Journal of Electrical Engineering and Automation
A High Efficiency Quadratic Gain DC–DC Converter for Low Voltage Renewable Sources
High gain DC–DC converters have become increasingly important in several applications including renewable energy systems, electric vehicles, and DC microgrids because of the necessity of converting low input voltages to high output voltages efficiently. This study focuses on the design and performance analysis of a high gain DC–DC converter that operates in the Continuous Conduction Mode (CCM). The use of CCM guarantees low current ripples, efficient energy transfer processes, and stable operations. The proposed converter utilizes combinations of inductors, capacitors, diodes, and controlled switches in order to ensure the conversion of low input voltages to higher outputs at a significantly high voltage gain without employing extremely high duty cycles. The converter topology has been designed to optimize the energy transfer and storage process by taking advantage of the various operating modes. The proposed converter design has been able to reduce the voltage stresses applied on the switching components hence improved reliability and better performances. Due to its high voltage gain capability, the suggested converter can be employed in applications such as photovoltaic systems and battery operated systems, which require step-up conversions. Simulations demonstrate the performance of the proposed converter in terms of efficiency and continuous currents.
Copyright (c) 2026 Journal of Electrical Engineering and Automation
Intelligent Smart Home Control Using NLP
The present research suggests an innovative smart home automation system using NLP and IoT technology, providing efficient control of household appliances through voice and text instructions. This system combines an NLP module with the Home module, offering natural communication between humans and devices without the need for pre-defined command set. The NLP module takes up the user commands by applying speech recognition, tokenization, intent classification, and entity extraction algorithms, whereas the Home module controls the appliances via Arduino UNO with the use of relays. Real time control of light, fan, and door in household environment becomes possible through text or voice communication. Experiments show that this approach is efficient in increasing the user convenience and accessibility, while decreasing dependence on complicated interfaces. The proposed intelligent automated control system is cost-effective to future intelligent smart home systems.
Performance Characterization of LoRa-Enabled Vehicle-to-Vehicle Safety Communication in Dynamic Road Environments
This research presents the design and experimental evaluation of LoRa-based vehicle-to-vehicle (V2V) communication system that can be used to transmit real-time hazard notifications without any dependence on cellular networks or roadside infrastructures. The design uses the ESP32 microcontroller with LoRa modules built using the SX1278 transceiver for the implementation of a peer-to-peer communication system in automobiles. The communication procedure includes hazard detection, packet creation, wireless transmission, and alerting components. The experimental analysis of the system was conducted by manipulating the environmental parameters like LOS (line-of-sight) and NLOS (non-line-of-sight). Evaluation performance measures include packet delivery ratio (PDR), received signal strength indicator (RSSI), latency, and communication distance. The experiment results demonstrate successful communication at a distance of 600 meters in LOS conditions, with a PDR of greater than 95% and latency lower than 250 ms.
Intelligent Transportation Systems (ITS)
Design and Implementation of an Android-Based Secure Circuit Breaker Control System for Lineman Safety
Maintenance of the electricity supply lines may be risky in case there is any accidental activation of the lines resulting in injuries to linemen. To address this problem, this study proposes incorporation of a secure and controlled circuit breaker with an Android application. This solution will use an AVR microcontroller, Bluetooth module, and relay to control the switching of the electric lines. This circuit breaker can only be activated through a mobile application by authorized linemen through entry of their password; no one else will restore the electric lines during maintenance activities. The hardware setup consists of a prototype power supply, an LCD display for displaying status, and wireless communication to enable real-time control of the electrical lines. The design of this system focuses on cost-effectiveness, simplicity, and ease of implementation while greatly increasing operational safety. The experimental results from implementing the proposed system show that this system prevent the accidental restoration of power and provide a reliable method for protecting lineman. This research illustrates a way to combine mobile technology with an embedded system to improve safety in electrical distribution.
