Volume 14 | Issue 6

 Abstract

Nowadays the world is shifting towards electrified mobility to reduce the pollutant emissions caused by non-renewable fossil-fueled vehicles and to provide an alternative to pricey fuel for transportation is an Electric Vehicle. But for electric vehicles, traveling range and charging process are the two major issues affecting its adoption over conventional vehicles WPT Enabled infrastructure has to be employed to achieve a Hybrid (dynamic/ stationary) EV charging concept. The weight of the battery pack can be reduced as the required energy storage is lower if the vehicle can be powered wirelessly while driving.

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WIRELESS ELECTRIC VEHICLE CHARGING STATION

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This paper presents Electric vehicles are today's zero emission vehicular technology which are considered as the future of automotive industry. The batteries of the vehicles get charged in order to drive the vehicle. The methodology of charging the electric vehicle currently is through plug-in method where the charging station charges the battery of an electric vehicle. However, an alternative method for charging the battery of an electric vehicle is through Wireless Power Transfer where it can be as a Static or Dynamic charging systems. Static Charging System can be implemented to charge the batteries of the electric vehicles when the vehicle is parked in static mode. Dynamic Charging System can be implemented to charge when the vehicle is in motion. This method of wireless charging of electric vehicle is done through inductive power transfer where wireless transmission of power is achieved by mutual induction of magnetic field between transmitter and receiver coil. The state of the battery is monitored using Battery Management system (BMS).

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Wire less Charging Station free Energy Transfer

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Energy Generation is a big problem in developing and developed countries. Its supply and demand is an issue as Energy Consumption is always higher than Energy production. Renewable Energy sources are playing a significant role in bridging the gap between energy generation and consumption. Photovoltaic Solar panels and wind generators are now widely available that can be used in order to conserve non-renewable resources. Wind Generator with Doubly-fed induction generator (DFIG) system is quite common in which the power electronic interface controls the rotor currents to achieve the variable speed necessary for maximum energy capture in variable winds. Exhaustion of Non-renewable Energy sources also leads to environmental problems. Therefore, there is the time to use renewable energy sources from non-renewable power sources such a way, which maximizes the use of power generated by renewable resources without affecting the power system. There should be an intelligent power system that can use the electrical power from renewable resources and only needs to use non-renewable power sources if renewable power is either not available or not fulfilling the required demand, which is the primary function of Smart Grid Power Network. Therefore, a smart grid is an electrical grid, which includes a vast variety of operations and energy measures, including smart meters, smart appliances, renewable energy resources, and energy-efficient resources. In this paper, different types of energy sources both non-renewable and renewable are connected and simulation is done with the help of MatLab Simulink Tool Box.

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Simulation of Smart Grid in MATLAB/Simulink

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PulseAI: Low-Cost IoT-Based Real-Time ECG Monitoring System Using AD8232, ADS1115, and ESP32

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PulseAI: Low-Cost IoT-Based Real-Time ECG Monitoring System Using AD8232, ADS1115, and ESP32

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The Password-Based Circuit Breaker is a smart electrical protection system designed to improve safety and prevent unauthorized use of electrical power. In traditional circuit breakers, anyone can switch the power ON or OFF manually, which may lead to misuse, accidents, or electric hazards. This project introduces a password security feature that allows only authorized users to control the electrical supply.

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Password-based Circuit Breaker

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Modelling and Simulation of a 100 kW Solar Power Plant Using MATLAB/Simulink

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Modelling and Simulation of a 100 kW Solar Power Plant Using MATLAB/Simulink

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This paper presents an IoT-Based Energy Monitoring and Control System using ESP32 and PZEM-004T sensor for real-time electrical parameter monitoring. The system measures voltage, current, power, and energy consumption and displays the data on an LCD display as well as on the ThingSpeak cloud platform through Wi-Fi connectivity. A relay module is integrated for remote load control using a mobile application. The proposed system enables real-time monitoring, cloud data storage, graphical analysis, and remote access for efficient energy management. The project helps reduce electricity wastage and supports smart home and smart campus applications.

