Anomaly detection of power battery pack using gated recurrent units based variational autoencoder

Abstract

Rapid and accurate detection of battery pack anomalies and timely fault-tolerant control is of great importance to the safe operation of electric vehicles (EVs). The occurrence of battery system fire accidents shows that if an anomaly cannot be detected early in the potential stage before thermal runaway, the anomaly may develop into a serious fault at any time and lead to irreparable damage. This paper presents such a semi-supervised anomaly detection model, by using a gated recurrent unit (GRU) based variational autoencoder (VAE) (GRU-VAE) framework that detects the early potential anomalies of EV power battery packs. Specifically, employing a GRU-based inference network enables the model to learn the robust latent feature representations of the input multivariate time series (MVTS), which are then used by the generator network to reconstruct the input data. In the training phase, minimizing the reconstruction error helps to learn the data distribution of normal samples. In the testing phase, a large reconstruction error of an input sample indicates that it contains potential abnormal events. In particular, the GRU is used to capture the complex time dependencies in MVTS, and the VAE is used to reconstruct the input sample with probability. The Peaks over Threshold (POT) model in the classical extreme value theory (EVT) is used to properly set the anomaly detection threshold. Experimentation over the real EV operation datasets shows that the model can effectively detect the potential anomalies in MVTS, which is expected to provide a reference for intelligent power battery pack anomaly detection technology.