Ph.D., Antioch University, 2015, Antioch New England: Environmental Studies

While there are as yet no wind energy facilities in New England coastal waters, a number of wind turbine projects are now operating on land adjacent to the coast. In the Gulf of Maine region (from Maine to Massachusetts), at least two such projects, one in Falmouth, Massachusetts, and another on the island of Vinalhaven, Maine, began operation with public backing only to face subsequent opposition from some who were initially project supporters. I investigate the reasons for this dynamic using content analysis of documents related to wind energy facility development in three case study communities. For comparison and contrast with the Vinalhaven and Falmouth case studies, I examine materials from Hull, Massachusetts, where wind turbine construction and operation has received steady public support and acceptance. My research addresses the central question: What does case study analysis of the siting and initial operation of three wind energy projects in the Gulf of Maine region reveal that can inform future governance of wind energy in Massachusetts state coastal waters? I consider the question with specific attention to governance of wind energy in Massachusetts, then explore ways in which the research results may be broadly transferable in the U.S. coastal context. I determine that the change in local response noted in Vinalhaven and Falmouth may have arisen from a failure of consistent inclusion of stakeholders throughout the entire scoping-to-siting process, especially around the reporting of environmental impact studies. I find that, consistent with the principles of ecosystem-based and adaptive management, design of governance systems may require on-going cycles of review and adjustment before the implementation of such systems as intended is achieved in practice. I conclude that evolving collaborative processes must underlie science and policy in our approach to complex environmental and wind energy projects; indeed, collaborative process is fundamen (open full item for complete abstract)

Committee: James Jordan Ph.D. (Committee Chair); Joy Ackerman Ph.D. (Committee Member); Herman Karl Ph.D. (Committee Member) Subjects: Alternative Energy; Energy; Environmental Management; Environmental Studies; Public Policy

Master of Science, University of Toledo, 2010, Electrical Engineering

Demand-Side Management (DSM) can be defined as the implementation of policies and measures to control, regulate, and reduce energy consumption. This document introduces home energy management through dynamic distributed resource management and optimized operation of household appliances in a DSM based simulation platform. The principal purpose of the simulation platform is to illustrate customer-driven DSM operation, and evaluate an estimate for home electricity consumption while minimizing the customer's cost. A heuristic optimization algorithm i.e. Binary Particle Swarm Optimization (BPSO) is used for the optimization of DSM operation in the platform. The platform also simulates the operation of household appliances as a Hybrid Renewable Energy System (HRES). The resource management technique is implemented using an optimization algorithm, i.e. Particle Swarm Optimization (PSO), which determines the distribution of energy obtained from various sources depending on the load. The validity of the platform is illustrated through an example case study for various household scenarios.

Committee: Dr. Lingfeng Wang PhD (Advisor); Dr. Vijay Devabhaktuni PhD (Advisor); Dr. Gursel Serpen PhD (Committee Member) Subjects: Computer Science; Electrical Engineering; Energy; Technology

Doctor of Philosophy, The Ohio State University, 2023, Environmental Science

The dissertation investigates how policies on trade and sustainability affect regional food, water, and energy systems in the Midwest region of the United States. Crop production activities in this region have significant environmental impacts, including greenhouse gas emissions and eutrophication. To evaluate the impacts of policies on trade and sustainability on nutrient runoff, a regional watershed model was developed by training a random forest model with observed data and results from a Soil and Water Assessment Tool (SWAT) model. The developed model was integrated with land use and economy models to assess whether five trade and sustainability scenarios could meet the phosphorus reduction target of the Maumee River Watershed. The findings suggest that consistent efforts to increase effective management practices have greater potential to decrease the harmful algae blooms compared to global trade impacts. Additionally, the dissertation evaluates GHG emissions of county-level corn farming in the Midwest of the United States with spatially explicit absolute environmental life cycle assessment. The results suggest further investigations of Utilities sector in Indiana to reduce GHG emissions from corn farming in the Midwest of the United States, and demonstrate needs of updating the framework and economy level models. When we consider the consequences of policies on both watershed and GHG emissions, it is essential to consider interactions and feedbacks to the economic and the land use models for more integrated approach. To meet this goal, future work is suggested. For instance, the regional watershed model and multi-regional hybrid life cycle assessment framework can be integrated to assess the temporal and spatial explicit life cycle impact of crop farming for global warming potential and eutrophication. Furthermore, the economy and land use model can provide temporal inputs and outputs for crop farming, which expands the study to dynamic LCA. Most importantly, (open full item for complete abstract)

