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For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle.

The food processing and preparation subsystem for bioregenerative lifesupport must combine food produced in-situ with resupplied food ingredients to provide a nutritionally balanced, palatable and varied diet for the station crew. We have approached these goals by developing a database of individual foods including recipe and processing information, panel acceptance data, nutritional analyses and cost estimates. Subsequently a linear programming optimization is used to select from among these foods a low-cost diet subject to constraints on nutrient content, overall acceptability, variety, and crew labor input. The database design, preliminary sensory testing results, and an optimized 1-day food list are presented..