Search Results (654)

Three-level non-inverting buck–boost converters are promising for electric vehicle charging stations due to their wide voltage regulation capability and bidirectional power flow. However, the number of three-level operating states is four times that of two-level operating states, and the lack of a unified switching state selection mechanism leads to serious challenges in its application. To address these issues, a finite control set model predictive control (FCS-MPC) strategy is proposed, which can determine the optimal set and select the best switching state from the excessive number of states. Not only does the proposed method achieve fast regulation over a wide voltage range, but it also maintains the input- and output-side capacitor voltage balance simultaneously. A further key advantage is that the number of switching actions in adjacent cycles is minimized. Finally, a hardware-in-the-loop experimental platform is built, and the proposed control method can realize smooth transitions between multiple operation modes without the need for detecting modes. In addition, the state polling range and the number of switching actions are superior to conventional predictive control, which provides an effective solution for high-performance multilevel converter control in energy systems. Full article

Solid Oxide Fuel Cells (SOFCs) offer high-efficiency electrochemical conversion of fuels like natural gas, yet detailed modeling is crucial for optimization. This paper presents a simulation study of a natural gas-fueled SOFC system, developed using Aspen Plus with Fortran integration. Distinct from prevalent paradigms assuming rated power output, this work adopts rated current density as the primary input, enabling a more direct investigation of the cell’s electrochemical behavior. We conducted a comprehensive sensitivity analysis of key parameters, including fuel utilization, water-carbon ratio, and current density, and further investigated the impact of different interconnection configurations on overall module performance. Results demonstrate that a single unit operating at a current density of 180 mA/cm², a fuel utilization of 0.75, and a water-carbon ratio of 1.5 can achieve a maximum net stack-level electrical efficiency of 54%. Furthermore, optimizing the interconnection of a 400 kW module by combining series and parallel units boosts the overall net system-level electrical efficiency to 59%, a 5-percentage-point increase over traditional parallel setups. This is achieved by utilizing a bottoming cycle for exhaust heat recovery. This research validates the rated current density approach for SOFC modeling, offering novel insights into performance optimization and modular design for integrated energy systems. Full article

Hybrid thermoelectric generators (HTEGs) play a pivotal role in sustainable energy conversion by harnessing waste heat through the Seebeck effect, contributing to global efforts in energy efficiency and environmental sustainability. In practical sustainable energy systems, HTEG output performance is significantly influenced by uncertainties in the operational parameters (such as temperature differences and load resistance), material properties (including Seebeck coefficient and resistance), and structural configurations (like the number of series/parallel thermoelectric components), which impact both efficiency and system stability. This study employs the Sobol-sequence-sampling method to characterize these parameter uncertainties, analyzing their effects on HTEG output power and conversion efficiency using mean values and standard deviations as evaluation metrics. The results show that higher temperature differences enhance output performance but reduce stability, a larger load resistance decreases performance while improving stability, thermoelectric materials with high Seebeck coefficients and low resistance boost efficiency at the expense of stability, increasing series-connected components elevates performance but reduces stability, parallel configurations enhance power output yet decrease efficiency and stability, and greater contact thermal resistances diminish performance while enhancing system robustness. This research provides theoretical guidance for optimizing HTEGs in sustainable energy applications, enabling the development of more reliable, efficient, and eco-friendly thermoelectric systems that balance performance with environmental resilience for long-term sustainable operation. Full article

A wearable closed-loop transcutaneous electrical nerve stimulation (TENS) platform has been developed to address the limitations of conventional open-loop neuromodulation systems. Unlike existing systems such as CLoSES—which targets intracranial stimulation—and electromyography-triggered functional electrical stimulation (EMG-FES) platforms primarily used for motor rehabilitation, the proposed device uniquely integrates low-latency surface electromyography (sEMG)-driven control with six-channel current stimulation in a fully wearable, non-invasive format aimed at ambulatory pain modulation. The system combines real-time sEMG acquisition, adaptive signal processing, a programmable multi-channel stimulation engine, and a high-voltage, boost-regulated power supply within a compact, battery-powered architecture. Bench-top evaluations demonstrate rapid response to EMG events and stable biphasic output (±22 mA) across all channels with high electrical isolation. A human-subject protocol using the Cold Pressor Test (CPT), heart rate variability (HRV), and galvanic skin response (GSR) has been designed to evaluate analgesic efficacy. While institutional review board (IRB) approval is pending, the system establishes a robust foundation for future personalized, mobile neuromodulation therapies. Full article

