Deployment Optimization For Charging Piles Based On Detouring Model

Global energy crisis and environmental pollution accelerate the development of electric vehicle industry.However,the “difficult to charge” problem has become the major problem faced by electric vehicles at present,due to limited battery capacity,long charging time and imperfect charging infrastructure construction.Determining the location and capacity of charging facilities is an important segment in the development of electric vehicle.Reasonable planning of charger stations can meet more users’ charging requirement,ensure the efficiency of traffic network,and promote the vigorous development of electric vehicles.Most of existing works focus on different optimization objectives,and there are no universal system models and planning methods.When deploying charging piles in cities,there is no comprehensive consideration of actual traffic conditions,limited charging resources and user behavior decisions.In view of the above problems,the main work of this dissertation is as follows:1.Based on the detouring models,road impedance functions and queuing models,we formulate the deployment optimization problem for charging piles in cities based on detouring model to maximize the coverage quality of traffic flow.Considering the electric vehicles can be detoured,three detouring models are proposed to describe the interaction between detouring time and user’s choice.BPR impedance function model is adopted to calculate the travel time between intersections in urban block map.The waiting time in the charging station is calculated based on M/M/1/K queueing model and M/M/S/K queueing model,and the coverage quality of charging station is formallydefined.2.A greedy algorithm,a flow-based algorithm and a particle swarm optimization algorithm are proposed to solve the problem of optimal deployment of charging piles in cities.Simulations are conducted based on construction of a road map.By varying the charging demand,the maximum detouring time and the number of charging piles under different model functions,the simulation results show that the coverage quality of the deployment solution obtained particle swarm optimization algorithm always performs the best,which is on average 9.15% higher than that of the flow-based and greedy algorithm.