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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.

Fully Variable Valve Actuation (FVVA) systems enable to employ a wide range of combustion strategies by providing the actuation of a gas exchange valve at an arbitrary point in time, with variable lift and adjustable ramps for opening and closing. Making such a system ready for the market requires appropriate fault-diagnostic functionality. Here, we focus on diagnosis possibilities by using air intake system sensors such as Manifold Absolute Pressure (MAP) sensors. Results obtained on a 4-cylinder test bench engine are presented for the early intake opening strategy under different loads, and at medium range rotational speeds on steady-state conditions. It is shown that detection and identification of the different critical faults on each actuator is possible by using a Fourier series signal model of the MAP sensor.