Digital Commons Network^™

Rural Families' At-Home Stem Tinkering Stimulates Creativity, Self-Expression, And Social-Emotional Engagement, Kristie S. Gutierrez, Kylie J. Swanson, Margaret R. Blanchard

Teaching & Learning Faculty Publications

Introduction: Informal STEM learning experiences include visits to museums, zoos, and aquariums as well as experiments and other activities performed at home. Family involvement in these experiences has been linked to increased student interest and participation in STEM fields; yet, scant research has been conducted on at-home STEM.

Methods: This descriptive case study investigated the tinkering experiences of nine rural middle school students and their families who participated in a series of interactive, at-home activities. The overarching research question was, in what ways do families engage in at-home STEM interventions? Data analyses of at-home audio recordings were guided by the …

Teaching Engineering To Enhance Beginning Elementary Preservice Teachers' Engineering-Related Knowledge And Beliefs, Jennifer Kidd, Danielle Rhemer, Kristie Gutierrez, Stacie I. Ringleb, Krishnanand Kaipa, Orlando Ayala, Pilar Pazos, Francisco Cima, Isaac Kumi

Teaching & Learning Faculty Publications

Background

Teacher educators have begun exploring the most effective ways to prepare preservice teachers (PSTs) to engage elementary students in engineering design. However, this remains challenging as PSTs continue to report a lack of exposure to engineering during their K-12 school experiences. This study investigates the engineering-related knowledge and beliefs of PSTs in their first education course, collaborating in small teams to lead elementary students in engineering design challenges. We explored two different iterations to understand how the structure of the teaching experiences contributed to PST outcomes as a first step in identifying helpful approaches. In spring 2022, PSTs collaborated …

Examining The Practice Of Teacher Leadership Across Nevada: How Is Context Associated With Practice?, Peter Wiens, Kaavonia Hinton, Ami Pritchard

Teaching & Learning Faculty Publications

Teacher leadership (TL) is recognized as vital to creating effective schools with demonstrated benefits to principals, teachers, and students. Teacher leadership is defined in this study as teachers leading from within and beyond the classroom in both formal and informal capacities. TL can take the form of a variety of behaviours such as professional learning, the leading of professional development, collaboration, shared leadership, and equity leadership. While there are commonalities, the practical applications of TL can vary greatly from context to context. Even within specific policy contexts, TL can vary from school to school. This article analyzed the responses of …

A Deep Dive In Preservice Teacher Self-Efficacy Development For Teaching Robotics (Rtp), Jennifer Jill Kidd, Kristie Gutierrez, Min Jung Lee, Danielle Marie Rhemer, Krishnanand Kaipa, Pilar Pazos, Stacie I. Ringleb, Orlando M. Ayala, Francisco Cima, Isaac Koduah Kumi

Teaching & Learning Faculty Publications

Nationwide K–6 engineering and coding standards have made it increasingly important to prepare elementary preservice teachers (PSTs) to teach these subjects confidently and effectively. Robotics, which combines coding and engineering, provides a rich context for developing PSTs’ expertise and self-efficacy. This study builds on prior work in which PSTs in an instructional technology course collaborated with undergraduate engineering students to co-teach robotics lessons to fifth graders. Using a multiple-embedded case study approach, we examine how the interactions and teaching roles within these partnerships influenced PSTs’ teaching self-efficacy. Drawing on reflections, lesson recordings, surveys, and interviews, we present the cases of …

Physics-Informed Deep Learning With Kalman Filter Mixture For Traffic State Prediction, Niharika Deshpande, Hyoshin (John) Park

Engineering Management & Systems Engineering Faculty Publications

Accurate traffic forecasting is crucial for understanding and managing congestion for efficient transportation planning. However, conventional approaches often neglect epistemic uncertainty, which arises from incomplete knowledge across different spatiotemporal scales. This study addresses this challenge by introducing a novel methodology to establish dynamic spatiotemporal correlations that captures the unobserved heterogeneity in travel time through distinct peaks in probability density functions, guided by physics-based principles. We propose an innovative approach to modifying both prediction and correction steps of the Kalman Filter (KF) algorithm by leveraging established spatiotemporal correlations. Central to our approach is the development of a novel deep learning model …

Proactive Distributed Emergency Response With Heterogeneous Tasks Allocation, Justice Darko, Hyoshin Park

