Spatiotemporal Prediction of Electric Vehicle Charging Demand Integrating Multidimensional Features and Its Application in Dynamic Scheduling of Mobile Charging Vehicles

To address the uneven spatiotemporal distribution of electric vehicle (EV) charging demand and the high complexity of mobile charging vehicle (MCV) scheduling, this study proposes an integrated “prediction–pre-scheduling–real-time scheduling” solution. It focuses on optimizing the charging demand prediction model while refining the MCV scheduling strategy. First, a new red-billed blue magpie optimizer (NRBMO) is proposed. By integrating three improved strategies—initialization via a Circle chaotic map with opposition-based learning, adaptive Lévy flight search, and dynamic attack intensity adjustment—over the original red-billed blue magpie optimizer (RBMO), the NRBMO algorithm optimizes the membership function parameters of a fuzzy neural network (FNN), thus establishing the NRBMO-FNN charging demand prediction model. Second, MCV scheduling is implemented in phases based on the predictive results: during the pre-scheduling phase, macro-level vehicle allocation is achieved to minimize the total system cost; in the real-time scheduling phase, a multi-objective optimization model is constructed and integrated with a four-input, four-output adaptive fuzzy controller to realize the coordinated optimization of the total system cost, service time, and user inconvenience. Finally, the results demonstrate that under the G = 3 test set, the prediction accuracy of NRBMO-FNN outperformed other algorithms by at least 26.3%, 33.4%, and 6.6% in RMSE, MAE, and R2, respectively. The proposed scheduling model reduced the three objective function values by an average of 3.41 yuan, 1.39 min, and 11.95 units during testing.