Committee: Bhavik Bakshi (Advisor); Gil Bohrer (Committee Member); Jay Martin (Committee Member); Jeffrey Bielicki (Advisor) Subjects: Environmental Science

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

This dissertation addresses energy-efficient operations for a fleet of diverse electrified vehicles at two system levels, the single-vehicle powertrain system, and the multi-vehicle transportation system, contributing to both with optimal control- and heuristic-based integrative approaches. At the single vehicle powertrain level, an electrified powertrain exhibits a continuum of complexities: mechanical, thermal, and electrical systems with nonlinear, switched, multi-timescale dynamics; algebraic and combinatorial path constraints relating a mix of integer- and real-valued variables. For optimal energy management of such powertrains, “PS3” is proposed, which is a three-step numerical optimization algorithm based on pseudo-spectral collocation theory. Its feasibility, convergence, and optimality properties are presented. Simulation experiments using PS3 on increasingly complex problems are benchmarked with Dynamic Programming (DP). As problem size increases, PS3's computation time does not scale up exponentially like that of DP. Thereafter, PS3 is applied to a comprehensive 13-state 4-control energy management problem. It saves up to 6% energy demand, 2% fuel consumption, and 18% NOx emissions compared to coarsely-modeled DP baseline. For generalizability, parallel and series electrified powertrain architectures running various urban delivery truck drive cycles are considered with multi-objective cost functions, Pareto-optimal study, energy flow analyses, and warm versus cold aftertreatment-start transients. At the multi-vehicle fleet level, energy-efficient vehicle routing approaches lack in integrating optimal powertrain energy management solutions. Extending single vehicle PS3 algorithm for a multi-vehicle fleet of plug-in hybrid (PHEV), battery electric (BEV), and conventional engine (ICEV) vehicles, an integrative optimization framework to solve green vehicle routing with pickups and deliveries (PDP) is proposed. It minimizes the fleet energy consumption a (open full item for complete abstract)

Committee: Qadeer Ahmed Dr. (Advisor); Kiryung Lee Dr. (Committee Member); Joel Paulson Dr. (Committee Member); Giorgio Rizzoni Dr. (Committee Member) Subjects: Aerospace Engineering; Alternative Energy; Applied Mathematics; Artificial Intelligence; Automotive Engineering; Civil Engineering; Computer Science; Electrical Engineering; Engineering; Environmental Engineering; Geographic Information Science; Industrial Engineering; Information Systems; Information Technology; Mechanical Engineering; Naval Engineering; Ocean Engineering; Operations Research; Robotics; Sustainability; Systems Design; Transportation; Transportation Planning; Urban Planning

Master of Science, The Ohio State University, 2022, Mechanical Engineering

Modern households are becoming increasingly electrified with all-electric appliances, renewable energy sources, and electric vehicles. While these homes ultimately mitigate the rate of climate change; without a resilient grid, large-scale market penetration is infeasible. To support grid resilience, utility companies have began incentivizing homeowners to defer appliance loads to times of lower electricity via day-time variable pricing schemes. Modern homeowners enrolled in these programs can maximize their financial benefits by installing energy storage systems and energy management strategies which can schedule appliance loads, energy distribution, and energy consumption. The work in thesis focuses on the design of a home energy management system that schedules multiple smart appliances including plug-in hybrid and battery electric vehicle charging, operation of heating, ventilation, and air conditioning system, energy usage from solar photo-voltaic cells, energy storage and usage from stationary energy storage system, and power consumed from the grid. Considering a day-time variable pricing scheme, the energy management strategy minimizes electrical grid cost to the user, while minimizing user's discomfort in the form of temperature deviation from set-point and time of appliance completion from request. To achieve this goal, the home energy management strategy is formulated as a model predictive controller and at every time step a multi-objective function is minimized using a meta-heuristic algorithm genetic algorithm. The performance of the the home energy management strategy is analyzed by comparing results to a simplistic control strategy. A simulation campaign is conducted to compare the relative performance of the the home energy management strategy at a multitude of plant model settings such as house location, house size, stationary energy storage size, and others. Additionally, to ensure the the home energy management strategy does not significantly de (open full item for complete abstract)

