Search Results (1,011)

This study investigates the impact of non-uniform carbon sphere diameter distributions on the structural and electrochemical performance of catalyst layers (CLs) in proton exchange membrane fuel cells (PEMFCs), utilizing the lattice Boltzmann method (LBM) for detailed simulations. The impact of carbon sphere diameter range and gradient distribution on oxygen transport, electrochemical reactivity, and catalyst layer morphology was investigated. The results show that gradient designs of carbon sphere diameters effectively modulate pore size distribution, electrochemically active surface area, and oxygen diffusion pathways within the CL. Specifically, placing larger carbon spheres near the gas diffusion layer improves pore connectivity and oxygen transport, while smaller spheres near the membrane enhance the availability of reaction sites. The three-layered gradient design, particularly the L-M-S configuration, demonstrated superior oxygen distribution, reduced concentration gradients, and increased current density by 15.4%. These findings underline the importance of optimizing carbon sphere diameter distributions for enhancing CL performance. This study offers a novel framework for designing catalyst layers with improved mass transport and electrochemical efficiency, providing significant insights for the future development of high-performance PEMFCs. Full article

The parameter extraction of proton exchange membrane fuel cells (PEMFCs) has been an active area of study over the past few years, relying on metaheuristic optimizers and experimental datasets to achieve accurate current/voltage (I/V) curves. This work develops a mirage search optimizer (MSO) to precisely estimate the PEMFC model parameters. The MSO employs two search techniques based on the physical phenomena of light bending caused by atmospheric refractive index gradients: a superior mirage for global exploration and an inferior mirage for local exploitation. The MSO employs optical physics to direct search behavior, in contrast to conventional optimization approaches, allowing for a dynamic balance between exploration and exploitation. Convergence efficiency is increased by its iteration-dependent control and fitness-based influence. Using two common PEMFC modules, a comparison study with previously published methodologies and new, recently developed optimizers—the Educational Competition Optimizer (ECO), basketball team optimization (BTO), the fungal growth optimizer (FGO), and the naked mole rat optimizer (NMRO)—was conducted to evaluate the proposed MSO for parameter identification. Furthermore, the two models were tested under various temperatures and pressures. For the three examples studied, the MSO achieved the best sum of squared errors (SSE) values with an intriguing overall standard deviation (STD). It is undeniable that the STD and cropped SSE values, among other difficult techniques, are quite competitive and display the fastest convergence. According to the MSO, the BCS 500W, Ballard Mark V, and Modular SR-12 each have MSO values of 0.011697781, 0.852056, and 1.42098181379214 × 10⁻⁴, respectively. Additionally, the comparison results demonstrate that the proposed MSO can be successfully used to quickly and accurately define the PEMFC model. Full article

As a critical component of high-temperature proton exchange membrane fuel cells (HT-PEMFCs), the catalytic layer (CL) significantly influences the overall performance of these systems. In this study, a pore-scale lattice Boltzmann (LB) model was established to simulate the multi-component mass transport in the HT-PEMFC catalyst layer. Based on the reconstruction of CL, the transport behavior of phosphoric acid was simulated. The effects of different carbon carrier diameters, porosity values, and Pt/C mass ratios on the transport of phosphoric acid in CL were studied. The distribution of phosphoric acid and air concentration, as well as the electrochemical surface area, was qualitatively and quantitatively analyzed. Finally, the optimal design parameters of CL structure were determined. The results show that, with increases in carbon carrier diameter, porosity, and Pt/C mass ratio, the distribution of phosphoric acid concentration shows an upward trend, and the distribution of air concentration shows a downward trend. The optimal ranges of carbon carrier diameter, porosity, and Pt/C mass ratio are 50–80 nm, 60–70%, and 40–50%, respectively. This study provides a new idea for further understanding the mass transport mechanism in the HT-PEMFC catalyst layer and provides effective suggestions for the optimization design of the HT-PEMFC catalyst layer structure. Full article

