Digital Commons Network^™

State-Of-Charge Estimation Using Deep Learning For Electric Vehicles, Samer Yahya Ribhe Tahboub

LSU Master's Theses

This thesis investigates the application of deep learning models for State of Charge (SOC) estimation in Battery Management Systems (BMS) for electric vehicles (EVs), focusing on optimizing EV range, lifespan, and performance while addressing challenges like range anxiety. The study explores three deep learning architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU)—each designed to capture complex temporal dependencies in battery data. The LSTM model is trained on EV battery data, including voltage, current, temperature, and SOC, providing a strong baseline for SOC estimation. The BiLSTM model enhances accuracy by processing data in both forward and backward …

A Survey On Food Ingredient Substitutions, Hyunwook Kim, Revathy Venkataramanan, Amit P. Sheth

Publications

Diet plays a crucial role in managing chronic conditions and overall well-being. As people become more selective about their food choices, finding recipes that meet dietary needs is important. Ingredient substitution is key to adapting recipes for dietary restrictions, allergies, and availability constraints. However, identifying suitable substitutions is challenging as it requires analyzing the flavor, functionality, and health suitability of ingredients. With the advancement of AI, researchers have explored computational approaches to address ingredient substitution. This survey paper provides a comprehensive overview of the research in this area, focusing on five key aspects: (i) datasets and data sources used to …

Maxwell's Second Equation: The Gauss's Law For Magnetostatic Fields, Ashanthi Maxworth Phd

EM Fields With Prof. Maxworth

Gauss's Law for magnetostatic fields says that the total magnetic flux through a closed surface is equal to zero. In otherwords, there are no magnetic monopoles. This law has been applied in multiple mathematical derivations. Watch the short video where Dr. Maxworth explains this law. In the worksheet you can observe the magnetic flux generated due to a current through a straight coaxial cable. Note that the magnetic flux always exisit as close loops making the total net flux through a closed surface zero and the direction of the magnetic field can be derived by the right hand rule, i.e …

Lagrange Multipliers As A Metric For Early-Stage Shipboard Power System Design, Magdalen A. Barnes

Theses and Dissertations--Electrical and Computer Engineering

Integrated power systems are essential in the operation of electric ships and must function under a diverse range of conditions. In addition to routine operation, ships should be prepared to respond to challenging events that require significant amounts of power. Demanding scenarios, coupled with complex and interconnected power systems, present challenging issues. The power and energy ratings of equipment are crucial factors in the operational success of the ship; however, to meet economic constraints, balance between performance and cost must be upheld. Details regarding equipment models and specifications are typically unavailable during early-stage design. Power system modeling and simulation can …

The Rhythm Of Renewables: Minute-By-Minute Insights From Kentucky, David S. Beyerle, Noah M. Stewart, Shaun E. Lavin, Chad C. Alkire, Heather Nikolic, Samuel Kelty, Ezekiel A. Boggs, Declan Boyle, Lawrence E. Holloway, Aron Patrick

Electrical and Computer Engineering Faculty Publications

Kentucky’s renewable energy landscape beats with a distinct rhythm shaped by ever-changing variations in sunlight, wind, rainfall, and the ever-modernizing grid that connects them. This paper analyzes minute-by-minute performance data from seven renewable and storage assets owned and operated by the PPL Corporation in Kentucky, including hydroelectric, solar, wind, and lithium-ion battery systems. Using a full year of synchronized, high-resolution data from multiple sites in the Commonwealth, the study examines daily and seasonal capacity factor trends, explores correlations among generation types, and evaluates their alignment with utility load profiles. Our analysis found 279 hours—about 3.2% of the year—with zero renewable …

Smart Homes, Grids, And Electric Vehicles Large-Scale Integration Studies Employing Machine Learning And Optimization Techniques, Rosemary E. Alden

Theses and Dissertations--Electrical and Computer Engineering

Residential digital twins are fundamental to the smart grid transition, and thus, must be both accurate and representative of existing homes and scalable for large distribution systems. Within this dissertation, new machine learning (ML) and physics-based methodologies are applied to the major individual residential loads, energy storage devices, and resources in the US to develop computationally efficient digital twins and new optimal control strategies for the virtual power plant (VPP) concept. Big data from experimental field demonstrations with dedicated metering, thousands of residential smart meter profiles, and large national human behavior surveys are employed to develop ultra-fast scalable residential load …

Fusion-Based Utilization And Synthesis Of Efficient Detections, Ethan C. Rogers, Parker H. Liberatore, Thomas G. James

