Automotive Engineering Commons^™

Comprehensive Analysis Of Spray Development And Low Temperature Combustion Characteristics In A Cvcc With Nvh Of Renewable Aerospace Fuels: Hefa & Ft Synthetic Kerosene (S8) Compared To Jet-A & Ulsd, Coleman Norton

Honors College Theses

A comprehensive analysis was conducted to research the viability of Hydroprocessed Esters and Fatty Acids (HEFA) and S8 Synthetic Kerosene fuels as a drop-in replacement for conventional petroleum-based fuels, Jet-A and ULSD (Ultra Low Sulfur Diesel). Global transportation remains heavily dependent on liquid fossil fuels, while renewable aerospace fuels offer a promising pathway to reducing life cycle greenhouse gas emissions and pollution. However, the combustion performance and long-term feasibility of these fuels remain insufficiently characterized. This study performed a thorough assessment of each fuel's thermophysical and combustion properties. Thermophysical characterization encompassed viscosity, freezing point, energy density, spray atomization, and volatility. …

Design Optimization Of Active-Stabilizing Canards For High-Powered Rockets, Simon Babcock

Senior Honors Theses

Rocket canards are a common means of stabilizing a high-powered rocket in flight by producing aerodynamic forces to correct the rocket's trajectory. The size, shape, and position of the canards relative to the rocket body determine their control effectiveness and aerodynamic efficiency – two objectives of canard design with an inverse relationship to each other. By integrating automated iterative rocket trajectory simulation with adaptive surrogate modelling, this research optimized the canard planform geometry for a high-powered rocket to maximize the canards’ control effectiveness and aerodynamic efficiency through multi-variable, multi-objective design optimization. The canard design was first parameterized into four design …

Safe Control Design For Quadruped Locomotion In Unstructured Environments Using Linear Transfer Operators, Sriram Sundar Krishnamoorthy Shankara Narayanan

All Dissertations

Deploying quadruped robots in unstructured, obstacle-rich environments requires control and planning methods that remain safe and reliable despite complex terrain geometry, limited sensing, and inevitable modeling errors. This thesis develops operator-theoretic tools for safe control design of robotic systems using linear transfer operators, with a focus on quadruped locomotion in unstructured environments. The central goal is to develop a unified operator-theoretic framework for safe control design based on the Perron–Frobenius (P–F) and Koopman operators. In particular, the thesis leverages \emph{density functions} to develop safe navigation frameworks in the dual space of densities. In the operator-theoretic perspective, the P–F operator governs …

Development Of A Mixing-Controlled Combustion Model For 0d Engine Modeling: Using High-Order Models To Guide The Formulation Of Reduced-Order Models, James Gohn

All Dissertations

Vehicles are becoming increasingly complex to comply with the increasingly stringent regulations placed on all market sectors while still maintaining performance requirements. This increased complexity leads to increases in the cost and time it takes to develop and produce these next generation vehicles. To meet demand therefore, it becomes necessary to evaluate numerous design iterations using computer models. There are multiple levels of models that may be necessary for different purposes or use cases. All levels of modeling for virtual prototyping, however, require a level of validation and predictive ability to be useful. To this end, the following thesis presents …

Increasing The Number Of Injections Per Cycle In A Gasoline Compression Ignition Strategy: An Experimental Study, Joshua Murray

All Theses

Gasoline Compression Ignition (GCI) is an advanced compression ignition strategy which utilizes the high octane number of gasoline to improve diesel soot and NOx emissions by enabling partially premixed injections of fuel. GCI work in the literature typically uses single, double, or triple injection strategies, with higher numbers of injections noted for their ability to achieve low MPRRs, improved soot and NOx emissions, and higher efficiency. Thus, this study extends the literature by evaluating quadruple and quintuple injection strategies for continued improvement in engine efficiency and emissions.

