Sustainable Planning of Electric Vehicle Charging Stations: A Bi-Level Optimization Framework for Reducing Vehicular Emissions in Urban Road Networks

Sania E. Seilabi, Mohammadhosein Pourgholamali, Mohammad Miralinaghi (), Gonçalo Homem de Almeida Correia, Zongzhi Li and Samuel Labi
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Sania E. Seilabi: Department of Civil and Structural Engineering, State University of New York, University at Buffalo, Buffalo, NY 14260, USA
Mohammadhosein Pourgholamali: Lyles School of Civil and Construction Engineering, Center for Connected and Automated Transportation, Purdue University, West Lafayette, IN 47907, USA
Mohammad Miralinaghi: Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Gonçalo Homem de Almeida Correia: Department of Transport & Planning, Delft University of Technology, 2600 GA Delft, The Netherlands
Zongzhi Li: Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
Samuel Labi: Lyles School of Civil and Construction Engineering, Center for Connected and Automated Transportation, Purdue University, West Lafayette, IN 47907, USA

Sustainability, 2024, vol. 17, issue 1, 1-23

Abstract: This paper proposes a decision-making framework for a multiple-period planning of electric vehicle (EV) charging station development. In this proposed framework, transportation planners seek to implement a phased provision of electric charging stations as well as repurposing gas stations at selected locations. The developed framework is presented as a bi-level optimization problem that determines the optimal electric charging network design while capturing the practical constraints and travelers’ decisions. The upper level minimizes overall vehicle CO emissions by selecting optimal charging stations and their capacities, while the lower-level models travelers’ choices of vehicle class (EV or conventional) and travel routes. A genetic algorithm is developed to solve this problem. The results of the numerical experiments describe the sensitive nature of EV market penetration rates in the urban traffic stream and overall vehicle CO emissions to EV charging station availability and capacity. The findings can assist transportation agencies in designing effective EV charging infrastructure by identifying optimal locations and capacities, as well as in creating policies to encourage EV use over time. This study supports broader efforts to reduce air pollution and promote sustainable transportation by promoting EV adoption in the long term.

Keywords: electric vehicles; electric charging stations; air pollution reduction; sustainable transportation; facility location problem (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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