Master of Science, The Ohio State University, 2025, Mechanical Engineering

Accurate parameter estimation is essential for improving lithium-ion battery mod- els, which are widely used to predict a cell's overall performance in electrical sys- tems. Traditional approaches to optimal experimental design (OED) rely on offline- generated current profiles, limiting their adaptability during testing. This thesis presents the development of a battery-in-the-loop (BIL) experimental setup that im- plements a real-time generated OED algorithm for Li-ion battery parameter estima- tion. A reinforcement learning (RL)-based OED algorithm was integrated with the BIL setup to dynamically generate an optimal input current profile during experimenta- tion. This approach was validated by calibrating the anode rate constant (kn) of a physics-based Li-ion battery model. The results demonstrate that real-time OED has the potential to achieve more accurate parameter estimation compared to traditional offline OED methods. In addition to validating real-time OED for parameter estimation, this work pro- vides a scalable, well-documented BIL framework that can be extended to other applications. Future research directions include simultaneous online parameter esti- mation, multi-parameter OED optimization, and the characterization of aged Li-ion cells utilizing the flexibility of the RL agent's OED strategy.

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

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, 2021, Mechanical Engineering

With ongoing research in intelligent transport systems and connected and automated vehicles, enabled by advancements in artificial intelligence, the large-scale advanced simulation has become an important part of product/software development for the automotive industry. Nowadays, traffic simulations are used to mimic real-world environment scenarios for connected vehicle technologies. The focus of this thesis lies in the development of microscopic traffic simulation calibration and enhance traffic signal control systems This thesis makes the following major contributions. First, a calibration framework is proposed which harnesses the exiting data set of OSU campus shuttles (CABS) to determine the traffic state and create a microscopic traffic simulation. The traffic simulation is implemented for a section of the OSU campus(“Woody Hayes Drive") which can be extended to the entire OSU campus. The second contribution is an investigation of an intelligent traffic signal control system. The signal control operation is formulated as a decision-making process where each controller or control component is modeled as an intelligent agent. The agents make decisions based on traffic conditions and their past knowledge of the environment. A state estimation method and an adaptive control scheme by reinforcement learning (RL) are introduced to implement such an intelligent system. Simulation experiments ii have been performed to verify the improvements of intelligent traffic control systems and compare them with the existing control policy. The third contribution summarises the initial integration work for the co-simulation framework completed by dSpace ASM and SUMO to create a complete real-time simulation of urban environments for ADAS testing. The demo scenario is the OSU campus with traffic demand generated using the calibrated model from the first part of the thesis.

Committee: Punit Tulpule Dr. (Advisor); Qadeer Ahmed Dr. (Committee Member); Shawn Midlam-Mohler Dr. (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2021, Mechanical Engineering

The EcoCAR Mobility Challenge is a student competition that tasks universities across North America with the hybridization and SAE Level 2 automation of a 2019 Chevy Blazer. In years 2 and 3 of the competition, the Ohio State EcoCAR team committed considerable effort to the development of an adaptive cruise control (ACC) feature. This paper provides a detailed discussion of what motivated the selection of a modified PID controller as the control method of choice for ACC. The state flow used by the team to achieve independent distance and velocity control is also reviewed. After designing the controller, the team performed particle swarm optimization to identify the ideal proportional, integral, and derivative gain values. In doing so, the team managed to greatly reduce maximum acceleration, RMS acceleration, and maximum jerk in simulation. While doing so, the efficiency of the vehicle was also improved by 8.45 percent. Then, in order to validate the real-world performance of the novel adaptive cruise controller, the team conducted a full range of anything-in-the-loop (XIL) testing. Across model, hardware, and vehicle closed-loop testing, Ohio State identified and resolved numerous potential issues in the controller and its implementation in the vehicle. Additionally, the safety and comfort of the ACC feature were verified across all testing environments, affirming the fidelity of the model and preparing the team for in-vehicle testing. Lastly, using a real target vehicle and live sensor data, Ohio State performed approach tests that demonstrate the functionality of its ACC in a real-world environment.

