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 for complex time-series data.
“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]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2014) · Cited by 33,419 · see citing works
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
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 21,793 · see citing works
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
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 · see citing works
On 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 · see citing works
Thermal runaway mechanism of lithium ion battery for electric vehicles: A review
Feng, X., Ouyang, M., Liu, X., Lu, L., Xia, Y., & He, X. (2017) · Energy storage materials · doi:10.1016/j.ensm.2017.05.013 · Cited by 4,251 · see citing works
Self-citationData-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228 · see citing works
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995 · see citing works
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847 · see citing works
A review of lithium ion battery failure mechanisms and fire prevention strategies
Wang, Q., Mao, B., Stoliarov, S. I., & Sun, J. (2019) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2019.03.002 · Cited by 1,768 · see citing works
Mitigating Thermal Runaway of Lithium-Ion Batteries
Feng, X., Ren, D., He, X., & Ouyang, M. (2020) · Joule · doi:10.1016/j.joule.2020.02.010 · Cited by 1,735 · see citing works
Self-citationMachine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727 · see citing works
Materials for lithium-ion battery safety
Liu, K., Liu, Y., Lin, D., Pei, A., & Cui, Y. (2018) · Science Advances · doi:10.1126/sciadv.aas9820 · Cited by 1,643 · see citing works
Nickel‐Rich and Lithium‐Rich Layered Oxide Cathodes: Progress and Perspectives
Manthiram, A., Knight, J. C., Myung, S., Oh, S., & Sun, Y. (2015) · Advanced Energy Materials · doi:10.1002/aenm.201501010 · Cited by 1,205 · see citing works
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., et al. (2020) · Nature · doi:10.1038/s41586-020-1994-5 · Cited by 1,180 · see citing works
Progress of enhancing the safety of lithium ion battery from the electrolyte aspect
Wang, Q., Jiang, L., Yu, Y. M., & Sun, J. (2018) · Nano Energy · doi:10.1016/j.nanoen.2018.10.035 · Cited by 917 · see citing works
A Review of Battery Fires in Electric Vehicles
Sun, P., Bisschop, R., Niu, H., & Huang, X. (2020) · Fire Technology · doi:10.1007/s10694-019-00944-3 · Cited by 901 · see citing works
Thermal Runaway of Lithium-Ion Batteries without Internal Short Circuit
Liu, X., Ren, D., Hsu, H., Feng, X., Xu, G., Zhuang, M., Gao, H., Lu, L., Han, X., Chu, Z., et al. (2018) · Joule · doi:10.1016/j.joule.2018.06.015 · Cited by 877 · see citing works
Self-citationMachine learning pipeline for battery state-of-health estimation
Roman, D., Saxena, S., Robu, V., PECHT, M. G., & Flynn, D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00312-3 · Cited by 689 · see citing works
High areal capacity battery electrodes enabled by segregated nanotube networks
Park, S., King, P. J., Tian, R., Boland, C. S., Coelho, J., Zhang, C. J., McBean, P., McEvoy, N., Kremer, M. P., Daly, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0398-y · Cited by 480 · see citing works
Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., & Reed, E. J. (2016) · Energy & Environmental Science · doi:10.1039/c6ee02697d · Cited by 445 · see citing works
High-throughput calculations of magnetic topological materials
Xu, Y., Elcoro, L., Song, Z.-D., Wieder, B. J., Vergniory, M. G., Regnault, N., Chen, Y., Felser, C., & Bernevig, B. A. (2020) · Nature · doi:10.1038/s41586-020-2837-0 · Cited by 422 · see citing works
Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
Chen, D., Hong, W., & Zhou, X. (2022) · IEEE Access · doi:10.1109/access.2022.3151975 · Cited by 420 · see citing works