This paper investigates three distinct hydrogen-related subsystems: production, storage, and the use. An analysis of the micro-combined heat and power production (mCHP) behavior using natural gas is conducted to understand how the system operates under different conditions and to evaluate its yearly performance. To reduce CO₂ emissions, hydrogen fuel consumption is proposed, and an emission analysis under different fuel-supply configurations is performed. The results show that hydrogen produced by artificial photosynthesis has the lowest CO₂ impact. Therefore, the paper examines this process and its main characteristics. An engineering model is proposed to rapidly estimate the mean volumetric hydrogen production rate. To ensure safe coupling between hydrogen production and mCHP demand, the study then focuses on solid-state hydrogen storage. Subsequently, in this framework, the state of charge (SOC) is defined as the central control variable linking storage thermodynamics to hydrogen delivery. Accurate SOC estimation ensures that the storage unit can supply the required hydrogen flow without causing starvation, pressure drops, or thermal drift during CHP operation. The proposed SOC estimation method is based on an analytical approach and experimentally validated while relying solely on external measurements. The overall objective is to enable a coherent, low-carbon, and safely controllable hydrogen-based mCHP system. Full article

Effective thermal management is critical for sustaining the performance, durability, and stability of a proton exchange membrane fuel cell (PEMFC). A thorough numerical investigation of six multi-fin zigzag cooling-channel geometries operating under three slope angles (75°, 90°, and 120°) is presented to monitor the combined impact of geometric complexity and channel inclination on cooling performance. In addition, temperature fields, velocity distributions, localized heat flow, total heat removal, and cooling efficiency were reviewed to characterize thermal–fluid behavior of the individual configuration. The results showed that geometric refinement had the strongest influence on cooling performance, with Type 5 (a = 2, b = 4, h = 2) and Type 6 (a = 4, b = 4, h = 2) progressively achieving declining temperature distributions, greater outlet velocities, and modified coolant mixing. Slope angles also affected flow behavior, where reduced inclination extended coolant residence time and elevated inclination intensified secondary flows, although the influence was secondary to geometry. Total heat flow, area-specific heat extraction, and cooling efficiency were highest in Type 5 (a = 2, b = 4, h = 2) and Type 6 (a = 4, b = 4, h = 2), with Type 5 exhibiting an optimal balance between flow disturbance and hydraulic resistance. This study generally presented practical design guidance for next-generation PEMFC cooling systems, proving that optimized multi-fin zigzag channels significantly advanced thermal uniformity and heat-transfer effectiveness under diverse operating conditions. Full article

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles. Full article

Proton exchange membrane fuel cells (PEMFCs) represent a new generation of clean and efficient energy conversion devices, demonstrating broad application prospects in transportation, distributed power generation, and other fields. The geometric configuration of the cathode gas channel (GC) and the surface microstructure of the gas diffusion layer (GDL) are core factors influencing the efficiency of reactant gas transport and water management performance. However, conventional rectangular flow channels suffer from insufficient convective enhancement and restricted oxygen supply beneath the fins. Furthermore, homogeneous GDLs exhibit limited diffusion and drainage capabilities, often leading to oxygen depletion and flooding downstream of the cathode, significantly limiting overall cell performance. To address these challenges, this study designs a novel centrally positioned fin-type barrier block. A three-dimensional multiphysics numerical model integrating GDL surface microporosity with the internal barrier block flow channels is constructed to systematically investigate the synergistic mechanisms of microporous topology and flow channel structure on two-phase flow distribution, oxygen mass transfer, and electrochemical performance. The results demonstrate that this model accurately captures the dynamic evolution of flow fields within the GDL. Compared to conventional structures, significant coupling effects exist between the GDL microporous structure and the novel barrier block. Their synergistic interaction forms multi-scale mass transfer enhancement and dewatering pathways, providing quantifiable optimization pathways and structural parameter references for high-power-density PEMFC cathode design. Full article

Hydrogen energy is vital for a clean, low-carbon society, and proton exchange membrane fuel cells (PEMFCs) represent a core technology for the conversion of hydrogen chemical energy into electrical energy. When PEMFC single cells are stacked under assembly force for high power output, their porous electrodes (gas diffusion layers, GDLs; catalyst layers, CLs) undergo compressive deformation, altering internal transport processes and affecting cell performance. However, existing microscale studies on PEMFC porous electrodes insufficiently consider compression (especially in CLs) and have limitations in obtaining compressed microstructures. This study proposes a combined framework from a microstructure perspective. It integrates the finite element method (FEM) with computational fluid dynamics (CFD). It reconstructs microstructures of GDL, CL, and GDL-bipolar plate (BP) interface. FEM simulates elastic compressive deformation, and CFD calculates transport properties (solid zone: heat/charge conduction via Laplace equation; fluid zone: gas diffusion/liquid permeation via Fick’s/Darcy’s law). Validation shows simulated stress–strain curves and transport coefficients match experimental data. Under 2.5 MPa, GDL’s gas diffusivity drops 16.5%, permeability 58.8%, while conductivity rises 2.9-fold; CL compaction increases gas resistance but facilitates electron/proton conduction. This framework effectively investigates compression-induced transport property changes in PEMFC porous electrodes. Full article

