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.