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

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, University of Akron, 2007, Electrical Engineering

The results of implementing a series-parallel control strategy for a heavily-hybridized parallel hybrid-electric vehicle are investigated. Simulation was used to estimate the effects of changing control strategy parameters on fuel economy, drive quality and tail-pipe emissions. A Simulink model of a heavily modified 2005 Chevrolet Equinox test vehicle equipped with a diesel internal combustion engine utilizing exhaust aftertreatments, two electric motors, and a series string of ultracapacitors was used for all simulations. Several control strategies were simulated using various drive cycles that represent a range of driving conditions and driver habits. No a priori drive cycle information was assumed to be available to the controller. The series-parallel control strategy was demonstrated through simulation to improve both fuel economy and drive quality when compared to the parallel control strategy. Further in-vehicle testing is necessary to determine the effects on emissions, but it was shown that choosing the ICE operating point to improve emissions results in near-optimal fuel economy when using either the parallel or the series-parallel control strategy.

Committee: Robert Veillette (Advisor) Subjects:

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, 2021, Mechanical Engineering

The EcoCAR Mobility Challenge is a four-year design cycle which tasks teams with designing a hybrid Chevrolet Blazer that serves the commuter market by efficiently providing a Mobility-as-a-Service. In Year 1 of the competition the OSU EcoCAR team selected a series-parallel hybrid architecture and defined vehicle performance goals to be achieved at the end of the development cycle. In Year 2, the stock GM Blazer was modified and hybrid propulsion components – a downsized 2.0L engine, P0 motor and P4 motor – were integrated and rear powertrain modifications were made. A full vehicle model, driver model, and HIL test harness for the EcoCAR hybrid vehicle was set up and the development of a Hybrid Supervisory Controller (HSC) was started. Components were bench tested after integration into the vehicle. In Year 3, the various algorithms necessary to achieve baseline functionality of the EcoCAR vehicle were developed and tested. A V-systems engineering process was followed to design control strategies from defined system requirements and constraints. Engine torque control was achieved by manipulating ACC (Adaptive Cruise Control) CAN messages through an Engine Control Module gateway. A simple REM torque assist strategy and a series charging algorithm utilizing the BAS were developed and implemented in the vehicle. The vehicle completed 200+ miles of VIL testing at the Transportation Research Center (TRC), maintaining SoC between 30-80% and meeting acceleration requests in performance mode. Methods to improve fuel economy with an energy management strategy has also been discussed for refining the HSC in Year 4.

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

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, 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 Ohio State EcoCAR team is a student project team at The Ohio State University providing real-world engineering experience and learning opportunities to engineering students. The EcoCAR Mobility Challenge is sponsored by the U.S. Department of Energy, General Motors, and The Mathworks and challenges twelve universities across the United States and Canada to redesign and reengineer a 2019 Chevrolet Blazer into a hybrid-electric vehicle. The goal of the competition is for students to develop and implement technologies to reduce the vehicle's environmental impact while maintaining performance and to enhance the vehicle with connected and automated technologies for a future in the mobility-as-a-service market. The transition from conventional to hybrid vehicle requires the addition of several hybrid powertrain components, including electric motors, power inverters, and a high voltage battery. These new components have thermal cooling requirements and require the integration of a dedicated thermal management system to prevent components from overheating and to maintain optimal operating temperature. This work models the thermal systems of the internal combustion engine and hybrid powertrain components to provide estimates for component temperatures during steady-state operation and predetermined drive cycles. The GT-Suite modeling software package from Gamma Technologies was chosen to model the two thermal systems because of its extensive library of pre-validated automotive grade component models. This library allowed component models to be built quickly and without extensive data collection. The thermal system models were integrated with a full-vehicle model of the OSU EcoCAR team's vehicle in Simulink. This work seeks to provide a reasonable approximation of the integrated thermal systems in the OSU EcoCAR vehicle, with provisions to update and calibrate the model in the future. The model provides both steady-state and drive cycle feedb (open full item for complete abstract)

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

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

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

To fuel transportation systems, there exists an opportunity to reduce launch costs by an order of magnitude by launching the necessary propellant on existing commercial launch vehicles (CLVs). This research analyzed various architectures that deliver propellant to near-rectilinear halo orbit (NRHO). An automated tool was developed and utilized to rapidly trade architectures. First-order results indicate many feasible architecture options exist for commercially launched propellant. Active cryogenic fluid management (CFM) tankers were shown to have negligible improvements over passive tankers that rendezvous with a reusable (active CFM) bus. CLV long-duration upper stages deliver more propellant than ZBO tankers if, on average, tanker inert mass is greater than 51% of the CLV usable payload. “Topping-off” long-duration upper stages with propellant in LEO permits a mean of 13 metric tons per launch delivered to NRHO. Reusable tugs were shown to increase delivered propellant per launch by 180% on average.

