The Hybrid Electric Vehicle (HEV) is a complex electromechanical system with complex interactions among various components. Due to the large number of design variables involved, the design flexibility in the HEV makes performance studies difficult. As the system complexity and sophistication increases, it becomes much more difficult to predict these interactions and design the system accordingly. Also, different variations in the design and manufacture of various components and systems involve a large amount of work and cost to keep updated of all these variations. While the above issues ask for a flexible design environment suitable for vehicle design, most of the existing powertrain design tools are based on experiential models, such as look-up tables, which use idealized assumptions and limited experimental data. The accuracy of the results produced by these tools is not good enough for designing these new generation vehicles. Also, sometimes the designs may lead to components or systems beyond physical limitations. To make the powertrain design more efficient, the models developed must be closely related to the underlying physics of the components. Only such physics-based models can facilitate high fidelity simulations for dynamics at different time scales. This results in the quest for a design tool that manages the vehicle?s development process while maintaining tight integration between the software and physical artifacts. The thesis addresses the above issues and focuses on the modeling of HEV using model integrated computing and employing physics-based resistive companion form modeling method. For this purpose, Generic Modeling Environment (GME), software developed by Institute of Software and Integrated Systems (ISIS), Vanderbilt University is used as the platform for developing the models. A modeling environment for hybrid vehicle design is prepared and a Battery Electric Vehicle (BEV) is developed as an application of the developed environment. Resistive companion form models of various BEV components are prepared and a model interpreter is prepared for integrating the developed component models and simulating the design.

The rise in overall powertrain complexity and the stringent performance requirements of a hybrid electric vehicle (HEV) have elevated the role of its powertrain control strategy to considerable importance. Iterative modeling and simulation form an integral part of the control strategy design process and industry engineers rely on proprietary ?legacy? models to rapidly develop and implement control strategies. However, others must initiate new algorithms and models in order to develop production-capable control systems. This thesis demonstrates the development and validation of a charge-sustaining control algorithm for a through-the-road (TTR) parallel hybrid (diesel-electric) powertrain. Some unique approaches used in powertrain-level control of other commercial and prototype vehicles have been adopted to incrementally develop this control strategy. The real-time performance of the control strategy has been analyzed through on-road and chassis dynamometer tests over several standard drive cycles. Substantial quantitative improvements in the overall HEV performance over the stock configuration, including better acceleration and fuel-economy have been achieved.

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