Automotive Engineering Commons^™

Investigation Of The Impact Of The Shape Of The Wings On Formula E Racing Car Performance: Enhancements For Optimal Aerodynamics, Ednie Marthe Adlaikah Jozil

Honors Undergraduate Theses

The aerodynamic performance of a Formula E chassis significantly dictates its overall race efficiency, directly impacting crucial parameters such as battery range and thermal management. This thesis investigates the external aerodynamics of the baseline Gen 2 Formula E car and evaluates the performance gains of two novel aerodynamic packages: a "Fully Modified" configuration and a "Flat Rear Wing" design. Computational Fluid Dynamics (CFD) simulations were conducted to analyze drag coefficients (Cd), downforce generation, and vehicle wake structures at race-relevant free-stream velocities (e.g., 37 m/s and 89 m/s). To ensure numerical robustness, the computational setup was validated using a smooth sphere …

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 …

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 …

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 …

Communication-Aware Energy Optimization For Electric Vehicles With Adaptive Cruise Control, Shahriar Shahram

Graduate Studies Theses and Dissertations 2026

This dissertation develops information-driven methods to reduce traction energy in battery electric vehicles during adaptive and cooperative cruise control. Physics-grounded energetics are embedded in a predictive controller that accounts for intermittent V2V preview, sensing noise, packet loss, and powertrain limits. To ensure deployability, the nonconvex traction–power map is replaced by locally convex surrogates so each step solves a small, strictly convex QP in real time (average ≈ 70 ms/step on a desktop CPU: 8 cores/16 threads, 4.2–5.0 GHz), leaving margin at typical sampling rates (Ts =0.05–0.10 s; N=15–25).

Across standardized drive cycles from NREL DriveCAT—including FTP–75 (light duty), NREL Class …

Lean Service System Optimization In U.S. Automotive Maintenance Centers: A Time Study And Simulation-Based Approach To Reducing Service Cycle Time And Increasing Efficiency, Rakibul Hasan Sarker

All Graduate Theses, Dissertations, and Other Capstone Projects

The primary objective of this study is to measure the current service time at a U.S. automobile service center, with the aim of reducing waste and optimizing service operations through time study and simulation modeling. Inefficiencies in those service centers increase service time and labor costs, reduce service quality, and reduce workshop productivity, thereby increasing customer waiting time. In this study, real-world shop floor data were collected from a single service center, namely Jiffy Lube. Over the course of ten working days, 205 vehicle data points were acquired. Service time, bay time, and overall process time were computed and examined …

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 …

Control Strategies For 6-Dof Quadcopter Uavs: Cascade Pid Stabilization In White Noise Conditions, An Vo Van, Hung Ha Duy

Makara Journal of Technology

In this study, a cascade PID control structure is proposed and implemented for a 6-degree-of-freedom (6-DOF) unmanned aerial vehicle (UAV) to enhance stability and trajectory tracking capabilities under both noise and non-noise conditions. The controller was designed based on the Tyreus–Luyben tuning method and was evaluated using quantitative metrics, including rise time, settling time, overshoot, and steady-state error. Simulation results on MATLAB/Simulink show that the controller achieves high performance in angular channels (ϕ, θ, ψ) and altitude (z) with a short rise time (< 2s), slight overshoot (< 1%), and nearly eliminated steady-state error. However, the horizontal position channels (x, y) have a longer settling time (~110s) and are sensitive to white noise. Quantitative comparisons with other control methods show that the cascade PID outperforms the standard PID in terms of accuracy and stability, achieving a performance comparable to LQR under noise-free conditions, but is less robust in the presence of noise than advanced methods like SMC and MPC. These results confirm the feasibility of cascade PID in UAV applications and indicate potential future improvements by integrating nonlinear, adaptive, or intelligent control strategies.

