Designing a Network of Electric Charging Stations to Mitigate Vehicle Emissions

M. Miralinaghi, G. Correia , S. E. Seilabi, S. Labi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

14 Citations (Scopus)
101 Downloads (Pure)

Abstract

Metropolitan authorities continue to seek programs and initiatives to reduce emissions in their jurisdictions. It has been shown that transitioning from fossil fuel to electric propulsion of transportation can help realize this goal. However, the current market penetration of electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) remains very small. This paper proposes a framework to address this problem over a long-term analysis period. The paper accounts for consumers' vehicle-purchasing propensities and their route choices, locations of EV-charging and ICEV-refueling stations. In the proposed framework, new EV charging stations are provided at selected locations and/or existing gas stations are repurposed by the transport agency's decision-maker (through policy) in conjunction with the private sector (through investment). The paper presents a bi-level mathematical model to capture the decision-making processes of the transport agency and the travelers. Underlying the framework is a solid theoretical foundation for the EV charging network design. The design problem is solved using an active-set algorithm. The study results can serve as guidance for metropolitan transport agencies to establish specific locations and capacities for EV stations and thereby to contribute to long-term reduction of emissions.
Original languageEnglish
Title of host publication2020 Forum on Integrated and Sustainable Transportation Systems (FISTS)
Subtitle of host publicationNovember 3-5, 2020, Delft - The Netherlands
PublisherIEEE
Pages95-100
Number of pages6
ISBN (Electronic)978-1-7281-9503-2
DOIs
Publication statusPublished - 2020

Bibliographical note

Accepted Author Manuscript

Keywords

  • Charging stations
  • Diffusion model
  • Electric vehicles
  • Network design

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