Increasing environmental, economic, and political concerns regarding the consumption of fossil fuels have highlighted the need for more efficient and alternative energy solutions. Hybrid electric vehicles represent a near-term opportunity for reducing liquid fossil fuel consumption and green-house gas emissions in the transportation industry, and as a result, many automotive manufacturers have invested heavily in hybrid vehicle development. The increased complexity of hybrid electric vehicles over standard internal combustion engine-powered vehicles has subsequently placed significant emphasis on development of advanced control methods geared towards efficient energy management. Real-time optimization-based methods represent the current state-of-the-art in terms of hybrid vehicle control and energy management. This thesis summarizes the development of an optimization-based real-time control system – which determines the optimal instantaneous system operating point, including gear, traction split between front rear axles, and engine speed and torque – and its application to an all-wheel drive extended-range electric vehicle that uses a General Motor’s front-wheel drive 2-Mode electronic continuously variable transmission and an additional rear traction motor. The real-time control system was developed and validated using a plant model and preliminarily tested in the vehicle using a four-wheel drive chassis dynamometer. Results of simulation and in-vehicle testing demonstrate engine operation focused on high-efficiency operating regions and minimal use of the rear traction motor. Further testing revealed that a rule-based traction split system may be sufficient to replace the optimization-based traction split determination, and that the limited rear traction motor use was not a function of the motor itself, but rather an inherent result of the selected architecture. / Graduate

The purpose of this study is to demonstrate a practical application of social network analysis in the field of education using a large-scale data source. Using the Early Childhood Longitudinal Base Year data, a network is identified by examining the connections that occur between supports, both inside and outside formal special education resources for kindergarteners with access to special education programs. Social network mappings and quantitative findings are presented for formal and informal supports and primary disability category, along with policy implications and suggestions for further research. Findings indicate that social network analysis offers a unique and innovative perspective to educational research.