Optimal Deployment of Electric Vehicles' Fast-Charging Stations. (3rd April 2023)

• Record Type: Journal Article
• Authors: Ullah, Irfan; Liu, Kai; Layeb, Safa Bhar; Severino, Alessandro; Jamal, Arshad
• Other Names: Wang Lei Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2023(2023)
• Publication Date: 2023-04-03
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2023/6103796
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : As climate change has become a pressing concern, promoting electric vehicles' (EVs) usage has emerged as a popular response to the pollution caused by fossil-fuel automobiles. Locating charging stations in areas with an expanding charging infrastructure is crucial to the accessibility and future success of EVs. Nonetheless, suitable planning and deployment for EV fast-charging stations is one of the most critical determinants for large-scale EV adoption. Installing charging stations in existing fuel/gas stations in the city may be an effective way to persuade people to adopt EVs. In this paper, we aim to optimally locate a fast-charging station in an existing gas station in the real-world scenario of Aichi Prefecture, Japan. The purpose is to locate and size fast-charging stations in such ways that drivers can get access to these charging facilities within a rational driving range while considering real-world constraints. Furthermore, we include the investment cost and the EVs users' convenience cost. This problem is formulated by five integer linear programming using a weighted set covering models. The developed model determines where to locate charging stations as well as how many chargers should be installed in each charging station. The experimental results demonstrate that an appropriate location scheme can be obtained using the model M 5 . A computational experiment identifies the best infrastructure solutions for policymakers to consider in the context of growing environmental policies.