Reasoning-supported Robustness Validation of Automotive E/E Components
2026-08-17 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors developed a method to make testing automotive electronic parts more efficient. They created a formal way to describe important factors like stress and usage patterns using a computer-friendly language called OWL. This helps automate the selection of tests, reducing errors and saving time. They tested their method on a real car electronics example and found it made the process faster and more thorough.
Robustness ValidationontologyOWLsemantic queriesMission Profilesautomotive electronicspower electronicsformalized knowledgeautomationdesign time
Authors
Jan Novacek, Alexander Viehl, Oliver Bringmann, Wolfgang Rosenstiel
Abstract
This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs). In contrast to the error-prone industrially established manual procedure, we show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process. The proposed approach is based on the idea of mapping MPs to an OWL representation so to allow to perform semantic queries against MP data to improve their integration into the RV process. The resulting ontology-supported application framework has been applied to an industrial use-case from automotive power electronics. We present experimental results showing that the RV process can be significantly improved in terms of reduced design time and increased exhaustiveness by automating the analyses selection step and the provisioning of all the relevant data to be used.