In today's world, automotive manufacturers face the difficult challenge of building vehicles that are capable of meeting the increasingly stringent fuel economy and emissions standards, while also maintaining the performance and drive quality that consumers have come to expect. The automotive industry's response to this has been to make increasingly advanced vehicles that require more complex control systems, often resulting in longer development times and higher costs. One way to help reduce the development time and cost associated with these advanced vehicles is to use a model-based design approach. This approach allows engineers to design more of the vehicle's control system in a virtual environment, before hardware is available to test the control software.;While model-based design techniques have helped reduce the amount of development time and cost that is needed to design the control system for a vehicle, these model-based techniques may not fully account for a vehicle's drive quality characteristics. Many of the energy management optimal control algorithms for hybrid vehicles designed in virtual environments today are capable of achieving high fuel economy numbers, but may result in poor drive quality characteristics when implemented on a vehicle. Therefore, a new methodology is needed to account for a vehicle's drive quality during the initial stages of a vehicle's control development.;The research presented here describes a new methodology where drive quality metrics are added to the optimal control algorithm's cost function, in order to allow the algorithm to find a good balance between fuel economy and drive quality. Although some research has been previously published in this area, the majority of research does not specifically link the criteria used to improve drive quality to the physical behavior of the vehicle. Other research solves the optimal energy management problem to minimize fuel consumption, but then filters the results to prevent drive quality problems. This filtering can potentially result in a non-optimal final solution. The research presented in this dissertation formally links the drive quality behavior of the vehicle to the criteria used in the formulation of the optimal energy management problem. This allows the final solution to the optimal energy management problem to be directly applicable to the vehicle, without the need to filter the results.
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