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A growing number of electric vehicles (EV's) are being used in fleet applications, creating a need for accurate estimates of vehicle mass while the vehicles are in operation. In this work, on-road energy use data are compared with simulated energy use to identify vehicle mass. The testing was performed on an electric Ford Transit Connect light-duty delivery vehicle in service with the Massachusetts Institute of Technology's facilities department. Driving data was collected using specific protocols designed to yield optimal inputs for identification, as well under normal driving conditions for evaluating the algorithms ability to identify parameters in worst-case scenarios. In this work, the identified mass is used to optimize fleet performance by providing more insight into the in-service weight of the vehicles, as well as by providing better electric vehicle range estimates to improve fleet utilization.

Evaluating options for reducing the environmental impact of light duty vehicles is complicated by the number of technologies being actively researched. In this work, various alternative transportation fuels such as ethanol, biodiesel, hydrogen, and electricity as well as advanced materials and drive-train concepts are analyzed for their environmental benefits compared to conventional diesel and gasoline vehicles. To objectively evaluate the impact of technology and fuel options on various stakeholder criteria under uncertainty (e.g. fuel and technology costs), it is necessary to consider how the technology may be applied to a broad set of vehicle designs. The heuristic vehicle design method uses rules from first principles and engineering practice to ensure vehicles in the design set (virtual fleet) are composed of self-consistent technology options.