Weiwei Qi

The increasing prevalence of lithium-ion batteries in energy storage and electric transportation has led to a rise in overcharge-induced thermal runaway (TR) incidents. Particularly, the TR of Lithium Iron Phosphate (LFP) batteries demonstrates distinct evolutionary stages and multimodal hazard signals. This study investigated the TR process of LFP batteries under various charging rates through five sets of gradient C-rate experiments, collecting multimodal data (temperature, voltage, gas, sound, and deformation). Drawing on the collected data, this study proposes a three-stage evolution model that systematically identifies key characteristic signals and tracks their progression pattern through each stage of TR. Subsequently, fusion-based models (for both single- and multi-rate scenarios) and a time-series-based LSTM model were developed to evaluate their classification accuracy and feature importance in the classification of TR stages. Results indicate that the fusion-based models offer greater generalization, while the LSTM model excels at modeling time-dependent dynamics. These models demonstrate complementary strengths, providing a comprehensive toolkit for risk assessment. Furthermore, for the severe TR stage, this study proposes an innovative three-dimensional dynamic emergency decision matrix comprising a toxicity index (TI), flammability index (FI), and visibility (V) to provide quantitative guidance for rescue operations in the post-accident phase. Ultimately, this study establishes a comprehensive, closed-loop framework for LFP battery safety, extending from multimodal signal acquisition and intelligent early warning to quantified emergency response. This framework provides both a robust theoretical basis and practical tools for managing TR risk throughout the entire battery lifecycle.