KI-LAN AI-based network-oriented charging management when parking under various use scenarios

The aim of the project is to investigate the optimisation of the dimensioning of the grid connection point, and the smart operation of (modular) charging points in parking scenarios with large numbers of charging points with local and also remote load management. This will involve the investigation of three use scenarios: charging at the place of work, at a shopping centre or place of work, and in rural areas using modular storage devices. The research approach employed by Esslingen University aims to explore methods of load and charging management on the basis of (control) methods by taking into account status logging and classification algorithms from artificial intelligence (AI), which are intended to lead to a reduction in the load peaks occurring and to minimise the investment for future charging concepts.

Principal researchers at Esslingen University:

Prof. Dr. Ralf W?rner

Prof. Dr.-Ing. Martin Neuburger

Project duration:

12/20219 to 06/2021

Funding:

Baden-Württemberg Ministry of the Environment, Climate Protection and the Energy Sector

Collaboration partners:

Fraunhofergesellschaft / Institut IAO
S3 Innovations GmbH
Marquardt GmbH
BridgingIT GmbH
Hochschule Furtwangen

People to contact:

Prof. Dr.-Ing. Martin Neuburger

M.Eng. Danting Cao