Battery Thermal Runaway Fault Prognosis in Electric Vehicles Based on Abnormal Heat Generation and Deep Learning Algorithms

Abstract

Efficient battery thermal runaway prognosis is of great importance for ensuring safe operation of electric vehicles (EVs). This presents formidable challenges under widely varied and ever-changing driving conditions in real-world vehicular operations. In this article, an enabling thermal runaway prognosis model based on abnormal heat generation (AHG) is proposed by combining the long short-term memory neural network (LSTM) and the convolutional neural network (CNN). The memory cell of the LSTM is modified and the resultant modified LSTM-CNN serves to provide accurate battery temperature prediction. The principal component analysis is used to optimize the model input factors to improve prediction accuracy and to reduce computing time. A random adjacent optimization method is employed to automatically optimize the hyperparameters. Finally, a model-based scheme is presented to achieve AHG-based thermal runaway prognosis. Real-world EV operating data are used to verify the effectiveness and robustness of the proposed scheme. The verification results indicate that the presented scheme exhibits accurate 48-time-step battery temperature prediction with a mean-relative-error of 0.28% and can realize 27-min-ahead thermal runaway prognosis.