Power and Energy Commons^™

Flame Boundary Effects Of Hydrogen-Air Premixing, Alecson L. De Lima Junior

Honors Undergraduate Theses

The objective of this research was to investigate the reaction of hydrogen and heated air premixing when injected into a combusting ethylene-air crossflow, and to study the flame stabilization location with varying premixing levels. The main parameter of this investigation was the flame stabilization diagnosed through chemiluminescence, based on recorded equivalence ratio, temperature, and flame boundaries, and maintaining a constant air temperature, hydrogen and air mass flow, and momentum flux ratios. Testing was conducted at different ratios of hydrogen and air premixing achieved through alternating the distance between the point where hydrogen and air are mixed and the combusting crossflow. …

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 Proximal Policy Optimization-Based Controller For Enhanced Power Sharing In Microgrids, Seyedmohammad Hasheminasab, Armin Lotfy, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

This paper introduces a Proximal Policy Optimization (PPO)-based virtual impedance (VI) controller to enhance both power sharing and system response under disturbances in inverter-interfaced microgrids. Traditional droop control methods often face challenges due to variations in feeder impedance, which degrade performance. The proposed controller continuously updates its policy based on changes in the operating environment. The control problem is modeled as a Markov Decision Process (MDP), in which the state and action spaces are explicitly defined, and a carefully designed reward function, satisfying system criteria and constraints, guides the learning process toward achieving the desired transient and steady-state performance. By …

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 …

Streamer Discharge Modeling For Plasma-Assisted Combustion, Stuart Reyes, Shirshak Kumar Dhali

Electrical & Computer Engineering Faculty Publications

Some of the popular and successful atmospheric pressure fuel/air plasma-assisted combustion methods use repetitive ns pulsed discharges and dielectric-barrier discharges. The transient phase in such discharges is dominated by transport under strong space charge from ionization fronts, which is best characterized by the streamer model. The role of the nonthermal plasma in such discharges is to produce radicals, which accelerates the chemical conversion reaction leading to temperature rise and ignition. Therefore, the characterization of the streamer and its energy partitioning is essential to develop a predictive model. We examine the important characteristics of streamers that influence combustion and develop some …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Determining The Transient Electric Field And The Effect Of Pulse Repetition Frequency On The Field In A Repetitive 10-Ns Pulsed Discharge In A Quiescent Ch₄-Air Mixture, Chunqi Jiang

Bioelectrics Publications

The goal of this research is to understand the underlying physics enabled by nanosecond pulsed power in a discharge initiation and the following physicochemical processes that favor lean-fuel plasma ignition for combustion. The hypothesis of this project is that pulsed power waveforms such as the pulse repetition frequency (PRF), voltage amplitude, and dielectric surface alter the reduced electric field during the initiation of the discharge, resulting in different plasma properties, which will vary the physicochemical processes for efficient and selective radical productions. This is especially important for lean-burn combustion and reducing emission.

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Determination Of Techno-Economic Indicators Of Pumped Storage Power Plants Under The Operational Conditions Of The Tuyamuyun Hydro Complex, Kurbon Salikhdjanovich Dzhuraev, Abdurauf Abduaziz Uulu, Fotima Tulkunovna Shadibekova

Technical science and innovation

The article presents the results of research on the analysis of the state of the electric power system of the Republic of Uzbekistan, taking into account the operating modes of hydraulic and thermal power plants with the study of the sharp-peak nature of the electric load in the winter and summer periods of the year and the problems of covering daily load schedules due to a lack of maneuverable capacities. The use of pumped storage power plants is proposed to cover and smooth daily load schedules across months and seasons. The necessity of utilizing the capacity of pumped storage power …

Method For Calculating Characteristic Frequency Ab (Rechargeable Batteries), Alisher Ganisher Ugli Bozorov

Technical science and innovation

A method for calculating the characteristic frequency is proposed, based on automatic interpolation of the frequency dependence of the reactance and subsequent calculation of the characteristic frequency as one of the roots of the interpolation function. The developed algorithm allows you to calculate the characteristic frequency from only 4 points of the frequency dependence of the reactance. In this case, the error in calculating the resonant frequency parameter does not exceed ~2 Hz.

Obviously, the values of the coefficients of the equation depend on the number of resistance values X and the corresponding frequencies of the test signal. The indicated …

Dendrite-Free Strategies For Aqueous Zinc-Ion Batteries: Structure, Electrolyte, And Separator, Gang Wu, Wu-Hai Yang, Yang Yang, Hui-Jun Yang

Journal of Electrochemistry

Continued growth in energy demand and increased environmental pollution constitute major challenges that need to be addressed urgently. The development and utilization of renewable, sustainable, and clean energy sources, such as wind and solar, are crucial. However, the instability of these intermittent energy sources makes the need for energy storage systems increasingly urgent. Aqueous zinc-ion batteries (AZIBs) have received widespread attention due to their unique advantages, such as high energy density, cost-effectiveness, environmental friendliness, and safety. However, AZIBs face significant challenges, mainly the formation of zinc dendrites that seriously affect the stability and lifetime of the batteries, leading to battery …

All-Solid-State Sodium-Ion Batteries: A Leading Contender In The Next-Generation Battery Race, Rui-Jie Zhu, Ze-Chen Li, Wei Zhang, Akira Nasu, Hiroaki Kobayashi, Masaki Matsui

