Optimization Of Dedicated Charging Network For Electric Taxicabs

With the development of eco-friendly cities in China,the number of electric taxicabs(ETs)is growing every year.The optimization and economical layout of dedicated fast-charging facilities based on their commercial operation characters is a key problem faced by large-scale popularization of ETs in urban areas.Considering the influence of the charging demand's transition between different charging stations,the optimization problem for dedicated charging network for ETs is studied in this paper using simulation results of spatial-temporal characters of ET's charging demand.Firstly,by combining taxicab's transportation OD matrix and its travel time-window,transition pattern between different operation status(occupied,available and charging),as well as the changing character of battery' s SOC in different situations,this paper proposes an accurate prediction model for ET's charging demand,and predicts the spatial-temporal characters of ET's charging demand using the proposed model,where the difference in the total number of ETs at different development stages is also considered.Secondly,considering the inequality of service capability between centralized and decentralized dedicated fast-charging station for ETs,as well as the influence of distance and queuing time,the Voronoi diagram and queuing theory with capacity limitation are introduced to build the collection and transition analysis model between the charging demand and charging station.Lastly,given the transition of charging demand between these two classes of dedicated fast-charging stations for ETs,a joint optimized model for the dedicated fast-charging network is established to minimize the total cost,including the additional time and energy cost for drivers' charging behavior,investment cost for fast-charging network and grid access.A scene of one city with 1000 electric taxis is considered in the simulation,and numerical results demonstrate that the model can effectively reduce the additional time and energy cost for charging by optimizing the investment ratio of centralized and decentralized stations.