Abstract
Under the challenge of climate change, fuel-based vehicles have been receiving increasingly harsh criticism. To promote the use of battery electric vehicles (BEVs) as an alternative, many researchers have studied the deployment of BEVs. This paper proposes a new method to choose locations for new BEV charging stations considering drivers’ perceived time cost and the existing infrastructure. We construct probability equations to estimate drivers’ demanding time for charging (and waiting to charge), use the Voronoi diagram to separate the study area (i.e., Shanghai) into service areas, and apply an optimization algorithm to deploy the charging stations in the right locations. The results show that (1) the probability of charging at public charging stations is 39.6%, indicating BEV drivers prefer to charge at home; (2) Shanghai’s central area and two airports have the busiest charging stations, but drivers’ time costs are relatively low; and (3) our optimization algorithm successfully located two new charging stations surrounding the central area, matching with our expectations. This study provides a time-efficient way to decide where to build new charging stations to improve the existing infrastructure.
Highlights
As greenhouse gasses increasingly affect the environment and human life, countries around the globe have been trying to reduce gas emissions
It requires strategic planning to locate the right number of charging stations in the right locations [2]
Researchers usually employ objective functions [6] to minimize these costs by minimizing missed trips [7], battery electric vehicles (BEVs) energy consumption [8,9], travel distance, and so on
Summary
As greenhouse gasses increasingly affect the environment and human life, countries around the globe have been trying to reduce gas emissions. In the study of minimizing energy consumption of BEVs to reach the charging station, a group of researchers adopted a weighted Voronoi diagram to calculate the size and shape of the service areas [8,9]. To locate the right number of charging stations (and piles) in the right locations, a number of researchers adopted the particle swarm optimisation algorithm to minimize costs. Xu et al [8] applied a binary particle swarm optimisation algorithm to determine the optimal configuration for central charging stations by minimizing the total travel distance to these central stations. This paper aims to consider the layout of existing charging stations to determine the locations for new BEV charging stations that minimize drivers’ time cost. We demonstrate the investment costs involved in constructing new charging stations (and piles)
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