The series arc fault is mainly caused by poor contact of the electrical contact points in the circuit, which is one of the main causes of electric vehicle fires, directly threatening the life safety of the occupants. In order to study it, an experimental platform for DC series arc fault of electric vehicles was established. The voltage signals of the power supply terminal were obtained under various working conditions, and the impact of arc faults on the power supply terminal voltage was analyzed. When constructing the detection model, the paper used a convolutional neural network, introduced a lightweight convolution operation, and considered its limitations in practical applications. Combining conventional convolution and lightweight convolution operations, a preliminary model for arc fault detection was constructed. Then, with the scale and accuracy of the network as the evaluation index, the genetic algorithm with elite preservation strategy was used to search for the external structure and internal parameters of the model. Finally, the model AFDNet suitable for arc fault detection (AFD) of electric vehicles was established. The detection accuracy of the model is 93.73%, and the running time on the embedded device Jetson Nano（JN） is 10.82 ms. After establishing the model, the paper compared the search results of the algorithm with other network structures in relation to network size, accuracy, and real-time performance, verified the validity of the results obtained by the search algorithm. By comparing AFDNet with other detection methods, it was proven that the performance of the electric vehicle arc fault detection model was superior.