In the world, Electric Vehicles (EVs) are a promising technology for developing a sustainable transportation sector because of their extremely low to zero carbon emissions, low noise levels, high efficiency, and flexibility in grid operation and integration. Thus, the number of EVs in use is growing annually. In recent years, Wireless Power Transfer (WPT) systems have been utilized as EV battery chargers. Designing effective power electronic converters enables the WPT system to operate at high frequencies, which is a typical feature for transmitting large amounts of power over longer distances. Therefore, this paper proposes a WPT system based on an efficient high frequency inverter for an EV charging system. A high frequency inverter decreases the size and resistance of passive components like inductors and is utilized for operating more effectively than a low frequency. The primary function of an isolation transformer is to provide electrical isolation between the high voltage battery and the low voltage systems. Finally, interleaved synchronous rectifiers are used to improve efficiency, reduce current ripple, and enhance charging power and energy received for load application. Further, a PI controller helps maintain stability and optimal performance. The MATLAB/Simulink simulation indicates that the proposed system has improved switching losses, reactive power compensation, and the highest efficiency of 98%.

The integration of Electric Vehicles (EVs) into microgrid based renewable energy systems presents significant challenges related to power conversion, energy management, and system stability. Efficient power electronic converters and control strategies are essential for optimizing energy exchange between Renewable Energy Sources (RES), EVs, and the grid. This review examines various DC DC converters, analysing their efficiency and impact on system performance. Additionally, it explores different control techniques, assessing their effectiveness in regulating power flow and ensuring stable operation. The findings highlight the role of converter design, and control approaches in enhancing the efficiency and reliability of microgrid based EV systems. This study provides significance into emerging technologies and methodologies that support the uninterrupted integration of RES and EVs, contributing to the development of sustainable and resilient power infrastructure.

The Electric Vehicles (EVs) are revolutionary in the transport sector and leads to the phasing out of conventional fuel vehicles. The Renewable Energy Sources (Res) is used for EV. The EV vehicles are cost effective, environmental friendly. For the proportion of EV, Permanent Magnet Brush Less Direct Current (PMBLDC) motor is used in the Photovoltaic (PV) system. The energy from the PV system is always low due to the environmental conditions, inorder to boost the voltage, this paper proposes Interleaved Boost Converter (IBC) to hybridize energy alternatives in EVs. The energy obtained from the PV system is DC. To step up the DC voltage from the PV array it is required level an interleaved boost converter is integrated. It ensure continous power flow, reduces ripple current and improves the overall power quality of the system.. The drive system’s performance for various operating modes, such as stable and varying load conditions are examined from the simulations. The PMBLDC motor helps in effectively managing speed with accordance with the PI controller. The developed system’s is evaluated by MATLAB simulations to verify its effectiveness. The presented converter achieves the highest efficiency of (93%) compared to other conventional methods.

This article introduces an innovative controller and motor architecture designed for an e rickshaw powered by Renewable Energy Sources (RES). In the absence of Hall effect sensors, brushless DC (BLDC) motor drives often experience commutation delays across both low and high speeds. Traditional control methods are limited by the small magnitude of back EMF, making them less effective. The proposed sensorless control strategy addresses this by implementing commutation error correction, enabling full range motor operation. For cost effective EV applications, a simple commutation error compensation technique is developed to detect freewheeling pulses, eliminating the need for low pass filters and streamlining control. Additionally, a zero crossing detection (ZCD) method is introduced, which corrects commutation delay without requiring a separate phase compensator. Any unwanted spikes in the ZCD circuit are removed using a fixed delay digital filter. A modified Landsman converter is used for efficient MPPT control, delivering a smooth current output and reducing the need for a front end ripple filter. The regenerative capability of the BLDC motor drive helps alleviate EV range anxiety, as the renewable energy source ensures a continuous power supply. A PI based Fuzzy Logic Controller is employed, and the effectiveness of the system is validated through MATLAB/Simulink simulations.

This paper presents an advanced Photovoltaic (PV) grid connected system utilizing a Superlift Luo Converter (SLC) to enhance energy conversion efficiency and system performance. The proposed system integrates a Crow Search Algorithm (CSA) optimized Proportional Integral (PI) controller to regulate the output of SLC. The SLC is employed to boost the voltage from the PV panels, enhancing energy extraction. The VSI, controlled by the optimized PI controller, ensures stable and efficient power transfer to the grid. An LC filter is incorporated to mitigate harmonics and ensure compliance with grid standards. The use of CSA for PI controller optimization improves dynamic response and system robustness. This results accomplished using MATLAB demonstrate that the proposed system achieves high efficiency, improved stability, and superior grid compliance compared to conventional approaches. The comparative analysis presented in this work demonstrates that the proposed approach significantly outperforms traditional methods, achieving an impressive efficiency of 95.2%. This highlights the effectiveness of the new system in optimizing performance, making it a valuable alternative to established techniques. This approach highlights the effectiveness of combining advanced converter topologies and optimization techniques to enhance PV grid connected systems.

