Automotive Engineering Commons^™

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 …

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 …

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.

Dynamic Direct Voltage Control Under Maximum Torque Per Ampere For Interior Pmsms, Mohamad Alzayed, Hicham Chaoui, Alaref Elhaj

Electrical & Computer Engineering Faculty Publications

A novel method for controlling the speed of interior permanent magnet synchronous motors (IPMSMs), known as the current-sensing-based dynamic direct voltage control method under the maximum torque per ampere (MTPA) concept, is introduced. This technique achieves precise tracking of machine velocity by determining the optimal combination of voltage amplitude and angle for each specific motor velocity and current/load condition. Unlike previous studies, this approach takes into account the transient model of the machine, resulting in improved accuracy during dynamic operating conditions compared with existing methods in the literature. Moreover, a comparative analysis is conducted involving different direct voltage MTPA speed …

Iuse: A Gamified Virtual Learning Platform For Connected Vehicle Applications To Enhance Undergraduate Transportation Education, Tianyu Shen, Di Yang, Kai Sun, Hong Yang, Kun Xie, Mansoureh Jeihani

Civil & Environmental Engineering Faculty Publications

Emerging transportation technologies are rapidly reshaping transportation systems and industry practice. However, most transportation undergraduate curricula still emphasize foundational topics such as geometric design, travel demand forecasting, pavements, and soil properties, typically delivered through lecture-centric instruction. While these subjects remain essential to the discipline, they do not fully reflect the pace of technological change or provide sufficient opportunities for experiential learning with modern tools and data. This gap limits students’ exposure to CV concepts and their ability to translate theory into practice.

Focusing on a key emerging technology, connected vehicles (CVs), this paper bridges the above gap by introducing a …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

Gem-Can: A Real-World Dataset Of Can-Bus Attack Scenarios On An Autonomous Vehicle For Intrusion-Detection Research, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini, Tienake Phuapaiboon, Milad Khaleghi, Daniel Tobias

Electrical & Computer Engineering Faculty Publications

This paper presents GEM-CAN, a labelled Controller Area Network (CAN) dataset captured from an autonomous GEM e6 platform under both normal operation and controlled cyber-attack conditions.

The dataset contains ∼143 K frames comprising (i) ∼ nominal autonomous operation (∼100k messages), (ii) DoS floods using arbitration ID 0 × 00000000 (∼41 K messages), and (iii) data-tampering injections that reuse legitimate IDs for brake and steering-lock (∼1.3 K messages). Each record includes timestamp, arbitration ID (11/29-bit), DLC, eight payload bytes, and a Normal/Attack label. A companion metadata file enumerates attack windows, PCAN bus-load traces, bitrate, and test conditions. Data were collected with …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

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 …

Optimizing Maintenance Routes For Highway Infrastructure Using Leader-Follower Autonomous Vehicles, Qing Tang, Chenxi Chen, Xianbiao Hu, Yuxin Ding, Tianjia Yang

Civil & Environmental Engineering Faculty Publications

The Autonomous Truck Mounted Attenuator (ATMA), a leader–follower style connected and automated vehicle system, enhances safety during transportation infrastructure maintenance in work zones. However, the significantly lower speed of ATMA, compared to regular vehicles, causes moving bottlenecks that reduce roadway capacity and prolong queuing, leading to further delays. Different ATMA routes lead to varying patterns of time-dependent capacity drop, affecting the user equilibrium traffic assignment and resulting in differing system costs. This study aims to optimize ATMA routing within a network to minimize the system cost associated with its slow-moving operation. To this end, a queuing-based traffic assignment approach is …

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), …

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

Harrisburg University Other Works

The goal of this research was to establish the viability of using hybrid manufacturing for automotive applications. By verifying that high-stress components can be created, it can be assumed that any other lower stress part could be made to match the strength requirements. A limiting factor of adoption for hybrid manufacturing is how new the technology is. Studies on time and cost were performed allowing for comparisons with traditional manufacturing technologies (casting, forging, milling) used in automotive applications. This research utilized a Haas Automation UMC750 5-axis CNC mill with a Meltio laser wire direct energy deposition attachment. Fusion 360 was …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

In-Field Tractor Operational Load Profile Generation In Support Of Advanced Tractor Testing In Mixedmode Power, Andrew Donesky