SVM-Based EV Battery Monitoring with Dual Active Bridge Converter
Estimating the health status of batteries used in electric vehicles is essential in ensuring safe operation and maximizing their life expectancy and reliability. Precise lifespan prediction reduces expenses and increases confidence. However, existing prediction techniques lack sufficient real-world battery degradation data, suffer from non-linear degradation patterns due to differences in usage cases, and require heavy computational capacity. To solve the above issues, this research work introduces a battery prognostics algorithm that uses the SVM algorithm to predict key parameters of lithium batteries' health state, including their SOH and RUL. The developed prognostics system is combined with a DC–DC converter. Thus, it allows transferring power in both directions between the converter and the vehicle battery. The power transfer control unit is based on the PWM generator and the digital signal controller dsPIC30F4011 that controls the operation of the system by controlling the power distribution. Besides, the system has voltage and current sensors that monitor the battery's parameters continuously. Finally, the developed system enables not only to predict but also charge the EV battery. TLP250 drivers help generate gate pulses to control the switching process, while the web-based monitoring service created using the ESP32 microcontroller helps to visualize data.
An Efficient Analysis of Cloud-based Energy Management System for Secure Data Transmission
The traditional method of manual electricity meter reading has limitations, including human errors, inefficiency, and resource intensity. This research proposes a Smart GSM-Based Automated Power Meter Reader System to streamline power consumption monitoring. The system utilizes a GSM module integrated with a digital energy meter to provide real-time, remote power consumption data to utility providers and consumers. This approach reduces manual intervention, enhances accuracy, and promotes energy management. Finally, a user-friendly interface is developed to visualize real-time energy statistics through interactive graphs and tables, enabling users to track consumption trends and make informed decisions.
Copyright (c) 2024 Journal of Electrical Engineering and Automation
Smart Women’s Safety Footwear with Moisture-Protected Taser and GPS-GSM Emergency Alert System
The increasing situations of harassment and violence on women leads to inquiries regarding their safety. Traditional methods of personal defense, such as pepper spray and mobile applications are not rapid responses in an emergency situation. This paper proposes a method using footwear as a self-defense system includes a non-lethal taser (electric shock), built-in GPS (global positioning system) and GSM (global system for mobile communications) technology allows real-time tracking of the users exact location. The taser may be activated by the user of the footwear but it also has a moisture sensor that deactivate the taser if moisture is detected in the air. Once the taser is turned off, the GPS and GSM modules transmit an emergency location alert to the wearer's emergency contacts. The smart footwear may be used on a regular basis designed to respond the situations rapidly (within seconds) and provide a cost-efficient, portable personal protection method.
Thermally Self-Regulating Battery Pack for Electric Vehicles
Thermal management plays a vital role in electric vehicle battery systems. It affects system performance, safety, life, and charge rate. This paper presents a study on a self-regulating electric vehicle battery pack that incorporates a novel hybrid thermal management system. It comprises a phase change material (PCM), a liquid coolant system, a temperature measurement system, and a control unit. This innovative system provides a combination of a passive system that utilizes a phase change material to buffer temperatures and an active system that utilizes a water-glycol coolant system to regulate temperatures. A thermal model of the system has been developed to simulate its behavior under various conditions. It considers heat generation, latent heat absorption, and convection heat transfer. This system can be implemented using a microcontroller. The experimental results show that the system can decrease the peak battery temperature by 8-12°C compared to the conventional air-cooled method, while maintaining a cell temperature variation of 2-3°C. The proposed hybrid cooling system can improve the thermal uniformity of the cells, increase the rate of heat dissipation, and maintain the battery at an optimal working range of 25-35°C. In addition, the system can reduce thermal stress, enhance safety, and increase energy efficiency by achieving moderate auxiliary power consumption. In summary, the experimental results prove that the proposed thermally self-regulating battery pack can be an effective, scalable, and energy-efficient solution for advanced thermal management of EV batteries.