A significant application of the proportional–integral (PI) controller in the automotive sector is in electric motors, particularly brushless direct current (BLDC) motors utilized in electric vehicles (EVs). This paper presents a high-performance boost converter regulated by a fractional-order proportional–integral (FoPI) controller to ensure stable output voltage and power delivery to effectively charge the battery under fluctuating input conditions. The FoPI controller parameters, including gains and fractional order, are optimized using an Arithmetic Harris Hawks Optimization (AHHO) algorithm with an integral absolute error (IAE) as the objective function. The primary objective is to enhance the system’s robustness against input voltage fluctuation while charging from renewable sources. Conversely, regenerative braking is crucial for energy recovery during vehicle operation. This study implements a fractional-order PI controller (FOPI) for the smooth and exact regulation of speed and energy recuperation during regenerative braking. The proposed scheme underwent extensive simulations in the Simulink environment using the FOMCON toolbox version 2023b. The results were validated with the traditional Ziegler–Nichols method. The simulation findings demonstrate smooth and precise speed control and effective energy recovery during regenerative braking and a constant voltage output of 375 V, with fewer ripples and rapid transient responses during charging of batteries from variable input supply. Full article

Combined cycle power plants are modern power generation systems that provide an efficient and environmentally friendly way of generating electricity. With the development of smart grids, higher requirements have been put forward for their power prediction. Using a dataset comprising 9568 observations from a combined cycle power plant operating at full load for 6 years, a high-precision power prediction model integrating CatBoost and domain knowledge is proposed. Twenty new features were designed based on domain expertise, and Recursive Feature Elimination was applied to select the most informative features, optimizing model performance. Experimental results demonstrate that CatBoost outperformed six commonly used machine learning algorithms, both with and without domain knowledge integration. And the incorporation of domain knowledge improved the predictive performance of all evaluated models, underscoring the effectiveness and general applicability of the proposed features. Moreover, Recursive Feature Elimination was applied to select 11 features. The optimized CatBoost model achieved the best predictive accuracy with a root mean square error of 2.8545, a mean absolute error of 1.9645, and an R-squared of 0.9702. A comparative analysis with existing literature methods further validated the superior performance of the proposed approach. These findings highlight the effectiveness of integrating domain knowledge with machine learning and its potential for improving power output prediction in combined cycle power plants. Full article

This article presents a new full-bridge converter with two series-connected transformers (TTFB), designed to meet the hold-up time requirements in power systems. The conventional TTFB topology offers low root mean square (RMS) output current, clamped voltage stress across the primary switches, and zero-voltage switching (ZVS) capability. However, under a wide input voltage range, it suffers from a significant circulating current during the freewheeling period, leading to efficiency degradation. To mitigate this issue, a new converter is proposed by integrating the TTFB with a boost circuit, which operates during the hold-up state when the input voltage drops below the nominal level. Thus, the proposed converter can increase the duty ratio under nominal input voltage conditions, thereby reducing the primary-side RMS current and improving efficiency. To validate the effectiveness of the proposed method, a prototype with a 12 V/400 W output was implemented. The proposed converter achieved a peak efficiency of 92.1% at 50% load, and maintained a higher efficiency across the entire load range compared to the conventional design. Thus, the proposed converter offers a solution for applications demanding extended hold-up time with improved efficiency. Full article

Atmospheric aerosols significantly impact solar photovoltaic (PV) energy generation through their effects on surface solar radiation. This study quantifies the impact of PM_2.5 pollution on PV power output using observational data from 10 stations across Hebei Province, China (2018–2019). Our analysis reveals that elevated PM_2.5 concentrations substantially attenuate solar irradiance, resulting in PV power losses reaching up to a 48.2% reduction in PV power output during severe pollution episodes. To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. The inclusion of PM_2.5 as a predictor variable systematically enhanced model performance across all algorithms. To further optimize prediction accuracy, we implemented a stacking ensemble framework that integrates multiple base learners through meta-learning. The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. These findings underscore the importance of aerosol–radiation interactions in PV forecasting and provide crucial insights for grid management in pollution-affected regions. Full article

With the development of wave energy, a promising renewable resource, oscillating water column (OWC) devices, has been extensively studied for its potential in harnessing this energy. However, traditional OWC devices face challenges such as corrosion and damage from prolonged exposure to harsh marine environments, limiting their long-term viability and efficiency. To address these limitations, this paper proposes a novel U-tube type dual chamber OWC wave energy conversion device integrated within a marine vehicle. The research involves the design of a U-tube dual-chamber OWC device, which utilizes the pitch motion of a marine vehicle to drive the oscillation of water columns within the U-tube, generating reciprocating airflow that drives an air turbine. Numerical simulations using computational fluid dynamics (CFD) were conducted to analyze the effects of various structural dimensions, including device length, width, air chamber height, U-tube channel width, and bottom channel height, on the aerodynamic power output. The simulations considered real sea conditions, focusing on low-frequency waves prevalent in China’s sea areas. Simulation results reveal that increasing the device’s length and width substantially boosts aerodynamic power, while air chamber height and U-tube channel width have minor effects. These findings provide valuable insights into the optimal design of U-tube dual-chamber OWC devices for efficient wave energy conversion, laying the foundation for future physical prototype development and experimental validation. Full article

