Search Results (765)

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries, these vehicles offer greater efficiency and zero emissions. However, their energy management remains a challenge requiring advanced strategies. This paper presents a comparative study of two developed energy management strategies: a deterministic rule-based approach and a fuzzy logic approach. The proposed system consists of a proton exchange membrane fuel cell (PEMFC) as the primary energy source and a lithium-ion battery as the secondary source. A comprehensive model of the hybrid powertrain is developed to evaluate energy distribution and system behaviour. The control system includes a model predictive control (MPC) method for fuel cell current regulation and a PI controller to maintain DC bus voltage stability. The proposed strategies are evaluated under standard driving cycles (UDDS and NEDC) using a simulation in MATLAB/Simulink. Key performance indicators such as fuel efficiency, hydrogen consumption, battery state-of-charge, and voltage stability are examined to assess the effectiveness of each approach. Simulation results demonstrate that the deterministic strategy offers a structured and computationally efficient solution, while the fuzzy logic approach provides greater adaptability to dynamic driving conditions, leading to improved overall energy efficiency. These findings highlight the critical role of advanced control strategies in improving FCHEV performance and offer valuable insights for future developments in hybrid-vehicle energy management. Full article

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

The aim of the article is to determine the control range of a multiphase squirrel cage induction generator with more than three stator phases, operating in a wide range of driving speeds. The generator produces an output DC voltage using a multiphase converter operating as a PWM rectifier. The entire speed range is divided into intervals in which the sequence of stator phase voltages and, in effect, the number of pole pairs, is changed. In each interval, the output voltage is regulated by the frequency and amplitude of the stator voltages causing the highest possible power efficiency of the generator. The system can be scalar controlled or regulated using field orientation. Generator characteristics are calculated based on the set of steady-state equations derived from differential equations describing the multiphase induction machine. The calculation results are compared with simulations and with the steady-state measurement of the vector-controlled nine-phase generator. Recognizing the reliability of the obtained results, calculations are performed for a twelve-phase generator, obtaining satisfactory efficiency from 70% to 85% in the generator speed range from 0.2 to 1.0 of the assumed reference speed of 314 rad/s. The generator producing DC voltage can charge an electrical energy storage system or can be used directly to provide electrical power. This solution is not patented. Full article

This article presents a comparative study of the storage of energy produced by photovoltaic panels by means of two types of batteries: Lead–Acid and Lithium-Ion batteries. The work involved the construction of a model in MATLAB-Simulink for controlling the loading/unloading of storage batteries with energy produced by photovoltaic panels through a buck-type DC-DC convertor, controlled by means of the MPPT algorithm implemented through the method of incremental conductance based on a MATLAB function. The program for the MATLAB function was developed by the author in the C++ programming environment. The MPPT algorithm provides maximum energy transfer from the photovoltaic panels to the battery. The electric power taken over at a certain moment by Lithium-Ion batteries in photovoltaic panels is higher than the electric power taken over by Lead–Acid batteries. Two types of batteries were successively used in this model: Lead–Acid and Lithium-Ion batteries. Based on the results being obtained and presented in this work it may be affirmed that the storage battery Lithium-Ion is more performant than the Lead-Acid storage battery. At the Laboratory of Electrical Machinery and Drives of the Engineering Faculty of Bacau, an experimental stand was built for a storing system for electric energy produced by photovoltaic panels. For controlling DC-DC buck-type convertors, a program was developed in the programming environment Arduino IDE for implementing the MPPT algorithm for incremental conductance. The simulation part of this program is similar to that of the program developed in C++. Through conducting experiments, it was observed that, during battery charging, along with an increase in the charging voltage, an increase in the filling factor of the PWM signal controlling the buck DC-DC convertor also occurred. The findings of this study may be applicable to the storage of battery-generated electrical energy used for supplying electrical motors in electric cars. Full article

To improve commutation accuracy and effectively suppress torque ripple in brushless DC motors (BLDCMs), this paper presents a novel commutation correction strategy integrated into an enhanced direct torque control (DTC) framework. The proposed method estimates the commutation angle error in real time by analyzing the integral difference in phase currents across adjacent 30° conduction intervals, enabling dynamic and accurate commutation correction. This correction mechanism is seamlessly embedded into a modified DTC algorithm that employs a three-level torque hysteresis comparator and introduces a novel zero-voltage vector selection strategy to minimize torque ripple. Compared with conventional DTC approaches employing dual-loop control and standard zero vectors, the proposed method achieves up to a 58% reduction in torque ripple along with improved commutation precision, as demonstrated through both simulation and experimental validation. These results confirm the method’s effectiveness and its potential for application in high-performance BLDCMs drive systems. Full article

