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

¹ Institute of Sustainable Energy Engineering and Mobility, University of Applied Sciences Esslingen, Kanalstreet 33, 73728 Esslingen
² Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO, Nobelstreet 12, 70569, Stuttgart, Germany

^* Corresponding author: Danting.Cao@hs-esslingen.de

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.