Committee: Marcello Canova (Committee Member); Stephanie Stockar (Advisor) Subjects: Electrical Engineering; Energy; Engineering; Mechanical Engineering; Sustainability; Systems Design

Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

Most vehicles on the road today are conventional vehicles which require the use of nonrenewable fuels to operate. Coupled with this need is a large amount of emissions released into the atmosphere throughout the duration of every trip. To alleviate the burden this places on the environment, governments worldwide have pushed for strict mandates which aim to reduce and, eventually, eliminate the use of fossil fuels. To meet government requirements, hybrid and electric vehicles have been the focus of many car manufacturers. Advancements in vehicle technology have significantly increased the potential of hybrid vehicle technology to reduce levels of emissions and fuel consumption. Advanced energy management strategies have been developed to properly handle the power flow through the vehicle powertrain. These range from rule-based approaches to globally optimal techniques such as dynamic programming (DP). However, cost of high-power computational hardware and lack of a-priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal frameworks. A viable solution to the problem is to leverage on-board sensors present in most vehicles equipped with basic advanced driver assistance systems (ADAS) to obtain a prediction of the future road conditions. Known as look-ahead predictive EMS, this approach partially solves the lack of a-priori knowledge since a detailed view of the road ahead is available. However, uncertainty in sensors and the computational burden of processing large amounts of data creates more difficulties. This research aims to address the challenges mentioned above. A look-ahead predictive EMS is proposed which combines the use of a globally optimal approach (DP) with the equivalent consumption minimization strategy (ECMS) to obtain an optimal solution for a future prediction horizon. ECMS is highly sensitive to the equivalence factor, s, making it necessary to adapt during a trip to account for dist (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Punit Tulpule (Committee Member); Shawn Midlam-Mohler (Advisor) Subjects: Engineering; Mechanical Engineering; Technology; Transportation

Master of Science, The Ohio State University, 2021, Mechanical Engineering

The sector of parcel delivery is rapidly growing along with the consumers' shift towards E-commerce. The market for light- and medium-duty trucks is increasing at the same pace to balance out the demand. In the current scenario, the hybrid electric vehicles represent a viable solution for the challenges posed by the logistic industry, which demands efficient vehicles (less fuel consuming) to increase profits, and policymakers requiring cleaner fleets. Powertrain electrification in conjunction with optimization-based control of the advanced powertrains will play a significant role in achieving the desired high efficiency and low emissions. The energy optimization of a Class 6 Pick-up and Delivery truck with a Range-Extended Electric Vehicle configuration is investigated in this work. Specific performance criteria necessary for the particular driving mission of the truck are established. Dynamic Programming is used to determine the energy consumption strategy that optimally trades off between these multiple objectives. Based on the optimal solution a real-time implementable supervisory controller is developed that takes advantage of a locally optimal Equivalent Consumption Minimization Strategy and simple heuristics. Performance comparable to the optimal results is achieved using the proposed online strategy. In the end, a hardware-in-the-loop simulation is performed to test the real-time implementation of the controller.

