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<p>The world transportation sector has been relying on the oil industry for more than a hundred years, accounting for the largest oil consumption and one third of the greenhouse gas emissions. However, with the boosting demand, escalating national energy security concerns and emerging environmental issues, reducing and displacing petroleum fuel in transportation sector has become an urging global target. As a result, hybrid electric vehicles evolve as one solution to displace petroleum fuel by utilizing vehicle onboard electrical systems, achieving higher fuel economy and less emissions by vehicle electrification and hybridization.</p> <p>However, since hybrid electric vehicles add additional electrical components and systems to realize better fuel economy, the system complexity increases and thus the cost increases. Hence, it is an objective of this thesis research to focus on the integrations and optimizations, aiming to simplify and optimize the hybrid power-trains in both system level and component level.</p> <p>This thesis contributes to a novel integrated electro-mechanical hybrid transmission that is potentially more compact and more operational flexible with fewer components compared to the GM Allison Two-Mode hybrid transmission. Comprehensive commercialized power-train transmissions are reviewed and analyzed to serve as background information for comparison. It also contributes to a family of double-rotor switched reluctance machines that are more integrated and suitable for hybrid electric vehicle applications. A prototype double-rotor switched reluctance machine has been built and tested for concept proving. Detailed machine design process is reported with the emphasis on design novelties. Finite element analysis and optimization techniques are applied and the accuracy is confirmed by the experiments. In addition, methods of machine loss analysis, thermal analysis and drive analysis are established; manufacturing and testing procedures are documented in detail that can be used for future machine designs guidance.</p> / Doctor of Philosophy (PhD)

This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada. / Graduate

Sophisticated power electronic apparatus and their digital control systems are finding increasing applications in electric power systems at generation, transmission, distribution and utilization levels. It is essential to carry out rigorous performance evaluation of such apparatus before commissioning. Field Programmable Gate Arrays (FPGAs) are becoming an attractive platform for accelerating computationally intensive applications. This thesis presents a FPGA-based real-time digital simulator for power electronic drives based on realistic device characteristics. A 3-level 12-pulse Voltage Source Converter (VSC) fed induction machine drive is implemented on the FPGA. The system components include the 3-level VSC, the induction machine, the direct field oriented controller, and the pulse width modulator. Both system-level and device-level IGBT models are utilized to implement the VSC. The VSC model is computed at a fixed time-step of 12:5ns allowing an accurate representation of the IGBT nonlinear switching characteristics. Altera Startix EP1S80 and EP3SL150F1152C2 FPGA boards utilized for the real-time simulation. All models were implemented in VHDL. The FPGA boards were interfaced to external DAC boards to display real-time results on the oscilloscope. The real-time results were validated using an off-line cosimulation set-up using the SABER and MATLAB/SIMULINK software. / Power Engineering and Power Electronics

Following the Model-Based-Design (MBD) development process used presently by the automotive industry, the control systems for a new Series-Parallel Multiple-Regime Plug-in Hybrid Electric Vehicle (PHEV), UVic EcoCAR2, have been developed, implemented and tested. Concurrent simulation platforms were used to achieve different developmental goals, with a simplified system power loss model serving as the low-overhead control strategy optimization platform, and a high fidelity Software-in-Loop (SIL) model serving as the vehicle control development and testing platform. These two platforms were used to develop a strategy-independent controls development tool which will allow deployment of new strategies for the vehicle irrespective of energy management strategy particulars. A rule-based energy management strategy was applied and calibrated using genetic algorithm (GA) optimization. The concurrent modeling approach was validated by comparing the vehicle equivalent fuel consumption between the simplified and SIL models. An equivalency factor (EF) of 1 was used in accounting for battery state of charge (SOC) discrepancies at cycle end. A recursively-defined subsystem efficiency-based EF was also applied to try to capture real-world equivalency impacts. Aggregate results between the two test platforms showed translation of the optimization benefits though absolute results varied for some cycles. Accuracy improvements to the simplified model to better capture dynamic effects are recommended to improve the utility of the newly introduced vehicle control system development method. Additional future work in redefining operation modes and mode transition threshold conditions to approximate optimal vehicle operation is recommended and readily supported by the control system platform developed. / Graduate / 0540 / 0548 / 0790 / d.e.prescott@gmail.com

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The lead-acid batteries are most commonly used electrochemical power source. The lead-acid battery is the oldest type of secondary battery cells. This lead-acid batteries have a great use in hybrid electric vehicles (HEVs), which operate in different modes of vehicle operation. This is related to changes in battery temperature, caused by Joule heat taking place during discharging and chargingg the battery in a vehicle operation. The lead-acid batteries in hybrid electric vehicles work in mode PSoC.

The goal of the thesis is to study literature and to become familiar with problems of accumulators operating in the mode of hybrid electric vehicles (HEV). To work up problems of a potential impact of the influence on the system of the lead accumulator. Assemble the experimental cells with discontinuous system of parallel fins and to treat them with different operating modes. Then to evaluate the results.

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