Doctor of Philosophy, The Ohio State University, 2019, Mechanical Engineering

Connected vehicles promise to increase transportation options and reduce travel times while improving the safety of road users. Convoying/platooning are the common use case of connected vehicles technology and the driveability performance impact of such convoy has never been researched before. The vehicles when following each other in a convoy, using adaptive cruise control (ACC), is augmented by the lead vehicle information (vehicle acceleration) through the vehicle to vehicle communication as a feedforward control is called Cooperative Adaptive Cruise Control (CACC). This dissertation analyses the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles. In order to assess the driveability performance, a framework consisting of various metrics has been developed. The parameter space robust control methodology has been used to design the controller that improves the convoy's driveability and the performance is compared to the convoy that is being tuned for maintaining the time gap. These simulation results were verified in a real-time setting using a Hardware-in-the-Loop (HIL) setup using a CARSIM high-fidelity car model. With the use of the V2X technology, the fuel economy of the connected vehicle can be improved and it is called Eco-Driving. This dissertation proposes a framework for Eco-driving that is comprised of Eco-Cruise, Greenwave algorithm, and Eco-CACC. The Eco-Cruise is the algorithm which calculates the optimal velocity profile based on the route information such as speed limit, stop sign and traffic sign location and the vehicle powertrain model. A Dynamic programming based algorithm which minimizes the fuel economy is developed. The Eco-Cruise algorithm stops at all the stop signs and traffic light (assuming red light) optimally. Driving scenario has a very big impact on the Eco-cruise algorithm, and a new methodology has been proposed in this dissertation, that formulates a metric based route selection t (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Vadim Utkin (Committee Member); Bilin Aksun-Guvenc (Committee Member); Abhishek Gupta (Committee Member) Subjects: Automotive 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 Philosophy, The Ohio State University, 2022, Mechanical Engineering

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies. This drive stemmed from the developments in communication technologies for Connected and Autonomous Vehicles (CAV), such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. Coupled with automated driving capabilities of CAVs, a new and exciting era has started in the world of transportation as each transportation agent is becoming more and more connected. To keep up with the times, research in the academia and the industry has focused on utilizing vehicle connectivity for various purposes, one of the most significant being fuel savings. Motivated by this goal of fuel saving applications of Connected Vehicle (CV) technologies, the main focus and contribution of this dissertation is developing and evaluating a complete Eco-Driving strategy for CAVs. Eco-Driving is a term used to describe the energy efficient use of vehicles. In this dissertation, a complete and comprehensive Eco-Driving strategy for CAVs is studied, where multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. The first step in making a CAV achieve Eco-Driving is to develop a route-dependent speed profile called Eco-Cruise that is fuel optimal. The methods explored to achieve this optimally fuel economic speed profile are Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP). Using a generalized Matlab function that minimizes the fuel rate for a vehicle travelling on a certain route with route gradient, acceleration and deceleration limits, speed limits and traffic sign (traffic lights and STOP signs) locations as constraints, a DP based fuel optimal velocity profile is found. The ego CAV (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Mrinal Kumar (Committee Member); Bilin Aksun-Guvenc (Committee Member) Subjects: Automotive Engineering; Computer Science; Design; Energy; Engineering; Experiments; Mechanical Engineering; Systems Design; Technology; Transportation

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

The fuel economy label (window sticker) is used by every vehicle manufacturer in the United States to report fuel economy for two purposes. First, the values reported on the sticker are certified by the United States Environmental Protection Agency (EPA) and are used for certifying emissions regulations like the Corporate Average Fuel Economy (CAFE). Second, the fuel economy numbers are used by consumers to compare competing vehicles in the marketplace. These fuel economy numbers are generated through a process that involves standardized testing on a chassis dynamometer using standard drive cycles. As a result, the test requires an accurate replication of the resistive forces that a vehicle experiences in the real-world, which requires an accurate estimation of road load applied by the road and the surroundings opposing the vehicle motion. The estimation also depends on the type (aerodynamic shape, drivetrain configuration, etc.) of vehicle being tested. To get a description of road load that is as close as possible to reality, several noise factors and residuals need to be estimated as well, which forms the bulk of this thesis. Vehicle coastdown method is widely used to determine road load coefficients for testing vehicles on a chassis dynamometer for fuel economy certification. However, apart from being a time-consuming procedure for each variant in a mass production vehicle lineup, the repeatability of track coastdown testing procedure is sensitive to environmental conditions, the track surface condition as well as on the type of vehicle being tested (for example, SUVs, sedans, hybrid vehicles, manual transmissions, etc.). As a result, several attempts have been made to accurately model the coastdown road load parametrically. This thesis explores various ways in which such parametric models can be obtained and methods to minimize risks related to overfitting and collinearity of variables. Since, the vehicle's road load is dependent on several physical phenomen (open full item for complete abstract)

