Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/111737
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Title: A joint model of infrastructure planning and smart charging strategies for shared electric vehicles
Authors: Liu, J 
Yang, X 
Zhuge, C 
Issue Date: Aug-2024
Source: Green energy and intelligent transportation, Aug. 2024, v. 3, no. 4, 100168
Abstract: This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). The model takes into account two prevalent smart charging strategies: the Time-of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users, utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset. Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations. For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 ​kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs. These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.
Graphical abstract: [Figure not available: see fulltext.]
Keywords: Big data
Charging infrastructure
Electric carsharing
Micro-simulation
Smart charging
Publisher: Elsevier Ltd
Journal: Green energy and intelligent transportation 
EISSN: 2773-1537
DOI: 10.1016/j.geits.2024.100168
Rights: © 2024 The Author(s). Published by Elsevier Ltd on behalf of Beijing Institute of Technology Press Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The following publication Liu, J., Yang, X., & Zhuge, C. (2024). A joint model of infrastructure planning and smart charging strategies for shared electric vehicles. Green Energy and Intelligent Transportation, 3(4), 100168 is available at https://doi.org/10.1016/j.geits.2024.100168.
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