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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.

The electrification of the powertrain, the diversity and the complexity of the more or less individual technical solutions which are preferred by different car manufacturers, create a steadily increasing challenge for the whole automotive industry. Missing standards and sales volumes still below the market expectations on the one hand, and the increasing interaction of the main powertrain domains (engine, transmission, e-drive) caused by upcoming cross domain functions on the other hand, lead to increasing development costs and non-optimal solutions concerning fuel economy improvement. Within the domain of engine management systems Bosch established in the mid-nineties the so called torque structure as the solution to a similar situation addressing the coordination of air management, fuel injection and ignition.