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, The Ohio State University, 2016, Industrial and Systems Engineering

Plug-in electric vehicle (PEV) market is growing. This increases the load on the electricity market and the need to maximize the utilization of renewable energy sources such as wind. This study analyzes the effects of wind uncertainty on PEV charging under varied levels of flexibility in charging, and thus on the overall electricity generation costs by modeling generation costs as a mixed-integer linear program, formulated as unit commitment problem with varied constraints to allow for flexibility in charging of PEV. It is seen that flexibility in charging of PEV accommodates for wind uncertainty and can utilize wind energy better, thus reducing generation costs.

Committee: Ramteen Sioshansi (Advisor); Antonio J. Conejo (Committee Member) Subjects: Energy; Industrial Engineering; Operations Research

Master of Science in Engineering, University of Akron, 2015, Electrical Engineering

The modeling, simulation and implementation of a range extender for an existing truck are presented in this document. The objective of this thesis is to re-engineer an existing electric truck into a series hybrid electric vehicle through a range extender. A LiFePO4 (Li-Ion) battery pack powered electric vehicle is used as a platform to implement a range extender using an advanced control strategy. A range extended electric vehicle has been simulated using series hybrid electric vehicle architecture to size the range extender by studying the behavior of the system under different drive cycles. To determine the size of the range extender, a specific drive cycle in which the vehicle is considered to be cruising at 65 Mph was selected to study the operation of the range extended electric vehicle. By analyzing the results of the simulations it has been concluded that a 30 kW engine and generator set is an appropriate size of the range extender to design a range extended electric vehicle. The range extender was designed, simulated and tested at a bench before it was implemented on a vehicle. A 30 kW range extender was developed by mechanically coupling a 40 hp V-twin horizontal shaft gasoline engine with a 30 kW permanent magnet generator from one of the electrical machines in the transmission of 2004 Toyota Prius. A range extended electric vehicle control algorithm was developed to control the operation of the engine and generator set relative to the state of charge (SOC) of the battery pack. The main objective of the developed algorithm is to maintain the SOC of the battery pack between a certain limits predefined by the programmer. It was determined that by maintaining the iii SOC of the battery pack in between 60% to 80% the targeted distance of 100 miles was achieved with 2 gallons of the gasoline. A novel power converter was developed to convert three phase AC output of the generator into an appropriate DC voltage to charge the battery pack. (open full item for complete abstract)

Committee: Yilmaz Sozer Dr (Advisor); Malik Elbuluk Dr. (Committee Member); Tom Hartley Dr. (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Engineering

Master of Science, The Ohio State University, 2011, Electrical and Computer Engineering

Technological systems today require increasingly robust and precise electronic controls to deliver improved safety, reliability, and performance while also delivering more numerous and diverse results faster than ever. Additionally, automotive technology is changing rapidly to meet increasingly stringent emissions and fuel economy government regulations while adding new consumer features and capabilities to interface with portable devices such as smart phones and music players. Automotive technology includes advanced vehicle technologies ranging from powerful electric machines to fuel cell systems and battery technologies. In this thesis, modern tools and methods such as hardware in the loop (HIL) simulation, rapid prototyping embedded control systems, and auto code generation are applied to a prototype vehicle design and the results and benefits are discussed. Hardware in the loop simulation is presented as a powerful tool for control validation because it can be applied to vehicle designs independent of vehicle availability, and the hardware and software tested using the process can be scalable and adaptive to relevant problems throughout the design process. Automotive manufacturers such as General Motors and Ford have been showing increased interest in HIL simulation and its benefits for improving vehicle reliability, safety, and maintenance costs. Controller validation and failure simulation have become increasingly popular uses of HIL simulation. A much faster design cycle has been a side effect, drawing the attention of other industries. The work described in this thesis has been applied to the Ohio State EcoCAR vehicle during all three years of the EcoCAR competition. EcoCAR is a student competition among sixteen North American universities in which students design and build advanced powertrain vehicles based on a donated platform to compete on metrics of fuel economy, emissions, performance, and utility. The OSU team demonstrated a refined vehicle using seve (open full item for complete abstract)

Committee: Dr. Stephen Yurkovich (Advisor); Dr. Giorgio Rizzoni (Committee Member) Subjects: Energy; Engineering

