Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89259
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | en_US |
dc.contributor | Department of Logistics and Maritime Studies | en_US |
dc.creator | Xu, M | en_US |
dc.creator | Yang, H | en_US |
dc.creator | Wang, SA | en_US |
dc.date.accessioned | 2021-03-02T03:55:12Z | - |
dc.date.available | 2021-03-02T03:55:12Z | - |
dc.identifier.issn | 0968-090X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/89259 | - |
dc.language.iso | en | en_US |
dc.publisher | Pergamon Press | en_US |
dc.rights | ©2020 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Xu, M., Yang, H., & Wang, S. (2020). Mitigate the range anxiety: Siting battery charging stations for electric vehicle drivers. Transportation Research Part C: Emerging Technologies, 114, 164-188 is available at https://dx.doi.org/10.1016/j.trc.2020.02.001. | en_US |
dc.subject | EV charging station location | en_US |
dc.subject | Range anxiety | en_US |
dc.subject | Compact formulation | en_US |
dc.subject | Outer-approximation algorithm | en_US |
dc.subject | Path deviation | en_US |
dc.title | Mitigate the range anxiety : siting battery charging stations for electric vehicle drivers | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 164 | en_US |
dc.identifier.epage | 188 | en_US |
dc.identifier.volume | 114 | en_US |
dc.identifier.doi | 10.1016/j.trc.2020.02.001 | en_US |
dcterms.abstract | This study addresses the location problem of electric vehicle charging stations considering drivers’ range anxiety and path deviation. The problem is to determine the optimal locations of EV charging stations in a network under a limited budget that minimize the accumulated range anxiety of concerned travelers over the entire trips. A compact mixed-integer nonlinear programming model is first developed for the problem without resorting to the path and detailed charging pattern pre-generation. After examining the convexity of the model, we propose an efficient outer-approximation method to obtain the ε-optimal solution to the model. The model is then extended to incorporate the charging impedance, e.g., the charging time and cost. Numerical experiments in a 25-node benchmark network and a real-life Texas highway network demonstrate the efficacy of the proposed models and solution method and analyze the impact of the battery capacity, path deviation tolerance, budget and the subset of OD pairs on the optimal solution and the performance of the system. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Transportation research. Part C, Emerging technologies, May 2020, v. 114, p. 164-188 | en_US |
dcterms.isPartOf | Transportation research. Part C, Emerging technologies | en_US |
dcterms.issued | 2020-05 | - |
dc.identifier.isi | WOS:000528280900009 | - |
dc.description.validate | 202103 bcwh | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a0588-n12 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | RGC :25207319; Others: P0030389; P0000250 | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xu_Mitigate_Range_Anxiety.pdf | Pre-Published version | 1.84 MB | Adobe PDF | View/Open |
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