Committee: Paul Barnhart PhD (Committee Chair); Sunniva Collins PhD (Committee Member); Yasuhiro Kamotani PhD (Committee Member) Subjects: Aerospace 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

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

MS, University of Cincinnati, 2018, Engineering and Applied Science: Mechanical Engineering

Dynamic systems incorporating physical plant and control systems should be designed in an integrated way to yield desirable and feasible solutions. Conventionally, these systems are designed in a sequential manner which often fails to produce system-level optimal solutions. However, combined physical and control system design (co-design) methods are able to manage the interactions between the physical artifact and the control part and consequently yield superior optimal solutions. Small-scale to moderate-scale dynamic systems can be addressed by using existing co-design methods effectively; nonetheless, these methods can be impractical and sometimes impossible to apply to large-scale dynamic systems which may hinder us from determining the optimal solution. This work addresses this issue by developing a new algorithm that combines decomposition-based optimization with a co-design method to optimize large-scale dynamic systems. Specifically, the new formulation applies a decomposition-based optimization strategy known as Analytical Target Cascading (ATC) to a co-design method known as Multidisciplinary Dynamic System Design Optimization (MDSDO) for the co-design of a representative large-scale dynamic system consisting of a plug-in hybrid-electric vehicle (PHEV) powertrain. Moreover, since many of dynamic systems may consist of several time-dependent linking variables among their subsystems, a new consistency measure for the management of such variables has also been proposed. To validate the accuracy of the presented method, the PHEV powertrain co-design problem has been studied with both simultaneous and ATC methods; results from the case studies indicate the new optimization formulation's ability in finding the system-level optimal solution.

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

Master of Science, The Ohio State University, 2017, Mechanical Engineering

EcoCAR 3 is a part of the Advanced Vehicle Technology Competition series hosted by the Department of Energy, and it challenges 16 North American university teams to re-engineer a 2016 Chevrolet Camaro and turn it into a hybrid electric vehicle, thus improving the environmental impact of the car while retaining its performance aspects. The Ohio State University's EcoCAR 3 vehicle has a plug-in hybrid architecture, with operation in series and parallel power flows. The architecture features a 5-speed manual transmission that was automated by the team to retain the efficiency of a manual transmission while providing the convenience of an automatic transmission. The team-developed controllers manage the clutch and shift actuators to provide supervisory control of the automated manual transmission. The simplicity and efficiency of a manual transmission combined with the advantages provided by the hybrid architecture make it a good candidate for an HEV. This thesis provides an overview of the modeling, component testing, and controls development for the AMT system. The controls development includes high level control for vehicle launch, gearshift process, and strategies used in different hybrid vehicle operation modes.

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

Doctor of Philosophy, The Ohio State University, 2016, Mechanical Engineering

One of the main goals of hybrid electric vehicle technology is to improve the energy efficiency. In industry and most of academic research, the powertrain control is designed and evaluated under standard driving cycles. However, the situations that a vehicle may encounter in the real world could be quite different from the standard cycles. Studies show that the human drivers have a great influence on the vehicle energy consumptions and emissions. The actual operating conditions that a vehicle faces are not only dependent on the roads and traffic, but also dependent on the drivers. A standard driving cycle can only represent the typical and averaged driving style under the typical driving scenarios, therefore the control strategies designed based on a standard driving cycle may not perform well for all different driving styles. This motivates the idea to design optimally-personalized hybrid electric vehicle control methods that can be adaptive to individual human driving styles and their driving routes. Human-subject experiments are conducted on a driving simulator to study the driving behaviors. A stochastic driver pedal model that can learn individual driver's driving style is developed first. Then a theoretic investigation on worst-case relative cost optimal control problems, which is closely related to vehicle powertrain optimal control under real-world uncertain driving scenarios, is presented. A two-level control structure for plug-in hybrid electric vehicles is proposed, where the parameters in the lower-level controller can be on-line adjusted via optimization using historical driving data. The methods to optimize these parameters are designed for fixed-route driving first, and then extended to multi-routes driving using the idea similar to the worst-case relative cost optimal control. The performances of the two proposed methods are shown through simulations using human driving data and stochastic driver model data respectively. The energy consumption resul (open full item for complete abstract)

Committee: Junmin Wang (Advisor); Ryan Harne (Committee Member); Chia-Hsiang Menq (Committee Member); Haijun Su (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2016, Mechanical Engineering

Increasingly stringent regulations on emissions require automobile manufacturers to find new ways to reduce the emissions produced by their vehicles. If current trends provide an indication of where the automotive industry is headed, hybrid electric vehicles (HEVs) and electric vehicles (EVs) will become prevalent in the market in coming years. These technologies are all relatively new and still need much development before they can hold a significant place in the automotive market. It is for this reason that companies are investing heavily in training the next generation of engineers to work on this problem. EcoCAR 3, a four year long Advanced Vehicle Technology Competition (AVTC), is one way companies are pursuing the training of future engineers. EcoCAR 3 challenges the engineering students to modify a stock Chevrolet Camaro, donated by GM, to reduce the vehicle's energy consumption and tailpipe emissions, while maintaining standard vehicle performance. Currently, the competition just ended its second year and is beginning year 3. During year 1, the team focused on selecting the powertrain architecture for the Chevrolet Camaro and procuring components from different suppliers. The vehicle architecture that met the goals of both the competition and the team is a Post-transmission Plug-in Hybrid Electric Vehicle (PHEV) configuration. During Year 2, the team's major goals were to mechanically and electrically design the different subsystems, have the entire vehicle integrated and have the vehicle run on its electric propulsion system. Having the vehicle to run by the end of the year 2 required the team to develop controls in parallel to the mechanical and electrical integration of the vehicle. In order to successfully accomplish all the goals in a timely manner the team followed a Model-Based Design (MBD) approach. The work described in this project focuses on the development process that was followed during the development of a full-vehicle model: EcoSIM 3 in th (open full item for complete abstract)

