Automotive Engineering Commons^™

Markov-Modulated Queueing Network For Mobile Traffic Aggregation With Threshold-Controlled Buffers, Anton A. Esin, Elmira Yu. Kalimulina

Mathematical Modelling and Numerical Simulation with Applications

We study the problem of data transmission from mobile platforms operating in high-speed transit between cellular base stations, under conditions of unstable and intermittent connectivity. Conventional queueing and connectivity models often fail to capture the combined effects of rapidly changing signal conditions, finite buffer capacity, and dynamic topology. We aim to develop a tractable yet expressive model that integrates stochastic link availability, queue dynamics, and buffer control. We propose a mathematical framework based on queues whose service intensities are modulated by a continuous-time Markov chain (CTMC) representing signal conditions along a high-speed trajectory. The core subsystem is a two-stage (aggregation …

Scenarioxp: A Complete Scenario–Based Testing Framework For The Exploration And Exploitation Of Autonomous Vehicle Validation Scenarios, Quentin Goss

Student Research Symposium (SRS)

Today is an age of exiting emerging technology where cutting-edge research in autonomous vehicles (AVs) reduces the active human participation in driving and extends awareness beyond human limitations of perception and reaction, improving driving safety and quality of the user experience as a result. The ever-increasing complexity of these autonomous systems poses many challenges towards the validation and verification (V\&V) of these complex systems under time and resource constraints, as the use of artificial intelligence and also the intricacy of the operating environment means that these systems are also black-box and non-deterministic. Scenario-based V\&V testing of such systems, which involves …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …

Rm Sotheby’S Construction Of Value In The High-End Car Market, Pablo Mijares Musi

MA Theses

The purpose of my thesis is to tell and enlighten any readers how the most prestigious and iconic auction house in the world, RM Sotheby’s sells more than just cars from the high-end car market and how the auction house adds value to through storytelling and provenance from each vehicle.  In today’s luxury market economics, we see somewhat of a radical shift. The high-end car market has moved into a very refined area where these beautiful machines are no longer seen and appraised just for their aesthetic design and mechanical engineering, now we see new value drivers for these works …

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 …

Development Of Stronger, More Extrudable 6xxx Series Alloys For Automotive Applications, Eli A. Harma

Dissertations, Master's Theses and Master's Reports

6xxx alloys are widely used in automotive extrusion structures for their high specific strength and formability. Advanced designs require greater formability and strength, creating an opportunity for high-strength, formable alloys. The 6xxx series forms β”-Mg₅Si₆ precipitates during age hardening and develops a fibrous microstructure during extrusion, both strengthening the alloy. Increasing Mg and Si to form more β” decreases formability. Thus, modifying texture can increase strength without reducing formability. Current alloys like 6005A add Mn and Cr to form dispersoids that inhibit recrystallization and promote strengthening textures; however, high Mn and Cr concentrations reduce formability. Replacing Mn …

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 …

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.

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 …

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 …

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 …

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), …

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 …

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 …

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 …

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 …

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 …

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 …