Unsupervised anomaly detection for power batteries: A temporal convolution autoencoder framework

Abstract

To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on temporal convolutional autoencoder (TCAE) that can quickly and accurately identify abnormal power battery data was proposed. Its encoder utilized a temporal convolutional network (TCN) structure with residuals to parallelly process data while capturing time dependencies. A novel TCN structure with an effect–cause relationship was developed for the decoder. The same-time-scale connection was established between the encoder and decoder to improve the model performance. The validity of the proposed model was confirmed using a real-world car dataset. Compared with the GRU-AE model, the proposed approach reduced the parameter count and mean square error by 19.5% and 71.9%, respectively. This study provides insights into the intelligent battery pack abnormality detection technology.