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This paper proposes an integration of renewable energy sources based on a modular multi-input bidirectional DC/DC buck-boost converter. The proposed four-port converter is realized with a reduced component count and simplified control strategy, which makes the converter more reliable and cost-effective. Also, this converter exhibits bidirectional power flow functionality, making it suitable for charging the battery during regenerative braking of an electric vehicle or hybrid electric vehicle. Photovoltaic (PV) panel, battery, and ultra-capacitor are the three different energy sources for the suggested topology. Dynamic modeling is derived, and the steady-state performance of the converter is well analyzed in the MATLAB/Simulink platform. Experimental results obtained from a 100 W prototype model validate the performance of the proposed four-port bidirectional buck/boost (FPB3C) DC/DC converter.

The demand for renewable energy-based Electric Vehicle (EV) charging infrastructure is increasing in recent years. Solar PV based EV charging method is preferred as it has simple energy harvesting technique. The PV system is an uncertain power source, where the power generation is varied with respect to the availability of sunlight. So, that the charging station requires a backup power supply for the uninterrupted charging. For the integrated power sources, the charging station requires a simple and efficient conversion unit for the DC/AC/DC conversion. In this work, a modified Z-source inverter (MZSI) is developed for the multiport EV charger using PV and grid. The proposed MZSI is connected between the input and output sides to boost the voltage as per the demand at the battery side. In order to connect many battery units with the charger, the capacitors used in the MZSI are split as per the required number of charging ports. This developed converter topology operates the systems in four different modes like PV-Grid, PV-battery, grid-battery, and battery-grid. The performance of this proposed work has been validated in MATLAB/Simulink® and in the experimental setup. The experimental setup has been developed with two charging ports for obtaining 250W at each charger end which cumulatively produces 500W output across both chargers with an efficiency of 90.18%. [ABSTRACT FROM AUTHOR]

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value. [ABSTRACT FROM AUTHOR]

The enduring life span of the machine insulation will be decided based on degradation level in motor and generator stator windings. The non-destructive diagnostic tools like dielectric loss and capacitance test and partial discharge (PD) analysis, recognized to access the deterioration in the insulation system of rotating machines. The experiments reveal various characteristic parameters such as leakage current, dielectric dissipation factor, the capacitance value, and PD magnitude. The integrity of the rotating machine can be find out by analyzing these parameters. This research study shows the hybrid method for prediction of insulation condition in the stator winding by utilizing the artificial neural network (ANN) with gravitational search algorithm in comparison with ANN and ANN-genetic algorithm. The advent of expert systems ensures the quality assurance and service life assessment of the high-voltage assets. It offers a predictive maintenance solution to personnel dealt with power utilities thereby increasing the uptime, reliability, and productivity, which in turn reducing the operating costs, downtime and unplanned outages. For testing and predicting the insulation status, several 11 kV machines are considered. The predicted results using hybrid techniques extend a close agreement with reference to the data obtained from the experiments performed. The proposed method indicate the competent and trustworthy, by the presented test results. [ABSTRACT FROM AUTHOR]

The capacitive coupled wireless power transfer (CCWPT) operating at megahertz (MHz) frequency is broadly considered as the promising solution for low power biomedical implants. The class E power amplifier is attractive in MHz range wireless power transfer (WPT) applications due to zero voltage switching (ZVS) and zero voltage derivative switching (ZVDS) properties. The existing design of class E amplifier is investigated only for inductive resonant coupled (IRC) WPT systems; the modelling and optimization of the class E amplifier for CCWPT systems are not deliberated with load variation. Meanwhile, the variations in the coupling distance and load are common in real time applications, which could reduce the power amplifier (PA) efficiency. The purpose of this paper is to model and optimize the class E amplifier for CCWPT systems used in MHz range applications. The analytical model of PA parameters and efficiency are derived to determine the optimal operating conditions. Also, an inductive-capacitive-inductive (LCL) impedance matching network is designed for the robust operation of the PA, which improves the efficiency and maintains required impedance compression. The maximum efficiency of the proposed design reached up to 96.34% at 13.56 MHz and the experimental results are closely matched with the simulation. [ABSTRACT FROM AUTHOR]

Wide band gap luminescent MoS 2 quantum dots (QDs) and MoS 2 nanocrystals (NCs) have been synthesized by using laser-assisted chemical vapour deposition and used as an electrode material in supercapacitors. Size-dependent properties of the MoS 2 QDs and NCs were examined by UV-vis absorption, photoluminescence, and Raman spectroscopy. The morphological evolution of the NCs and QDs were characterized by using field emission scanning electron microscopy, high-resolution transmission electron microscopy, and atomic force microscopy. The as-synthesized uniform QDs with a size of ∼2 nm exhibited an extended electrochemical potential window of 0.9 V with a specific capacitance value of 255 F/g, while the NCs values were 205 F/g and 0.8 V and the pristine MoS 2 with values of 105 F/g and 0.6 V at a scan rate of 1 mV s -1 . A shorter conductive pathway and 3D quantum confinement of MoS 2 QDs that exhibited a higher number of active sites ensure that the efficient charge storage kinetics along with the intercalation processes at the available edge sites enable significant widening of operating potential window and enhance the capacitance. The symmetric device constructed with the QDs showed a remarkable device capacitance of 50 F/g at a scan rate of 1 mV s -1 with an energy density of ∼5.7 W h kg -1 and achieved an excellent cycle stability of 10,000 consecutive cycles with ∼95% capacitance retention., Competing Interests: The authors declare no competing financial interest., (© 2021 American Chemical Society.)