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

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

The EcoCAR Mobility Challenge is the current iteration of the Advanced Vehicle Technology Competitions that challenges twelve universities across North America to re-engineer a 2019 Chevrolet Blazer into a connected and automated vehicle. The competition goal is to design, prototype, test, and validate a SAE Level 2 advance driver assistance system. This work outlines the development process of a SAE Level 2 perception system. The process began by defining system and component level requirements that iniated a sophisticated sensor and hardware selection process. Then to protoype, test, and validate the system, a V-model approach was followed, which included validation and verification of the system in multiple test environments. The role of each test environment in the validation process along with its advantages and shortcomings is discussed in detail, followed by the evolution of the perception system throughout Year 1 and Year 2 of the competition. Next, three case studies outlining the different subsystems in the perception controller: the I/O layer, the fault diagnostics, and sensor calbration are discussed. Each of these sub-algorithms used various modeling environment to increase the realiability and accuracy of the perception system. This work serves as the foundation of the connected and automated vehicle perception system and will be vital in the implementation of advance driver assistance features such as adaptive cruise control, lane centering control, and lane change on demand in future years of this competition.

Committee: Shawn Midlam-Mohler (Advisor); Lisa Fiorentini (Committee Member); Punit Tulpule (Other) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2018, Mechanical Engineering

Vehicle integration testing has been increasingly front-loaded in the automotive development cycle to reduce prototyping and testing costs. One method of performing these tests is X-in-the-loop integration, which allows a verification and validation workflow from the design of control algorithms in offline high-fidelity models (MIL) to the online integration verification with prototype control hardware (HIL). A 10-speed automatic transmission is used as an example to traverse the gap between an offline high-fidelity model and a real-time capable online model. The model is split into three subsystems and each is built up from the component level using one-dimensional mechanics and zero-dimensional hydraulic fluid flow. The high-fidelity model parameters are perturbed to judge sensitivity of output performance metrics. The model is reduced by removing higher-order derivatives and faster dynamics at the component level. Multiple reduced models were generated and tested for errors relative to the high-fidelity version and increases in model execution speed. After reduction, a full automatic transmission model with hydraulic actuation circuit and dynamic torque converter has been implemented on a dSpace HIL simulator for real-time testing without control hardware in the loop.

Committee: Shawn Midlam-Mohler (Advisor); Krishnaswamy Srinivasan (Committee Member); Punit Tulpule (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2015, Aero/Astro Engineering

This dissertation provides a flight-testing framework for assessing the performance of fixed-wing, small-scale unmanned aerial systems (sUAS) by leveraging sub-system models of components unique to these vehicles. The development of the sub-system models, and their links to broader impacts on sUAS performance, is the key contribution of this work. The sub-system modeling and analysis focuses on the vehicle's propulsion, navigation and guidance, and airframe components. Quantification of the uncertainty in the vehicle's power available and control states is essential for assessing the validity of both the methods and results obtained from flight-tests. Therefore, detailed propulsion and navigation system analyses are presented to validate the flight testing methodology. Propulsion system analysis required the development of an analytic model of the propeller in order to predict the power available over a range of flight conditions. The model is based on the blade element momentum (BEM) method. Additional corrections are added to the basic model in order to capture the Reynolds-dependent scale effects unique to sUAS. The model was experimentally validated using a ground based testing apparatus. The BEM predictions and experimental analysis allow for a parameterized model relating the electrical power, measurable during flight, to the power available required for vehicle performance analysis. Navigation system details are presented with a specific focus on the sensors used for state estimation, and the resulting uncertainty in vehicle state. Uncertainty quantification is provided by detailed calibration techniques validated using quasi-static and hardware-in-the-loop (HIL) ground based testing. The HIL methods introduced use a soft real-time flight simulator to provide inertial quality data for assessing overall system performance. Using this tool, the uncertainty in vehicle state estimation based on a range of sensors, and vehicle operational environments is pre (open full item for complete abstract)

Committee: James W. Gregory (Advisor); Charles Toth (Committee Member); Cliff Whitfield (Committee Member); Jeffery P. Bons (Committee Member) Subjects: Aerospace Engineering

Doctor of Philosophy, The Ohio State University, 2015, Mechanical Engineering

Modern passenger and commercial vehicles share many of the same safety systems. Advanced Cruise Control, Anti-lock Brakes and Electronic Stability Control have all been shown to be an effective means of improving safety on both classes of vehicles. Dynamic Brake Support (DBS) is a system which has been implemented successfully on passenger cars but no record of implementation on heavy vehicles has been found. This is largely due to the belief that commercial vehicle drivers, as professionals, apply the brakes more effectively than passenger car drivers, and therefore do not need this system. This document presents a multi-point study of the applicability of DBS to commercial vehicles. Beginning with analyzing commercial vehicle driver braking behavior to show that commercial vehicle driver braking behavior is fundamentally similar to passenger car driver behavior. Therefore, systems that assist passenger car drivers should also assist commercial vehicle drivers. Next, a revised method of braking behavior analysis is proposed to better characterize this behavior and model it stochastically. Based on data indicating that this system could be effective, commercial vehicle driver braking behavior was evaluated to show that braking behavior in emergency situations could be reliably distinguished from behavior in non-emergency situations. This is important in that it allows the system to act only in situations in which it is necessary. Lastly a prototype DBS system is developed and is shown to be effective at reducing vehicle stopping distance and collision velocity in situations in which the vehicle cannot stop.

