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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.

A fuel cell hybrid powertrain design is implemented and optimized by the University of Waterloo Alternative Fuels Team for the ChallengeX competition. A comprehensive set of bench-top and in-vehicle validation results are used to generate accurate fuel cell vehicle models for SIL/HIL control strategy testing and tuning. The vehicle is brought to a “99% buy-off” level of production readiness, and a detailed crashworthiness analysis is performed. The vehicle performance is compared to Vehicle Technical Specifications (VTS).

Supervisory control strategies for a hybrid fuel cell powertrain are developed and simulated using Simulink models and the Powertrain Systems Analysis Toolkit (PSAT). The control strategy selects the power splitting ratio between a 65kW Hydrogenics fuel cell power module and a 70kW Cobasys Nickel Metal Hydride (NiMH) battery pack. Simple control algorithms targeting a battery pack State of Charge (SOC), or maximizing the instantaneous powertrain efficiency are initially considered and analyzed. A comprehensive control strategy optimizing powertrain efficiency, vehicle performance, emissions, and long-term reliability is then developed and simulated. The simulated vehicle using the comprehensive control strategy with reliability considerations exhibits a 21% mileage improvement as compared to a simple rule-based control algorithm.

A fuel cell-battery hybrid powertrain SUV vehicle is designed using an optimized model-based design process. Powertrain and fuel storage components selected include a 65 kW Polymer Electrolyte Membrane Fuel Cell (PEMFC) power module, two 67 kW electric traction motors, a 35 MPa compressed hydrogen storage tank, a 70 kW nickel metal hydride battery pack, and a University of Waterloo in-house DC/DC converter design. Hardware control uses two controllers, a main supervisory controller and a subsystem controller in addition to any embedded component control modules. Two key innovations of this work include the hybrid control strategy and the DC/DC converter. The final powertrain characteristics are expected to meet a set of Vehicle Technical Specifications (VTS).

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.

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.

A recently developed laser Doppler velocimetry system for making two-point spatial correlation measurements of velocity fluctuations has been applied to the turbulent flow field of an IC engine. Fluctuation integral length scales have been measured within the clearance volume of a ported, single cylinder engine with a disc-shaped chamber and a compression ratio of 8.0. The engine was motored at 600 rpm and the engine flow field had a swirl ratio at top dead center of approximately 4. These measurements were made at the center of the clearance height at three-quarters of the cylinder radius. The integral length scale was found to reach a minimum of approximately one-fifth of the clearance height near IDC. Comparison of the results obtained using this technique with the integral length scales measured in engines by other authors using different methods gives agreement to within a factor of two.