Citation

Hii, S. J. and Tan, Ai Hui and Cham, Chin Leei (2026) Lithium-Ion Battery Internal Resistance Estimation Using Kernel-Based Regularization. 2026 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2026 - Conference Proceedings. pp. 173-178. ISSN 2995-2859, 2995-2840

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Abstract

Accurate estimation of lithium-ion battery internal resistance is essential for reliable state-of-health monitoring and battery management. This paper investigates the use of kernel-based regularization for internal resistance estimation and compares its performance with direct resistance and least squares approaches. A detailed simulation study was conducted based on a third-order equivalent circuit model of a battery, where the parameters vary randomly across a bounded range from the nominal parameters to simulate changes in battery dynamics caused by operating conditions. Oracle, tuned-correlated, diagonal-correlated and stable spline kernels were evaluated using the mean squared error of the estimated total resistance. Results show that kernel-based regularization provides improved robustness against parameter variations. Finally, the kernel-based methods were validated using experimental data from a real lithium titanate battery.

Item Type:	Article
Uncontrolled Keywords:	Kernel , Batteries , Lithium-ion batteries
Subjects:	T Technology > TP Chemical technology > TP155-156 Chemical engineering
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 04:23
Last Modified:	04 Sep 2026 04:23
URII:	http://shdl.mmu.edu.my/id/eprint/16713

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