期刊
PATTERNS
卷 2, 期 8, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.patter.2021.100302
关键词
-
类别
资金
- Ministry of Science and Technology of the People's Republic of China [SQ2019YFB170029]
- Guangzhou Science and Technology Project [202002030323]
- Faraday Institution Multi-scale Modelling programme [EP/S003053/1, FIRG003]
- EPSRC [EP/R030243/1, EP/P004636/1]
- Foshan-HKUST Project [FSUST19-FYTRI01]
By combining industrial data with accelerated aging tests, high-quality battery aging datasets can be recovered to improve the assessment of battery aging with up to 90% experimental time saved.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据