Modeling and evaluating the impact of electricity price on commute network flows of battery electric vehicles

In many urban areas and urban-rural regions, the price of electricity is not only subject to time-of-use tariffs, but also dependent on the usage purpose or service sector, significantly varying across different geographic locations or land uses. Such a spatial price difference greatly affects the charging behavior of electric vehicle drivers. This paper models and analyzes the possible impact of electricity-charging price on the routing choice and flow pattern of electric vehicles in a home-workplace commute traffic network. The particular circumstance in such a network accommodating electric vehicles is that commuters driving battery electric vehicles have opportunities to charge their vehicles at both home and workplace and take into account different electricity-charging prices in their travel choices. For the illustration and evaluation purposes, the authors presented a small synthetic example and proposed a convex programming model for characterizing the commute network flow pattern of electric vehicles that may be charged at home and/or workplace, encapsulating a piecewise electricity-charging cost function that implies commuters’ combined home-based and workplace-based charging behaviors. Given different electricity-charging prices at home and workplace, solving this problem requires tracking all individual paths in the network. As such, a path-based solution algorithm sketched in the disaggregated simplicial decomposition framework, with some modifications in the path generation phase and an added k-shortest path search procedure, is designed for problem solutions. The authors implemented the modeling and solution methods for deriving the network flow pattern of electric vehicles and comparing it with that of gasoline vehicles, as well as evaluating the network performance change caused by varying electricity-charging prices with multiple network-level and link-level evaluation matrices.

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    • © 2022 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
  • Authors:
    • Xie, Chi
    • Hou, Jue
    • Zhang, Ti
    • Waller, Travis
    • Chen, Xiqun
  • Publication Date: 2023-3


  • English

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  • Accession Number: 01884244
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 31 2023 10:58AM