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Oxygen excess ratio (OER), which is influenced by the air flow input to the cathode, is one of the important factors affecting the performance of fuel cell system. Insufficient oxygen supply will result in a sharp drop in output voltage, while an excessive supply will bring about a large parasitic power load on the air compressor, reducing the net output power of the system and operating efficiency. Both insufficient and excess oxygen supply can adversely affect the operating life of the proton exchange membrane fuel cell (PEMFC). Due to the serious nonlinear, hysteresis, uncertainty and interference in the process of OER control, this paper proposes an OER feedforward control based on extended state observer (ESO) for a PEMFC auxiliary system with the ESO to predict the total disturbance to improve the dynamic response of the system with large time delay utilizing terminal sliding mode control (TSMC). The control strategy is divided into two stages. The first stage constructs an ESO to realize real-time observation of the state variables required for the internal operation of the auxiliary machine system and the PEMFC, and yields the air compressor voltage control signal through terminal sliding mode control. In the second stage, combined with V/f control for the compressor, the transmitted air flow is adjusted, ultimately achieving the goal of changing the OER of the PEMFC and improving operating efficiency. The simulation results show that the proposed control strategy has good anti-interference capabilities, a faster response, and less overshoot. The gas flow transmission in the gas supply system is more robust, the PEMFC operation is safer, and its lifespan is extended. • The control difficulty caused by time delay characteristics of gas transmission in hydrogen production system considering gas-electric coupling is overcome. • The additional ESO can well observe the state variables and external disturbance that cannot be directly measured in the PEMFC system. The closed-loop control based on TSMC of the system is realized more accurately. • This paper considers the situation that when the energy storage system cannot meet the energy demand, PEMFC still wants to provide continuous power output. An adjusting strategy for OER in PEMFC through the electromagnetic effect of air compressor to extend the life span of the PEM fuel cell is proposed. • The proposed OER feedforward control strategy demonstrated superior robustness and fast response characteristics for oxygen excess ratio optimization. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFCs) have not been applied on a large scale due to their expensive platinum-based catalysts and scarce resources. Metal-nitrogen doped carbon (M-N-C, M=Fe, Ni, Mn, Co) monoatomic catalysts have been considered as promising catalysts for oxygen reduction reaction (ORR), among which Fe-N-C catalysts have high catalytic activity for oxygen reduction in acidic media with low cost. However, most of the reported Fe-N-C catalysts are still not as good as the platinum-based catalysts in fuel cells and have poor stability in acidic media rather than being widely used. Here, we reported a method for constructing Fe-Nx and Co-Nx catalysts on a stable and highly graphitic NC support pyrolyzed from ZIF-67. The obtained Co/Fe-N-C diatomic catalyst achieved an onset potential (Eoneset) of 0.96 V (vs. RHE) and a half-wave potential (E1/2) of 0.79 V (vs. RHE), with more remarkable stability and superior ORR activity compared with the Fe-N-C and Co-N-C catalysts, providing a new insight into enhance the activity and durability of Fe-N-C catalysts. [ABSTRACT FROM AUTHOR]

Minimizing the use of platinum (Pt) in proton exchange membrane fuel cells (PEMFCs) is crucial for expanding the PEMFC market. The most straightforward approach would be to reduce the size of Pt particles. However, small Pt clusters, particularly those <2 nm in size, typically exhibit reduced activity for the oxygen reduction reaction (ORR) due to the overly strong adsorption of oxygen intermediates. Additionally, these small Pt clusters tend to degrade more quickly, resulting in lower durability. In this study, carbon‐embedded Pt alloy cluster catalysts (PtFe, PtCo, PtNi) that demonstrate high activity and durability in the PEMFC cathode are presented. Density functional theory calculations indicate that carbon atoms stably adsorb onto the Fe sites of PtFe clusters, making the neighboring Pt sites active for ORR with an optimal adsorption strength for oxygen intermediates. This research can pave the way for developing durable and efficient ORR catalysts while significantly reducing Pt usage in PEMFCs. [ABSTRACT FROM AUTHOR]

The proton exchange membrane fuel cell (PEMFC) in a fuel cell electric bus (FCEB) converts hydrogen's chemical energy into electrical energy. The fuel cell system comprises a fuel cell stack and a balance of plant (BOP) system, which efficiently controls the stack. Fuel cell and battery are sensitive to operational temperature, which directly impacts performance, lifespan, and safety. Therefore, a thermal management system (TMS) is necessary to maintain an appropriate temperature by dissipating the heat generated by the fuel cell and battery. In this study, the exponential or quadratic relationships between the power consumption of the major components of an FCEB and various factors, such as temperature and flow rate influencing the operational behavior and control of the components, were analyzed based on the results of a dynamometer vehicle test. Additionally, the vehicle's energy flow was calculated under different fuel cell load conditions. When the fuel cell operated at 56.3 kW, TMS power was 6.6 times higher than at 20 kW. At full load, under 90 kW, it increased to 17.4 times higher. The rise in fuel cell load correlated with higher heat generation, resulting in a significant increase in power consumption for both the radiator fan and coolant pump. [ABSTRACT FROM AUTHOR]

