Early Thermal-Risk Prediction and Probabilistic Analysis of Lithium-Ion Batteries Using the TRI-S Model

The present paper introduces TRI-S, a probabilistic early warning model for predicting lithium-ion battery thermal runaway. The model integrates the Self-Heating Rate (SHR) and Mass Loss Rate (MLR) indicators into a unified Risk Index, thereby offering a comprehensive risk assessment that surpasses the limitations of conventional single-parameter methods. TRI-S employs Monte Carlo simulations to generate probabilistic risk distributions, enabling robust differentiation between high-risk and normal operating states. The validation of the system across LCO, LFP, and NCA chemistries demonstrates superior early detection performance compared to traditional threshold-based systems. Integration with battery management systems enables proactive thermal runaway mitigation, offering a generalizable safety solution for diverse lithium-ion battery applications.