Solar Charge Research Articles

Dual MPPT Algorithm Implementation in Solar Charge Controller

The implementation of a Dual Maximum Power Point Tracking (MPPT) algorithm in a solar charge controller is aimed at enhancing the efficiency of solar energy utilization. This algorithm optimizes power extraction from solar panels by dynamically adjusting the operating point to track the maximum power output under varying environmental conditions. By utilizing two MPPT trackers, the controller can efficiently handle scenarios with multiple peaks in the power-voltage curve, common in partially shaded or non-uniformly illuminated panels. This implementation involves real-time monitoring of panel voltages and currents, coupled with intelligent algorithms to swiftly respond to changes in irradiance levels. Through advanced control strategies, such as Perturbation and Observation (P&O) or Incremental Gradient (Incgrad), the system maximizes energy harvest, improving overall system performance and reliability.

Optimized Smart Home Energy Management System: Reducing Grid Consumption and Costs through Real-Time Pricing and Hybrid Architecture

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6%) and energy costs (57.7%) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

Electric Bicycle Battery Charging System Design Using Solar Panel

This solar-powered electric bicycle charging system design and development uses solar panels as a renewable energy source to charge the battery, which drives the electric motor. The system consists of four main components: solar panel, solar charge controller, battery, and electric motor. The solar panels are connected in parallel to generate 36V, which is then stored in the battery. The system is designed to optimize energy harvesting and reduce dependence on fossil fuels. Testing was conducted with research stages, including system design, hardware implementation, and testing. The results show that the system can charge the battery efficiently, with a charging time of 3 hours. The system was also tested under different conditions, including morning, afternoon, and night, to evaluate its performance. It can be concluded that the solar-powered electric bicycle charging system is a viable alternative to traditional fossil fuel-based systems. The system is environmentally friendly, reduces energy dependence, and provides a sustainable solution for transportation. The results of this research can be used to develop a more efficient and sustainable transportation system.

Optimizing Solar Power Generation and Integration in Automotive Systems Through IoT

Objective: This study presents an Internet of Things (IoT)- based system with edge intelligence that predicts power production with over 98% accuracy and monitors substations and smart solar installations, ensuring reliable power distribution in industrial IoT environments. This system enhances sustainability, safety, and energy management in smart buildings by reducing power fluctuations by 30% and improving decision-making, leading to a 95% reduction in energy management costs. Method: An IoT-enabled power monitoring system was implemented for smart solar panels and substations, incorporating edge intelligence for instantaneous prediction and decision-making. An IoT-enabled solar charging station was deployed for smart homes and Industry 4.0 applications. The cloud was used for analyzing sensor data, with a response time of less than 1 second. Findings: The proposed framework increased the efficiency and reliability of power production and distribution by 25% across commercial, residential, and industrial contexts. It significantly mitigated power fluctuations, reducing downtime by 40% and achieving a 95% cost reduction in energy management compared to traditional systems. IoT integration improved safety and sustainability metrics by 20% in smart buildings. Novelty: The framework integrates edge intelligence with IoT in smart solar systems and substations, providing a sophisticated control system that enhances power distribution decision-making. It facilitates real-time monitoring and prediction of power production with over 98% accuracy, emphasizing sustainability, safety, and energy management improvements of up to 30% in smart buildings. Keywords: IoT-based control system, Smart solar systems, Edge intelligence, Power substations, Load management, Energy sustainability

Design of 30 A PWM Type Solar Charge Controller Module for 100 Watt Lamp Load

The electrical energy produced by solar panels has been used as a renewable energy solution to support human life. In reality, this alternative energy has not been widely used due to the constraints of expensive initial investment, so it is necessary to design solar power generation components, one of which is a reliable, optimal, efficient and economical solar charge controller. The controller is made using the switching method. The switching process can occur by setting the duty cycle through the embedded controller in the solar charge controller circuit. Based on the test results, a duty cycle value of 1% to 100% can occur the switching process. The duty cycle that has been set on the solar charge controller is 90%. The duty cycle is a duty cycle where the output voltage approaches the setpoint of the minimum charger voltage. The input voltage from the solar panel when connected to the battery of ±16 volts can be regulated to ±14 volts by the solar charge controller. Based on the battery charging specifications, the controller output can be used to charge a 12 volt battery.

