Analysis and prediction of thermal runaway propagation interval in confined space based on response surface methodology and artificial neural network

Abstract

Thermal runaway (TR) of lithium-ion batteries (LIBs) and its propagation in battery packs may bring significant losses and restrict the wide application of LIB. It is important to study the propagation characteristics of TR. Based on a series of experiments, this work analyses the influence of state of charge, environment temperature, and heating power on the thermal runaway propagation interval (TRPI) between two adjacent cells in a confined space. The results show that they all have a significant impact on TRPI. Furthermore, response surface methodology (RSM) is employed to study the interactions among these three factors. The minimum TRPI is predicted to be 61.08 s. Based on artificial neural network (ANN), a prediction model trained by back-propagation algorithm is constructed for temperature variations of two cells. The results show that the model is effective in prediction, with the maximum prediction error of 6.88 % and the average prediction error of 3.42 %. It is found that TR can be propagated within 37 s, which brings great challenges to the management of battery packs. This research provides effective methods for identifying the safety problems of LIB packs based on RSM and predicting the temperature variations of cells based on ANN methodology.