Doctor of Philosophy, The Ohio State University, 2008, Mechanical Engineering

The gradual decline of oil reserves and the increasing demandfor energy over the past decades has resulted in automotive manufacturers seeking alternative solutions to reduce the dependency on fossil-based fuels for transportation. A viable technology that enables significant improvements in the overall tank-to-wheel vehicle energy conversion efficiencies is the hybridization of electrical and conventional drive systems. Sophisticated hybrid powertrain configurations require careful coordination of the actuators and the onboard energy sources for optimum use of the energy saving benefits. The term optimality is often associated with fuel economy, although other measures such as drivability and exhaust emissions are also equally important. This dissertation focuses on the design of hybrid-electric vehicle (HEV) control strategies that aim to minimize fuel consumption while maintaining good vehicle drivability. In order to facilitate the design of controllers based on mathematical models of the HEV system, a dynamic model that is capable of predicting longitudinal vehicle responses in the low-to-mid frequency region (up to 10 Hz) is developed for a parallel HEV configuration. The model is validated using experimental data from various driving modes including electric only, engine only and hybrid. The high fidelity of the model makes it possible to accurately identify critical drivability issues such as time lags, shunt, shuffle, torque holes and hesitation. Using the information derived from the vehicle model, an energy management strategy is developed and implemented on a test vehicle. The resulting control strategy has a hybrid structure in the sense that the main mode of operation (the hybrid mode) is occasionally interrupted by event-based rules to enable the use of the engine start-stop function. The changes in the driveline dynamics during this transition further contribute to the hybrid nature of the system. To address the unique characteristics of the HEV driv (open full item for complete abstract)

Committee: Giorgio Rizzoni PhD (Advisor); Yann Guezennec PhD (Committee Member); Andrea Serrani PhD (Committee Member); Steve Yurkovich PhD (Committee Member) Subjects: Mechanical Engineering

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

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, Electrical and Computer Engineering

Performing numerical optimization in large scale simulations environments is complicated by the fact that the overall objective function might be too computationally intensive or impossible to define in its closed form. In these cases, simulation-based optimization algorithms, which do not need the exact closed form objective function are the only viable solution method. Derivative Free Optimization algorithms are one such class of algorithms that does not need the derivative of the objective function in order to find the optimum. They instead use function evaluations to traverse the search space. This dissertation addresses the optimization challenges of large scale simulators that do not lend themselves to gradient based optimization. While the field of simulation-based optimization has been in existence for a few decades, the growing complexity of models in recent years puts a focus on the field to provide effective strategies to efficiently perform the required optimization. The difference between simulations and the real world systems they represent is that simulations use assumptions. It is important that these assumptions are within an acceptable tolerance which enable them to model reality with an appropriate level of certainty, within a reasonable amount of time, and using limited computational resources. Simulators use various ways to simplify reality and one way this is done is through the use of look-up tables (LUT). A look up table is an matrix that enables complicated computation to be replaced with relatively simpler array indexing. Finding optimal solutions to simulators which use LUTs is complicated by LUTs being discrete and event based. In addition, most simulation models that are used to model decision making mechanisms such as embedded control systems consist of both discrete and continuous state dynamics. These hybrid system models need both the discrete and continuous state dynamics to be analyzed and optimized simultaneously. This disser (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Yingbin Liang (Committee Member); Abhishek Gupta (Committee Member); Tunc Aldemir (Committee Member) Subjects: Computer Engineering; Electrical Engineering; Mechanical Engineering

Master of Science, University of Akron, 2008, Electrical Engineering

An energy usage adaptation (EUA) strategy to manage the charge/discharge profile of the energy storage system for plug-in hybrid vehicles is presented in this thesis. The objective of the EUA strategy is to bring the stored energy to a low level at the end of the daily drive cycle, and to limit the number of deep discharge cycles. The EUA algorithm first predicts the energy usage for a given day based on historical usage data. The predicted energy is then compared with the actual energy used and the battery energy available to set the SOC limits in the energy management algorithm. The EUA strategy has been tuned and tested using simulations of both a series and a series-parallel plug-in hybrid vehicle (model) with vehicle control algorithms developed for the purpose. The strategy is shown to improve the fuel economy of the vehicle and to reduce the cost per mile of operation by efficiently using the off board supplied energy. It also helps to extend the life of the battery by limiting the number of deep discharge cycles to no more than one per day. A well-to-wheel analysis of the designed plug-in hybrid is also done using the standard GREET model and through vehicle simulation to investigate the overall efficiency of plug-in hybrid vehicles. The well-to-wheel efficiency of the plug-in hybrids is found to be lower than those of the conventional gasoline and electric vehicles.

