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

  • 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.

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Metadaten
Author:C. Danting, J. Lerch, D. Stetter, M. Neuburger, R. Wörner
DOI:https://doi.org/10.1051/e3sconf/202019104003
Parent Title (English):E3S Web of Conferences
Publisher:EDP Sciences
Place of publication:Les Ulis
Document Type:Conference Proceeding
Language:English
Year of Completion:2020
Contributing Corporation:2020 The 3rd International Conference on Renewable Energy and Environment Engineering (REEE 2020)
Release Date:2021/01/27
Volume:191
Page Number:6
Institutes:Fakultäten / Mechatronik und Elektrotechnik
Open Access?:nur im Hochschulnetz
Relevance:Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren)
Licence (German):License LogoVeröffentlichungsvertrag ohne Print-on-Demand