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

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

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

Doctor of 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:

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

Doctor of Philosophy (PhD), Ohio University, 0, Mass Communication (Communication)

Turkish television series has a global popularity and loyal non-Turkish audience fandom. Many of these fans do not see Turkish television shows as simply another version of soap operas or telenovelas. Rather, the Turkish dizi is a unique form, reflected by Turkey's specific hybrid culture and historical East-West synthesis. This dissertation used amulti-method approach and aimed to provide a holistic explanation for the global popularity of Turkish television series. Three aspects were investigated: the role of media creators in the process of making a television series, non-Turkish audience perspectives on structural elements in the shows, and Middle Eastern audience perspectives on Turkish actors and actresses. In-depth interviewing techniques were used in the first study to probe decisions made by creative media workers in the process of making a television series. In the second study, in-depth and structured interviews were provided to global fans of Turkish television series to uncover their reasons for viewing the series. In both studies, admiration towards Turkish performers was a common emerging theme so a third experimental design study was conducted to measure audience casting preferences.Interpretation of the collected qualitative data suggests media creators and executives do not explicitly create Turkish television series for non-Turkish audiences. Instead, structural factors in the process of making the series and casting decisions influence watching behaviors of non-Turkish audiences. This also inadvertently contributes to Turkey's soft power efforts in the region. Furthermore, the third study showed that Middle Eastern audiences prefer stereotypical Western appearances for performers in the series and such casting choices might influence the popularity of Turkish television series.

Committee: Drew McDaniel (Committee Chair); Suetzl Wolfgang (Committee Member); Chawla Dewika (Committee Member); Eliaz Ofer (Committee Member) Subjects: Mass Communications

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 in Mechanical Engineering, University of Toledo, 2010, Mechanical Engineering

Hybridization of vehicles has been proven a good way to reduce fuel consumption significantly. Working prototypes of a series hydraulic hybrid vehicle (SHHV) are already under testing. The power split strategy for those prototypes is a rule-based controller, or called a “bang-bang” controller. The controller is designed based on engineer's intuition, to keep the engine working in the region with high efficiency and low fuel consumption rate. One of the problems of that design is that it only takes one component of the hydraulic hybrid system, the internal combustion engine, into account. It is a device centered rather than system centered design. As a result, the potential of the hydraulic hybrid system is not fully realized. A more efficient power split strategy is conducted based on the Deterministic Dynamic Programming (DDP), which has been proved a powerful tool for optimal control. However, the DDP is a looking-forward tool, which means it uses the future driving conditions to split the power between the two sources for optimization. Successful applications of DDP used standard driving cycles as the known driving conditions. However, DDP is not applicable where the driving cycle is unknown. This means that the DDP could not be applied in real-time, unless the future driving conditions could be found. The driving conditions in our everyday commute are extremely different with the typical driving cycles. And different drivers have different driving habits. However, a specific driver has a certain “driving cycle” for a certain commute, although which is not a standard one. As long as the certain “driving cycle” is known, The DDP algorithm could be applied for optimization. Artificial neural network (ANN) has the ability to “learn” the “driving cycle” from a certain driver and then to “predict” the driving conditions before its happening. The “prediction” method is the “time-series forecasting” method. ANN is a good tool for time series forecasting and has also be (open full item for complete abstract)

Committee: Walter Olson (Advisor); Terry Ng (Committee Member); Yong Gan (Committee Member) Subjects: Mechanical Engineering