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

Ning Gao, You Gong (), Xiaobei Yang, Disai Yang, Yao Yang, Bingyu Wang and Haifei Long
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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

Energies, 2025, vol. 18, issue 17, 1-13

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.

Keywords: lithium–sulfur batteries; online parameter estimation; equivalent circuit network model (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: 2025
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