The experiments in this work are conducted on a heavy-duty Detroit Diesel DD13 …

Bayesian Assembly Tool For Intelligent Digital Twin Selection, Shishir Sharma

All Theses

Digital twins can be used for system performance optimization, health monitoring, predictive maintenance, and investigating system anomalies. A ground vehicle digital twin may be assembled using representations of the subsystem models (tires, suspension system, powertrain, etc.) of different fidelity. Lower fidelity Digital Twins have relatively simpler design and physics, hence are generally computationally less expensive, but may not represent the physical system accurately. On the contrary, higher fidelity Digital Twins are generally more precise in their ability to represent the physical system, but due to complexity, are usually computationally more demanding. This thesis describes the application of Bayesian Classification within …

Optimizing Controller Speed And Torque To Reduce Drivetrain Stress, Selene J. Welch

SACAD: Scholarly Activities

This project investigates how torque delivery from the motor controller affects mechanical stress on the rear freewheel ratchet mechanism of a three-wheel electric race car. Excessive initial torque has caused accelerated wear and tear on the ratchet mechanism, reducing drivetrain reliability during Kansas ElectroRally competitions. Preliminary testing showed that slower, gradual acceleration prevented malfunction and allowed the vehicle to maintain top speed reliably. Prior research shows that torque-control strategies strongly influence electric-drive performance. Current research focuses on optimizing the controller’s torque and speed using the Alltrax Software ToolKit to reduce drivetrain tension while preserving the car’s performance capabilities.

Performance Analysis Of A Converted Electric Underbone Motorcycle: A Combined Dynamometer And Road Test Approach, Dhimas Oki Permata Aji, Sigit Suwarto, Yunus Ari Rokhim

Journal of Mechanical Engineering Science and Technology (JMEST)

In developing countries, converting traditional underbone motorcycles to electric vehicles transforms sustainable transportation from a theoretical concept to a practical implementation. This paper describes the design and operational performance evaluation of an electric underbone motorcycle conversion system that integrates a 2.0 kW, 72 V BLDC motor and a 72 V, 20 Ah lithium-ion battery pack. Compared to previous studies, this paper combines, for the first time, both dynamometer and road tests as the basis for a comprehensive performance assessment of low-cost motorcycle conversion. The dynamometer test was conducted under laboratory conditions to evaluate the test system for torque, power output, …

Plasmoid Vortex System Retrofit A Sustainability And Efficiency Study On Internal Combustion Engines, Walker Hall

Doctoral Dissertations and Master's Theses

The thesis addresses the persistent inefficiency and environmental degradation caused by internal combustion engines in modern vehicles, a major issue as the automotive industry faces increasing pressure to reduce fuel consumption and greenhouse gas emissions. Internal combustion engines, which power most cars today, convert only about 20-30% of fuel energy into useful work, with the remainder lost as heat and exhaust waste, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx). This inefficiency contributes to global carbon emissions, with transportation accounting for approximately 29% of U.S. greenhouse gases in 2021 [1]. As regulatory standards tighten (e.g., …

Markov-Modulated Queueing Network For Mobile Traffic Aggregation With Threshold-Controlled Buffers, Anton A. Esin, Elmira Yu. Kalimulina

Mathematical Modelling and Numerical Simulation with Applications

We study the problem of data transmission from mobile platforms operating in high-speed transit between cellular base stations, under conditions of unstable and intermittent connectivity. Conventional queueing and connectivity models often fail to capture the combined effects of rapidly changing signal conditions, finite buffer capacity, and dynamic topology. We aim to develop a tractable yet expressive model that integrates stochastic link availability, queue dynamics, and buffer control. We propose a mathematical framework based on queues whose service intensities are modulated by a continuous-time Markov chain (CTMC) representing signal conditions along a high-speed trajectory. The core subsystem is a two-stage (aggregation …

Scenarioxp: A Complete Scenario–Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Student Research Symposium (SRS)

Today is an age of exiting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …

End-To-End Development And Experimental Validation Of A 1/10-Scale Autonomous Vehicle, Rikkin Pankaj Panchal

Electrical Engineering Theses

Autonomous vehicle development demands vast resources, making scaled down platforms a critical alternative for solving core algorithmic challenges. The primary contribution of this thesis is the end to end development and validation of a complete real time autonomous driving pipeline deployed on a one tenth scale vehicle. To streamline platform development, an AI assisted annotation framework automates dataset generation, significantly reducing manual labor while improving training data quality. The system perception stack features a reinforcement learning guided online multi camera calibration framework that enables adaptive surround view stitching without the need for offline recalibration. This is paired with robust lane …

Rm Sotheby’S Construction Of Value In The High-End Car Market, Pablo Mijares Musi

