International Journal of Power Electronics and Drive Systems (IJPEDS)
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A comprehensive review of efficient wireless power transfer for electric vehicle charging: advancements, challenges, and future directions
Get PDFElectric vehicles (EVs) have transformed the transportation sector, offering a sustainable alternative to fossil-fuel-powered vehicles. However, their widespread adoption faces challenges such as inadequate charging infrastructure, range anxiety, and concerns about user convenience. Wireless power transfer (WPT) technology provides an efficient, reliable, and user-friendly charging solution that eliminates physical connections, enabling both static and dynamic charging applications. This review explores key components of WPT systems, including wireless charging schemes, compensation circuits, coupling pad structures, and misalignment tolerance, emphasizing their impact on system efficiency and reliability. Findings highlight that WPT can enhance charging convenience, reduce dependence on large battery capacities, and support seamless EV integration into daily life. Additionally, WPT systems improve safety, lower maintenance needs, and create opportunities for autonomous charging. Key advancements in compensation topologies, coupling pad geometries, and misalignment-tolerant capabilities are discussed alongside their role in enhancing power transfer efficiency. By offering insights into the current state-of-the-art and future directions, this paper aims to support the development and deployment of WPT systems, contributing to the global transition toward sustainable transportation
Bidirectional power converter for electrical vehicle with battery charging and smart battery management system
Get PDFIn electric vehicles (EVs), efficient energy management is critical for reliable power transfer between the battery and motor. This paper presents the design and implementation of a bidirectional DC-DC converter equipped with a smart battery management system (BMS). The system supports bidirectional power flow, operating in boost mode during acceleration and buck mode during regenerative braking, thereby enhancing overall energy efficiency and vehicle performance. A PIC microcontroller governs the system, performing real-time monitoring of key battery parameters such as state of charge (SOC), state of health (SOH), voltage, and temperature. Safety features include automatic cooling fan activation when the temperature exceeds 45 °C and generator startup when battery voltage falls below 23 V. Real-time data is displayed via an LCD interface to improve user interaction and system transparency. The proposed system achieved a conversion efficiency of 90-93% during experimental testing, with stable switching, reliable automation, and effective thermal protection. The embedded energy management system optimizes charging and discharging cycles while preventing overcharging, deep discharge, and thermal stress. This intelligent, automated power converter enhances battery life, improves EV reliability, and contributes to sustainable transportation by enabling features like vehicle-to-grid (V2G) energy transfer. The proposed architecture is well-suited for integration into modern EV infrastructure. Although the system architecture supports future V2G integration, V2G functionality was not implemented or tested in the present experimental setup
Dehydration of Moringa leaves using microcontroller and IoT controlled electrical dryer
Get PDFThe dehydration of Moringa Oleifera leaves is crucial to preserving their high nutritional value and extending shelf life for use in food and pharmaceutical applications. Traditional drying methods often result in nutrient degradation and lack precise environmental control. This study presents the design and implementation of an internet of things (IoT)- enabled electrical dryer system controlled by a microcontroller for the efficient dehydration of Moringa leaves. The system integrates temperature and humidity sensors, an Arduino Mega microcontroller, and a web-based interface for real-time monitoring and control. The electrical dryer maintains optimal drying conditions, significantly reducing moisture content while preserving essential nutrients. Data is logged and visualized through IoT connectivity, allowing for remote access and performance analysis. The dehydration of Moringa leaves requires approximately one kg of electricity for batteries in dual-energy dryers, which are based on microcontrollers and the IoT. The results demonstrate that the proposed system offers a reliable, energy-efficient, and scalable solution for the controlled dehydration of Moringa leaves, with potential applications in smart agriculture and postharvest processing. The excellent drying time is achieved in a greenhouse dryer, which maintains a temperature of 45 °C within the drying chamber, resulting in a median drying time of 6 hours. The standard moisture percentage of clean and dry Moringa leaves is measured at 18.5% (wb) and 8% (wb), respectively
Predictions of solar power using ensemble machine learning techniques
Get PDFPredicting solar power production accurately is becoming more and more crucial for efficient power management and the grid's integration of renewable energy sources. Using data from an Australian photovoltaic (PV) power station, this study employs a variety of machine learning (ML) ensemble techniques, such as gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGBoost), to forecast solar power production. ML models are developed utilizing pertinent information from electricity and meteorological data in order to forecast solar power. The predictive performance of trained ML models is verified in terms of metrics like mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R2). With higher R2 values and lower error results (MAE and RMSE), XGBoost performs better than GB and RF. Optimizing the hyperparameters of the XGBoost model significantly improves its performance. The tweaked XGBoost model shows a significant improvement in R2 (more than 5% to 10%) and error results (reduced MAE and RMSE by 0.01 to 0.06), when compared to other ensemble approaches. Compared to other ensemble approaches, the tuned XGBoost methodology is more robust and generates more accurate forecasts in solar power
Enhancement of large PV-integrated grid stability using an advanced UPQC
