ABSTRACT Sealing reliability is critical to the efficiency, durability, and safety of proton exchange membrane fuel cell (PEMFC) stacks for hydrogen‐energy applications. In multilayer stacks, manufacturing errors, interlayer misalignment, gasket and gas‐diffusion‐layer thickness variations, and non‐uniform clamping loads are transmitted through compliant components and modify local sealing‐interface states. This review examines the chain from assembly‐deviation sources and propagation mechanisms to contact‐pressure redistribution, micro‐gap connectivity, interfacial leakage, bulk permeation, and process regulation. Compared with previous reviews that mainly focus on PEMFC sealing materials, sealing structures, stack assembly techniques, or clamping‐load design, this review highlights the variable‐transfer pathway by which assembly deviations are converted into contact‐state degradation and leakage‐rate inputs. Recent finite‐element contact analyses, rough‐interface leakage models, and multiscale prediction methods are summarized, with emphasis on variables linking assembly quality to leakage risk, including minimum local contact pressure, real contact area, equivalent leakage‐channel height, and leakage‐path connectivity. This review establishes a reliability‐oriented framework linking assembly deviation, contact pressure degradation, micro‐gap formation, leakage‐path evolution, and leakage‐rate prediction in PEMFC stacks. It critically compares analytical, numerical, experimental, and data‐driven approaches for leakage analysis and summarizes regulation strategies for improving stack assembly quality and sealing reliability. Future directions involving intelligent assembly, online monitoring, and digital‐twin‐assisted leakage control are also discussed. Future research should integrate manufacturing‐deviation databases, assembly‐process monitoring, leakage testing, online diagnostics, and service‐degradation data to support predictive sealing‐reliability design and closed‐loop assembly regulation for PEMFC stacks.
ABSTRACT The microstructure of the gas diffusion layer (GDL) in proton exchange membrane fuel cell (PEMFC) significantly influences the freezing behavior of supercooled water. Microscopic contact angles of water droplets within various GDLs were measured using an environmental scanning electron microscope (ESEM). These measurements reveal a notable discrepancy between microscopic and macroscopic contact angles: the macroscopic contact angle increases more substantially following hydrophobic treatment than its microscopic counterpart. The incorporation of polytetrafluoroethylene (PTFE) slightly increases the microscopic contact angle. Freezing experiments were conducted on isolated GDL samples to examine the supercooling degrees and freezing times for both commercial and laboratory‐treated GDLs. Subsequently, the freezing process of supercooled water was modeled based on reconstructed three‐dimensional (3D) GDL structures and heterogeneous nucleation theory to predict freezing times and supercooling degrees. The results indicate that the microscopic contact angle is one of the primary factors governing the ice nucleation of supercooled water inside the GDL. Notably, even after hydrophobic treatment, residual weakly hydrophobic regions within the pores remain dominant, thereby critically influencing freezing behavior.
ABSTRACT This study explores a 10‐kW hybrid energy system combining a medium‐temperature solid oxide fuel cell (SOFC) with a turbocharger, using ammonia as a hydrogen carrier. The principal innovation lies in the comparative analysis of unified integrated system with two configurations through diverse operating conditions, power range, application, and simulation tools against previous literatures, aimed at optimizing overall performance while maintaining exhaust temperatures below . Effective thermal management via heat exchangers and regulated air supply for optimizing SOFC cooling and power output, while precise combustion control supports nitrogen oxide () emission reduction. TRNSYS simulations validate the system's effectiveness, showing notable gains in fuel utilization, electrical efficiency, and reliability. Furthermore, the study applied optimization under two scenarios to compare equipment configurations. One scenario prioritized cost‐effective efficiency with fixed power outputs, while the other assessed performance based on tailored components. The initial configuration (1) consistently proves superior in power efficiency and SOFC utilization. These insights aid in developing efficient, sustainable SOFC‐GT hybrid energy systems.
