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

• Published in: IEEE transactions on power electronics Vol. 37; no. 7; pp. 8513 - 8525
• Main Authors: Li, Da, Liu, Peng, Zhang, Zhaosheng, Zhang, Lei, Deng, Junjun, Wang, Zhenpo, Dorrell, David G., Li, Weihan, Sauer, Dirk Uwe
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Batteries; Computer memory; Computing time; Convolutional neural network (CNN); Convolutional neural networks; Deep learning; Driving conditions; Electric vehicles; electric vehicles (EVs); fault prognosis; Heat generation; Heating systems; Lithium; lithium-ion batteries; long short-term memory neural network (LSTM); Machine learning; Neural networks; Optimization; Predictive models; Principal components analysis; Prognosis; Prognostics and health management; Temperature measurement; Temperature sensors; Thermal runaway
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/TPEL.2022.3150026

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.