Abstract: In response to the current research on charging vehicle(EV)load prediction,the accuracy of real-time prediction of charging vehicle origin-destination(OD)is not high and considers the influence of road information on users' charging behavior choices.On the data-driven side,a combination of long short-term memory(LSTM)networks and graph convolutional networks(GCN)is used to analyze the existing charging load data and realize the prediction of charging vehicle origin-destination(OD),with respect to the spatial and temporal characteristics of the OD matrix of charging vehicle trips.On the model-driven side,a combination of road information is taken into consideration to predict the OD in real time.In terms of model driving,based on the comprehensive consideration of traffic network composition,ambient temperature,real-time traffic flow and other methods,a dri-ving behavior model of electric vehicle users is established,including dynamic traffic information,mileage energy consumption of electric vehicles in various road segments in the city and user path planning,and the improved A^* algorithm is used to plan driving paths for the starting and ending points of electric vehicles in accordance with the users' choices,so as to simulate the driving behavior of electric vehicle users.The improved A^* algorithm is used to plan driving paths for the starting and ending points of EVs that meet the user's choice,and simulate the driving behavior of EV users.Finally,the path planning test and charging demand prediction test for different types of EVs are completed under different application scenarios.The results show that the spatial and temporal distribution characteristics of charging demand are consistent with the objective demand.

Key words: Electric vehicles, Spatio-Temporal charging load forecasting, LSTM, GCN, OD matrix, Dynamic traffic information, Path planning