Battery Prognostics and Health Management for Electric Vehicles Under Industry 4.0
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Traditional SOH estimation methods based on electrochemical/physical models face limitations in practical applications due to complexity of aging and individual variability.
“These models can accurately simulate the internal behavior of batteries, but due to the complexity of battery aging processes and individual variability, they face many limitations in practical applications.24,25,26,27”
From Battery state of health estimation under fast charging via deep transfer learning (2025) · cites this paper as [27]
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- Authors
- Zhao, J., & Burke, A. F.
- Affiliations
- University of California, Davis (US)
- Corresponding author
- Burke, Andrew F.
- Venue
- Journal of Energy Chemistry
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 84
- Pages
- 30-33
- ISSN
- 2095-4956 (ISSN-L)
- OpenAlex
- W4376878909
- Cited by
- 44
- References
- 27
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 19 · 2023: 11
- FWCI
- 3.20
- 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 › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- battery prognostics, battery health management, electric vehicles, Industry 4.0, battery reliability, battery safety, battery performance optimization
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