A novel multi-scenario battery health assessment method combining semi-supervised learning and data augmentation techniques

Data-driven methods are widely claimed to be the most promising candidates for online battery health assessment estimation.

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Publication Details

Journal
Sustainable Energy & Fuels
Published
2024-12-24
DOI
https://doi.org/10.1039/d4se01231c
Citations
2
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.41

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article

A novel multi-scenario battery health assessment method combining semi-supervised learning and data augmentation techniques

Jisheng Ren, Xianghui Qiu, Shuangfeng Wang
2 citations
Sustainable Energy & Fuels
Advanced Battery Technologies Research
0.41
article

A novel multi-scenario battery health assessment method combining semi-supervised learning and data augmentation techniques

Jisheng Ren, Xianghui Qiu, Shuangfeng Wang
article en
2 citations

Abstract

Data-driven methods are widely claimed to be the most promising candidates for online battery health assessment estimation.

Sustainable Energy & FuelsVol. 9(3)
South China University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 38%
Advanced Battery Technologies Research
0.41
DOISourceOpenAlex
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A novel multi-scenario battery health assessment method combining semi-supervised learning and data augmentation techniques — Jisheng Ren, Xianghui Qiu, et al. · Sustainable Energy & Fuels (2024) | TGRS Research Map | TGRS