Research On Optimized Scheduling Of Intelligent Charging Service For Electric Vehicles

As the energy crisis and environmental pollution become more and more serious,the pressure of the traditional automobile industry is increasing day by day.With the breakthrough of many key technologies of electric vehicles and the popularization of low-carbon concepts,electric vehicles have received strong support from the government as a kind of new energy vehicle with fast-growth.However,the mileage of electric vehicles is short and it is difficult for electric vehicles to charge on the road because of the insufficient charging infrastructure,which are the main causes hindering the popularization of electric vehicles.The utilization rates are huge different among the existing charging stations,the charging piles are in short supply in some charging stations,while the charging piles are often idle in some charging stations,causing a great waste of charging facilities.In order to improve this phenomenon,it is very meaningful to provide a charging scheme that can reduce the cost of the charging users,increase the revenue of the charging stations,and utilize the public resources fully at the same time.Therefore,it is very necessary to research the charging service of electric vehicles.At present,the domestic and foreign research on charging service for electric vehicles mainly focuses on the indirect analysis of electric vehicle charging problems through the dispatch management system.In addition,the path planning for the charging service of electric vehicles is also a research hotspot.However,most of the researches are static path analysis,and the dynamic paths are seldom considered.Besides,there are few researches on the analysis of electric vehicle charging services from the perspectives of the charging stations and users.As a matter of fact,the interests of charging stations and users are closely related.It is capable of providing better charging services to the users only if the economic benefits of the charging stations are good enough.Therefore,the optimal dispatching system of intelligent charging service for electric vehicles was researched,and the user,station and integrated models were analyzed in this thesis.Firstly,the electric vehicle charging demand was analyzed,and the distance,time,cost,economic benefit and service level were determined as the key factors which affect the choices of the charging users.In this section,the path planning and the prediction of charging queuing time are mainly described.The A* algorithm was improved with a dynamic schedule,and the modified algorithm was used to plan the driving routes of electric vehicles.The charging queue time is calculated according to the number of original vehicles in the charging station and the numbers of leaving and arriving vehicles during the charging electric vehicle's running to the charging station.Secondly,the charging demand of electric vehicles was quantified,and the intelligent charging user and station models of electric vehicles were established.The multi-objective user and station models were transformed into single-objective models by using the fuzzy programming algorithm.Based on the two single-objective models,the integrated model of electric vehicles was obtained by giving the weighted coefficients.The genetic algorithm was used to solve the user,station and integrated models.Finally,the established user,station and integrated models were verified by simulations.Comparing with the single-factor optimal charging scheme,the user model had the lower travel cost,and the station model could improve the overall level of charging services.The integrated model had the advantage over the user and station models of taking into account the interests of both user and charging station.The integrated model not only reduced the charging cost of the users,but also improved the economic efficiency of the charging stations.