The proton exchange membrane fuel cell (PEMFC) converts chemical energy into electricity without pollution or noise. PEMFC has a stable electrolyte, reliable, fast start‐up, and lightweight portability. Optimal PEMFC parameter prediction is difficult. Due to inaccurate estimation, many meta‐heuristic models are inaccurate. Particle swarm, Ant colony, and grey wolf algorithms are tested for fuel cell parameter estimation. These traditional algorithms benefit from chaotic maps for exploitation and exploration. Analyzing V–I and P–I polarization curves, hybrid approaches estimate unknown parameters. Results are compared with others to support the identifying strategy. This project examined SR‐12 and NedSstack PS‐6 PEMFC stacks. The objective function was shown by the sum of square errors between the experiments, and once an appropriate map is identified, the single chaotic map inclusion method can perform better than the multiple chaotic map scheme, and the "Iterative" approach in chaotic swarm is the most promising for multidimensional challenges. [ABSTRACT FROM AUTHOR]

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The flow channel structure is a key core component in the fuel cell, which affects the performance of the fuel cell by affecting the distribution of the reaction gas and liquid water. To solve the problem of liquid water accumulation in the contact area between the flow channel and the ridge in the single flow channel of the fuel cell, a three-dimensional oblique prism flow channel was designed, and its three-dimensional structure and quantity layout was optimized, inspired by the principle of field cooperation and waveform flow field structure. Through these optimization measures, the goal of accelerating the flow of directional guiding gas to the side wall of the flow channel is successfully achieved, and the hydrophobicity of the flow channel wall is also improved. Compared with the straight channel, the liquid water saturation of the OPB-IV flow channel with N = 9 in the flow channel and the gas diffusion layer (GDL) was reduced by 62.7%, and the oxygen molar concentration at the interface between the GDL and the catalytic layer was increased by 19.1%. In addition, we analyzed the influence of inlet humidity on the performance of fuel cells. These optimization measures are essential for improving the performance of fuel cells, reducing costs, and promoting the development of green energy. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) is considered as one of the most promising green energy devices. Although fruitful results can be available for the remaining useful life (RUL) prediction of PEMFC, stochastic uncertainties have never been considered. To tackle this problem, a hybrid method is proposed in this paper. Wiener process with temporal uncertainty and individual uncertainty is adopted to model the degradation of the state of health (SOH), which is then estimated from monitoring voltage with measurement noise using the unscented Kalman filter (UKF), where unknown filtering and model parameters are jointly identified by expectation-maximization (EM) algorithm and Rauch-Tung-Striebel (RTS) smoother. Finally, gated recurrent unit (GRU) network is employed to realize the RUL prediction with the prediction uncertainty quantified by the Bayesian variational inference technology. The proposed method is verified on the experimental data. Results indicate that smaller mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) values can be obtained compared with other methods. When 60% data are used for prediction, the proposed method can achieve a RUL prediction accuracy with 1.63% and 2.17% relative errors under static and dynamic conditions, respectively, which illustrates the feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Temperature and humidity cycling during frequent startup-shutdown of proton exchange membrane (PEM) fuel cell (PEMFC) can lead to crack formation in the catalyst layer (CL) or even at the interface between the CL and PEM, decreasing the effective activation area and degrading the fuel cell performance. This study develops a two-dimensional PEMFC model using COMSOL software which is well validated, and investigates the effect of different lengths and positions of CL/PEM interface crack (delamination) on the mass transport and performance of PEMFC. The results show that both the crack length and position have significant influence on the cell performance, especially at high current density. The presence of crack leads to an uneven distribution of cell current density, and the performance of the cell decreases with the increase of the crack length due to decreased activation area. The crack near the inlet has the greatest adverse impact on the cell performance, compared to other positions near the outlet or in the middle of the rib. Compared to the cell without cracks, the decrement in current density is 5.05%, 9.55% and 15.52%, respectively, for the crack in the middle of the rib, near the outlet and near the inlet. [ABSTRACT FROM AUTHOR]

