Batteries endommagées
Ali, M. M. E. H., Tahtali, M. et Ghodrat, M. (2025). Real-time CCTV-based deep learning for early detection of lithium-ion battery fires. Journal of Power Sources, 659, article 238452. https://doi.org/10.1016/j.jpowsour.2025.238452 
Li, T., Zhao, W., Yuan, J., Wei, X., Zhao, J. et Yang, X. (2025). Prediction of heat release rates from lithium-ion battery fires: A methodology incorporating digital imaging and experimental analysis. Journal of Energy Storage, 137, article 118645. https://doi.org/10.1016/j.est.2025.118645
Wang, Y., Yu, T., Zheng, L., Ji, W., Chen, Z., Zhu, J., . . . Chen, J. (2026). Revealing the generation mechanisms and explosion risks of emissions from abused lithium-ion batteries: Progress and challenges. Journal of Energy Storage, 146, article 120023. https://doi.org/10.1016/j.est.2025.120023
Yao, Y., Jiang, Y., Chen, F., Wang, Y., Zhao, J., Zhou, H. et Kang, R. (2025). Optimal spray strategy for synergistic suppression of thermal runaway propagation in lithium-ion batteries using gaseous extinguishing agents and intermittent water mist. Journal of Energy Storage, 137, article 118579. https://doi.org/10.1016/j.est.2025.118579
Zhou, D., Guo, R., Liu, J., Wu, W., Niu, J., He, X., . . . Zheng, Q. (2026). Quantitative assessment of falling and fire risks of lithium batteries in shipping containers based on multi-physics coupling and intelligent algorithms. Journal of Energy Storage, 146, article 120124. https://doi.org/10.1016/j.est.2025.120124
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