Location Design of Electric Vehicle Charging Facilities: A Path-Distance Constrained Stochastic User Equilibrium Approach

Abstract

Location of public charging stations, range limit, and long battery-charging time inevitably affect drivers' path choice behavior and equilibrium flows of battery electric vehicles (BEVs) in a transportation network. This study investigates the effect of the location of BEVs public charging facilities on a network with mixed conventional gasoline vehicles (GVs) and BEVs. These two types of vehicles are distinguished from each other in terms of travel cost composition and distance limit. A bilevel model is developed to address this problem. In the upper level, the objective is to maximize coverage of BEV flows by locating a given number of charging stations on road segments considering budget constraints. A mixed-integer nonlinear program is proposed to formulate this model. A simple equilibrium-based heuristic algorithm is developed to obtain the solution. Finally, two numerical tests are presented to demonstrate applicability of the proposed model and feasibility and effectiveness of the solution algorithm. The results demonstrate that the equilibrium traffic flows are affected by charging speed, range limit, and charging facilities' utility and that BEV drivers incline to choose the route with charging stations and less charging time.

Multitype Recharge Facility Location for Electric Vehicles

“…As EV‐related technology is still under development, most EVs currently have a very limited range. In this section, the distance range of capacitor powered EVs is set to be within 30 miles (Jiang and Xie, ; Xie and Jiang, ; Jing et al., ). The ICEV is also considered, the distance range of which is assumed to be infinity (Jiang et al., ; Agrawal et al., ).…”

“…We adopt the Bureau of Public Roads (BPR) function in this study for demonstration purposes. Although the BPR function is not able to well describe the highly congested traffic with spillbacks, it is the most commonly used tool to estimate link travel time in the area of transport planning, including recharge facility deployment (Ukkusuri et al., ; Luathep et al., ; Unnikrishnan and Lin, ; Fontaine and Minner, ; Jing et al., ). Furthermore, the link travel speed, which is the link length divided by travel time obtained by the BPR function, has been widely employed as an input to compute the network‐wide energy consumption and emission of EVs and ICEVs (Sharma and Mathew, ; Ferguson et al., ; Gardner et al., ; Duell et al., ).…”

Locating electric vehicle charging stations under uncertain battery energy status and power consumption

“…Stochastic driver route choice behaviour is considered in [11] and [24], where the route between an origin node O and a destination node D is selected based on criteria such as the traffic congestion and the availability of charging facilities instead of being systematically assigned to the shortest path between O and D. In [24], a single level non linear model under stochastic user equilibrium (SUE) constraints is proposed while in [11], the authors develop a bi-level programming approach under uncertain driver route choice behaviour. The upper level is a maximum flow covering problem while the lower level deals with a stochastic traffic assignment problem under path distance constraints.…”

Research on Location and Capacity Optimization Method for Electric Vehicle Charging Stations Considering User’s Comprehensive Satisfaction

“…One study [8] uses the quantum behavior particle swarm optimization algorithm (QPSO), and another [9] optimizes the particle swarm optimization algorithm based on the chaotic simulated annealing idea. In order to maximize the coverage of charging stations, Jin WT et al proposed a hybrid integer non-linear model and solved it by a simple heuristic algorithm based on equilibrium [10]. The genetic algorithm is a heuristic optimization algorithm widely used in the field of artificial intelligence.…”