Dual-active bridge (DAB) converters have emerged as a preferred topology in electric vehicle charging and energy storage applications, owing to their structurally symmetric configuration and intrinsic galvanic isolation capabilities. However, conventional triple-phase shift (TPS) control strategies face significant challenges in maintaining high efficiency across ultra-wide output voltage and load ranges. To exploit the inherent structural symmetry of the DAB topology, a symmetric optimization strategy based on triple-phase shift (SOS-TPS) is proposed. The method specifically targets the forward buck operating mode, where an optimization framework is established to minimize the root mean square (RMS) current of the inductor, thereby addressing both switching and conduction losses. The formulation explicitly incorporates zero-voltage switching (ZVS) constraints and operating mode conditions. By employing the Karush–Kuhn–Tucker (KKT) conditions in conjunction with the Lagrange multiplier method (LMM), the refined control trajectories corresponding to various power levels are analytically derived, enabling efficient modulation across the entire operating range. In the medium-power region, full-switch ZVS is inherently satisfied. In the low-power operation, full-switch ZVS is achieved by introducing a modulation factor λ, and a selection principle for λ is established. For high-power operation, the strategy transitions to a conventional single-phase shift (SPS) modulation. Furthermore, by exploiting the inherent symmetry of the DAB topology, the proposed method reveals the symmetric property of modulation control. The modulation strategy for the forward boost mode can be efficiently derived through a duty cycle and voltage gain mapping, eliminating the need for re-derivation. To validate the effectiveness of the proposed SOS-TPS strategy, a 2.3 kW experimental prototype was developed. The measured results demonstrate that the method ensures ZVS for all switches under the full load range, supports ultra-wide voltage conversion capability, substantially suppresses RMS current, and achieves a maximum efficiency of 97.3%. Full article

This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features. Full article

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article

The escalating consumption of fossil fuels and the rapid development of portable electronics have increased interest in alternative energy solutions that can sustainably self-power wearable devices. Triboelectric nanogenerators (TENGs), which convert mechanical energy into electricity through contact electrification and electrostatic induction, have emerged as a promising technology due to their high voltage output, lightweight design, and simple fabrication. However, the performance of TENGs is often limited by a low surface charge density, inadequate dielectric properties, and poor charge retention of triboelectric materials. To address these challenges, recent research has focused on the use of polymer composites that incorporate various functional fillers. The filler materials play roles in improving dielectric performance and enhancing mechanical durability, thereby boosting triboelectric output even in harsh environments, while also diminishing charge loss. This review comprehensively examines the role of polymer composite design in TENG performance, with particular emphasis on materials categorized by their triboelectric polarity. Tribo-negative polymers, such as PDMS and PVDF, benefit from filler incorporation and phase engineering to enhance surface charge density and charge retention. By contrast, tribo-positive materials like nylon and cellulose have demonstrated notable improvements in mechanical robustness and environmental stability through composite strategies. The interplay between material selection, surface engineering, and filler design is highlighted as a critical path toward developing high-performance, self-powered TENG systems. Finally, this review discusses the current challenges and future opportunities for advancing TENG technology toward practical and scalable applications. Full article

High-power lithium-ion batteries impose stringent requirements on output power. Tetramethylene sulfone (TMS), serving as a novel electrolyte additive, effectively enhances the stability of electrolytes under high-voltage conditions due to its high flash point and high dielectric constant, thereby boosting the output performance of lithium-ion batteries. In this work, we selected lithium hexafluorophosphate (LiPF₆) as the lithium salt, using a solvent carrier consisting of a mixture of ethylene carbonate (EC), dimethyl carbonate (DMC), and ethyl methyl carbonate (EMC). TMS was added as an additive to create a novel high-power electrolyte system. We prepared five electrolytes with different TMS concentrations and conducted in-depth investigations into their impacts on the performance of lithium-ion batteries. The findings indicate that the electrolytes with TMS ratios of 2 wt% and 5 wt% demonstrated good synergistic cathode–anode stability in the NCM//soft carbon system, and the electrolyte with a 5 wt% TMS ratio demonstrated the most significant improvement in the overall performance of the full battery. Full article

This research article examines the CO₂ laser cutting performance of Fused Filament Fabricated Acrylonitrile Styrene Acrylate (ASA) thermoplastics by analyzing the influence of plate thickness, laser power, and cutting speed on four quality characteristics: surface roughness (Ra), top kerf width (Top KW), bottom kerf width (Bottom KW), and bottom heat-affected zone (Bottom HAZ). Forty-five experiments were conducted using five thickness levels, three power levels, and three cutting speeds. To model and predict these outputs, seven machine learning approaches were employed: Autoencoder, Autoencoder–Gated Recurrent Unit, Autoencoder–Long Short-Term Memory, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression, and Linear Regression. Among them, XGBoost yielded the highest accuracy across all performance metrics. Analysis of Variance results revealed that Ra is mainly affected by plate thickness, Bottom KW by cutting speed, and Bottom HAZ by power, while Top KW is influenced by all three parameters. The study proposes an effective prediction framework using multi-output modeling and hybrid deep learning, offering a data-driven foundation for process optimization. The findings are expected to support intelligent manufacturing systems for real-time quality prediction and adaptive laser post-processing of engineering-grade thermoplastics such as ASA. This integrative approach also enables a deeper understanding of nonlinear dependencies in laser–material interactions. Full article