Committee: Iqbal Husain Dr. (Advisor); Robert J. Veillette Dr. (Committee Member); Malik Elbuluk Dr. (Committee Member) Subjects: Electrical Engineering

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:

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, 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

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

The Ohio State University has participated in Advanced Vehicle Technology Competitions (AVTCs) for over 21 years. These competitions challenge universities throughout North American to reengineer a vehicle with technologies advancing the automotive market. This work explores the use of systems engineering practices during the eleventh iteration of the AVTC program, the EcoCAR 3 competition. The document presents the systems engineering process and two case studies implementing the process. The systems engineering process presented is a simplification of the “Vee” and “Agile” systems engineering processes applicable to a high-cost, long-term, prototype program. The process is broken into five stages: Concept Creation and Refinement, Architecture and Metric Creation, Development, Verification, and Assessment and Validation. The two case studies present uses of the process at a low-level applied to a software algorithm and at a high-level applied to an entire project. The first case study reviews the development of a diagnostic algorithm for the automated manual transmission used in the EcoCAR 3 competition vehicle. The team automated a manual transmission and needed an algorithm to detect and isolate failures to components of the transmission system. The concept and requirements for this algorithm are detailed in Chapter 1 before continuing to discussion of development and testing. Testing of the algorithm utilizes a model-based environment. The second case study reviews the construction and execution of a behavioral study project evaluating driver performance during a vehicle to driver transition of an SAE Level 3 partially automated vehicle. Research was conducted in a model-based environment, simulating an autonomous vehicle by utilizing a driving simulator. The project requirements are derived from the applicable parent requirements, implemented, and tested.

Committee: Shawn Midlam-Mohler Ph.D. (Advisor); Giorgio Rizzoni Ph.D. (Advisor); Lisa Fiorentini Ph.D. (Committee Member); Sandra Metzler Ph.D. (Committee Member) Subjects: Electrical 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, The Ohio State University, 2018, Mechanical Engineering

This thesis details efforts towards developing a methodology that enables the design of a look ahead based energy management system. It explores various technologies that are required to enable such a system to function on a physical vehicle. A new simulation framework known as `Traffic-In-Loop' (TIL) simulation is developed to mimic real-world driving. It serves as a drive cycle independent controls development platform. The framework is enabled by combining microscopic traffic simulation with a detailed mathematical powertrain model. The TIL simulation technique facilitates emulation of on-board sensors, V2X communication and capture causal behavior of real-world scenarios. Data collected from these virtual sensors are used to forecast future drive scenarios -- called `Look ahead predictions'. Further a strategy to integrate future drive scenario forecasts with powertrain control is introduced. The above advances, catalyzed the design of a look ahead based energy management controller, called 'Delta Energy Controller'. It aims at improving a vehicle's fuel economy by utilizing available drive scenario forecasts. Simulation results are used to prove the optimality of this controller and study the improvement in fuel economy as a function of better look ahead predictions.

Committee: Giorgio Rizzoni (Advisor); Marcello Canova (Committee Member); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering; Transportation

Doctor of Engineering, Cleveland State University, 2017, Washkewicz College of Engineering

High Intensity Laser Power Beaming is a wireless power transmission technology developed at the Industrial Space Systems Laboratory from 2005 through 2010, in collaboration with the Air Force Research Laboratory to enable remote optical `refueling' of airborne electric micro unmanned air vehicles. Continuous tracking of these air vehicles with high intensity lasers while in-flight for tens of minutes to recharge the on-board battery system is not operationally practical; hence the recharge time must be minimized. This dissertation presents the development and system design optimization of a hybrid electrical energy storage system as a solution to this practical limitation. The solution is based on the development of a high energy density integrated system to capture and store pulsed energy. The system makes use of ultracapacitors to capture the energy at rapid charge rates, while lithium-ion batteries provide the long-term energy density, in order to maximize the duration of operations and minimize the mass requirements. A design tool employing a genetic algorithm global optimizer was developed to select the front-end ultracapacitor elements. The simulation model and results demonstrate the feasibility of the solution. The hybrid energy storage system is also optimized at the system-level for maximum end-to-end power transfer efficiency. System response optimization results and corresponding sensitivity analysis results are presented. Lastly, the ultrafast supply/extended energy storage system is generalized for other applications such as high-power commercial, industrial, and aerospace applications.

