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]
Citations
Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
(2014) · Cited by 33,419
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Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Early-warning signals for critical transitions
Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., & Sugihara, G. (2009) · Nature · doi:10.1038/nature08227 · Cited by 4,854
Thermal runaway mechanism of lithium ion battery for electric vehicles: A review
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Self-citationOn the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310
Cited by
Sorted by citations
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216
Self-citationBattery safety: Fault diagnosis from laboratory to real world
Zhao, J., Feng, X., Tran, M., Fowler, M. W., Ouyang, M., & Burke, A. F. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234111 · Cited by 229
Self-citationEnhancing Autonomous System Security and Resilience With Generative AI: A Comprehensive Survey
Lopez, M. A., Lunardi, W. T., Lawton, G., & Thakkar, S. (2024) · IEEE Access · doi:10.1109/access.2024.3439363 · Cited by 128
Artificial intelligence-driven real-world battery diagnostics
Zhao, J., Qu, X., Wu, Y., Fowler, M. W., & Burke, A. F. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100419 · Cited by 113
Self-citationCao, R., Zhang, Z., Shi, R., Lu, J., Yifan, Z., Sun, Y., Liu, X., & Yang, S. (2025) · Nature Communications · doi:10.1038/s41467-025-56832-8 · Cited by 97
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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