Tang, X, Liu, K, Li, K orcid.org/0000-0001-6657-0522 et al. (3 more authors) (2021) Recovering large-scale battery aging dataset with machine learning. Patterns, 2. 100302. ISSN 2666-3899
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | battery aging assessment; battery aging dataset generation; lithium-ion battery management; incremental capacity analysis; model migration; machine learning; accelerated battery aging experiments |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Jul 2021 09:15 |
Last Modified: | 02 Jul 2021 09:21 |
Status: | Published |
Publisher: | Cell Press |
Identification Number: | 10.1016/j.patter.2021.100302 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175810 |