Citation

Tan, Ai Hui and Sihvo, Jussi (2026) A Robust Resistance-Like Feature for Health Estimation of Lithium-Ion Batteries Based on Impulse Response and Regularization. IEEE Transactions on Transportation Electrification. p. 1. ISSN 2372-2088

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Abstract

In a battery management system, internal ohmic resistance is often used for the prediction of state of health (SOH). However, battery resistance is dependent on conditions such as state of charge (SOC), charging/discharging rates and temperature. These complicate the prediction of the SOH via battery resistance by causing unwanted fluctuations that are unrelated to the SOH. To overcome this problem, this paper proposes a built-in self-scaling (BS) estimator based on the regularized impulse response of the battery. The BS method estimates a new resistance-like health feature that is resilient to immediate and moderate-time changes in the actual resistance caused by the SOC, current rates and temperature. Meanwhile, it remains to be affected by slow changes in the actual resistance that are caused by aging and, thus, the degradation of the SOH. Datasets from the CALCE database on a lithium nickel manganese cobalt oxide battery are employed for validation. The performance of the BS method is compared with several popular resistance estimation methods, namely the direct resistance, Kalman filter and recursive least squares techniques. The results show that the BS estimator is less affected by changes in SOC, current profile, temperature and the effects of noise. A final aging experiment on another similar commercial battery confirms the capability of the BS method to track very slow changes in the battery resistance caused by the SOH.

Item Type:	Article
Uncontrolled Keywords:	Internal resistance, impulse response, lithium-ion batteries, state of health, hybrid electric vehicles
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	31 Jul 2026 06:19
Last Modified:	31 Jul 2026 06:19
URII:	http://shdl.mmu.edu.my/id/eprint/16410

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