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Application and machine learning methods for dynamic load point controls of electric vehicles (xEVs)

 
: Cao, Danting; Lerch, Jonathan; Stetter, Daniel; Neuburger, Martin; Wörner, Ralf

:
Volltext urn:nbn:de:0011-n-6219388 (2.1 MByte PDF)
MD5 Fingerprint: 818d473713152e36364aed6682d3964f
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Erstellt am: 3.2.2021


Loredo, J.:
3rd International Conference on Renewable Energy and Environment Engineering, REEE 2020 : Lisbon, Portugal, August 16-18, 2020, held online
Les Ulis: EDP Sciences, 2020 (E3S Web of Conferences 191)
ISSN: 2267-1242
Art. 04003, 6 S.
International Conference on Renewable Energy and Environment Engineering (REEE) <3, 2020, Online>
Ministerium für Umwelt, Klima und Energiewirtschaft Baden-Württemberg UM BW
KI-LAN
Künstliche Intelligenz–basiertes netzdienliches Lademanagement beim Parken unter verschiedenen Nutzungsszenarien
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAO ()

Abstract
From the customer's perspective, the appeal of electric vehicles depends on the simplicity and ease of their use, such as flexible access to electric power from the grid to recharge the batteries of their vehicles. Therefore, the expansion of charging infrastructure will be an important part of electric mobility. The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs. This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques. The module will be integrated into a real charge management concept with optimization procedures for controlling the dynamic load point. The value of load forecasting through practical load data of a car park were taken to illustrate the proposed methods. The prediction performance of different ML methods under the same data condition (e.g., holiday data) are compared and evaluated.

: http://publica.fraunhofer.de/dokumente/N-621938.html