Prognostic Modeling of Thermal Runaway Risk in Lithium-Ion Power Batteries Based on Multivariate Degradation Data

Lithium-ion batteries serve as critical energy storage units for electric vehicles, unmanned aerial vehicles, and other emerging transportation systems. Numerous real-world incidents have demonstrated that thermal runaway (TR) remains a predominant cause of spontaneous combustion in these applications. Concerns over TR risks have significantly hindered broader adoption of lithium-ion batteries. While existing research predominantly focuses on battery heat generation mechanisms, TR initiation processes, and advanced materials with enhanced safety, limited attention has been paid to TR risk evolution induced by cycle-induced performance degradation. To address this gap, this study proposes a data-driven prognostic framework for quantifying TR risks under battery aging scenarios. Leveraging the Open Access XJTU Battery Dataset, we first identify eight degradation-sensitive parameters (including mean current, current standard deviation, and charging time, etc.) by analyzing temporal degradation patterns within characteristic segments of charging curves. These parameters are then fused into a composite degradation index through Physics-Informed Neural Networks (PINNs). Recognizing the stochastic nature of both degradation trajectories and TR-triggering stresses, a Wiener process-based random failure threshold model is developed to probabilistically predict TR risks under time-varying operational conditions. The proposed methodology enables quantitative risk assessment throughout battery service life, offering a novel perspective for aging-aware battery safety management.