Regionally Differentiated Real-Time Energy Consumption Prediction of Electric Vehicles Oriented to Travel Characteristics

Real-time prediction of electric vehicle energy consumption is of great significance to users’ travel planning and charging decisions. This paper analyzed the influence of travel characteristics and regional differences on the power consumption of electric vehicles, and built a regional electric vehicle energy consumption model based on travel characteristics prediction: In this paper, a large number of travel samples are obtained by preprocessing the real-time operation data of electric vehicles, and the influencing factors of power consumption in the travel samples are analyzed to determine that the most relevant characteristic parameters are travel mileage and time, which are used as the main characteristic indicators of energy consumption prediction. On this basis, a single-region BP neural network energy consumption prediction model was built, and the optimal network model structure was adjusted and determined through error feedback, which achieved a prediction accuracy of 93.2%; then, the travel samples of different cities are modeled and cross predicted, and established a multi-regional energy consumption prediction model; finally, the prediction results of different models are compared. The results show that this model has the highest accuracy in the energy consumption prediction of the actual operation of urban electric vehicles, which can reach 92% and above. Combining the existing electricity with the predicted energy consumption results can provide effective support for users to make reasonable charging decisions before travel.