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation addresses the need to better characterize real-world tractor power requirements across drawbar, power take-off (PTO), and hydraulic modes to support more representative tractor testing. Conventional testing protocols, such as OECD Code 2, emphasize steady-state performance under controlled high-load conditions, which do not reflect the mixed and dynamic demands of modern field operations. To address this gap, a Tractor Instrumentation System (TIS) was developed, validated, and deployed to collect high-resolution, mixed-mode power data during planting, anhydrous ammonia application, and grain cart operations. The TIS integrates physical sensors, including custom load pins, hydraulic pressure/flow sensors, and a redesigned PTO torque …

Development And Validation Of A Thermal Model For An Electric Vehicle Powertrain, Claudia Fajardo

Waldo Library Student Exhibits

No abstract provided.

Development Of A Cost-Effective Daq For Measuring Brake Performance In Race Cars, Adrin Alias

2025 Spring Honors Capstone Projects - Archive

This project explores the feasibility of creating a cost-effective data acquisition (DAQ) system for high-speed, real-time brake performance testing of Formula SAE racecars. The research addresses the limitations of the current MoTeC DAQ system currently employed by the team, which is costly and time-consuming to set up for on-car testing. The team will use a brake dynamometer for steady-state comparisons of different brake pad compounds (senior design project), but evaluating real-world performance requires on-car testing. By systematically comparing various hardware platforms, sensors, communication protocols, and storage solutions, this project aims to balance cost-efficiency with reliability and performance. The research evaluates …

Computational Modeling Of Torque Arm And Load Cell Interactions In Uta Racing Brake Dynamometer, Anthony A. Aiyedun

2025 Spring Honors Capstone Projects - Archive

The UTA Racing Team is developing a brake dynamometer to measure the coefficients of friction of various brake pads and calipers under controlled conditions. The dynamometer simulates on-track braking scenarios, testing brake pads at temperatures up to 1300ºF and pressures similar to racing environments. Its primary goal is to generate accurate friction vs. temperature graphs for various brake pad and rotor combinations, addressing the need for dedicated testing equipment. This project creates an ANSYS simulation of the torque arm and load cell interaction. The computational model accurately represents the relationship between applied torque and measured load cell force, incorporating precise …

Carbon Fiber Cv Tripod Axle Design: Husker Motorsports Formula Sae, Taylor D. Boudreaux

Honors Program: Senior Projects (Public)

The 2024–2025 HMS Formula SAE senior design team successfully reduced axle system weight, cost, and improved manufacturability using a bonded tripod joint and ±45° intermediate modulus carbon fiber tube. The final design was validated on HMS 25, with no failures under rigorous testing.

Budget:
/="/"> Total project cost was $1,165.64, exceeding the initial $1,000 target due to unanticipated test rig expenses. However, per-axle production cost was reduced from $525.31 to $383.37 through in-house manufacturing, use of on-hand epoxy (DP460), and switching to cost-effective 7068 aluminum. The axle-specific cost was $766.74.

Timeline:
/="/"> Despite early delays from building a torsional testing …

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 …

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 …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

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 …

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 …

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 …

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; …

Potential Of Lidar And Hyperspectral Sensing For Overcoming Challenges In Current Maritime Ballast Tank Corrosion Inspection, Sergio Pallas Enguita, Jiajun Jiang, Chung-Hao Chen, Samuel Kovacic, Richard Lebel

Electrical & Computer Engineering Faculty Publications

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral …

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 …

Flux-Weakening Control Methods For Permanent Magnet Synchronous Machines In Electric Vehicles At High Speed, Samer Alwaqfi, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Permanent magnet synchronous motors (PMSMs) are widely favored by manufacturers for use in electric vehicles (EVs) because of their many benefits, which include high power density at high speeds, ruggedness, potential for high efficiency, and reduced control complexity. However, since the Back Electromotive Force (EMF) increases proportionally with the motor’s rotational speed, it must be carefully controlled at high speeds. Flux-weakening (FW) control is required to avoid excessive electromagnetic flux beyond the power source and inverter’s voltage restrictions. This paper aims to compare various FW control strategies and analyze their effectiveness in maximizing the speed of PMSMs in EV applications …