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This paper describes how distributive computing along with statistical subsystem simulation can be applied to produce near production ready embedded vehicle software and calibrations. Coupling distributive computing and statistical simulation was first employed over a decade ago at General Motors to design and analyze propulsion subsystem hardware. Recently this method of simulation has been enhanced extending its capabilities to both test embedded vehicle code as well as develop calibrations. A primary advantage of this simulation technique is its ability to generate data from a statistically significant population of subsystems. The result is the acquisition of an optimal data set enabling the development of a robust design now including both embedded code and calibrations. Additionally it has been shown that there are significant economic advantages in terms of time and cost associated with this type of development when compared to traditional method.

The objective of this paper is to propose that the automotive engineering “legacy” approach of assuming that vehicle electrical return paths using conductive structural components have zero (0) ohms of impedance, needs to be reviewed. The process in this review consists of the development of an equivalent circuit composed of both real (“resistive”) and reactive elements (due to inductance and capacitance). This issue needs to be addressed due to the increased complexity of electrical and electronics systems in vehicles and the use of the vehicle structural (such as chassis, frame, or bracket) components to provide a path for the return of electrical power and/or signals as a way to eliminate additional wiring manufacturing issues, cost, and weight contribution to a vehicle.

Adopting robust design principles is a proven methodology for increasing design reliability. General Motors Powertrain (GMPT) has incorporated robust design principles into their Signal Delivery Subsystem (SDSS) development process by moving traditional prototype manufacturing and test functions from hardware to software. This virtual manufacturing technique, where subsystems are built and tested using simulation software, increases the number of possible prototype iterations while simultaneously decreasing the time required to gather statistically meaningful test results. This paper describes how virtual manufacturing was developed using distributed computing.