This paper proposes an energy-efficient current control strategy for drive modules of permanent magnetic actuators (PMAs) to reduce the cost and volume of DC-link capacitors. The drive module of the PMA does not receive the input power from an external power source during operation. Instead, the externally charged DC-link capacitors are used as internal backup power sources to guarantee the reliable operation even in the case of an emergency. Therefore, it is important to use the charged energy efficiently within the limited DC-link capacitors. However, conventional control strategies using a voltage open loop have trouble reducing the energy waste. This is because the drive module with the voltage open loop uses unnecessary energy even after the PMA mover has finished its movement. To figure it out, the proposed control strategy adopts a current control loop to save energy even if the displacement of the PMA mover is unknown. In addition, the proposed strategy can ensure the successful operation of the PMA by using the driving force analysis. The efficacy of the proposed strategy is verified through the experimental test. It would be expected that the proposed strategy can reduce the cost and volume of the PMA drive system. Full article

The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a unified nonlinear modal analysis framework integrating second-order analytical solutions with novel nonlinear indices. Validated across diverse systems (DC microgrids and grid-connected PV), the framework yields significant findings: (1) second-order solutions outperform linearization in capturing critical oscillation/damping distortions under realistic disturbances, essential for fault analysis; (2) nonlinear effects induce modal dominance inversion and generate governing composite modes; (3) key interaction mechanisms are quantified, revealing distinct voltage regulation pathways in DC microgrids and multi-path dynamics driving DC voltage fluctuations. This approach provides a systematic foundation for dynamic characteristic assessment and directly informs control design for power electronics-dominated grids. Full article

Cutaneous melanoma is an aggressive cancer with an increasing incidence worldwide, highlighting the need for research into its pathogenesis. The tumor microenvironment (TME) plays a critical role in melanoma progression and consists of cellular components and an extracellular matrix (ECM) rich in cytokines and signaling molecules. The most abundant stromal cells within the TME are cancer-associated fibroblasts (CAFs), which remodel the ECM and modulate immune responses. Among immune cells, tumor-associated macrophages (TAMs) predominate, and their polarization toward the M2 phenotype supports tumor progression. Tumor-infiltrating lymphocytes (TILs) have diverse functions, including cytotoxic T-cells, helper T-cells that modulate immune response, B-cells forming tertiary lymphoid structures (TLS), and regulatory T-cells with immunosuppressive properties. Dendritic cells (DCs) also play a complex role in the TME. A notable subpopulation are mature regulatory dendritic cells (mregDCs), which contribute to immune evasion. All of these TME components may drive tumorigenesis. Advancements in melanoma treatment—including immunotherapy and targeted therapies—have significantly improved outcomes in advanced-stage disease. In parallel, emerging approaches targeting the tumor microenvironment and gut microbiome, as well as personalized strategies such as neoantigen vaccines and cell-based therapies, are under active investigation and may further enhance therapeutic efficacy in the near future. Full article

