This paper presents a bidirectional AC-DC converter system designed for seamless power exchange between electric vehicles (EVs) and the utility grid. The proposed converter facilitates the conversion of 230 V, 50 Hz AC input to 380 V DC during grid-to-vehicle operation, allowing for efficient battery charging through a bidirectional DC-DC converter. Conversely, during vehicle-to-grid operation, it converts the 380 V DC input from the DC-DC converter to 230 V, 50 Hz AC output for grid supply. The system employs PI controllers to ensure precise voltage and current regulation, ensuring stable and efficient operation during grid interaction. Simulation results demonstrate the system’s effectiveness in managing power conversion for both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications This paper presents a bidirectional AC-DC converter system designed for seamless power exchange between electric vehicles (EVs) and the utility grid. The proposed converter facilitates the conversion of 230 V, 50 Hz AC input to 380 V DC during grid-to-vehicle operation, allowing for efficient battery charging through a bidirectional DC-DC converter. Conversely, during vehicle-to-grid operation, it converts the 380 V DC input from the DC-DC converter to 230 V, 50 Hz AC output for grid supply. The system employs PI controllers to ensure precise voltage and current regulation, ensuring stable and efficient operation during grid interaction. Simulation results demonstrate the system’s effectiveness in managing power conversion for both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications

This paper presents an AC-DC converter system tailored for grid-to-vehicle (G2V) applications, aimed at facilitating efficient power flow while achieving a Unity power factor (UPF). The system employs a rectifier for AC-DC conversion, which effectively steps up a 230V AC input to a 380V DC output. This DC output can be further regulated using a buck converter to meet specific load requirements. A Proportional-Integral (PI) controller is implemented to oversee the voltage and current regulation, thereby minimizing harmonic distortion and enhancing the overall power factor. By actively managing the input voltage and current, the controller ensures that the system operates within desired parameters, thus optimizing power quality. Comprehensive simulation results validate the system’s performance, demonstrating its capability to maintain a UPF in G2V mode. The findings indicate significant reductions in total harmonic distortion (THD), reinforcing the system’s effectiveness in managing power quality. This AC-DC converter design not only enhances the efficiency of power flow in electric vehicle charging systems but also contributes to the stability of the grid by minimizing reactive power and harmonics. Overall, this work represents a significant advancement in converter technology for sustainable transportation and energy management

This study examines the integration of permanent magnet synchronous motors (PMSM) with renewable energy sources, focusing on solar photovoltaic (SPV) arrays to improve efficiency and sustainability in electric vehicle (EV) applications. PMSM, renowned for its high efficiency, silent operation, and precise control, is managed using a proportional-integral (PI) controller to handle variable load conditions, including fluctuations in torque and current. By fine-tuning the PI controller’s gains, the desired motor speed is achieved efficiently. A DC-DC Buck-Boost converter serves as an intermediary power conditioning unit, optimizing energy extraction from the SPV array and enhancing system efficiency. This setup ensures that PMSM meets the power and operational demands of EVs. Additionally, a voltage source inverter (VSI) facilitates electronic commutation of the PMSM, providing accurate control using fundamental frequency pulses. The system is modelled and simulated in MATLAB/Simulink, demonstrating its reliability under diverse load conditions. The findings underscore the potential of this approach in promoting renewable energy integration in EVs, paving the way for cleaner and more sustainable transportation solutions

Due to environmental concerns, renewable energy has gained significant popularity over the past two decades. Integrating distributed generation and renewable energy sources, particularly through microgrids in power distribution systems, has become feasible. Additionally, there has been a notable increase in the adoption of electric vehicles (EVs) driven by environmental initiatives and their advantages over internal combustion engines. As a result, the planning and operation of microgrids in distribution systems have become more complex. To address these complexities, computational evolutionary algorithms have emerged as effective solutions. The Differential Evolution (DE) algorithm stands out for its speed and userfriendly simplicity. The proposed study uses the Fuzzy Adaptive Differential Evolution (FADE) analysis for microgrid planning integrated with EV charging infrastructure, using the IEEE 33bus system. The FADE algorithm combines the power of fuzzy logic and adaptive strategies within the DE framework to tackle the planning and optimization challenges of microgrids integrated with Electric Vehicle Charging Stations (EVCS). The findings provide valuable insights into the effectiveness of the FADE algorithm in addressing the challenges associated with the planning and operation of microgrids with EVCS in modern power systems