Battery Thermal Management System (BTMS)
Arduino-Based Automated Waste Segregation System for Efficient Recycling
Now-a-days, the environment faces many challenges relating to effective waste management, particularly with the rapid population growth and increasing levels of solid waste produced in urban areas. The waste misclassification of generation contributes to the ineffectively recycling and eventually contributes to pollution. Traditional systems for sorting waste are largely dependent on manual methods that are labour-intensive and may not produce the desired result. This paper presents solutions to the previously mentioned issues by presenting a prototype for an automated waste sorting system using an Arduino platform. The system will enable the separation of wet and dry waste through sensor technology. The moisture content of the waste will be determined from moisture sensors identify the type of waste. Data from these sensors is sent and processed by an Arduino Uno microcontroller. When a waste material is determined to have a certain classification, the waste will automatically be sorted into the appropriate bin via one of the servo motor mechanisms., Ultrasonic fill level sensors exist to measure bin contents and buzzer alerts activate when bins are full to prevent waste overflow. The design demonstrated that the waste classification satisfied the classification and completed testing by reducing the amount of time spent performing manual tasks. The existing system is also cost effective and easily expanded for the use of any number of applications including residential spaces, educational facilities and small-scale waste collection businesses.
Autonomous and Collaborative Robots for Energy Systems
The global change of sustainable energy has increased the demand for robotic systems that can work independently from humans to work together safely with their connections through the energy systems (e.g. energy generation plants, transmission lines, renewable energy fields, substations and underground cable networks). Now-a-days, installing robots into the energy systems creates very hazardous and complex environments with time sensitive constraints in which traditional automation methods will not work. The use of autonomous robots (robots that can operate without human interaction) and collaborative robots (robots that can operate with humans) has the potential to deliver safe, reliable and efficient operation of robots in these work environments using technologies for perception (how a robot sees its environment), learning based decision making (make decisions using the information it has learned), distributed control (how robots operate as a distributed system) and collaborate with humans (work with human operators). This review presents a systematic, synthesis of recent advancements in collaborative and autonomous robotics with energy related applications. The review of technology advancements and deployment constraints will use 26 studies published in the major scientific software databases (IEEE Xplore, Scopus, Web of science, ScienceDirect, SpringerLink, & MDPI repositories) from 2024 - 2025. This review focuses on enabling technologies of cognitive and explainable autonomy, navigation using reinforcement learning, multi-robot coordinate, digital twin integration, fault tolerant control systems, energy aware task orchestration and Human-centred interactions through industry 5.0. This study has been categorized based on the type of contribution (research vs. development), Deployment context and technology advancement. It shows that individual capability areas such as navigation intelligence, collaborative interaction and multi-robot coordination continue to progress significantly. It identifies three primary challenges for the continuous improvement of autonomous robotic systems operation and additional research areas like the completeness & reliability of system operation under severe environmental conditions, Energy aware mission planning and scalable coordination of heterogeneous teams of robots with effective human-in-the-loop supervision. This review contains the identification of the most significant research gaps, an outline of how to develop robust, energy aware and Human-centred robotic systems that will facilitate energy infrastructure of the future.
Design and Development of Intrinsically Safe Lightning Arrester System for Firework Industries
The fireworks production process is highly risky for lightning-related accidents due to the presence of explosive substances and exposure to severe weather conditions. The existing fire protection system depends on ineffective surge and grounding management techniques without any automated emergency service in real-time. This article discusses the design and implementation of a fundamentally safe, affordable, IoT-enabled lightning protection and early alert system developed for the fireworks industry. This model combines environmental sensing, artificial lightning modeling, electrical fault monitoring, fire detection and automated fire control. Weather conditions such as temperature, humidity, rainfall, noise levels and water levels are continually monitored to detect the irregular variations caused by simulated lightning strikes using voltage and current sensors. The experimental results show fire detection (~0.3s), automated fire protection activation within 1.5s, reliable cloud data updates every 15s, and WhatsApp alert notifications sent within 6–8s. This system operated continuously for 8 hours with stable performance and achieved AI-based weather risk prediction accuracy of approximately 82%. These results confirm that the proposed work is an effective smart safety measure for improving lightning-related fire protection in fireworks manufacturing industries.
