Deep Learning-Based Multifeature Fusion Model for Accurate Open-Circuit Fault Diagnosis in Electric Vehicle DC Charging Piles

With electric vehicles' popularity, a surge has been created in demand for charging infrastructure. As a result, the maintenance of charging piles has become a critical issue that requires attention. To effectively utilize the fault features of the front and back circuits in case of the charging pile fails, a multifeature fusion model is proposed in this article. First, use the front- and back-stage feature information fusion module to fuse the collected front-stage fault feature quantity signals and the back-stage fault feature quantity signals. Then, the spatial and temporal feature extraction modules are used to mine the spatial and temporal high-dimensional features in parallel. Finally, through the spatiotemporal feature fusion classification module, the spatial and temporal features are fused and classified to achieve the purpose of fault diagnosis. The proposed method employs deep learning techniques, which avoids the cumbersome steps involved in graphical input and the errors arising from manually selecting features in traditional deep learning algorithms and gives full play to the parallel diagnostic performance of deep learning. The simulation results demonstrate that the proposed method outperforms other comparative algorithms in terms of diagnostic accuracy, convergence speed, and overfitting suppression, and has excellent noise immunity, which can cope with the noisy situation of charging piles. In the experimental test, the fault diagnosis accuracy of this method reached 96.36%, and its recognition sensitivity for most fault categories was higher than that of the comparison model, which further verified the superiority and robustness of this method.