Research On Operation Optimization Of Electric Taxi Charging Station

The implementation of preferential policies for new energy vehicles and the further improvement of charging infrastructure have greatly promoted the rapid development of the electric vehicle industry.As a public transportation tool in a broad sense,electric taxis are an important part of the promotion of electric vehicles.Their extensive promotion has effectively promoted the improvement of energy conservation and emission reduction levels and the efficiency of social and economic operations.However,due to the large daily operation volume of taxis and the wide range of operating areas,in such a complex operating environment,the problem of unbalanced utilization of existing charging stations has seriously affected the daily service level of charging stations and even taxis.Therefore,optimizing the operating capacity of charging stations is necessary for the operation of the electric taxi system.This study takes electric taxis and charging stations as the research objects,analyzes the problems existing in existing charging stations,and draws relevant conclusions by constructing models and verification examples.The analysis of the results of the calculation examples in this paper confirms the scientificity and rationality of the above model,which can improve the operation level and resource utilization rate.The establishment of a reasonable pricing plan can provide a certain theoretical support and decision-making reference for the operation of the charging station.This study mainly carried out the following research and work:Firstly,the development of electric vehicles is explained,and then the research object of this article is determined to be electric taxis.The existing research is summarized from two aspects of charging selection behavior and charging station operation strategy,and the optimization of electric taxi charging station operation is studied.Content and determine the technical route.Secondly,surveying of taxi charging choices.Considering the impact of operating scenarios,charging station service levels,personal and operational attributes,RP survey and SP survey are used to complete the design of the questionnaire.Among them,RP survey is used to collect information such as the personal attributes of taxi drivers and taxi operations;SP survey analyzes their charging selection behavior preferences from the perspective of behavior analysis and explores the influencing factors of taxi drivers’ charging selection behaviors under hypothetical scenarios.Thirdly,based on the results of the questionnaire and combined with the disaggregated theory,a charging selection BL(Binary Logit)model and a charging station selection MNL(Multinomial Logit)model were constructed to quantify the impact of personal attribute variables and operating scenario variables and charging station service level variables on charging the influence of selection behavior and charging station selection behavior.Then,given the existing problems of charging stations,a two-tier optimization model for charging station pricing is constructed.The upper-level model uses the balance of the charging station utilization rate and the utility of the charging station selection by taxi users as calculation indicators to quantify the optimization effects under different pricing schemes.The lower model is a user equilibrium model based on the selection of MNL charging stations,which is used to output the charging station selection results of taxi users under different pricing schemes.For the constructed model,genetic algorithm and successive weighted average algorithm are used to solve the problem.Finally,the data of charging stations and taxis in Tianjin Binhai New Area are selected for analysis of calculation examples to verify the validity of the constructed model and solve the pricing plan.Taking the charging station utilization rate and the taxi user utility index as the objective functions,the two objective functions are solved separately through the two-level programming model,and the results obtained by the two objective functions in different scenarios are compared and analyzed,and the comparison is carried out.The objective function values before and after optimization are used to verify the correctness of the model.The results show that the model has universal applicability under different characteristics of charging demand.Compared with the utilization balance as the objective function,the optimization result obtained by the user utility objective function is in terms of the number of taxi charging demands at the charging station,the queue time,etc.better.