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

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Adaptive Mamba-LSTM Hybrid Network with Joint State Estimation and Fault Diagnosis for Lithium Batteries

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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

Cross-modal physics-constrained fault diagnosis for lithium-ion batteries based on bidirectional gated recurrent unit and transformer

Xing, 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

Realistic multi-fault diagnostics of millions-scale Li-ion batteries with rapid unsupervised learning

Xie, S., Dong, G., Chen, H., & Lou, Y. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103154 · Cited by 1

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

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

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

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Zhao, J., Cai, M., Wang, Z., Wang, Y., Li, Y., Dong, B., Wang, H., Burke, A. F., Onori, S., & Harris, S. J. (2026) · Advanced Science · doi:10.1002/advs.202524179 · Cited by 2

Transformers 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

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

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

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