Optimized Deployment of Charging Piles Based on Extended Traffic Equilibrium in Highway
Electric vehicles(EVs)face mileage issues during medium-to long-distance travel.Improving highway charging facilities can alleviate charging problems and improve travel service levels.To characterize the charging selection behavior of EV users,a discrete selection model based on a multivariate logit was established,where utility functions were proposed using the remaining mileage of EVs and the average necessary service time in the charging service district(CSD).An extended traffic equilibrium model was developed to obtain the charging demand for each CSD,which combined the traffic assignment model with the CSD selection model.Considering the minimization of the system charging service time,a bi-level programming model for charging stations was proposed based on an extended traffic equilibrium model to obtain the optimal number of charging stations in each CSD.However,solving traffic equilibrium models for large-scale road networks is difficult.The traffic equilibrium assignment problem was transformed into a nonlinear complementarity problem(NCP)for the solution.A genetic algorithm and a nonlinear complementarity algorithm were used to solve the bi-level programming model.The Hunan Provincial Expressway Network was used to verify the proposed model.The results show that the optimized distribution plan reduces the system service time by 53.1％compared with the uniformly distributed charging pile deployment plan.In addition,a sensitivity analysis revealed that an increase in EV users'perception of information can help improve the utilization rate of charging stations in some CSDs and reduce the number of empty CSDs.This study provides significant guidance for the deployment of charging facilities on highways to improve service levels.
traffic engineeringelectric vehicledeployment of charging stationtraffic equilibri-um modelbi-level programming model
万方数据
维普