Master of Science, The Ohio State University, 2024, Mechanical Engineering

EcoCAR EV Challenge is a four-year competition to redesign, integrate, test and refine an all-wheel drive (AWD) propulsion system in a Cadillac LYRIQ. The objective of this project is to improve the performance of the vehicle while maintaining the efficiency of the system along with added autonomous capabilities. This thesis shines light on the controls implementation of the torque distribution strategy to the two electric machines that constitute the AWD architecture. The controller architecture is defined and developed to meet the requirements set by the competition and the vehicle to interact seamlessly while implementing controller strategies to improve efficiency. Various methods to optimize the efficiency are discussed with simulations of expected system behavior and performance comparisons. Two optimization cost functions and their solutions are compared. Each solution is implemented and evaluated in simulation and vehicle testing for efficiency, performance, drive quality and system safety. The strategy with minimization for the losses of the electric machines is projected to improve the overall energy consumption by 12% with the use of the rear disconnect unit and reduce the rolling losses in highway cruising conditions.

Committee: Shawn Midlam-Mohler (Advisor); Giorgio Rizzoni (Committee Member) Subjects: Automotive Engineering

Master of Science, The Ohio State University, 2024, Mechanical Engineering

The United States has made a commitment to de-carbonization of the transportation sector. Around seventeen percent of all US greenhouse gas emissions come from light-duty passenger vehicles [1]. Electric vehicles (EVs) provide a promising pathway to achieving that goal. EVs are an emerging technology in the automotive sector and are now just being produced at the same scale as internal combustion engine vehicles. During this time, rapid innovation in the EV space is possible, and unique EV architectures and designs can be explored. Further, a standardized testing and development process is needed for vehicle models and controllers. This work presents an architecture selection process that explores a large range of unique EV architectures and tabulates all of the data. Next, from that architecture selection process, a physical vehicle is constructed, and a new drivetrain controller is developed. The processes used for requirements generation and management, model development, and controller testing are presented in this work. The presented processes are all done to an industry-level standard and present a future steppingstone for further exploration of the EV design space.

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

Doctor of Philosophy (PhD), Wright State University, 2024, Engineering PhD

Implantable leads used in pacemakers, defibrillators, and cardiac resynchronization therapy are designed for in-vivo applications, yet their longevity is inevitably shaped by the conditions within the human body. The mechanical behavior of these leads can be affected over time, necessitating the evaluation of their residual properties. Two main insulators, silicone, and polyurethane are commonly used for the outer insulation of cardiac leads. Understanding the long-term performance of these insulators is crucial for ensuring the reliability and safety of cardiac implantable devices. The research aims to assess the long-term mechanical properties and performance of implantable leads utilized in cardiovascular implantable electronic devices (CIEDs), which are subjected to the in-vivo environment with finite lifespans. Utilizing more than 300 samples obtained from the Wright State University Anatomical Gift Program. Tests were conducted according to ASTM standard D 1708-02a and ASTM Standard D 412-06a using the Test Resources Q series system. Electromagnetic interference (EMI) from electric vehicles on CIEDs, particularly Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) were quantified within a Tesla Model 3. SolidWorks and MIMICS 25.0 were used for three-dimensional heart modeling, and were developed with CIED leads inside the heart for finite element analysis. ANSYS Workbench 2022R1 was utilized for simulating cardiac leads behavior inside the heart with specific residual properties, and used computational simulations to predict lead performance. This research found silicone insulation to show some degradation in mechanical properties after 94 months of in-vivo environment, and polyurethane insulation demonstrated consistent performance without significant degradation after 108 months of in-vivo exposure. The proposed mechanical testing and FEM provide an insight into the durability and performance of different insulation materials, and how these materia (open full item for complete abstract)

Committee: Tarun Goswami D.Sc. (Advisor); Abdul Wase MBBS (Committee Member); Vic Middleton Ph.D. (Committee Member); Jaime E. Ramirez-Vick Ph.D. (Committee Member) Subjects: Biomedical Engineering

