Automotive Engineering Commons^™

Advances Towards A Robotic Management Vehicle Suited To Nurse Row Crops To More Efficient Outcomes, Adam Mk Gronewold

Dartmouth College Ph.D Dissertations

Enhancing agricultural production while reducing input costs remains a central challenge in modern row-crop management. Recent advances in computation, imagery, and sensors are enabling more efficient practices across various agricultural domains, and automation technologies are increasingly available to manage tasks central to perennial crop development. Automation in row-crop agriculture, by contrast, lags behind. This thesis explores utilizing small, unmanned ground vehicles to transform row cropping through the implementation of unconventional, in-season management strategies. The first focus of this work considers improvements to nitrogen fertilization using small, autonomous vehicles. An agronomy experiment in corn assessed the effects of gradually applying nitrogen …

Hyperparameter Tuning For Robust Autonomous Vehicle Vision, Nico D. De Ros

Theses and Dissertations

Classification “flickering,” where the classification of an object changes inconsistently between consecutive video frames, remains a persistent issue in modern object classification algorithms. This problem undermines the reliability of autonomous vision systems and poses significant risks in high-stakes applications such as autonomous vehicles. This thesis explores the use of response surface methodology, a statistical design of experiments technique, to optimize hyperparameters across three object classification pipelines. The first pipeline combines YOLOv8 with SORT to establish a benchmark. The second integrates a Bayesian back-end, while the third employs an exponential smoothing back-end. Hyperparameter tuning was conducted using a two-step process: an …

Comparative Evaluation Of Linear Regression, Cross Validation And Regularization Approaches In Multivariate Data Analysis, Ransford Owusu, Felix Yeboah, Francis Effah Boateng

Data Science and Data Mining

This study evaluates linear regression and its enhanced variants incorporating cross-validation and regularization techniques for high-dimensional, multivariate datasets. We address challenges such as multicollinearity and overfitting. Methods including Ridge, LASSO, and Elastic Net are compared against ordinary least squares regression. Empirical analysis using an automobile dataset for fuel efficiency prediction shows that while OLS regression captures basic relationships, its limitations are mitigated through regularization and cross-validation, resulting in improved model interpretability. The findings provide a comprehensive framework for predictive modeling in complex data environments and offer insights into statistical methodology and practical applications in the automobile industry.

Mixed Ion-Electron Conducting Lixag Alloy Anode Enabling Stable Li Plating/Stripping In Solid-State Batteries Via Enhanced Li Diffusion Kinetic, Anran Cheng, Pei Gao, Ruxing Wang, Kangli Wang, Kai Jiang

EKU Faculty and Staff Scholarship

Although showing huge potential in prospering the marketplace of all-solid-state lithium metal batteries (ASSLMBs), garnet-type solid electrolytes (Li6.5La3Zr1.5Ta0.6O12, LLZTO) are critically plagued by interface instability with Li anode and the vulnerability to Li dendrite, which are attributed to poor Li diffusion kinetic in bulk Li metal. Herein, a LixAg solid solution alloy with high Li diffusion kinetic is reported as a mixed ion- electron conductor (MIEC) alloy anode. The high Li diffusion kinetic stemming from a low eutectic point and a high mutual solubility of LixAg could reduce the Li concentration gradient in the anode, regulate Li electrochemical potential, and …

Navigating The Future Advancing Autonomous Vehicles Through Robust Target Recognition And Real-Time Avoidance, Mohammed Ahmed Mohammed Hussein

Theses and Dissertations

The problem being tackled by this thesis is a very important one and very relevant to our days and times: it is about making improved target recognition and enhanced real-time response skills in AVs under simulated conditions. Our plan is to put some enhanced sensory capabilities into these vehicles and see if that makes them safer and more reliable. We are using as our base a particular object recognition algorithm (YOLOv7) and a particular simulation environment (CARLA). We utilized the CARLA 0.9.14 simulator on Ubuntu 20.04 as a more stable option than the initially used CARLA 0.9.15 on Ubuntu 22.04, …

Performance And Emissions Characteristics Of Multi-Cylinder Direct Injection Diesel Engine Fuelled With Diesel/Biodiesel And Toluene Additives, Hagar Alm-Eldin Bastawissi, Medhat Elkelawy Prof. Dr. Eng., Mohammed Osama Elsamadony Dr. Eng., Moustafa Ghazaly Eng.

