Automotive Engineering Commons^™

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

Safe Control Design For Quadruped Locomotion In Unstructured Environments Using Linear Transfer Operators, Sriram Sundar Krishnamoorthy Shankara Narayanan

All Dissertations

Deploying quadruped robots in unstructured, obstacle-rich environments requires control and planning methods that remain safe and reliable despite complex terrain geometry, limited sensing, and inevitable modeling errors. This thesis develops operator-theoretic tools for safe control design of robotic systems using linear transfer operators, with a focus on quadruped locomotion in unstructured environments. The central goal is to develop a unified operator-theoretic framework for safe control design based on the Perron–Frobenius (P–F) and Koopman operators. In particular, the thesis leverages \emph{density functions} to develop safe navigation frameworks in the dual space of densities. In the operator-theoretic perspective, the P–F operator governs …

Optimizing Controller Speed And Torque To Reduce Drivetrain Stress, Selene J. Welch

SACAD: Scholarly Activities

This project investigates how torque delivery from the motor controller affects mechanical stress on the rear freewheel ratchet mechanism of a three-wheel electric race car. Excessive initial torque has caused accelerated wear and tear on the ratchet mechanism, reducing drivetrain reliability during Kansas ElectroRally competitions. Preliminary testing showed that slower, gradual acceleration prevented malfunction and allowed the vehicle to maintain top speed reliably. Prior research shows that torque-control strategies strongly influence electric-drive performance. Current research focuses on optimizing the controller’s torque and speed using the Alltrax Software ToolKit to reduce drivetrain tension while preserving the car’s performance capabilities.

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 …

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 …

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 …

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), …

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 …

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 …

Robustness Investigation, Detection, And Defense Of Deep Learning Models Against False Data Injection, Amirhossein Nazeri

All Dissertations

This dissertation addresses the critical challenge of adversarial robustness in deep learning systems, focusing on two fundamental domains: time-series prediction and object detection. As these AI systems become increasingly deployed in safety-critical applications from power grid management to autonomous vehicles their vulnerability to adversarial attacks poses significant risks to infrastructure and human safety.

The first contribution introduces a novel stealthy black-box False Data Injection (FDI) attack specifically designed for quasi-periodic time-series data. Unlike existing attacks that produce easily detectable anomalies, our method generates adversarial perturbations that preserve the underlying periodicity and statistical properties of the data, effectively bypassing traditional anomaly …

Predicting Battery Efficiency: A Theoretical Approach, Haiti Schafers

SACAD: Scholarly Activities

The Kansas ElectroRally Races continuously demand faster electric vehicles (EV). When designing a vehicle one of the most important components is the battery. This study was designed to find the following; What is the optimal throttle percentage to run a 36V 20Ah (768Wh) LiFePO4 Rechargeable Battery Pack[1].

The optimal throttle percentage was found with methods detailed in a different section. Based on the performance of the older cars this study choose to focus on 50% and 70%, while obtaining other relevant data to further that inference.

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.

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 …

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 …

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 …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam

Computer Science Faculty Publications

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an …

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Green Defense: Evaluating Potentials And Limitations In Transitioning Military Vehicles To Clean Energy, Aurora M. V. Bellingeri, Danielle M. Drumm, Zoe S. Koutsokostas

Student Publications

Army operations rely on fuel-intensive vehicles and aircraft, which contribute substantially to global emissions and the looming threat of global warming. This study evaluates the potential of transitioning five key U.S. Army vehicles, the M1301 ISV, HMMWV M1165, JLTV M1280, LMTV M1078, and M3A3 Bradley, from traditional diesel and JP8 powertrains to electric vehicle (EV) and hydrogen fuel cell vehicle (FCV) technologies. Vehicle specifications including engine power, fuel efficiency, weight, and payload were gathered for each vehicle and compared. Efficiency was estimated for EV and FCV powertrains using linear regressions with data from the AFLEET Online tool, which provided national …