transformer-based lithium battery overcharge-induced thermal runaway prediction model

The unified loss function and training strategy facilitate efficient end-to-end optimization, addressing practical requirements for battery safety prediction[11]. Based on the previously described innovative architecture design, the core structure of the overcharge-induced thermal runaway prediction model consists of four sequentially executed modules: (1) a ‌feature embedding module‌ that maps raw features into high-dimensional representation space, (2) a ‌temporal modeling module‌ capturing sequential dependencies through Transformer layers and multi-scale convolutions, (3) a ‌feature fusion module‌ that integrates global and local feature information, and (4) a ‌classification prediction module‌ outputting the risk probability of lithium battery overcharge-induced thermal runaway.