Hong, J., Wang, Z., & Yao, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113381 · Cited by 384 · see citing works
Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
Correa‐Baena, J., Hippalgaonkar, K., van Duren, J., Jaffer, S. A., Chandrasekhar, V., Stevanović, V., Wadia, C., Guha, S., & Buonassisi, T. (2018) · Joule · doi:10.1016/j.joule.2018.05.009 · Cited by 350 · see citing works
Li, D., Zhang, Z., Liu, P., Wang, Z., & Zhang, L. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3008194 · Cited by 348 · see citing works
The coming electric vehicle transformation
Crabtree, G. W. (2019) · Science · doi:10.1126/science.aax0704 · Cited by 345 · see citing works
Safety modelling and testing of lithium-ion batteries in electrified vehicles
Deng, J., Bae, C., Marcicki, J., Masias, A. G., & Miller, T. (2018) · Nature Energy · doi:10.1038/s41560-018-0122-3 · Cited by 331 · see citing works
Ma, G., Xu, S., Jiang, B., Cheng, C., Yang, X. P. O., Shen, Y., Yang, T., Huang, Y., Ding, H., & Yuan, Y. (2022) · Energy & Environmental Science · doi:10.1039/d2ee01676a · Cited by 323 · see citing works
The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety
Finegan, D. P., Zhu, J., Feng, X., Keyser, M. A., Ulmefors, M., Li, W., Bazant, M. Z., & Cooper, S. J. (2020) · Joule · doi:10.1016/j.joule.2020.11.018 · Cited by 305 · see citing works
Self-citationExponential expressivity in deep neural networks through transient chaosPreprint
Poole, B., Lahiri, S., Raghu, M., Sohl‐Dickstein, J., & Ganguli, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.05340 · Cited by 299 · see citing works
Thermal Runaway Triggered by Plated Lithium on the Anode after Fast Charging
Li, Y., Feng, X., Ren, D., Ouyang, M., Lu, L., & Han, X. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b16589 · Cited by 282 · see citing works
Self-citationDual-Aspect Self-Attention Based on Transformer for Remaining Useful Life Prediction
Zhang, Z., Song, W. W., & Li, Q. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3160561 · Cited by 273 · see citing works
Safety issues caused by internal short circuits in lithium-ion batteries
Liu, B., Jia, Y.-K., Li, J., Yin, S., Yuan, C.-H., Hu, Z., Wang, L.-B., Li, Y.-X., & Xu, J. (2018) · Journal of Materials Chemistry A · doi:10.1039/c8ta08997c · Cited by 238 · see citing works
Data-Driven Safety Envelope of Lithium-Ion Batteries for Electric Vehicles
Li, W., Zhu, J., Xia, Y., Gorji, M. B., & Wierzbicki, T. (2019) · Joule · doi:10.1016/j.joule.2019.07.026 · Cited by 223 · see citing works
Machine learning for predicting battery capacity for electric vehicles
Zhao, J., Ling, H., Liu, J., Wang, J., Burke, A. F., & Lian, Y. (2022) · eTransportation · doi:10.1016/j.etran.2022.100214 · Cited by 201 · see citing works
Self-citationHannan, M. A., How, D. N. T., Lipu, M. S. H., Mansor, M., Ker, P. J., Dong, Z. Y., Sahari, K. S. M., Tiong, S. K., Muttaqi, K. M., Mahlia, T. M. I., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-98915-8 · Cited by 196 · see citing works
Mechanism of Thermal Runaway in Lithium-Ion Cells
Galushkin, N. E., Yazvinskaya, N. N., & Galushkin, D. N. (2018) · Journal of The Electrochemical Society · doi:10.1149/2.0611807jes · Cited by 185 · see citing works
Shen, H., Zhou, X., Wang, Z., & Wang, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103768 · Cited by 160 · see citing works
Self-citationInternal short circuit detection in Li-ion batteries using supervised machine learning
Naha, A., Khandelwal, A., Agarwal, S., Tagade, P., Hariharan, K. S., Kaushik, A., Yadu, A., Kolake, S. M., Han, S., & Oh, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-58021-7 · Cited by 157 · see citing works
Aging Mechanisms of Electrode Materials in Lithium‐Ion Batteries for Electric Vehicles