Introduction: Substance use disorders (SUDs) pose a significant public health challenge, with current treatments often exhibiting limited effectiveness and high relapse rates. Transcranial direct current stimulation (tDCS), a noninvasive neuromodulation technique that delivers low-intensity direct current via scalp electrodes, has shown promise in various psychiatric and neurological conditions. In SUDs, tDCS may help to modulate key neurocircuits involved in craving, executive control, and reward processing, potentially mitigating compulsive drug use. However, the precise neurobiological mechanisms by which tDCS exerts its therapeutic effects in SUDs remain only partly understood. This review addresses that gap by synthesizing evidence from clinical studies that used neuroimaging (fMRI, fNIRS, EEG) and blood-based biomarkers to elucidate tDCS’s mechanisms in treating SUDs. Methods: A targeted literature search identified articles published between 2008 and 2024 investigating tDCS interventions in alcohol, nicotine, opioid, and stimulant use disorders, focusing specifically on physiological and neurobiological assessments rather than purely behavioral outcomes. Studies were included if they employed either neuroimaging (fMRI, fNIRS, EEG) or blood tests (neurotrophic and neuroinflammatory markers) to investigate changes induced by single- or multi-session tDCS. Two reviewers screened titles/abstracts, conducted full-text assessments, and extracted key data on participant characteristics, tDCS protocols, neurobiological measures, and clinical outcomes. Results: Twenty-seven studies met the inclusion criteria. Across fMRI studies, tDCS—especially targeting the dorsolateral prefrontal cortex—consistently modulated large-scale network activity and connectivity in the default mode, salience, and executive control networks. Many of these changes correlated with subjective craving, attentional bias, or extended time to relapse. EEG-based investigations found that tDCS can alter event-related potentials (e.g., P3, N2, LPP) linked to inhibitory control and salience processing, often preceding or accompanying changes in craving. One fNIRS study revealed enhanced connectivity in prefrontal regions under active tDCS. At the same time, two blood-based investigations reported the partial normalization of neurotrophic (BDNF) and proinflammatory markers (TNF-α, IL-6) in participants receiving tDCS. Multi-session protocols were more apt to drive clinically meaningful neuroplastic changes than single-session interventions. Conclusions: Although significant questions remain regarding optimal stimulation parameters, sample heterogeneity, and the translation of acute neural shifts into lasting behavioral benefits, this research confirms that tDCS can induce detectable neurobiological effects in SUD populations. By reshaping activity across prefrontal and reward-related circuits, modulating electrophysiological indices, and altering relevant biomarkers, tDCS holds promise as a viable, mechanism-based adjunctive therapy for SUDs. Rigorous, large-scale studies with longer follow-up durations and attention to individual differences will be essential to establish how best to harness these neuromodulatory effects for durable clinical outcomes. Full article

To deal with the interphase circulating-current problem of modular multilevel converters (MMCs) in multiphase wind power systems, a cooperative circulating-current suppression strategy based on a second-order generalized integrator (SOGI) and passivity-based control–integral sliding mode control (PBC-ISMC) is proposed in this paper. Firstly, a multiphase permanent magnet direct-drive wind power system topology without a step-up transformer is established. On this basis, SOGI is utilized to construct a circulating current extractor, which is utilized to accurately extract the double-frequency component in the circulating current, and, at the same time, effectively filter out the DC components and high-frequency noise. Secondly, passivity-based control (PBC), with its fast energy dissipation, and integral sliding mode control (ISMC), with its strong robustness, are combined to construct the PBC-ISMC circulating-current suppressor, which realizes the nonlinear decoupling and dynamic immunity of the circulating-current model. Finally, simulation results demonstrate that the proposed strategy significantly reduces the harmonic content of the circulating current, optimizes both the bridge-arm current and output current, and achieves superior suppression performance and dynamic response compared to traditional methods, thereby effectively enhancing system power quality and operational reliability. Full article

DC link power supply systems that integrate power electronic converters are increasingly being adopted. In particular, emerging “source–load” systems, in which the DC link interfaces with converters, have attracted increasing research interest due to concerns about power quality and system stability. This paper addresses mid- and low-frequency oscillation issues in DC link voltage supplied induction motor drives (IMDs). It begins by constructing a multiple-input multiple-output (MIMO) state-space model of the induction motor. For the first time, the dq-axis control system is represented as an equivalent admittance model that forms two single-input single-output (SISO) loops. The PI controller and induction motor are integrated into the inverter’s input impedance model; Furthermore, the effectiveness and accuracy of the derived impedance model are experimentally validated under various operating conditions of the induction motor using a custom-built test platform. The experimental results offer a practical reference for system enhancement and stability evaluation. Full article

Electric forklifts are increasingly adopted in industrial environments due to their energy efficiency, reduced emissions, and lower operating noise compared to combustion alternatives. This paper presents a novel methodology for estimating the transported load weight in electric forklifts based on the output power signal of the DC–DC converter driving the electro-hydraulic lifting system. The proposed method leverages non-intrusive measurements of voltage and current to compute the lifting power, lifting speed, and energy, also allowing the computation of the lifting efficiency. The analysis confirmed that lifting energy is not linearly correlated with transported weight but lifting efficiency can be reasonably approximated as a function of lifting power and lifting speed, subsequently allowing the estimation of the transported mass. Experimental validation using 53 lifting events demonstrated that the methodology can estimate load weight with a reasonable mean absolute percentage error of 10.6% and 6.4% when using linear or multivariable regression analysis, respectively. These results demonstrate that the approach is sufficiently accurate for practical applications such as triggering load warnings when the estimated mass exceeds predefined safety thresholds. Full article