Smart Vehicle-to-Vehicle Wireless Charging System with IoT-Based Location Tracking
The increased use of Electric Vehicles (EVs) provides smart charging solutions to prevent limitations and dependence on fixed charging stations. This research work presents a Smart Vehicle-to-Vehicle (V2V) wireless charging system with IoT based location tracking to allow direct energy transmission between vehicles. This system integrates a dual-switch method to control the energy transfer. One switch will initiate a charging request and the other switch will allow or reject the request. The Wireless Power Transfer (WPT) method is used for efficient power transmission and an ESP32 microcontroller allows real-time data collection and system management. The main hardware components used in this research are a voltage and current sensor, a temperature sensor, and a GPS module for monitoring energy flow, safety and tracking the vehicle’s location. This work also improves the charging range, reduces dependency and allows decentralized energy transmission. This study provides a cost effective solution to improve EV charging performance and access by combining IoT technology with wireless charging.
Autonomous and Collaborative Robots for Energy Systems: State-of-the-art Review
Energy systems are becoming larger, more complicated and distributed system. Such environments make manual operation and investigation based on unsafe, slow and inefficient operations. The independent and cooperative robots have been realized as viable systems in energy infrastructures to monitor, maintain and assist in operating the facilities. Recent research indicates a gradual increase in automated, energy-sensitive work and combined task performance of robots. Simultaneously, energy systems have severe limitations in safety, reliability and power efficiency. According to research explained in recent surveys, energy-efficient movement planning, smart power controlling and collaborative working among robots in energy-based tasks are most important. This paper reviews autonomous and collaborative robots in energy systems. It considers energy infrastructures as robotic workplaces, discusses autonomous and collaborative robots and reviews energy consumption modelling and optimization plans. The system integration and real-life implementations are also discussed. Finally, the main problems and future study directions are described. This survey aims to provide a brief and methodical source of data for researchers and engineers working on robotics and energy systems.
Future of Dynamic Electric Vehicle Challenges and Solutions
Dynamic Electric Vehicles are an advanced, sustainable, alternative to traditional Electric Vehicle technology that reduces the major issues associated with traditional EV technology (i.e. range anxiety, long recharging times and the requirement of high-capacity batteries). The application of Dynamic Wireless Charging (DWC) systems and new smart energy systems will improve the performance and accelerate the adoption of EVs. The methodology for this study performs a longitudinal review of the literature addressing DWC systems and included a number of case study examples from the various previous works. DWC systems based on either inductive or resonant power transfer, V2I (vehicle-to-infrastructure) communication, integration with smart grid, improved battery technology and dynamic load management systems were evaluated. The results indicate that dynamic charging systems are technologically capable and will provide improved performance compared to traditional EVs based on the case studies that used from Europe, North America and China. The results of this research show that charging EVs will decrease the time that EVs spend while they are in movement. It will remove the range anxiety and reduce the amount of battery power that EVs require.
Robust Fault Detection and Classification in Power Systems via Physics-Informed and Data-Driven Learning
Electrical faults in power transmission systems can severely affect grid stability, equipment safety, and operational reliability. Traditional protection schemes, particularly distance relays, depend on apparent impedance computation that changes with error, creating a risk of misclassification. The results from relay overreach, underreach, or complete maloperation due to CT/PT saturation lead to developing problems in high impedance situations. These limitations highlight the need for adaptive, data-driven alternatives. This paper proposes an intelligent fault detection and classification model based on supervised machine learning techniques that overcome these challenges. The system’s robustness was validated under different training sizes and Gaussian noise levels, demonstrating consistent accuracy and generalization across diverse learning conditions. The presented approaches learn the complex nonlinear mapping between three-phase voltage/current patterns and the associated fault type, without assuming fixed impedance paths like traditional protection schemes. This method extracts a high set of derived features to represent the distinguishing characteristics of six fault categories by utilizing line voltages and currents. Different models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests, XGBoost, Long Short-Term Memory (LSTM), and Physics-Informed Neural Networks (PINN) are developed and processed on SMOTE-balanced datasets. These models classify the errors without fixed thresholds or fault loop assumptions, improving sensitivity and robustness. The supervised machine learning approaches bridge the gap between traditional impedance-based protection and smart, scalable data-driven grid analytics that are implemented into a wide area monitoring and control system. The PINN achieved the highest fault detection accuracy of 99.86% while sustaining 99.79% multiclass classification accuracy on the clean dataset. The PINN maintains high accuracy under 2–5% noise and 1–60% training data, providing millisecond-level inference by embedding power system equations, which enables accurate real-time protection by understanding the missing simple data-driven parameters.