Dem Simulations For Tractive Performance Of Rigid Wheel In Granular Media, Aidan Noah Dickerson

Theses and Dissertations

Accurately predicting vehicle mobility in granular media is essential for evaluating off-road mobility in agriculture, defense, and planetary exploration. Traditional empirical models often fail in capturing complex micromechanics of soil deformation, especially under dynamic conditions such as high slips and maneuvering. This study demonstrates the value of the discrete element method (DEM) in modeling wheel-soil interactions with higher fidelity. Using Altair EDEM and pre-calibrated GEMM materials, simulations were conducted for a range of forward (-5.9% to 54.8%) and side slip angles (3°, 6°, 12°) in dry sand. DEM enabled detailed analysis of sinkage, traction forces, and lateral loads, revealing trends …

Study On The Knock Resistance Offered By Thermally Stratifying Water Injections In A Single Cylinder Spark Ignition Engine, Aditya Datar

All Theses

Stochastic end-gas autoignition in SI engines, commonly called ‘knock’, limits attainable engine efficiencies. Multiple pathways to extend SI engine operation into knock-limited regions have been studied, including direct water injection (DWI). This study employs single-cylinder engine experiments and modeling to investigate the knock resistance offered by compression stroke water injections, which have shown to thermally stratify the cylinder in HCCI. In SI, thermally stratifying injections are expected to forcibly widen the cylinder temperature distribution by preferentially cooling the cylinder periphery. The end-gas is in the cylinder periphery; therefore, a cooler end-gas would result in longer ignition delays, thus providing knock …

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 …

Power Distribution Controller For Formula Sae Vehicles, Michael Yuen

Electrical Engineering

The Power Distribution Controller (PDC) is designed for low-voltage (12V) automotive applications, specifically Formula SAE vehicles. It aims to replace the current and outdated MOSFET-based system, which suffers from inefficiencies such as lossy current sense resistors and limited configurability. Instead, this new system uses a PROFET-based architecture controlled over the Controller Area Network (CAN) and an updated microcontroller, enabling intelligent power management and data logging.

Designed with FSAE regulations in mind, the PDC not only enhances the reliability of low-voltage systems but also provides a quickly adaptable solution to fuse boxes. It achieves this by providing firmware-adjustable overcurrent protection, incorporating …

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 …

Tradespace Exploration For Multiple Collaborative Vehicles, Mrunal Deshmukh

All Theses

Modern military operations demand systems that adapt to uncertain, rapidly changing missions across diverse terrains. Traditional single-platform vehicle design is insufficient for such complexity. This research introduces a hierarchical tradespace exploration framework for designing and evaluating families of heterogeneous ground vehicles under a System-of-Systems (SoS) architecture. The framework treats vehicle design as a co-optimization problem, where a “parent” vehicle (e.g., a Squad Multipurpose Equipment Transport) coordinates specialized “child” vehicles for reconnaissance, amphibious tasks, terrain traversal, and stealth missions. Unlike conventional approaches that optimize vehicles individually, this study emphasizes collaborative performance, resource sharing, and adaptability at the family level. Central to …

Multi-Modal Data-Efficient Learning For 3d Machine Vision, Zhimin Chen

All Dissertations

The rapid progress of 3D computer vision has enabled a wide range of applications in autonomous driving, robotics, and augmented reality. Despite this growth, training robust 3D perception models remains challenging due to limited labeled data, the complexity of integrating multiple modalities, and the inherently imbalanced and long-tailed nature of 3D datasets. This dissertation addresses these challenges by proposing data-efficient, multi-modal learning frameworks that improve the accuracy, generalization, and scalability of 3D scene understanding.

In the semi-supervised setting, this work presents novel approaches that combine limited annotations with large amounts of unlabeled data to enhance 3D object classification and retrieval. …

Hybrid Learning For Rough Terrain Navigation Of Actively Articulated Wheeled Vehicles, Dhruv Mehta

All Dissertations

Conventional wheeled ground vehicles have been used for rough terrain navigation in the recent years. They consist of a chassis connected to wheels through passive, semi-active, or active suspension systems. However, their fixed configurations limit mobility and maneuverability, constraining their ability to autonomously navigate diverse and rough terrains. Autonomous Ground Vehicles (AGVs) face significant challenges in this regard, including varying terrain roughness, soil hardness, and obstacle crossing.

