Open Access
| Issue |
EPJ Web Conf.
Volume 355, 2026
4th International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics (STAAAR 2025)
|
|
|---|---|---|
| Article Number | 01010 | |
| Number of page(s) | 12 | |
| Section | Robotics, Exoskeletons and AI Modeling | |
| DOI | https://doi.org/10.1051/epjconf/202635501010 | |
| Published online | 03 March 2026 | |
- J. Zhang, Y. Wang, B. Jiang, H. He, S. Huang, C. Wang, Y. Zhang, X. Han, D. Guo, G. He, M. Ouyang, Realistic fault detection of Li-ion battery via dynamical deep learning. Nat. Commun. 14, 5940 (2023). https://doi.org/10.1038/s41467-023-41689 [Google Scholar]
- A. Naha, A. Khandelwal, S. Agarwal, P. Tagade, K.S. Hariharan, A. Kaushik, A. Yadu, S.M. Kolake, S. Han, B. Oh, Internal short circuit detection in Li-ion batteries using supervised machine learning. Sci. Rep. 10, 1301 (2020). https://doi.org/10.1038/s41598-020-58298-0 [Google Scholar]
- J. Ouyang, Z. Lin, L. Hu, X. Fang, Voltage faults diagnosis for lithium-ion batteries in electric vehicles using optimized graphical neural network. Sci. Rep. 15, 27328 (2025). https://doi.org/10.1038/s41598-025-01234-5 [Google Scholar]
- Q. Xue, G. Li, Y. Zhang, S. Shen, Z. Chen, Y. Liu, Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution. J. Power Sources 482, 228964 (2021). https://doi.org/10.1016/j.jpowsour.2020.228964 [Google Scholar]
- G. Hu, P. Huang, Z. Bai, Q. Wang, K. Qi, Comprehensive analysis of failure evolution and safety evaluation of automotive lithium-ion battery. ETransport. 10, 100140 (2021). https://doi.org/10.1016/j.etran.2021.100140 [Google Scholar]
- R.K. Daniels, V. Kumar, S.S. Chouhan, A. Prabhakar, Thermal runaway fault prediction in air-cooled lithium-ion battery modules using machine learning through temperature sensors placement optimization. Appl. Energy 355, 122352 (2024). https://doi.org/10.1016/j.apenergy.2023.122352 [Google Scholar]
- X. Liu, Y. Li, X. Jiang, K. Xu, Lithium-ion battery of an electric vehicle short circuit caused by electrolyte leakage: A case study and online detection. J. Energy Storage 97, 112950 (2024). https://doi.org/10.1016/j.est.2024.112950 [Google Scholar]
- X. Feng, M. Ouyang, X. Liu, L. Lu, Y. Xia, X. He, Thermal runaway mechanism of lithium-ion battery for electric vehicles: A review. Energy Storage Mater. 10, 246–267 (2018). https://doi.org/10.1016/j.ensm.2017.05.013 [CrossRef] [Google Scholar]
- Z. Liao, S. Zhang, K. Li, G. Zhang, T.G. Habetler, A survey of methods for monitoring and detecting thermal runaway of lithium-ion batteries. J. Power Sources 436, 226879 (2019). https://doi.org/10.1016/j.jpowsour.2019.226879 [Google Scholar]
- L. Song, Y. Zheng, Z. Xiao, C. Wang, T. Long, Review on thermal runaway of lithium-ion batteries for electric vehicles. J. Electron. Mater. 51, 30–46 (2022). https://doi.org/10.1007/s11664-021-09263-3 [Google Scholar]
- Y. Zhang, R. Xiong, H. He, X. Qu, M. Pecht, Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries. ETransport. 1, 100004 (2019). https://doi.org/10.1016/j.etran.2019.100004 [Google Scholar]
- S. Park, Y. Song, S.W. Kim, Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval. Energy 290, 130220 (2024). https://doi.org/10.1016/j.energy.2024.130220 [Google Scholar]
- Y. Liao, H. Zhang, Y. Peng, Y. Hu, J. Liang, Z. Gong, Y. Wei, Y. Yang, Electrolyte degradation during aging process of lithium-ion batteries: mechanisms, characterization, and quantitative analysis. Adv. Energy Mater. 14, 2304295 (2024). https://doi.org/10.1002/aenm.202304295 [Google Scholar]
- R. Cao, Z. Zhang, R. Shi, J. Lu, Y. Zheng, Y. Sun, X. Liu, S. Yang, Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions. Nat. Commun. 16, 1651 (2025). https://doi.org/10.1038/s41467-025-01915-7 [Google Scholar]
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