Electric vehicles (EVs) have steadily developed over the last decade as more energy-efficient alternatives to internal combustion engine cars. With increased range and performance, EVs are growing more reliable. Expanding the availability of electric vehicles can be facilitated via wireless charging. Wireless charging (WC) is an interesting remodelling makes electric cars to charge more convenient. Wireless Power Transfer (WPT) enables power to be transmitted from a power source without a trouble of connections, and this charging option increases the use of electric cars. This paper examines the most effective wireless power transmission techniques Magnetic Resonance Coupling (MRC), and its real-world application in EVs. It also examined how wireless power transfer efficiency declines with distance.

In this paper, Standalone PV system is designed to operate without the need of electric utility grid which are commonly designed to supply specific DC electric loads. These type of PV system are generally powered using photovoltaic array and consist of solar charging modules, controllers and regulators. The objective of this work is to implement the Radial Basis Function Neural Network (RBFNN) based Maximum Power Point Tracking (MPPT) technique to attain constant DC link voltage of the ZETA converter. This method utilizes a ZETA converter to achieve better voltage gain and lower ripple in the output current and voltage. Using an Artificial Neural Network (ANN) controller to operate a high frequency converter, which improves power conversion efficiency. To ensure safety and prevent electrical faults, an isolation transformer provides galvanic isolation between the high frequency converter and the PV system.  Galvanic isolation avoids electrical faults from being transmitted and safeguards connected loads. The rectifier is used to supply the Direct Current (DC) load with the output from the isolation transformer. The rectifier changes the transformer’s Alternating Current (AC) output into DC current to power DC loads. An intelligent MPPT is employed to stabilize the converter’s output voltage and address the intermittent characteristics of PV, and also assists in sustaining the DC link voltage without alterations and ripples from the converter. The efficiency of this work is authenticated via MATLAB simulation 2021a.

Power demand is rising worldwide, forcing the search for an alternative source to solve the imminent power crisis, which is expected to be accomplished through the deployment of grid synchronized electric vehicles (EV). Wind and solar energy are incorporated into this integration to those concerns. One smart grid innovation that permits energy exchange between the EV and the grid is vehicle to grid (V2G) technology. For the purpose of charging EVs, this research proposes employing hybrid energy. Here, the neighboring household’s linear and non linear demands are powered by the excess electricity from the photovoltaics (PV) panel, wind turbine, and AC generator that is utilized to charge the EV battery. Because of its dependability and independence from the real system model, a proportional integral (PI) controller is used to maximize the DC voltage extracted from the PV panel. The improved DC voltage overall is a result of the SEPIC converter’s improved DC output. Concurrently, an AC generator is incorporated into the system to supply extra power to the grid. An artificial neural network (ANN) controller is used to convert the power generated by the PV panel’s Sepic conversion process into an improved output that is then converted to AC using a 3 phase Voltage Source Inverter (VSI).  Harmonic components in the VSI output are corrected with an LC filter to ensure grid compatibility and safe current injection into the grid. This paper discusses the usage of a bidirectional converter to adjust the EV battery’s voltage. MATLAB 2021a / Simulink software is used to simulate and validate the proposed approach.

Renewable Energy Systems (RES) harness natural sources like sunlight, wind, and water to generate sustainable power. They decrease dependency on fossil fuels and minimize greenhouse gas emissions, promoting environmental sustainability and energy security. Photovoltaic (PV) systems are a prominent application of renewable energy, converting sunlight directly into electricity. However, the efficiency of PV systems adversely affected by varying environmental conditions and climatic changes, leading to fluctuations in voltage output. To address these challenges and optimize energy extraction, the integration of DC-DC converters into PV system is essential. In this paper an exhaustive review of various DC-DC converters technologies used in PV connected grid systems, including SEPIC converter, Buck boost converter, Boost converter, and CUK converter. Every converter plays a crucial role in managing and stabilizing voltage levels by adjusting the output voltage to meet the needs of the grid or load. A comparative analysis of recent research highlights various methodologies for enhancing converter performance. The review includes studies on ultra gain converters, Fourth order boost DC-DC converter (FBDC), high step up interleaved converters, and Quasi Z source converters. Each approach presents unique benefits and challenges, reflecting ongoing efforts to optimize DC-DC conversion in PV systems. The paper concludes with insights into the future direction of converter technologies and their role in enhance the stability and efficiency of PV connected systems.

Palm print recognition has emerged as a robust biometric authentication method due to its distinctiveness and reliability. This paper explores the revolution in palm print recognition driven by advanced feature analysis techniques, focusing on the integration of sophisticated preprocessing and feature extraction and classification methods. Effective palm print recognition relies heavily on preprocessing technique such as noise reduction, contrast enhancement, and image filtering. Thus, this study emphasize the role of Gabor filter excel in image processing by effectively capturing spatial frequency and orientation information. The paper highlights the use of Local Ternary Patterns (LTrP) and Local Modified Ternary Patterns (LMTrP) to enhance the accuracy and robustness of feature extraction in palm print matching and identification. Additionally, the Scale Invariant Feature Transform (SIFT) used to further strengthens the system by providing further enhancing the reliability and rotation. Moreover, this paper implemented by using python programming language.  Overall, this work provides insights into their combined impact on the accuracy and reliability of palm print recognition systems, marking a significant advancement in biometric authentication technology.