MS, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

We are witnessing a profound transformation within the automotive industry, propelled by the mass adoption of Electric Vehicles (EVs). As this transition unfolds at a rapid pace, it unveils security gaps within the emerging infrastructure, necessitating urgent attention and solutions. This thesis investigates the resilience to cybersecurity threats of a new standalone branch of EV charging application, which we propose terming "Fleet Charging Infrastructure" (FCI). We conducted a case study on one of the emerging leaders in EV fleet charging in the US market, Electrada. To start the study, we analyzed Electrada's operation, the structure of their charging infrastructure, and the importance of internal processes that guarantee a 99% uptime commitment to their customers. Our research questions focused on characterizing FCI, identifying cybersecurity threats specific to it, and devising strategies for enhancing its resilience against cyberattacks. The study utilized STRIDE and DREAD frameworks for threat modeling and prioritization, along with well-established industry frameworks like NIST IR8473 and CIS CSC for threat mitigation planning. As a result, we succeeded in outlining the key processes within FCI, enumerating major components and dataflows, and establishing distinctive features that later allowed us to identify 27 unique threats, propose mitigation actions for each threat, and develop a 3-step mitigation strategy plan for FCI operators based on their resource availability. Our findings highlight the distinctiveness of FCI as a standalone branch of EV charging applications, due to the uniqueness of internal processes, components, motivations, and goals compared to other iterations of EV Charging.

Committee: Isaac Kofi Nti Ph.D. (Committee Member); M. Murat Ozer Ph.D. (Committee Chair) Subjects: Information Technology

Master of Science in Engineering, University of Akron, 2023, Mechanical Engineering

With advancements in modern battery technology, electric vehicles (EVs) have become increasingly more prevalent on the road. While the technology is still evolving, it has become clear that EVs have numerous benefits, and some of which are performance oriented. One of these benefits is the ability to package electric motors that are directly connected to individual wheels or axles. With this, combined with the considerably reduced feedback loop of electric motors with respect to a combustion engine, it has become easier to implement advanced motor control systems, such as traction control (TC) and torque vectoring (TV). In this thesis, a MATLAB program to generate Milliken Moment Diagrams (MMDs) will be created using tire models and vehicle parameters, in which the cornering response of a given vehicle will be calculated given a set of input conditions. Various motor configurations will be simulated in these MMDs to compare the differences in vehicle behavior to demonstrate the potential benefits of torque vectoring. Several input conditions are also varied to explore the robustness of the models, in which a TV control “map” is created and applied to show how the model can be utilized as a tool to improve vehicle performance in coordination with testing.

Committee: Daniel Deckler (Advisor); Ajay Mahajan (Committee Member); Alper Buldum (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Engineering; Mechanical Engineering

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

PhD, University of Cincinnati, 2022, Engineering and Applied Science: Mechanical Engineering

Combined plant and control design (control co-design, or CCD) methods are generally used to address the synergistic coupling between the plant and control parts of a dynamic system. Recently, reliability-based design optimization (RBDO) principles have been used within CCD to address the design of stochastic dynamic systems. However, since the new reliability-based CCD (RBCCD) algorithms use all-at-once (AAO) formulations of CCD, only most-probable-point (MPP) methods can be used as a reliability analysis technique. This is a limitation as the use of such methods for highly-nonlinear RBCCD problems introduces solution error that could lead to system failure. A multidisciplinary feasible (MDF) formulation for RBCCD problems would eliminate this issue as the dynamic equality constraints would be satisfied through forward simulation. Since the RBCCD problem structure would be similar to traditional RBDO problems, any accurate reliability analysis method could be used. Therefore, in this work, a novel reliability-based MDF formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) has been proposed for RBCCD. To quantify the uncertainty propagation, an accurate reliability analysis method using generalized polynomial chaos (gPC) expansions has been proposed. The effectiveness of the RB-MDF-MDSDO formulation and the proposed reliability analysis method are established via two test problems. The performance of the gPC method relative to the current state of the art, MPP methods, is relatively unknown for RBCCD applications. Specifically, the only known information pertains to RBDO applications, where the gPC expansion method is generally known to be more accurate, but also computationally more expensive than the MPP methods. Therefore, to benchmark the performance of the gPC expansion method against MPP methods, the first-ever double-loop and single-loop MPP-based formulations of RB-MDF-MDSDO are developed, and their solution accuracy and e (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Sam Anand Ph.D. (Committee Member) Subjects: Engineering

