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In this paper, loads acting on driveline components during an entire proving ground (PG) durability schedule are used to demonstrate the methodology of optimizing driveline performance reliability using both physical and computational methods. It is well known that there is an effect of driver variability on the driveline component loads. Yet, this effect has not been quantified in the past for lack of experimental data from multiple drivers and reliable data analysis methods. This paper presents the data reduction techniques that are used to identify the extreme driver performance and to extrapolate the short-term measurement to long-term data for driveline performance reliability. The driveline loading variability is made evident in the rotating moment histogram domain. This paper also introduces the concept for a simulation model to predict the driveline component loads based on a complete proving grounds schedule. A model-to-test correlation is also performed in this paper.

Once a vehicle powertrain is designed and the first prototype is built, extensive on-board instrumentation and testing needs to be carried out at the proving grounds (PG) to generate load histograms for various components. The load histograms can then be used to carry out durability tests in the laboratory. When a component in the vehicle powertrain is changed, the load histograms need to be generated again at the proving grounds. This adds much time and money to the vehicle's development. The objective is to develop a virtual powertrain model that can be simulated through a powertrain endurance driving cycle in order to predict torque histograms and total damage. The predictions are then correlated against measured data acquired on a test vehicle that was driven through the same driving cycle at the proving grounds.