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.

Understanding volcanic processes using UV camera measurements of sulfur dioxide and coincident infrasound and seismicity

The exsolution of volatiles from magma maintains an important control on volcanic eruption styles. The nucleation, growth, and connectivity of bubbles during magma ascent provide the driving force behind eruptions, and the rate, volume, and ease of gas exsolution can affect eruptive activity. Volcanic plumes are the observable consequence of this magmatic degassing, and remote sensing techniques allow us to quantify changes in gas exsolution. However, until recently the methods used to measure volcanic plumes did not have the capability of detecting rapid changes in degassing on the scale of standard geophysical observations. The advent of the UV camera now makes high sample rate gas measurements possible. This type of dataset can then be compared to other volcanic observations to provide an in depth picture of degassing mechanisms in the shallow conduit. The goals of this research are to develop a robust methodology for UV camera field measurements of volcanic plumes, and utilize this data in conjunction with seismoacoustic records to illuminate degassing processes. Field and laboratory experiments were conducted to determine the effects of imaging conditions, vignetting, exposure time, calibration technique, and filter usage on the UV camera sulfur dioxide measurements. Using the best practices determined from these studies, a field campaign was undertaken at Volcán de Pacaya, Guatemala. Coincident plume sulfur dioxide measurements, acoustic recordings, and seismic observations were collected and analyzed jointly. The results provide insight into the small explosive features, variations in degassing rate, and plumbing system of this complex volcanic system. This research provides useful information for determining volcanic hazard at Pacaya, and demonstrates the potential of the UV camera in multiparameter studies.

Optimal game theoretic distributed control methodologies in small scale power systems

This dissertation presents the competitive control methodologies for small-scale power system (SSPS). A SSPS is a collection of sources and loads that shares a common network which can be isolated during terrestrial disturbances. Micro-grids, naval ship electric power systems (NSEPS), aircraft power systems and telecommunication system power systems are typical examples of SSPS. The analysis and development of control systems for small-scale power systems (SSPS) lacks a defined slack bus. In addition, a change of a load or source will influence the real time system parameters of the system. Therefore, the control system should provide the required flexibility, to ensure operation as a single aggregated system. In most of the cases of a SSPS the sources and loads must be equipped with power electronic interfaces which can be modeled as a dynamic controllable quantity. The mathematical formulation of the micro-grid is carried out with the help of game theory, optimal control and fundamental theory of electrical power systems. Then the micro-grid can be viewed as a dynamical multi-objective optimization problem with nonlinear objectives and variables. Basically detailed analysis was done with optimal solutions with regards to start up transient modeling, bus selection modeling and level of communication within the micro-grids. In each approach a detail mathematical model is formed to observe the system response. The differential game theoretic approach was also used for modeling and optimization of startup transients. The startup transient controller was implemented with open loop, PI and feedback control methodologies. Then the hardware implementation was carried out to validate the theoretical results. The proposed game theoretic controller shows higher performances over traditional the PI controller during startup. In addition, the optimal transient surface is necessary while implementing the feedback controller for startup transient. Further, the experimental results are in agreement with the theoretical simulation. The bus selection and team communication was modeled with discrete and continuous game theory models. Although players have multiple choices, this controller is capable of choosing the optimum bus. Next the team communication structures are able to optimize the players’ Nash equilibrium point. All mathematical models are based on the local information of the load or source. As a result, these models are the keys to developing accurate distributed controllers.