A simple feature extraction method for estimating the whole life cycle state of health of lithium-ion batteries using transformer-based neural network

Kai Luo, Huiru Zheng, Zhicong Shi

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number233139
Pages (from-to)1-8
Number of pages8
JournalJournal of Power Sources
Volume576
Early online date21 May 2023
DOIs
Publication statusPublished (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

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