Cross-Material Battery Capacity Estimation Using Hybrid-Model Fusion Transfer Learning
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Battery state of health estimation under fast charging via deep transfer learning
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“53 Transfer learning enhances the generalization ability of the model by pre-training on one domain or dataset and then fine-tuning it for application in another domain, thus reducing reliance on large-scale labeled data.”
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- Authors
- Zhao, J., Qu, X., Han, X., Wu, Y., & Burke, A. F.
- Affiliations
- University of California, Davis (US)
- Hubei University of Arts and Science (CN)
- Tsinghua University (CN)
- Stanford University (US)
- Corresponding author
- Zhao, Jingyuan
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2025-01
- Volume
- 625
- Pages
- 235674
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W4403753127
- Cited by
- 45
- References
- 70
- Citations by year
- 2026: 19 · 2025: 24 · 2024: 2
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Power Sources (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
- battery capacity estimation, battery health assessment, convolutional neural networks, self-attention, lithium iron phosphate batteries, NMC batteries, ternary cathode materials, battery degradation, operational history
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