Copyright (c) 2025 Journal of Electrical Engineering and Automation
Multi-Purpose Voice Control Patient Assistance Robot
Timely and regular treatment of the physically disabled, bedridden, and elderly is a mandated health requirement. A multi-purpose voice-controlled patient care robot (VCPAR) designed and implemented for on-demand drug, food, and liquid delivery in natural language is presented in this paper. In contrast to conventional button/joystick-based or human-attention-based robotic systems, the approach utilizes Arduino-based control, Bluetooth communication, and speech recognition to facilitate completely hands-free usage. IR sensor-based obstacle sensing and accurate actuating devices ensure safe transport and efficient dispensing of goods. Another of the research's novel contributions is its low-cost, locally manufactured, and multimodal platform for use in resource-constrained settings where advanced healthcare robots are cumbersome. Experimental findings demonstrate agreement with voice instructions from the patient at a distance of 10 m or less from Bluetooth, precise delivery of objects with minimal latency, and greater user control compared to current semi-automatic systems. The project aims to explore the potential of low-cost voice-controlled robots for maximum patient autonomy, caregivers' load reduction, and new opportunities for smart healthcare support systems. Intellectually enabled assistive technology has been in high demand due to the increasing numbers of elderly, bedridden, and physically disabled patients who otherwise need to remain constantly supervised and receive timely interventions.
Voice Controlled Application using ESP32
The ESP32 microcontroller is used in this project to develop voice applications for device automation. The ESP32's built-in Bluetooth and Wi-Fi capabilities make it the perfect platform for Internet of Things (IoT) applications, where it can perform voice-activated tasks both online and offline. An external microphone records the voice commands, which the system stores before being interpreted by edge-based speech recognition software like TensorFlow Lite and Vosk, or cloud-based services like Google Assistant or Amazon Alexa. Relays or the ESP32's GPIO pins are used to translate the voice commands into control signals that can be used to operate lights, fans, and other devices. The system can function in any network condition thanks to its support for both offline and cloud computing. The application showcases the versatility of the ESP32 in real-time voice-activated home systems, which improve accessibility and user convenience through voice control.
Smart Thermal Regulation for Patient Care using Body Temperature and Room Temperature Monitoring
A speed control fan system has been designed to enhance the comfort level of patients and conserve energy. It is extremely helpful for those patients who may not be able to shift their positions, as it continues to monitor the body temperature of the patient along with the room temperature continuously. The fan itself automatically changes speed using sensors and PWM technology, leaving the user with no work to manually do so. The fan speed is modulated by temperature in a linear and accurate way, as determined by testing with Tinkercad simulations. A manual override through a potentiometer can be used if needed, and an LCD display using an I2C adapter provides for the ability to monitor system activity. Other enhancements to improve the system further involve more advanced control algorithms, integration with wearable technology for enhanced temperature sensing, and remote control and monitoring.
Environmental Air Pollution and Water Quality Systems in Educational Institutions
An intelligent environmental monitoring system receives real-time conditions through continuous interpretation of several environmental parameters to provide dependable results alongside ongoing updates. The data communication through IoT connectivity creates a smooth transmission to central platforms which enables remote observation along with preventive decision-making to boost health and environmental sustainability. The system operates over extended periods by using automated analytics that track environmental changes with data-driven evaluation methods. It provides an accessible platform that helps users track essential parameters effectively, supporting public security and healthcare initiatives. The system enables environmental management alongside public health due to its feature set, which includes early alerts, quick responses, and strategic decision-making abilities. It requires the integration of machine learning and improved data analytics to advance its prediction functions.