Accurate wind power forecasting is essential for the effective integration of wind energy into power grids. Yet, the inherent variability of wind and the intricate interplay of meteorological factors make prediction a challenging task. This study introduces a novel short-term wind power forecasting method, improving the traditional convolutional neural network and long short-term memory (CNN-LSTM) model through two significant innovations. First, we introduce a parallel convolutional architecture that employs both 1dimensional (1D) and 2-dimensional (2D) convolutions to simultaneously capture temporal patterns and inter-variable relationships in wind power data. This structure, inspired by Explainable-CNNs, enables more comprehensive feature extraction. Second, we integrate an attention mechanism that dynamically weights the importance of different input features and time steps, improving both forecast accuracy and model interpretability. The proposed model is evaluated using data from two wind farms in Croatia, comparing its performance against benchmark models including standard CNN-LSTM, LSTM, and gated recurrent unit (GRU) networks. Results demonstrate that our enhanced CNN-LSTM model achieves superior forecasting accuracy, with improvements in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 15% and 12% respectively, compared to the best-performing benchmark. Furthermore, the attention mechanism provides valuable insights into the relative importance of different features over time, offering a new level of interpretability in wind power forecasting models. This work contributes to the advancement of accurate and explainable wind power prediction, supporting more efficient renewable energy integration and grid management

The rapid rise in electric vehicle (EV) adoption highlights the critical need for a reliable charging infrastructure to ensure the stability of power distribution networks. This research introduces a fuzzy inference system (FIS) designed to forecast daily EV loads essential for developing microgrids to meet the increasing demand for EVs. The present work considers four factors for FIS designing: travel distance, parking duration, battery state of charge (SoC), and expected arrival times at charging stations. By developing fuzzy logic rules for these variables, a probabilistic charging is generated, improving both the precision and adaptability of load forecasts. This study also explores the impact of future EV adoption on microgrid load demand, analyzing adoption rates of 53%, 68%, and 84%, providing crucial insights for planning microgrids. The discrepancy between estimated and actual EV loads is found to be 0.078, demonstrating a reduction in prediction error. This effectively mitigates uncertainties related to EV user behavior and supports the design of resilient and flexible microgrid systems

Energy storage systems with a high voltage transfer ratio (VTR) play an important role in integrating modern electric power systems with large-scale renewable energy integration. This article suggests a modified Switched Capacitor non-isolated Bidirectional DC–DC Converter (SCBDC) topology to achieve a high VTR. The presented converter has a simple circuit, simple control, a switched capacitor structure that increases the voltage-gain range, and low-voltage stress on switches, making it suitable for renewable and hybrid energy source electric vehicle applications. Continuous conduction mode is used for the operation principles, steady-state analysis, and extraction of voltage and current equations. Simulation results for the proposed converter were obtained in a MATLAB environment, demonstrating the converter's feasibility.

This work presents an optimization approach for optimal operation of a grid connected microgrid (MG) considering EV charging station, renewable-based generators, DSTATCOM, load shifting, and both active and reactive power loads. DSTATCOM is used to supply the reactive power loads locally instead of purchasing it from the grid. Load shifting strategy is used to reduce dependency on the grid during high electricity pricing hours. The minimization of total annual operating cost of MG is considered as the objective function which includes cost of buying/selling active power from/to the grid, costs related to PV, wind power generator, and DSTATCOM, cost of reactive power purchase from the grid, and operating cost of EV charging station. EV charging station load profile is generated using fuzzy-based approach considering number ofEVs, SOC levels of EVs, and arrival time of EV s to the charging station. The proposed optimization problem is solved using CPLEX solver of GAMS. The simulation results show that the uses of load shifting and DSTACOM facilities significantly reduce the total annual operating cost of MG.

Bi-directional DC-DC converters (BDC) are required for power flow regulation between storage devices and DC buses in renewable energy based distributed generation systems. The fundamental requirements of the BDC are simple structure, reduced switching components, a wide range of voltage gain, low voltage stress, high efficiency, and reduced size. There are different BDC topologies for various applications based on their requirements in the literature. Various BDC are categorized according to their impedance networks. Isolated BDC converters are large due to high-frequency transformers and hence used for static energy storage applications whereas non-isolated BDC is lightweight and suitable for dynamic applications like electric vehicles. This paper reviews types of non-isolated BDC topologies. The performance of five non-isolated BDC converters under steady state condition is evaluated by using theoretical analysis. On this basis, suitability of BDC for different applications is discussed. Further advantages and limitations of converters are discussed by using comparative analysis. The optimization of BDC for distributed generation systems from the perspectives of wide voltage gain, low electromagnetic interference, low cost with higher efficiency is identified. Theoretical analysis of the converters is validated by simulating 200W converters in MATLAB Simulink.

This paper proposes BKY converter, which is made to run in continuous conduction mode during both the charging and discharging cycles for low power EV applications. An analysis is conducted on the converter's dynamic behavior, and an approach to control is put forth to manage the power transfer between the traction system and battery in an electric vehicle. The suggested converter is designed using an extracted small-signal model. A significant ripple in the detected current causes switching instability in the current-mode control approaches at low duty ratios. A computation delay occurs when the controller is implemented in the microcontroller. The control algorithm's design takes this into account. A theoretical framework for current and voltage loop gain transfer functions are created using the realistic parameters of a BKY converter. Further, dynamic performance under load variations is explained and validated by simulations.