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IoT Based Energy Monitoring System

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In this proposed system, two zero crossing detectors are used for detecting zero crossing of voltage and current. The project is designed to minimize penalty for industrial units using Automatic Reactive Power Compensation unit. The PIC microcontroller used in this project.

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Automatic Reactive Power Compensation System

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Inefficient waste segregation at the source remains a significant challenge for effective waste management and recycling efforts. This project presents the design and implementation of an automated, IoT-enabled smart garbage separation system capable of classifying waste into distinct categories: Dry, Wet, and Metal.

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IOT, pico w, E waste, machine Learning, Sensor, WI- FI

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The project " EV BICYCLE" focuses on studying, designing, and understanding the mechanical and electrical systems that enable bicycles to be one of the most efficient human-powered machines ever developed. This report aims to present a detailed analysis of the basic working principles, structural components, efficiency factors, and innovations in modern bicycles.

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Bicycle Design, Mechanical Transmission, Gear Systems, Brake Efficiency, Energy Conservation, Sustainable Transport

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Air Quality Monitoring Using Drone: A Quadcopter-Based Real-Time Atmospheric Sensing System

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Air Quality Monitoring Using Drone: A Quadcopter-Based Real-Time Atmospheric Sensing System

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Modern agriculture requires efficient water management, especially in hilly regions where uneven terrain makes irrigation difficult. Traditional irrigation methods often result in improper water distribution, increased wastage, and reduced crop productivity. To overcome these challenges, this project presents an IoT-based smart irrigation system designed for multi-crop cultivation, specifically for Wheat and Jowar crops.The system uses NodeMCU modules as wireless sensor nodes to monitor soil moisture, temperature, and humidity in real time across different crop zones. The collected data is transmitted to the Blynk cloud platform via Wi-Fi, enabling continuous monitoring through a mobile application. A Raspberry Pi acts as the central controller, processing the data and controlling irrigation components such as a water pump and solenoid valves using a relay module.The system operates in both automatic and manual modes. In automatic mode, irrigation is controlled based on soil moisture thresholds to ensure optimal water usage. In manual mode, users can monitor data and control the system remotely through the Blynk mobile application.This system improves water efficiency, reduces manual effort, and ensures proper irrigation in hilly environments. It is cost-effective, scalable, and supports real-time agricultural applications, contributing to sustainable farming and improved crop yield.

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IoT, Smart Irrigation, Raspberry Pi, NodeMCU, Soil Moisture Sensor, Blynk, Automation

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Tampering in weighing and measuring instruments poses a significant threat to commercial accuracy, consumer protection, and fair-trade practices. With the increasing shift from mechanical to digital systems, new forms of tamperingsuch as sensor bypassing, data manipulation, magnetic interference, and microcontroller hacking have emerged. This project focuses on the development of an intelligent tamper-detection and prevention system designed to ensure the integrity and reliability of weighing instruments. The proposed system integrates multiple security mechanisms including load cell monitoring through HX711 amplifier modules, tamper switches for enclosure security, magnetic field detection, and anomaly-based detection algorithms within a microcontroller unit. Any detected irregularity triggers visual and audible alarms and can log the event for regulatory inspection. Additionally, protective circuit design, shielding techniques, and secure firmware architecture are employed to minimize vulnerabilities. The outcomes demonstrate that a multi-layered approach combining hardware, software, and mechanical safeguards significantly increases resistance to tampering and enhances accuracy and trustworthiness in measurement systems. This solution supports legal metrology standards and promotes transparency in commercial transactions.

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Tampering detection, Weighing instruments, Legal metrology, Load cell security, HX711 amplifier, Microcontroller-based monitoring

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AgroPredict: Intelligent Analysis of Soil Data for Crop Suggestion

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AgroPredict: Intelligent Analysis of Soil Data for Crop Suggestion

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The integration of automation technologies in the hospitality industry is becoming increasingly important for improving service quality and reducing operational costs. This paper presents the development of a cost-effective autonomous service robot designed for indoor hospitality environments such as hotels, restaurants, and office spaces. The system is specifically designed to be low-cost, reliable, and easy to deploy, making it suitable for small and medium-sized enterprises.