The stand-alone creatinine test kit design is home care monitoring technology, i.e., patients at risk of kidney injury or chronic kidney disease (CKD). The new kit enables individuals to self-calculate their own creatinine levels better and more precisely without necessarily utilizing the services of well-trained professional health care providers. The primary intention of this kit is to reduce the need for professional medical care to the utmost level, thus emulating an automated process. The test kit is provided with a simple, easy-to-handle system, including a urine sample collection device and colorimetric assay for the determination of creatinine. It can provide results in a few minutes with instant feedback on kidney function. The self-testing creatinine kit aims to ensure patient autonomy and allow for the early detection of kidney disorders, thus facilitating early medical care. The kit has been prepared for use by common patients who are far from hospitals or do not have easy access to hospitals.
Artifact Sentinel: A Real-Time IoT System for Artifact Safety and Conservation
As everyone knows, museums are houses for priceless artifacts that need to be preserved at all costs. It is important to note that these artifacts need to be stored in a suitable environment for preservation. The proposed "Deformation Evidence Guard" is an Internet of Things device that provides protection for deformation evidence and ensures that its environmental norms are maintained. Various sensors in this device work together to ensure that desired results are obtained. An alarm beeps, and an immediate alert is issued when movement is detected near an artifact, and an ESP takes a picture of a 32-came intruder. Technology also enables the sensor to continuously monitor temperature and humidity levels, presenting results on an LED display to help preserve as many artifacts as possible. This simple, automated device increases museum protection by reducing the possibility of theft and deterioration through real-time monitoring and environmental tracking. The proposed model using high energy efficiency, the ability to monitor surroundings, motion sensing, and an attached camera, as well as features such as real-time reactions, consistent monitoring, and detailed tracking, making it a novel approach compared to existing.
Smart IoT Energy Metering System with Real-Time Theft Detection and Prevention
This article describes the design and development of a real-time, IoT-based smart energy meter for detecting electricity theft using a low-cost Arduino Uno, ESP8266 Wi-Fi module, and Blynk IoT platform, relying on threshold-based detection methods. In contrast to conventional energy meters that do not have tampering detection features, the designed system constantly checks for energy consumption using current sensors and monitors data to detect unauthorized power consumption. The uniqueness of this research is its cost-effective dual-load configuration that mimics both legal and illegal utilization scenarios, allowing real-time detection and instant notification through cloud-based dashboards. Experimental validation proves effective bypass scenario detection and distant notifications with negligible latency, making it ideal for smart grid integration in developing countries. The system improves energy accountability, enables remote monitoring, and offers a scalable solution to help fight electricity theft effectively.
Design and Implementation of CMOS K-Band Phase Shifter using 90nm
We suggested designing a 24 GHz K band CMOS phase shifter using 90nm technology in this paper to satisfy the need for integrated and high-performance components for wireless system development. For the best phase shift and the least amount of insertion loss, the phase shifter operates between 18 and 27 GHz using a combination of lumped and distributed components. Important considerations are phase selectivity, linearity, and impedance matching. Theory and experiments demonstrate that it has a 180° range of phase shift and is utilized in high-frequency applications. The study demonstrates how 90nm CMOS technology can be used to improve wireless communication.
Complementary Metal Oxide Semiconductor (CMOS)
Automatic Smart Defense based Surveillance Security System using IoT
The Automatic Smart Defense-Based Surveillance Security System is an advanced system proposed in this study to improve real-time security surveillance and eliminate danger. Traditional surveillance technology relies heavily on human interaction which, leads to inefficiencies, delays, and mistakes made by humans. To overcome these limitations, the proposed system uses radar technology, AI, and IoT to detect, monitor, and remove potential threats such as drone attacks and illegal activities. Automating the surveillance will reduce false alarms, minimize dependency on human involvement, and provide continuous monitoring with quick reactions. High-security areas, including military camps, border patrol zones, airports, and other important buildings are the most suitable for this system to enhance security.
