An integration framework based on deep learning and CFD for early detection of lithium-ion battery thermal runaway

Lithium-ion batteries (LIBs) have been extensively adopted in various fields, leading to a rapid increase in fire risks and accidents. Early detection of potential LIB thermal runaway has a profound influence on reducing fire risks. In this work, we proposed an integration framework based on computational fluid dynamics (CFD) and deep learning to detect the battery thermal runaway early enough. Firstly, a dataset of the temperature contours for the battery thermal runaway has been built. A coupled model with the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) is applied to train the dataset and predict the potential fire risks of the battery pack by identifying the abnormal heat generation. The performance of the proposed model has been proven to have a maximum accuracy of 0.967. The trained model performed an 85.02 F1-score, and all the risks can be detected timely. This framework can further expand the LIB safety margin by detecting the battery thermal runaway quickly and accurately and reducing potential battery fire risks and accidents.