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Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries

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  • Ning Gao

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • You Gong

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Xiaobei Yang

    (School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore)

  • Disai Yang

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Yao Yang

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Bingyu Wang

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Haifei Long

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

Abstract

While Forgetting Factor Recursive Least Square (FFRLS) algorithms with evaluation mechanisms have been developed to address SOC-dependent parameter mapping shifts and their efficacy has been proven in Li-ion batteries, their applicability to lithium–sulfur (Li-S) batteries remains uncertain due to different electrochemical characteristics. This study critically evaluates the applicability of a Fisher information matrix-constrained FFRLS framework for online parameter identification in Li-S battery equivalent circuit network (ECN) models. Experimental validation using distinct drive cycles showed that the identification results of polarization-related parameters are significantly biased between different current excitations, and root mean square error (RMSE) variations diverge by 100%, with terminal voltage estimation errors more than 0.05 V. The parametric uncertainty under variable excitation profiles and voltage plateau estimation deficiencies confirms the inadequacy of such approaches, constraining model-based online identification viability for Li-S automotive applications. Future research should therefore prioritize hybrid estimation architectures integrating electrochemical knowledge with data-driven observers, alongside excitation capturing specifically optimized for Li-S online parameter observability requirements and cell nonuniformity and aging condition consideration.

Suggested Citation

  • Ning Gao & You Gong & Xiaobei Yang & Disai Yang & Yao Yang & Bingyu Wang & Haifei Long, 2025. "Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries," Energies, MDPI, vol. 18(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4493-:d:1731221
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    References listed on IDEAS

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    1. Fotouhi, Abbas & Auger, Daniel J. & Propp, Karsten & Longo, Stefano & Wild, Mark, 2016. "A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1008-1021.
    2. Abbas Fotouhi & Daniel J. Auger & Laura O’Neill & Tom Cleaver & Sylwia Walus, 2017. "Lithium-Sulfur Battery Technology Readiness and Applications—A Review," Energies, MDPI, vol. 10(12), pages 1-15, November.
    3. Zizhou Lao & Bizhong Xia & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares," Energies, MDPI, vol. 11(6), pages 1-15, May.
    4. Guangmin Zhou & Hao Chen & Yi Cui, 2022. "Formulating energy density for designing practical lithium–sulfur batteries," Nature Energy, Nature, vol. 7(4), pages 312-319, April.
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