Engineering Management & Systems Engineering Faculty Publications

Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However, ignoring these inherent interdependencies while making current deployment decisions is shortsighted, and the resulting naive deployment strategy can significantly worsen the overall incident delay impact on the network. The interdependencies on incident evolution in the environment, including those between incident occurrences and those between resource availability in near-future requests and the anticipated duration of the immediate incident request, should be considered through a look-ahead model when making current-stage deployment decisions. This study develops …

Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz

Engineering Management & Systems Engineering Faculty Publications

The growing sophistication of cyberattacks exposes small- and medium-sized businesses (SMBs) to a widening range of security risks. As these threats evolve in complexity, the need for advanced security measures becomes increasingly pressing. This necessitates a proactive approach to defending against potential cyber intrusions. Emerging technologies, such as blockchain, artificial intelligence, and Zero Trust security framework, offer crucial tools for strengthening the digital infrastructure of SMBs. The Zero Trust architecture (ZTA) holds significant promise as a critical strategy for protecting SMBs. While existing literature explores the implementation of ZTA in various business settings, discussions specifically addressing the financial, human resource, …

Cyberattacks On Port Infrastructures: A Decade Of Trends, Incidents, And Mitigation Strategies (2011-2024), Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Port infrastructures are critical to global trade, handling over 80% of the world's cargo by volume. However, their increasing reliance on digital technologies has exposed them to a wide range of cyber threats. This paper provides a comprehensive analysis of cyberattacks targeting port infrastructures from 2011 to the present. We examine the types of attacks, geographical distribution, notable incidents, and underlying vulnerabilities. Additionally, we discuss mitigation strategies and future directions for enhancing cybersecurity in the maritime sector. Our findings highlight the urgent need for robust regulatory frameworks, advanced technological solutions, and collaborative efforts to safeguard critical port operations.

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

Maritime Industry Cybersecurity Threats In 2025: Advanced Persistent Threats (Apts), Hacktivism And Vulnerabilities, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Mihaela Hnatiuc, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020-2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the …

A Review Of Infrared Thermography Applications For Civil Infrastructure, Prabal Shrestha, Onur Avci, Sahabeddin Rifai, Feras Abla, Michael Seek, Karl Barth, Udaya Halabe

Engineering Technology Faculty Publications

Civil infrastructure is continuously subject to aging and deterioration due to multiple factors, which lead to a decline in performance and impact structural health. Accumulated damage on structures increases operational costs and poses significant risks to public safety. Effective maintenance, repair, and rehabilitation strategies are needed to ensure civil infrastructure’s overall safety and reliability. Non-Destructive Evaluation (NDE) methods are utilized to assess latent damage and provide decision-makers with real-time information for mitigating hazards. Within the last decade, there has been a significant increase in the research and development of innovative NDE techniques to improve data processing and promote efficient and …

Developing Employability Skills: A Case Study Of Synchronous And Asynchronous Modalities, A. Mark Gray, Michelle E. Bartlett

Educational Leadership & Workforce Development Faculty Publications

In today's workforce, soft skills such as communication and adaptability are increasingly critical for career success, yet many graduates lack these competencies. As online learning continues to expand, questions arise about its effectiveness in cultivating essential workplace skills. This mixed-methods case study examines how synchronous and asynchronous distance learning modalities support the development of soft skills, focusing on communication and adaptability. Drawing on data from two online courses, an asynchronous professional development course (n = 24 pre, n = 19 post) and a synchronous construction leadership course (n = 2), the study utilizes surveys, reflections, and assignments to …

A Pathway To Completion: Student Veterans Pursuing Engineering Degrees, Kim E. Bullington, Cynthia L. Tomovic, Anthony W. Dean, Vukica Jovanovic

Educational Leadership & Workforce Development Faculty Publications

This 5‐year research project was based on an NSF S‐STEM research grant (1742118) focused on student veteran education and workforce preparation. We addressed STEM workforce and defence community needs by providing targeted scholarships and support for veteran students pursuing engineering degrees. Our aim was to enhance the workforce by improving access and success rates through financial and institutional support, while developing evidence‐based practices for recruitment, retention and graduation of student veterans. Through targeted interventions, we examined obstacles student veterans face in education and in engineering programs and increased their sense of camaraderie, career awareness, engineering identity, professional development, financial security …

Food Insecurity And Academic Self-Efficacy: Military Students And Student Veterans, Kim E. Bullington, Matthew J. Smith, William L. Nuckols, Dennis E. Gregory, Joshua L. Howell