Get PDFThis paper presents an enhancement to the stability of large PV-integrated grids using an advanced power quality control system. The proposed unified power quality conditioner (UPQC) system control technique combines synchronous reference frame (SRF) theory and modified unit vector template generation (MUVTG), supplemented by an additional proportional-integral-derivative (PID) controller to regulate reactive power flow to the grid. The results indicate a reduction in the total harmonic distortion (THD) levels. The study also demonstrates the system’s stability for different harmonic orders and various cases of voltage sag and swell, in compliance with IEEE standards. The proposed approach effectively addresses power quality issues and achieves a THD of 0.30%, meeting the IEEE-519 standards using MATLAB Simulink
Development of dual functional converter for drive and charging power conversion for EV drive
Get PDFThe adaptability of electric vehicle drives is primarily concerned with the size and efficiency of power conversion. This paper presents a unified power converter for the drive and charge functions of brushless direct current-based electric vehicle drives (BLDC). The symmetrical utilization of BLDC phase windings during charging operation is implemented for efficient power conversion. The unified converter operation, configuration, and control are presented. The proposed converter is simulated in the MATLAB/Simulink platform. The performance is evaluated using operational variables such as voltage, current, torque, and speed. A comparative study is presented regarding the size and efficiency of the proposed and existing drives. The proposed drive achieved 0.01 p.u. ripple in torque, 10-sec transient time for a change in speed full throttle command, and unity power factor current for charging operation, proving its robustness over the comparable drives
Enhanced reaching law for improved response in sliding mode control of PMSM motors with fuzzy logic integration
Get PDFThe rising demand for high-performance permanent magnet synchronous motors (PMSMs) is responsible for the development of PMSM speed control. Although the proportional-integral controller is often used in field-oriented control (FOC) for motor speed regulation, it has drawbacks like slow response and instability. This paper proposed an enhanced sliding mode controller with a modified sliding surface to achieve better speed control performance. In comparison to proportional-integral or PI controller, fuzzy logic controller, conventional sliding mode controller, the proposed control approach uses a reaching law that incorporates a fuzzy logic controller. A smoother and faster response time is targeted by the proposed approach compared to conventional sliding mode control. Practical small-scale PMSM experiments certify the effectiveness of our proposed enhanced sliding mode control
Integrated proportional-integral control for enhanced grid synchronization and power quality in photovoltaic-electric vehicle systems
Get PDFPhotovoltaic (PV)-grid with electrical vehicle penetration introduces harmonics to the main power system. This paper explores the disturbances introduced due to both PV and electric vehicles (EVs) in the grid. PV acting as the source and EV acting as both the load and the source introduces harmonics to the main grid. The combined harmonics from both the PV and EV are controlled using the integrated DQ controller on the voltage source converter (VSC) that connects to the grid from the PV source. The real and reactive power is controlled in a decoupled manner to obtain better control of the harmonic reduction introduced in the grid. This study investigates the use of proportional-integral (PI) control techniques to develop an integrated controller that can effectively handle both PV synchronization and power quality when using electric vehicles. To reduce harmonics in the grid current, the study combines multicarrier space vector pulse width modulation (SVPWM) with PI control on the grid-connected converter through a dual-control loop system devoted to PV grid synchronization, with one loop specifically addressing EV battery charging control. DQ method yields a total harmonic distortion (THD) of 2.74% for voltage and 3.44% for current according to the IEEE 519 standards
Real-time implementation of a combined controller-observer approach for shunt active filters
Get PDFThe crucial role of shunt active power filter (SAPF) is to compensate for reactive power, balances unbalanced currents and counteract harmonics produced due to non-linear loads, by injecting phase-opposed compensation current by designing an appropriate controller. In this work, a combined controller-observer state and disturbances estimation scheme for a SAPF is proposed. To avoid the requirement of full-state feedback, unknown input observers (UIO) is designed. This is conducted in OPAL-RT OP4510 environment. Real-time simulations are used to show how successful the suggested controller-observer architecture for SAPF is; wherein the estimated states from the observer are fed back to the controller, and finally, the disturbance is also estimated. UIO is designed for SAPF to deal with nominal conditions and in the presence of sinusoidal disturbance.The OPAL-RT results clearly show that LO introduces steady state error between the reference input and the estimated state of SAPF in the presence of disturbances. This steady state error is completely eliminated in presence of all disturbances using UIO. The results also show that UIO perfectly tracks the reference input in the presence of disturbances. Further, disturbances are also estimated perfectly with UIO
Global solar energy estimation using improved greedy based genetic algorithm with deep convolutional neural network
Get PDFDemand for solar energy increases and it is required to manage the supply of energy effectively. Accurate detection on patterns of energy consumed assist in taking appropriate decisions on generating energy. Even though many traditional techniques have predicted the consumption rate, still improvement is needed in prediction accuracy. The pre-processing is performed initially for handling missing values. The feature selection is accomplished using improved greedy based genetic algorithm (GGA) to extract best features to enhance the performance of the model. Output from feature-selection is passed as input to the classification phase using proposed deep convolutional neural network (CNN) in which future solar energy patterns are classified and predicted timely basis power consumption and it optimize the model by minimize the error. The prediction accuracy is estimated through evaluation metrics such as mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE) 0.423, 0.652, and 0.215, respectively. The outcomes achieved in terms of accuracy at 99.75, precision at 99.28, sensitivity, and recall at 100 revealed the efficiency of the proposed classification model. As a result, the proposed future prediction of solar energy was considered efficient since it achieved reduced error values than other prediction algorithms. It assists in maintaining stability in solar-energy based systems