Multifeatured Electronic Helmet to Enhance Road Safety and Rider’s Comfort

This paper presents a multi-featured electronic helmet designed to tackle critical road safety issues, including accidents, drunk driving, and over-speeding. The design incorporates a global system for mobile communications (GSM) and global positioning system (GPS) modules to accurately capture driver’s current location and send messages to predefined contacts. When encountering an accident, the helmet promptly notifies designated contacts and authorities, ensuring swift assistance. By integrating features for detecting over-speeding, accidents, and drunk driving, the helmet renders real-time alerts to both the driver’s family and traffic police, enhancing accountability and safety measures. Additionally, the helmet includes solar charging functionality for mobile devices receive 100% charged in 3.37 hours, thereby optimizing usability and emergency communication. A rain detection system protects mobile devices, while an internal warming mechanism caters to adverse weather conditions, and proffers enhanced comfort by maintaining internal form temperature between 26-27 ℃ and safety, especially in cold regions or for military personnel.

Implementation of Solar PV Protection System in Indonesia: A Review

Conventional energy sources will run out if used continuously and damage the environment. Therefore, it is necessary to develop other energy sources that are safe, environmentally friendly, and inexhaustible known as renewable energy sources to meet energy needs in Indonesia. This article presents a review that discusses the protection of solar panels and PLTS. Solar panels are vulnerable to lightning strikes and other electrical disturbances, so an effective protection system is needed. This study uses a qualitative method through a literature study, reviewing various protection methods such as conventional and electrostatic lightning rods, good grounding systems, and overcurrent detection devices such as Solar Charge Controller (SCC) and Maximum Power Point Tracking (MPPT). In addition, battery protection with Automatic Transfer Switch (ATS) and Low Voltage Disconnected (LVD) is also discussed. Using Arduino Uno, ESP 32, and PLC, automation approaches offer real-time monitoring and control solutions for solar power plant performance. Innovations in the lightning protection zone concept and minimal quantity measurement schemes enable more cost-effective design of protection systems without reducing the level of protection. The results show that these various protection methods can provide effective and efficient protection for solar power plants, enabling optimization of system function and safety. In conclusion, the protection systems reviewed in this study offer a sustainable solution to Indonesia's energy challenges by utilizing the maximum potential of solar energy. The potential for further research is presented in this article to develop more efficient and effective protection methods.

Photo-charging sodium-ion battery by gallium arsenide solar cell generating an overall efficiency exceeding 30 %

Photo-charging energy storage system integrating photovoltaic harvest and energy storage functions creates a sustainable power source. The system not only alleviates the inherent limitations of intermittent, fluctuating, and unstable sunlight irradiation, but also facilitates the effective and sustainable utilization of green energy. However, the photo-charging efficiency of the system is relatively low nowadays. Herein, we report a photo-chargeable sodium-ion battery (PC-SIB) that leverages a self-designed multi-functional modulator to directly charge sodium-ion battery using GaAs solar cells. By harmonizing function portfolio management, PC-SIB achieves a photo-charging efficiency milestone of 30.24 %, along with excellent charge-discharge stability. Even in −20 °C, PC-SIB still maintains outstanding performance. The great leap in efficiency will strongly promote the high-efficient development of solar charging and storage integrated devices.