Water management is a critical challenge in ensuring the performance and durability of low-temperature proton exchange membrane fuel cells (PEMFCs). This study utilizes the phase-field lattice Boltzmann method to conduct three-dimensional numerical simulations, investigating the dynamics of two-phase flow in the cathode gas channel (GC) of PEMFC with a trapezoidal cross-section. The effects of the water inlet location, the surface wettability of GC, the open angle of GC, and the air velocity on liquid water distribution and discharge are analyzed. The results indicate that positioning the water inlet in the middle of the GC significantly enhances PEMFC performance while having minimal impact on average pressure drop. A wall contact angle of 70° or 110°Can optimize fuel cell performance from different directions. Setting a wall contact angle at 70° optimizes gas flow stability and maintains a low-pressure drop value for the GC. A wall contact angle of 110° focuses on optimizing gas reactant diffusion ability into porous electrodes and GC drainage rate. An opening angle of 55° for the trapezoidal gas channel improves overall fuel cell performance. Increasing air velocity facilitates the film flow formation of liquid water on top wall surfaces within the GC. [ABSTRACT FROM AUTHOR]

Durability of membrane electrode assembly (MEA) is a serious problem to be overcome in the commercial development of proton exchange membrane fuel cell (PEMFC). The change in volume due to water–ice conversion has an irreversible effect on the MEA, which affects the performance of PEMFC. For investigating the optimal initial water content of MEA that minimizes the impact on PEMFC performance after freeze–thaw (F/T) cycles, this study first measured the high‐frequency resistance to determine the water content of MEA, and then subjected five MEAs with different water contents to 60 F/T cycles at −20°C to 30°C. The fuel cell output performance of five MEAs was found to be inconsistently degraded by polarization curve tests, with the cells of the two MEAs with the lowest and highest water contents exhibiting the worst output performance. Electrochemical impedance spectroscopy curves proved that the difference in resistance change after F/T cycles is one reason why the cell output performance is degraded differently. Finally, the degradation of cell performance was further explained by cyclic voltammetry. These results indicate that MEA has the best output performance for F/T cycles at an initial water content of 3.0. [ABSTRACT FROM AUTHOR]

The sealing technology of proton exchange membrane fuel cells (PEMFCs) is a critical factor in ensuring their performance, impacting driving safety and range efficiency. To guarantee the safe operation of PEMFCs in complex environments, it is essential to conduct related sealing research. The structure of the fuel cell sealing system is complex, with components in close contact, and identifying factors that affect its sealing performance is crucial for the development and application of the cells. This paper briefly describes the sealing mechanism of PEMFCs and introduces four typical sealing structures. It considers both the assembly and operation processes, summarizing assembly errors, sealing gaskets, and sealing leaks as well as vibration, cyclic temperature and humidity, and cyclic assembly. The research status of the sealing system in simulations and experiments is reviewed in detail. The key factors affecting the sealing performance of fuel cells are emphasized, highlighting the significance of dynamic detection of the gasket status, stack performance improvement under cumulative errors, and multi-objective optimization models combining contact pressure with the characteristics of stack components. [ABSTRACT FROM AUTHOR]

Designing a functional end plate for the proton exchange membrane fuel cell (PEMFC) is a challenging task since the structure that simultaneously satisfies uniform contact pressure and lightweight features must be achieved. To design an end plate for both requirements, a multi‐objective topology optimization model of the PEMFC based on the concept of equivalent stiffness is established with the aim of maximum stiffness and minimum root mean square (RMS) of contact displacement. Based on the final topology, the filled honeycomb‐based end plates with excellent mechanical properties and energy absorption effects are optimized through the particle swarm optimization algorithm (PSO). The results demonstrate that the end plate mass is reduced by 39.5% and the stress distribution is more uniform. The standard deviation of pressure decreases by 30.8% compared to the original model data. The proposed optimization method is helpful in the design of future end plates. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFCs) have garnered considerable interest owing to their high conversion efficiency and eco-friendliness. However, the components of PEMFCs are prone to degradation caused by generated radicals, hindering their widespread application. To counteract the chemical attack of radicals on PEMFC components, the key solution is to incorporate radical scavengers, as they can effectively eliminate harmful radicals. Ce-based scavengers exhibit the highest radical scavenging capacity, whereas they are susceptible to dissolution and may exert poisoning effects on ionomers, thereby impacting proton conductivity and gas mass transfer. Additionally, Ce-based radical scavengers may degraded and migrated due to varying chemical and electrochemical conditions. This study delves into the scavenging mechanisms and influences of Ce-based radical scavengers. Furthermore, it addresses the issues of poisoning and migration and outlines appropriate measures to mitigate these challenges. This review aims to offer an innovative solution to enhance the durability of PEMFCs. [Display omitted] • Reviewed the scavenging mechanisms of Ce-based radical scavengers and their effects. • Discussed the poisoning effects of Ce-based radical scavengers and provided some mitigation strategies. • Highlighted the migration of Ce species and the corresponding stabilization strategies. • Presented the technical challenges and future development prospects of Ce-based radical scavengers. [ABSTRACT FROM AUTHOR]

