Doctor of Engineering, University of Dayton, 2022, Electrical and Computer Engineering

Beamforming is a technique in which an array of antennas is exploited to achieve maximum reception in a specified direction by estimating the signal arrival from a desired direction (in the presence of noise) while signals of the same frequency from other directions are rejected. This is achieved by varying the weights of each of the sensors (antennas) used in the array. It basically uses the idea that, though the signals emanating from different transmitters occupy the same frequency channel, they still arrive from different directions. This spatial separation is exploited to separate the desired signal from the interfering signals. In adaptive beamforming the optimum weights are iteratively computed using complex algorithms based upon different criteria. Several adaptive filter structures are proposed for noise cancellation; however, the present research uses a hybrid least square algorithm (HLMS). The main objective of this adopting this algorithm in this system is to utilize the filter weights w[i] for two algorithms, which are LMS and Sign error algorithms. These use a hybrid LMS (HLMS) algorithm to adjust filter weights according to mean filter weights. Fur- thermore, simulation studies show that the HLMS algorithm gives better performance as compared to LMS and Sign error algorithms. Finally, the validity of the proposed algorithm is illustrated using three numerical examples. 3

Committee: Guru Subramanyam (Committee Chair) Subjects: Academic Guidance Counseling; Electrical Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

The automotive industry is driving towards electrification. As the emission and fuel economy standards get more stringent, manufactures are electrifying their vehicle platforms by developing more hybrid electric vehicles. Although new technology boosts the fuel economy, it also brings new challenges. One of them is that customers often find discrepancies between the rated fuel economy number and the number they get during real world operation. Therefore, there is a need to investigate the issue and develop a new calibration process for optimizing the HEV fuel economy over both certification and real-world operation. In this research, a model-based calibration process is developed. The process uses meta-heuristic algorithms to optimize five look-up tables that are relevant to fuel economy of the HEV. Four different meta-heuristic algorithms, namely PSO, GA and two hybrids, are investigated and compared. It is found that PSO has reasonably good performance and can deliver its performance consistently under different conditions. Other algorithms may have better performance under certain scenarios, but they are sensitive to constraints in test problems and fail to get rational solutions in the real problem. The research also investigates methods to reduce number of parameters to optimize, the initialization of the optimization set and ways to generate representative drive cycles based on real-world driving data. The important thing is that these methods are not vehicle-specific and therefore can be migrated to calibration of other HEVs easily.

Committee: Giorgio Rizzoni (Advisor); Marcello Canova (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

Increasing cost of fuel and global regulatory targets are driving the automotive industry towards fuel efficient vehicles. Hybrid electric vehicles (HEVs) can significantly improve the fuel economy by the application of an efficient control strategy. Additionally, the look-ahead information available from advanced driver assistance systems and cloud applications in a connected and automated vehicle can make the powertrain more predictive in nature. This would enable the implementation of a global optimization algorithm such as Dynamic Programming (DP). In this thesis, DP is implemented to co-optimize the vehicle velocity and energy management of a 48V mild-HEV over real world driving scenarios. Velocity optimization is performed by considering the look-ahead route characteristics such as the speed limit constraints along with the position of traffic lights and stop signs. To enable close to real-time implementation of DP, efforts have been put to alleviate the well-known "Curse of Dimensionality." A variable step size strategy is adopted instead of a constant step size. Furthermore, this thesis aims at building the Rollout Algorithm using Approximate Dynamic Programming for the 48V optimal control problem. This algorithm yields a look-ahead suboptimal control policy and under certain conditions, the sub-optimality can be minimized which is shown in this thesis. To compare the benefits obtained from the rollout, an experimentally validated driver model is developed which serves as the baseline for this project.

Committee: Marcello Canova (Advisor); Giorgio Rizzoni (Committee Member); Punit Tulpule (Committee Member) Subjects: Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2018, Aero/Astro Engineering

Unmanned vehicles research and application has become a major industry that is at the forefront of innovation. Commercial and hobbyist users alike have begun to harness the ability for these systems to be used in a range of applications such as, environmental monitoring, search and rescue, and more recently package delivery. However, these systems are limited in their ability to complete these missions as they typically require prolonged operational times that are currently infeasible. Instead of taking a more traditional approach of optimizing each component, which has its own limitations, a more novel approach is extended in this work, incorporating solar harvesting capabilities into the unmanned vehicles. When taking this approach, understanding the mission and environment is fundamental for successful mission planning and operation. This manuscript explores multiple mission planning problems, such as information gathering and persistent traveling vehicle problem with unmanned ground vehicles. In each of these problems, the mission, hardware, and environmental constraints are modeled as conventional optimal controls problems and heuristic methodologies are presented that can handle the nonlinearity and discontinuities of the problems. While the proposed methodologies can effectively handle the individual problems, in order to develop more reliable, effective and efficient outdoor, solar-robotics a new real-time mission planning framework is presented to handle the difficulty of environmental analysis and efficient path planning of nonlinear problems. The resulting simulations and experimental tests of all the developed methods are presented and discussed.