Solar Power Plant Optimization using Automatic Transfer Switch (ATS) and Low Voltage Disconnect (LVD)

The automatic system model uses a Solar Charge Controller (SCC) to turn off the solar panel system at the minimum battery point so that the battery is safe and durable and an Automatic Transfer Switch (ATS) to automatically transfer the electricity network from State Electricity Company or Grid or solar panels. In this study, Solar Panel Priority Grid electricity is used if the solar panel is insufficient and hybrid system with solar panel priority with 2 cut out battery usage. The results of this study are 450WP Solar Panel, 100A SCC MPPT, 12V/100Ah Battery, and 3000 Watt Modified Sine Wave Inverter, Miniature Circuit Breaker, Contactor, Relay Switch, Time Delay Relay and Indicators. The results of the switching process test between the Solar Power Plant source and grid with ATS control can run automatically in Grid Priority Mode, meaning Solar Power Plant as a backup, or Solar Power Plant Priority Mode where the grid source is used as a power backup system. In the Battery Capacity Optimization System, Low Voltage Disconnect (LVD) Protection can work well, namely it can cut off the voltage from the inverter if the battery is in a low voltage state at a rating below 10.8 Volts, Auto Cut Charging Protection testing can charge the battery up to 13.8 Volts and Cycle Use, namely the process of this system can work to store energy while releasing energy to run the load. The results of this study can be used as a reference for the best choice of the most efficient Solar Power Plant system.

The SRG/eROSITA diffuse soft X-ray background

Context. The SRG/eROSITA All-Sky Surveys (eRASSs) combine the advantages of complete sky coverage and the energy resolution provided by the charge couple device and offer the most holistic and detailed view of the diffuse soft X-ray background (SXRB) to date. The first eRASS (eRASSl) was completed at solar minimum, when solar wind charge exchange emission was minimal, providing the clearest view of the SXRB. Aims. We aim to extract spatial and spectral information from each constituent of the SXRB in the western Galactic hemisphere, focusing on the local hot bubble (LHB). Methods. We extracted and analysed eRASSl spectra from almost all directions in the western Galactic hemisphere by dividing the sky into equal signal-to-noise bins. We fitted all bins with fixed spectral templates of known background constituents. Results. We find the temperature of the LHB exhibits a north-south dichotomy at high latitudes (|b| > 30°), with the south being hotter, with a mean temperature at kT = 121.8 ± 0.6 eV and the north at kT = 100.8 ± 0.5 eV. At low latitudes, the LHB temperature increases towards the Galactic plane, especially towards the inner Galaxy. The LHB emission measure (EMLHB) enhances approximately towards the Galactic poles. The EMLHB map shows clear anti-correlation with the local dust column density. In particular, we found tunnels of dust cavities filled with hot plasma, potentially forming a wider network of hot interstellar medium. We also constructed a three-dimensional LHB model from EMLHB, assuming constant density. The average thermal pressure of the LHB is Pthermal/k = 10100−1500+1200 cm−3 K, a lower value than typical supernova remnants and wind-blown bubbles. This could be an indication of the LHB being open towards high Galactic latitudes.

Design analysis and techno-economic assessment of a photovoltaic-fed electric vehicle charging station at Dhaka-Mawa expressway in Bangladesh

Electric vehicles are crucial for sustainable transport and energy solutions, particularly in developing countries like Bangladesh where their popularity is rising. This study primarily focuses on the techno-economic design of a 300 kWp solar photovoltaic-powered electric vehicle charging station along the Dhaka-Mawa Expressway in Bangladesh, capable of charging 20 electric vehicles simultaneously. The design utilizes the commercially available software package PVsyst 7.2 to ensure the feasibility and efficiency of the charging infrastructure. The use of solar photovoltaics for electric vehicle charging, compared to traditional grid-based methods, offers substantial environmental benefits, including significant reductions in carbon emissions. This shift is driven by decreasing costs, improved module efficiencies, and increased environmental awareness. The estimated levelized cost of energy is calculated at 7.1556 BDT/kWh (BDT is Bangladeshi Taka), with an annual energy generation cost of 1436285.32 BDT. Over a projected lifespan of 25 years, the system is expected to replace 8065 tons of CO2 emissions with its own emissions totaling 537.56 tons, resulting in a net decrease of 6460.2 tons of CO2. This approach not only aligns with Bangladesh’s emission reduction goals but also exemplifies the potential for solar photovoltaic systems to enhance sustainability in transportation. It also emphasizes the importance of integrating renewable energy sources into the electric vehicle-based infrastructure to achieve true sustainability and supports the country’s commitment to combating climate change through technological innovation.

