Electric vehicle license plate detection is of great significance in non-motor vehicle management and urban road supervision. Aiming at the problems of large changes in shooting Angle, complex background, small target size and serious occlusion of license plate images in actual scenes, an improved YOLO11 license plate detection algorithm integrating lightweight convolution structure and feature enhancement mechanism, called YOLO11-GCE, was proposed. Firstly, the C3k2_GhostConv module was constructed by fusing the C3k2 structure and Ghost convolution, which enhances the feature extraction capability of the backbone network while reducing computational redundancy. Secondly, EMA (efficient multi-scale attention) module was introduced at the end of the network, which stabilizes deep semantic information through a cross-spatial learning mechanism and improves the model's ability to discriminate small targets and blurred license plates in complex backgrounds. Finally, the lightweight GSConv structure was used to replace some standard convolutions, which optimizes the computational efficiency of the feature fusion network and enhances the detection performance for multi-pose targets. Through the synergistic effect of these improvements, YOLO11-GCE achieves significant enhancements in both detection accuracy and inference efficiency. Experiments conducted on a self-built electric vehicle license plate dataset show that the mAP@50:95 of YOLO11-GCE reaches 84.4%, an improvement of 1.7 percentage points over the original YOLO11, with a parameter reduction of about 19.8%, achieving better inference efficiency while ensuring detection accuracy.