Arc fault is one of the main causes of electrical fire. In the electric system of electric vehicle, the DC series arc fault usually occurs at the loose contact point or the line connection damage, which will cause serious accidents such as fire and explosion. In order to quickly and accurately detect the series electric arc fault of electric vehicles, an experimental platform for electric car fault arc has been constructed to collect time series data on trunk current under various operating situations and create a sample library. Through the lightweight convolutional neural network, a series fault arc detection model based on the improved Mobilenet network is established. By comparing and analyzing the learning rate, network layer number and sample length, the model is optimized. The optimization model can realize the detection of series fault arc, the selection of fault lines of electric vehicles through the trunk current, and the detection accuracy reaches 96. 39%. This paper provides a feasible scheme for the arc fault detection of electric vehicle’s electrical system.