Long-time and effective control of water temperature is critical for both commercial and domestic use. This project designs a new dual-application water temperature control system for drinking and bathing water use by integrating thermoelectric cooling and immersion heating technology. The system employs a Peltier coil water flow block in cooling and an immersion water heater in heating. A fan-based heat dissipation scheme enhances thermal efficiency to ensure optimal system performance. An ESP-based microcontroller offers automated water level control and real-time monitoring through tank refilling control and monitoring of water levels. A temperature sensor DS18B20, with its precise thermal measurements within the storage tank, offers precise feedback control. The switching process of heating and cooling is maintained through a relay-based switching mechanism, ensuring that maximum energy efficiency is retained with optimal temperature levels. This system provides an innovative, energy-efficient, and affordable means of regulating the temperature of water in domestic and business premises. Monitoring enabled through IoT further increases user-friendliness by allowing real-time monitoring of temperatures and automated regulation of the system. The suggested method increases convenience for users and promotes eco-friendly use of resources by offering a flexible and scalable solution to existing issues of water heating and cooling.
Decentralized Control Strategy for Voltage Regulation in Islanded DC Microgrids
Every distributed energy resource (DER), including battery systems and solar panels, has local controllers that respond to variations in frequency and voltage to modify power generation in order to maintain voltage stability. In this context, traditional droop control is commonly used to control variable states of charge (SoC) in battery energy storage systems (BESS) and to enable decentralized control for load sharing among distributed generator (DG) units. While grid integration provides increased flexibility and resilience, decentralizing energy management allows the system to optimize local energy resources independently, creating a resilient and flexible system that can react to changing grid conditions.
Battery Energy Storage Systems (BESS) Units
Design of Hand Assistive Device for Autistic Children
Autism spectrum disorder (ASD) is a developmental disability related to neurological variations, affecting children's social communication, behavior, learning, and attention. These children often struggle with movement, as they require external support to move their hands and legs. A hand assistive device has been designed to help these children, providing them with movement exercises and physiotherapy. This device also uses EMG (electromyography) sensors to diagnose neuromuscular activities in autistic children. This device is essential for their overall well-being and development.
Lithium-ion batteries are an important component in electric vehicles (EVs), and ensuring their optimal performance is vital for the longevity and efficiency of the vehicle. Efficient testing of battery cells plays a major role in verifying their performance. Traditionally, a single-cell testing unit is used for batch processing during battery pack assembly. However, this method is time-consuming and lacks scalability, which limits productivity. To address this, the research proposes a multicell testing approach that concurrently estimates the state of charge (SOC) and state of health (SOH) of multiple battery cells in parallel. By doing so, the approach significantly reduces testing time and improves efficiency. The dual filter concept is incorporated to categorize cells based on their performance, ensuring only high- quality cells are selected for inclusion in the battery pack. Furthermore, a custom Temporal Convolutional Network (TCN) model, achieving an accuracy of 89%, is employed to accurately estimate SOC and SOH. In addition, a predictive battery temperature forecasting model is introduced to forecast the temperature of the battery cells over the next three days, which aids in proactive temperature management and prevents potential degradation. Overall, the proposed approach enhances battery testing productivity and ensures higher accuracy in SOC and SOH estimation, contributing to the development of more reliable and efficient EV batteries.
Optimized Dual Loop Control in PV – based LVDC Microgrid With Hybrid Energy Storage for Enhanced Power Management
This study proposes a power management plan for an LVDC (Low-Voltage Direct Current) microgrid that is linked with solar energy and connected to a HESS (Hybrid Energy Storage System) that consists of a supercapacitor and battery. This study analyses the microgrid's performance with a focus on integrating hybrid energy storage devices to manage the unpredictable nature of renewable energy resources and load demands. The primary goals are creating a DC link voltage controller, building the battery and supercapacitor current controller, and activating a solar power generating unit using the Perturb and Observe MPPT algorithm. Additionally, a dual loop control strategy is created for the HESS. The power management method controls DC voltage and maintains power balance.
Improving Lithium-ion Battery Longevity through Predictive Thermal Management
Lithium-ion batteries are vital in various storage applications like electric vehicle, renewable energy system and industrial applications. One of the major drawbacks with lithium- ion batteries are overheating. A cost-effective thermal management system is proposed where predictive analysis is employed to reduce thermal stress, thereby increasing reliability and longevity of batteries. Linear regression techniques is employed to forecast temperature fluctuation and activate cooling system when temperature of the lithium-ion battery reaches unsafe limit. The proposed methodology is developed and validated by a low-cost hardware.