ABSTRACT Hydrogen fuel cell technologies are promising power sources for marine propulsion owing to their high efficiency and low emissions. However, their practical application on small‐scale vessels presents severe challenges due to fragmented onboard space and rapid load fluctuations under complex marine conditions. Herein, a modular layout and a triple‐hybrid power architecture are proposed to enable the efficient integration and stable dynamic operation of the fuel cell system, and are validated through sea trials on an 8‐m small yacht. A distributed modular framework was first developed, dispersing a 40 kW proton exchange membrane fuel cell (PEMFC) system and a 10 kWh primary lithium‐ion battery within the yacht's bulkheads. Then, a high‐rate lithium‐ion battery was introduced as a dedicated power buffer, forming a triple‐hybrid architecture that physically decouples transient power regulation from long‐term energy management. Experimental results demonstrate that the triple‐hybrid architecture instantaneously absorbs surplus transient power of up to 11.5 kW, greatly suppresses DC‐link voltage spikes, eliminates voltage‐protection shutdowns, and maintains stable system operation under highly dynamic marine conditions. This work provides a scaled technical dynamic prototype and empirical data for integrating zero‐emission hydrogen fuel cells into space‐constrained small vessels.
ABSTRACT Gas diffusion layers (GDLs), a critical component in / proton exchange membrane fuel cells (PEMFCs), are commonly treated with polytetrafluoroethylene (PTFE) for controlling hydrophobicity. However, the influence of the GDL's native microstructure on the final PTFE spatial distribution is still poorly understood. We compare the PTFE distribution in a dry‐laid GDL containing a native binder with that in a wet‐laid, binder‐free GDL. To analyze the GDL microstructure, we perform scanning electron microscopy (SEM), energy‐dispersive X‐ray spectroscopy (EDX), and confocal Raman spectroscopy, with resolution at identical locations. We find that the binder acts as a scaffold for PTFE agglomerates to deposit, independent of the fiber locations. We show that PTFE deposited on the binder phase results in a significantly lower effect on the dry oxygen transport resistance, measured using a limiting current method at low oxygen concentration.
ABSTRACT Water management remains a critical challenge in proton exchange membrane fuel cells (PEMFCs) owing to the need to balance flooding and membrane dry‐out. However, the lack of direct measurements of liquid water saturation in the catalyst layer hinders effective water‐state monitoring and control. To address this issue, a nonlinear dynamic observer is proposed for the online estimation of liquid water saturation based on a lumped‐parameter state‐space model with a triangular structure. A radial basis function neural network (RBFNN) is incorporated to approximate nonlinear terms associated with unmeasurable current derivatives and compensate for modeling uncertainties. Observer gain matrices and adaptive RBFNN weight update laws are systematically derived to guarantee estimation performance and stability. Furthermore, a computationally tractable observer design framework is established through the integration of linear matrix inequalities (LMIs) and algebraic constraint parameterization. Comparative simulations show that the proposed observer improves the estimation accuracy of stack temperature and liquid water saturation by 67.7 and 46, respectively, compared with a conventional high‐gain observer. These results demonstrate the effectiveness and potential of the proposed approach for real‐time PEMFC water‐state monitoring.
ABSTRACT Microbial electrosynthesis (MES) has emerged as a promising bioelectrochemical technology for sustainable carbon dioxide (CO 2 ) utilization, enabling the direct conversion of CO 2 into value‐added fuels and chemicals using electroactive microorganisms under mild operating conditions. MES provides a unique pathway for carbon recycling by coupling microbial metabolism with renewable electricity while preventing high complexity, temperature, and pressures associated with conventional thermochemical and electrochemical CO 2 conversion technologies. Despite its good potential, the practical implementation of MES remains restricted by slow microbial activity, electrode instability, low electron transfer efficiency, and challenges in long‐term system performance. Among the various electrode materials explored for addressing these limitations, MXene‐based electrodes have attracted increasing attention due to their high electrical conductivity, large surface area, and tunable surface chemistry. These properties position MXenes as strong candidates for enhancing electron transfer, promoting microbial attachment and improving overall MES performance. Nevertheless, the application of MXene‐based electrodes in MES is still emerging, primarily constrained by their susceptibility to degradation in aqueous media that may affect electrode durability under prolonged operation. This review provides a comprehensive overview of MES systems, followed by critical examination of MXene‐based electrodes for MES applications, with particular emphasis on studies reported in recent years. Key aspects discussed include the fundamentals of MES, recent progress in electrode materials, MXene synthesis, modifications, composite electrode designs, and their catalytic roles in enhancing CO 2 reduction performance. In addition, development in reactor configurations, performance metrics such as current density and product yield relevant to MXene‐enhanced MES systems are evaluated. Furthermore, the remaining challenges related to long‐term stability, scalability, microbial compatibility is identified, and potential strategies to overcome these limitations are proposed. Overall, this study provides comprehensive insights into the role of MXene‐based electrodes in advancing MES technologies and outlines future research directions focused at facilitating their integration into efficient and sustainable CO 2 conversion systems.
