Modeling, control analysis, and multi-physics co-simulation supporting high-performance hybrid-electric vehicles

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Loghavi, Saeid
Leamy, Michael J.
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This thesis presents a series of model-based studies and associated considerations supporting the development of a high-performance HEV. Due to increasingly strict governmental regulations and consumer demand, automakers have taken steps to reduce fuel consumption and greenhouse emissions. HEV's can provide a balance between fuel economy and vehicle performance by exploiting engine load-point shifting, regenerative braking, pure electric operation, and hybrid traction modes. The existence of a multitude of HEV architectures with different emissions and performance characteristics necessitates the development of simulation platforms which can assist in specifying and selecting critical components. Recent advancements in the automotive industry, especially the introduction of hybrid technology, have resulted in lower emissions and improved fuel economy; however, hybrid technology can also be utilized in order to enhance the performance characteristics of traditional internal combustion high-performance vehicles. The complexity of the hybrid systems and high power demand of high-performance vehicles requires a detail analysis of critical system components, such as the energy storage systems, to ensure safe and optimal operation. The collaboration between Georgia Tech researchers and Ferrari S.p.A. is illustrative of the need for the further development of innovative and model-based tools to enhance the design and performance of high-performance hybrid electric vehicles. This thesis also features a series hybrid electric vehicle model developed using Simulink modeling software as part of a tutorial which may be provided to students in order to teach the basic principles underlying the operation, control, and design of hybrid electric vehicles. The final chapter of this thesis features a modeling approach developed in order to analyze the battery pack in high-performance hybrid-electric vehicles using a multi-physics co-simulation approach. This modeling capability can be extended to other multi-physics systems in order to develop high fidelity models while significantly decreasing computational costs.
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