Lin, C. W., Tang, A., Mu, H., Wang, W., & Wang, C. (2015) · Journal of Chemistry · doi:10.1155/2015/104673 · Cited by 148 · see citing works
Battery prognostics and health management from a machine learning perspective
Zhao, J., Feng, X., Pang, Q., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.233474 · Cited by 140 · see citing works
Self-citationSpecialized Deep Neural Networks for Battery Health Prognostics: Opportunities and Challenges
Zhao, J., Han, X., Ouyang, M., & Burke, A. F. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.08.047 · Cited by 129 · see citing works
Self-citationData‐Driven Safety Risk Prediction of Lithium‐Ion Battery
Jia, Y., Li, J., Yuan, C., Gao, X., Yao, W., Lee, M., & Xu, J. (2021) · Advanced Energy Materials · doi:10.1002/aenm.202003868 · Cited by 125 · see citing works
Hong, J., Wang, Z., Chen, W., & Yao, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113648 · Cited by 112 · see citing works
Tomographic reconstruction of oxygen orbitals in lithium-rich battery materials
Hafiz, H., Suzuki, K., Barbiellini, B., Tsuji, N., Yabuuchi, N., Yamamoto, K., Orikasa, Y., Uchimoto, Y., Sakurai, Y., Sakurai, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03509-z · Cited by 92 · see citing works
Data-driven prediction of battery failure for electric vehicles
Zhao, J., Ling, H., Wang, J., Burke, A. F., & Lian, Y. (2022) · iScience · doi:10.1016/j.isci.2022.104172 · Cited by 92 · see citing works
Self-citationForeign matter defect battery and sudden spontaneous combustion
Kong, X., Lu, L., Yuan, Y., Sun, Y., Feng, X., Yang, H., Zhang, F., Zhang, J., Liu, X., Han, X., et al. (2022) · eTransportation · doi:10.1016/j.etran.2022.100170 · Cited by 92 · see citing works
Self-citationElectric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis
Zhao, J., & Burke, A. F. (2022) · Batteries · doi:10.3390/batteries8100142 · Cited by 82 · see citing works
Self-citationSpatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation
Shi, D., Zhao, J., Wang, Z., Zhao, H., Wang, J., Lian, Y., & Burke, A. F. (2023) · Electronics · doi:10.3390/electronics12122598 · Cited by 63 · see citing works
Self-citationCloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health
Shi, D., Zhao, J., Wang, Z., Zhao, H., Eze, C., Wang, J., Lian, Y., & Burke, A. F. (2023) · Energies · doi:10.3390/en16093855 · Cited by 61 · see citing works
Self-citationBattery Prognostics and Health Management for Electric Vehicles Under Industry 4.0
Zhao, J., & Burke, A. F. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.04.042 · Cited by 44 · see citing works
Self-citation2023 IEEE Vehicle Power and Propulsion Conference (VPPC)
(2023) · doi:10.1109/vppc60535.2023 · Cited by 43 · see citing works
STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety
Li, M., Dong, C., Xiong, B., Mu, Y., Yu, X., Xiao, Q., & Jia, H. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119965 · Cited by 42 · see citing works
Xie, J., Zhang, L., Yao, T., & Li, Z. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101957 · Cited by 28 · see citing works
Battery Diagnosis: A Lifelong Learning Framework for Electric Vehicles
Zhao, J., Nan, J., Wang, J., Ling, H., Lian, Y., & Burke, A. F. (2022) · IEEE Vehicle Power and Propulsion Conference (VPPC) · doi:10.1109/vppc55846.2022.10003378 · Cited by 21 · see citing works
Self-citationThe 6G frequency switch that spares scientific services
McNair, J. Y. (2022) · Nature · doi:10.1038/d41586-022-01306-w · Cited by 18 · see citing works
Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge PredictionPreprint
Biggio, L., Bendinelli, T., Kulkarni, C. S., & Fink, O. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.02555 · Cited by 15 · see citing works
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
Flagged first, then by citations
1 of the 63 references couldn’t be matched to a known paper.