Committee: Giorgio Rizzoni (Advisor); Punit Tulpule (Committee Member); Manfredi Villani (Committee Member) Subjects: Mechanical Engineering

Master of Science in Engineering, Youngstown State University, 2020, Department of Civil/Environmental and Chemical Engineering

Plastic waste management is one of the environmental problems facing the United States and the world at large. This project suggests solutions to the problem by using the plastic waste to produce a green and clean energy which will reduce the amount of plastic waste as well as making the environment a haven. With the increase in pollution level, the world is adopting the use of clean and green fuel. This work considers using steam reforming of volatile products from pyrolysis of high-density polyethylene (HDPE) at 500°C to mass-produce hydrogen gas. 50 metric tonnes/day of waste HDPE plastic and 7841litre/ day of generated steam feed will be converted into 5,128.2 metric tonnes of pure hydrogen gas per year using a adiabatic, fixed-bed catalytic reactors operating at 649.3°C and 1 bar. The catalyst is a commercial Ni in the form of 0.4-0.8m particle size.

Committee: Douglas Price PhD (Advisor); Pedro Cortes PhD (Committee Member); Park Byung-Wook PhD (Committee Member) Subjects: Alternative Energy; Chemical Engineering; Energy; Environmental Engineering

Master of Science (MS), Ohio University, 2020, Electrical Engineering (Engineering and Technology)

This thesis discusses the continued development of a Predictive Alerting of Energy (PAE) algorithm that has been integrated into an Information Management and Decision Support Tool (IMDS). The tool was designed to increase Aircraft State Awareness (ASA) by reducing pilot workload through a set of visual alerts on the Vertical Situation Display (VSD) and the Engine Indicating and Crew Alerting System (EICAS) display along with aural alerts. This work relies heavily on past work in which the fundamental principles of the core Fast Time Simulations (FTS) functionality were created. A new method of detecting High and Fast (HF) and Low and Slow (LS) conditions during runway side-steps on approach is discussed. Lastly, the feasibility of a machine learning energy prediction method is explored using a Long Short-Term Memory (LSTM) network, a type of Recurrent Neural Network (RNN).

Committee: Chad Mourning (Advisor); Maarten Uijt de Haag (Advisor) Subjects: Aerospace Engineering; Computer Science; Electrical Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

The increasing complexity of the Powertrain model with the emerging trends in the hybrid and connected vehicles industry demands new approaches. As an Optimal Control Problem for the Energy Management of these class of vehicles becomes more complicated and larger in size due to addition of several mixed integer (continuous and discrete) states and controls variables in a dynamical system, the currently used offline global optimization techniques such as Dynamic Programming may not find a practical application due to a significantly high computational effort or in some cases, even failing to provide any solution at all. Thus, it becomes important to investigate a substitute optimization-based algorithm that can offer a good scalability in terms of numerical efficiency and computational effort as the Optimization Control Problem (OCP) becomes larger in size. In this thesis, we attempt to explore and solve different sizes of Optimal Energy Management Problems concerned with a Parallel P2 Hybrid Electric Vehicle using DP as well as a new algorithm called Pseudospectral Collocation method or PSC (using CasADi). Due to PSC's promising performance and a possible interface with MATLAB/Simulink as shown in the last chapter, this thesis essentially aims to stimulate researchers' interest even further to explore and solve much complicated and larger Hybrid/Electric Vehicle EMS problems using the proposed methodology.

Committee: Qadeer Ahmed Dr. (Advisor); Giorgio Rizzoni Dr. (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2020, Mechanical Engineering

With the on-going electrification and data-intelligence trends in logistics industries, enabled by the advances in powertrain electrification, and connected and autonomous vehicle technologies, the traditional ways vehicles are designed by engineering experience and sales data are to be updated with a design for operation notion that relies intensively on operational data collection and large scale simulations. In this work, this design for operation notion is revisited with a specific combination of optimization and control techniques that promises accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pick-up and delivery truck that is limited to a given driving mission. A Gaussian Process (GP) based statistical learning approach is used to refine the search for the most accurate, optimal designs. Five hybrid powertrain architectures are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. A cross-architecture performance and cost comparison is performed and the selected architecture is developed further in the form of a forward simulator with a dedicated ECMS controller. In the end, a traffic-in-the-loop simulation is performed by integrating the selected powertrain architecture with a SUMO traffic simulator to evaluate the performance of the developed controller against varying driving conditions.

