In large-scale energy storage cabinets

chain propagation of thermal runaway initiated by single cells within densely arranged battery modules may lead to cascading failures at the module or even cabinet level

posing severe threats to energy storage system safety. Considering the thermal runaway triggering and propagation characteristics in battery modules

this study proposes a fault inversion localization methodology for energy storage batteries constrained by thermal propagation paths. First

using 280Ah lithium-iron phosphate (LiFePO4) cells

modules

and clusters representative of practical cabinet configurations

this paper conducts thermal abuse experiments to induce cell thermal runaway. Critical parameter thresholds and evolutionary characteristics at the cell level are systematically analyzed through multi-point data acquisition. Second

leveraging experimentally validated thermal abuse failure data

a three-dimensional multiphysics coupled simulation model is developed by integrating the forgetting factor recursive least squares (FFRLS) parameter identification method. This establishes an optimized thermal propagation model with time-varying mappings between state of charge (SOC) and thermal runaway parameters

enabling precise simulation of thermal runaway propagation paths and energy release characteristics at the cell/module level under varying SOC conditions. Finally

a hybrid genetic algorithm (GA) and grey wolf optimizer (GWO) enhanced time difference of arrival (TDOA) fault inversion framework is proposed

integrating thermal propagation temperature evolution with fault inversion optimization so as to achieve cluster-level thermal runaway source localization. Results demonstrate that the optimized thermal propagation modeling framework enables high- precision module-level thermal runaway simulations

while the proposed fault inversion method robustly identifies failure sources across complex operational scenarios.