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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.

In this paper, responses from a vehicle's suspension, chassis and body, are used to demonstrate a methodology to optimize physical test results. It is well known that there is a variability effect due to an increase of wheel unsprung mass (due to loads measurement fixturing), tire pressure, speed, etc. This paper quantifies loading variability due to Wheel Force Transducer (WFT) unsprung mass by using a rainflow cycle counting domain. Also, presents a proving ground-to-test correlation study and the data reduction techniques that are used in road simulation test development to identify the most nominal road load measurement. Fundamental technical information and analytical methodology useful in overall vehicle durability testing are discussed. Durability testing in a laboratory is designed to correlate fatigue damage rig to road. A Proving Ground (PG) loading history is often acquired by running an instrumented vehicle over one or more PG events with various drivers.