Recovering large-scale battery aging dataset with machine learning

Xiaopeng Tang, Kailong Liu, Kang Li, Widanalage Dhammika Widanage, Emma Kendrick, Furong Gao

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
19 Downloads (Pure)

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

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

ASJC Scopus subject areas

  • Decision Sciences(all)

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