Uncertainty assessment method for thermal runaway propagation of lithium-ion battery pack

To some extent, the thermal runaway issue pertaining to lithium-ion battery still present challenging problems, to put the safety-related issue under control in high-power and high-energy applications. One of these challenging issues relates to the underpinning stochastic uncertainties associated with thermal runaway propagation. Therefore, this study proposes a methodology to evaluate the uncertainty of thermal runaway propagation in Li-ion battery modules. Firstly, a numerical thermal runaway propagation model is developed for phase change material-based thermal management system and verified by experiments. Subsequently, the Adaptive Kriging High-dimensional model representation method is employed to construct a surrogate model, followed by global sensitivity analysis to evaluate the impact of six different influencing factors on thermal runaway propagation. Finally, the random fluctuation of the critical factors affecting thermal runaway propagation is integrated to analyze and quantify the uncertainty. The results show that the state of charge, the thermal conductivity of phase change material, and inter-cell spacing are critical factors affecting thermal runaway propagation. With the system parameters proposed in this paper, the critical state of charge range that significantly impacts the uncertainty of thermal runaway propagation is approximately 40% to 55%. The analysis of the propagation results and the cell temperature indicates that the highest level of uncertainty occurs at around 45% state of charge. In this case, the thermal runaway propagation is significantly impacted by changes in key factors, and the battery temperature exhibits high fluctuation levels. Furthermore, although there is a 59% likelihood of no propagation, the potential damage to all batteries becomes exceedingly significant once thermal runaway propagation occurs. The method proposed in this study effectively evaluates the uncertainty of thermal runaway propagation in battery systems. It identifies the causes of the uncertainty, which can be used to implement effective suppression measures to enhance safety.