The adoption of proton exchange membrane fuel cells (PEMFCs) in unmanned aerial vehicles (UAVs) offers a sustainable pathway to zero-emission propulsion, supporting aviation decarbonization by replacing battery or fossil fuel systems with efficient hydrogen technology. This work presents the development, validation, and application of a comprehensive dynamic model of a 1 kW open-cathode PEMFC system, including complete balance of plant (BOP) and control logic for four cooling fans, a purge valve, and a short-circuit unit (SCU). The model was validated through extensive experiments with step, triangular, and real-world UAV current profiles. Under steady-state conditions, it reproduces stack voltage with a <1 V average error and a temperature of 2.5 °C. Dynamic modeling accurately predicts fan behavior, purge/SCU events, and transient voltage drops. Applied to a 25 min UAV flight, the model quantifies reactant-management impacts: purge events increase H₂ usage by 4.8%, with SCU raising total to 5.6% above stoichiometric consumption. Altitude analysis shows ambient temperature reduction dominates the oxygen partial pressure effects, yielding net cell voltage increase under current-based fan control. These insights underscore explicit BOP and ambient modeling for accurate UAV endurance estimation and strategy optimization, providing a basis for future altitude-chamber validation. By enabling precise BOP dynamics simulation and H₂ optimization, this model advances the achievement of affordable clean energy, facilitating an extended endurance with minimal environmental impact. Full article

Accurate and rapid fault diagnosis is paramount to stabilizing proton exchange membrane fuel cells (PEMFC). To achieve this, this study proposes a novel fault diagnosis method that integrates a convolutional neural network (CNN), a bi-directional long short-term memory network (BiLSTM), and a waveform fault attention (WFA) mechanism. In the proposed framework, data are classified into five distinct categories utilizing a hierarchical clustering algorithm. Additionally, data augmentation techniques are implemented to bolster model performance. The introduction of amplitude attention and temporal difference attention, in conjunction with the construction of WFA, enables the accurate extraction of temporal-spatial features, significantly improving the distinguishability of fault diagnosis. Furthermore, feature contribution is evaluated using permutation feature importance (PFI) to identify key features, enhancing the interpretability of the model. Experimental findings verify that the proposed method enables high-precision fault identification, with precision values spanning 97–100% and an average stability of 98.3%, demonstrating robust performance even when the volume of original sample data is limited. This performance markedly surpasses that of extant methodologies. The comprehensive approach augments the accuracy, reliability, and interpretability of PEMFC fault diagnosis, and introduces a novel research paradigm for feature extraction, thereby possessing significant theoretical and practical application value. Full article

Integrating electrochemical fuel cells and internal combustion engines can enhance the total efficiency and sustainability of power systems. This study presents a promising solution by integrating a Proton Exchange Membrane Fuel Cell (PEMFC) with a mini gas turbine, forming a hybrid system called the “Oya System.” This approach aims to mitigate the efficiency losses of gas turbines during high ambient temperatures. The hybrid model was designed using Aspen Plus for modelling and the EES simulation program for solving mathematical equations. The primary objective of this research is to enhance the efficiency of gas turbine systems, particularly under elevated ambient temperatures. The results demonstrate a notable increase in efficiency, rising from 37.97% to 43.06% at 10 °C (winter) and from 31.98% to 40.33% at 40 °C (summer). This improvement, ranging from 5.09% in winter to 8.35% in summer, represents a significant achievement aligned with the goals of the Oya System. Furthermore, integrating PEMFC contributes to environmental sustainability by utilising hydrogen, a clean energy source, and reducing greenhouse gas emissions. The system also enhances efficiency through waste heat recovery, further optimising performance and reducing energy losses. This research highlights the critical role of interface engineering in the hybrid system, particularly the interaction between the PEMFC and the gas turbine. Integrating these two systems involves complex interfaces that facilitate the transfer of electrochemistry, energy, and materials, optimising the overall performance. This aligns with the conference session’s focus on green technologies and resource efficiency. The Oya System exemplifies how innovative hybrid systems can enhance performance while promoting environmentally friendly processes. Full article

