Predictive control of hybrid vehicle powertrain for intelligent energy management

This thesis studies the minimization of the fuel consumption for a Hybrid Electric Vehicle (HEV) using Model Predictive Control (MPC). The presented MPC – based controller calculates an optimal sequence of control inputs to a hybrid vehicle using the measured plant outputs, the current dynamic states, a system model, system constraints, and an optimization cost function. The MPC controller is developed using Matlab MPC control toolbox. To evaluate the performance of the presented controller, a power-split hybrid vehicle, 2004 Toyota Prius, is selected. The vehicle uses a planetary gear set to combine three power components, an engine, a motor, and a generator, and transfer energy from these components to the vehicle wheels. The planetary gear model is developed based on the Willis’s formula. The dynamic models of the engine, the motor, and the generator, are derived based on their dynamics at the planetary gear. The MPC controller for HEV energy management is validated in the MATLAB/Simulink environment. Both the step response performance (a 0 – 60 mph step input) and the driving cycle tracking performance are evaluated. Two standard driving cycles, Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Driving Schedule (HWFET), are used in the evaluation tests. For the UDDS and HWFET driving cycles, the simulation results, the fuel consumption and the battery state of charge, using the MPC controller are compared with the simulation results using the original vehicle model in Autonomie. The MPC approach shows the feasibility to improve vehicle performance and minimize fuel consumption.

Optimal current control for sub-unity power factor correction

Switching mode power supplies (SMPS) are subject to low power factor and high harmonic distortions. Active power-factor correction (APFC) is a technique to improve the power factor and to reduce the harmonic distortion of SMPSs. However, this technique results in double frequency output voltage variation which can be reduced by using a large output capacitance. Using large capacitors increases the cost and size of the converter. Furthermore, the capacitors are subject to frequent failures mainly caused by evaporation of the electrolytic solution which reduce the converter reliability. This thesis presents an optimal control method for the input current of a boost converter to reduce the size of the output capacitor. The optimum current waveform as a function of weighing factor is found by using the Euler Lagrange equation. A set of simulations are performed to determine the ideal weighing which gives the lowest possible output voltage variation as the converter still meets the IEC-61000-3-2 class-A harmonics requirements with a power factor of 0.8 or higher. The proposed method is verified by the experimental work. A boost converter is designed and it is run for different power levels, 100 W, 200 W and 400 W. The desired output voltage ripple is 10 V peak to peak for the output voltage of 200 Vdc. This ripple value corresponds to a ± 2.5% output voltage ripple. The experimental and the simulation results are found to be quite matching. A significant reduction in capacitor size, as high as 50%, is accomplished by using the proposed method.

MILITARY VEHICLE OPTIMIZATION AND CONTROL

It is remarkable that there are no deployed military hybrid vehicles since battlefield fuel is approximately 100 times the cost of civilian fuel. In the commercial marketplace, where fuel prices are much lower, electric hybrid vehicles have become increasingly common due to their increased fuel efficiency and the associated operating cost benefit. An absence of military hybrid vehicles is not due to a lack of investment in research and development, but rather because applying hybrid vehicle architectures to a military application has unique challenges. These challenges include inconsistent duty cycles for propulsion requirements and the absence of methods to look at vehicle energy in a holistic sense. This dissertation provides a remedy to these challenges by presenting a method to quantify the benefits of a military hybrid vehicle by regarding that vehicle as a microgrid. This innovative concept allowed for the creation of an expandable multiple input numerical optimization method that was implemented for both real-time control and system design optimization. An example of each of these implementations was presented. Optimization in the loop using this new method was compared to a traditional closed loop control system and proved to be more fuel efficient. System design optimization using this method successfully illustrated battery size optimization by iterating through various electric duty cycles. By utilizing this new multiple input numerical optimization method, a holistic view of duty cycle synthesis, vehicle energy use, and vehicle design optimization can be achieved.

SELECTED CHEMOAUTOTROPHIC PROCESSES IN WASTEWATER TREATMENT: LOW TEMPERATURE NITRIFICATION INHIBITION BY AN AZO DYE AND BIOFILTRATION FOR ODOR CONTROL OF HYDROGEN SULFIDE

Biochemical processes by chemoautotrophs such as nitrifiers and sulfide and iron oxidizers are used extensively in wastewater treatment. The research described in this dissertation involved the study of two selected biological processes utilized in wastewater treatment mediated by chemoautotrophic bacteria: nitrification (biological removal of ammonia and nitrogen) and hydrogen sulfide (H2S) removal from odorous air using biofiltration. A municipal wastewater treatment plant (WWTP) receiving industrial dyeing discharge containing the azo dye, acid black 1 (AB1) failed to meet discharge limits, especially during the winter. Dyeing discharge mixed with domestic sewage was fed to sequencing batch reactors at 22oC and 7oC. Complete nitrification failure occurred at 7oC with more rapid nitrification failure as the dye concentration increased; slight nitrification inhibition occurred at 22oC. Dye-bearing wastewater reduced chemical oxygen demand (COD) removal at 7oC and 22oC, increased i effluent total suspended solids (TSS) at 7oC, and reduced activated sludge quality at 7oC. Decreasing AB1 loading resulted in partial nitrification recovery. Eliminating the dye-bearing discharge to the full-scale WWTP led to improved performance bringing the WWTP into regulatory compliance. BiofilterTM, a dynamic model describing the biofiltration processes for hydrogen sulfide removal from odorous air emissions, was calibrated and validated using pilot- and full-scale biofilter data. In addition, the model predicted the trend of the measured data under field conditions of changing input concentration and low effluent concentrations. The model demonstrated that increasing gas residence time and temperature and decreasing influent concentration decreases effluent concentration. Model simulations also showed that longer residence times are required to treat loading spikes. BiofilterTM was also used in the preliminary design of a full-scale biofilter for the removal of H2S from odorous air. Model simulations illustrated that plots of effluent concentration as a function of residence time or bed area were useful to characterize and design biofilters. Also, decreasing temperature significantly increased the effluent concentration. Model simulations showed that at a given temperature, a biofilter cannot reduce H2S emissions below a minimum value, no matter how large the biofilter.