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Solar energy is the most promising source for generating residential, commercial, and industrial electricity. However, solar panels should be eco-friendly to increase sustainability during manufacturing and recycling. This study investigates the potential of using natural fibre composites as eco-friendly alternatives to conventional polyethylene terephthalate (PET) back sheets in solar panels. Furthermore, it examines the performance of sisal fibres coated with zeolite-polyester resin. The chemical composition, structural integrity, and crystalline properties of the composites were evaluated through extensive microstructural analysis. The results from the experimental analysis revealed significant improvements in voltage (8%) and current (6%) for the coated sisal fibre panels compared to conventional panels. Power output increased by 12%, and overall efficiency improved from 9.75 to 10.8%. Solar panels with sisal fibre sheets exhibit adequate tensile strength and impact resistance and reduce operating temperature by 2–3 °C, ensuring stable operation and minimizing heat loss. Statistical analysis confirmed the reliability and significance of these results. The life cycle analysis demonstrated a 60% reduction in CO2 emissions and a 50% decrease in energy consumption during the production, utilization and disposal of sisal fibre sheets. These findings underscore the viability of natural fibre composites in enhancing the performance and sustainability of solar panels. [ABSTRACT FROM AUTHOR]

The recent developments in electric vehicle (EV) necessities the requirement of a human intervention free charging system for safe and reliable operation. Wireless power transfer (WPT) technology shows promising options to automate the charging process with user convenience. However, the operation of the WPT system is designed to operate at a high-frequency (HF) range, which requires proper control and modulation technique to improve the performance of power electronic modules. This paper proposes a dead-time (DT) integrated Pulse Density Modulation (PDM) technique to provide better control with minimal voltage and current ripples at the switches. The proposed technique is investigated using a LCC-LCL compensated WPT system, which predominantly affects the high-frequency voltage and current ripples. The performance analysis is studied at different density conditions to explore the impact of the integrated PDM approach. Moreover, the PDM technique gives better control over the power transfer at different levels of load requirement. The simulation and experimental analysis was performed for a 3.7 kW WPT prototype test system under different modes of operation of the high-frequency power converters. Both the simulated and experimental results demonstrate that the proposed PDM technique effectively enhances the efficiency of the HF inverter while significantly reducing output current ripples, power dissipation and improving the overall WPT system efficiency to 92%, and leading to a reduction in the power loss in the range of 10% to 20%. This leads to improved overall system control and performance. [ABSTRACT FROM AUTHOR]

Traffic congestion is a prevalent problem in modern civilizations worldwide, affecting both large cities and smaller communities. Emergency vehicles tend to group tightly together in these crowded scenarios, often masking one another. For traffic surveillance systems tasked with maintaining order and executing laws, this poses serious difficulties. Recent developments in machine learning for image processing have significantly increased the accuracy and effectiveness of emergency vehicle classification (EVC) systems, especially when combined with specialized hardware accelerators. The widespread use of these technologies in safety and traffic management applications has led to more sustainable transportation infrastructure management. Vehicle classification has traditionally been carried out manually by specialists, which is a laborious and subjective procedure that depends largely on the expertise that is available. Furthermore, erroneous EVC might result in major problems with operation, highlighting the necessity for a more dependable, precise, and effective method of classifying vehicles. Although image processing for EVC involves a variety of machine learning techniques, the process is still labor intensive and time consuming because the techniques now in use frequently fail to appropriately capture each type of vehicle. In order to improve the sustainability of transportation infrastructure management, this article places a strong emphasis on the creation of a hardware system that is reliable and accurate for identifying emergency vehicles in intricate contexts. The ResNet50 model's features are extracted by the suggested system utilizing a Field Programmable Gate Array (FPGA) and then optimized by a multi-objective genetic algorithm (MOGA). A CatBoost (CB) classifier is used to categorize automobiles based on these features. Overtaking the previous state-of-the-art accuracy of 98%, the ResNet50-MOP-CB network achieved a classification accuracy of 99.87% for four primary categories of emergency vehicles. In tests conducted on tablets, laptops, and smartphones, it demonstrated excellent accuracy, fast classification times, and robustness for real-world applications. On average, it took 0.9 nanoseconds for every image to be classified with a 96.65% accuracy rate. [ABSTRACT FROM AUTHOR]

