In the context of achieving carbon peak and carbon neutrality in transportation, high-precision, fine-grained, and highly feasible real-time prediction methods for motor vehicle energy consumption have become key components in reducing carbon emissions. Addressing the issue of limited universality in traditional regression-based vehicle energy consumption models, a prediction model based on the radial basis function neural network (RBFNN) has been developed. Firstly, the influencing factors of vehicle energy consumption were analyzed, and the influence factor matrix was normalized using the Min-Max standardization method. Then, the grey wolf optimization (GWO) algorithm was employed to optimize the training of the centers of the hidden layer, the width of the Gaussian function, and the weights connecting the hidden layer to the output layer in the RBFNN algorithm. Finally, a comprehensive analysis of the model's prediction accuracy was conducted through horizontal model comparisons and real-world vehicle measurements. The test results demonstrate that the RBFNN algorithm improves prediction accuracy by approximately 12% compared to traditional regression models, achieving an overall accuracy of over 90%. This makes it highly effective in accurately predicting the energy consumption of urban motor vehicles.