Endeavors: Mississippi State Undergraduate Research Journal

This study aimed to develop hardware and software for an object detection fusion system, using three different sensors. The system was built and studied with the motivating application of autonomous drones searching for and detecting people in a search-and-rescue scenario. The system’s performance was compared to that of individual sensors deployed for the same task. The focus of the research was to prove the competence and benefits of a decision-level fusion method as it was applied to a lightweight object detection architecture, and the driving motivators behind the study were simplicity in implementation and good computational performance. In short, the …

Data-Driven Forecasting Of Power Demand Via Convex Optimization, Mersedeh Ashraphijuo

Electrical Engineering Dissertations - Archive

This dissertation develops interpretable, data-driven frameworks for short-term power demand forecasting using convex optimization and advanced feature engineering. The models combine historical load data, calendar structures, and meteorological variables to deliver accurate point, quantile, and probabilistic forecasts. By leveraging multi-periodic Fourier features, temperature-based regressors, and autoregressive memory, the proposed approach balances predictive performance with interpretability and scalability. Evaluations on multi-year datasets across US regions show consistent accuracy gains over benchmarks, while preserving transparency critical for real-world deployment. Beyond power systems, the framework generalizes to other time series applications in data science and AI, offering a robust, explainable alternative to black-box …

Characterization And Modeling Of Polyphase Machines And Power Conditioning Components, Nathan Lockhart

Electrical Engineering Dissertations - Archive

Microgrid power configurations have become increasingly prevalent in recent power systems due to the rise of power electronic energy generation, energy storage, and the many diverse electrical demands. Microgrids offer numerous advantages over traditional power electronic networks, which rely on large rotating generators to supply power over extensive distances to multiple users. Remote power grids are particularly beneficial for smaller networks that may be isolated or have unique power requirements, often incorporating energy storage to enhance operational flexibility. Advances in power electronics, such as medium voltage DC distribution, are enhancing the reliability, redundancy, and integration capabilities of isolated microgrids.

To …

Autonomous Vehicle Platooning, Tony Abelson

McNair Summer Research Program

This research investigates the construction and performance optimization of two autonomous vehicles with using "Platooning," a strategy aimed at reducing fuel consumption and enhancing transportation efficiency. Platooning allows one vehicle to follow another closely, minimizing aerodynamic drag and improving fuel economy. This study addresses the growing need for sustainable transportation solutions in the context of increasing urbanization and environmental concerns, emphasizing the importance of efficient autonomous vehicle operation. The primary objectives of this research are to assemble autonomous vehicles from scratch and to optimize their performance in both individual and platoon operations. The methodology involves using Traxxas Slash chassis and …

3d Perception, Mapping, And Navigation For Mobile Cobot, Tuan T. Dang

Computer Science and Engineering Dissertations - Archive

Service robots are migrating from tightly controlled factory lines into offices, hospitals, and homes, where they must perceive, remember, and act amid people, clutter, and perpetual change. Humans solve this daily by forming compact, task-relevant “cognitive maps”: we sample just enough sensory detail to guide the moment, stitch those snapshots into a sparse topological scaffold, and continuously refine it as we move. Guided by that insight, this dissertation proposes a biologically inspired mapping framework that turns partial RGB-D observations into a hybrid temporal-spatial memory—locally metric for centimeter-scale navigation yet globally topological for room-to-building navigation. The system first distills raw depth …

Smart Irrigation System Using Iot And Lstm For Optimal Water Management, Farley Y. Ruiz

Electrical Engineering Theses - Archive

This thesis presents the design and implementation of a smart irrigation system that combines Internet of Things hardware with a Long Short-Term Memory (LSTM) neural network for predictive soil moisture management. The goal is an affordable and reliable solution that uses real-time sensor data and environmental data to schedule irrigation before the substrate moisture drops below its target range. The system integrates soil moisture, temperature, humidity, and sensors on an Arduino Nano that communicates wirelessly with a Raspberry Pi. The Raspberry Pi runs a Python/Flask backend that collects and processes data, executes the LSTM model, and serves a secure web …

Power Quality Event Diagnosis Using Multi-Rate Neural Networks, Jordan D. Lloyd

Electrical Engineering Theses - Archive

Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) …

Auto-Tuning And Applications Of Transient Voltage Suppressor Models In Full-Wave Simulations, Daniel Peter Szanto