Committee: Dennis A. Guenther (Advisor); Gary J. Heydinger (Committee Member); Ahmet Kahraman (Committee Member); Junmin Wang (Committee Member) Subjects: Engineering; Mechanical Engineering

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

Battery technology is evolving rapidly with growing energy and power densities. While this energy storage device becomes more prominent and ubiquitous in the automotive industry, safety and reliability of the battery system are attracting more attention than ever before. The battery management system (BMS) becomes the key to improving vehicle safety, prolonging battery life, and reducing cost. This project focuses on constructing a test bench for BMS with Hardware-In-the-Loop (HIL) technique. A battery model that is single-cell-capable and real-time-capable simulates a battery pack's behavior as a series of connected cells. It takes into account the manufacturing differences between cells that cause voltage deviation from the ideal reference voltage. Said model is then implemented into the dSpace mid-size HIL simulator. Several experiments were performed with the virtual battery model to validate prototyped BMS components and BMS software. These components include a module balancing board, a battery controller module, and a battery data logger. These tests indicate it is feasible to validate BMS with HIL techniques, and the developed battery model can reduce the cost of developing BMS software and hardware.

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

Master of Science, The Ohio State University, 2013, Mechanical Engineering

The work presented within this thesis consists of the validation of a supervisory controller and vehicle simulator for the ECO Saver IV demonstration bus being developed as part of the National Fuel Cell Bus Program (NFCBP). The goal of the NFCBP is to develop fuel cell transit buses such that a U.S. industry for fuel cell bus technology can be established through both technology innovation and increased public awareness of fuel cell vehicles. The use of fuel cells in vehicles is desirable due to their high efficiencies and zero emissions, allowing the transportation sector to rely less heavily on petroleum and carbon based fuels that emit hazardous greenhouse gases. The ECO Saver IV, as designed by the DesignLine Corporation through a contract with the Center for Transportation and the Environment, is a battery dominant fuel cell hybrid bus that takes advantage of the benefits of hybridization in conjunction with the benefits of the fuel cell. The team of researchers at The Ohio State University (OSU) Center for Automotive Research (CAR) served as a subcontractor to develop a supervisory controller and fuel cell hybrid bus simulator, modeled after the chosen powertrain architecture. The validation performed involved the use of software-in-the-loop and hardware-in-the-loop simulations, where the results were compared to baseline model-in-the-loop simulations. The driving conditions of the intended application of the demonstration bus, i.e., integration into the OSU Campus Area Bus Services (CABS) fleet, were taken into consideration through the development of real-world drive cycles that were representative of actual CABS bus routes. A new driver model was developed that solved issues related to tracking distance, velocity and road grade to enable the use of real-world drive cycles. The results of the validation are to be used in the final phases of development and construction of the ECO Saver IV fuel cell hybrid transit bus to prove the effectiveness of (open full item for complete abstract)

Committee: Shawn Midlam-Mohler Dr. (Advisor); Yann Guezennec Dr. (Committee Member) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2013, Mechanical Engineering

According to NHTSA's 2011 Traffic Safety Facts, large-truck occupant fatalities increased from 530 in 2010 to 635 in 2011, which is a 20% increase. This was a second consecutive year in which large truck fatalities have increased (9% increase from 2009 to 2010). There was also a 15% increase in large truck occupant injuries from 2010. Moreover, the fatal crashes involving large trucks increased by 1.9%, in contrast to other-vehicle-occupant fatalities that declined by 3.6% from 2010. Given the high accident involvement rate of heavy trucks, the research presented in this dissertation focussed on methods of improving and testing heavy truck ESC performance. The first part of the research, aimed at estimating trailer parameters and states using sensors on the tractor to enhance the capabilities of the tractor based ESC unit. The mass of the vehicle and road grade are first estimated using recursive least square estimation. The trailer CG position is then estimated using the load on the tractor drive axels. This is followed by a planar model of an articulated vehicle to calculate the lateral acceleration, longitudinal acceleration and yaw rate of the trailer CG. Finally a Dual Extended Kalman Filter is developed to estmate trailer roll angle and roll parameters. The second phase of the research involved the development of a state of the art Hardware in the Loop simulation setup to test heavy truck ESC systems. The design of the HIL system is briefly discussed followed by the modeling of the vehicles in TruckSim. This is followed by a rigorous validation of the vehicle models and the HIL setup. Finally some of the applications of the validated HIL setup is discussed. This includes an indepth study of the Sine with Dwell maneuver and effects of vehicle speed, surface friction and CG height on the vehicle stability. This is followed by the design of a steering controller which is used to study the advantages afforded by the ESC system in an actual crash sc (open full item for complete abstract)