Committee: Giorgio Rizzoni (Advisor); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering; Sustainability; Systems Design; Transportation

Doctor of Management, Case Western Reserve University, 2016, Weatherhead School of Management

In spite of a distinct global demand for microgrid energy, companies receive limited investments. First, we examine in a qualitative study of 21 microgrid firms and 21 investors the determinants of each other's business logic. Second, we use a sample of 118 publicly listed companies from 1991–2015 in a quantitative finance study to estimate our original microgrid business model's systematic risk, relative time-variation and cyclicality effects from macroeconomic variables. Our original microgrid business model suggested three components in the value chain: electricity supply, last mile1 electrical business services provided to the microgrid user community, and the monetization of community user intelligent power data 2analysis through product and service providers. The findings indicate 1) that private capital investors have a limited understanding of the microgrid business logic caused by translation incoherence, different mental models, and weak social networks inherent within microgrid management; 2) that the microgrid business model retains two additional unrecognized value-chains of intelligent power data and electrical business services; 3) our microgrid business model has significantly lower systematic risk relative to renewable energy firms; 4) during the 2008–2015 economic crisis cycle, our microgrid business model's systematic risk declined significantly while that for industrials increased significantly; and 5) industrial betas generate significant multivariate regression results against 90-day treasury bills (T-bills). This study exemplifies that our suggested microgrid business model has valuable implications for entrepreneurial management competencies, institutional investors that depend on systematic risk evidence for investment decisions and CEOs of utilities, industrials, renewable energy that are expanding into microgrids. 1 The last mile is the end link between consumers and connectivity and has proved to be disproportionately expensive to s (open full item for complete abstract)

Committee: Richard Boland, Ph.D. (Advisor); Anurag Gupta, Ph.D. (Advisor); Chris Laszlo, Ph.D. (Advisor); Kalle Lyytinen, Ph.D. (Advisor) Subjects: Energy; Systems Design

Doctor of Philosophy, The Ohio State University, 2019, Mechanical Engineering

This dissertation examines the benefit of energy optimization in the operation of a vehicle system at an individual vehicle level and the fleet level. For energy optimization in an individual vehicle, a hybridized Class 6 Pickup and Delivery truck with a Range Extended Electric Vehicle configuration is considered. The truck's components were chosen for minimal energy consumption while meeting all the performance requirements of a conventional, diesel-powered vehicle of that class and application. Dynamic Programming is used to determine the best possible energy consumption performance over the course of a working day for the hybrid truck. Energy consumption is then determined using a causal energy management controller on a forward simulator that is compatible with implementation in real-time, where this dissertation introduces the use of a distance-based driver that accurately matches the distance traveled by the vehicle from every start-to-stop in the drive cycle even if the performance constraints of the components prevent the exact matching of the drive cycle speed. The energy consumption results with the forward simulator demonstrate that with increasing levels of information of the expected duty cycle of the day, the onboard energy management can be easily adapted to obtain better fuel consumption performance. For energy optimization in a vehicle fleet, a delivery vehicle fleet is considered that consists of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs) and conventional Internal Combustion Engine Vehicles (ICEVs) operating over the same service area, from a shared depot. This dissertation develops a methodology for route optimization of such a heterogeneous delivery vehicle fleet while taking into account information related to static parameters of the service area (such as topography, payload and driving distance) and dynamic driving conditions (such as traffic incidents and traffic lights). The benefit of route optimization of the fleet f (open full item for complete abstract)

Committee: Giorgio Rizzoni PhD (Advisor); Qadeer Ahmed PhD (Committee Member); Shawn Midlam-Mohler PhD (Committee Member); Marcello Canova PhD (Committee Member); Ran Dai PhD (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 2019, Environmental Studies (Voinovich)

