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Planning and Management of Charging Facilities for Electric Vehicle Sharing

In: Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities

Author

Listed:
  • Long He

    (National University of Singapore)

  • Guangrui Ma

    (Shanghai Jiao Tong University)

  • Wei Qi

    (McGill University)

  • Xin Wang

    (University of Wisconsin-Madison)

  • Shuaikun Hou

    (University of Wisconsin-Madison)

Abstract

Electric vehicle (EV) sharing has experienced rapid development and has served as a flexible and environmental friendly means for urban transportation. However, charging an EV sharing fleet is still a challenge for business operators because of limited or costly access to charging facilities. In this chapter, we focus on how to charge a fleet to make EV sharing viable and profitable. Adopting the real data of car2go, we propose a queueing network model to characterize how customers endogenously pick EVs according to energy levels, as well as the implementation of a charging-up-to policy. In order to solve the proposed nonlinear optimization program, we develop mixed-integer second order cone programs as tractable lower- and upper-bound formulations. These models lead to practical insights related to charger resource availability and locations, EV charging policy, battery technological advancements, and urban spatial structure.

Suggested Citation

  • Long He & Guangrui Ma & Wei Qi & Xin Wang & Shuaikun Hou, 2022. "Planning and Management of Charging Facilities for Electric Vehicle Sharing," Springer Optimization and Its Applications, in: Panos M. Pardalos & Stamatina Th. Rassia & Arsenios Tsokas (ed.), Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities, pages 135-153, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-84459-2_8
    DOI: 10.1007/978-3-030-84459-2_8
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