Search Results (13,069)

Smart transformers (STs), which are power electronic-based transformers with control and communication capabilities, facilitate managing future distribution grids with distributed generators (DGs) and battery energy storage systems (BESSs). This paper presents a current-fed quadruple active-bridge (CF-QAB) DC–DC converters-based cascaded H-bridge (CHB) ST architecture in which it is easy to coordinate the system-level power transmission and distribution. Compared with the QAB/DAB + Boost baseline, this topology achieves a reduction of approximately 20% in device count. For the core component of the proposed ST architecture, the operation principles are illustrated and the small-signal model is derived. Based on that, the control system obtained by using the individual channel design method is proposed to decouple the highly coupled LV and BESS DC ports, which significantly simplify the control system structure and design process. The experimental results are shown to validate the effectiveness of the proposed DC–DC converter and associated control system. Full article

With the increasing penetration of renewable energy such as solar and wind power into the grid as well as the addition of modern types of versatile loads such as electric vehicles, the grid system is more prone to system failure and instability. One of the possible solutions to mitigate these conditions and increase the system efficiency is the integration of virtual power plants into the system. Virtual power plants can aggregate distributed energy resources such as renewable energy systems, electric vehicles, flexible loads, and energy storage, thus allowing for better coordination and optimization of these resources. This paper proposes a genetic algorithm-based optimization to coordinate the different elements of the energy management system of a virtual power plant, such as the energy storage system and charging/discharging of electric vehicles. It also deals with the random behavior of the genetic algorithm and its failure to meet certain constraints in the final solution. A novel method is proposed to mitigate these problems that combines a genetic algorithm in the first stage, followed by a gradient-based method in the second stage, consequently reducing the overall electricity bill by 50.2% and the simulation time by almost 95%. The performance is evaluated considering the reference set-points of operation from the obtained solution of the energy storage and electric vehicles by performing tests using a detailed model where power electronics converters and their local controllers are also taken into account. Full article

The unimolecular alkene elimination reaction class of fatty acid alkyl esters (FAAEs) is a crucial component in the low-temperature combustion mechanism for biodiesel fuels. However, thermo-kinetic parameters for this reaction class are scarce, particularly for the large-size molecules over four carbon atoms and intricate branched-chain configurations. Thermo-kinetic parameters are essential for constructing a reaction mechanism, which can be used to clarify the chemical nature of combustion for biodiesel fuels. In this paper, the B3LYP method, in conjunction with the 6-311G(d,p) basis set, is used to carry out geometry optimization of the species participating in the reactions. Frequency calculations are further executed at the same level of theory. Additionally, coupled with the 6-311G(d,p) basis set, the B3LYP method acts as the low-level ab initio approach, while the Gaussian-4 (G4) composite method serves as the high-level ab initio approach within the isodesmic reaction correction scheme. The CCSD(T) approach is employed to verify the consistency of the electronic energy ascertained through the G4 method. The isodesmic reaction method (IRM) is used to obtain the energy barriers and reaction enthalpies for unimolecular alkene elimination reaction class of FAAEs. Based on the reaction class transition state theory (RC-TST), high-pressure-limit rate coefficients were computed, with asymmetric Eckart tunneling corrections applied across 500~2000 K temperature range. Rate rules at the high-pressure-limit are obtained through the averaging of rate coefficients from a representative collection of reactions, which incorporate substituent groups and carbon chains with different sizes and lengths. Ultimately, the energy barriers, reaction enthalpies, and rate rules at the high-pressure-limit and kinetic parameters expressed as (A, n, E) are supplied for developing the low-temperature combustion mechanism of biodiesel fuels. Full article

Induction motors piloted by voltage source inverters constitute a major source of acoustic noise in industry. The discrete tonal bands generated by induction motor stator current spectra controlled by the fixed Pulse Width Modulation (PWM) technique have damaging effects on the electronic noise source. Nowadays, the investigation of new advanced control techniques for variable speed drives has developed a potential investigation field. Finite state model predictive control has recently become a very popular research focus for power electronic converter control. The flexibility of this control shows that the switching times are generated using all the information on the drive status. Predictive Torque Control (PTC), space vector PWM and random PWM are investigated in this paper in terms of acoustic noise emitted by an induction machine fed by a three-phase two-level inverter. A comparative study based on electrical and mechanical magnitudes, as well as harmonic analysis of the stator current, is presented and discussed. An experimental test bench is also developed to examine the effect of the proposed PTC and PWM techniques on the acoustic noise of an induction motor fed by a three-phase two-level voltage source converter. Full article

