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

Kai-Rong Lin, Chien-Chung Huang and Kin-Cheong Sou ()
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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

Energies, 2023, vol. 16, issue 20, 1-20

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.

Keywords: state of health; aging feature parameters; incremental capacity analysis; piecewise linear interpolation regression; back-propagation neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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