Master data

Title: Model-based Testbed Design for Electric Vehicles
Subtitle:
Abstract:

Electric cars boom. This puts pressure on providing and improving tools and systems for electric car development. Electric vehicle testbeds (EVTs) are such systems: they serve for testing all high voltage vehicle components like batteries, inverters or complete engines and help to reduce the need of cost intensive road tests. EVT users like manufacturers of automobiles, aircrafts or train engines mostly have individual requirements. EVTs are therefore typically tailor-made solutions. Today’s approach to customized testbed (component) design starts with drawing the overall architecture using tools like MS Visio; based here-on, software developers, circuit plan designers, and engineers use their specific low-level design and development environments, obviously with no transformation or generation out of the initial drawing with causes all known challenges of such procedure. This paper presents a novel, innovative and scalable approach to EVT design based on an ontology grounded Domain Specific Modeling Language (DSML). It enables the user to describe the customer requirements in the familiar form. The resulting model can then be used to generate circuit diagrams and software configurations. Such approach not only may reduce development time and cost but may increase the quality of the resulting EVT.

Keywords: Modeling, Meta-Model, Ontology, Circuit plan, Testbed, MDA, DSML, Electric Vehicle
Publication type: Article in Proceedings (Authorship)
Publication date: 05.05.2020 (Print)
Published by: 40 Years EMISA - Digital Ecosystems of the Future: Methodology, Techniques and Applications. Proc. 40th Conf. of the GI SIG EMISA
40 Years EMISA - Digital Ecosystems of the Future: Methodology, Techniques and Applications. Proc. 40th Conf. of the GI SIG EMISA (2020)

H. Mayr, S. Rinderle-Ma, S. Strecker
Gesellschaft für Informatik e.V.

to publication
 ( Gesellschaft für Informatik e.V.; H. Mayr, S. Rinderle-Ma, S. Strecker )
Title of the series: Lecture Notes in Informatics
Volume number: P-304
First publication: Yes
Page: pp. 45 - 58

Versionen

Keine Version vorhanden
Publication date: 05.05.2020
ISBN:
  • 978-3-88579-698-5
ISSN: 1617-5468
Homepage: https://dl.gi.de/handle/20.500.12116/33138?locale-attribute=en

Assignment

Organisation Address
Fakultät für Technische Wissenschaften
 
Institut für Artificial Intelligence und Cybersecurity
Universitätsstr. 65-67
A-9020 Klagenfurt
Austria
  -993705
   aics-office@aau.at
https://www.aau.at/en/aics/
To organisation
Universitätsstr. 65-67
AT - A-9020  Klagenfurt

Categorisation

Subject areas
  • 102 - Computer Sciences
Research Cluster No research Research Cluster selected
Peer reviewed
  • Yes
Publication focus
  • Science to Science (Quality indicator: n.a.)
Classification raster of the assigned organisational units:
working groups
  • Application Engineering

Cooperations

Organisation Address
AVL List GmbH
Hans-List-Pl. 1
8010 Graz
Austria - Styria
Hans-List-Pl. 1
AT - 8010  Graz

Articles of the publication

No related publications