Battery Electric Vehicles (BEVs) technology is rapidly emerging as the cornerstone of sustainable transportation, driven by advancements in battery technology, power electronics, and modern drivetrains. This paper presents a comprehensive review of current and next-generation BEV powertrain architectures, focusing on five key subsystems: battery energy storage system, electric propulsion motors, energy management systems, power electronic converters, and charging infrastructure. The review traces the evolution of battery technology from conventional lithium-ion to solid-state chemistries and highlights the critical role of battery management systems in ensuring optimal state of charge, health, and safety. Recent innovations by leading automakers are examined, showcasing advancements in cell formats, motor designs, and thermal management for enhanced range and performance. The role of power electronics and the integration of AI-driven strategies for vehicle control and vehicle-to-grid (V2G) are analyzed. Finally, the paper identifies ongoing research gaps in system integration, standardization, and advanced BMS solutions. This review provides a comprehensive roadmap for innovation, aiming to guide researchers and industry stakeholders in accelerating the adoption and sustainable advancement of BEV technologies. Full article

The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through these devices. Understanding and monitoring these harmonics is crucial to ensure the smooth and seamless operation of these networks, as well as to protect and manage the renewable energy sources-based power system. In this paper, we propose an advanced method of dynamic modal decomposition, called Higher-Order Dynamic Mode Decomposition (HODMD), one of the recently proposed data-driven methods used to estimate the frequency/amplitude and phase with high resolution, to identify the harmonic spectrum in power systems dominated by renewable energy generation. In the proposed method, several time-shifted copies of the measured signals are integrated to create the initial data matrices. A hard thresholding technique based on singular value decomposition is applied to eliminate ambiguities in the measured signal. The proposed method is validated and compared to Synchrosqueezing Transform based on Short-Time Fourier Transform (SST-STFT) and the Concentration of Frequency and Time via Short-Time Fourier Transform (ConceFT-STFT) using synthetic signals and real measurements, demonstrating its practical effectiveness in identifying harmonics in emerging power networks. Finally, the effectiveness of the proposed methodology is analyzed on the energy storage-based laboratory-scale microgrid setup using an Opal-RT-based real-time simulator. Full article

Digital connectivity depends not only on infrastructure, but also on the material devices used to access networks. This study examines electronic devices’ availability and prices in Saint-Louis, a mid-sized Senegalese city, to address the lack of empirical research on African digital markets. With data on material connectivity being scarce, this paper provides a baseline description as grounds for future research. Using a participatory mapping approach over three weeks in September 2024, the research assessed the range, condition, and distribution of smartphones across central and neighborhood markets. Descriptive statistics and spatial analysis illustrate key trends. Results show a market heavily structured around second-hand smartphones, where device quality and prices adjust to economic power. Imported second-hand devices are often high-end, with prices above many new items of cheaper brands, while locally used items have much depreciated prices compared to either new or imported second-hand ones. Market locations are widespread for common items and clustered for specialized devices, consistent with central place theory. By documenting the material foundations of digital communication, this study provides new empirical evidence on African urban device markets and highlights the need to consider material access alongside infrastructure in digital connectivity debates. Full article

In this paper, the authors analyze the applicability of artificial intelligence algorithms for classifying file encryption methods based on statistical features extracted from the binary content of files. The prepared datasets included both unencrypted files and files encrypted using selected cryptographic algorithms in Electronic Codebook (ECB) and Cipher Block Chaining (CBC) modes. These datasets were further diversified by varying the number of encryption keys and the sample sizes. Feature extraction focused solely on basic statistical parameters, excluding an analysis of file headers, keys, or internal structures. The study evaluated the performance of several models, including Random Forest, Bagging, Support Vector Machine, Naive Bayes, K-Nearest Neighbors, and AdaBoost. Among these, Random Forest and Bagging achieved the highest accuracy and demonstrated the most stable results. The classification performance was notably better in ECB mode, where no random initialization vector was used. In contrast, the increased randomness of data in CBC mode resulted in lower classification effectiveness, particularly as the number of encryption keys increased. This paper provides a comprehensive analysis of the classifiers’ performance across various encryption configurations and suggests potential directions for further experiments. Full article

