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.
Bibliographical noteFunding Information:
We gratefully acknowledge the financial support from the National Key Research and Development Program of China ( 2022YFE0202400 ).
© 2023 Elsevier B.V.
- Electrochemical impedance spectroscopy
- Lithium-ion battery
- State of health
- Transformer neural network