Background: While mRNA vaccines effectively limit hospitalization and severe COVID-19 disease, the precise early innate immune mechanisms associated with their efficacy and reactogenicity remain underexplored. The identification of innate immune correlates prior to vaccination could provide mechanistic insights and potentially predict responses. Methods: We developed an in vitro model to study the innate immune activation of pre-vaccination peripheral blood mononuclear cells (PBMCs) collected from participants enrolled in a well-characterized COVID-19 BioNTech/Pfizer BNT162b2 vaccine (BNT162b2 vaccine) cohort. Pre-vaccination PBMCs were stimulated with empty lipid nanoparticle (LNP), mRNA-LNP, or Toll-like receptor (TLR) agonists. Using multiparameter spectral flow cytometry, we analyzed the baseline immune state, innate responsiveness to stimuli, and cytokine profiles of study participants. These pre-vaccination in vitro results were analyzed for correlations with post-vaccination symptoms and spike-specific IgG responses. Results: Baseline dendritic cell (DC) states inversely correlated with the magnitude of symptoms following BNT162b2 vaccination. Heightened conventional (cDC) and weaker plasmacytoid DC (pDC) responses to RNA stimuli correlated with the magnitude of an acute IgG response. IgG durability modestly correlated with a lower pDC state but higher cDC2 and monocyte baseline states and inversely correlated with TLR3 agonist responsiveness. Conclusions: The pre-vaccination assessment of innate immune function and resting states can be used to fit models potentially predictive of immunogenicity and reactogenicity to BNT162b2 vaccination. Pre-vaccination DC states may influence reactogenicity, while the response to RNA may impact antibody responses. Our data suggest that pre-vaccination assessment offers insights into the innate mechanisms driving mRNA vaccine responses and has predictive potential. Full article

A new control system implemented with a single-stage DC-DC controller to power an LED headlamp for automotive applications is presented in this work. Daytime running light (DRL), low beam (LB), high beam (HB) and adaptive driving beam (ADB) are typical functions requiring a dedicated LED driver solution to fulfill car maker requirements for front-light applications. Single-stage drivers often exhibit a significant overshoot in LED current during transitions from driving a higher number of LEDs to a lower number. To maintain LED reliability, this current overshoot must remain below the maximum current rating of the LEDs. If the overshoot overcomes this limit, it can cause permanent damage to the LEDs or reduce their lifespan. To preserve LED reliability, a comprehensive system has been proposed to minimize the peak of LED current overshoots, especially during transitions between different operating modes or LED string configurations. A key feature of the proposed system is the implementation of a parallel discharging path to be activated only when the current flowing in the LEDs is higher than a predefined threshold. A prototype incorporating an integrated test chip has been developed to validate this approach. Measurement results and comparison with state-of-the-art solutions available in the market are shown. Furthermore, a critical aspect to be considered is the proper dimensioning of the discharging path. It requires careful considerations about the gate driver capabilities, the discharging resistor values, and the thermal management of the dumping element. For this purpose, an extensive study on how to size the relative components is also presented. Full article

The rapid development of electric transport necessitates efficient energy storage and redistribution in traction systems. A key challenge is the utilization of regenerative braking energy, which is often dissipated in resistors due to network saturation and limited consumption capacity. The paper addresses the problem of inefficient energy utilization in electric rail vehicles due to the absence of effective energy recovery mechanisms. A specific challenge arises when managing energy recuperated during regenerative braking, which is typically lost if not immediately reused. This study proposes the integration of on-board energy storage systems (ESS) based on supercapacitor technology to temporarily store excess braking energy. A mathematical model of a traction drive with a DC motor and supercapacitor-based ESS is developed, accounting for variable load profiles and typical urban driving cycles. Simulation results demonstrate potential energy savings of up to 30%, validating the feasibility of the proposed solution. The model also enables system-level analysis for optimal ESS sizing and placement in electric rail vehicles. Full article