User Equilibrium–Based Location Model of Rapid Charging Stations for Electric Vehicles with Batteries that have Different States of Charge

Abstract

A model was developed for the location of rapid charging stations for electric vehicles (EVs) in urban areas, taking into account the batteries' state of charge and users' charging and traveling behaviors. EVs are one means of preparing for the energy crisis and of reducing greenhouse gas emissions. To help relieve range anxiety, an adequate number of EV charging stations must be constructed. Rapid charging stations are needed in urban areas because there is inadequate space for slow-charging equipment. The objective function of the model is to minimize EVs' travel fail distance and the total travel time of the entire network when the link flow is determined by a user equilibrium assignment. The remaining fuel range (RFR) at the origin node is assumed to follow a probabilistic distribution to reflect users' charging behavior or technical development. The results indicate that the model described in this paper can identify locations for charging stations by using a probabilistic distribution function for the RFR. The location model, which was developed on the basis of user equilibrium assignment, is likely to consider the congested traffic conditions of urban areas, to avoid locating charging stations where they could cause additional traffic congestion. The proposed model can assist decision makers in developing policies that encourage the use of EVs, and it will be useful in developing an appropriate budget for implementing the plan.

Electric vehicles charging infrastructure location: a genetic algorithm approach

“…By incorporating charging requirements in a distance constrained equilibrium model, they evaluated alternative charging locations. Lee et al [18] developed a bi-level optimization framework with the objective of minimizing fail distance and total network travel time. The model uses a probabilistic distribution function of the remaining fuel range.…”

Application of Hexagonal Fuzzy MCDM Methodology for Site Selection of Electric Vehicle Charging Station

“…You and Hsieh [48] used the mixed-integer programming model to determinine the best location of the EVCS which would maximize the number of people who could complete round-trip itineraries, along with developing an efficient hybrid genetic algorithm which would obtain a compromised solution in a reasonable time. Lee et al [49] first collected users' charging and traveling behaviors along with the batteries' state of charge and then proposed a location model of the rapid EVCS by using a probabilistic distribution function for the remaining fuel range. Baouche et al [50] formulated an integer linear optimization model, taking the electric vehicle's input consumption as the optimal model.…”

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

“…GWO is used to solve the EVCS allocation problem. Compared to other metaheuristic algorithms such as GA and PSO, GWO is considered as one of the fastest and the most efficient metaheuristic algorithms for the reason of using three solutions at the convergence to achieve better global solution [28]. Finally, both budget and routing constraints are also considered including the cost of building a CS, waiting time at the CS, available routes between CSs, and the maximum distance an EV can go after a full charge.…”