A transformer guided multi modal learning framework for predictive and causal assessment of thermal runaway in high energy batteries - Gajghate, Sameer Sheshrao; Noor, M. M.; Kumar, Subhash; Bansod, Premendra Janardan; Shelare, Sagar Dnyaneshwar; Nikam, Keval Chandrakant; Jathar, Laxmikant Dattatray; Dennison, Milon Selvam (Nature Research, 2025)
Machine Learning approaches from the present state either use unimodal data, unable to model elegant long spatial-temporal dependencies in warning systems or create early warning response datasets with limited quantitative interpretability sets. To address these shortcomings, this work introduces T-RUNSAFE, a multi-pronged, machine learning-based predictive prototype for thermal runaway assessment. The framework integrates five specialized modules: (1) ST-Former, a spatiotemporal transformer that encodes thermal gradients from thermal images and sensor logs using temporal self-attention over LSTMs, thus is superior to traditional LSTMs for capturing evolving thermal patterns; (2) FUSE-GEN, adversarial trained dual-encoder variational autoencoder, fusing acoustic emission (AE) signals and thermal embeddings into a shared latent space for earlystage internal degradation detection; (3) DEGRA-GNN, a graph attention network that capitalizes on battery electrode topology to model the spatial propagation of thermal faults; (4) CAUS-RUN, a counterfactual simulation engine employing structural causal models to attribute risk to specific spatial zones for interpretability; and (5) SENSOR-RL, a reinforcement learning module optimizing sensor sampling policies on real-time risk levels that cuts down on sensor power while still holding to detection accuracy. The experimental results show great early prediction accuracy (AUC-ROC > 0.96), high spatial degradation localization accuracy (93.5%), and a 37% decrease in power consumption of sensing. T-RUNSAFE predicts, interprets, and optimizes resource utilization for thermal runaway risk assessment. By integrating deep learning, physics-informed modeling, and causal reasoning, it enables real-time battery safety monitoring. Although challenges remain regarding sensor cost, computational overhead, and chemistry generalization, the study demonstrates the feasibility of advanced onboard battery management systems tailored for next-generation energy applications.