Committee: Giorgio Rizzoni (Advisor); Yann Guezennec (Committee Member); Adithya Jayakumar (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering; Statistics

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, 2018, Electrical and Computer Engineering

In response of the world's increasing concern on carbon emissions, vehicle real-world fuel economy potential is being further developed by autonomy and connectivity, so as to achieve superior judgment and to eliminate fuel waste by imperfect human operations. A starting point is by redesigning the existing Adaptive Cruise Control (ACC) system with route preview and optimal control calculation, which is regarded as predictive optimal control in this work. The work targets to provide algorithm solutions for designing and implementing predictive optimal control in a real-world vehicle system, covering the aspects of control, estimation, and prediction. For control development, two algorithms are designed for the scenarios of optimal car-following speed control and optimal cruise control on a hilly route. The two designs share a common concept that by previewing the upcoming condition change, the vehicle control can be scheduled with a constrained modulation range in trade of improved operation cost. For the problem of optimal car-following speed control, which contains a mixed-integer programming problem caused by gearshifts, optimization complexity is broken down by a hybrid solver of Quadratic Programming (QP) and Pontryain's Minimum Principle (PMP). The solver partitions the problem into simplified sub-problems with quick quasi-optimal solutions, so that the search space is efficiently reduced to achieve constrained optimal control solutions in real time. Control results show major fuel saving benefits with clean gear shifts. For the problem of cruising on a hilly route, where the vehicle drives on a high gear and the engine operates near the torque capacity curve, control solving encounters the challenges by non-convex & non-affine constraints, along with state-dependent system switching. To achieve flexibility in optimal control solving, a PMP analytical solution set is developed to self-expand forward in time, detecting the system's constraints and switches wh (open full item for complete abstract)

Committee: Umit Ozguner (Advisor); Giorgio Rizzoni (Committee Member); Vadim Utkin (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2016, Agricultural, Environmental and Developmental Economics

What information is captured in home prices? Clearly prices should reflect characteristics such as square footage, build quality, and the number of bedrooms. Economists also believe that house prices reflect local characteristics, such as school and air quality, presence of open space, and crime rates. Traditionally, researchers employ hedonic models, where the marginal willingness to pay of these characteristics is obtained by running a linear regression of housing and neighborhood characteristics on the log of house price. These models have been used to study the value of a myriad of topics, from pollution and crime rates, to views of windmills and presence of nearby methamphetamine labs. As with many methods of analysis, hedonic models are subject to numerous assumptions and caveats, many of which are often ignored.This dissertation explores several complexities of and proposes new means of employing house prices in economic analysis. The first chapter asks a question that has received little study: why do buyers pay different prices for the same house? In most studies utilizing house transactions, the researcher does not know who the buyers and sellers are, and thus implicitly assumes that specific types of individuals have no effect on home prices. The chapter measures the effect of experience: the relative number of transactions the buyer and seller have taken part in over a given period of time. First, I develop a two-sided real estate search model that incorporates information costs, search costs, and Nash bargaining power. I test the implications of this model using repeat-sales housing data on 113,272 transactions from 1998-2006 in two large metropolitan regions of Ohio. The main results show that more experienced buyers purchase properties at a discount, experienced sellers sell at a premium, and that the magnitude of these differences varies depending on the relative and absolute levels of buyer and seller experience and geographic location. On average, (open full item for complete abstract)

Committee: Elena Irwin (Advisor); Mark Partridge (Committee Member); Allen Klaiber (Committee Member) Subjects: Agricultural Economics; Economics; Environmental Economics