MA Theses

The purpose of my thesis is to tell and enlighten any readers how the most prestigious and iconic auction house in the world, RM Sotheby’s sells more than just cars from the high-end car market and how the auction house adds value to through storytelling and provenance from each vehicle.  In today’s luxury market economics, we see somewhat of a radical shift. The high-end car market has moved into a very refined area where these beautiful machines are no longer seen and appraised just for their aesthetic design and mechanical engineering, now we see new value drivers for these works …

Uncertainty-Aware Estimation, Planning, And Control For Tracking Multiple Drifting Patches In Flow Fields, Daniel O. Akanji, Krishnanand N. Kaipa, Cong Wei

Mechanical & Aerospace Engineering Faculty Publications

In this study, we present a replay-based framework for uncertainty-aware persistent tracking of multiple advected surface patches using an autonomous marine vehicle operating in spatiotemporal-varying currents. The method combines three components: local flow estimation, covariance-aware patch-boundary propagation with intermittent boundary fusion, and mission-level scheduling over multiple patches. Each patch is represented by a polygonal boundary, whose vertices are propagated through the estimated flow field while carrying per-vertex covariance, thereby quantifying uncertainty growth during advection. A flow-aware gain-scheduled linear quadratic regulator (LQR) was designed to shape the desired surge speed to take advantage of favorable currents. When the vehicle services a …

Development Of Stronger, More Extrudable 6xxx Series Alloys For Automotive Applications, Eli A. Harma

Dissertations, Master's Theses and Master's Reports

6xxx alloys are widely used in automotive extrusion structures for their high specific strength and formability. Advanced designs require greater formability and strength, creating an opportunity for high-strength, formable alloys. The 6xxx series forms β”-Mg₅Si₆ precipitates during age hardening and develops a fibrous microstructure during extrusion, both strengthening the alloy. Increasing Mg and Si to form more β” decreases formability. Thus, modifying texture can increase strength without reducing formability. Current alloys like 6005A add Mn and Cr to form dispersoids that inhibit recrystallization and promote strengthening textures; however, high Mn and Cr concentrations reduce formability. Replacing Mn …

Machine Learning And Multi-Scale Optimization For Control And Energy Management In Connected And Automated Vehicle Propulsion Systems, Joshua D. Orlando

Dissertations, Master's Theses and Master's Reports

This dissertation presents a multi-scale optimization framework leveraging machine learning (ML) to enhance energy efficiency in connected and automated vehicle (CAV) propulsion systems. As transportation transitions toward hybridization and automation, the integration of vehicle-to-everything (V2X) connectivity and advanced control algorithms offers unprecedented opportunities for energy reduction. This research addresses three critical scales of vehicle energy management: multiple vehicle-level coordination, component-level powertrain dynamics, and real-time vehicle parameter estimation.

First, the research investigates the energy consumption characteristics of heterogeneous propulsion systems—ranging from internal combustion engines to battery electric vehicles across light- and heavy-duty sectors—on arterial roadways. Utilizing Particle Swarm Optimization (PSO) and …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

Statewide Corridor Evacuation Response And Re-Entry Behaviors In Florida During Hurricane Irma, Xin Wang, Yuan Zhu, Hong Yang, Kun Xie

Electrical & Computer Engineering Faculty Publications

Hurricane Irma stands as one of the most destructive tropical storms to make landfall in the United States, particularly impacting the State of Florida, where it prompted the largest evacuation in history with approximately 7 million residents. The profound consequences of mass evacuation underscore the critical need to understand travel behaviors during hurricane evacuation and the recovery process. This research analyzes statewide evacuation and re-entry patterns, leveraging diverse datasets, including TTMS data from main corridors and GIS data. A statewide corridor-based empirical analysis framework is constructed to characterize evacuation and re-entry response patterns using sensor-based traffic observations. The results show …

The Efficacy Of Hybrid Manufacturing For High Stress Automotive Parts, Logan Trimmer

Harrisburg University Other Works

Presentation covering the broad strokes of the project. The goal was to prove the viability of hybrid/additive manufacturing for high stress automotive applications. This project focused on recreating a piston from an old engine to examine if hybrid manufacturing could be used for such applications. On a small scale, hybrid manufacturing was able to be more cost effective if the costs of the equipment and electricity were ignored. The decision to ignore those costs came from the inability to find accurate prices for industrial casting.