Educational Leadership & Workforce Development Faculty Publications

Food insecurity (FI) affects households worldwide and is also seen on college and university campuses with adverse effects on student success. We examine FI and academic self-efficacy levels of military students and student veterans enrolled in higher education in the United States which has not been extensively studied regarding FI. Military students and student veterans may have access to G.I. Bill funds and may not be perceived as requiring additional assistance. The results reveal that military students and student veterans have some level of FI. The qualitative analysis yielded five themes that center on FI, economic vulnerability, inequities in the …

An Overview Of Video Game Biometrics Collection And Considerations For Cyberbiosecurity, Lucas Potter, Christen Westberry, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Over the past fifty years, the global cost of consumer electronics has significantly decreased, leading to greater accessibility to both biosensing systems and interactive entertainment platforms. This increased access has naturally resulted in higher usage of medical and entertainment electronics. However, the intersection of these technologies, combined with invasive data harvesting practices, has raised concerns about the potential misuse of biological signals to manipulate individuals' behavior both within and beyond the video game environment. Currently, biometric data in video games are employed in various ways, such as using Heart Rate Variability (HRV) as a performance metric and integrating eye tracking …

Mxene-Based Materials For Enhanced Water Quality: Advances In Remediation Strategies, Ali Mohammad Amani, Milad Abbasi, Atena Najdian, Farzaneh Mohamadpour, Seyed Reza Kasaee, Hesam Kamyab, Shreeshivadasan Chelliapan, Mostafa Shafiee, Lobat Tayebi, Ahmad Vaez, Atefeh Najafian, Ehsan Vafa, Sareh Mosleh-Shirazi

Electrical & Computer Engineering Faculty Publications

Two-dimensional MXenes are promising candidates for water treatment because of their large surface area (e.g., exceeding 1000 m²/g for certain structures), high electrical conductivity (e.g., >1000 S/m), hydrophilicity, and chemical stability. Their strong sorption selectivity and effective reduction capacity, exemplified by heavy metal adsorption efficiencies exceeding 95 % in several studies, coupled with facile surface modification, make them suitable for removing diverse contaminants. Applications include the removal of heavy metals (e.g., achieving >90 % removal of Pb(II)), dye removal (e.g., demonstrating >80 % removal of methylene blue), and radioactive waste elimination. Furthermore, 3D MXene architecture exhibit enhanced performance in antibacterial …

Obesity Prediction From Structural Mri Using Conformal Deep Learning With Uncertainty Quantification, W. Farzana, A. G. A. Temtam, B. Humud-Arboleda, L. Ma, M. Bean, F. Gerard Moeller, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Obesity arises from a neurobehavioral disorder in which the brain’s regulation of hunger and food intake is impaired, leading to an imbalance between energy consumption and expenditure. One key phenotype associated with obesity is body mass index (BMI). BMI is influenced by multiple causal pathways driven by behavioral, metabolic, and genetic factors. Traditional obesity prediction studies often rely on magnetic resonance imaging (MRI) voxel-based morphometry to correlate BMI with obesity-related clinical measurements and brain structure, predominantly gray matter volume (GMV). However, the altered brain regions are variable and widespread between studies, with some literature presenting contradictory results between BMI and …

Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …

A Potentially Fruitful Path Toward A Cleaner And Safer Environment: Mxenes Uses In Environmental Remediation, Ali Mohammad Amani, Milad Abbasi, Atena Najdian, Farzaneh Mohamadpour, Seyed Reza Kasaee, Hesam Kamyab, Shreeshivadasan Chelliapan, Hanieh Ardeshiri, Lobat Tayebi, Ehsan Vafa, Sarah Mosleh-Shirazi, Alireza Jahanbin, Saravanan Rajendran, Daniel Simancas-Racines

Electrical & Computer Engineering Faculty Publications

The rapid industrialization of the world has resulted in severe environmental pollution, necessitating the development of new materials such as pollution remediation. Two-dimensional (2D) MXenes have emerged as a promising family of materials due to their unique physicochemical properties, making them ideal for environmental remediation. The article sheds light on the new opportunities of MXenes in the removal of organic and inorganic contaminants, including organic dyes, pharmaceuticals, heavy metals, radionuclides, and gas pollutants. MXenes also show excellent performance in photocatalytic degradation, adsorption, and microbial inactivation with environmental safety. Moreover, their application in recovering valuable elements from waste streams is also …

Biomedical Mxene-Polymer Nanocomposites: Advancing Photothermal Therapy, Antibacterial Action, And Smart Drug Delivery: A Review, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Mohammad Javad Azizli, Milad Abbasi, Ahmad Vaez, Hesam Kamyab, Daniel Simancas-Racines, Shreeshivadasan Chelliapan