ABSTRACT Proton exchange membrane fuel cells (PEMFCs) are important in the move towards clean energy and decarbonization of automotive power systems around the world. Adoption faces challenges such as changing component performance and degradation in real‐world conditions. Data science models and artificial intelligence (AI) methods, including machine learning, neural networks, and hybrid physics‐informed approaches, are applied to improve the reliability and efficiency of PEMFCs. Neural networks help with fault detection, spatial mapping of current distribution, and prediction of performance decline. Learning reinforcement is used to optimize cell humidity and identify thermal hotspots in single cell and fuel‐cell stacks, which show the need for standardized and benchmarked AI‐based diagnostic protocols. This review examines and compares current data science models for diagnosis in different operating scenarios, evaluates their success in forecasting chemical, electrochemical, and thermal failures, and explores the challenges and performance of these predictive tools in automotive fuel‐cell applications. The findings support the development of regulatory standards and help speed up the industrial adoption of AI‐driven and data science–based diagnostic solutions for fuel‐cell technologies.
Nonuniform distributions of reactants, liquid water, and temperature constrain the performance and durability of proton exchange membrane fuel cells (PEMFCs), especially at high current densities. This study numerically evaluates a novel flow field design where metal foam is symmetrically embedded in both the anode and cathode of straight channels, focusing on the effect of porosity grading on multiphysics transport and overall cell performance. A three-dimensional, nonisothermal, two-phase PEMFC model is developed and validated against published polarization data. Nine porosity configurations are examined, comprising four uniform foams and five with a linear porosity gradient from the channel side (with higher porosity) to the gas diffusion layer (GDL) side (with higher density). Compared to a conventional parallel channel, the optimized graded foam channel increases the peak power density from 0.494 to 0.588 (a 19.0% improvement), raises the total current by 22.3%, and reduces the average membrane temperature by approximately 10% while improving temperature uniformity. Field analyses reveal that the graded structure enhances under-rib oxygen supply, directs liquid water toward high-porosity regions for removal, and strengthens lateral heat spreading. These mechanisms collectively expand the effectively utilized reaction zone and mitigate flooding and local hot spots. The findings provide preliminary numerical guidance for the further development of gradient-porosity metal foam flow fields in PEMFCs.
Membrane dehydration is one of the major factors that reduces the performance of proton exchange membrane fuel cells under dry, low-humidity conditions. This research work mainly focuses on incorporating novel molybdenum disulfide (MoS2) via the hydrothermal synthesis method onto the Pt/C anode catalyst layer to promote moisture retention. Comprehensive structural (X-ray diffraction and scanning electron microscopy-energy-dispersive X-ray spectroscopy) and electrochemical assessment (cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge-discharge) studies were performed on a coated electrode. A single cell delivers a power density of 0.59 W/cm2 during dry conditions, which is equivalent to the performance of a cell at humidified conditions when compared to commercial Pt/C (humid: 0.45 W/cm2). The findings from the result shows MoS2 coated electrode exhibits enhanced moisture retention due to the synergistic effects of the additive.
