Over the last decade, hybrid-electric vehicles have progressed from a futuristic icon to a firm production reality for a growing number of automobile manufacturers. While the motivation for this trend may vary, hybrid-electric vehicles today symbolize a recognition of the necessity to evolve advanced automotive technologies in order to sustain a culture of freedom of mobility. The Challenge X program communicates this message towards academia and future automotive engineers with strong support from both government and industry.
The work of this thesis was aimed toward The Ohio State University's objectives as a participant in the Challenge X competition. As an initial task, the Ohio State team defined a set of vehicle technical specifications to steer and motivate the vehicle design and control strategy development. After an extensive decision-making process, a specific architecture emerged with the potential to meet the vehicle technical specifications. The chosen configuration is a charge-sustaining, power-split, hybrid-electric vehicle design. A downsized Diesel engine and integrated starter/alternator drive the front wheels through an automatic transaxle. A larger, tractive electric machine and single-speed gearbox exist on the rear drivetrain. Both electric machines and their respective inverters connect electrically to a single high-voltage battery pack. The validation procedure for both the vehicle architecture and a control strategy involves use of a computer vehicle simulator. A quasi-static vehicle model acts as a basis for a simulator to validate the design and control strategy with respect to energy management. A dynamic vehicle model establishes a foundation for eventual creation of a second simulator for drivability validation. Both simulators operate in a forward-moving fashion and contain three primary sections: (i) the driver, (ii) the hybrid-electric powertrain, and (iii) the vehicle. Both models are also highly nonlinear, but the main differentiating property is the relatively large system order of the dynamic model as compared to the quasi-static model.