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Energies
Volume 18
Issue 17
10.3390/en18174493
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Ning Gao
1,
You Gong
1,*,
Xiaobei Yang
2,
Disai Yang
1,
Yao Yang
1,
Bingyu Wang
1 and
Haifei Long
1
1
School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China
2
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Energies 2025, 18(17), 4493; https://doi.org/10.3390/en18174493 (registering DOI)
Submission received: 11 July 2025 / Revised: 5 August 2025 / Accepted: 19 August 2025 / Published: 23 August 2025
(This article belongs to the Special Issue Lithium-Ion and Lithium-Sulfur Batteries for Vehicular Applications)
Versions Notes

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 lithium–sulfur batteries; online parameter estimation; equivalent circuit network model

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MDPI and ACS Style

Gao, N.; Gong, Y.; Yang, X.; Yang, D.; Yang, Y.; Wang, B.; Long, H. Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries. Energies 2025, 18, 4493. https://doi.org/10.3390/en18174493

AMA Style

Gao N, Gong Y, Yang X, Yang D, Yang Y, Wang B, Long H. Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries. Energies. 2025; 18(17):4493. https://doi.org/10.3390/en18174493

Chicago/Turabian Style

Gao, Ning, You Gong, Xiaobei Yang, Disai Yang, Yao Yang, Bingyu Wang, and Haifei Long. 2025. "Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries" Energies 18, no. 17: 4493. https://doi.org/10.3390/en18174493

APA Style

Gao, N., Gong, Y., Yang, X., Yang, D., Yang, Y., Wang, B., & Long, H. (2025). Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries. Energies, 18(17), 4493. https://doi.org/10.3390/en18174493

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