As the percentage of Hybrid Electric Vehicles (HEV) is increasing, On-Board Diagnosis (OBD) faces new challenges such as limited combustion engine runtime. Moreover, predictive driving strategies for HEV assure that more vehicles are equipped with navigation systems. These systems can provide information about the road conditions such as limit speed, curvature and slope. In this study, navigation road information is used to predict monitoring conditions of OBD functions so that the available OBD time can be used effectively. As an example, catalyst monitoring is considered and a simple vehicle model is proposed which takes velocity and slope prediction from the navigation system to predict torque and exhaust mass flow. The model is composed of a combination of longitudinal motion and a power train torque transition model. Results of this effort are presented for different velocity profiles.