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 · see citing works
Self-citationBattery safety: Fault diagnosis from laboratory to real world
Zhao, J.-Y., Feng, X.-N., Tran, M.-K., Fowler, M., Ouyang, M., & Burke, A. F. (2024) · Journal of Power Sources · doi:10.1016/j.jpowsour.2024.234111 · Cited by 215 · see citing works
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 115 · see citing works
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 104 · see citing works
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 · see citing works
Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery
Wang, C., & Chen, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123280 · Cited by 86 · see citing works
Battery health diagnostics: Bridging the gap between academia and industry
Wang, Z., Shi, D., Zhao, J.-Y., Chu, Z.-Y., Guo, D.-X., Eze, C., Qu, X.-D., Lian, Y., & Burke, A. F. (2023) · eTransportation · doi:10.1016/j.etran.2023.100309 · Cited by 66 · see citing works
Self-citationPredictive pretrained transformer (PPT) for real-time battery health diagnostics
Zhao, J., Wang, Z., Wu, Y., & Burke, A. F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124746 · Cited by 71 · see citing works
Self-citationBig data-driven prognostics and health management of lithium-ion batteries:A review
Chen, K., Luo, Y., Long, Z., Li, Y., Nie, G., Liu, K., Xin, D., Gao, G., & Wu, G. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115522 · Cited by 68 · see citing works
Review of vehicle to grid integration to support power grid security
Yang, Y. G., Wang, W., Qin, J. P., Wang, M., Ma, Q., & Zhong, Y. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.08.069 · Cited by 70 · see citing works
Quan, R., Zhou, Y., Li, W., & Wan, H. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116788 · Cited by 62 · see citing works
Zheng, B., Deng, Z., Luo, Z., Mao, S., Ouyang, M., Han, X., Wang, H., Li, Y., Sun, Y., Wang, D., et al. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126688 · Cited by 64 · see citing works
Self-citationBattery state of health estimation under fast charging via deep transfer learning
Zhao, J., Li, D., Li, Y., Shi, D., Nan, J., & Burke, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.112235 · Cited by 53 · see citing works
“45,46 Convolutional neural networks (CNNs) excel at extracting local features from the spatial dimensions of battery data, 47 while long short-term memory (LSTM) can capture long-term dependencies in time series.”
Self-citationSpecialized convolutional transformer networks for estimating battery health via transfer learning
Zhao, J., & Wang, Z. (2024) · Energy storage materials · doi:10.1016/j.ensm.2024.103668 · Cited by 49 · see citing works
Self-citationWaseem, M., Gundebommu, S. L., Sreeshobha, E., & Khan, S. (2024) · Energy Storage and Saving · doi:10.1016/j.enss.2024.09.002 · Cited by 48 · see citing works
Wu, P., Tian, E., Tao, H., & Chen, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109756 · Cited by 44 · see citing works
Li, S., Zhang, C., Du, J., Zhang, L., & Jiang, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124634 · Cited by 42 · see citing works
Zhao, J., Lv, Z., Li, D., Feng, X., Wang, Z., Wu, Y., Shi, D., Fowler, M. W., & Burke, A. F. (2024) · eTransportation · doi:10.1016/j.etran.2024.100364 · Cited by 42 · see citing works
Self-citationNaresh, G., Praveenkumar, T., Madheswaran, D. K., Varuvel, E. G., Pugazhendhi, A., Mohanraj, T., & Muthiya, S. J. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.106919 · Cited by 29 · see citing works
Fault detection for lithium-ion batteries of electric vehicles with spatio-temporal autoencoder
Li, H., Liu, Z., Kaleem, M. B., Ruan, S., Ruan, S., & Liu, W. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125933 · Cited by 29 · see citing works
Ye, J., Huang, J., Kuang, J., Tang, J., Liu, C., Li, R., & Tan, G. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118043 · Cited by 31 · see citing works