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Design and Implementation of a Low-Cost Autonomous Hospitality Robot for Smart Indoor Service Application

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India's 146 million smallholder farming households continue to rely heavily on diesel-powered tillers for soil cultivation, weeding, and trenching. These machines consume 1.2-1.5 litres of diesel per operating hour, generating roughly 3.4 kg of CO? per hour and imposing an operating fuel cost of INR108-135/hr on marginal farmers. This paper presents the fabrication, testing, and detailed energy analysis of a 48 V brushless DC (BLDC) motor-driven electric soil cultivator, developed by third-year B.Tech students of the Electrical Engineering department at R.C. Patel Institute of Technology (RCPIT), Shirpur, Maharashtra, as part of their 2025-26 departmental project.

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Electric cultivator, battery-powered weeder, BLDC motor, 48 V LiFePO?, FOC controller, Indian agriculture, energy saving, rural student fabrication, RCPIT Shirpur, JAP-O design.

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The rapid adoption of electric vehicles (EVs) worldwide has created an urgent need for sophisticated monitoring systems capable of tracking vehicle performance, battery health, energy consumption, and location in real time. This review paper examines the current state of real-time EV monitoring systems, exploring their architecture, key technologies, communication protocols, and applications. We analyse the challenges facing these systems and discuss emerging trends that will shape their future development. The integration of Internet of Things (IoT), cloud computing, machine learning, and advanced sensor technologies has enabled unprecedented capabilities in EV monitoring, [1] contributing to improved safety, efficiency, and user experience.

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Electric vehicles, real-time monitoring, battery management system, IoT, telematics, cloud computing, machine learning, fleet management

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This work presents an IoT-enabled hybrid renewable energy charging station designed for electric vehicle applications. The system integrates a 12V/10W polycrystalline solar photovoltaic panel and a 3V DC generator motor functioning as a small-scale Wind Energy Conversion System to address the intermittency challenges inherent in single-source renewable installations. The station employs an ESP32 DevKit V1 microcontroller with dual-core Xtensa 32-bit LX6 processor, integrated 12-bit analog-to-digital conversion capability, and built-in Wi-Fi connectivity.

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Hybrid renewable energy systems, wireless power transfer, Internet of Things, electric vehicle charging, ESP32 microcontroller, Blynk cloud platform.

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 Abstract

The rapid global integration of Photovoltaic (PV) systems requires robust monitoring and diagnostic mechanisms to ensure high efficiency and operational safety. PV systems are highly susceptible to various anomalies, including line-to-line faults, open circuits, partial shading, and degradation, which can severely diminish power output and cause permanent damage. Traditional fault detection methods rely on predefined thresholds, which often fail under fluctuating environmental conditions. This paper proposes a Machine Learning (ML) based approach for the automatic detection and diagnosis of faults in PV systems. By extracting key electrical features (voltage, current, power) and environmental data (irradiance, temperature), several supervised ML algorithms, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forest (RF), are evaluated. The methodology involves data preprocessing, feature extraction, and model training using simulated I-V characteristic curves under healthy and faulty conditions. The proposed model demonstrates high diagnostic accuracy, offering a reliable, sensor-efficient, and computationally viable solution for real-time predictive maintenance in solar energy systems.

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Photovoltaic System, Fault Diagnosis, Machine Learning, Artificial Neural Networks (ANN), Predictive Maintenance, Partial Shading.

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The rapid growth of photovoltaic (PV) solar systems has increased the need for efficient monitoring and maintenance techniques to ensure optimal performance. Faults such as hotspots, shading, cracks, and connection failures significantly degrade system efficiency. This paper presents a comprehensive performance analysis of PV systems using thermal imaging technology for anomaly detection. Infrared thermography enables non-contact, real-time monitoring of PV modules by detecting temperature variations associated with defects. The study analyzes various anomalies, evaluates their impact on system performance, and highlights the effectiveness of thermal imaging combined with image processing techniques.

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Photovoltaic systems, thermal imaging, anomaly detection, hotspots, infrared thermography, fault diagnosis.

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