In electric vehicle charging facilities, to prevent non electric vehicles from occupying charging spaces, cameras can be installed on charging piles and combined with target recognition technology to achieve recognition of vehicle type and license plate number. However, ensuring recognition accuracy in more complex conditions such as low lighting environments will be challenging. The manual method is time-consuming and laborious, with its efficiency not guaranteed. In order to solve the above problems, this paper proposes a lightweight low-light license plate recognition method based on improved YOLOv5n-LPRNet, which can be easily deployed in edge computing devices. The main idea of this method is Enhance-Segmentation-Recognition, which achieves end-to-end license plate recognition by combining CLAHE-GAMMA transform, YOLOv5n segmentation network and LPRNet character recognition network. The specific improvement is as follows: Using the idea of "Low FLOPs pitfall", the CBS module in YOLOv5n backbone network is replaced by DynamicConv, and the C3 module in backbone network is combined with DynamicConv; The C3 module in the neck network of YOLOv5n was replaced by the RepNCSPELAN module proposed by YOLOv9. The EMA attention mechanism is added after two Dropout layers of the LPRNet network. The experimental results show that compared with the original model, the mask_mAP of the improved model is improved by about 2%, and a small number of frames are lost while maintaining real-time performance. The accuracy of the recognition model improved by about 9%.