Lyu, D., Liu, E., Chen, H., Zhang, B., & Xiang, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124290 · Cited by 29 · see citing works
Fault detection for Li-ion batteries of electric vehicles with segmented regression method
Kaleem, M. B., Zhou, Y., Jiang, F., Liu, Z., & Li, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-82960-0 · Cited by 26 · see citing works
The Role of Generative Artificial Intelligence in Internet of Electric Vehicles
Zhang, H., Niyato, D. T., Zhang, W., Zhao, C., Du, H., Jamalipour, A., Sun, S., & Pei, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3511961 · Cited by 26 · see citing works
Yao, L. Y., Chang, Y., Xiao, Y., Cui, G., Fei, Z., & Qu, C. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118447 · Cited by 23 · see citing works
Xie, L., Wei, J., Li, X., Chen, C., Chen, H., & Dong, G. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3556068 · Cited by 21 · see citing works
Liu, K.-L., Zhao, S., Wang, Y., Li, K., Wang, J., Sun, Y.-J., Wu, Q.-W., & Peng, Q. (2025) · Advances in Applied Energy · doi:10.1016/j.adapen.2025.100247 · Cited by 24 · see citing works
Self-citationBattery state estimation for electric vehicles: Translating AI innovations into real-world solutions
Li, H., Xie, X., Zhang, X., Burke, A. F., & Zhao, J. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.116000 · Cited by 22 · see citing works
Self-citationLiu, S., Xu, D., Ye, Y., & Pan, T. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121399 · Cited by 20 · see citing works
Artificial intelligence for battery reuse, recycling and remanufacturing
Tao, S., Moura, S. J., Brandell, D., Han, Z., Urréhman, S., Zou, C., Zhang, X. B., & Zhou, G. (2026) · Nature Reviews Clean Technology · doi:10.1038/s44359-026-00167-0 · Cited by 22 · see citing works
Zhao, J., Burke, A. F., Miller, M. R., & Fulton, L. M. (2024) · Transportation Research Interdisciplinary Perspectives · doi:10.1016/j.trip.2024.101169 · Cited by 19 · see citing works
Self-citationHou, G., Ye, L., Huang, T., & Huang, C. (2024) · Energy · doi:10.1016/j.energy.2024.132879 · Cited by 19 · see citing works
Towards digitized electrochemical power source for electric vehicles
Zhu, J., Xu, W., Tao, S., Cai, J., Shen, Y., Tian, M., Jiang, Y., Jiang, B., Wang, X., Schade, W., et al. (2025) · eTransportation · doi:10.1016/j.etran.2025.100426 · Cited by 18 · see citing works
Multi-scenario failure diagnosis for lithium-ion battery based on coupling PSO-SA-DBSCAN algorithm
Yang, S., Wang, X., Zhou, S., Zhuang, Y., Jin, H., Chen, J., Liu, J., Gao, Z., Zhou, X., Wang, Q., et al. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113393 · Cited by 18 · see citing works
Jeon, J., Cheon, H., Jung, B., & Kim, H. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123757 · Cited by 15 · see citing works
Yang, J., Chen, W., Xiao, X., & Zhang, Z. (2025) · Measurement · doi:10.1016/j.measurement.2025.116781 · Cited by 15 · see citing works
Fault Detection of Li–Ion Batteries in Electric Vehicles: A Comprehensive Review
Li, H., Shaukat, H., Zhu, R., Kaleem, M. B., & Wu, Y. (2025) · Sustainability · doi:10.3390/su17146322 · Cited by 14 · see citing works
Vehicle choice modeling for emerging zero-emission light-duty vehicle markets in California
Burke, A. F., Zhao, J., Miller, M. R., & Fulton, L. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32823 · Cited by 15 · see citing works
Self-citationBattery field data and why it matters: Foundations for real-world electric vehicles
Kim, K.-H., Cho, K., Lee, K., Yoon, J., & Choi, J. (2025) · eTransportation · doi:10.1016/j.etran.2025.100494 · Cited by 13 · see citing works
Wang, Q., Yan, H., Yang, Y., Zhao, X., Huang, G., Huang, Z., Zhu, Z., Liu, S. V., Yi, B., Huang, G., et al. (2025) · Batteries · doi:10.3390/batteries11110414 · Cited by 13 · see citing works