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

Design and optimization of a dynamic system must account for synergy between plant and the associated controller to obtain a system-level optimum solution. If an active dynamic system is designed without considering the synergy between the plant design variables and the controller input based on the states, the optimizer might give sub-optimal results. This synergy between the plant and control is considered in combined plant and control design optimization which is also known as control co-design optimization. Currently control co-design has been applied to active dynamic systems such as hybrid electric vehicles and active suspension systems. Broadly, there are two classes of optimization algorithms, gradient-based and derivative-free methods, that can be used to solve any co-design problem. Most co-design problems are currently solved using derivative-based methods. These methods are best at giving an optimum solution when the problem is convex. However, multiple local optimum solutions are possible when the problem is non-convex. This might obscure the chances of achieving a global optimum solution. Derivative-based methods can be used to solve such problems by using a multi-start approach. This approach could be cumbersome though since it requires the initialization of plant and control design variables which spans the entire design space and thereby would be an arduous task. A more practical approach at doing a global search would be to use a derivative-free method to solve such problems. Although many derivative-free methods exist, Mesh Adaptive Direct Search (MADS) has got proven convergence proofs and has a computational expense that makes the solution to non-convex codesign problems tractable while maintaining sufficient global search capability. This work examines the capability of MADS at performing a global search by way of an autonomous electric vehicle powertrain control co-design study. The results from solving the control co (open full item for complete abstract)

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); David Thompson (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Engineering

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

The increase in the usage of Unmanned Aerial Vehicles or UAVs for surveillance, aid and other purposes has compounded the detrimental impact of the aviation industry on the environment. To counter its increasing contribution to the climate crisis, the industry needs timely energy efficient solutions. Combined optimal design and control study or co-design aims to lower the energy consumption of UAVs through various propulsion systems, one of them being a power-split hybrid model. However, this approach is limited in its consideration of uncertain losses or changes in these systems. In this thesis, we address these uncertain parameters of a Group 5 UAV using a power-split Hybrid Electric Propulsion System (HEPS) architecture. We will be investigating several random variations which such an aircraft could encounter during its flight. The thesis explores the outcome of the application of a stochastic dynamic optimization technique, called Robust-Multidisciplinary Dynamic System Design Optimization (R-MDSDO), to the power split HEPS architecture. This helps to ascertain the optimal value of design, state trajectories and control trajectories to minimize the energy utilized by the Group 5 UAV. The end result, which is achieved through a comparison of the robust and deterministic solution, indicates that accounting for system uncertainties has a significant impact on the power-split HEPS design.

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Manish Kumar (Committee Member); Ahmed Elgafy Ph.D. (Committee Member) Subjects: Aerospace Materials

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

A major emphasis within the automotive industry today is autonomous driving. Many recent studies in this area deal with the development of real-time optimal control strategies to improve overall vehicle energy efficiency. Although such research is critically important, it overlooks the potential need to reevaluate the design of an autonomous vehicle itself, especially as it relates to the powertrain. Failing to thoroughly examine the impact of autonomous driving on vehicle powertrain design could limit the potential opportunities to augment the energy-efficiency gains from optimal powertrain control (power demand) strategies. Therefore, this thesis addresses this situation by investigating the impact of autonomous driving on the design (sizing) and control strategies (energy management + power demand) of a plug-in hybrid-electric vehicle (PHEV) powertrain. In particular, a dynamic optimization method known as multidisciplinary dynamic system design optimization (MDSDO) is used to formulate and solve a combined optimal design and control optimization (or control co-design) problem for an autonomously-driven PHEV powertrain under two simulation conditions: in the first, only an autonomous driving cycle represented by a hypothetical lead (HL) duty cycle is considered, whereas the second also includes acceleration and all-electric range (AER) performance along with the HL duty cycle in order to generate an overall powertrain design solution. The optimal solutions for both simulation conditions are then compared to those corresponding to a control co-design problem for a human-driven PHEV powertrain, with the results indicating that autonomous driving does indeed have a significant impact on both powertrain design and control. Therefore, this implies a compelling need to reevaluate current powertrain design conventions when developing autonomous vehicles.