In this study, a novel ether-free natural comb-like polyvinyl butyral-based anion exchange membrane (AEM) was synthesized via the Mitsunobu reaction, aiming to achieve a balanced combination of alkaline stability, ionic conductivity, and mechanical strength. To improve the structural integrity of the material and promote microphase separation, benzimidazole (BIM) and benzotriazole (BTA), both featuring rigid conjugated frameworks, were incorporated into the polymer matrix. Experimental results demonstrated that the Q-PVB-BIM0.6 membrane achieved a hydroxide ion conductivity of 71.28 mS cm-1 at 80 degrees C. Furthermore, after immersion in 2 M NaOH solution for 1200 h, the membrane retained 78.8% of its initial conductivity, confirming its excellent alkaline stability. This remarkable performance can be attributed to the synergistic effects of charge delocalization and steric hindrance provided by the BIM cation. In contrast, the Q-PVB-BTA0.6 membrane retained 70.6% of its original ionic conductivity under the same conditions, which may result from the higher electron density of the triazole ring, rendering it more susceptible to nucleophilic attack by hydroxide ions. However, the Q-PVB-BTA0.6 membrane exhibited superior mechanical strength (21.75 MPa), likely due to the presence of pi-pi stacking interactions. Through a systematic comparative analysis, this study offers a comprehensive understanding of how different heterocyclic structures influence the electrochemical and mechanical properties of AEMs.
This study examines the impact of gas diffusion layer (GDL) perforation characteristics-perforation size, center-to-center spacing, and location-on the performance and transport behaviors of polymer electrolyte fuel cells (PEFCs) under varying flow field configurations. Using both single-channel serpentine and parallel flow fields, we demonstrate that perforating the cathode GDL, particularly under the gas flow channel, significantly improves water management by facilitating liquid water removal, thus enhancing mass transport and cell performance. The findings reveal that larger perforation sizes and smaller center-to-center spacings are more effective in high-velocity regions, such as those found in serpentine flow fields, due to increased convective effects. However, in low-velocity regions like parallel flow fields, these effects are less pronounced, resulting in only marginal improvements. Importantly, perforating only the cathode under the channel was shown to sufficiently enhance cell performance while maintaining design simplicity. Strategic perforation adjustments-denser perforations in high-velocity regions and larger perforations in low-velocity regions-can optimize water removal and mitigate mass transport limitations.
Recently fuel cell becomes more popular as renewable energy source to produce electrical energy using hydrogen gas. Optimal modelling of fuel cell is important to extract specified power. In this paper precise calibration of proton exchange membrane fuel cell (PEMFC) is analyzed using optimization process. Hybrid grey wolf optimization-cuckoo search (hGWO-CS) algorithm is employed to minimize squared error between measured and simulated terminal voltage. Performance of the algorithm is analyzed for six different bench mark functions. Different optimization algorithms and published results are compared in this paper for Ballad Mark V50 KW, BCS 500 W PEMFC, and NedStack PS6 stacks fuel cell. Among these algorithms with hGWO-CS lower SSD is achieved for three cases. The effectiveness of the proposed method is also validated comparing theoretical and experimental simulations. Further, computational time and statistical indices like mean, minimum, standard deviation, maximum value RMSE, MAE of SSD for hybrid method specify smallest value amongst all other algorithms which clarifies hybrid method as more robust and effective. Moreover, convergence curves and non-parametric test additionally confirm sturdiness and consistency of hGWO-CS in detecting unidentified parameters of PEMFC. Sensitive analysis with variation in optimized parameters are performed to provide insights for optimizing the PEMFC performance. Major contribution of the paper is to design hGWO-CS algorithm accurately optimizing PEMFC parameter which can improve overall performance of fuel cell.
Low-temperature water electrolysis technologies exhibit a significant potential not only to replace grey hydrogen use in existing chemical industries but also to decarbonize hard-to-abate sectors. The main objective of this work is to assess the techno-economic viability of using an MW-scale electrolysis-based green hydrogen plant as a supplier for an industrial heating furnace. HYTECSIM simulation tool is employed to physically model alternative plant configurations and to estimate both onsite footprint and economic metrics, including the levelized cost of hydrogen (LCOH). Consumption measurements from an internal zone of an ingot heating rotary furnace are used as demand profiles in the simulations. Under these premises, three plant configurations are sized and simulated in order to quantify the capital expenditures (CAPEX), footprint, electrolyzer performance, and operational expenditures (OPEX) and to evaluate their combined impact on the LCOH. Results reveal that extending the electrolyzer's stack lifespan by optimizing its operation has great potential to achieve competitive LCOH values.