This position paper extends the author’s keynote address from the 2024 IEEE European Solid-State Electronics Research Conference, offering a perspective on effective strategies for the advancement of analog and mixed-signal (AMS) integrated circuit (IC) design. It is argued that traditional methodologies, characterized by their focus on transistor-level optimization within individual sub-blocks, are insufficient for satisfying the stringent performance and power consumption demands of contemporary information and communication technologies (ICT), especially in the context of expanding AI applications. Consequently, a paradigm shift is necessary, emphasizing “full-stack” solutions that prioritize comprehensive system-level analysis and aim to minimize physical resources and reduce complexity by innovating across the established boundaries of design abstraction levels. Building on prior work, this manuscript offers a more thorough justification for the proposed full-stack analog design methodology, supported by broader evidence and more comprehensive discussion. It also identifies key considerations regarding EDA and workforce development as topics for future work. Full article

A three-phase inverter generates non-sinusoidal voltages, contains high order harmonics, and concentrates on switching frequency multiples. Supplying an induction machine (IM) with a voltage source inverter (VSI) increases the acoustic noise content which becomes unbearable, particularly for systems needing a moderate level of electric traction. The discrete tonal bands produced by the IM stator current spectrum controlled by the fixed pulse width modulation (PWM) technique have damaging effects on the electronic noise source. Moreover, it has been factually proven that the noise content is strongly associated with the harmonics of the source feeding electric machine. Thus, the harmonic content is influenced by the control strategy VSI to produce pulse width modulation (PWM). Currently, the investigation of new advanced control techniques for variable speed drives has developed into a potential investigation file. Two fundamental topologies for a three-phase inverter have been suggested in the literature, namely two- and three-level topologies. Therefore, this paper investigated the effect of variable and fixed PWM strategies, such as random PWM (RPWM) and space vector PWM (SVPWM), on the noise generated by an IM, powered with a two- or three-level inverter. Simulation results showed the validity and efficiency of the proposed variable RPWM strategy in reducing sideband harmonics for both the two and three levels at different switching frequencies and modulation indexes. The proposed PWM strategies were further evaluated by the results of equivalent experiments on an IM fed by a two-level VSI. The experimental measurements of harmonic current and noise spectra demonstrate that the acoustic noise is reduced and dispersed totally for the RPWM strategy. Full article

Azomethine ylides (AMYs) have a nitrogen–carbon double bond and an electron lone pair on the nitrogen atom. They are essential 1,3-dipoles for [3+2] cycloadditions in the synthesis of pyrrolidine-containing heterocycles. Significant progress in 1,3-diplolar cycloadditions has been made in the construction of novel heterocyclic scaffolds, with efforts to broaden substrate scope, enhance stereoselectivity, and integrate green chemistry principles. This article summarizes double cycloadditions of AMYs derived from amino esters and amino acids for the synthesis of novel polyheterocycles. The design of double cycloadditions through the pot, atom, and step economic (PASE) method to increase the reaction efficiency is discussed. The examples presented in this paper may be applied to the synthesis of biologically active molecules. Full article

Rejuvenating aged asphalt is critical for sustainable road construction and resource utilization. This paper systematically reviews the current research on rejuvenators, focusing on their classification and the micro-, and macro-properties of rejuvenated asphalt. Rejuvenators are categorized into mineral oil-based, bio-based, and compound types. Each type offers distinct advantages in recovering the performance of aged asphalt. Mineral oil-based rejuvenators primarily enhance low-temperature cracking resistance through physical dilution, while bio-based rejuvenators demonstrate superior environmental sustainability and stability. Compound rejuvenators, particularly those incorporating reactive compounds, show the best results in repairing degraded polymer modifiers and improving both low- and high-temperature properties of aged, modified asphalt. Atomic Force Microscopy (AFM), Fluorescence Microscopy (FM), and Scanning Electron Microscopy (SEM) have been applied to analyze the micro-properties of rejuvenated asphalt. These techniques have revealed that rejuvenators can restore the microstructure of aged asphalt by dispersing agglomerated asphaltenes and promoting molecular mobility. Functional groups and molecular weight changes, characterized by Fourier Transform Infrared Spectroscopy (FTIR) and Gel Permeation Chromatography (GPC), indicate that rejuvenators effectively reduce oxidation products and molecular weight of aged asphalt, restoring its physicochemical properties. Macro-property evaluations show that rejuvenators significantly improve penetration, ductility, and fatigue resistance. Finally, this review identifies the key characteristics and challenges associated with rejuvenator applications and provides an outlook on future research directions. Full article