Committee: Dennis Guenther Dr. (Advisor); Gary Heydinger Dr. (Committee Member); Ahmet Kahraman Dr. (Committee Member); Junmin Wang Dr. (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2013, Mechanical Engineering

The global automotive industry continually strives to develop advanced automotive control systems that can reduce fuel consumption and emissions towards a greener future, while improving efficiencies and enhancing safety and comfort.This effort is collaborative, with not just OEMs and their suppliers working amidst stiff competition and strict regulations, but various research organizations, both independent as well as academic collaborating with these OEMs. As a result of this collaborative effort, there is a huge potential for synergy, but also the possibility of huge disconnects in the process itself. Model-Based System Development methodologies that use simulation models representing the controlled and controlling systems have a very important role to play in today's scenario when developing control systems. For a research organization with strong academic background such as the Center for Automotive Research at The Ohio State University, a comprehensive process for automotive model-based control that encapsulates various practice and standards already in place in the industry can help develop better solutions when collaborating with industry. While systematic and detailed approaches exist already, there are sufficient variations amongst internal approaches and methodologies which calls for a more unified approach that is industry-inspired. This thesis presents a comprehensive process that helps a developer gain an overall perspective to the bigger problem. The proposed methodology starts right at the fundamental opportunity identification phase, and is driven in the early stages by product development methodologies. Systematic approaches towards identifying opportunities, generating concepts and selecting concepts, with a case-study based on the usage of model-based simulations tools to select concepts are presented. With constantly changing requirements in the automotive industry, the need for traceability to the initial requirements has b (open full item for complete abstract)

Committee: Shawn Midlam-Mohler PhD (Advisor); Giorgio Rizzoni PhD (Committee Member); Marcello Canova PhD (Committee Member); Fabio Chiara PhD (Committee Member) Subjects: Automotive Engineering

Master of Science, The Ohio State University, 2010, Mechanical Engineering

Increased pressure for fuel economy improvement in combination with rapid development of battery technology has brought focus to new vehicle architectures like: hybrid electric vehicles (HEV), plug-in hybrid vehicles (PHEV), and extended range electric vehicles (EREV). These architectures require different engine characteristics which must be considered during the component selection phase of a vehicle development program. Throughout the development program a variety of different engine simulation techniques can be used to increase the efficiency of the program. Current literature has shown that a wide variety of engine simulation models have been developed and applied to many different engine research problems. These models vary greatly in their fidelity and computational efficiency. The methods which are used to build and calibrate the different models require varying amounts of empirical data and model calibration effort. With a large number of model based resources available, a key question is how to select and implement models which are best targeted for a project's goals. When developing a new engine control strategy, some of the driving issues are cost and resource minimization and quality improvement. This thesis outlines how a model based approach was used to choose an engine and develop an engine control strategy for an EREV. The outlined approach allowed the development team to minimize the required number of experiments and to complete much of the control development and calibration before implementing the control strategy in the vehicle. It will be shown how models of different fidelity, from map-based models, to mean value models, to 1-D gas dynamics models were generated and used to develop the engine control system. The application of real time capable models for Hardware-in-the-Loop testing and the development of an electronic throttle control strategy will also be shown. The application of this work is the EcoCAR advanced vehicle competition. T (open full item for complete abstract)

