Br(e)aking the Boundaries of Physical Simulation Models: Neural Functional Mock-up Units for Modeling the Automotive Braking System

Abstract

Testing real hardware and simulation models in combinationin a software- or hardware-in-the-loop set-up ischallenging. One of the key factors is the high demand foraccuracy in the simulation model. If classical modelingbased on physical principles is not sufficient to reach thedesired level of accuracy, hybrid modeling, the combinationof physical simulation models and machine learning can beapplied. In this publication, we train a hybrid model for acontrolled electric motor within the electro-hydraulicbraking system of a car under the conditions andrestrictions of a real engineering application in thefield. We apply state-of-the-art modeling patterns forthis, and further extend them with application specificmethodological optimizations. Finally, we investigate andshow the quantitative and qualitative advantages of theproposed approach for this specific application, resultingin a gain in accuracy by multiple factors.