基于热蔓延路径约束框架下的磷酸铁锂储能电池级联热失控建模与故障反演定位方法研究OA

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.