The increasing demand for wireless power transfer (WPT) systems for electric vehicles (EVs) has necessitated advancements in charging solutions, with a particular focus on speed and efficiency. However, power transfer efficiency is the major concern in static and dynamic wireless charging (DWC) design. Design consideration and improvements in all functional units are necessary for an increase in overall efficiency of the system. Recently, different research works have been presented regarding DWC at the power converter, coil structure and compensators. This paper provides a comprehensive review of power converters incorporating high-order compensation topologies, demonstrating their benefits in enhancing the DWC of EVs. The review also delves into the coupling coil structure and magnetic material architecture, pivotal in enhancing power transfer efficiency and capability. Moreover, the high-order compensation topologies used to effectively mitigate low-frequency ripple, improve voltage regulation, and facilitate a more compact and portable design are discussed. Furthermore, optimal coupling and different techniques to achieve maximum power transfer efficiency are discussed to boost magnetic interactions, thereby reducing power loss. Finally, this paper highlights the essential role of these components in developing efficient and reliable DWC systems for EVs, emphasizing their contribution to achieving high-power transfer efficiency and stability. [ABSTRACT FROM AUTHOR]

Foreign object debris (FOD) includes any unwanted and unintentional material lying on the charging lane or parking lots, posing a risk to the wireless charging system, the vehicle, or the people inside. FOD in an Electric Vehicle (EV) wireless charging system can cause problems, including decreased charging efficiency, safety risks, charging system damage, communication issues, and health risks. To address this problem, this paper proposes the deep learning object detection network approach of using YOLOv4 (You Only Look Once), which is a single-shot detector. Additionally, for real-time implementation, YOLOv4-Tiny is suggested, which is a compressed version of YOLOv4 designed for devices with low computational power. YOLOv4-Tiny enables faster inferences and facilitates the deployment of FOD detectors on edge devices. The algorithm is trained using the FOD dataset, consisting of images of common debris on runways or taxiways. Furthermore, utilizing the concept of transfer learning, the last few layers of the pre-trained YOLOv4 model are modified using the COCO (Common Objects in Context) dataset to transfer features to the new network and retrain the model on the FOD dataset. The results obtained using this YOLOv4 model yielded a precision rate of 99.05%, while the results from YOLOv4-Tiny achieved a precision rate of 97.74%, with an average inference time of 150 ms under the ambient light and weather conditions. [ABSTRACT FROM AUTHOR]

Dynamic wireless power transfer (DWPT) has garnered significant attention as a promising technology for electric vehicle (EV) charging, eliminating the need for physical connections between EVs and charging stations. However, the improvement in power transfer efficiency is a major challenge among the research community. Different techniques are investigated in the literature to maximize power transfer efficiency. The investigations include the power electronic circuit, magnetic coupler design, compensating capacitance and control technique. Also, the investigations are carried out based on the type of wireless charging system, which is either a static or dynamic scenario. There are a good number of review articles available on the power electronic circuit and compensator design aspects of WPT. However, studies on the controller design and tracking maximum efficiency are some of the important areas that need to be reviewed. This paper provides a comprehensive review of bibliometric analysis on the DWPT technology, design procedure, and control technique to increase the power transfer and socio-economic acceptance analysis. The manuscript also provides information on the challenges and future direction of research in the field of DWPT technology. [ABSTRACT FROM AUTHOR]

Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) performance is affected by increased panel temperature. Maintaining an optimal PV panel temperature is essential for sustaining performance and maximizing the productive life of solar PV panels. Current temperature sensors possess a long response time and low resolution and accuracy. Advanced fibre-optic sensors offer distinct advantages of greater accuracy, a more comprehensive range, and a very high sampling rate. The present experimental work focuses on fibre Bragg grating sensor-based solar PV panel temperature monitoring. The unique capabilities of fibre-optic sensors are demonstrated by studying the rapid perturbations in panel temperature over time for indoor and outdoor conditions. The effects of incident radiation flux and the inclination angle on panel temperature are analyzed. Temperature sensitivity of 6 pm/°C is obtained. The results are helpful in dynamic monitoring of panel temperature, understanding the thermal interactions at the microscale level, and streamlining the measured temperature of multiple panels on solar farms. Thus, the proposed advanced method of FBG-based temperature monitoring of solar panels could be helpful to operate an integrated cooling system to improve the performance of solar panels.

The United Nations has set an ambitious goal to achieve net zero carbon emissions by 2050. This objective requires shifting towards green and renewable energy sources instead of conventional fossil fuels to address the global energy crisis without emitting greenhouse gases. While the energy radiated by the sun is one of the most abundant sources of energy available, its efficient and optimal use remains a significant challenge. To facilitate solar-energy-based applications, estimating the amount of solar energy available is crucial. Empirical and soft computing is the most-used method to estimate solar energy. This paper aims to analyze the existing techniques used in various models for estimating and predicting the quantity and quality of solar radiation using readily available data. Additionally, the study aims to identify the most appropriate techniques for developing prediction models using available explanatory variables. To fully harness the potential of solar energy, it is necessary to limit the terrestrial loss of solar radiation by minimizing the harmful effects of anthropogenic factors that reduce the quantity and quality of solar radiation in the area. This paper provides valuable insights to identify opportunities to maximize the potential of solar energy in different locations. [ABSTRACT FROM AUTHOR]