| Title |
Early Thermal?Risk Prediction and Probabilistic Analysis of Lithium?Ion Batteries Using the TRI?S Model |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.4.804 |
| Keywords |
Lithium-ion battery safety; Thermal runaway prediction; Early warning system; Probabilistic risk assessment; Battery management system (BMS) |
| Abstract |
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. |