Yang, J., Hong, J., Zhang, X., Du, Y. J., Hou, Y., Chen, Y., Wang, F., Chen, Y., & Wang, H. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.128075 · Cited by 12 · see citing works
Zhou, J., Zhang, S., & Wang, P. (2024) · Processes · doi:10.3390/pr12040816 · Cited by 12 · see citing works
Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis
Mumcuoğlu, M. E., Farea, S. M., Ünel, M., Mise, S., Unsal, S., Cevik, E., Yılmaz, M., & Köprübasi, K. (2024) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2024.108811 · Cited by 12 · see citing works
Umathe, S., Daigavane, P. M., & Daigavane, M. B. (2024) · EAI Endorsed Transactions on Energy Web · doi:10.4108/ew.5950 · Cited by 12 · see citing works
Wu, P., Tian, E., Tao, H., & Chen, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107699 · Cited by 11 · see citing works
Shen, H., Liu, Y., Zhao, Q., Xue, G., Zhang, T., & Tan, X. (2024) · Batteries · doi:10.3390/batteries10100373 · Cited by 11 · see citing works
Hou, G., Zhou, Y., & Zhang, F. (2025) · Energy · doi:10.1016/j.energy.2025.136958 · Cited by 12 · see citing works
Wang, Z., Tao, J., Hu, Y., Zhang, J., Ma, L., & Xu, M. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.05.048 · Cited by 10 · see citing works
Fan, T., Chen, F., Chen, K., & Feng, F. (2025) · Energy · doi:10.1016/j.energy.2025.138227 · Cited by 9 · see citing works
A hybrid data-driven method for voltage state prediction and fault warning of Li-ion batteries
Huang, Y., Gong, X., Lin, Z., & Xu, L. (2024) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2024.105420 · Cited by 8 · see citing works
Challenges and prospects in real-world battery status prediction within Industry 4.0
Qu, X., Zhao, J., Pang, H., Fowler, M. W., & Burke, A. F. (2025) · Green Energy and Intelligent Transportation · doi:10.1016/j.geits.2025.100298 · Cited by 8 · see citing works
Self-citationZhao, Y., Guo, H., & Guo, X. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3576954 · Cited by 8 · see citing works
Lv, L., Zhu, L., Gao, S., Xiang, X., Wang, L., Chang, C., Tian, A., & Jiang, J. (2025) · Energy · doi:10.1016/j.energy.2025.138849 · Cited by 8 · see citing works
Yao, L. Y., Deng, J., Dai, H., Dai, F., Chen, S., Xiao, Y., Cui, G., & Fei, Z. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116465 · Cited by 8 · see citing works
A cognitive digital twin for process chain anomaly detection and bottleneck analysis
Iyer, S. V., Sangwan, K. S., & Dhiraj (2024) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2024.2381728 · Cited by 7 · see citing works
Ding, J., Wang, J., & Wang, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128523 · Cited by 7 · see citing works
Self-citationBhanu, P., Vibin, R., Gnanaprakasam, C. N., & Srilakshmi, K. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118250 · Cited by 7 · see citing works
Ametefe, D. S., Sukor, N. S. A., John, D., Ametefe, G. D., Aliu, A. A., Owen, M. M., & Salaudeen, T. O. (2026) · Battery energy · doi:10.1002/bte2.70099 · Cited by 7 · see citing works
Cheng, Y.-M., Gao, D., Zhao, F.-M., & Yang, Q. S. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad9d68 · Cited by 6 · see citing works
Li, F., Min, Y., Zhang, Y. A., Zuo, H., Bai, F., & Zhang, Y. (2025) · Energy · doi:10.1016/j.energy.2025.137825 · Cited by 6 · see citing works
Charge Diagnostics and State Estimation of Battery Energy Storage Systems Through Transformer Models
Zequera, R. A. G., Rassõlkin, A., Vaimann, T., & Kallaste, A. (2025) · IEEE Access · doi:10.1109/access.2025.3532858 · Cited by 6 · see citing works
Li, S., Zhang, H., Misul, D. A., Miretti, F., Acquarone, M., Ding, N., Ni, D., Hou, N., He, Y., Zhang, Y., et al. (2025) · eTransportation · doi:10.1016/j.etran.2025.100445 · Cited by 5 · see citing works
Multimodal mechanical wear fault diagnosis: Fusion of signal characterization and image information
