Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network

Sibo Zeng, Sheng Chen () and Babakalli Alkali
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Sibo Zeng: Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou 412001, China
Sheng Chen: School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
Babakalli Alkali: School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK

Energies, 2024, vol. 17, issue 6, 1-14

Abstract: Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.

Keywords: lithium-ion battery; capacity estimation; incremental capacity analysis; gaussian regression; convolutional 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: 2024
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