Proton exchange membrane fuel cells (PEMFCs) are highly promising for producing clean and efficient energy. However, their complex electrochemical behavior, shaped by activation, ohmic, and concentration losses, requires accurate modeling and precise parameter estimation to ensure optimized performance. In this study, guided manta ray foraging optimization (GMANTA), an improved version of the original Manta Ray Foraging Optimization algorithm, is used to estimate key parameters in semiempirical PEMFC voltage models. Studies that simultaneously analyze multiple commercially available PEMFC stacks, such as the Horizon 500 W, BCW 500 W, and SR-12, are relatively scarce in the literature. Consequently, incorporating three experimentally obtained datasets in this study helps fill this gap and provides a more comprehensive and realistic validation framework for metaheuristic optimization algorithms. The proposed method offers a unique contribution by enabling highly accurate parameter identification across varying pressures and temperatures, using a small population size and few iterations. The approach reduces the error between simulated and measured voltage-current (V-I) data, ensuring that the models effectively capture the underlying physical phenomena. To assess the robustness and reliability of the method, GMANTA is compared with eight other well-established metaheuristic algorithms, and differences in error rates among these algorithms are analyzed statistically.
Clamping pressure applied during proton exchange membrane fuel cell (PEMFC) assembly reduces interfacial contact resistance but impedes mass transport, yielding coupled beneficial and adverse effects on performance, which makes its optimal specification nontrivial. This study establishes an integrated framework that couples three-dimensional multiphase non-isothermal computational fluid dynamics (CFD) with two-dimensional finite element analysis (FEA) and data-driven surrogate modeling to quantify these interactions and to enable rapid optimization. Deformation induced changes in geometry, gas diffusion layer porosity and permeability, and pressure dependent interfacial contact resistance are propagated into the transport and electrochemical model. Three surrogate models, namely the radial basis function neural network (RBFNN), support vector regression (SVR), and Gaussian process regression (GPR) are trained on the coupled CFD and FEA dataset to predict power density over the design space of operating voltage and clamping pressure. Results reveal region-specific effects of clamping pressure: In the activation-loss-dominated regime, performance is low and insensitive to pressure; in the ohmic-loss-dominated regime, power density peaks at moderate pressure; in the concentration-loss-dominated regime, power density decreases monotonically with increasing pressure. All surrogate models achieved R 2 values exceeding 0.995, with fast predictions. Coupled CFD-FEA simulation yielded a maximum power density of 0.777 W & centerdot;cm-2, with a recommended range of 0.75-1.25 MPa clamping pressure and 0.60-0.65 V voltage. The artificial intelligence-genetic algorithm framework refined it to 0.8-1.2 MPa and 0.61-0.63 V, where the predicted power density consistently exceeds 0.78 W & centerdot;cm-2. These findings provide quantitative insights for the optimal assembly and operation of PEMFCs.
The decarbonization of the transportation sector is imperative for achieving targeted reductions in greenhouse gas emissions, necessitating the integration of renewable energy pathways. While battery electric vehicles (BEVs) are widely promoted as an environmentally sustainable solution, their utility is often constrained by the significant battery mass and corresponding weight penalty required to achieve extended operational ranges. This fundamental limitation has motivated the rigorous development of fuel cell hybrid electric vehicles (FCHEVs) as a viable technological alternative. In recent years, simulation of powertrain systems has emerged as a prevalent methodology for assessing vehicle performance and energy efficiency across various electric, hybrid, and FCHEV architectures. However, a significant portion of the existing literature on hybrid vehicles focuses on component-level optimization or specific hybrid topologies, often relying on simulations that assume idealized, flat-terrain road profiles, thereby neglecting the impact of topographical gradients. This study addresses this research gap by developing a comprehensive powertrain system model for a battery/fuel cell hybrid vehicle implemented in MATLAB/Simulink. The model's performance is dynamically evaluated under the WLTP, ARTEMIS, and NEDC driving cycles, which are distinctly applied to the GraphHooper Maps-derived real-world inclined route in Elaz & imath;& gbreve;. The simulations yield critical performance indicators, including the power distribution dynamics among powertrain components, changes in battery state of charge (SoC), and vehicle speed control. Furthermore, an adaptive PID controller is implemented to ensure that the vehicle's instantaneous speed precisely tracks the reference speed trajectory. This work aims to provide a high-fidelity simulation approach that more accurately reflects real-world driving conditions, facilitating a robust evaluation of hybrid vehicle performance in realistic operational scenarios. In this context, for the WLTP, ARTEMIS, and NEDC driving cycles applied to a 3.1-km real-world route, the battery SoC was improved by 1.5%, 1.8%, and 1.6%, respectively, in the hybrid configuration. Moreover, owing to the proposed adaptive control strategy, the vehicle speed tracked the reference profile with an average accuracy of 99.8%.
