Recovering large-scale battery aging dataset with machine learning

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

doi:10.1016/j.patter.2021.100302

Recovering large-scale battery aging dataset with machine learning

Patterns (N Y). 2021 Jun 30;2(8):100302. doi: 10.1016/j.patter.2021.100302. eCollection 2021 Aug 13.

Authors

Xiaopeng Tang¹, Kailong Liu², Kang Li³, Widanalage Dhammika Widanage^{2

4}, Emma Kendrick^{5

4}, Furong Gao^{1

6}

Affiliations

¹ Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China.
² WMG, The University of Warwick, Coventry CV4 7AL, UK.
³ School of Electronic and Electric Engineering, University of Leeds, Leeds LS2 9JT, UK.
⁴ The Faraday Institution, Quad One, Becquerel Avenue, Harwell Campus, Didcot OX11 0RA, UK.
⁵ School of Metallurgy and Materials, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
⁶ Guangzhou HKUST Fok Ying Tung Research Insititue, Guangdong 511458, China.

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.

Keywords: accelerated battery aging experiments; battery aging assessment; battery aging dataset generation; incremental capacity analysis; lithium-ion battery management; machine learning; model migration.