Committee: Dai Ran Dr. (Advisor); Mrinal Kumar Dr. (Committee Member); David Hoelzle Dr. (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering; Experiments; Robotics

PHD, Kent State University, 2018, College of Arts and Sciences / Department of Mathematical Sciences

The solution of the linear system of algebraic equations that arise from the finite element discretization of the advection-diffusion equation is considered here. In this dissertation, we study three hybrid Schwarz domain decomposition algorithms to solve this non-symmetric problem. We use the GMRES and BiCGStab methods to solve the resulting preconditioned system. In each iteration step, we solve a coarse finite element problem and a number of local problems depending on the algorithm. Local problems are solved in non-overlapping subdomains and ring-shaped overlapping subdomains into which the original domain is subdivided. These three algorithms combine the advantages of additive and multiplicative methods. We show that these algorithms are scalable in the sense that the rate of convergence is independent of the mesh size and the number of subdomains. The performance of these algorithms in two dimensions is illustrated by numerical experiments.

Committee: Jing Li (Advisor); Lothar Reichel (Committee Member); Chuck Gartland (Committee Member); Arden Ruttan (Committee Member); Ye Zhao (Committee Member) Subjects: Applied Mathematics

MS, University of Cincinnati, 2016, Engineering and Applied Science: Electrical Engineering

Image segmentation is a core component of image processing. It is a very important part of image understanding. It is the technique of partitioning an image into similar components. The similarity can be based on color, texture, edges or any other user defined quality. The objective of this thesis is to develop a framework for segmenting, quantifying and understanding complex structures. These complex structures do not have clear, closed boundaries which make them difficult to segment. We work with complex structures of the mouse lung images. The first step is to approximately delineate the structures by using an edge detection technique. This information is used in the next step to segment the structure. The segmentation technique used here is a hybrid technique which uses an augmented version of the original region growing algorithm.

Committee: Anca Ralescu Ph.D. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Dan Ralescu Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2015, Mechanical Engineering

In today's world, automotive manufacturers face the difficult challenge of building vehicles that are capable of meeting the increasingly stringent fuel economy and emissions standards, while also maintaining the performance and drive quality that consumers have come to expect. The automotive industry's response to this has been to make increasingly advanced vehicles that require more complex control systems, often resulting in longer development times and higher costs. One way to help reduce the development time and cost associated with these advanced vehicles is to use a model-based design approach. This approach allows engineers to design more of the vehicle's control system in a virtual environment, before hardware is available to test the control software. While model-based design techniques have helped reduce the amount of development time and cost that is needed to design the control system for a vehicle, these model-based techniques may not fully account for a vehicle's drive quality characteristics. Many of the energy management optimal control algorithms for hybrid vehicles designed in virtual environments today are capable of achieving high fuel economy numbers, but may result in poor drive quality characteristics when implemented on a vehicle. Therefore, a new methodology is needed to account for a vehicle's drive quality during the initial stages of a vehicle's control development. The research presented here describes a new methodology where drive quality metrics are added to the optimal control algorithm's cost function, in order to allow the algorithm to find a good balance between fuel economy and drive quality. Although some research has been previously published in this area, the majority of research does not specifically link the criteria used to improve drive quality to the physical behavior of the vehicle. Other research solves the optimal energy management problem to minimize fuel consumption, but then filters the results to prevent dri (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Shawn Midlam-Mohler (Committee Member); Wei Zhang (Committee Member); Manoj Srinivasan (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy in Engineering, University of Toledo, 2012, College of Engineering

