Tran, CQ, Ngoduy, D, Keyvan-Ekbatani, M et al. (1 more author) (2021) A user equilibrium-based fast-charging location model considering heterogeneous vehicles in urban networks. Transportmetrica A: Transport Science, 17 (4). pp. 439-461. ISSN 2324-9935
Abstract
Inappropriate deployment of charging stations not only hinders the mass adoption of Electric Vehicles (EVs) but also increases the total system costs. This paper attempts to address the problem of identifying the optimal locations of fast-charging stations in the urban network of mixed gasoline and electric vehicles with respect to the traffic equilibrium flows and the EVs' penetration. A bi-level optimization framework is proposed in which the upper level aims to locate charging stations by minimizing the total travel time and the installation costs for charging infrastructures. On the other hand, the lower-level captures re-routing behaviours of travellers with their driving ranges. A cross-entropy approach is developed to deliver the solutions with different levels of EVs' penetration. Finally, numerical studies are performed to demonstrate the fast convergence of the proposed framework and provide insights into the impact of EVs' proportion in the network and the optimal location solution on the global system cost.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Hong Kong Society for Transportation Studies Limited. This is an author produced version of an article published in Transportmetrica A: Transport Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Electric vehicles, user equilibrium, fast-charging stations, bi-level optimization, cross-entropy method |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Jun 2020 13:35 |
Last Modified: | 12 Jul 2022 15:09 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/23249935.2020.1785579 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161919 |