Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/118251
DC FieldValueLanguage
dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorZhao, Zen_US
dc.creatorLee, CKMen_US
dc.creatorYan, Xen_US
dc.date.accessioned2026-03-26T04:27:14Z-
dc.date.available2026-03-26T04:27:14Z-
dc.identifier.issn0360-8352en_US
dc.identifier.urihttp://hdl.handle.net/10397/118251-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectEV charging networken_US
dc.subjectEV charging stationen_US
dc.subjectMulti-state modelen_US
dc.subjectService quality evaluationen_US
dc.subjectUniversal generating functionen_US
dc.titleA multi-state model for the service quality evaluation of an electric vehicle charging network via universal generating functionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume200en_US
dc.identifier.doi10.1016/j.cie.2024.110839en_US
dcterms.abstractWith the large-scale proliferation of electric vehicles (EVs), a comprehensive evaluation of service quality for EV charging networks is crucial to develop effective policies that address the needs and concerns of EV users and operators. This paper investigates the service quality evaluation (SQE) problem for EV charging networks consisting of multiple public EV charging stations (EVCSs). The charging service of each EVCS is formalized from the standpoint of both queuing management and power grid operation considering demand interaction and epistemic uncertainty, where the mean waiting time in the queue and loss of load probability (LoLP) are utilized as the evaluation metrics. A universal generating function (UGF)-based technique is employed to derive the service quality of the overall charging network based on the performance level of each EVCS from a multi-state perspective. A case study is conducted to assess the validity and feasibility of the developed model and explore the relationship between parameter selection and policymaking for the planning and operation of the charging network. The proposed method will be helpful for policymakers in formulating appropriate policies to enhance service quality, thereby further promoting the mass adoption of EVs and accelerating the transportation electrification process.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationComputers and industrial engineering, Feb. 2025, v. 200, 110839en_US
dcterms.isPartOfComputers and industrial engineeringen_US
dcterms.issued2025-02-
dc.identifier.scopus2-s2.0-85213863694-
dc.identifier.eissn1879-0550en_US
dc.identifier.artn110839en_US
dc.description.validate202603 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.SubFormIDG001329/2025-12-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis research is funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP2-1) under the InnoHK Research Clusters, Hong Kong Special Administrative Region Government.en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2028-02-29en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2028-02-29
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