The effective use of renewable energy sources (RES) and advanced energy management strategies ensures an environmentally friendly and cost-effective energy supply. The study presented investigates the performance of a hybrid microgrid system (HMGS) designed to meet the energy demand of the Ild & imath;r Bay region in & Idot;zmir, T & uuml;rkiye, representing a remote coastal community. The system operates in island mode, integrating wave energy into a configuration that already includes continuous energy sources, such as a fuel cell, to exploit the potential of suitable locations without compromising stability. The HMGS consists of four main energy sources: a 50 kW DC fuel cell (FC) stack, a 100 kW photovoltaic (PV) system, a 30 kW wind energy (WE) unit, and a 20 kW oscillating water column (OWC) wave energy (WvE) system, totaling 200 kW. MATLAB/Simulink is used for system modeling, and a fuzzy-logic (FL)-based load management (LM) system, combined with a Smart FC Dispatch strategy, is proposed to minimize energy waste and enhance operational flexibility. A 24 h simulation is performed to evaluate system performance under different load conditions. Simulation results show that without LM, the simultaneous demand of 150 kW leads to rapid battery depletion and potential system blackout. In contrast, the proposed strategy dynamically sheds noncritical loads and regulates FC output based on the battery state of charge (SOC). This approach ensures a continuous supply for the 20 kW critical load, maintains the battery SOC above 80%, and reduces unnecessary hydrogen consumption by preventing excessive FC operation. Furthermore, harmonic analysis confirms that the system complies with IEEE-519 power quality standards. Overall, the proposed HMGS offers an innovative and sustainable solution to meet the continuous energy needs of modern coastal communities.
Sulfite-based gold electrodepositions are explored as a cyanide-free route for protective and conductive coatings in proton exchange membrane (PEM) water electrolysis. Multilayered Au/Ni/Cu coatings exhibit dense microstructures, well-defined interfaces, and typical Au reflections of the fcc crystal system. Contact resistance remains low across the relevant compaction range, reaching 9.39 m ohm cm2 at 150 N cm-2, consistent with the performance of commercial cyanide-derived coatings, meeting the international standards. Corrosion testing showed similar corrosion potentials and currents for both systems, whereas chronoamperometry revealed an under 2 mA current at 2 V and a more stable course for the sulfite-based coating. In situ cell measurements under representative PEMWE operating conditions with polarization up to 2 A cm-2 reached cell potentials of under 1.9 V and no indication of significant mass-transport limitations. Operation exceeding 160 h showed overlapping polarization curves before and after chronoamperometry testing and a degradation rate 11.8 & micro;V h-1, indicating stable electrochemical behavior. Overall, the sulfite-complexed deposition approach delivers physical characteristics and acidic electrochemical behavior consistent with established industrial gold coatings while eliminating cyanide from the process.
The flow rates of the air and hydrogen and the gas pressures of the cathode and anode have a significant effect on the performance of the proton exchange membrane fuel cell (PEMFC). In order to improve the robustness of the PEMFC gas supply control system and thus its ability to regulate the gas flow rates and the gas pressures, firstly, two observers, whose error feedback terms are constructed using a neural network, are designed to estimate the gas pressures within the stack and thus to estimate the excess ratio of oxygen and hydrogen. Secondly, four extended observers are used for the feedback linearization of the gas supply system, and the sliding mode control laws based on the Hamilton-Jacobi inequality are designed for fast and accurate adjustment of the air and hydrogen flow rates supplied to the stack and the gas pressures of the two poles within the stack. Simulation results indicate that the neural network observer has a stronger pressure estimation capability and noise rejection ability than the linear extended observer, and the proposed controllers have minimal overshoot and settling times for the regulation of air and hydrogen flow rates and pressures compared to the proportional-integral-derivative controllers or the feedback linearization controllers.