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

Master of Science, The Ohio State University, 2016, Mechanical Engineering

Automobiles affect growth of humanity. Vehicles affect everything from economy to our environment. One of the advancements to ensure the energy consumed by vehicles is sustainable are Hybrid-electric vehicles. Hybrid-electric vehicles cover various domains under its umbrella. They are a combination of different systems including Electrical, Mechanical, Chemical, and Thermal. One of the tools used to model the dynamics of Hybrid-electric vehicles is MATLAB/Simulink which is a general purpose modelling tool. The language of Modelica and its simulation environments allows multi-domain modeling while modeling from the physical perspective. The focus of this thesis was to model components of Hybrid–electric vehicles in a Modelica based simulation environment, in the process learning the advantages and disadvantages of using the Modelica language and the tools which act as the front ends for the language. A model was created to simulate the electrical dynamics of a battery cell and pack. A model of an electric drive was taken from a commercial library and its dynamics were observed by integrating the model of the battery pack along with the electric drive. The models created using Dymola simulated in Dymola and were exported to Simulink to compare results and simulation times. The differences in creating a model in Simulink and a Modelica Simulation environment were examined and noted along with the benefits and shortcomings of the different modeling methodologies.

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

Master of Science, The Ohio State University, 2016, Mechanical Engineering

The EcoCAR 3 project is a four-year competition sponsored by General Motors and the U.S. Department of Energy challenging 16 university teams to re-engineer a 2016 Chevrolet Camaro to be a performance hybrid electric vehicle. The Ohio State University designed a parallel hybrid electric vehicle with a 0 to 60 mph acceleration goal of 5.6 seconds and a 44 mile all electric range. Before the performance and emissions goals can be met the team must fully mechanically and electrically integrate their hybrid vehicle architecture. Concurrently to the vehicle integration, the controls team developed the basic vehicle controls that would be required to meet the goals for the second year of the competition. The controls development started with fully defining the vehicle controls requirements and then evaluating which requirements would be met in each part of the development process. The controls validation occurred using a team-developed vehicle model in both the Software- and Hardware-in-the-Loop environments. The main focus for this part of the development was defining and implementing the basic controls, such as controller communication and vehicle startup, which are critical to eventually having a fully functional vehicle. With the Year 2 controls validated in the HIL environment, a vehicle implementation plan was developed to be implemented and validated by May of 2016. The full controls development plan that was developed to meet the high level team goals included both performance and efficiency modes that will be implemented and validated in Year 3 and 4 of the EcoCAR 3 project.

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

Doctor of Philosophy, The Ohio State University, 2015, Electrical and Computer Engineering

The Dual Mechanical Port (DMP) electric machine has two rotors that can be set to rotate at different speeds and directions. Compared to conventional electric machines with only one rotor, the DMP machine provides higher torque density and much better control flexibility. However, the DMP machine has a relatively complex structure, which makes it a challenge to model and control. In addition, the existing model and control algorithms for single rotor machines cannot directly be applied to the DMP machine. In this study, it has been explore that how the DMP machine can be applied to hybrid electric vehicles as an avenue for explaining the electromagnetic characteristics and functionality of this more complex mechanism. The model and the control algorithms for two different DMP machines are also investigated. The first DMP machine, which is called the PMDMP, uses two layers of permanent magnets within the outer rotor. The second one, which is referred to as the SCDMP machine, uses a single layer of squirrel cage within the outer rotor, The study of the modeling and control for the SCDMP machine is the major contribution of this work. Compared to other DMP machines, the PMDMP machine stands out for its high torque density and high efficiency. A detailed model derivation for the PMDMP is presented later in the work. The independent control of its two rotors is investigated and verified by simulations and experiments. To overcome the problems brought about by the position sensors, the effectiveness of position sensorless control algorithms for the PMDMP is investigated. High frequency injection and sliding mode sensorless control algorithms are applied to the PMDMP machine at low speed and high speed, respectively. The performance of the sensorless control algorithms in experiments matches well with the simulation results. To verify the functionality of the DMP machine in power split hybrid application, the power flow pattern in various operational modes are discusse (open full item for complete abstract)

Committee: Longya Xu Dr. (Advisor); Jin Wang Dr. (Committee Member); Mahesh Illindala Dr. (Committee Member) Subjects: Electrical Engineering; Electromagnetics; Energy

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