Transforming Hydroponics with IoT for Sustainable Water Management

General Background: Urbanization is reducing agricultural land, necessitating innovative cultivation techniques like hydroponics to thrive in limited spaces. Specific Background: The study investigates the use of IoT technology in hydroponic systems, specifically focusing on remote monitoring and control of water pumps for efficient resource management. Knowledge Gap: Limited research has explored the use of IoT in optimizing water pump operations in hydroponic systems, focusing on energy management and remote accessibility. Aims: The research aims to create an IoT-based automated water pump control system for hydroponics, enabling remote monitoring and evaluating its energy efficiency and operational effectiveness. Results: The experimental setup, including an ESP32 microcontroller, ADS1115 module, solar charger controller, and submersible pump, demonstrated optimal performance with a time-based control strategy, achieving 96.7% current and 97% voltage accuracy. Novelty: The study showcases a successful model for integrating IoT in hydroponic systems, thereby enhancing sustainability and operational efficiency. Implications: The study underscores the potential of IoT technologies in enhancing water management in hydroponics, thereby providing a feasible solution to contemporary agricultural challenges in space-constrained environments. Highlights: IoT Integration: Remote control enhances hydroponic system monitoring and efficiency. Energy Optimization: Effective solar charging improves automated water pump performance. Real-time Monitoring: Enables accessible tracking from significant distances via smartphones. Keywords: Hydroponics, IoT, Water Management, ESP32, Energy Efficiency

Optimization of a Smart Greenhouse with a Solar Energy System for Floating Raft Hydroponic Cultivation: Implementation and Performance Evaluation

Abstract The rapid growth of the global population has increased food demand, posing challenges for traditional agriculture due to limited natural resources. Hydroponic systems offer a sustainable solution by optimizing the growing environment. This study evaluates the performance of a floating-raft hydroponic smart greenhouse powered by solar energy. The system integrates advanced technologies and renewable energy sources, including three 120 Wp and 130 Wp solar panels arranged in a series-parallel configuration, a solar charge controller, two parallel 12V 65Ah and 12V 200Ah batteries, a microcontroller, DHT22 sensors, and actuators such as LED lights and a mist-type DC pump sprayer. These components effectively regulate humidity, temperature, lighting, and spraying, with data transmission to the Antares platform via a monitoring system utilizing NodeMCU ESP8266. The greenhouse maintained optimal environmental conditions, reducing the temperature from 38°C to 32°C within 30 minutes. The system’s daily electrical load was 1006.36 Wh, leaving an energy reserve of 68.35%. The plants cultivated in the greenhouse responded favorably to the system. This study highlights the potential of combining renewable energy with smart agricultural technologies to address the limitations of traditional farming practices and enhance sustainability.

Minimizing charging task time of WRSN assisted with multiple MUVs and laser-charged UAVs

This paper investigates the framework of wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs) and laser-charged unmanned aerial vehicles (UAVs). On the basis of framework, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the time consumption of UAV that takes the most time of all UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the multiple UAVs-based TSP (MUTSP) problem to balance the charging tasks assigned to every UAV. Then, based on the MUTSP problem, we propose the TOUM algorithm (TOUMA) to design the detailed travel plan of UAVs and MUVs. We also present an algorithm named TOUM-DQN to make intelligent decisions about the travel plan of UAVs and MUVs by extracting valuable information from the network. The effectiveness of proposed algorithms are verified through extensive simulation experiments. The results demonstrate that the TOUMA algorithm outperforms the solar charging method, the base station charging method, and the TOUM-DQN algorithm in terms of time efficiency. Simultaneously, the experimental results show that the execution time of TOUM-DQN algorithm is significantly lower than TOUMA algorithm.

