Development of a DC fast charging station model for use with EV infrastructure projection tool

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dc.citation.epage938en_US
dc.citation.spage934en_US
dc.contributor.authorUcer, E. Y.en_US
dc.contributor.authorKisacikoglu, M. C.en_US
dc.contributor.authorErden, Fatihen_US
dc.contributor.authorMeintz, A.en_US
dc.contributor.authorRames, C.en_US
dc.coverage.spatialLong Beach, CA, USAen_US
dc.date.accessioned2019-02-21T16:04:31Zen_US
dc.date.available2019-02-21T16:04:31Zen_US
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 13-15 June 2018en_US
dc.description.abstractThe deployment of public charging infrastructure networks has been a major factor in enabling electric vehicle (EV) technology transition, and must continue to support the adoption of this technology. DC fast charging (DCFC) increases customer convenience by lowering charging time, enables long-distance EV travel, and could allow the electrification of high-mileage fleets. Yet, high capital costs and uneven power demand have been major challenges to the widespread deployment of DCFC stations. There is a need to better understand DCFC stations' loading, utilization, and customer service quality (i.e. queuing time, charging duration, and queue length). This study aims to analyze these aspects using one million vehicle-days of travel data within the Columbus, OH, region. Monte Carlo analysis is carried out in three types of areas - urban, suburban, and rural- to quantify the effect of uncertain parameters on DCFC station loading and service quality.en_US
dc.description.provenanceMade available in DSpace on 2019-02-21T16:04:31Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipThis work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with the National Renewable Energy Laboratory. This report and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The authors acknowledge John Smart of Idaho National Laboratory for leading the Alternative Fueling Infrastructure Pillar of the SMART Mobility Laboratory Consortium. The following DOE Office of Energy Efficiency and Renewable Energy managers played important roles in establishing the project concept, advancing implementation, and providing ongoing guidance: David Anderson, Sarah Olexsak, and Rachael Nealer.en_US
dc.identifier.doi10.1109/ITEC.2018.8450158en_US
dc.identifier.isbn978-1-5386-3049-5en_US
dc.identifier.urihttp://hdl.handle.net/11693/50191en_US
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttps://doi.org/10.1109/ITEC.2018.8450158en_US
dc.relation.projectNational Renewable Energy Laboratory, NREL - Office of Energy Efficiency and Renewable Energy, EERE - U.S. Department of Energy, DOE: DE-AC36-08GO28308 - Vehicle Technologies Office, VTO - Idaho National Laboratory, INL - U.S. Department of Energy, DOE - Singapore-MIT Alliance for Research and Technology Centre, SMARTen_US
dc.source.title2018 IEEE Transportation Electrification Conference and Expo (ITEC)en_US
dc.titleDevelopment of a DC fast charging station model for use with EV infrastructure projection toolen_US
dc.typeConference Paperen_US

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