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Lithium-Ion Battery State of Health Estimation Using Simple Regression Model Based on Incremental Capacity Analysis Features

Author

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  • Kai-Rong Lin

    (Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan)

  • Chien-Chung Huang

    (Green Energy and Environment Research Laboratory, Industrial Technology Research Institute, Tainan 71150, Taiwan)

  • Kin-Cheong Sou

    (Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan)

Abstract

Batteries are the core component of electric vehicles (EVs) and energy storage systems (ESSs), being crucial technologies contributing to carbon neutrality, energy security, power system reliability, economic efficiency, etc. The effective operation of batteries requires precise knowledge of the state of health (SOH) of the battery. A lack of proper knowledge of SOH may lead to the improper use of severely aged batteries, which may result in degraded system performance or even a risk of failure. This makes it important to accurately estimate battery SOH using only operational data, and this is the main topic of this study. In this study, we propose a novel method for online SOH estimation for batteries featuring simple online computation and robustness against measurement anomalies while avoiding the need for full cycle discharging and charging operation data. Our proposed method is based on incremental capacity analysis (ICA) to extract battery aging feature parameters and regression using simple piecewise linear interpolation. Our proposed method is compared with back-propagation neural network (BPNN) regression, a popular method for SOH estimation, in case studies involving actual data from battery aging experiments under realistic discharging and temperature conditions. In terms of accuracy, our method is on par with BPNN results (about 5% maximum relative error), while the simplicity of our method leads to better computation efficiency and robustness against data anomalies.

Suggested Citation

  • Kai-Rong Lin & Chien-Chung Huang & Kin-Cheong Sou, 2023. "Lithium-Ion Battery State of Health Estimation Using Simple Regression Model Based on Incremental Capacity Analysis Features," Energies, MDPI, vol. 16(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7066-:d:1258600
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    References listed on IDEAS

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    Cited by:

    1. Mónica Camas-Náfate & Alberto Coronado-Mendoza & Carlos Vargas-Salgado & Jesús Águila-León & David Alfonso-Solar, 2024. "Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms," Energies, MDPI, vol. 17(4), pages 1-22, February.

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