Electrical & Computer Engineering Faculty Publications

MXenes are hydrophilic, conductive, tunable, and biocompatible two-dimensional ceramic materials prepared by etching the 'A' layer from their precursor MAX phases. Although MXenes show exceptional promise in photothermal therapy, biosensing, and regenerative medicine, they face challenges such as oxidative instability in physiological environments, limited drug-loading capacity, and unpredictable immune responses. To address these limitations, MXene/polymer nanocomposites incorporating both synthetic polymers (e.g., polyethylene glycol (PEG), polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), and polylactic-co-glycolic acid (PLGA)) and natural biopolymers (e.g., cellulose nanofiber, gelatin, chitosan, hyaluronic acid, and soybean phospholipids) have been developed. These composites enhance functionality for biomedical applications such as photothermal cancer …

Optimizing Superconducting Nb Film Cavities By Mitigating Medium-Field Q-Slope Through Annealing, B. Abdisatarov, G. Eremeev, H. E. Elsayed-Ali, D. Bafia, A. Murthy, Z. Sung, A. Netepenko, A. Romanenko, C. P.A. Carlos, G. J. Rosaz, S. Calatroni, S. Leith, A. Grassellino

Electrical & Computer Engineering Faculty Publications

Niobium films are of interest in applications in various superconducting devices, such as superconducting radiofrequency cavities for particle accelerators and superconducting qubits for quantum computing. In this study, we address the persistent medium-field Q-slope issue in Nb film cavities, which, despite their high-quality factor at low RF fields, exhibit a significant Q-slope at medium RF fields compared to bulk Nb cavities. Traditional heat treatments, effective in reducing surface resistance and mitigating the Q-slope in bulk Nb cavities, are challenging for Nb-coated copper cavities. To overcome this challenge, we employed DC bias high-power impulse magnetron sputtering to deposit …

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …

Polarized Photocathode R&D At Bnl And Spin Consideration For The Eic Preinjector, Jyoti Biswas, Erdong Wang, Omer Rahman, John Skaritka, Adam Masters, Sylvain Marsillac, Tai-De Li

Electrical & Computer Engineering Faculty Publications

Superlattice GaAs photocathodes are vital for producing polarized electron beams for the Electron-Ion Collider (EIC) at Brookhaven National Laboratory. The electron pre-injector at the EIC requires a 7 nC bunch with at least 85% spin polarization from a GaAs-based superlattice cathode. The doping density of the very surface layer of the cathode needs to be optimized to extract a high bunch charge beam from the high-voltage DC gun. The polarization axis of the emitted beam is longitudinal, and it will be rotated to transverse direction using two Wien filters, each rotating the spin by 45 degrees. In this paper, we …

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

Electrical & Computer Engineering Faculty Publications

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

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

A Customized Large Single-Piece Bifrontal Implant For Post-Craniectomy Defect Reconstruction: A Case Study, Omid Ghaderzadeh, Ehsan Amirbeyk, Seyed Roholah Ghodsi, Zahra Namazi, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Background: Large bifrontal defects pose unique reconstruction challenges due to their complex curvature and mechanical requirements. This case demonstrated how computer-aided design/manufacturing (CAD/CAM) enabled precise single-piece polymethyl methacrylate (PMMA) implant fabrication, thereby overcoming traditional limitations. Case presentation: A 25-year-old male who had undergone bifrontal decompressive craniectomy suffered a severe traumatic brain injury. The autologous bone flap had been temporarily stored in a subcutaneous fat area of the abdomen for 3 months to preserve its viability. A secondary cranioplasty was then performed using titanium miniplates and self-tapping screws for final fixation. After 2 years, the patient developed empyema and a brain …

Leveraging Large Language Models To Create Learner Personas For Training Design, N. Lovett, M. Yang, K. Herman, B. Li, M. Sosonkina, W. Purwanto, P. Jiang, M. H. Wu

Electrical & Computer Engineering Faculty Publications

This case examines the innovative use of Large Language Models (LLMs) to generate learner personas for developing learner-centered cybersecurity training materials when direct access to initial learner data is not available. The team developed a nine-stage iterative process for creating and refining AI-generated personas to address this constraint, integrating ethical review, stakeholder feedback, and action research principles. The process expanded upon Kouprie and Visser’s (2009) empathic design framework to ensure cultural responsiveness and mitigate potential biases in LLM outputs. Through multiple refinement cycles, initial generic personas evolved into detailed, context-rich archetypes which informed the development of effective and context-responsive training …

Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

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 …