Strategic deployment of GIS-optimized solar charging stations for electric vehicles: A multi-criteria decision-making approach

Climate change and the rise in carbon dioxide levels due to gasoline vehicles are global challenges that require innovative and sustainable solutions; this study presents an innovative strategy to promote electric vehicles (EVs) adoption through the establishment of solar-powered charging stations. Utilizing ArcGIS10.8.2 software, a comprehensive analysis was conducted to identify optimal locations for these stations, considering technical, economic, environmental, and geological data. This research, unlike other past research, leverages public spaces such as gas stations, shopping centers, and parking lots for station construction, minimizing operational costs and fostering a sustainable infrastructure. This approach applies not only to the study area of this research, which is Khuzestan province, but can also be extended to other regions and serves as a model for reducing CO2 emissions in the transportation sector worldwide. This research evaluates the location for establishing electric vehicle charging stations using solar energy innovatively, from both technical and operational perspectives. By using the systematic and new method presented in this research, it is possible to identify the highest potential for the construction of electric car charging stations that simultaneously use solar energy all over the world, and the results of applying this new method in the studied area showed that 90% of the cities within this province have the potential to establish charging stations for electric vehicles utilizing sunlight. Specifically, Mahshahr County in this province can supply 90.55% of the required energy for charging stations through solar energy. Operationally, 70% of the cities in the region possess such potential. The study demonstrated that this area has the capacity to convert 11% of vehicles to electric cars by 2040 and can reduce CO2 emissions by more than 30 tons.

Design and analysis of sustainable photovoltaic solar charging system with battery storage for electric vehicles

Design and analysis of sustainable photovoltaic solar charging system with battery storage for electric vehicles

Energy-saving path planning navigation for solar-powered vehicles considering shadows

Globally, the adverse climate effects caused by greenhouse gas emissions are becoming increasingly apparent, and solutions to increase the use of eco-friendly transportation methods are urgently needed. Introducing solar-powered vehicles (SPVs), which are cars integrated with solar panels capable of generating power, presents a promising solution to reduce urban carbon footprints. However, the low adoption rate of SPVs implies that the benefits—such as environmental friendliness and ability to charge while driving—need to be more palpably experienced by consumers. To address this aspect, in this study, we aimed to develop a navigation system algorithm that guides users along routes that optimize energy consumption and solar energy production from the starting point to the destination. This was done with the objective of providing more tangible benefits from using SPVs. The study focused on the high-traffic urban center of Seoul, where determining solar power availability for a moving SPV is challenging, given the presence of shadows cast by roadside features such as buildings and trees. To achieve this, panoramic images from Google Street View were collected at 10 m intervals from all roads within the research area. From these images, sky and non-sky elements were separated. Subsequently, a hemispherical map was constructed and superimposed with the sun's path. The presence of shadows was determined by assessing whether the sun's path was obstructed by non-sky elements; if the path was unimpeded in the sky, no shadow was recorded. The shadow data obtained at each spot were efficiently stored in a database for quick retrieval and application based on specific locations and departure times. Using this shadow information, the navigation algorithm calculates power generation along a given route and considers the energy consumption of the SPV. Analysis led to the identification of an energy-saving route, which enabled the achievement of energy conservation and CO2 reduction benefits. Furthermore, a comprehensive sensitivity analysis was conducted to examine the impact of four critical parameters—module efficiency, solar panel area, vehicle speed, and departure time—on route selection and net energy consumption. The energy-saving path planning algorithm enhances the economic feasibility of solar charging for SPVs during travel; thus, this study can contribute significantly to the widespread adoption of SPVs, which play a definitive role in reducing transportation's carbon footprint.

A solar-powered multi-functional portable charging device (SPMFPCD) with internet-of-things (IoT)-based real-time monitoring—An innovative scheme towards energy access and management

