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.

Human and embodied energy analysis applied to water source protection and household water treatment interventions used in Mali, West Africa

As water quality interventions are scaled up to meet the Millennium Development Goal of halving the proportion of the population without access to safe drinking water by 2015 there has been much discussion on the merits of household- and source-level interventions. This study furthers the discussion by examining specific interventions through the use of embodied human and material energy. Embodied energy quantifies the total energy required to produce and use an intervention, including all upstream energy transactions. This model uses material quantities and prices to calculate embodied energy using national economic input/output-based models from China, the United States and Mali. Embodied energy is a measure of aggregate environmental impacts of the interventions. Human energy quantifies the caloric expenditure associated with the installation and operation of an intervention is calculated using the physical activity ratios (PARs) and basal metabolic rates (BMRs). Human energy is a measure of aggregate social impacts of an intervention. A total of four household treatment interventions – biosand filtration, chlorination, ceramic filtration and boiling – and four water source-level interventions – an improved well, a rope pump, a hand pump and a solar pump – are evaluated in the context of Mali, West Africa. Source-level interventions slightly out-perform household-level interventions in terms of having less total embodied energy. Human energy, typically assumed to be a negligible portion of total embodied energy, is shown to be significant to all eight interventions, and contributing over half of total embodied energy in four of the interventions. Traditional gender roles in Mali dictate the types of work performed by men and women. When the human energy is disaggregated by gender, it is seen that women perform over 99% of the work associated with seven of the eight interventions. This has profound implications for gender equality in the context of water quality interventions, and may justify investment in interventions that reduce human energy burdens.

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.

Online distribution management framework by incorporating wind generation

In distribution system operations, dispatchers at control center closely monitor system operating limits to ensure system reliability and adequacy. This reliability is partly due to the provision of remote controllable tie and sectionalizing switches. While the stochastic nature of wind generation can impact the level of wind energy penetration in the network, an estimate of the output from wind on hourly basis can be extremely useful. Under any operating conditions, the switching actions require human intervention and can be an extremely stressful task. Currently, handling a set of switching combinations with the uncertainty of distributed wind generation as part of the decision variables has been nonexistent. This thesis proposes a three-fold online management framework: (1) prediction of wind speed, (2) estimation of wind generation capacity, and (3) enumeration of feasible switching combinations. The proposed methodology is evaluated on 29-node test system with 8 remote controllable switches and two wind farms of 18MW and 9MW nameplate capacities respectively for generating the sequence of system reconfiguration states during normal and emergency conditions.

Driver Safety in Far-side and Far-oblique Crashes: A Study of Patrol Vehicles in the United States of America

The dissertation titled "Driver Safety in Far-side and Far-oblique Crashes" presents a novel approach to assessing vehicle cockpit safety by integrating Human Factors and Applied Mechanics. The methodology of this approach is aimed at improving safety in compact mobile workspaces such as patrol vehicle cockpits. A statistical analysis performed using Michigan state's traffic crash data to assess various contributing factors that affect the risk of severe driver injuries showed that the risk was greater for unrestrained drivers (OR=3.38, p<0.0001) and for incidents involving front and far-side crashes without seatbelts (OR=8.0 and 23.0 respectively, p<0.005). Statistics also showed that near-side and far-side crashes pose similar threat to driver injury severity. A Human Factor survey was conducted to assess various Human-Machine/Human-Computer Interaction aspects in patrol vehicle cockpits. Results showed that tasks requiring manual operation, especially the usage of laptop, would require more attention and potentially cause more distraction. A vehicle survey conducted to evaluate ergonomics-related issues revealed that some of the equipment was in airbag deployment zones. In addition, experiments were conducted to assess the effects on driver distraction caused by changing the position of in-car accessories. A driving simulator study was conducted to mimic HMI/HCI in a patrol vehicle cockpit (20 subjects, average driving experience = 5.35 years, s.d. = 1.8). It was found that the mounting locations of manual tasks did not result in a significant change in response times. Visual displays resulted in response times less than 1.5sec. It can also be concluded that the manual task was equally distracting regardless of mounting positions (average response time was 15 secs). Average speeds and lane deviations did not show any significant results. Data from 13 full-scale sled tests conducted to simulate far-side impacts at 70 PDOF and 40 PDOF was used to analyze head injuries and HIC/AIS values. It was found that accelerations generated by the vehicle deceleration alone were high enough to cause AIS 3 - AIS 6 injuries. Pretensioners could mitigated injuries only in 40 PDOF (oblique) impacts but are useless in 70 PDOF impacts. Seat belts were ineffective in protecting the driver's head from injuries. Head would come in contact with the laptop during a far-oblique (40 PDOF) crash and far-side door for an angle-type crash (70 PDOF). Finite Element analysis head-laptop impact interaction showed that the contact velocity was the most crucial factor in causing a severe (and potentially fatal) head injury. Results indicate that no equipment may be mounted in driver trajectory envelopes. A very narrow band of space is left in patrol vehicles for installation of manual-task equipment to be both safe and ergonomic. In case of a contact, the material stiffness and damping properties play a very significant role in determining the injury outcome. Future work may be done on improving the interiors' material properties to better absorb and dissipate kinetic energy of the head. The design of seat belts and pretensioners may also be seen as an essential aspect to be further improved.