Journal of Engineering Research

This study explores the impact of using a diesel, biodiesel, and toluene additive fuel blend in a multi-cylinder direct injection diesel engine, focusing on both performance and emissions characteristics. Biodiesel, made from renewable sources like vegetable oils, is often added to diesel to reduce reliance on fossil fuels and improve combustion due to its higher oxygen content. In this work, a 45% biodiesel blend (B45) led to significant reductions in particulate matter (PM) emissions by up to 40% compared to conventional diesel. Additionally, carbon monoxide (CO) and hydrocarbon (HC) emissions decreased by up to 30% and 25%, respectively, due to …

Effect Of Dual-Fuelled Cng And Gasoline On Spark Ignition Engine Performance And Emissions Behaviors At Different Loads, Medhat Elkelawy Prof. Dr, Eng., M.M. Bassuoni Prof. Dr., Hagar Alm-Eldin Bastawissi, Saied I. Haiba Eng.

Journal of Engineering Research

Engine emissions are one of numerous factors that have detrimentally affected the environment, such as global warming, which is the result of growing exhaust gases, practically carbon dioxide (CO2). These emissions have substantially influenced researchers to develop strategic initiatives to minimize the carbon contents of fuels. The purpose of this research is to investigate the characteristics of internal combustion (SI) engines fueled by gasoline and compressed natural gas (CNG) blends. The experiments are performed using a HONDA 4-stroke, single-cylinder, and air-cooled SI engine. Five CNG concentrations are evaluated, the concentrations range from 0.5 to 2.5 L/min with a 0.5 L/min …

Dashar: An Implementation Of Augmented Reality Technology For Automotive Applications, Trevor D. Brown

Masters Theses & Specialist Projects

Since the advent of the modern automobile, manufacturers have provided means of tracking various critical data points associated with automobile operation, with the most prominent and standardized method being the instrument cluster. These data points include, but are not limited to, automobile speed, engine speed, fuel level, oil temperature, radiator (water) temperature, and battery charge. While this data is updated in real-time as the automobile is running, traditional instrument clusters cannot be modified or adjusted to the automobile driver’s needs, unless extensive after-market modifications are made. These modifications can be expensive, and require great understanding of the automobile’s assembly.

Alongside …

Integration Of Agile Approach Into The Implementation Of The Iso/Sae 21434 On Top Of The V-Model To Enable Continuous Secure-By-Design Automotive Cybersecurity Development, Pooja Patil

Master's Theses and Doctoral Dissertations

The rapid evolution of technology is revolutionizing the automotive industry, with connected and autonomous vehicles at the forefront. These vehicles rely on complex digital ecosystems to enhance safety and efficiency but are increasingly vulnerable to cybersecurity threats. Addressing these challenges requires following robust development methodologies, while complying with cybersecurity standards. This study introduces a framework that merges the widely used agile methodology practices with the ISO/SAE 21434 standard to support secure-by-design automotive product development. Traditional development approaches like the V-model provide structured and linear project phases, but they often lack the flexibility and the ability to adapt to evolving security …

ต้นแบบระบบตรวจจับ คัดแยก และป้องกันภัยจากอากาศยานไร้คนขับขนาดเล็ก, รักษ์ตะวัน พูลสุวรรณ

