2025-09-10 –, Audi-Midi
Testing real hardware and simulation models in combination in a software- or hardware-in-the-loop set-up is challenging. One of the key factors is the high demand for accuracy in the simulation model. If classical modeling based on physical principles is not sufficient to reach the desired level of accuracy, hybrid modeling, the combination of physical simulation models and machine learning can be applied. In this publication, we train a hybrid model for a controlled electric motor within the electro-hydraulic braking system of a car under the conditions and restrictions of a real engineering application in the field. We apply state-of-the-art modeling patterns for this, and further extend them with application specific methodological optimizations. Finally, we investigate and show the quantitative and qualitative advantages of the proposed approach for this specific application, resulting in a gain in accuracy by multiple factors.
... is an advanced doctoral student at University of Augsburg. He completed both his Bachelor's and Master's degree in “Engineering and Computer Sciences” at this same university. His research focuses on Scientific Machine Learning, and he is specifically engaged with the area of Hybrid Modeling, the combination of simulation models – often from engineering – and novel machine learning approaches. His research includes the development of new methods in this field, but explicitly also the improvement of new and established approaches so that they can be applied in a demanding real-world environment.
