli; Fan, Yongcun; Fernandez, Carlos

Abstract

Due to the strong nonlinear characteristics of multi-state coupling of physical fields such as different charge states and discharge rates in lithium-ion batteries, it poses a great challenge for the parameter identification of the model and the estimation of the state of charge (SOC). The battery coupling characteristics are analyzed, and a coupled equivalent circuit model (CECM) is constructed. A novel multi–input–multi–output–fuzzy uncoupling model parameter identification (MIMO-FUMPI) strategy is proposed to estimate the parameter for CECM. The strategy uses the uncoupling feature of fuzzy logic to achieve uncoupled online parameter identification of CECM. A fuzzy logic controller considering discharge rate and SOC is designed to realize the uncoupling parameter identification of the model, combined with the filtering algorithm to realize the SOC estimation. The proposed SOC estimation strategy is validated under two experimental conditions. The maximum error is improved in the DST condition by 44.901% and in the BBDST condition by 31.206%. The proposed estimation strategy achieves high-precision estimation in the lithium-ion battery SOC and verifies the accuracy and robustness of the fuzzy logic controller in the identification of uncoupling parameters in the equivalent circuit model for lithium batteries and the estimation of the SOC on lithium-ion batteries.

Citation

LIU, D., LI, X., WANG, S., FAN, Y. and FERNANDEZ, C. [2025]. A novel multi–input–multi–output–fuzzy uncoupling model parameter identification strategy for the online state of charge estimation of high-power lithium-ion batteries. Ionics [online], Online First. Available from: https://doi.org/10.1007/s11581-025-06330-0

• Journal Article Type: Article
• Acceptance Date: Apr 17, 2025
• Online Publication Date: Apr 28, 2025
• Deposit Date: May 15, 2025
• Publicly Available Date: Apr 29, 2026
• Journal: Ionics
• Print ISSN: 0947-7047
• Electronic ISSN: 1862-0760
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s11581-025-06330-0
• Keywords: Equivalent circuit model; Fuzzy logic strategy; Lithium-ion batteries; State of charge; Uncoupling identification methods
• Public URL: https://rgu-repository.worktribe.com/output/2830437