A two-sided equilibrium model of Vehicle-to-Vehicle charging platform

Xuekai Cen, Kanghui Ren, Enoch Lee, Hong K. Lo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

A novel mobile charging service utilizing vehicle-to-vehicle (V2V) charging technology has been proposed as a complement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study develops a two-sided equilibrium model for a V2V charging platform, where the demand-side charging vehicles (CVs) choose between charging piles and the V2V platform, and the supple-side discharging vehicles (DVs) decide whether to provide discharging services. The V2V platform matches CVs with DVs, receiving compensation from CV owners and reimbursing DV owners. To explore the strategic interactions between the V2V platform and fixed CI operators, we construct an analytical bi-level model that examines different operation regimes, including competitive and cooperative scenarios, i.e., non-cooperative game and Nash Bargaining game. The lower-level model is a two-sided equilibrium model that quantifies charging and discharging choices of EV owners, while the upper-level model optimizes the pricing decisions of the V2V platform and fixed CI operator. Numerical results indicate that the introduction of the V2V platform can reduce the overall charging costs, thus encourage the use of public charging services. Furthermore, it is suggested that the V2V platform be managed by independent enterprises, rather than existing fixed CI operators, to promote the EVs at relatively low charging costs.

Original languageEnglish
Article number104821
JournalTransportation Research Part C: Emerging Technologies
Volume168
DOIs
Publication statusPublished - Nov 2024

Keywords

  • Electric Vehicle
  • Nash Bargaining
  • Non-cooperative game
  • Vehicle-to-Vehicle Charging

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Management Science and Operations Research

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