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On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study

Author

Listed:
  • Eleftherios Anastasiadis

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

  • Panagiotis Angeloudis

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

  • Daniel Ainalis

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

  • Qiming Ye

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

  • Pei-Yuan Hsu

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

  • Renos Karamanis

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

  • Jose Escribano Macias

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

  • Marc Stettler

    (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can produce solutions for large problem instances. Our results refer to a representative set of the total data for Chicago and indicate that nearly 180 CSs need to be installed to handle the demand of a TNC fleet of 3000 vehicles.

Suggested Citation

  • Eleftherios Anastasiadis & Panagiotis Angeloudis & Daniel Ainalis & Qiming Ye & Pei-Yuan Hsu & Renos Karamanis & Jose Escribano Macias & Marc Stettler, 2020. "On the Selection of Charging Facility Locations for EV-Based Ride-Hailing Services: A Computational Case Study," Sustainability, MDPI, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:168-:d:468738
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    References listed on IDEAS

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