Doctor of Philosophy, University of Akron, 2016, Electrical Engineering

The prospective spread of Electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) arises the need for fast charging rates. Higher charging rates requirements lead to high power demands, which can't be always supported by the grid. Thus, the use of on-site sources alongside the electrical grid for EVs charging is a rising area of interest. In this dissertation, a photovoltaic (PV) source is used to support the high power EVs charging. However, the PV output power has an intermittent nature that is dependable on the weather conditions. Thus, battery storage are combined with the PV in a grid tied system, providing a steady source for on-site EVs use in a renewable energy based fast charging station. Verily, renewable energy based fast charging stations should be cost effective, efficient, and reliable to increase the penetration of EVs in the automotive market. Thus, this Dissertation proposes a novel power flow management topology that aims on decreasing the running cost along with innovative hardware solutions and control structures for the developed architecture. The developed power flow management topology operates the hybrid system at the minimum operating cost while extending the battery lifetime. An optimization problem is formulated and two stages of optimization, i.e online and offline stages, are adopted to optimize the batteries state of charge (SOC) scheduling and continuously compensate for the forecasting errors. The proposed power flow management topology is validated and tested with two metering systems, i.e unified and dual metering systems. The results suggested that minimal power flow is anticipated from the battery storage to the grid in the dual metering system. Thus, the power electronic interfacing system is designed accordingly. Interconnecting bi-directional DC/DC converters are analyzed, and a cascaded buck boost (CBB) converter is chosen and tested under 80 kW power flow rates. The need to perform power factor correction (PF (open full item for complete abstract)

Committee: Yilmaz Sozer Dr. (Committee Chair); Malik Elbuluk Dr. (Committee Member); Seungdeog Choi Dr. (Committee Member); Ping Yi Dr. (Committee Member); Kevin Kreider Dr. (Committee Member) Subjects: Electrical Engineering; Energy

Master of Science, The Ohio State University, 2008, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science in Cyber Security (M.S.C.S.), Wright State University, 2023, Computer Science

The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three attack types, Denial of Service, Machine-in-the-Middle, and Log4shell.

Committee: Junjie Zhang Ph.D. (Advisor); Krishnaprasad Thriunarayan Prasad Ph.D. (Committee Member); Bin Wang Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

Master of Science in Mechanical Engineering, Cleveland State University, 2021, Washkewicz College of Engineering

Recent developments in aircraft propulsion electrification are motivated by economic and environmental factors such as lowering greenhouse gas emissions, reducing noise, and increasing fuel efficiency. This thesis focuses on a hybrid gas-electric propulsion concept combining a gas turbine jet engine with an electromechanical (EM) system. An optimal control system allows energy to be recovered from the gas turbine engine or injected into it from an electric storage unit. Energy extraction or injection can be obtained by selecting a performance weight in the optimization function that trades off fuel consumption with stored electrical energy utilization. The goal of this research is to validate the effectiveness and plausibility of the optimal controller during representative acceleration and deceleration maneuvers and at steady state. To accomplish this, the gas turbine engine dynamics are simulated using NASA's T-MATS package and used in a hardware co-simulation approach along with physical hardware representative of the EM system, namely motors, power converter, and an energy storage device. A time scaling methodology was used to reconcile the power levels of the physical EM system (in the order of a kilowatt) with those of the engine simulation (in the order of megawatts). Multiple steady state missions were represented within a full simulation environment and in the lab test environment that covered a wide range of fuel-electric optimization weights. In addition, a chop-burst study was conducted to ensure the readiness of the system to handle flight missions. Based upon captured data, specifically that of shaft torque, supercapacitor voltage, and fuel flow measurements, it was determined that the optimal control objective was met. An increase in fuel-electric optimization weight corresponded to a desired change in torque to the engine and voltage to the energy storage device.

