A Time-spatial Location Model For Locating Electric Vehicle Charging Stations

Concerns about the gradually exhausted non-renewable energy resources, growing greenhouse effect, and unchecked energy consumption in transportation have stimulated global interests in electric vehicles. Electric vehicles are expected to play an important role in a transition to a sustainable transportation system by relieving the contradiction of energy crisis, climate change and increasing demand for transportation. The lack or unreasonable layout of the charging infrastructure, however, has seriously inhibited the promotion of electric vehicles. An effective method for locating electric vehicle charging stations is essential to the rapid development of electric vehicle industry.The conventional and popular alternative-fuel stations location models that considering the space distribution of traffic demand have already realized to minimize total demand-weighted travel distance or to maximize capturing, as well as to satisfy multiple objectives. While for the case of locating electric vehicle charging stations, the minimum requested charging time introduces time constraints, since it not only intensifies time and space uncertainties of charging demand working with the time restrictions among origin and destination, but also brings the possible delay time due to waiting or searching the next station. Unfortunately, these kinds of time-spatial constraints were always ignored in previous models. Therefore, this paper aims to develop a new layout method considering simultaneously the dual-constrain of time and space.The related influence factors for locating electric vehicle charging stations are firstly analyzed, mainly include the vehicle features characterized by limited mileage range and longer charging time, the simultaneous service capacity constraints of chargong stations, traveler’s charging behavior decision-making about when and where to charge, and how to deal with the queuing problem due to the simultaneous charging capacity limitations, the time and space distribution of charging demand, as well as the electric vehicle development planning. Then followed by the reasonable assumptions and restrictions, a dynamic model with objective of minimum total travel time is developed by considering the time and space constraints simultaneously, and a micro simulation method is employed on an example network to figure out the optimal locations.Results depict that time constraints do have great effects on the locations of electric vehicle charging stations, which is characterized in that the different definitions of charging time lead to the different optimal scales and layout structures of location network. At the same time, the optimal results both cover links with large demand flows and near home or workplace, coinciding with current travel behaviors. Additionally, when the optimization objective is replaced by minimizing detour time while other conditions remain unchanged, the solutions are relatively stable as the number of stations changes, satisfying the needs of staged development of electric vehicles. So the proposed dynamic model improves the optimization results by giving more attention on the time constraints, which also enhances the practical application of this model.