Committee: Michael Alexander-Ramos Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Engineering

Master of Science, The Ohio State University, 2021, Electrical and Computer Engineering

Advancements in semiconductor technology present new challenges in electric machine construction, operation, and control. Silicon carbide (SiC)-based power electronics are becoming the new standard for high-power consumer and commercial devices, and are implemented in technologies such as power inverters, converters and rectifiers. This paper focuses on the effects of inverter drives for traction motors in electric vehicles with high dV/dt rates on bar-wound machine windings, including the expected impacts on insulation materials under prolonged periods of high voltage stress. Partial discharge inception voltage testing was performed to evaluate the voltage bus level at which breakdown will start to occur. A simulation model was constructed using finite element analysis, the results of which were validated with experimental results using a commercially available SiC inverter and traction motor. Correlation has been established between the preliminary simulation results and experimental data. It is proven that as DC bus voltages increase with the capabilities of SiC devices, the voltage stresses inside the stator windings approach levels which could cause partial discharge and premature insulation degradation in existing stator designs.

Committee: Julia Zhang (Advisor); Jin Wang (Committee Member) Subjects: Alternative Energy; Design; Electrical Engineering; Electromagnetics; Electromagnetism; Energy; Engineering; Solid State Physics; Sustainability; Transportation

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

Volatile oil prices, rapidly increasing air travel demand, growing concern for health and climate impacts of emissions, and limited performance of existing aircraft technologies have generated substantial interest in distributed propulsion and hybridization, even without significant government regulation. This work details the development of the Map-based Aircraft Propulsion Simulator (tMAPS), a modular framework of reduced-order models of aircraft propulsion subsystems, and its application in optimizing the size and control of a battery energy storage system (BESS) in a hybrid-electric, distributed-propulsion (HEDP) regional jet aircraft. tMAPS is validated against the NPSS-based Georgia Tech Hybrid Electric Aircraft Test-bed. The supervisory energy management strategy is formulated into a discrete-time optimal control problem and solved via dynamic programming. Both state-of-the-art and future battery technologies are evaluated, with energy density ranging from 230-400 Wh/kg and power density ranging from 350-1200 W/kg. The optimal energy management strategy is evaluated as a function of BESS size, cell chemistry, and mission range to deduce system-level implications. The performance of HEDP is compared to a turbo-electric, distributed-propulsion (TEDP) aircraft that assumes improvements in weight, drag, and engine efficiency consistent with a regional jet entering operation in 2035.

Committee: Marcello Canova Ph.D. (Advisor); Yann Guezennec Ph.D. (Committee Member); Giorgio Rizzoni Ph.D. (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering; Energy; Engineering; Mechanical Engineering; Technology; Transportation

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

The increasing penetration of renewable sources of energy has resulted in an increased likelihood of power over-generation and ramp rate requirements at the electricity supplier end. By incorporating temporally varying costs of electricity provided to the customer, the grid supplier may choose to offer demand-response programs that encourage the customer to defer high load activities to periods of low grid load, effectively overcoming these challenges and increasing machine life. Smart homes optimally activate appliances at the appropriate time with an objective to minimize load at high-price periods, so that at the user end, the total electricity price is lowered. The work presented in this thesis focuses first on the development of models for energy demand and generation associated with electric vehicle (EV) charging and solar power generation, and their integration in an existing residential energy modeling framework. For this enhanced residential power demand model, machine learning (ML) techniques are used to develop a prediction of the user activities for single-resident and multi-resident households. The predicted power demand can be integrated into the smart home algorithm to enhance the optimal activation of appliances to minimize electricity cost and inconvenience.

Committee: Stephanie Stockar (Advisor); Manoj Srinivasan (Committee Member) Subjects: Alternative Energy; Artificial Intelligence; Energy; Engineering; Mechanical Engineering

Master of Sciences (Engineering), Case Western Reserve University, 2020, EMC - Mechanical Engineering

In this thesis, copper indium gallium selenide (CIGS) solar cells were studied for the manufacturing processes, material consumed, energy consumed and carbon dioxide emissions. Throughout the processes of forming, cleaning, sputtering, coevaporation and chemical bath deposition, it is found that manufacturing a square meter of an entire CIGS panel, with a weight of 6.27 kg, consumes 157 kg of water, 5.95 kg of methanol, 2.97 kg of acetone, and 0.453 kg of panel layers' material and the reactants needed during deposition, consumes 139 kWh of energy, and generates 130 lbs of carbon dioxide emissions. The application of the CIGS solar cells on a solar-powered commuter car operating in Cleveland, Los Angeles and Phoenix as three representative locations was modeled and analyzed. The actual solar irradiance in Cleveland, Los Angeles, and Phoenix was considered in the analysis to calculate the solar power availability, daily distance capacity, total charging time, and the viability for commutes. Numerical analysis focusing on the solar panels rather than the whole vehicle showed that the energy payback time of the panels ranges from 113 to 183 days across the three locations. Compared with grid-based electric vehicles, the carbon dioxide payback time is 279 to 361 days. The carbon dioxide emissions from the CIGS solar panels is approximately 3.96 g/mile, in comparison with the grid ranging 40 to 52 g/mile. This study shows that employing solar panels to power electric vehicles has great potential in reducing the carbon footprint of electric vehicles.