Zhou, Q., Chai, B., Guo, Y., Li, T., Zhou, S., Wang, K., & Ye, Y. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107204 · Cited by 5 · see citing works
Liu, X., Xiao, H., Yu, D., Yi, C., & Zeng, J. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adcd91 · Cited by 5 · see citing works
A Multifault Diagnosis Method for Lithium-Ion Battery Packs Based on Multisource Feature Map Fusion
Geng, H., Li, J., Gu, X., Bi, F., Chen, Z., & Shang, Y. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3597614 · Cited by 4 · see citing works
Large-scale model driven real-time economic generation control for integrated energy systems
Huang, W., & Yin, L. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126733 · Cited by 4 · see citing works
Zhang, Q., Yang, M., Sun, G., Xiang, Y., Wang, S., Zhang, J., & Li, S. (2026) · Energy storage materials · doi:10.1016/j.ensm.2026.104897 · Cited by 4 · see citing works
Li, L., Li, X., Yang, D., Wu, T., & Jiang, J. (2024) · International Journal of Green Energy · doi:10.1080/15435075.2024.2376707 · Cited by 4 · see citing works
Wan, D., Peng, L., & Zhan, H. K. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16040218 · Cited by 3 · see citing works
Wang, L., Tan, C., Wu, J., Yang, D., & Wu, M. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117325 · Cited by 3 · see citing works
Tian, C., Li, M., Yan, A., Zhang, H., & Liu, Q. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116922 · Cited by 3 · see citing works
Robust Sensor Fault Diagnosis for Lithium-Ion Battery Systems Using an Electro-Thermal Model
Seyyedhosseini, M., Gholaminejad, A., Naeini, N. M., Nahid‐Mobarakeh, B., & Ahmed, R. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3649812 · Cited by 3 · see citing works
Generative Artificial Intelligence in Supply Chain: Review, Trends, and Future Directions
Baruah, A., & Moshref‐Javadi, M. (2026) · Logistics · doi:10.3390/logistics10080190 · Cited by 3 · see citing works
Battery abnormality detection based on important sample selection for large-scale vehicle monitoring
Zhang, Z., Ma, J., Ma, Y., Gong, X., Xiangli, K., & Zhao, X. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125667 · Cited by 3 · see citing works
Design of Temperature Monitoring and Fault Warning System for Lithium Ternary Battery Case
Liu, X., & Han, K. (2025) · Micromachines · doi:10.3390/mi16030345 · Cited by 3 · see citing works
Machine Learning for Anomaly Detection in Electric Transportation Networks
Usanova, K. I., Rani, G. S., Mishra, N., Kaur, S., & Sidhu, J. (2024) · E3S Web of Conferences · doi:10.1051/e3sconf/202451101039 · Cited by 3 · see citing works
Xiong, X., Zhang, Y., & Feng, W. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.237965 · Cited by 3 · see citing works
Chen, K., Lin, N., Li, X., Huang, S., Xu, Y., & Wang, Z. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.118050 · Cited by 2 · see citing works
Chen, H., Zou, L., Zhuang, K., Deng, B., & Hu, J. L. (2025) · Structural Health Monitoring · doi:10.1177/14759217251364745 · Cited by 2 · see citing works
Edge AI-Driven Battery Management for Electric Vehicles: Models, Hardware, and Future Directions
Zangaruche, S. J., Pakhare, R., Gadekar, S. M., Sangelia, S. D., Allagi, S. B., & Sonwalkar, P. K. (2025) · Global Conference on Information Technology and Communication Networks (GITCON) · doi:10.1109/gitcon65266.2025.11377019 · Cited by 2 · see citing works
Guo, Y., Zhen, D., Kong, J., Hou, B., Lyu, D., & Wang, D. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112622 · Cited by 2 · see citing works
Toward Pre-Trained Model-Enabled Intelligent Fault Prognosis for Lithium-Ion Batteries
Liu, C., Li, K.-X., Li, H., & Lyu, B. (2026) · Applied Sciences · doi:10.3390/app16073515 · Cited by 2 · see citing works
Tang, A., Chen, Y., Peng, S., Yu, Q., Shu, X., Shen, W., & Xiong, R. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121828 · Cited by 2 · see citing works
