Abstract
With the rapid development of electric vehicles (EVs), one of the urgent issues is how to deploy limited charging facilities to provide services for as many EVs as possible. This paper proposes a bilevel model to depict the interaction between traffic flow distribution and the location of charging stations (CSs) in the EVs and gasoline vehicles (GVs) hybrid network. The upper level model is a maximum flow-covering model where the CSs are deployed on links with higher demands. The lower level model is a stochastic user equilibrium model under elastic demands (SUE-ED) that considers both demands uncertainty and perceived path constraints, which have a significant influence on the distribution of link flow. Besides the path travel cost, the utility of charging facilities, charging speed, and waiting time at CSs due to space capacity restraint are also considered for the EVs when making a path assignment in the lower level model. A mixed-integer nonlinear program is constructed, and the equivalence of SUE-ED is proven, where a heuristic algorithm is used to solve the model. Finally, the network trial and sensitivity analysis are carried out to illustrate the feasibility and effectiveness of the proposed model.
Highlights
The market share of new energy vehicles without fossil fuels has been increasing rapidly in recent years, especially electric vehicles (EVs), which provide better performance, higher efficiency, and zero emissions [1,2]
This paper focus on a hybrid network, denoted as G = (N,A), where N is the set of nodes, A is the is used toon represent set of ODdenoted pairs, and is an element, W.set
For EVs, the feasible paths were less than gasoline vehicles (GVs) because of the range limit, and the travel cost was affected by the link flow as GVs, and affected by the utility of charging facilities, charging speed, and waiting cost at charging stations
Summary
The market share of new energy vehicles without fossil fuels has been increasing rapidly in recent years, especially electric vehicles (EVs), which provide better performance, higher efficiency, and zero emissions [1,2]. It is especially important to study how to effectively and reasonably deploy EV public charging stations within the hybrid network of rapidly increasing EVs and dominated gasoline vehicles (GVs), which should help reduce the range anxiety of EV users and maximize the coverage rate of EVs. According to previous studies, a variety of factors affect the location of charging stations, including, among others, preference and travel choice behavior of users [6,7], travel demand of users [8], information of the en-route energy consumption of EVs [9,10,11,12], information of the remained. The lower model considers the elastic demands and the distance constraints of EVs, which have a significant influence on route choice and the distribution of link flow.
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