Committee: Chris Yuan (Advisor) Subjects: Alternative Energy; Energy; Engineering; Mechanical Engineering; Transportation

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

Today's world faces numerous environmental challenges as we attempt to meet the growing demands for mobility while tackling its negative externalities. In recognition of these negative externalities, world governments have enacted increasingly stricter standards that the means of mobility must meet. To meet these demands, private and public industries have invested tremendous resources into alternative means of mobility. The most promising step to immediately reduce mobility's negative externalities is the use of hybrid technologies. Hybrid technologies utilizes traditional petrochemical energy sources combined with electrical sources to power mobility. If properly controlled, this allows for a reduction in energy consumption and pollutant production in vehicles. The challenge for mobility engineers is how to properly control these multi-domain systems to reduce negative externalities. Extensive research in the field of optimal control applied to hybrid vehicles has already shown that fuel consumption can be minimized within a charge sustaining hybrid through optimized torque splitting. Furthermore, research into pollutant production and control has greatly reduced air pollution from vehicles. Both fields have already penetrated the consumer market and helped form control strategies that are already on the road. However, the increasing demands placed by regulations require constantly pushing the bounds for the extra reduction in fuel consumption or pollutant production. As such, this research develops a methodology that can be applied to HEVs to establish a controls strategy for the simultaneous reduction of fuel consumption and pollutant production. This work relies on model-based techniques to simulate vehicle operation, and optimal control techniques to use the developed vehicle models to establish control policies to reduce fuel consumption and pollution production. This work goes through the development of a catalyst and emissions model that predicts tailpipe (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Shawn Midlam-Mohler (Advisor); Ahmet Selamet (Committee Member); Vadim Utkin (Committee Member); Punit Tulpule (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, Case Western Reserve University, 0, EMC - Mechanical Engineering

The transportation sector is one of the largest contributors to global Greenhouse Gas (GHG) emissions, takes 23% of total energy-related carbon emissions globally. More than 53% of primary oil is consumed in the transportation sector to meet 94% of energy needs (93 exajoule). Furthermore, from the perspective of transportation-related goods and services, 8.9% of the total U.S. Gross Domestic Product (GDP) in 2016, is responsible for the transportation sector. In order to handle these issues, electric vehicles (EVs) are widely promoted as clean alternatives to conventional vehicles for reducing fossil fuel consumption, GHG emissions and improving energy efficiency from ground transportation. However, there are remaining concerns about the actual techno-economic impacts of EVs due to the complexity in vehicular operation conditions and battery degradation processes. Vehicular operation conditions, such as travel demand and ambient temperature, can substantially affect the vehicle power need within the operation process and further influence their energy consumption and GHG emissions. Meanwhile, the battery within EV undergoes a complex degradation process that is highly sensitive to vehicular operations condition, and the effects of battery degradation on EV techno-economic performance are yet understudied. Therefore, this dissertation developed a systematic life cycle framework incorporating the mathematic physical-based battery degradation model to address the context-dependent electric vehicle techno-economic performance assessment problems, including both the vehicle and battery pack operating processes. The objective of this dissertation is to provide a robust analytical approach for supporting policymaking in prioritizing EV deployment to achieve both the environmental and economic goals. These findings help understanding of the spatiotemporal application of EV automotive and battery technologies from both the economic and techno perspective. Furthermore, the re (open full item for complete abstract)

Committee: Chris Yuan (Committee Chair); Fumiaki Takahashi (Committee Member); Sunniva Collins (Committee Member); Li Yue (Committee Member); Heo YeongAe (Committee Member) Subjects: Mechanical Engineering