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

Master of Science, University of Akron, 2007, Electrical Engineering

This thesis explains various stages of the vehicle controller development, especially for a Hybrid Electric Vehicle (HEV), and documents the development of a platform for vehicle controller testing. Two stages of testing a vehicle controller, namely Software-in-Loop (SIL) simulation and Hardware-in-Loop (HIL) simulation, are explained in a stepwise manner for the series-parallel 2x2 HEV. The idea of using a common tool from the design stage to the prototyping stage is demonstrated. The series-parallel 2x2 HEV is modeled using the Powertrain Systems Analysis Toolkit (PSAT) in Matlab/Simulink. A rule based vehicle control strategy is added to the existing control libraries in PSAT. The SIL testing of the HEV model is done by exercising it over various drive cycles. A HIL platform is built from the ground up using commercially available off-the-shelf computers and Input/Output cards. The offline model of the HEV is simulated on the HIL platform to start the vehicle controller testing process. The preliminary HEV model was used to demonstrate the capabilities of the HIL setup. The HIL simulation setup is scalable and allows the incorporation of additional computational nodes for distributed simulation of complex systems without a major change to the original setup. The HEV model is run in real time on two computation nodes and the differences between offline and online simulations are discussed. The HIL simulation platform is successfully built and can be used for testing and tuning the vehicle controller.

Committee: Iqbal Husain (Advisor) Subjects:

Master of Science, The Ohio State University, 2013, Mechanical Engineering

The purpose of this thesis is to evaluate the findings brought forth from a research project conducted at The Ohio State University Center for Automotive Research. The objective of the research was to accurately model a 6x4 tractor-trailer rig using TruckSim and simulate severe braking and handling maneuvers with hardware in the loop and software in the loop simulations. For the hardware in the loop simulation (HIL), the tractor model was integrated with a 4s4m anti-lock braking system (ABS) and straight line braking tests were conducted. In addition to this, CAN messages were transmitted and received with the electronic control unit utilized by the ABS system. For the software in the loop simulation (SIL), anti-lock braking (ABS) and roll stability control (RSC) algorithms were developed using Simulink and tested with the TruckSim model. By properly simulating the tractor-trailer rig using HIL and SIL simulations, severe maneuvers could be performed and the rig's response characteristics could be evaluated within a lab environment. The first step in creating the HIL and SIL simulations was to develop a model of a 6x4 tractor using TruckSim. In order to accomplish this, over 100 vehicle parameters were acquired from a real production tractor and entered into TruckSim. Similarly, parameters from a production trailer were acquired and entered as well. By entering these parameters into TruckSim, the dynamic behavior of the actual tractor-trailer could be simulated within a computer environment. The tractor-trailer model was then subjected to simple handling maneuvers without the aid of any vehicle stability controls and its performance was compared against experimental data from the tractor manufacturer. This was done in order to validate the accuracy of the TruckSim model. After the tractor-trailer model was validated, the HIL simulation was developed. Essentially, the HIL simulation integrates actual braking hardware with the computer based tractor mod (open full item for complete abstract)

Committee: Dennis Guenther Dr. (Advisor); Gary Heydinger Dr. (Advisor) Subjects: Automotive Engineering; Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2013, Mechanical Engineering

The purpose of this thesis is to develop a vehicle dynamics model of a 6 X 4 cab-over tractor and a 2-axle semitrailer combination and a model-based design of ABS and ESC controllers. In addition to this, a Hardware-in-the-Loop (HIL) simulation of an Anti-lock Braking System (ABS) for a heavy truck was performed using dSPACE. TruckSim, developed by Mechanical Simulation Corporation (MSC), was used to model the vehicle dynamics. The tractor was equipped with disc brakes and the trailer was equipped with drum brakes. Model validation was by performing various dynamic maneuvers like J-turn, double lane change, decreasing radius curve test, high dynamic steer input and constant radius test with increasing speed. The model was validated in all three loading conditions: Bobtail or solo tractor, low CG trailer and high CG trailer condition. The vehicle responses obtained from TruckSim were compared against the experimental field test data obtained from the Heavy Truck Manufacturer (HTM). A hardware-in-the-loop (HIL) simulation of a heavy truck ABS system was setup in order to better understand the ABS control strategy and various activation thresholds involved. The test bench consists of six (6) brake chambers, ABS modulator valves, ABS electronic control unit from a commercial supplier, two air reservoirs, wheel speed sensors and pressure sensors for measuring the individual brake chamber pressures. dSPACE midsize was used to interface the vehicle model in TruckSim with the hardware components in the physical realm. The simulator converts the digital signals from TruckSim such as lateral acceleration, yaw rate and tractor speed into suitable analog signals which serve as inputs to the control module. For this simulation, the wheel speed signals coming from TruckSim were converted into an analog signal of sinusoidal form whose frequency is proportional to the wheel spin rate. TruckSim along with the hardware components thus forms a closed-loop system. The algorithm in (open full item for complete abstract)

Committee: Dennis Guenther PhD (Advisor); Gary Heydinger PhD (Committee Member) Subjects: Automotive Engineering; Mechanical Engineering