RAISE: Real-Time Adaptive Artificial Intelligence System for Safety Enforcement
Thakur, R., Sajid, Z., Amin, M. T., Kwon, J., & Khan, F. (2026) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.6c00106 · Cited by 3 · see citing works
Ji, T., & Li, J. (2026) · Symmetry · doi:10.3390/sym18050857 · Cited by 2 · see citing works
Battery Safety: Mechanisms, Monitoring, and Machine Intelligence
Jing, H., Ou, S. S., Lv, Z., Guo, H., Burke, A. F., & Zhao, J. (2026) · Advanced Energy Materials · doi:10.1002/aenm.71093 · Cited by 3 · see citing works
Self-citationAutoencoder with Multi-Head Attention for Voltage Anomaly Detection in Electric Vehicle Battery
Kaleem, M. B., Li, H., Saeed, M. U., Liu, Z., & Liu, W. (2024) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc64274.2024.00229 · Cited by 2 · see citing works
Mezaal, Y. S., Abed, F. T., ALRubeei, I. R. N., Alrikabi, A. R. H., ALRikabi, H. T. S., Mutar, H. A., Alaidi, A. H. M., Farhan, M. S., & Obod, I. I. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-82881-2_4 · Cited by 1 · see citing works
Peng, L., Wu, Y., Jiang, Y., Huang, G., Yang, Q., & Zhang, K. (2026) · Journal of Wind Engineering and Industrial Aerodynamics · doi:10.1016/j.jweia.2026.106449 · Cited by 1 · see citing works
Li, P., Ye, J., Zhang, Y., Hou, J., Xiang, S., & Zhou, J. (2025) · CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) · doi:10.1109/safeprocess67117.2025.11268008 · Cited by 1 · see citing works
End-to-end millisecond-level battery aging diagnostics for electric vehicles
Zhao, J., Lv, Z., Che, Y., Li, Y., & Burke, A. F. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119638 · Cited by 2 · see citing works
Self-citationXing, S., Zhang, S., Pang, B., Yu, M., Sun, H., & Shen, Y. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.121151 · Cited by 1 · see citing works
Xie, S., Dong, G., Chen, H., & Lou, Y. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103154 · Cited by 1 · see citing works
Large language model-based fault diagnosis for lithium-ion batteries in cloud-edge systems
Li, J., Shang, Y., Gu, X., Geng, H., Liu, K., Li, X., Bi, F., & Zhang, C. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103210 · Cited by 1 · see citing works
Cross-Scenario Multitype Fault Diagnosis for Lithium-Ion Batteries via Deep Transfer Learning
Li, X., Wei, J., Xie, L., Chen, C., & Dong, G. (2026) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2026.3694043 · Cited by 1 · see citing works
Machine learning is rewiring Li-ion battery RUL prediction: A systematic review and outlook
Tu, K., Yuan, W., Wu, X., Zhang, X., & Liu, Q. (2026) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2026.05.023 · Cited by 1 · see citing works
Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers
Zhao, J.-Y., Cai, M., Wang, Z., Wang, Y., Li, Y.-Q., Dong, B., Wang, H.-W., Burke, A. F., Onori, S., & Harris, S. J. (2026) · Advancement of science · doi:10.1002/advs.202524179 · Cited by 2 · see citing works
Self-citationTransformers for Internet of Things Security and Communications: A Comprehensive Survey
Salman, M., Saeed, N., & Khurshid, K. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3717847 · Cited by 1 · see citing works
Prediction of Failures in Air Pressure System: A Semi-Supervised Framework Based on Transformers
Farea, S. M., Mumcuoğlu, M. E., Ünel, M., Mise, S., Unsal, S., Cevik, E., Yılmaz, M., & Köprübasi, K. (2024) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin58382.2024.10774220 · Cited by 1 · see citing works
Stacked neural filtering network for reliable NEV monitoring
Wang, Y., Yu, C., Zhu, X., Gao, H., & Shang, J. (2025) · Displays · doi:10.1016/j.displa.2025.102976 · Cited by 1 · see citing works
- 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 › Advanced Battery Technologies Research
- 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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