Recovering large-scale battery aging dataset with machine learning

Xiaopeng TANG, Kailong LIU*, Kang LI, Widanalage Dhammika WIDANAGE, Emma KENDRICK, Furong GAO

*Corresponding author for this work

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

78 Citations (Scopus)

Abstract

Batteries are crucial for building a clean and sustainable society, and their performance is highly affected by aging status. Reliable battery health assessment, however, is currently restrained by limited access to sufficient aging data, resulting from not only complicated battery operations but also long aging experimental time (several months or even years). Refining industrial datasets (e.g., the field data from electric vehicle batteries) unlocks opportunities to acquire large-volume aging data with low experimental efforts. We introduce the potential of combining industrial data with accelerated aging tests to recover high-quality battery aging datasets, through a migration-based machine learning. A comprehensive dataset containing 8,947 aging cycles with 15 operational modes is collected for evaluation. While saving up to 90% experimental time, aging data can be recovered with ultra-low error within 1%. It provides an alternative solution to significantly improve data shortage issues for assessment of battery and energy storage aging.

Original languageEnglish
Article number100302
JournalPatterns
Volume2
Issue number8
Early online date30 Jun 2021
DOIs
Publication statusPublished - 13 Aug 2021
Externally publishedYes

Bibliographical note

Funding Information:
This paper was financially supported, in part, by the Ministry of Science and Technology of the People's Republic of China (SQ2019YFB170029), the Foshan-HKUST Project (FSUST19-FYTRI01), Guangzhou Science and Technology Project (202002030323), the Faraday Institution Multi-scale Modelling programme (faraday.ac.uk; EP/S003053/1 and FIRG003), and EPSRC grants EP/R030243/1 and EP/P004636/1. We would like to thank Prof. Qi Liu from the Hong Kong Polytechnic University; Dr. Ke Yao, Mr. Minqi Hu, Mr. Zhenwei He, and Mr. Zhou Lyu from the HKUST Fok Ying Tung Research Institute; and Ms. Xin Wang from the Hong Kong University of Science and Technology, for the useful discussions and suggestions. The first author would like to give thanks for the continuing support from the Guangzhou HKUST Fok Ying Tung Research Institute during the Hong Kong's unrest and the outbreak of the COVID-19. X.T. and F.G. conceived the study and carried out the experiments. X.T. and K.L. developed the machine learning solution, implemented the programming, analyzed the experimental results, and drafted the manuscript. K.L. W.D.W. and E.K. discussed the technical details, analyzed the data, and revised and polished the manuscript. All authors commented on the manuscript. The authors are preparing a CN patent and a US patent related to this work.

Publisher Copyright:
© 2021 The Authors

Keywords

  • accelerated battery aging experiments
  • battery aging assessment
  • battery aging dataset generation
  • DSML 1: Concept: Basic principles of a new data science output observed and reported
  • incremental capacity analysis
  • lithium-ion battery management
  • machine learning
  • model migration

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