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The Deep Orange [1] initiative is an integral part of the automotive graduate program at Clemson University International Center for Automotive Research. The initiative was developed to provide the graduate students with hands-on experience of the knowledge attained in the various engineering disciplines and related disciplines (such as marketing and human factors psychology). For the 3rd edition of Deep Orange, the goal was to develop a blank sheet hybrid mainstream sports car concept targeted towards the Generation Y (Gen Y) market segment. The objective of this paper is to elaborate on the overall development process and the technology that was created and integrated. A unique all-wheel-drive (AWD) parallel hybrid concept was derived based on extensive analyses of the Gen Y market. The data revealed that Gen Y, as an environmentally conscious generation, is willing to invest in sustainable powertrain technologies and also has a significant interest in all-wheel-drive.

Modern turbocharged spark-ignition engines are being equipped with an increasing number of control actuators to meet fuel economy, emissions, and performance targets. The response time variations between engine control actuators tend to be significant during transients and necessitate highly complex actuator scheduling routines. Model Predictive Control (MPC) has the potential to significantly reduce control calibration effort as compared to the current methodologies that are based on decentralized feedback control strategies. MPC strategies simultaneously generate all actuator responses by using a combination of current engine conditions and optimization of a control-oriented plant model. To achieve real-time control, the engine model and optimization processes must be computationally efficient without sacrificing effectiveness. Most MPC systems intended for real-time control utilize a linearized model that can be quickly evaluated using a sub-optimal optimization methodology.