The biological breakdown of organic waste material in anaerobic digestion (AD) systems can produce biogas, a methane (CH4) rich gas, which can be purified or upgraded and used like fossil fuel–derived pipeline quality natural gas. A pilot-scale AD system was built on the campus of Ohio University (OU) in 2015 to test the potential for converting variable food waste (FW) from dining halls into biogas. AD systems can produce suboptimal biogas quality and yields because the variability of the FW causes the bacterial communities consuming it to become unstable or to acidify. Suboptimal system performance can be prevented by adjusting the feedstock C:N ratio to be more favorable and consistent for the bacteria. This study tests the potential of mixing Miscanthus x giganteus (miscanthus) crop residues with FW to achieve an optimum C:N ratio and buffer against variability in biogas production. In a pilot-scale (2006 liters) anaerobic digester, a FW and miscanthus grass (MC) mixture with a ratio of 1:1 was tested as feedstock for three months, and the average biogas produced was 72.35% CH4 and 27.65% CO2. We determined that the system was able to convert 71.2% of the carbon in the feedstock into CH4. A second study was conducted in the laboratory using small vessel (1 l) batch digesters to test MC and FW, and also MC and beer waste (BW) at 1:1, and 2:1 ratios. In the batch digesters, the feedstock mixture of FW and MC at a 2:1 ratio produced biogas with the most CH4, averaging 23.84% CH4, but MC and BW at a ratio of 1:1 was similar, with 22.66% CH4. The results of this study lends support to the existing literature that co-digestion of wastes with an optimized C:N ratio improves digester performance in comparison to the digestion of a single variable waste stream. The results of the laboratory study suggest that other factors, such as the particle size of feedstocks can also improve the biodegradability of the feedstocks, and thus offer other insight for methods to stabiliz (open full item for complete abstract)

Committee: Sarah Davis Dr. (Committee Chair) Subjects: Environmental Studies

Doctor of Philosophy, The Ohio State University, 2019, Mechanical Engineering

48V mild hybrid technology along with electrification of auxiliary loads is a promising solution to enhance fuel economy and reduce tailpipe emissions. However, the increased complexity of advanced electrified powertrains brings also significant challenges in the control design and calibration process. Conventional methods based on decentralized or hierarchical control architectures inevitably ignore the interactions among subsystems, and hence cannot achieve system-wide optimal performance. Meanwhile, developing and implementing centralized control architectures are practically intractable, due to the presence of multiple control inputs, different optimization objectives, and reconfigurable system structures. This dissertation aims at developing a novel Distributed Model Predictive Control (MPC) framework, tailored for a 48V Diesel mild hybrid powertrain, coupled with an electrically driven booster (E-Booster) and an electrically heated catalyst (EHC). The proposed methodology exploits the benefits of a distributed control system consisting of interconnected, local optimal controllers that approach system-wide optimal performance by cooperation, and also exhibit a flexible system structure to accommodate actuator on/off operations. In specific, this dissertation addresses two essential control problems in the field of electrified Diesel powertrains. First, a low-level engine air path control is designed for reference tracking, covering both turbocharging and electrical boosting modes. A nonlinear distributed MPC is developed, which is able to achieve the system-wide optimal performance and closed-loop stability, while rendering the E-Booster module plug-and-play. This approach is extended to a Lyapunov-based distributed MPC, where a nonlinear control law is embedded in local controllers to ensure the closed-loop stability with no communication. Then, a high-level supervisory control is designed for system-level energy management of a hybrid electric vehicl (open full item for complete abstract)

Committee: Marcello Canova (Advisor); Giorgio Rizzoni (Committee Member); Vadim Utkin (Committee Member); Wei Zhang (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 2019, Electrical Engineering & Computer Science (Engineering and Technology)

