Cited by 129Advanced Battery Technologies ResearchAdvancements in Battery MaterialsStructural Health Monitoring Techniques

Specialized Deep Neural Networks for Battery Health Prognostics: Opportunities and Challenges

Zhao, Jingyuan, Han, Xuebing, Ouyang, Minggao, Burke, Andrew F.·2023·Journal of Energy Chemistry

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Crossref · 10.1016/j.jechem.2023.08.047

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52 of 52 references verified0 citation errors · 0 not checkable

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Authors
Zhao, J., Han, X., Ouyang, M., & Burke, A. F.
Affiliations
  • University of California, Davis (US)
  • Tsinghua University (CN)
Corresponding author
  • Ouyang, Minggao
  • Burke, Andrew F.
Venue
Journal of Energy Chemistry
Publisher
Elsevier BV
Published
2023-12
Volume
87
Pages
416-438
ISSN
2095-4956 (ISSN-L)
OpenAlex
W4386566202
Cited by
129
References
52
Citations by year
2026: 23 · 2025: 55 · 2024: 44 · 2023: 7
FWCI
9.36
Type
journal article
Language
en
Open access
closed
Locations
Journal of Energy Chemistry (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 › Civil and Structural Engineering › Structural Health Monitoring Techniques
Keywords
lithium-ion batteries, battery health prognostics, deep learning, transformer models, transfer learning, physics-informed machine learning, generative adversarial networks, deep reinforcement learning, battery state estimation, battery management

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