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dc.contributor.authorYeregui, Josu
dc.contributor.authorUrkizu, June
dc.contributor.authorAizpuru, Iosu
dc.date.accessioned2024-03-06T08:01:48Z
dc.date.available2024-03-06T08:01:48Z
dc.date.issued2023
dc.identifier.isbn979-8-3503-4445-5en
dc.identifier.issn2769-4186en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174594en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6265
dc.description.abstractThis paper presents a tool to generate realistic traffic profiles in Electric Vehicle (EV) charging stations. The tool emulates non-deterministic traffic cases based on data from similar applications. This obtained data does not often follow a normal distribution function, so the tool uses the Kernel Density Estimation (KDE) data-based technique to obtain the probability functions for the arrival and departure of the vehicles along with their missing energy at arrival. Scenarios without traffic data availability but fixed schedules like in private companies are also considered. For these cases the user may define expected schedules and shift types to generate possible traffic cases based on normal distributions around the rush hours. Based on the probability distribution analysis performed, the user obtains information of individual cases of vehicles using the charging station, which follows the trend of a real scenario.en
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2023 IEEEen
dc.subjectelectric vehicleen
dc.subjectCharging stationsen
dc.subjectElectric mobilityen
dc.subjectdata-based modelsen
dc.subjectprobability distributionsen
dc.titleData-based traffic profile generation tool for electric vehicle charging stationsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceIEEE Vehicle Power and Propulsion Conference (VPPC)en
local.contributor.groupAlmacenamiento de energíaes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1109/VPPC60535.2023.10403317en
local.embargo.enddate2026-01-31
local.source.detailsMilan (Italia), 24-27 October, 2023en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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