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Specialized deep neural networks for battery health prognostics: Opportunities and challenges
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Self-citationArtificial Intelligence in Gas Sensing: A Review
Chowdhury, M. A. Z., & Oehlschlaeger, M. A. (2025) · ACS Sensors · doi:10.1021/acssensors.4c02272 · Cited by 150
Rapid health estimation of in-service battery packs based on limited labels and domain adaptation
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Metadata
- 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 › Battery Modeling and Management
- 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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