Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89259
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorXu, Men_US
dc.creatorYang, Hen_US
dc.creatorWang, SAen_US
dc.date.accessioned2021-03-02T03:55:12Z-
dc.date.available2021-03-02T03:55:12Z-
dc.identifier.issn0968-090Xen_US
dc.identifier.urihttp://hdl.handle.net/10397/89259-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectEV charging station locationen_US
dc.subjectRange anxietyen_US
dc.subjectCompact formulationen_US
dc.subjectOuter-approximation algorithmen_US
dc.subjectPath deviationen_US
dc.titleMitigate the range anxiety : siting battery charging stations for electric vehicle driversen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage164en_US
dc.identifier.epage188en_US
dc.identifier.volume114en_US
dc.identifier.doi10.1016/j.trc.2020.02.001en_US
dcterms.abstractThis 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.accessRightsembargoed accessen_US
dcterms.bibliographicCitationTransportation research. Part C, Emerging technologies, May 2020, v. 114, p. 164-188en_US
dcterms.isPartOfTransportation research. Part C, Emerging technologiesen_US
dcterms.issued2020-05-
dc.identifier.isiWOS:000528280900009-
dc.description.validate202103 bcwhen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera0588-n12-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRGC :25207319; Others: P0030389; P0000250en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2022-05-31en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2022-05-31
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