Masters Theses

System efficient electrostatic design (SEED) combines full-wave geometry information with SPICE models of non-linear protection devices, typically transient voltage suppression (TVS) diodes, to allow optimization and validation of electrostatic design (ESD) protection early in the design process. TVS models have previously been developed which may be used in SPICE simulation tools like Keysight ADS, but these models could not be used directly in full-wave simulation tools like CST Studio. A process was developed for converting existing ADS models of TVS devices to a form that can be used within a CST full wave/SPICE hybrid simulation. Three TVS models were converted …

Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring, Marina Materikina

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Despite the numerous research studies and interest in the non-intrusive load monitoring (NILM) area to improve energy efficiency, the problem of accurate and precise disaggregation of electrical devices has not been solved yet. The goal of our research is to build a method with a focus on higher accuracy on complex state-based appliances, which most approaches struggle to detect due to their power signal complexity and low consumption. Our approach is NILM with data-driven signatures (DS), with the ability to potentially predict power usage over time that would work great for suitable applications such as demand response, anomaly detection, and …

Enhancing Irs Localization Via Ml-Driven Aoa And Distance Estimation, Nikhila Kotlapara Surendran

Theses and Dissertations

Intelligent Reflecting Surfaces (IRSs) are transforming next-generation wireless networks by enabling dynamic control of the propagation environment for improved localization and security. This thesis advances Angle-of-Arrival (AoA) and distance estimation in IRS-assisted systems using a hybrid architecture with active sensing and passive reflecting elements. A modified MUSIC algorithm, optimized for near-field localization, decouples 2‑D AoA and 1‑D range estimation using a grid-and-peak pseudo-spectrum scan, achieving sub-degree AoA (0.003 rad) and sub-meter range (0.05 m) accuracy with over 100× lower complexity than classical 3D MUSIC.

The study further applies the algorithm to the DeepMIMO dataset (O1_28 scenario, 3.5 GHz) using …

Ms-Yolo: Infrared Object Detection For Edge Deployment Via Mobilenetv4 And Slideloss, Jiali Zhang, Thomas S. White, Haoliang Zhang, Wenqing Hu, Donald C. Wunsch, Jian Liu

Mathematics and Statistics Faculty Research & Creative Works

Infrared imaging has emerged as a robust solution for urban object detection under low-light and adverse weather conditions, offering significant advantages over traditional visible-light cameras. However, challenges such as class imbalance, thermal noise, and computational constraints can significantly hinder model performance in practical settings. To address these issues, we evaluate multiple YOLO variants on the FLIR ADAS V2 dataset, ultimately selecting YOLOv8 as our baseline due to its balanced accuracy and efficiency. Building on this foundation, we present MS-YOLO (MobileNetv4 and SlideLoss based on YOLO), which replaces YOLOv8's CSPDarknet backbone with the more efficient MobileNetV4, reducing computational overhead by 1.5% …

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.

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

Optimal Manipulation Motion Action Planner Enabled By Physics Informed Neural Networks, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy …

Review Of Coreless Axial Flux Permanent Magnet Machines With Applications Including Electric Aircraft Propulsion, Matin Vatani, Donovin D. Lewis, John F. Eastham, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Axial flux permanent magnet (AFPM) machines with entirely or stator coreless configurations offer significant advantages, including low rotational losses, the absence of cogging torque, minimal torque ripple, and the potential for high specific power density. Advances in permanent magnet (PM) technology and cooling techniques have enabled fully coreless AFPM machines to achieve high magnetic and current loadings without core saturation, making them suitable for high performance applications like electric aircraft propulsion. This paper reviews recent advancements in coreless AFPM machines, tracing their historical and chronological development. Various topologies, including single- and double-sided and multi-disk designs, are examined to identify the …

Two-Level Multi-Objective Design Optimization Including Torque Ripple Minimization For Stator Excited Synchronous And Flux Switching Machines, Ali Mohammadi, Oluwaseun A. Badewa, Yaser Chulaee, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Electric machine performance can be enhanced through torque ripple mitigation, which reduces mechanical vibrations and improves system stability and operation. This paper proposes a novel two-level optimization method for synchronous flux-switching and hybrid excitation machines, featuring an innovative multi-point spline shaping approach for torque ripple minimization. The study employs experimentally validated models based on a prototype with a similar topology. The optimization process is implemented on two distinct designs: a 20-pole inner rotor PM-excited stator machine and a 28-pole outer rotor DC-excited stator machine. Analysis results demonstrate that the proposed innovative method significantly reduces torque ripple in both configurations. Force …

