Battery Fault Detection Using Enhanced Spatial-Temporal Features for Electric Vehicles

The rapid and accurate detection of faults for lithium-ion batteries plays a critical role in ensuring the safe operation of electric vehicle systems. This paper proposes a fault detection method for electric vehicle batteries by exploiting the temporal smoothness and spatial similarity of battery pack data. Firstly, a temporal convolutional network (TCN) is utilized to learn the latent spatial and temporal features of the data. Then, a self-attention mechanism is employed to capture the correlations and importance between different features. Furthermore, an autoencoder is employed to reconstruct input data based on the extracted spatial-temporal features. This encoder-structured approach is trained only using normal data and the anomalies are detected as conspicuous differences between the input data and the reconstructed data. The detection performance of the proposed method is validated by utilizing real-world electric vehicle operational datasets.