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This volume of the series ARENA2036 compiles the outcomes of the first Stuttgart Conference on Automotive Production (SCAP2020).
It contains peer-reviewed contributions from a theoretical as well as practical vantage point and is topically structured according to the following four sections: It discusses (I) Novel Approaches for Efficient Production and Assembly Planning, (II) Smart Production Systems and Data Services, (III) Advances in Manufacturing Processes and Materials, and (IV) New Concepts for Autonomous, Collaborative Intralogistics.
Given the restrictive circumstances of 2020, the conference was held as a fully digital event divided into two parts. It opened with a pre-week, allowing everyone to peruse the scientific contributions at their own pace, followed by a two-day live event that enabled experts from the sciences and the industry to engage in various discussions. The conference has proven itself as an insightful forum that allowed for an expertly exchange regarding the pivotal Advances in Automotive Production and Technology.
Unfallforschung 2021
(2021)
Am 16. und 17. Juni 2021 fand das „Symposium für Unfallforschung und Sicherheit im Straßenverkehr“ (UFO 2021) der ADAC Stiftung als Online-Veranstaltung statt. Schwerpunktthema des alle zwei Jahre stattfindenden Symposiums war 2021 die Verkehrssicherheit von ungeschützten Verkehrsteilnehmern.
Dieser Tagungsband enthält die Langfassungen der Beiträge, welche von einem Expertenbeirat ausgewählt wurden. Inhaltlich befassen sie sich mit den für die Erhöhung der Verkehrssicherheit relevanten Bereichen Mensch, Fahrzeug und Infrastruktur.
Mit dem Symposium und diesem Tagungsband wird Experten und Nachwuchswissenschaftlern die Möglichkeit gegeben, ihre Forschungsarbeiten der Fachwelt zu präsentieren und sich zu informieren.
The widespread adoption of electric vehicles makes investments in charging parks both immediate and necessary to lower range anxiety and allow longer trips. However, many charging park operators struggle with sustainable and profitable operation due to high fees on peak loads and volatile availability of renewable energy. Smart charging strategies may enable such operation, but the computational complexity of most available algorithms increases significantly with the number of charging points. Thus, operators of larger charging parks need information systems that provide real-time decision support without immense cost for computation. This paper presents a model that uses recent methods from the field of Reinforcement Learning. Our model is trained on a charging park simulation with realworld data on highway traffic and day ahead energy prices. The results indicate that Reinforcement Learning is a feasible solution to improve the sustainable and profitable operation of large electric vehicle charging parks.