Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning

Xiaopeng TANG, Xin LAI, Changfu ZOU*, Yuanqiang ZHOU, Jiajun ZHU, Yuejiu ZHENG, Furong GAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)

Abstract

The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highlights the opportunities to diagnose lifetime abnormalities via “big data” analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost-benefit, and improved environmental friendliness.

Original languageEnglish
Article number2305315
JournalAdvanced Science
Early online date11 Dec 2023
DOIs
Publication statusE-pub ahead of print - 11 Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.

Keywords

  • big data
  • early-stage detection
  • few-shot learning
  • lifetime abnormality
  • lithium-ion battery

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