With the energy consumption of data centers continuously increasing in recent years, green data centers as a transformative solution have grown increasingly significant. In this paper, a proton exchange membrane fuel cell-based combined cooling, heating, and power (PEMFC-CCHP) system coupled with wind and solar energy is proposed to ensure an energy supply that matches the dynamic load requirements of data centers. Taking a data center located in Guiyang, China, as a case study, a TRNSYS 18 simulation model for the integrated energy system is developed, and the analysis on the energy, economic, and environmental performance of the system is performed. The results demonstrate that the integrated energy system can effectively accommodate the load fluctuations of data centers through multi-energy complementarity. The PEMFC-CCHP system achieves a high energy utilization efficiency of 0.85–0.90. Furthermore, the payback period of the integrated energy system is estimated to be between 8.2 and 13.1 years, yielding an annual reduction in CO₂ emissions of 1847 t. Full article

Accurate modeling of proton exchange membrane fuel cells (PEMFCs) is essential for predicting system performance under diverse operating conditions. This study introduces a refined semi-empirical modeling that combines experimental validation with an enhanced parameter estimation method based on the Cloud Drift Optimization (CDO) algorithm. The approach focuses on identifying seven key parameters of the nonlinear PEMFC model by minimizing the difference between experimentally measured and simulated cell voltages. To assess its effectiveness, the proposed CDO-based estimator was compared with several established metaheuristic algorithms, including the particle swarm optimizer and the tetragonula carbonaria optimization algorithm. The evaluation was performed using three commercial PEMFC stacks rated at 250 W, 500 W, and the NedStack PS6, as well as experimental data obtained from the Renewable Energy Laboratory at A’Sharqiyah University. Results demonstrate that the CDO algorithm consistently produced the lowest sum of squared errors (SSE) of 1.0337 and exhibited stable convergence across multiple independent runs with a standard deviation of 1.2114 × 10⁻⁷. Its reliable performance under both normal and degraded conditions confirms the algorithm’s robustness and adaptability, establishing CDO as an efficient and dependable technique for PEMFC modeling and parameter identification. Full article

This work explores water hydrolysis using magnesium as a decentralized dihydrogen source for off-grid households. A dedicated reactor design enabled on-demand dihydrogen generation, coupled with a Proton Exchange Membrane Fuel Cell (PEMFC) for electricity and heat production. Different energy management strategies were compared, highlighting the limitations of single-purpose approaches and the benefits of converting surplus electricity to heat. The integration of photovoltaic generation further reduced magnesium demand by 30%, thus reducing storage requirements to close to 1565 kg of magnesium powder per year, i.e., a volume of 0.9 m³ to cover the heat and electricity needs of a four-person household. Results demonstrate that combining water hydrolysis with magnesium and renewables provides a feasible and sustainable solution for autonomous energy supply in isolated sites. Full article

Hydrogen energy is a pivotal alternative to lithium-ion batteries for low-altitude aircraft, offering a pathway to sustainable aviation with its zero emissions and high energy density. Nevertheless, its broader application is hindered by challenges in storage, safety, and performance under extreme conditions such as low pressure and low temperature at high altitudes. This paper systematically evaluates various hydrogen power technologies—including water-cooled and air-cooled proton exchange membrane fuel cells (PEMFCs) as well as hydrogen turbines—highlighting their respective advantages, limitations, and suitability for different aircraft types. Among these, water-cooled PEMFCs are identified as the most viable option for manned low-altitude aircraft due to their balanced performance in power density and startup capability. In contrast, air-cooled PEMFCs demonstrate distinct cost-effectiveness for lightweight drones, while hydrogen turbines show promise for long-range regional transport. Furthermore, we analyze current progress in integrating PEMFCs into aircraft platforms and discuss persistent challenges in system compatibility and environmental adaptation. Finally, potential future development directions for PEMFC applications in low-altitude aviation are outlined. Full article