Energies, Vol. 18, Pages 4493: Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries

Energies, Vol. 18, Pages 4493: Applicability Evaluation of an Online Parameter Identification Method: From Lithium-Ion to Lithium–Sulfur Batteries

Energies doi: 10.3390/en18174493

Authors:
Ning Gao
You Gong
Xiaobei Yang
Disai Yang
Yao Yang
Bingyu Wang
Haifei Long

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.