To address these limitations, Actively Articulated Wheeled Vehicle (AAWV) architectures have recently emerged, offering real-time geometric adaptability. AAWVs have chassis and wheels connected via articulated serial or parallel linkages. However, increased articulation introduces …

Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar

All Dissertations

This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …

Influence On Shot Peening And Blast Polishing For Rotating Bending Fatigue Strength Of Vacuum Carburized Steel With Circumferential Notch, Yuji Kobayashi, Hayato Taniguchi, Kiyotaka Masaki

15th International Conference on Shot Peening

In order to improve fuel efficiency in automobiles, weight reduction is necessary as well as improving engine efficiency. For transmission gears, thickness reduction is necessary for weight reduction. When the thickness is reduced, the cross-sectional area becomes smaller, and the load stress increases. Therefore, higher fatigue strength is required. Most gears carry out carburizing. Shot peening is often used to improve the fatigue strength of carburized heat-treated parts. After shot peening, compressive residual stress is introduced, but at the same time, surface roughness is increased. Therefore, the processing conditions of shot peening are very important. Blast polishing is a processing …

Adaptation Of Shot Peen Parameters For Gear Geometry, David J. Breuer, Beth Matlock

15th International Conference on Shot Peening

Shot peening is a well established process for the surface enhancement of gearing. Gearing is a primary example of a high cycle fatigue application that can benefit from residual stress enhancement. Designing shot peening parameters to specific gear geometry based on material, heat treatment, and surface finish is a more precise way to achieve better performance outcomes. One of the best tools to assist in the optimal shot peening is x-ray diffraction (XRD) and its ability to measure small differences that can result in significant performance outcomes. XRD residual stress measurements are a direct measurement of elastic strain. The diffraction …

The E-Strip® As A Tool For Predictive Maintenance To Monitor And Control Shot Peening Processes, Walter A. Beach, Scott Glasier

15th International Conference on Shot Peening

The primary objective of this paper is to demonstrate the E-Strip® is capable of providing real-time feedback to the machine controller for making necessary process adjustments due to external forces, such as machine wear. Adjustments may include regulating air pressure and shot flow within pre-defined tolerances dictated by specification. It is anticipated that this integrated approach will significantly reduce variations in intensity levels observed from part to part and lot to lot.

Application Of Burnishing Process Expected To Frictional Heat To Non-Oriented Electromagnetic Steel Sheet, Yuji Kobayashi, Masato Okada

15th International Conference on Shot Peening

Burnishing is a technology used to improve the surface roughness of metals. On the other hand, it is expected that the metal structure near the surface will change due to strong distortion.

The magnetic properties of electromagnetic steel sheets depend on large crystal grains and fine crystals near the surface[1]. In particular, thinner fine crystal grain layers on the surface are expected to reduce iron loss. In this study, the power generation characteristics of Burnished electromagnetic steel sheets were evaluated.

Electromotive force waveform was evaluated by rotating the laminated electromagnetic steel sheets and using a spindle motor. The electromotive force …

The Effects Of Shot Peening Highly Loaded Compression Springs, David J. Breuer

15th International Conference on Shot Peening

The compression spring in this test study is typical of one used in a demanding off-road vehicle suspension. The cyclic loads applied during fatigue tests were severe enough to cause failure in approximately 11,000 cycles without shot peening.

The objectives of this study were the following: (1) Design a compression spring similar to those used in the off-road recreational industry. (2) Design & manufacture a fatigue test stand that could withstand high loads for months of testing. (3) Generate a matrix of shot peening treatments to produce different amounts of residual compressive stress. (4) Measure the shot peening residual compressive …

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

Waldo Library Student Exhibits

No abstract provided.