Electromagnetic Design Characterization Of Synchronous Machines With Flux Switching Effect Employing Reluctance Rotors And Stators With Pms And Ac Concentrated Coils, Oluwaseun A. Badewa, Ali Mohammadi, Donovin D. Lewis, Somasundaram Essakiappan, Madhav Manjrekar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces an innovative design for a synchronous electric motor that has phase coils and permanent magnets (PM) embedded in the stator. The electric machine contains both concentrated winded phase coils in dedicated slots and spoke-type permanent magnets solely in the stator. The spoke-type magnet arrangement achieves a high flux intensity even with low remanence non-rare earth permanent magnets. As the rotor has no active electromagnetic components, the risk for demagnetization is reduced and advanced cooling applied to the stator can enable high-speed operation. The principle of operation and polarity of the motor are investigated with a unique approach …

St-Hybrid: Dynamic Graph Learning With Multi-Scale Spatio-Temporal Attention For Traffic Forecasting, Dhe Yeong Ewaza Tchalla

Electronic Theses and Dissertations

Accurate short-term traffic forecasting is central to modern Intelligent Transportation Systems, supporting route guidance, adaptive signal control, and incident response. Yet producing reliable predictions remains difficult because traffic is highly non-stationary. The relationships among roadway sensors shift during congestion, incidents, weather changes, or fluctuations in demand, and the temporal structure of traffic spans several scales from abrupt minute-level variations to broader daily and weekly rhythms. Models that rely on fixed spatial graphs or a single temporal scale tend to miss these evolving and layered dependencies. This thesis addresses these challenges by developing a graph-learning framework that adapts to changing traffic …

Multi-Objective Deep Reinforcement Learning For Dynamic Algorithm Selection In Open Ran, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open Radio Access Networks (Open RAN) provide flexible, modular multi-vendor interoperability. Growing mobile data demand requires balancing network performance with power efficiency. Mobile operators need intelligent resource management to achieve Key Performance Indicator (KPI) targets while maintaining operational efficiency. This paper proposes a solution using a multi-objective deep reinforcement learning (MODRL) model deployed on the Open RAN Intelligent Controller (RIC). Three customizable operator profiles (Power Saving, Balanced, and Performance) are used which define specific priority ratios between performance and power saving objectives.

To evaluate, individual algorithms (CPU scheduling and UE connection state switching) are implemented in Open RAN, achieving 5–20%CPU …

Performance Evaluation Of Managed Switch Configurations For Secure And Efficient Plc-Based Industrial Automation Networks, Ahmed Salama

All Graduate Theses, Dissertations, and Other Capstone Projects

This thesis explores how managed switches can improve network performance and security in PLC-based industrial systems. Using simulations in GNS3 and Cisco Packet Tracer, and packet analysis via Wireshark, the study compares unmanaged and managed switch configurations. Redundancy protocols, particularly STP and RSTP, are evaluated under failure scenarios. Results show that RSTP offers faster recovery times, making it more suitable for time-sensitive environments. Additionally, managed switch features like VLANs, port security, and MAC filtering significantly reduce vulnerabilities and improve network segmentation. The findings highlight the importance of incorporating both cybersecurity and redundancy in industrial network design as systems move toward …

C 3 An: Custom, Compact And Composite Ai Systems - A Neurosymbolic Approach: 4Th-Generation Evolution Of Intelligent Systems, Amit P. Sheth, Kaushik Roy, Revathy Venkataramanan, Venkatesan Nadimuthu

Publications

Artificial Intelligence (AI) systems continue to evolve rapidly. From the architecture perspective, it is evolving from large, monolithic models trained on massive internet data to complex, multi-component “compound” systems and “agentic” frameworks capable of semi-autonomous decision-making. These systems show immense promise yet face numerous challenges in reliability, consistency, transparency, and alignment with user goals. In this article, we propose Custom, Compact and Composite AI with Neurosymbolic (C3AN) approach, a framework that paves way to 4th-generation of AI that integrates data, knowledge, and human expertise to build robust, intelligent and trustworthy AI systems defined by 14 foundation elements.

