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Developing a hydrogen fuel cell vehicle in three years is not a trivial task for any group of engineers. It is even worse still when you consider the climate it will be subjected to in Canada. For four months of the year, our vehicle remains inside of a heated garage, away from the cold ice and snow. Actual vehicle data is collected during the eight warm months of the year to construct empirical models. Software-in-the-loop and hardware-in-the-loop methodologies were used to tune our vehicles using the models that were constructed using actual vehicle data. Without MATLAB and Simulink from The MathWorks, our winters would be a lot less productive. In this paper, you will find a brief overview of our vehicle's architecture as well as how model-based design was valuable to our design and inplementation of our vehicle.

Transportation, with its high energy consumption, is commonly recognized as a major contributor to local, regional, and global environmental impacts. With around 95% of transportation energy originating from petroleum and an increasing emphasis on the associated environmental impacts, alternative transportation fuels are receiving great attention from industry, government, researchers, and the public. When the motivation for developing alternative fuels is to reduce environmental impact, a rigorous tool is needed for comparing the effects of very different alternative and conventional fuels. Such an evaluation tool must consider not only the effects of fuel combustion, but also the effects of producing, refining/processing, distributing, and disposing of wastes associated with that fuel… in other words, the life cycle effects of the fuel.

Life Cycle Assessment (LCA) is commonly used to measure the environmental and economic impacts of engineering projects and/or products. However, there is some uncertainty associated with any LCA study. The LCA inventory analysis generally relies on imperfect data in addition to further uncertainties created by the assessment process itself. It is necessary to measure the effects that data and process uncertainty have on the LCA result and to communicate the level of uncertainty to those making decisions based on the LCA. To accomplish this, a systematic and rigorous means to assess the overall uncertainty in LCA results is required. This paper demonstrates the use of Monte Carlo Analysis to track and measure the propagation of uncertainty in LCA studies. The Monte Carlo technique basically consists of running repeated assessments using random input values chosen from a specified probable range.