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้ได้นำเสนอการออกแบบและความเป็นไปได้ของระบบกระบวนการหลายขั้นตอนแบบครบวงจรสำหรับการตอบโต้โดรน (UAV) โดยใช้แพลตฟอร์ม Software-Defined Radio (SDR) ในกระบวนการแรก การตรวจจับใช้วิดีโอเรดาร์แบบคลื่นต่อเนื่องปรับความถี่ (FMCW) ที่มีความถี่พาหะ 2.4GHz สำหรับการตรวจจับและติดตามเป้าหมายทางอากาศ ระบบย่อยนี้ได้รับการออกแบบมาเพื่อระบุระยะทางและความเร็วของวัตถุที่เข้ามา ขั้นตอนที่สองเกี่ยวข้องกับการจำแนกสัญญาณคลื่นความถี่วิทยุ (RF) โดยใช้โครงข่ายประสาทเทียมแบบคอนโวลูชัน (CNN) เพื่อวิเคราะห์สัญญาณควบคุมและการถ่ายโอนข้อมูลที่ส่งระหว่างโดรนและผู้ควบคุมภายในย่านความถี่ ISM (2.4GHz) ขั้นตอนสุดท้ายคือการทำให้ภัยคุกคามที่ได้รับการยืนยันเป็นกลางโดยใช้เทคนิคการตอบโต้ทางอิเล็กทรอนิกส์สองวิธี วิธีแรกคือการปล่อยสัญญาณรบกวนเพื่อขัดขวางการเชื่อมโยงคำสั่งและการควบคุมในย่านความถี่ ISM (2.4GHz) เพื่อแยกโดรนออกจากผู้ควบคุมได้อย่างมีประสิทธิภาพ ในขณะเดียวกัน ระบบจะทำการหลอก GPS ภายในย่านความถี่ L1 โดยการส่งสัญญาณ GPS L1 จำลองในย่านความถี่ 1.575GHz เพื่อทำให้ตำแหน่งของโดรนสับสน ระบบนี้ได้ทดสอบกับโดรน DJI Mini 4K ที่มีพื้นที่หน้าตัดเรดาร์ 0.01 ตารางเมตร โดยมีผลลัพธ์คือระยะการตรวจจับ 20 เมตร และระยะการรบกวนและการหลอก GPS ที่ 50 เมตร ภายใต้ข้อจำกัดกำลังส่งที่ 20 dB ตามข้อจำกัดของย่านความถี่ ISM

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Intelligent Systems For Synergistic Optimization Of Vehicle Dynamics And Energy Consumption In Connected And Automated Vehicles, Aman Poovalappil

Dissertations, Master's Theses and Master's Reports

This dissertation focuses on the development and validation of on-board, real-time estimation algorithms and the investigation of energy-saving strategies for modern light-duty vehicles, including connected and automated vehicles (CAVs). The accurate estimation of key vehicle parameters is critical for enhancing energy efficiency, enabling advanced driver-assistance systems (ADAS), and optimizing vehicle performance and energy efficiency. First, this research introduces a novel real-time algorithm to dynamically learn vehicle mass using readily available sensor data. Based on longitudinal vehicle dynamics, a sensitivity analysis was conducted to identify the conditions under which mass estimation is most robust. The algorithm was extensively validated through on-road …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

Vector Estimation For Continuous Tracking Of Observed Radio Signals (V.E.C.T.O.R.) Lunar Navigation System, Dimitry Melnikov, Evan Bartel, Andrew Burrier, Goran Gjorgievski

Williams Honors College, Honors Research Projects

NASA's Artemis program requires precise navigation capabilities to establish the first sustained presence on the lunar surface. However, as launches bring necessary orbital infrastructure, the Artemis program will face a critical period during which reliable lunar navigation is not possible. To address this challenge, the V.E.C.T.O.R. system tracks assets, such as rovers and astronauts, as User Terminals relative to a pre-existing cell tower, or Base Station. To do so, the system leverages existing Base Station hardware to calculate the location of User Terminals in conjunction with existing communications infrastructure.

