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.