Research And Development Of Electric Vehicles Charging Demand Distribution System In Network Charging Station Environment

At present,the world is facing the dual test of environmental pollution and energy crisis.Electric vehicles(EVs)are developing rapidly because of their advantages of low pollution and high efficiency.However,the unknown location information and use status of the existing charging facilities,together with the blindness and randomness of users' travel path and charging behavior,lead to the unbalanced service level distribution of charging facilities and the increase of users' travel costs.However,charging demand distribution and path induction for EVs can guide the scientific travel of EVs and then balance the service level of transportation infrastructure and charging facilities.By analyzing the travel characteristics and charging characteristics of electric vehicles,this paper establishes a prediction model for charging demand of electric vehicles and a prediction model for the distribution of charging queuing time in the context of networked charging stations.Then,aiming at the minimum total travel cost for EV users,a charging demand distribution and path selection model for EV is proposed based on real-time dynamic data and user preference.Finally,an intelligent charging analysis system for EVs is designed and developed,which can provide decision basis for charging station operators and users.The main research work of this paper is as follows:(1)Based on the investigation of users' intention of choosing a charging station,the influencing factors of users' choice of a charging station are determined.On this basis,the influencing factors of charging stations are quantitatively analyzed,the utility function of users' choice of charging stations is established,and the charging demand in each charging station is predicted by the multi-logit model.Finally,an example is given to verify the accuracy of the charging demand prediction method,which provides a basis for the calculation of queuing time distribution prediction in the next step;(2)The charging behavior characteristics of EVs are analyzed to determine the distribution of the number of vehicles arriving at the charging station and the distribution of charging time.M/G/n queuing model is adopted to simulate the queuing system of charging station,and the distribution function of charging waiting time of EV is solved to display the theoretical solution.The variation coefficient of service time was determined to eliminate the influence of charging time on queue waiting time in the station,and a prediction algorithm for charging queue waiting time distribution of EV was established.The square variation coefficient and average arrival rate were updated according to the real-time data of networking charging stations and the predicted charging demand,and the charging waiting time distribution of EV was predicted.Finally,an example is given to verify the accuracy and effectiveness of the method.(3)Based on the high definition smart bayonet data,the section running time data is obtained,and the matrix decomposition is used to complete the missing data of real-time running time matrix.The travel cost of electric vehicles is calculated,and its travel cost is minimized as the goal of path optimization.The path selection improvement model is constructed by considering path selection constraints,arrival time constraints,battery capacity constraints,charging station constraints along the way,and user waiting time reliability threshold of charging stations.Through the comparison of path optimization results,it is shown that the improved model can effectively reduce users' travel cost and service level of balanced charging facilities.(4)The requirements of the EV intelligent charging analysis system are analyzed and the overall design scheme is determined.On the basis of snatched charging station environment data,combined with the charging demand forecasting model,queue waiting time charge distribution prediction model,driving the completion time matrix decomposition algorithm,charge distribution and route choice model,developed based on client/server(C/S)architecture analysis of the electric vehicle intelligent charging system,and in detail elaborated the system each function module and its implementation process.