Committee: Hanz Richter Ph.D. (Committee Chair); Taysir Nayfeh Ph.D. (Committee Member); Lili Dong Ph.D. (Committee Member); Majid Rashidi Ph.D. (Committee Member); Petru Fodor Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2017, Mechanical Engineering

The dissertation offers a systematic analysis on the interdependency between fuel economy and battery capacity degradation in hybrid electric vehicles. Optimal control approaches including Dynamic Programming and Pontryagin's Minimum Principle are used to develop energy management strategies, which are able to optimally tradeoff fuel consumption and battery aging. Based on the optimal solutions, a real-time implementable battery-aging-conscious Adaptive Equivalent Consumption Management Strategy is proposed, which is able to achieve performance that is comparable to optimal results. In addition, an optimal control based charging strategy for plug-in hybrid electric vehicles and battery electric vehicles is developed, which minimizes battery capacity degradation incurred during charging by optimizing the charging current profile. Combining a generic control-oriented vehicle cabin thermal model with the battery aging model, the benefit of this strategy in terms of decreasing battery aging is significant, when compared with the existing strategies, such as the widely accepted constant current constant voltage (CC-CV) protocol. Thus this dissertation presents a complete set of optimal control solutions related to xEVs with consideration of battery aging.

Committee: Giorgio Rizzoni (Advisor) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

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

The design of large-scale, complex systems such as plug-in hybrid electric vehicles (PHEVs) motivates the use of formal optimization methods from both multidisciplinary design optimization (MDO) and optimal control theory. Traditionally, MDO methods have been used to address the integrated design of engineering systems comprised of multiple, interacting components and/or disciplines for superior static system performance. Optimal control theory, on the other hand, is often used to select the best operation strategy of a given dynamic system for superior dynamic system performance. Although many times in practice the optimal design and control of such dynamic systems are addressed almost independently, this approach generally yields sub-optimal overall design solutions. This is because the system architecture, or physical design, is inherently coupled with its operation strategy, or control design. Combined optimal design and control techniques, also known as co-design, can address this issue by using an integrated approach to enable superior design solutions for dynamic systems. This thesis focuses on the co-design of large-scale systems, specifically PHEVs based on simultaneous multidisciplinary dynamic system design optimization (MDSDO) methods using direct transcription (DT). In order to enable a simultaneous approach for optimizing the design and control of the PHEV, a toolbox was developed to design all the critical component of a PHEV powertrain including: electric motor, generator, engine, transmission, and high voltage battery. This toolbox takes the size related design variables as inputs and by using the embedded analytical equations, generates the output performance characteristics of each component. The MDSDO problem formulation is then solved using GPOPS-II,a DT-based MATLAB software for solving multiple-phase optimal control problems. DT-based simultaneous problem formulations in MDSDO has already been successfully used in moderate scale problems, howe (open full item for complete abstract)

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

Master of Science, The Ohio State University, 2014, Mechanical Engineering

Increasingly stringent government regulations and the rising price of oil are causing automotive manufactures to develop vehicles capable of obtaining higher fuel economies and lower emissions. To achieve these goals, automotive manufactures have been developing hybrid-electric vehicles (HEV) and plug-in hybrid-electric vehicles (PHEV) that use both electricity and petroleum based fuels as their power sources. While hybridizing the vehicle powertrain improves the fuel efficiency of the vehicle, the addition of multiple power sources and modes creates many unique issues with vehicle driveability that are difficult and time-consuming to calibrate. The drive quality (also referred to as driveability) of a vehicle refers to the vehicle's responsiveness to driver inputs and overall smoothness of the vehicle in operation. A vehicle with good drive quality results in a comfortable ride for both the driver and passengers. Drive quality is a subjective measure of the driver's perception of the dynamic responses of the vehicle. It is typically measured using highly trained test drivers that analyze the vehicle's behavior under specific driving conditions such as take-off, acceleration, pedal tip-in/tip-out, gear shifts, and braking. Assessing and improving vehicle drive quality in this way can be time consuming and is subject to human error. Recently, major automobile manufacturers have begun to move towards the use of specially-designed software packages to evaluate drive quality objectively. This thesis discusses the development of control strategies for The Ohio State University's EcoCAR 2 competition vehicle with an in-depth analysis of the effects of the control strategies on the objectively-measured drive quality of the vehicle. The OSU EcoCAR 2 vehicle had serious issues tip-in drive quality due to the high amount of gear backlash present in the powertrain. A feedforward control was implemented to smooth the driver's torque request to minimize the negative effec (open full item for complete abstract)

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