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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

Authors

Donglei Liu

Xiaoxia Li

Shunli Wang

Yongcun Fan



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