Journal of Electrochemistry

All-solid-state lithium-ion batteries (LIBs) using ceramic electrolytes are considered the ideal form of rechargeable batteries due to their high energy density and safety. However, in the pursuit of all-solid-state LIBs, the issue of lithium resource availability is selectively overlooked. Considering that the amount of lithium required for all-solid-state LIBs is not sustainable with current lithium resources, another system that also offers the dual advantages of high energy density and safety— all-solid-state sodium-ion batteries (SIBs) —holds significant sustainable advantages and is likely to be the strong contender in the competition for developing next-generation high-energy-density batteries. This article briefly introduces the research …

Electrochemical-Method-Induced Strong Metal-Support Interaction In Pt-Cnt@Sno2 For Co-Tolerant Hydrogen Oxidation Reaction, Shen-Zhou Li, Zi-Jie Lin, Qi-An Chen, Zhao Cai, Qing Li

Journal of Electrochemistry

Inducing the classic strong metal-support interaction (SMSI) is an effective approach to enhance the performance of supported metal catalysts by encapsulating the metal nanoparticles (NPs) with supports. Conventional thermal reduction method for inducing SMSI processes is often accompanied by undesirable structural evolution of metal NPs. In this study, a mild electrochemical method has been developed as a new approach to induce SMSI, using the cable structured core@shell CNT@SnO₂ loaded Pt NPs as a proof of concept. The induced SnO_x encapsulation layer on the surface of Pt NPs can protect Pt NPs from the poisoned of CO impurity in …

Contributions To Journal Of Electrochemistry, The Flagship Journal Of Chinese Chemical Society Electrochemical Committee, Will Be Listed As A New Recommendation Policy For China Youth Awards Of Electrochemist, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Design, Control, And Evaluation Of A Photovoltaic Snow Removal Strategy Based On A Bidirectional Dc-Dc Converter For Photovoltaic–Electric Vehicle Application, Salma Elakkad, Mohamed Hesham, Hany Ayad Bastawrous, Peter Makeen

Electrical Engineering

A novel self-heating technique is proposed to clear snow from photovoltaic panels as a solution to the issue of winter snow accumulation in photovoltaic (PV) power plants. This approach aims to address the shortcomings of existing methods. It reduces PV cell wear, resource loss, and safety risks, without the need for additional devices. A self-heating current is applied to the solar panel to melt the snow covering its surface, which is then allowed to slide off the panel due to gravity. The proposed system consists of a bidirectional DC-DC converter, which removes the snow cover by heating the solar PV …

Leveraging P4 Programmable-Hardware Switches For In-Network Pmu Packet Recovery, Evan Michael Bonar

Electrical Engineering and Computer Science Undergraduate Honors Theses

Phasor Measurement Unit (PMU) systems are essential for real-time power grid monitor- ing but often face data loss due to network delays, equipment malfunctions, or transmis- sion errors. Traditional centralized recovery solutions introduce significant latency and scalability challenges. This thesis presents a P4-based in-network recovery mechanism that embeds detection and recovery directly into the data plane of P4-enabled programmable switches, significantly reducing recovery time and infrastructure complexity. Using the Aurora 610 switch, the system detects missing packets via sequence number analysis and recovers magnitudes with an efficient register-based algorithm.

Evaluation demonstrates high accuracy and low latency, achieving a mean absolute …

Artificial Intelligence Computational Techniques Of Flywheel Energy Storage Systems Integrated With Green Energy: A Comprehensive Review, Abdelmonem Draz, Hossam Ashraf, Peter Makeen

Electrical Engineering

In recent years, the operation of the electric power grid has become more efficient and resilient due to the integration of renewable energy sources (RESs). Solar and wind energy are being incorporated aggressively into the main grid, while other RESs like biomass and geothermal energy are also on the rise. However, the intermittent nature of these RESs necessitates the use of energy storage devices (ESDs) as a backup for electricity generation such as batteries, supercapacitors, and flywheel energy storage systems (FESS). This paper provides a thorough review of the standardization, market applications, and grid integration of FESS. It examines the …

Building Energy Management: A Data-Driven Approach Using Clustering And Load Forecasting, Aviral Kandel

LSU New Orleans Theses and Dissertations

The increase of smart meters in the grid has led to the generation of a vast amount of high dimensional energy data with improving temporal resolution. During analysis, relying on short samples like a day or week of data, could lead to wrong conclusion due to seasonal dynamics and customer behavior variations. To effectively utilize the vast amount of information, it must be compressed into a low-dimensional representation. This thesis explores the state-of-the-art dimensionality reduction techniques for a high-dimensional, non-linear energy dataset and proposes a novel deep learning based method to address the limitation of existing approaches. The proposed method …

Reference Dependence In Queue Design And Pricing Strategies, Jian Liu, Yongpin Zhou, Jian Chen, Peng Li

Electrical and Computer Engineering Faculty Research & Creative Works

This research investigates the effect of reference dependence on waiting times in service systems which formerly used a first-in-first-out (FIFO) service but have introduced a priority line with a fee. Our model combines reference-dependent gain-loss utility with standard customer utility, and we posit that customers are pleased with shorter-than-expected waiting times, whereas longer-than-expected times lead to dissatisfaction and an increased likelihood of balking. The study explores two scenarios: a captive customer system (CCS) and a noncaptive customer system (NCCS), with a focus on optimal pricing and segmentation strategies for revenue and social welfare maximization. The results reveal that, in a …