Automotive Engineering Commons^™

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 …

Multi-Level Energy Optimization For Connected And Automated Vehicles: From Cooperative Multi-Vehicle Control To Individual Powertrain Management, Pruthwiraj Santhosh

Dissertations, Master's Theses and Master's Reports

The transportation sector currently accounts for nearly 30% of global energy consumption, necessitating urgent advancements in vehicle efficiency to meet Net Zero targets. Leveraging connectivity and automation, this dissertation proposes and validates methodologies to reduce the energy consumption of light-duty vehicles at both fleet and individual levels.

First, a validation framework is developed to bridge the “simulation-to-real world” gap in Cooperative Automated Vehicle (CAV) research. Moving beyond virtual simulations, the study establishes a methodology for physically validating centralized control architectures via a custom Cellular V2X network. By synchronizing vehicle-powertrain models with physical test vehicles, the framework successfully orchestrates complex arterial …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Autonomous Weapons And Strategic Stability, Chick Edmond

Political Science & Geography Faculty Publications

The increasing number of artificial intelligence (AI) elements within military systems has introduced new forms of security dilemmas related to speed, level of secrecy, and transfer of responsibility from humans to machines. This article addresses the question of whether or how AI enabled autonomous weapons can lead to greater levels of strategic instability. Three causal mechanisms were determined by this study to potentially create destabilizing effects due to the introduction of autonomy; the first mechanism is a reduction in time available for decision making. The second mechanism involves the creation of multiple pathways of escalation. The third mechanism is the …

Real-Time Lidar-Based Vehicle Detection Using Pointpillars For Connected And Automated Vehicle Applications, Jared Townsend Behr

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work explores the implementation of a real-time LiDAR processing model for autonomous vehicle design. Automotive perception systems are becoming increasingly important as Advanced Driver Assistance Systems (ADAS) and Connected and Automated Vehicle (CAV) technologies continue to progress toward higher levels of autonomy. Reliable perception is paramount for autonomous features, such as adaptive cruise control (ACC), where identifying and tracking a lead vehicle is a safety critical task.

Many production ADAS perception systems primarily rely on radar and camera sensors for object detection and sensor fusion. These sensors work well to achieve classification and accurate distance detections; however, they are …

Analysis Of Battery Degradation Effects On Optimized Torque Splitting In Dual-Motor Electric Vehicles, Homer E. Butcher

Graduate Theses, Dissertations, and Problem Reports (ETD)

Battery electric vehicle performance depends on both powertrain efficiency and battery-health. In dual-motor all-wheel-drive battery electric vehicles, driver-requested torque can be distributed between front and rear electric drive units, creating an opportunity to reduce electrical energy demand through torque allocation. However, lithium-ion battery aging changes the electrical behavior of the energy storage system through capacity fade and internal resistance growth. Capacity fade reduces the usable energy and vehicle range, while resistance growth increases voltage drop, current-related losses, and battery electrical loading. This thesis evaluates how these degradation effects influence the energy consumption, simulated range, and battery electrical behavior of optimized …

Development And Validation Of A Multi-Output Neural Network-Based Virtual Temperature Sensor For Electric Vehicle Thermal Management, Dalton Michael Wiggins

Graduate Theses, Dissertations, and Problem Reports (ETD)

Battery electric vehicles (BEVs) rely on accurate thermal management to ensure component performance, efficiency, safety, and long-term durability. Critical propulsion system components, including electric motors and high-voltage battery packs, are commonly monitored using physical temperature sensors. However, these sensors increase system cost, introduce additional hardware complexity, and may be impractical for directly measuring temperatures at critical internal locations. Virtual temperature sensors (VTSs) provide a software-based alternative by estimating component temperatures using readily available vehicle operating data.

This research presents the development and validation of a long short-term memory (LSTM) neural network-based virtual temperature sensor capable of simultaneously estimating the motor …

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 …

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 …

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 …