Committee: Hanz Richter (Advisor); Jerzy Sawicki (Committee Member); Lili Dong (Committee Member) Subjects: Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Mechanical Engineering

Vehicle safety is one of the critical elements of modern automobile development. With increasing automation and complexity in safety-related electrical/electronic (E/E) systems, and given the functional safety standards adopted by the automotive industry, the evolution and introduction of electrified and automated vehicles had dramatically increased the need to guarantee unprecedented levels of safety and security in the automotive industry. The automotive industry has broadly and voluntarily adopted the functional safety standard ISO 26262 to address functional safety problems in the vehicle development process. A V-cycle software development process is a core element of this standard to ensure functional safety. This dissertation develops a model-based diagnostic methodology that is inspired by the ISO-26262 V-cycle to meet automotive functional safety requirements. Specifically, in the first phase, system requirements for diagnosis are determined by Hazard Analysis and Risk Assessment (HARA) and Failure Modes and Effect Analysis (FMEA). Following the development of system requirements, the second phase of the process is dedicated to modeling the physical subsystem and its fault modes. The implementation of these models using advanced simulation tools (MATLAB/Simulink and CarSim in this dissertation) permits quantification of the fault effects on system safety and performance. The next phase is dedicated to understanding the diagnosability of the system (given a sensor set), or the selection of a suitable sensor set to achieve the desired degree of diagnosability, using a graph-theoretic method known as structural analysis. By representing a system in directed-graph or incidence-matrix form, structural analysis allows the determination of analytical redundancy in the system and of the detectability and isolability of individual faults. Further, it provides a logical computation sequence for solving for system unknowns, by identifying analytical redundant relat (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Manoj Srinivasan (Committee Member); Ran Dai (Committee Member); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 2007, Mechanical Engineering (Engineering)

Methodology of and details on designing, constructing, and testing an efficient low power electric vehicle for alternative energy testing purposes. Experimental analysis of the drive motor operating temperature to determine feasibility of a thermal management system to preheat ammonia for improved efficiency of an electro-chemical reformer. Description of steps taken in preparation for the eventual inclusion of a hydrogen fuel cell and ammonia electrochemical reformer.

Committee: Gregory Kremer (Advisor) Subjects: Engineering, Mechanical

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2006, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2007, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2007, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2024, Environment and Natural Resources

Passenger vehicles are a leading contributor to the United States' (U.S.) greenhouse gas emissions. Electrifying the U.S. vehicle fleet is a key strategy to reduce the magnitude of emissions and reduce environmental damage; however, electric vehicle (EV) adoption remains low, particularly among women. In the U.S., women own about half of all vehicles (McElroy, 2023) but less than 1/3rd of EVs (Naughton, 2023). This study sheds light on why women, traditionally strong adopters of pro-environmental behaviors, are buying less EVs than men. Study 1 shows that relative to men, women have lower EV purchasing intention. Women evaluated EVs as being less safe and as having less instrumental and affective value, and these factors mediated the relationship between gender and purchasing intention. Women also reported that they would feel greater feelings of range anxiety and concern for their personal safety while using a public charging station. I theorize that these feelings may relate in part to concern about one's children, which may be more salient for women, and explored this through Study 2. My manipulation was unsuccessful in getting women to think solely about themselves in the context of a hypothetical vehicle purchase, but these results did show that men and women do not differ in their willingness to purchase an EV as a family vehicle relative to a personal vehicle, though overall, women are less willing to purchase an EV relative to men. Finally, Study 3 demonstrated that in general, people are equally willing to recommend an EV for a man or a woman but are less willing to recommend an EV to a person with children; however, a three-way contrast showed that women (but not men) participants were less willing to recommend an EV for a woman with children relative to a man with children. The effect of presence of children on willingness to recommend was partially mediated by perceptions of safety and instrumental value, such that safety and instrumental value positivel (open full item for complete abstract)

Committee: Nicole Sintov (Advisor); Jacquet Jeffrey (Committee Member); Brooks Jeremy (Committee Member) Subjects: Environmental Studies; Psychology

MDES, University of Cincinnati, 2022, Design, Architecture, Art and Planning: Design

