Abstract
Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) can avoid safety accidents and economic losses, and it remains a big research challenge. In this paper, electrochemical impedance spectroscopy (EIS) is used as the feature for the SOH prediction. EIS contains rich information such as material properties and electrochemical reactions, which directly reflects the aging state of LIBs. In order to obtain valuable data for SOH estimation, we propose a new feature extraction method from the perspective of electrochemistry, and then apply the transformer-based neural network for SOH estimation. Through feature extraction, the mean absolute percentage error of the estimation is reduced to 1.63% in the whole life cycle, which is decreased by 70% compared to the original data before feature extraction.
Original language | English |
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Article number | 233139 |
Pages (from-to) | 1-8 |
Number of pages | 8 |
Journal | Journal of Power Sources |
Volume | 576 |
Early online date | 21 May 2023 |
DOIs | |
Publication status | Published (in print/issue) - 30 Aug 2023 |
Bibliographical note
Funding Information:We gratefully acknowledge the financial support from the National Key Research and Development Program of China ( 2022YFE0202400 ).
Publisher Copyright:
© 2023 Elsevier B.V.
Keywords
- Data-driven
- Electrochemical impedance spectroscopy
- Lithium-ion battery
- State of health
- Transformer neural network