Research On Charging Guidance Strategy Considering Electric Vehicle Charging Demand And Future Passenger Flow Of Charging Stations

In recent years,the electric vehicle industry has grown by leaps and bounds along with global decarbonization and the evolution of policies towards traditional fuel vehicles.In the context of the expanding scale of EV(Electric Vehicle)industry and its user group,it is important to provide EV users with appropriate charging guidance service.On one hand,for EV users,appropriate charging guidance strategy can reduce their wait time and strongly increase customer experience while reducing difficulty in finding charging piles.On the other hand,when EV users have spatial aggregation charging demand,appropriate charging guidance can reduce their wait time and enhance their charging convenience.Also,through appropriate charging guidance,EV users can be dispersed to the surrounding charging stations to meet their charging demand while relieving the service pressure of some charging stations and balance the workload of different charging stations,thus improving the overall utilization rate of charging facilities.Based on this,this paper integrates the charging benefits of EV users and the balanced utilization of charging station resources,and proposes a charging guidance strategy that considers the charging demand of users and the future passenger flow of charging stations.The main research contents are as follows:(1)Analysis of charging demand considering the behavioral characteristics of electric vehicle users.First,based on the travel behavior data of urban residents in Xi’an and the charging behavior data in the business area covered by the third-party charging operation service provider,the travel and charging behavior characteristics of EV users are analyzed.Second,based on the TF-IDF method,the functional areas of the study area are identified and the arrival probability matrix of different areas in the same functional area is established.Finally,based on the travel chain theory,the travel simulation of EV users’ charging demand is carried out,and then the charging demand of users and its spatio-temporal distribution are obtained.Also,the rationality of charging demand is verified based on the real data of charging stations.(2)Passenger flow prediction of charging stations based on spatio-temporal map convolutional network.First,based on the charging station passenger flow data from the comprehensive management platform of new energy vehicles and charging facilities in Xi’an,the temporal characteristics of charging station passenger flow and the spatial correlation among charging stations are analyzed.Second,the classification of charging stations is realized by combining the functional area attributes of the actual locations of charging stations.Finally,considering the spatio-temporal information of charging station passenger flow,a charging station passenger flow prediction model based on spatio-temporal graph convolutional network is established to realize charging station passenger flow prediction,and the effectiveness of the proposed prediction model is verified by comparing with various prediction algorithms.(3)Electric vehicle charging guidance strategy considering user charging demand and future passenger flow at charging stations.Based on the behavioral characteristics of EV users and the prediction model of charging station passenger flow,the charging strategy for EV users is formulated with the objective function of minimizing the cost of EV users and maximizing the service balance of charging stations,taking into account the remaining power driving accessibility and dynamic traffic information of EV users.Also,the effectiveness of the charging guidance strategy proposed in this paper is verified by simulation in the research area.