Improving The Accuracy Of Neighborhood Median Pixel Method (Nmpm) In Classifying Landsat-8 Oli Images By Optimizing The Scoring System’S Point Values, Abraham T. Magpantay, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary …

Numerical Modelling And Co-Optimization Of Gasoline Fuels For Gasoline Compression Ignition Using Multi Component Approach, Ashwin Karthik Purushothaman

Dissertations, Master's Theses and Master's Reports

Future mobility is expected to rely on a broad spectrum of powertrain technologies, including battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), fuel cell electric vehicles (FCEVs) and traditional internal combustion engine (ICE) vehicles. Despite the shift toward electrification, internal combustion engines are projected to remain a key component of future powertrains, either as the primary power source or as range extenders to generate electricity in hybrid systems. As such, significant research efforts continue to focus on enhancing the efficiency and reducing the emissions of ICEs to meet increasingly stringent regulatory and environmental targets. Gasoline compression ignition (GCI) has been …

Investigations Of Conjugate Heat Transfer And Fluid Flow In Partitioned Porous Cavity Using Darcy-Forchheimer Model: Finite Element-Based Computations, Nasir Yasin, Shafee Ahmad, Muhammad Umair, Zahir Shah, Narcisa Vrinceanu, Ghadah Alhawael

Mathematics & Statistics Faculty Publications

The conjugate heat transfer and fluid flow has vast applications in thermal engineering, particularly for cooling in thermal devices, and automobile engines. This study investigates conjugate heat transfer in 2D enclosures, featuring thin solid fins attached to a porous bottom wall. The porous medium is considered isotropic and homogeneous by the Darcy-Forchheimer model, with fluid phases in local thermal equilibrium. The boundary conditions at the porous fluid interface ensure continuity of the velocities, stresses, temperature, and heat flux. The phenomenon is mathematically modelled by obtaining a set of partial differential equations. The finite element method (FEM) is used to perform …

Development And Verification Of Testing Platform For Torque Vectoring Controller, Nicholas Petersen

Dissertations, Master's Theses and Master's Reports

A foremost concept in the automotive industry in recent years has been that of a vehicle “digital twin”. A digital twin is an accurate simulation of a dynamic system for the use of rapid development. These digital twins have a critical advantage over physical testing, which have dominated vehicle development up to now. As the cost of physical testing continues to rise, simulation can deliver rapid system development in a low-cost format. No simulation environment is a perfect representation of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially …

Influence Of Time Pressure And Flood Information Type On Flood Alert Effectiveness In Driving, Katherine R. Garcia, Scott Mishler, Jing Chen

Psychology Faculty Publications

Flood alerts are a means of risk communication that alerts the public to potential floods. The purpose of this research was to investigate factors that affected drivers' understanding and actions given a flood presented through a mobile navigation application. Two experiments were conducted to examine the effects of time pressure and type of flood information on drivers' planned actions when faced with potential flooding. Participants were asked about their planned actions given one type of flood information in a driving scenario either with or without time pressure. Our results indicated significant differences in participants' behaviors across the different flood information …

Similarity May Be Safer: The Effect Of Similarity Between Speech-Based Takeover Request Style And Driver Personality On Self-Driving Takeover Performance, Keer Ma, Jianfeng Wu, Yanxi Lin, Zihan Li, Songyang Guo, Dongfang Jiao, Shihan Yu

Psychology Faculty Publications

In Level 3 automated driving, it is critical that drivers can rapidly and effectively shift from non-driving related tasks (NDRT) back to the driving task. While previous research has examined the modality, timing, and vocal characteristics of takeover requests (TORs), little is known about how the style of speech-based TORs interacts with drivers’ personality traits. This study conducted a driving simulator experiment with 49 participants using a 2 × 2 within-subjects design. Drawing on the dominant-submissive dimension of personality, we examined the similarity of personality tendencies between speech-based TORs and drivers under takeover scenarios of varying urgency (low: road construction; …