A cross-domain electromagnetic compatibility data platform for the digital development of automated vehicles
Publication date
2024-12-20
Document type
Sammelbandbeitrag oder Buchkapitel
Author
Wagner, Jan
Thoma, Peter
Aichele, Hermann
Cuevas Rosa, Salvador
Eidher, Roland
Organisational unit
Book title
dtec.bw-Beiträge der Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg : Forschungsaktivitäten im Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr dtec.bw : Band 2 – 2024
First page
119
Last page
124
Peer-reviewed
✅
Part of the university bibliography
✅
Keyword
dtec.bw
Digital twin
Autonomous driving
Electromagnetic compatibility
Electromagnetic interference
Power electronics
Artificial intelligence
Abstract
Maintaining compliance to electromagnetic compatibility (EMC) standards becomes an increasing challenge for the automotive industry in the course of the ongoing automation of vehicles. Novel extended design procedures and test standards are required to ensure safety of automated driving functions, particularly in an adverse electromagnetic (EM) environment. In order to keep the number of tests within a reasonable and practical limit, an evaluation framework based on virtual design methods and information drawn from legacy experiments and simulations can support the automotive industry. With this digital framework appropriate technological solutions can be identified during the pre-compliance phase and efficient experimental designs can be generated to ensure EMC compliance. Furthermore, such a framework paves the way for digital EMC twins of automated vehicles (AVs) considering the complex interrelations of AV’s (sub-)systems to accurately predict the behaviour of new AV functions in various EM environments. To this purpose, a cross-domain platform is being developed in this work as the backbone of such a virtual framework. It supports the handling, storage and processing of various datasets from EMC test campaigns, including (intentional) electromagnetic interference ((I)EMI) tests, as well as simulations of automotive devices-under-test (DUTs). The platform allows for the establishment of interconnections between various data sources and deeper analyses based on artificial intelligence (AI) methods to deduce EMC information for new developments, whilst maintaining traceability.
Version
Published version
Access right on openHSU
Open access