2026 Ullah Physics Guided Thermal Runaway Warning

Thermal runaway in lithium-ion batteries poses a significant 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 runaway. 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, warming, or danger regimes from mechanical signals. These regime estimates then condition a causal temporal convolutional backbone through feature-wise input modulation, physics-based 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 a prediction 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 accuracy of 0.92, and an experiment-level false alarm rate of 1.5%. Its lead time exceeds that of the strongest baseline by 69.6%. Removing force reduces the lead time by 63.0%, highlighting the value of mechanofusion as a promising strategy for earlier and more reliable thermal-runaway warning under controlled abuse conditions.