Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/111737
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | - |
| dc.contributor | Mainland Development Office | - |
| dc.contributor | Otto Poon Charitable Foundation Smart Cities Research Institute | - |
| dc.creator | Liu, J | - |
| dc.creator | Yang, X | - |
| dc.creator | Zhuge, C | - |
| dc.date.accessioned | 2025-03-14T03:56:44Z | - |
| dc.date.available | 2025-03-14T03:56:44Z | - |
| dc.identifier.uri | http://hdl.handle.net/10397/111737 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Ltd | en_US |
| dc.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/). | en_US |
| dc.rights | 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. | en_US |
| dc.subject | Big data | en_US |
| dc.subject | Charging infrastructure | en_US |
| dc.subject | Electric carsharing | en_US |
| dc.subject | Micro-simulation | en_US |
| dc.subject | Smart charging | en_US |
| dc.title | A joint model of infrastructure planning and smart charging strategies for shared electric vehicles | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 3 | - |
| dc.identifier.issue | 4 | - |
| dc.identifier.doi | 10.1016/j.geits.2024.100168 | - |
| dcterms.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. | - |
| dcterms.abstract | Graphical abstract: [Figure not available: see fulltext.] | - |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Green energy and intelligent transportation, Aug. 2024, v. 3, no. 4, 100168 | - |
| dcterms.isPartOf | Green energy and intelligent transportation | - |
| dcterms.issued | 2024-08 | - |
| dc.identifier.scopus | 2-s2.0-85199286631 | - |
| dc.identifier.eissn | 2773-1537 | - |
| dc.identifier.artn | 100168 | - |
| dc.description.validate | 202503 bcch | - |
| dc.description.oa | Version of Record | en_US |
| dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | National Natural Science Foundation of China; Public Policy Research Funding Scheme of The Government of the Hong Kong Special Administrative Region; Hong Kong Polytechnic University | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.description.oaCategory | CC | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 1-s2.0-S2773153724000203-main.pdf | 2.93 MB | Adobe PDF | View/Open |
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