Doctor of Philosophy, The Ohio State University, 2014, Mechanical Engineering

The application of modern Diesel engines in automotive industry has been widely recognized for reasons of their distinguished performances on fuel economy, durability, and reliability. Meanwhile, NOx and particulate matters (PM) emissions have been the main concerns in the evolution of Diesel engines as more and more stringent emission standards have been legislated against Diesel engine emissions worldwide. In addition, as the Greenhouse gas emissions are receiving more and more concerns due to global warming issues, the demand of fuel economy improvement is increasing significantly. The objective of this research is to develop systematic control methodologies, based on fundamental insight into the system characteristics, to improve the overall fuel economy and emission performance of integrated Diesel engine and aftertreatment systems. The test platform of this research is a medium-duty Diesel engine equipped with high-pressure common-rail fuel injection system, dual-loop exhaust gas recirculation systems, variable geometry turbocharger system, and an integrated aftertreatment system including a Diesel oxidation catalyst (DOC), Diesel particulate filter (DPF), and two-catalyst selective catalytic reduction (SCR) system. The topics of this research fall into two groups. The first group focuses on the modeling, estimation, and control of integrated aftertreatment systems based on the interactions between the subsystems with the objective of maintaining low tailpipe emissions at low cost. Topics covered in this group include the modeling and observer-based estimations for oxygen concentration and thermal behaviors across the DOC and DPF, state estimator design for SCR system using production NOx sensor measurements, and the active NO/NO2 ratio controller design for DOC and DPF to improve the SCR performance. The second group mainly concentrates on the modeling, estimation, and control of integrated engine-aftertreatment systems grounded on the interactions between en (open full item for complete abstract)

Committee: Junmin Wang PhD (Advisor); Jack McNamara PhD (Committee Member); Chia-Hsiang Menq PhD (Committee Member); Ahmet Selamet PhD (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy in Urban Studies and Public Affairs, Cleveland State University, 2014, Maxine Goodman Levin College of Urban Affairs

There has been, in recent decades, a concerted effort to promote energy efficiency as a means to reduce energy consumption, along the supply and demand sides. The general thesis is that, ceteris paribus, an increase in energy efficiency would lead to a decrease in the consumption of the good or service rendered efficient. This is in opposition to Jevons' Paradox which states that “It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth…” (Jevons, 1865). While many studies have applied Jevons' Paradox to various sectors to estimate rebound effects, few have examined if Jevons' Paradox holds when all available factors that could affect consumption of an efficient good/service are controlled for. This study hoped to fill that gap in literature. The study looked at vehicle fuel economy and vehicle miles travelled (VMT) and examined if, all else being equal, a vehicle that was more fuel efficient accrued greater VMT. Using data from the National Household Travel Survey (NHTS, 2009), a multivariate regression model was built (N = 82,485) controlling for driver, household and vehicle attributes. The findings indicated that, at the microlevel, Jevons' Paradox does hold true; a 1% increase in fuel efficiency was associated with a 1.2% increase in VMT.

Committee: William Bowen PhD (Committee Chair); John Holcomb PhD (Committee Member); Nicholas Zingale PhD (Committee Member) Subjects: Economic Theory; Energy; Environmental Studies; Sustainability; Transportation Planning; Urban Planning

Master of Science in Mechanical Engineering, Cleveland State University, 2014, Washkewicz College of Engineering

Rising fuel costs and concerns over fossil fuel emissions have resulted in more stringent fuel economy and emissions standards globally. As a result, motor vehicle manufacturers are constantly pushed to develop more efficient engine and drivetrain systems. Along with advances in hardware, the development of highly fuel efficient engine oils and driveline lubricants can have a significant impact on total system efficiency. Recently motorcycle fuel economy and emissions have come under increased scrutiny. While the passenger vehicle and heavy duty vehicle industries employ a variety of American Society for Testing and Materials (ASTM) standardized tests to measure fuel economy and exhaust emissions, the motorcycle industry has very little standardization and no industry standard fuel economy engine tests. The objective of this work is to fill this void with the development of a motorcycle fuel economy test methodology. The developed testing methodology is demonstrated experimentally using a Honda PCX150 motorcycle engine, which is commercially available and of a size and architecture which is representative of a wide range of motorcycles throughout the world. The fuel economy test is developed to incorporate four unique, steady-state stages in which engine load and speed are controlled while fuel consumption is measured. Each stage is tailored to produce lubrication in different operating regimes. After suitable test conditions are determined, three oils are prepared and tested. Each test oil was prepared and selected to investigate differences in both oil viscosity and chemical additives. The developed test is shown to have the ability to quantitatively evaluate test oils based on each oil's effect on fuel consumption.

