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Research On Location Selection Of Urban Public Electric Vehicle Charging Stations Based On Multi-objective Programming Model

Posted on:2024-04-02Degree:MasterType:Thesis
Country:ChinaCandidate:Z Y WangFull Text:PDF
GTID:2542307145452914Subject:Master of Civil Engineering and Hydraulic Engineering
Abstract/Summary:PDF Full Text Request
In recent years,affected by the energy crisis and the importance of human pollution problems,various new energy products are welcomed by more and more consumers.New energy vehicles have become the first choice for the mobility of residents in cities due to their environmental protection,low cost of car use,and high degree of intelligence.In this context,my country’s electric vehicles have increased rapidly,but the problem followed is that consumers are generally worried that the battery life is insufficient,cannot meet the needs,and cannot find charging stations in time when driving."Mileage anxiety" and "difficulty in charging" are currently the biggest obstacles to Chinese consumers when buying electric cars.The site selection of electric vehicle charging stations is unreasonable and unscientific is seriously hindering the further development and promotion of the electric vehicle industry.Therefore,the reasonable layout and construction of electric vehicle charging stations can not only help the extensive use of electric vehicles,but also play an important role in promoting low-carbon operations in society.Today,when many cities need to arrange charging stations,building a scientific and reasonable charging pile site selection system is particularly urgent.Therefore,based on the theories of behavioral geography,location of public space facilities and spatial balance,this paper studies the location of charging stations.The main contents of this paper include:Firstly,the research background,research significance,main research content and research methods of this study are introduced,and the domestic and foreign literature related to the research content is combed and summarized.Then,electric vehicles and electric vehicle charging stations are introduced respectively,and the relevant theories of this study are expounded to provide theoretical basis for subsequent research.Secondly,the paper analyzes the important basic principles that must be followed for the site selection of EV charging stations.At the same time,the relevant factors affecting the site selection of EV charging stations are analyzed.On this basis,the site selection model of multi-objective EV charging stations is constructed.Starting from the two parties with conflicting interests,supplier and user,a multi-objective EV charging station location model is established by taking the minimum cost of supplier and the maximum satisfaction of user as two objective functions,respectively considering the interests of both parties.Thirdly,the non-dominant sorting genetic algorithm(NSGA-II)of elite retention strategy and TOPSIS method were used to solve the model.In order to verify the feasibility and effectiveness of the proposed model and algorithm,this paper takes a certain region as an example,uses NSGA-II algorithm and TOPSIS method to solve the constructed EV charging station location model,and finds out the construction location of the charging station and the number of charging piles.The construction scheme of electric vehicle charging station is obtained,and the feasibility and effectiveness of the proposed model and algorithm are verified.Then,the model and algorithm in this paper were used to locate charging stations in Longzihu Street,Zhengdong New District,Zhengzhou City.The site selection scheme of electric vehicle charging stations was obtained,and the site selection suggestions were put forward according to the scheme.Finally,this paper summarizes the research of this subject comprehensively,and deeply recognizes the shortcomings of the research and the problems that need further research.
Keywords/Search Tags:Electric vehicle, Location of charging station, Multi-objective programming, Genetic algorithm
PDF Full Text Request
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