Powder bed fusion of copper has been extensively investigated using both laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) additive manufacturing technologies. Each technique offers unique benefits as well as specific limitations. Near-infrared (NIR) laser-based LPBF is widely accessible; however, the high reflectivity of copper limits energy absorption, thereby resulting in a narrow processing window. Although optimized parameters can yield relative densities above 97%, issues such as keyhole porosity, incomplete melting, and anisotropy remain concerns. Green lasers, with higher absorptivity in copper, offer broader process windows and enable more consistent fabrication of high-density parts with superior electrical conductivity, often reaching or exceeding 99% relative density and 100% International Annealed Copper Standard (IACS). Mechanical properties, including tensile and yield strength, are also improved, though challenges remain in surface finish and geometrical resolution. In contrast, Electron Beam Powder Bed Fusion (EB-PBF) uses high-energy electron beams in a vacuum, eliminating oxidation and leveraging copper’s high conductivity to achieve high energy absorption at lower volumetric energy densities (~80 J/mm³). This results in consistently high relative densities (>99.5%) and excellent electrical and thermal conductivity, with additional benefits including faster scanning speeds and in situ monitoring capabilities. However, EB-PBF faces its own limitations, such as surface roughness and powder smoking. This paper provides a comprehensive review of the current state of laser-based (PBF-LB/M) and electron beam-based (PBF-EB/M) powder bed fusion processes for the additive manufacturing of copper, summarizing key trends, material properties, and process innovations. Both approaches continue to evolve, with ongoing research aimed at refining these technologies to enable the reliable and efficient additive manufacturing of high-performance copper components. Full article

Light-emitting diodes (LEDs) have gained widespread adoption as solid-state lighting sources due to their compact size, long operational lifetime, high brightness, and mechanical robustness. This paper presents the development and implementation of an isolated AC-DC LED electronic lighting driver circuit that integrates a modified flyback converter with a lossless snubber circuit, along with inherent power factor correction (PFC). The proposed design operates the transformer’s magnetizing inductor in the discontinuous conduction mode (DCM), thereby naturally achieving PFC without the need for complex control circuitry. Furthermore, the circuit is capable of recycling the energy stored in the transformer’s leakage inductance, improving overall efficiency. The input current harmonics are shown to comply with the IEC 61000-3-2 Class C standard. A 72 W (36 V/2 A) prototype has been constructed and tested under a 110 V AC input. Experimental results confirm the effectiveness of the proposed design, achieving a power factor of 0.9816, a total harmonic distortion (THD) of 12.094%, an output voltage ripple factor of 9.7%, and an output current ripple factor of 11.22%. These results validate the performance and practical viability of the proposed LED driver architecture. Full article

Electronic nose (e-nose) systems have emerged as transformative tools for odor and gas analysis, leveraging advances in nanomaterials, sensor arrays, and machine learning (ML) to mimic biological olfaction. This review synthesizes recent developments in e-nose technology, focusing on innovations in sensor design (e.g., graphene-based nanomaterials, MEMS, and optical sensors), drift compensation techniques, and AI-driven data processing. We highlight key applications across healthcare (e.g., non-invasive disease diagnostics via breath analysis), food quality monitoring (e.g., spoilage detection and authenticity verification), and environmental management (e.g., pollution tracking and wastewater treatment). Despite progress, challenges such as sensor selectivity, long-term stability, and standardization persist. The paper underscores the potential of e-noses to replace conventional analytical methods, offering portability, real-time operation, and cost-effectiveness. Future directions include scalable fabrication, robust ML models, and IoT integration to expand their practical adoption. Full article