Automotive Engineering Commons^™

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Development And Validation Of A Thermal Model For An Electric Vehicle Transmission, Ghazal Rajabikhorasani

Dissertations

This research investigates the thermal behavior of a high-speed electric vehicle (EV) helical gearbox with the goal of improving the prediction accuracy of component temperatures and total power losses under a range of operating conditions. The study focuses on developing a physics-based lumped-parameter thermal network model capable of capturing the main heat generation and dissipation mechanisms within the transmission. The model integrates experimentally validated loss correlations for gears, bearings, seals, and churning, as well as convective and radiative heat transfer paths.

The thermal network model was coded in MATLAB and validated through a series of controlled experiments performed on an …

Techniques For Enabling Advanced Adas Features: Operation In Inclement Weather And Use Of Infrastructure Information, Parth Kadav

Dissertations

Modern vehicles have undergone a transformation with the widespread integration of Advanced Driver Assistance Systems (ADAS) technology becoming the new standard and are set to be mandated by the by the National Highway Traffic Safety Administration (NHTSA) for all passenger vehicles and light trucks. ADAS features have proven to prevent or mitigate crashes by either alerting or assisting the driver. ADAS typically utilizes a forward-facing camera, which comes standard in modern vehicles to provide limited automation features such as Lane Keeping Assist (LKA), and Lane Centering Assist (LCA) to improve driver safety. These systems rely on the assumption that vehicle …

Faulty Perception Correction Of Autonomous Vehicles In Real-Life Driving Scenarios, Mark Omwansa

Dissertations

Driving is one of the most popular modes of transportation in the world. The United States Department of Transportation’s (USDOT) Federal Highway Administration (FHWA) reported 2.8 trillion vehicle-miles traveled (VMT) in 2020, and the National Highway Traffic Association (NHTSA) recorded 3.2 trillion VMT in 2019. Also recorded in the NHTSA report were 39,096 fatalities and 2.7 million injuries due to traffic accidents, costing the economy an estimated $242 billion. Most of these recorded accidents can be attributed to human error or misjudgment. It is for this reason that governments and the automotive industry are looking at autonomous vehicle (AV) technologies …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Av Operation And Energy Efficiency Improved Through The Evaluation And Demonstration Of Av Sensor Technology, Nicholas Brown

Dissertations

The majority of states have passed legislation or have signed an executive order enacting safe testing, development, and deployment of level 4 and level 5 autonomous vehicles (AVs) in accordance with SAE standard J3016, which has led to an increase in the frequency of AV testing. The major driving force behind the push for AVs on public roads appears to be the increases the number of AVs on the road to decrease the chances of fatalities from distracted drivers. There are reports of disengagement from companies, which are required to report them to operate within California. The continuance of disengagements …

Improving Autonomous Vehicles Operational Performance Using Resilience Engineering, Johan Fanas Rojas

Dissertations

Autonomous vehicles are expected to revolutionize the transportation industry by providing a safer and more efficient means of transportation. However, as autonomous vehicles are deployed on public roads, they are exposed to significant risks, both in terms of safety and system performance. Recent studies have highlighted a range of errors and accidents associated with autonomous vehicles, underscoring the need for a systematic approach to improve their operational resilience. Resilience engineering, a discipline focused on designing and analyzing complex systems to better cope with unexpected events and disruptions, offers a promising framework for addressing these challenges. Despite the potential benefits of …

Enabling Energy Efficiency In Connected And Automated Vehicles Through Predictive Control Techniques, Farhang Motallebiaraghi

Dissertations

The transportation sector is a significant contributor to global energy consumption and emissions, necessitating the development of sustainable transportation systems. In this regard, connected and automated vehicles (CAVs) have emerged as a potential solution to transform the transportation industry. By harnessing advanced mapping and location technologies, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, CAVs offer the promise of improving efficiency, reducing traffic congestion, and enhancing safety and comfort. However, the adoption of CAVs also brings about various challenges, including energy efficiency concerns that need to be addressed to fully realize their potential benefits. This dissertation investigates energy-efficient control techniques for transportation …

Instance Segmentation-Based Depth Completion Using Sensor Fusion And Adaptive Clustering For Autonomous Vehicle Perception, Mohammad Z. El-Yabroudi

Dissertations

Depth sensing is critical for safe and accurate maneuvering in robotics and self-driving car (SDC) applications. Most recent LiDAR sensors, such as Ouster and Velodyne, offer 360 degrees of scanning at the rate of ten frames per second, making them very appropriate for autonomous driving applications. However, LiDAR point cloud data show many shortcomings, especially its data sparsity and unassigned nature, making it very challenging to utilize in applications such as perception, 3D object detection, 3D scene reconstruction, and simultaneous localization and mapping.

In this study, a novel framework using instance image segmentation and the raw LiDAR data for the …

Model Predictive Controller Design For Internal Combustion Engines Based On The Second Law Of Thermodynamics, Muataz Abotabik

Dissertations

Energy resources depletion and worldwide strict emissions policies pose challenges that automotive manufacturers try to overcome through researching advanced powertrain technologies such as lean-burn gasoline, direct injection, homogeneous charge compression ignition engines, powertrain electrification, etc. Most of these developments have been focused on conventional internal combustion engines (ICE) emissions and performance enhancements. Most ICE control strategies are built based on the First Law of Thermodynamics (FLT) i.e., to deliver a specific load requirement, enhancing thermal efficiency, etc. The FLT doesn’t account for in-cylinder high temperature thermodynamics process irreversibilities that cause losses in the work potential; up to 25% of the …

Turbulence Investigations In The Core-Flow Of An Internal Combustion Engine, James R. Macdonald

Dissertations

Turbulence significantly impacts the operation of energy conversion devices. In internal combustion (IC) engines, mixing, heat transfer, and combustion are all strongly dependent on the turbulence inside the cylinder. Consequently, knowledge of the state of turbulence is critical for improving our understanding and modeling of engine processes.

Turbulence states may be determined through analysis of the Reynolds stress tensor, which can in turn be experimentally quantified using velocity data. In this research, stereoscopic particle image velocimetry (stereo-PIV) experiments were conducted in a single-cylinder, motored engine with optical access to measure the two-dimensional, three-component (2D-3C) velocity fields throughout the compression stroke. …

Retinex-Based Visibility Enhancement System For Inclement Weather With Tracking And Distance Estimation Capabilities, Marwan S. Alluhaidan

Dissertations

Road conditions affected by weather are well known to have an impact on the number of vehicle accidents and fatalities, due to low- to no-visibility conditions. According to the U.S. Department of Transportation, there are more than 1,259,000 crashes each year. On average, 6,000 people are killed and more than 445,000 people are injured annually due to severe weather conditions. These accidents could be significantly reduced if real-time visibility enhancement systems were made available. However, eliminating the impact of weather conditions on visibility is still lacking and beyond our control. The time has come to develop technology that is capable …