In the absence of portable charging devices, sectors such as transportation, communication, and emergency services deal with various challenges towards electric power needs while compromising on (1) operational efficiency, (2) insufficient portable charging solutions, and (3) limited versatility. This highlights the critical need for reliable and multi-functional power solutions. To provide a portable charging solution across diverse sectors, this paper proposes an innovative development of a solar-powered multi-functional portable charging device (SPMFPCD) with internet- of-thing (IoT)-based monitoring capabilities. The proposed scheme introduces a comprehensive model integrating advanced technologies which include a highly efficient solar panel, charge controller, sensors, and IoT module. The proposed system facilitates versatile charging solutions for a wide range of power requirements with real-time monitoring and data analysis through the IoT platform. Moreover, the proposed work explores the applications of the SPMFPCD in (1) emergency medical scenarios, (2) outdoor adventures, (3) disaster management, and 4) public spaces. Performance evaluation was made by proposing case studies to validate the (1) economic viability, (2) power management, and (3) environmental impact of widespread deployment of SPMPFCD in public spaces. Furthermore, detailed analysis of battery energy storage system (BESS) and photovoltaic (PV) integration for load management, seasonal dynamics, and renewable energy integration (REI) contribute to a comprehensive understanding of the proposed solution.

Design and simulation of zeta solar charge controller with artificial neural network MPPT

Fossil fuel are non-renewable energy sources with constrained reserves. This advocates renewable energy as a viable alternative for electricity generation. PV is a renewable energy source that harnesses solar energy. The current issue with PV is its low efficiency and elevated cost. Photovoltaic control devices and Maximum Power Point Tracking (MPPT) mimic human neural networks in processing multiple conditions and providing solutions from current reference data to optimise photovoltaic power. This technique is known as Artifical neural network (ANN). This method uses a zeta converter to adjust the photovoltaic voltage to supply specific loads, allowing ANN to optimize power as sunlight intensity changes. In this work, a charge controller for solar applications was designed using MPPT algorithms. This is responsible for overseeing the battery circuit and producing PWM signals to regulate zeta converter according to the battery voltage. The artificial neural network (ANN) received as inputs temperature and irradiation (G). Several simulations verified the efficiency of the suggested MPPT algorithm. The Zeta solar charge controller (ZSCC) device achieved up to 92% efficiency with low irradiance. The proposed ANN-based MPPT controller and perturb-observed Maximum Power Point Tracking (MPPT) algorithm were compared in different solar insolations. The simulation results show that MPPT-ANN can track power up to 2000 Watts for a 2000 WP photovoltaic in 0.056 seconds, with no power oscillations at the MPP.

Intelligent hybrid deep learning models for enhanced shipboard solar irradiance prediction and charging station

Electrifying marine routes with solar energy significantly reduces ocean carbon emissions, playing a vital role in climate regulation. Solar irradiance forecasting ensures reliable power despite unpredictable sea weather, necessitating innovative model development. This research presents a forecasting model designed for the optimal placement of solar charging stations, providing hour-ahead solar irradiance predictions for onboard solar vessels. India (Jawaharlal Nehru Port Trust)– United States (Port of New York and New Jersey) sea route is selected to test our proposed forecasting model, as it aligns with busy shipping operations and long-distance trade requirements for electrification. Two distinct procedures by two case studies are used to select optimum data to predict the suitable locations for solar charging stations along the navigation route. In the first case study (Medium data analysis – MDA), the data are selected based on a statistical analysis of twenty years of hourly solar irradiance data from 201 locations. In the second case study (Large data analysis – LDA), two years of seasonal data from 201 locations are chosen and combined to form a large amount of hourly data. For both case studies, a hybrid solar irradiance forecasting model (ARIMA – Bi – LSTM) is developed by integrating the deep learning model with the time series model and fine-tuning them using optimization algorithms (PSO and GA). In MDA, the PSO optimization achieves mean absolute error (MAE) values of 0.32, 0.85, 1.83, 1.40, and 1.33 W/m2 for locations 1, 2, 3, 4, and 5, respectively, significantly outperforming the GA, which has MAE values of 1.36, 1.65, 3.25, 0.92, and 1.41 W/m2 for the same locations. In LDA, the PSO model achieves MAE values of 0.79, 0.91, 0.28, and 0.39 W/m2 for winter, rain, summer, and spring, respectively, also outperforming the GA, which has MAE values of 1.54, 2.17, 0.99, and 1.46 W/m2 for the same seasons. Comparatively, the LDA reinforces its best alignment to predict optimal charging station locations.