In electric vehicles (EVs), the type of electric motor and converter technology have a significant impact on regulating the operational characteristics of the vehicle. Therefore, in this work, the modified bi-directional KY converter (BKYC) is proposed for EV applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, bi-directional power flow, simplified control structure, continuous current, common ground, low volume, and high efficiency. An inductor on either side of the converter ensures continuous current flow and passive components are arranged to operate in series to offer high step-up/step-down conversion. The charging and discharging operations, steady-state analysis, and design process of the proposed converter are discussed in detail and compared with similar bi-directional converter topologies. Further, the efficiency analysis of the proposed converter is presented and found that the efficacy of 95.51 % in charging operation and 96.52 % in discharging operation of operation. The simulations are carried out using MATLAB/Simulink environment. Further, a prototype of a modified bi-directional KY converter is implemented with a TMS320F28335 processor and validated with theoretical and simulation counterparts.

The increasing focus on environmental sustainability has led to a significant rise in the use of renewable energy within distributed generation systems. Microgrids play a crucial role in facilitating the integration of renewable energy into distribution networks, making effective strategic planning essential for achieving the best financial and environmental results. Advanced software tools for microgrid planning and design, such as HOMER, are vital in this context. HOMER stands out for its ability to incorporate contemporary factors such as demand-side management, generator reliability, and Electric Vehicle Charging Fleets (EVCF). The proposed work investigates the planning process for a campus microgrid that includes EVCF, exploring various renewable energy configurations and tariff options. It offers a thorough assessment of different planning scenarios, emphasizing both the potential benefits and challenges associated with incorporating EVCF into university microgrids. The analysis determined that the optimal sizes for the microgrid components could yield annual energy charge savings of 12,027,annualutilitybillsavingsof281,905, and a payback period of 5.2 years

Over the past 20 years, the popularity of renewable energy has sharply increased due to environmental concerns. Integrating Distributed Generation (DG) and renewable energy sources, particularly through microgrids, into power distribution systems has become increasingly feasible. Simultaneously, there has been a notable surge in the adoption of electric vehicles (EVs), driven by environmental initiatives and their advantages over internal combustion engines. Consequently, the planning and management of microgrids within distribution networks have grown increasingly complex. To tackle these complexities, computational evolutionary algorithms have emerged as effective solutions. Among these algorithms, the Differential Evolution (DE) algorithm stands out for its speed and user-friendly simplicity. The proposed work analyzes Hybrid Differential Evolution (HDE) integrated with EV charging infrastructure for microgrid planning. The HDE algorithm combines the power of fuzzy logic and adaptive strategies within the DE framework to address the planning and optimization challenges of microgrids integrated with Electric Vehicle Charging Stations (EVCS). The paper gives insights into the effectiveness of the HDE algorithm in addressing the challenges related to the planning and operation of microgrids with EV charging stations in modern power systems. Furthermore, the optimization results are compared with those achieved using the DE algorithm.

Lithium ion batteries are a promising energy source for electric vehicles due to their high specific energy and power output. Overall system reliability and stability can be improved by effectively planning battery replacement intervals and monitoring their condition. To guarantee the battery system operates safely, steadily, and effectively, it is necessary to accurately assess the state of health (SOH) of the lithium-ion battery. Capacity might be used to anticipate it directly. To improve the accuracy of the SOH estimate, hyperparameter-optimized Gaussian process regression (GPR) is used. Gaussian process models have the advantage of being flexible, stochastic, nonparametric models with uncertainty forecasts, and may have variance around the mean forecast to account for the associated uncertainties in evaluation and forecasting. The lithium-ion battery data set made available by NASA is examined in this article. The outcomes demonstrate its efficacy and demonstrate that the algorithm may be successfully used for battery monitoring and prognostics. Additionally, the prediction for battery health has been improved through the comparison of predictions with various quantities of training data.

A compact Inertial Electrostatic Confinement (IEC) system is designed and fabricated for D-D fusion neutron generation. The IEC system consists of two concentric spherical grids connected to high voltage power supply inside a vacuum chamber filled with deuterium gas. The diameter of inner grid cathode is 40 mm and the diameter of outer grid anode is 120 mm. These grids are placed inside a SS304L cylindrical vacuum chamber of 300 mm diameter and 450 mm length. The IEC system has been operated at 24 kV in deuterium gas medium at 0.01–0.02 mbar, and the neutron yield of ~ 10 5 n/s is measured with neutron monitor. The temperature inside the IEC system is also measured using Fiber Bragg Grating (FBG) during D-D gas discharges. Degradation in vacuum inside the chamber causes the instability in deuterium plasma which reduces the neutron yield and increases the cathode temperature.