Due to the increased computing power in the last decade, more and more complex vehicle models were developed. Nowadays even complex multibody models can be generated via graphical user interfaces in object-oriented simulation tools like Dymola or SimulationX. On the other hand, the available computing power in electronic control units is still limited, mostly by the cost pressure in the automotive industry. Hence, it is not possible to generate a complex model by drag and drop via a graphical user interface and run it in real-time within a desired time cycle on an ECU inside the vehicle. The same holds for HIL-testbeds and driving simulators, where the model must run in real-time as well. Thus, generally the model is adjusted in an iterative process until the model can be integrated in real-time on the particular ECU. In other words, a model has to be generated that is on the one hand complex enough to reproduce the desired physical effects and on the other hand simple enough to fulfill the real-time requirements. As it is easy to generate a complex model nowadays, an algorithm for the automated reduction of the model is required. Equation-based reduction techniques are a tool for the automated reduction of a given DAE-system for a defined error bound. This approach was already adopted and extended to generate vehicle models with an adjustable accuracy. In this contribution, equation-based reduction techniques are extended to generate models, which are guaranteed to run in real-time on a given real-time target within a given real-time cycle.