Network-on-chip (NoC) continues to be the preferred communication fabric in multicore and manycore architectures as the NoC seamlessly blends the resource efficiency of the bus with the parallelization of the crossbar. However, without adaptable power management the NoC suffers from excessive static power consumption at higher core counts. Static power consumption will increase proportionally as the size of the NoC increases to accommodate higher core counts in the future. NoC also suffers from excessive dynamic energy as traffic loads fluctuate throughout the execution of an application. Power-gating (PG) and Dynamic Voltage and Frequency Scaling (DVFS) are two highly effective techniques proposed in literature to reduce static power and dynamic energy in the NoC respectively. DVFS is a popular technique that allows dynamic energy to be saved but may potentially lead to a loss in throughput. Power-gating allows static power to be saved but can introduce new problems incurred by isolating network routers. Further complications include the introduction of long wake-up delays and break-even times. However, both DVFS and power-gating are critical for realizing energy proportional computing as core counts race into the hundreds for multi-cores. In this thesis, we propose two distinct but related techniques that enable energy proportional computing for NoC. We first propose LEAD - Learning-enabled Energy Aware Dynamic voltage/frequency scaling for NoC architectures. LEAD applies machine learning (ML) techniques to enable improvements in both energy and performance with reduced overhead cost. This allows LEAD to enact a proactive energy management strategy that relies on an offline trained regression model while also providing a wide variety of voltage/frequency (VF) pairs. In this work, we will refer to various VF pairs as modes. LEAD groups each router and the router's outgoing links locally into the same V/F domain allowing energy management at a finer granularity wit (open full item for complete abstract)

Committee: Avinash Karanth (Advisor); Razvan Bunescu (Committee Member); Savas Kaya (Committee Member) Subjects: Computer Engineering; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Mechanical Engineering

Hybrid electric vehicles are a result of a global push towards cleaner and fuel-efficient vehicles. They use both electrical and traditional fossil-fuel based energy sources, which makes them ideal for the transition towards much cleaner electric vehicles. A key part of the hybridization effort is designing effective energy management algorithms because they are crucial in reducing fuel consumption and emission of the hybrid vehicle. In the automotive industry, energy management systems are designed, prototyped, and validated in a software simulation environment before implementation on the hybrid vehicle. The software simulation uses model-based design techniques which reduce development time and cost. Traditionally, the design of energy management systems is based on statutory drive-cycles. Drive-cycle based solutions to energy management systems improve fuel economy of the vehicle and are well suited for statutory certification of fuel economy and emissions. In recent times however, the fuel economy and emissions over real-world driving is being considered increasingly for statutory certification. In light of these developments, methodologies to simulate and design new energy management strategies for real-world driving are needed. The work presented in this dissertation systematically addresses the challenges faced in the development of such a methodology. This work identifies and solves three sub-problems which together form the methodology for model-based real-world look-ahead energy management system development. First, a simulation framework to simulate real-world driving and look-ahead sensor emulation is developed. The simulation framework includes traffic simulation and powertrain simulation capabilities. It is termed traffic integrated powertrain co-simulation. Second, a comprehensive algorithm is developed to utilize look-ahead sensor data to accurately predict the vehicle's future velocity trajectories. Finally, through the use of optimal c (open full item for complete abstract)

Committee: Giorgio Rizzoni PhD (Advisor); Shawn Midlam-Mohler PhD (Committee Member); David Hoelzle PhD (Committee Member); Abhishek Gupta PhD (Committee Member); Qadeer Ahmed PhD (Committee Member) Subjects: Mechanical Engineering; Transportation

Master of Science, The Ohio State University, 2018, Electrical and Computer Engineering

This thesis addresses the coordinated operation of networked microgrids (MGs), distributed energy resources (DERs), and Volt-VAR control devices in the implementation of Volt-VAR optimization (VVO). Although our formulation is focused on implementing conservation voltage reduction (CVR), it can be extended for other VVO objectives e.g. losses minimization, peak demand shaving, or energy consumption reduction. We assume that the distribution network operator (DNO) has to make decisions anticipating the decisions of the MG operators. The hierarchy shown in this problem is related to a Stackelberg game. Hence, we formulate this problem as a bi-level optimization problem where the upper-level problem corresponds to the DNO and the lower-level problems correspond to each MG. The DNO as well as each MG are assumed to be independent entities with their individual objective functions. The objective of the DNO depends on the objective of the VVO objective. In particular, in the case of CVR, the objective is to minimize the load demand and losses of the distribution system. Conversely, we assume that the objective of the MG operators is to minimize the operation costs of dispatching DGs within the MG and buying/selling electricity from/to DNO i.e. an economic dispatch. The integration of DERs at the distribution level changes the response of the grid to a VVO strategy. We consider that DERs are located at the distribution network and within each microgrid. We study the four voltage-power control modes of DERs stated in the IEEE Std. 1547-2018, namely constant power factor, voltage-reactive power, active--reactive power, and constant reactive power. Finally, we validate our formulation in a modified IEEE 33-node test system. The effectiveness of CVR with the different voltage-power control modes of DERs is analyzed. The findings of this work are significant for the implementation of VVO in active distribution systems with networked microgrids.

