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Cited by 72Advanced Battery Technologies ResearchAdvancements in Battery MaterialsElectrical Fault Detection and Protection

Lithium-Ion Battery Health State and Remaining Useful Life Prediction Based on Hybrid Model MFE-GRU-TCA

Wang, Xiaohua, Dai, Ke, Hu, Min, Ni, Nanbing·2024·Journal of Energy Storage
DOI 10.1016/j.est.2024.112442
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References 44 · Cited by 72

Authors
Wang, X., Dai, K., Hu, M., & Ni, N.
Affiliations
Hefei University of Technology (CN)
Corresponding author
Dai, Ke
ORCID
Venue
Journal of Energy Storage
Publisher
Elsevier BV
Published
2024-08
Volume
95
Pages
112442
ISSN
2352-152X (ISSN-L)
DOI
10.1016/j.est.2024.112442
OpenAlex
W4399709751
Cited by
72
References
44
Citations by year
2026: 23 · 2025: 44 · 2024: 5
FWCI
7.76
Type
journal article
Language
en
Open access
closed
Licence
Locations
Journal of Energy Storage (publishedVersion)
Topics
  • Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
Keywords
lithium-ion batteries, state of health estimation, remaining useful life prediction, hybrid deep learning model, multi-feature extraction, gated recurrent unit, capacity decay, capacity recovery, charge-discharge cycling, battery degradation

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