The DOI matches its Crossref record; 62 of the 63 references it cites were found in a registry or an index.
Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks
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“Deep learning models can automatically learn and extract features from data, showing significant advantages, especially when dealing with complex time-series data.45,46”
From Battery state of health estimation under fast charging via deep transfer learning (2025) · cites this paper as [46]
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Metadata
- Authors
- Zhao, J., Feng, X., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F.
- Affiliations
- University of California, Davis (US)
- Tsinghua University (CN)
- BYD (China) (CN)
- Corresponding author
- Ouyang, Minggao
- Burke, Andrew F.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 352
- Pages
- 121949
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W4386873880
- Cited by
- 152
- References
- 63
- Citations by year
- 2026: 58 · 2025: 58 · 2024: 35 · 2023: 1
- FWCI
- 11.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Battery Modeling and Management
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
- Keywords
- battery fault diagnosis, failure prediction, electric vehicles, lithium-ion batteries, thermal runaway, spatio-temporal transformer, early warning indicators, onboard sensor data, battery safety
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