Custom emphasizes …

Vehicle Routing Problem With Time Constraint From Multiple Origins To Selective Destinations By Using Genetic Algorithm, Manocha Kruetet

Chulalongkorn University Theses and Dissertations (Chula ETD)

This thesis addresses the vehicle routing problem with time constraints from multiple origins to selective destinations by using a genetic algorithm for efficient path planning. The focus is on optimizing field collection and repossession activities in the financial industry to enhance operational efficiency and customer satisfaction. The proposed approach integrates two main components: a tailored genetic algorithm for multi-objective optimization and a graph neural network for precise travel time estimation. The genetic algorithm is designed to minimize total travel distance or travel time and balance the workload among field collectors, while the graph neural network provides travel time estimates to …

Real-Time Monitoring System For Sedentary Behavior Using Pose-Estimation Approach, Pheng Ou Chea

Chulalongkorn University Theses and Dissertations (Chula ETD)

Long-term sedentary behavior, defined as extended periods of sitting or inactivity, increases the risk of obesity, cardiovascular disease, and other health problems. These risks are often underestimated due to low levels of physical activity. This study proposes the use of You Only Look Once (YOLO)–based pose estimation for dwell-time analysis to monitor sedentary behavior. We examined posture over an entire day to assess prolonged inactivity and encourage individuals to balance energy expenditure with available time—such as engaging in exercise in the early morning or evening according to recommended guidelines. For employees across generations, as well as for vulnerable groups such …

การจัดสรรการเดินเครื่องกำเนิดไฟฟ้าพร้อมระบบกักเก็บพลังงานด้วยแบตเตอรี่เพื่อสนับสนุนกำลังสำรองพร้อมจ่ายในระบบไฟฟ้าที่มีพลังงานแสงอาทิตย์, ชาติเชื้อ ปานพรหมมินทร์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในปัจจุบัน ระบบไฟฟ้าของประเทศไทยและทั่วโลกกำลังอยู่ในช่วงของการเปลี่ยนผ่านสู่ระบบพลังงานสะอาด (Energy Transition) โดยมีการส่งเสริมการผลิตไฟฟ้าจากพลังงานหมุนเวียน เช่น พลังงานแสงอาทิตย์และพลังงานลม เพื่อทดแทนการใช้เชื้อเพลิงฟอสซิลที่ก่อให้เกิดการปล่อยก๊าซเรือนกระจก อย่างไรก็ตาม พลังงานหมุนเวียนมีลักษณะผันผวนตามสภาพอากาศและช่วงเวลาของวัน จึงสร้างความท้าทายต่อความมั่นคงและความน่าเชื่อถือของระบบผลิตไฟฟ้า การรักษาสมดุลระหว่างการผลิตและการใช้ไฟฟ้าในทุกช่วงเวลาจึงต้องอาศัยการเตรียมกำลังผลิตสำรองพร้อมจ่าย เพื่อรองรับความผันผวนและเหตุขัดข้องของหน่วยผลิต การจัดสรรกำลังผลิตสำรองพร้อมจ่าย ในระบบที่มีสัดส่วนพลังงานหมุนเวียนสูงจึงกลายเป็นประเด็นสำคัญที่ต้องมีการวางแผนอย่างรอบคอบ โดยเฉพาะอย่างยิ่งในช่วงที่การผลิตจากพลังงานหมุนเวียนลดลงอย่างกะทันหันซึ่งอาจส่งผลกระทบต่อความมั่นคงของระบบไฟฟ้าโดยรวมวิทยานิพนธ์ฉบับนี้นำเสนอการศึกษาการเดินเครื่องหน่วยผลิตไฟฟ้า (Unit Commitment) ที่พิจารณาการใช้ระบบกักเก็บพลังงานด้วยแบตเตอรี่ (Battery Energy Storage System) เพื่อช่วยเตรียมกำลังสำรองพร้อมจ่ายในระบบไฟฟ้าที่มีการผนวกพลังงานแสงอาทิตย์ โดยพัฒนาแบบจำลองทางคณิตศาสตร์แบบ Mixed Integer Linear Programming (MILP) เพื่อหากำหนดการเดินเครื่องที่เหมาะสมที่สุดภายใต้ข้อจำกัดของโรงไฟฟ้าและแบตเตอรี่ พร้อมทั้งสร้าง สถานการณ์จำลองของความผันผวนของพลังงานแสงอาทิตย์ เพื่อวิเคราะห์ผลกระทบของความไม่แน่นอนต่อการจัดสรรกำลังผลิตและต้นทุนของระบบ การศึกษานี้มุ่งหวังที่จะประเมินว่าการนำระบบแบตเตอรี่มาใช้ร่วมกับโรงไฟฟ้าพลังงานฟอสซิลจะช่วยลดต้นทุนโดยรวมของระบบได้มากน้อยเพียงใด ตลอดจนสะท้อนบทบาทที่แท้จริงของแบตเตอรี่ในการเพิ่มความยืดหยุ่นและความมั่นคงของระบบไฟฟ้าในยุคพลังงานหมุนเวียน