Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks

Attention Is All You Need

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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

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

Untitled work

(2014) · Cited by 33,419

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 22,191

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

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 4,383

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

Data-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

Probabilistic machine learning and artificial intelligence

Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995

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

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

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

Machine-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

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 1,677

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

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

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 924

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

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

Machine 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 713

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

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., & Reed, E. J. (2017) · Energy & Environmental Science · doi:10.1039/c6ee02697d · Cited by 448

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 436

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

Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks

Hong, J., Wang, Z., & Yao, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113381 · Cited by 384

Exponential 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 367

Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model

Li, D., Zhang, Z., Liu, P., Wang, Z., & Zhang, L. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3008194 · Cited by 358

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 354

The coming electric vehicle transformation

Crabtree, G. W. (2019) · Science · doi:10.1126/science.aax0704 · Cited by 345

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

Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning

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

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 309

Dual-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 288

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

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

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

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 206

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model

Hannan, 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

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

State of charge estimation for lithium-ion battery using Transformer with immersion and invariance adaptive observer

Shen, H., Zhou, X., Wang, Z., & Wang, J. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103768 · Cited by 160

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 158

Internal 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

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 144

Specialized 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

Data‐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

Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks

Hong, J., Wang, Z., Chen, W., & Yao, Y. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113648 · Cited by 111

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

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

Foreign 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

Electric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis

Zhao, J., & Burke, A. F. (2022) · Batteries · doi:10.3390/batteries8100142 · Cited by 87

Spatial-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 68

Cloud-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

Battery 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

2023 IEEE Vehicle Power and Propulsion Conference (VPPC)

(2023) · doi:10.1109/vppc60535.2023 · Cited by 43

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

Quantitative diagnosis of internal short circuit for cylindrical li-ion batteries based on multiclass relevance vector machine

Xie, J., Zhang, L., Yao, T., & Li, Z. (2020) · Journal of Energy Storage · doi:10.1016/j.est.2020.101957 · Cited by 28

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

The 6G frequency switch that spares scientific services

McNair, J. Y. (2022) · Nature · doi:10.1038/d41586-022-01306-w · Cited by 18

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

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

Battery 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

Enhancing 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

Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions

Cao, 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

Early Warning of Thermal-Runaway Risk and Faulty-Cell Localization for Electric-Vehicle Battery Packs Using a Hotspot-Suppressed and Alarm-Consistent Dual-Branch Attention TransformerPreprint

王, 迅., & 范, 昕. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6727673

Unsupervised early warning of thermal runaway in electric vehicle batteries via a hierarchical multi-scale Transformer and extreme value theory

Li, B., Li, B., & Wen, J. (2026) · Energy · doi:10.1016/j.energy.2026.142573

State of Health Estimation of Electric Vehicle Batteries Using Transformer-Based Neural Network

Zhao, Y., & Behdad, S. (2024) · Journal of Energy Resources Technology · doi:10.1115/1.4065762 · Cited by 18

Multi-Step Temperature Prognosis of Lithium-Ion Batteries for Real Electric Vehicles Based on a Novel Bidirectional Mamba Network and Sequence Adaptive Correlation

Shen, H., Liu, Y., Zhao, Q., Xue, G., Zhang, T., & Tan, X. (2024) · Batteries · doi:10.3390/batteries10100373 · Cited by 11

Early Fault Diagnosis Framework Based on Transformer Model for Internal Short Circuits and Connection Faults in Battery SystemsPreprint

Xu, X., Zhu, M., Yan, W., Zhu, J., Wang, P., Wang, C., & Zhang, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6621922