The proper development of an electric vehicle charging infrastructure will become paramount to how quickly the United States transitions from Internal Combustion Engine (ICE) vehicles to Electric Vehicles (EVs). The research on Next Generation Charging Infrastructure highlights both a methodological and theoretical approach to that infrastructure problem, which in years to come will only become more acute as the percentage of registered EVs starts to grow. With Geographic Information Systems (GIS), this research identifies sites within the City of Cincinnati, that can be further developed into future charging points. This initial methodology can be applied to other cities that meet the same or better qualifications and become the backbone for designing a template that exists in a much larger framework for the development of EV infrastructure. The second methodology in this work highlights primary research on user preferences that was conducted through surveys. The primary research is used to develop user values and design principles, and identify the greatest user needs. While this thesis focuses on the City of Cincinnati as a case study, the combined research and methodologies can help guide the infrastructure architecture in any city or region.

Committee: David Edelman Ph.D. (Committee Member); Yong-Gyun Ghim M.Des. M.S. (Committee Member) Subjects: 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, 2020, Mechanical Engineering

The air we breathe is getting dangerously polluted with passenger vehicles and heavy-duty vehicles being one of the major sources of this pollution, producing significant amounts of nitrogen oxides, carbon monoxide, and other harmful gases. The U.S. Environmental Protection Agency (EPA) has laid stringent rules and aggressive policies to curb this pollution. Hybrid Electric Vehicles (HEV) and Electric Vehicles (EV) are a promising option considering their efficient operation and reduced emissions. These technologies are being developed at a rapid pace and can occupy a significant place in the automotive market. Companies are investing heavily to enhance the skills of future generation of engineers to develop these technologies through student competitions and workshops. EcoCAR Mobility Challenge (ECMC), a four-year Advanced Vehicle Technology Competition (AVTC) is one-way companies are pursuing this challenge. ECMC challenges teams to apply advanced propulsion systems, as well as connected and automated vehicle technology to improve the energy efficiency, safety, and consumer appeal of a 2019 Chevrolet Blazer – specifically for the carsharing market. The work described in this thesis focuses on the Model Based design approach adopted for the vehicle plant model and controls development during years one and two of the competition. The process includes the vehicle architecture selection process, component and soft ECU model development and finally describes the framework developed for testing of the control algorithm using an example of a fault scenario.

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

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, 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 in Materials Science and Engineering (MSMSE), Wright State University, 2018, Materials Science and Engineering

Demands for electric vehicles and flexible electronics have escalated research in developing high-performance lithium batteries based on solid-state chemistry. The present work is to develop highly-conductive and flexible solid electrolyte for such applications. Lithium aluminum titanate phosphate (LATP or Li1.3Al0.3Ti1.7(PO4)3), both in ceramic pellets and free-standing composite membranes, have been fabricated. The crystal structure, surface morphology, and ionic conductivity are systematically studied. LATP pellets are prepared using solid state reaction approach. The results indicate that calcine temperature has significant impacts on the phase impurity and sintering temperature and duration have more impacts on the grain size and porosity of LATP pellets. At the optimal conditions, the highest bulk conductivity of LATP electrolyte reaches 1.5*10-3 S/cm at room temperature with an activation energy of 0.206 eV. The as-prepared LATP has high conductivities comparable with liquid electrolytes, which is feasible for applications to all-solid-state lithium batteries. Ceramic electrolyte can be composited with polymer electrolyte to enable flexible battery design. In this study, LATP-based electrolyte membranes are fabricated in composite with a lithiated polymer, i.e. polyvinylidene fluoride (PVDF) dissolved with lithium perchlorate (LiClO4), via the casting method. It is found curing temperature has influences on ionic conductivities of the composite membrane and high casting temperature can cause the decomposition of PVDF. Appropriate LATP composition can increase the ionic conductivity, mechanical strength while maintaining the flexibility of the composite membrane. Raman spectroscopic analysis suggests there exists certain interactions among the three components in the composite membrane.

Committee: Hong Huang Ph.D. (Advisor); Michael Rottmayer Ph.D. (Committee Member); Amir A. Farajian Ph.D. (Committee Member) Subjects: Materials Science; Mechanical Engineering; Metallurgy; Polymers