Abstract

The mass adoption of Electric Vehicles (EVs) might raise pressure on the power system, especially during peak hours. Therefore, there is a need for delayed charging. However, to optimize the charging system, the progression of charging from an empty battery to a full battery of the EVs, based on real-world data, needs to be analyzed. Currently, many researchers view this charging profile as a static load and ignore the actual charging behavior during the charging session. However, this study investigates how different factors influence the charging profile of individual EVs based on real-world data of charging sessions in The Netherlands, and thereby enable optimization analysis of EV smart charging schemes.

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The different versions of the original document can be found in:

http://dx.doi.org/10.3390/wevj9020017 under the license cc-by
https://www.mdpi.com/2032-6653/9/2/17,
https://pure.hva.nl/ws/files/4863137/wevj_09_00017.pdf,
https://www.narcis.nl/publication/RecordID/oai%3Ahbokennisbank.nl%3Aamsterdam_pure%3Aoai%3Apure.hva.nl%3Apublications%2Fdef16713-7fd1-457e-a4b6-326f6397e686,
http://www.mdpi.com/2032-6653/9/2/17,
https://academic.microsoft.com/#/detail/2801715398 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2018

Volume 2018, 2018
DOI: 10.3390/wevj9020017
Licence: Other

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