Committee: Mahesh Illindala Ph.D. (Advisor); Jiankang Wang Ph.D. (Committee Member) Subjects: Electrical Engineering

Master of Science in Mechanical Engineering (MSME), Wright State University, 2018, Mechanical Engineering

As technology advances, the abilities of civilian and military vehicles, both air and ground, will undoubtedly increase as well. One of the main areas of improvement is in the electronics area. The new electronics are ever smaller, use ever higher amounts of electrical power, and require ever smaller temperature tolerances. This leads to the problem of effectively managing the increasing thermal loads and temperature tolerances on these systems. One electronic system that causes concern is a high energy pulse system (HEPS). These devices have very high thermal loads (100s of kW). On an air vehicle, where thermal management by legacy methods (i.e. fuel as the heat sink) is already problematic, a HEPS will certainly overload the thermal management system (TMS). HEPS performance must be understood and quantified more accurately to understand the design requirements of a TMS for this device. To aid in this understanding, the HEPS itself and a palletized system to thermally manage the HEPS will be modeled. Previous analysis of a cryogenic palletized HEPS contained a simplified power model for a HEPS that had a set efficiency and always gave a certain amount of optical power out and a certain amount of power dissipated as heat based on that set efficiency. The HEPS model developed and presented takes into account the temperature of internal HEPS components and changes the efficiency accordingly. The HEPS efficiency changes with component temperature to provide a better understanding of the consequences of not thermally managing a HEPS effectively. Along with the HEPS model, a cryogenic-based palletized TMS using Liquefied Natural Gas (LNG) for indirectly cooling the HEPS was modeled. Using LNG as a method of cooling is a possible alternative to using very large legacy systems (fuel as heat sink) to cool a HEPS. The architecture of this palletized system uses LNG to cool the heat loads. The LNG then becomes the fuel for the turbo-generator, which produces electrical po (open full item for complete abstract)

Committee: Rory Roberts Ph.D. (Advisor); Mitch Wolff Ph.D. (Committee Member); Zifeng Yang Ph.D. (Committee Member) Subjects: Aerospace Engineering; Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

Larger-scale data centers are well known for their significant energy cost. Among all the power consumers within a data center, data center network (DCN) can account for as much as 10% to 20% of the total power. Therefore, the power optimization for DCNs has recently received increasing research attention. Currently, an effective type of power-saving approach for DCNs is traffic consolidation, which consolidates traffic flows onto a small set of links and switches such that unused network devices can be dynamically turned to sleep for power savings when the total workload is low, and be turned on again when workload increases. However, this type of approach has many limitations, such as 1) Current DCN optimization approaches are usually applied without the coordination with other data center components (e.g., servers, cooling), thus losing the chance for improved energy savings, or even leading to network congestions; 2) Existing DCN power optimization approaches are mostly centralized and do not scale well for nowadays larger-scale DCNs. 3) Current approaches usually only focus on the power optimization, but ignore the impact of traffic consolidation on network performance, especially one of the most important metrics of network performance as the flow completion time (FCT). In this dissertation, we try to address above limitations of the state-of-the-art power optimization approaches, by proposing and analyzing different solutions respectively. Firstly, we propose PowerNetS, a power optimization framework that jointly coordinate the DCN and servers during the consolidation leveraging correlation analysis. Evaluation on both a physical testbed and large-scale simulation with real DCN trace files from Wikipedia, Yahoo! and IBM data centers show that, PowerNetS can provide as much as 51.6% energy savings, and outperforms two state-of-the-art baselines by 44.3% and 15.8%, respectively. Secondly, we try to address the scalability issue by proposing DISCO, a highly s (open full item for complete abstract)

Committee: Xiaorui Wang (Advisor) Subjects: Computer Engineering