The probabilistic reliability evaluation of power systems is a complex and highly dimensional problem that often requires a large amount of computational resources, particularly processing power and time. The complexity of this problem is only increasing with the advent of the smart grid and its accompanying technologies, such as plug-in hybrid electric vehicles (PHEVs). Such technologies, while they add convenience, intelligence, and reduce environmental impacts, also add dynamic and stochastic loads that challenge the current reliability and security of the power grid. One method that is often used to evaluate the reliability of power systems is Monte Carlo simulation (MCS). As the complexity and dimensionality of a power system grows, MCS requires more and more resources leading to longer computational times. Multiple methods have previously been developed that aid in reducing the computational resources necessary for MCS in order to achieve a more efficient and timely convergence while continuing to accurately assess the reliability of a given system. Examples include analytical state space decomposition, population based metaheuristic algorithms (PBMs), and the use of high performance computing (HPC). In order to address these issues, this dissertation is focused on improving the performance of algorithms used to examine the level of reliability in composite power systems through the use of computational intelligence (CI) and HPC, while also investigating the impact of PHEVs on the power grid at the composite and distribution levels. Contributions include the development and exploration of 3 variations of a new, hybrid algorithm called intelligent state space pruning (ISSP) that combines PBMs with non-sequential MCS in order to intelligently decompose, or prune, a given state space and improve computational efficiency, an evaluation of the use of latin hypercube sampling and low discrepancy sequences in place of MCS, the use of serial and parallel support vecto (open full item for complete abstract)

Committee: Lingfeng Wang Ph.D. (Committee Chair); Mansoor Alam Ph.D. (Committee Co-Chair); Jackson Carvalho Ph.D. (Committee Member); Vijay Devabhaktuni Ph.D. (Committee Member); Mohsin Jamali Ph.D. (Committee Member); Weiqing Sun Ph.D. (Committee Member) Subjects: Artificial Intelligence; Computer Science; Electrical Engineering

Master of Science, The Ohio State University, 2012, Mechanical Engineering

Increasingly stringent government regulations and the rising price of oil are causing automotive manufactures to develop vehicles capable of obtaining higher fuel economies and lower emissions. To achieve these goals, automotive manufactures have been developing hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV) that use both electricity and petroleum based fuels as their power sources. The additional power the vehicle receives from the high voltage batteries and the electric machines allow automotive manufacturers to downsize the engine inside of the vehicle. Vehicles with smaller engines are able to obtain a higher overall fuel economy because the smaller engine is able to operate at its more efficient high load operating points more frequently. The addition of a high voltage battery pack and at least one electric machine to a vehicle's conventional powertrain significantly increases the complexity of optimizing the operation of the vehicle's powertrain components. In a hybrid vehicle, the driver's power demand from the accelerator pedal can be met by the engine, the electric machines or a combination of the two. Therefore the vehicle needs a sophisticated control strategy that can divide the driver's power demand between the different torque producing powertrain components as efficiently as possible. The process of designing an optimal control strategy for a vehicle can require a significant amount of time, money and in-vehicle testing. Therefore many automotive manufacturers use Software-in-the-Loop (SIL) simulation to both speed up and reduce the cost of developing a vehicle's control strategy. Software-in-the-Loop simulation allows multiple versions of a control strategy to be tested in a virtual environment, in order to find the control strategy version most likely to increase the vehicle's fuel economy. The best version of the control strategy from the SIL simulations can then be tested later on the vehicle. The work described in this (open full item for complete abstract)

Committee: Dr. Giorgio Rizzoni (Advisor); Dr. Shawn Midlam-Mohler (Advisor); Dr. Yann Guezennec (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2011, Mechanical Engineering

Battery lifespan is one of the largest considerations when designing battery packs for electrified vehicles. Even during vehicle operation, it is essential to monitor the progression of a battery health as it degrades and predict battery life. This thesis presents a preliminary severity factor analysis based on available experimental data and details the development of an algorithm for predicting, while in operation, the remaining life of a battery based on the growth of internal resistance. Nine lithium ion batteries were systematically aged through severe aging protocols spanning multiple C-rates (2C, 4C and 8C), low ranges of SOC (0-10, 0-20 and 0-30%), and elevated temperature (55 deg C). Their internal resistance was continuously calculated at each sharp current transition, and these values were filtered and processed. Severity factors were calculated for each battery by determining the average rate of resistance growth over a battery life and a preliminary analysis of these factors was carried out. A resistance growth dynamic model was developed to identify rates of resistance growth on a local basis as resistance values were collected. These local rates of resistance growth were then used to calculate predicted future rates of resistance growth, which were in turn used to predict remaining life. The life prediction algorithm produced continuously updated predictions of remaining battery life that proved relatively accurate for cases of constant battery aging conditions. This computationally simple algorithm could be implemented onboard an electrified vehicle to provide estimates of remaining battery life based on resistance growth. This methodology can in principle be readily extended to track capacity degradation as well, provided that a feasible capacity estimator can be developed on the basis of vehicle measurements.

Committee: Yann Guezennec (Advisor); Giorgio Rizzoni (Committee Member); Simona Onori (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 1996, Industrial and Manufacturing Systems Engineering (Engineering)

A Hybrid algorithm to solve the traveling-salesman problem using operations research heuristics and artificial neural networks

Committee: Luis Rabelo (Advisor) Subjects: Engineering, Industrial