Committee: Jerzy Sawicki PhD (Committee Chair); Stephen Duffy PhD (Committee Member); Ana Stankovic PhD (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

Master of Science in Mechanical Engineering, University of Toledo, 2009, Mechanical Engineering

Hydraulic hybrid vehicle (HHV) is a new technology being developed in order to improve fuel economy for road vehicles. This technology also has limitations for example: low energy density, no power grid plug-in capability. This research is on the evaluation of a new concept for improving the HHV technology. With an added air system to HHV, the air system can be charged through grid plug-in or by the internal combustion engine (ICE). The new scheme has the potential to significantly improve the energy density of the hydraulic hybrid vehicles and also provide plug-in capability for these vehicles.Basing on a symbolic program developed in MATLAB/Simulink, a parallel hybrid simulation model for the new system is developed in this thesis. The simulation model includes all the system components such as the vehicle, the air tank, the accumulators, the pressure exchangers, the hydraulic pump/motor, the compressor and the ICE. The power management is implemented based on using all the available hydraulic power. The main objective of this model is to evaluate the average fuel economy (FE) for the HHV with the added compressed-air system. This model is tested basing on the federal urban drive schedule (FUDS). The simulations results with various configurations have not shown significant improvement in the fuel economy. This thesis provides a detailed analysis about the results from the system structure and the energy loss. In this system, there are two alternating accumulators. Every time the accumulator switches to reservoir, energy will be lost. When the engine drives the compressor to recharge the air system, a large engine would be needed to power such a compressor. These are the main reasons for the poor fuel economy of the proposed HHV system.

Committee: Mohammad Elahinia (Advisor); Walter Olson (Committee Member); Maria Coleman (Committee Member) Subjects: Engineering

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

Since solid oxide fuel cells can operate on fuel containing both hydrogen and carbon monoxide, it may prove possible to remove hydrogen from syngas streams for other purposes and allow the fuel cell to operate with higher carbon monoxide levels. In this study, electrolyte-supported solid oxide fuel cells were tested using hydrogen, syngas, and hydrogen-depleted syngas (HDS) as fuel sources. It was found that reducing the hydrogen flow rate by 50% while maintaining an equivalent fuel utilization rate increases the polarization of the electrode by less than 5%. Carbon deposition was avoided when the water content of the fuel reflected that of actual syngas. The drop in the ideal voltage plus the increase in the resistance of the cell equated to a measured loss in power density of 7.8%.

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

Master of Science (MS), Ohio University, 2003, Mechanical Engineering (Engineering)

In conventional vehicles, the entire power is derived from the IC engine, so, it is obligatory to size the engine larger than necessary for its cruising speed. The engine is to be designed to account for peak power requirements like acceleration. This over sizing the engine shifts the operating point from its efficient zone and this adversely affects the fuel economy and emissions. The idea of hybridization is that a part of the total power required can be replaced by an auxiliary power source, generally a motor powered by batteries. Hence, the IC engine can be designed for average load and can be operated with better fuel rfficiency. A simulation tool called ADVISOR (Advanced Vehicular Simulator) is used for this study. The software takes the vehicle input and the drive cycle from the user, simulates the vehicle drive and gives fuel economy, acceleration performance and emissions. In this study, each of the three vehicle platforms (light SUV, full size SUV and Heavy Truck) is selected and a reasonable power level for that vehicle platform is taken from the data of the current conventional vehicle type. The powertrain is then hybridized by replacing (in steps) this power by an equivalent motor power and a simulation is run, such simulations are run in Advisor at three different battery charge capacities to understand the effect of on board charge. The fuel economy and the time to accelerate from rest to 60 mph are noted down from the ADVISOR results. The cost optimization is also done by considering the cost of the motor along with the cost and space of the batteries. It also includes the replacement cost of the batteries. The benefits due to the decrease of operating costs will be shown in the fuel economy and the penalty due to the weight of the batteries is shown both in performance and fuel economy of the vehicle. Results showed that hybridization can improve the overall performance of the vehicle, but with the current cost of the batteries it may be a little co (open full item for complete abstract)

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

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: