This thesis investigates modeling and simulation of hybrid electric vehicles with particular emphasis on transient modeling and real-time simulation. Three different computer models, i.e. a steady state model, a fully-detailed transient model and a reduced-intensity transient model, are developed for a hybrid drive-train in this study. The steady-state model, which has low computational intensity, is used to determine the optimal battery size and chemistry for a plug-in hybrid drive-train. Simulation results using the developed steady state model show the merits of NiMH and Li-ion battery technologies. Based on the obtained results and the reducing cost of Li-ion batteries, this battery chemistry is used throughout this research. A fully-detailed transient model is developed to simulate the vehicle behaviour under different driving conditions. This model includes the dynamics of the power train components such as the engine, the power-electronic converters and vehicle controllers of all levels. The developed transient model produces an accurate representation of the drive-train including the switching behaviour of the power electronic converters. A reduced-intensity transient model (also referred to as a dynamic average model) is developed for real-time hardware-in-loop simulation of the vehicle. By reducing the computational demand of the detailed transient model using averaging techniques, the reduced-intensity model is implemented on a real-time simulator and is interfaced to an external subsystem such as an actual battery. The setup can be used to test existing and emerging battery technologies, which may not have an accurate mathematical model. Extensive tests are performed to verify the accuracy and validity of the results obtained from the developed hardware-in-loop simulation setup.

This thesis investigates modeling and simulation of hybrid electric vehicles with particular emphasis on transient modeling and real-time simulation. Three different computer models, i.e. a steady state model, a fully-detailed transient model and a reduced-intensity transient model, are developed for a hybrid drive-train in this study. The steady-state model, which has low computational intensity, is used to determine the optimal battery size and chemistry for a plug-in hybrid drive-train. Simulation results using the developed steady state model show the merits of NiMH and Li-ion battery technologies. Based on the obtained results and the reducing cost of Li-ion batteries, this battery chemistry is used throughout this research. A fully-detailed transient model is developed to simulate the vehicle behaviour under different driving conditions. This model includes the dynamics of the power train components such as the engine, the power-electronic converters and vehicle controllers of all levels. The developed transient model produces an accurate representation of the drive-train including the switching behaviour of the power electronic converters. A reduced-intensity transient model (also referred to as a dynamic average model) is developed for real-time hardware-in-loop simulation of the vehicle. By reducing the computational demand of the detailed transient model using averaging techniques, the reduced-intensity model is implemented on a real-time simulator and is interfaced to an external subsystem such as an actual battery. The setup can be used to test existing and emerging battery technologies, which may not have an accurate mathematical model. Extensive tests are performed to verify the accuracy and validity of the results obtained from the developed hardware-in-loop simulation setup.

Developing analytical models that can accurately describe behaviors of Internet-scale networks is difficult. This is due, in part, to the heterogeneous structure, immense size and rapidly changing properties of today's networks. The lack of analytical models makes large-scale network simulation an indispensable tool for studying immense networks. However, large-scale network simulation has not been commonly used to study networks of Internet-scale. This can be attributed to three factors: 1) current large-scale network simulators are geared towards simulation research and not network research, 2) the memory required to execute an Internet-scale model is exorbitant, and 3) large-scale network models are difficult to validate. This dissertation tackles each of these problems. First, this work presents a method for automatically enabling real-time interaction, monitoring, and control of large-scale network models. Network researchers need tools that allow them to focus on creating realistic models and conducting experiments. However, this should not increase the complexity of developing a large-scale network simulator. This work presents a systematic approach to separating the concerns of running large-scale network models on parallel computers and the user facing concerns of configuring and interacting with large-scale network models. Second, this work deals with reducing memory consumption of network models. As network models become larger, so does the amount of memory needed to simulate them. This work presents a comprehensive approach to exploiting structural duplications in network models to dramatically reduce the memory required to execute large-scale network experiments. Lastly, this work addresses the issue of validating large-scale simulations by integrating real protocols and applications into the simulation. With an emulation extension, a network simulator operating in real-time can run together with real-world distributed applications and services. As such, real-time network simulation not only alleviates the burden of developing separate models for applications in simulation, but as real systems are included in the network model, it also increases the confidence level of network simulation. This work presents a scalable and flexible framework to integrate real-world applications with real-time simulation.

International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California / A real-time telemetry simulator of the IUS spacecraft has recently entered operation to train Flight Control Teams for the 1aunch of the AXAF telescope from the Shuttle. The simulator has proven to be a successful higher fidelity implementation of its predecessor, while affirming the rapid development methodology used in its design. Although composed of COTS hardware and software, the system simulates the full breadth of the mission: Launch, Pre-Deployment-Checkout, Burn Sequence, and AXAF/IUS separation. Realism is increased through patching the system into the operations facility to simulate IUS telemetry, Shuttle telemetry, and the Tracking Station link (commands and status message).

This thesis presents the real-time simulation of a realistic-size Modular Multilevel Converter (MMC) based High-Voltage Direct Current (HVDC) transmission system. Based on the concept of time-varying Thevenin equivalent, a computationally efficient model of the MMC is developed and deployed on an FPGA platform. The salient features of the developed MMC model are: (i) The decoupling of the solutions of the MMC model and the of the rest of the system, (ii) it provides an equivalent representation of the full MMC, (iii) it is suitable for parallel implementation. The model is used as part of the simulation of the 401-level France/Spain MMC-HVDC link between, in which 2 separate MMCs are included, showing the expandability of the designed system to larger DC grid scenarios. Hardware in the loop (HIL) testing capabilities of the system are also demonstrated with the addition of an external controller to the simulation system.

This thesis presents the real-time simulation of a realistic-size Modular Multilevel Converter (MMC) based High-Voltage Direct Current (HVDC) transmission system. Based on the concept of time-varying Thevenin equivalent, a computationally efficient model of the MMC is developed and deployed on an FPGA platform. The salient features of the developed MMC model are: (i) The decoupling of the solutions of the MMC model and the of the rest of the system, (ii) it provides an equivalent representation of the full MMC, (iii) it is suitable for parallel implementation. The model is used as part of the simulation of the 401-level France/Spain MMC-HVDC link between, in which 2 separate MMCs are included, showing the expandability of the designed system to larger DC grid scenarios. Hardware in the loop (HIL) testing capabilities of the system are also demonstrated with the addition of an external controller to the simulation system.

Transient stability analysis is necessary for the planning, operation, and control of power systems. However, its mathematical modeling and time-domain solution is computationally onerous and has attracted the attention of power systems experts and simulation specialists for decades. The ultimate promised goal has been always to perform this simulation as fast as real-time for realistic-sized systems. In this thesis, methods to speedup transient stability simulation for large-scale power systems are investigated. The research reported in this thesis can be divided into two parts. First, real-time simulation on a general-purpose simulator composed of CPU-based computational nodes is considered. A novel approach called Instantaneous Relaxation (IR) is proposed for the real-time transient stability simulation on such a simulator. The motivation of proposing this technique comes from the inherent parallelism that exists in the transient stability problem that allows to have a coarse grain decomposition of resulting system equations. Comparison of the real-time results with the off-line results shows both the accuracy and efficiency of the proposed method. In the second part of this thesis, Graphics Processing Units (GPUs) are used for the first time for the transient stability simulation of power systems. Data-parallel programming techniques are used on the single-instruction multiple-date (SIMD) architecture of the GPU to implement the transient stability simulations. Several test cases of varying sizes are used to investigate the GPU-based simulation. The simulation results reveal the obvious advantage of using GPUs instead of CPUs for large-scale problems. In the continuation of part two of this thesis the application of multiple GPUs running in parallel is investigated. Two different parallel processing based techniques are implemented: the IR method, and the incomplete LU factorization based approach. Practical information is provided on how to use multi-threaded programming to manage multiple GPUs running simultaneously for the implementation of the transient stability simulation. The implementation of the IR method on multiple GPUs is the intersection of data parallelism and program-level parallelism, which makes possible the simulation of very large-scale systems with 7020 buses and 1800 synchronous generators. / Energy Systems

In this thesis, we explore the topic of designing an architecture and processing algorithms for a multi-agent system, where agents need to address potential unexpected events in the environment, operating under soft real-time constraints. We first develop a classification of unexpected events into Opportunities, Barriers and Potential Causes of Failure, and outline the interaction required to support the allocation of tasks for these events. We then propose a hybrid architecture to provide for agent autonomy in the system, employing a central coordinating agent. Certain agents in the community operate autonomously, while others remain under the control of the coordinating agent. The coordinator is able to determine which agents should form teams to address unexpected events in a timely manner, and to oversee those agents as they perform their tasks. The proposed architecture avoids the overhead of negotiation amongst agent teams for the assignment of tasks, a benefit when operating under limited time and resource constraints. It also avoids the bottleneck of having one coordinating agent making all decisions before work can proceed in the community, by allowing some agents to work independently. We illustrate the potential usefulness of the framework by describing an implementation of a simulator loosely based on that used for the RoboCup Rescue Simulation League contest. The implementation provides a set of simulated computers, each running a simple soft real-time operating system. On top of this basic simulation we implement the model described above and test it against two different search-and-rescue scenarios. From our experiments, we observe that our architecture is able to operate in dynamic and real-time environments, and can handle, in an appropriate and timely manner, any unexpected events that occur. We also comment on the value of our proposed approach for designing adjustable autonomy multi-agent systems and for specific environments such as robotics, where reducing the overall level of communication within the system is crucial.

In this thesis, we explore the topic of designing an architecture and processing algorithms for a multi-agent system, where agents need to address potential unexpected events in the environment, operating under soft real-time constraints. We first develop a classification of unexpected events into Opportunities, Barriers and Potential Causes of Failure, and outline the interaction required to support the allocation of tasks for these events. We then propose a hybrid architecture to provide for agent autonomy in the system, employing a central coordinating agent. Certain agents in the community operate autonomously, while others remain under the control of the coordinating agent. The coordinator is able to determine which agents should form teams to address unexpected events in a timely manner, and to oversee those agents as they perform their tasks. The proposed architecture avoids the overhead of negotiation amongst agent teams for the assignment of tasks, a benefit when operating under limited time and resource constraints. It also avoids the bottleneck of having one coordinating agent making all decisions before work can proceed in the community, by allowing some agents to work independently. We illustrate the potential usefulness of the framework by describing an implementation of a simulator loosely based on that used for the RoboCup Rescue Simulation League contest. The implementation provides a set of simulated computers, each running a simple soft real-time operating system. On top of this basic simulation we implement the model described above and test it against two different search-and-rescue scenarios. From our experiments, we observe that our architecture is able to operate in dynamic and real-time environments, and can handle, in an appropriate and timely manner, any unexpected events that occur. We also comment on the value of our proposed approach for designing adjustable autonomy multi-agent systems and for specific environments such as robotics, where reducing the overall level of communication within the system is crucial.

This dissertation describes the wave particles technique for simulating water surface waves and two way fluid-object interactions for real-time applications, such as video games. Water exists in various different forms in our environment and it is important to develop necessary technologies to be able to incorporate all these forms in real-time virtual environments. Handling the behavior of large bodies of water, such as an ocean, lake, or pool, has been computationally expensive with traditional techniques even for offline graphics applications, because of the high resolution requirements of these simulations. A significant portion of water behavior for large bodies of water is the surface wave phenomenon. This dissertation discusses how water surface waves can be simulated efficiently and effectively at real-time frame rates using a simple particle system that we call "wave particles." This approach offers a simple, fast, and unconditionally stable solution to wave simulation. Unlike traditional techniques that try to simulate the water body (or its surface) as a whole with numerical techniques, wave particles merely track the deviations of the surface due to waves forming an analytical solution. This allows simulation of seemingly infinite water surfaces, like an open ocean. Both the theory and implementation of wave particles are discussed in great detail. Two-way interactions of floating objects with water is explained, including generation of waves due to object interaction and proper simulation of the effect of water on the object motion. Timing studies show that the method is scalable, allowing simulation of wave interaction with several hundreds of objects at real-time rates.