AI 中文总结
研究锂离子电池热失控早期预警，提出状态感知物理引导框架，整合多测量信号，经卷积分类器和因果时间卷积主干联合学习，在多条件测试中取得良好效果，支持该融合策略用于更可靠预警。
AI 中文摘要
锂离子电池热失控对电动汽车和储能系统构成重大安全风险。当前早期预警方法主要依赖温度，可能错过快速加热前出现的机械先兆。本文引入一种状态感知、物理引导框架，整合温度、电压、力、变形和荷电状态测量用于在可控机械滥用下早期预警。通过轻量级卷积分类器从机械信号推断安全、预警或危险状态，这些状态估计通过特征线性调制、物理偏置注意力和状态依赖门控来调节因果时间卷积主干。联合学习统一状态识别、热失控检测和灾难时间估计。通过留一实验交叉验证评估框架，该方法在不同荷电状态水平和加载协议下取得了良好效果，支持状态感知热机械融合作为在可控滥用条件下实现更早、更可靠热失控预警的有前景策略。
英文摘要
Thermal runaway in lithium-ion batteries poses a major safety risk to electric vehicles and energy storage systems. Current early-warning methods depend mainly on temperature and may therefore miss mechanical precursors that emerge before rapid heating. We introduce a regime-aware, physics-guided framework that integrates temperature, voltage, force, deformation, and state-of-charge measurements for early warning under controlled mechanical abuse. A lightweight convolutional classifier first infers safe, warning, or danger regimes from mechanical signals. These regime estimates then condition a causal temporal convolutional backbone through feature-wise linear modulation, physics-biased attention, and regime-dependent gating. Joint learning unifies regime identification, thermal-runaway detection, and time-to-disaster estimation. We evaluate the framework using leave-one-experiment-out cross-validation on 30 mechanical-abuse tests across state-of-charge levels of 10%, 50%, and 90% and two loading protocols. The method achieves an F1 score of 0.89, a high-temperature prediction root-mean-square error of 12.3 °C, a mean warning lead time of 15.6 s, a detection success rate of 0.92, and an experiment-level false alarm rate of 2.7%. Its lead time exceeds that of the strongest baseline by 69.6%. Removing force reduces the lead time by 60.3%, highlighting the value of mechanical precursors. These results support regime-aware thermo-mechanical fusion as a promising strategy for earlier and more reliable thermal-runaway warning under controlled abuse conditions.