The current limitations of air-cooled proton exchange membrane fuel cells (AC-PEMFCs) in water and heat management remain a major obstacle to their commercialization. A 90 cm2 full-size AC-PEMFC multi-physical field-coupled numerical model was constructed; isothermal and non-isothermal calculations were performed to explore the effects of univariate and multivariate variables on cell performance, respectively. The isothermal results indicate that lower temperature is beneficial to increase the humidity of MEA, and distribution uniformity at lower stoichiometric ratios and lower temperatures is better. The correlation between current density distribution and temperature, water content, and concentration distribution shows that the performance of AC-PEMFCs is influenced by multiple factors. Notably, under high current operation, the large heat generation may lead to high local temperature and performance decline, especially in the under-channel region with drier MEA. The higher stoichiometric ratio can enhance heat dissipation, improve the uniformity of current density, and increase power density. Optimal fuel cell performance is achieved with a stoichiometric ratio of 300, balancing the mixed influence of multiple factors. [ABSTRACT FROM AUTHOR]

The control strategy of the gas supply subsystem is very important to ensure the performance and stability of the fuel cell system. However, due to the inherent nonlinear characteristics of the fuel cell gas supply subsystem, the traditional control strategy is mainly based on proportional integral (PI) control, which has the disadvantages of large limitation, large error, limited immunity, and inconsistent control performance, which seriously affects its effectiveness. In order to overcome these challenges, this paper proposes an optimal control method for air supply subsystems based on nonlinear active disturbance rejection control (ADRC). Firstly, a seven-order fuel cell system model is established, and then, the nonlinear ADRC and traditional PI control strategies are compared and analyzed. Finally, the two strategies are simulated and compared. The validation results indicate that the integral absolute error (IAE) measure of PI control is 0.502, the integral square error (ISE) measure is 0.1382, and the total variation (TV) measure is 399.1248. Compared with the PI control, the IAE and ISE indexes of ADRC were reduced by 61.31% and 58.03%, respectively. ADRC is superior to PI control strategy in all aspects and realizes the efficient adjustment of the system under different working conditions. ADRC is more suitable for the nonlinear characteristics of the gas supply system and is more suitable for the oxygen excess ratio (OER). [ABSTRACT FROM AUTHOR]

The ordered membrane electrode assembly (MEA) has gained much attention because of its potential in improving mass transfer. Here, a comprehensive study was conducted on the influence of the patterned microporous layer (MPL) on the proton exchange membrane fuel cell performances. When patterned MPL is employed, grooves are generated between the catalyst layer and the gas diffusion layer. It is found that the grooves do not increase the contact resistance, and it is beneficial for water retention. When the MEA works under low humidity scenarios, the MEA with patterned MPL illustrated higher performance, due to the reduced inner resistance caused by improved water retention, leading to increased ionic conductivity. However, when the humidity is higher than 80% or working under high current density, the generated water accumulated in the grooves and hindered the oxygen mass transport, leading to a reduced MEA performance. [ABSTRACT FROM AUTHOR]

The economy and stability of the hydrogen recirculation system in high-power fuel cells significantly impact the performance of the stack system. However, there is no consensus on the optimal hydrogen recirculation mode to be employed. To determine the most effective solution across the entire power range of the stack, this study compares the performance of various coupling modes. The dynamic performance variations of the system were investigated under different modes by the Computational Fluid Dynamics (CFD) method. The simulation results demonstrated that within the low and medium power range of the stack, the PUE (Pump Upstream of Ejector) mode had the lowest power consumption of all modes, at 112 W. The power of the PUE mode had a reduction of 56.3% compared to the full-power single hydrogen pump mode, and a reduction of 22.8% compared to the PDE (Pump Downstream of Ejector) mode. [Display omitted] • Various coupling models of hydrogen pumps and ejectors are studied. • The pressure rise distribution of coupling modes are compared and analyzed. • Coupling mode reduces power consumption by more than 50%. • The optimal solution for the full power range of the stack is derived. [ABSTRACT FROM AUTHOR]

Accurate and swift estimation of the future performance degradation of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for the health management of PEMFC. However, most existing studies primarily concentrate on uncovering the inherent patterns of performance degradation parameters, neglecting the fundamental relationships and pattern variations between performance degradation and other characteristic parameters (such as flow rate, temperature, and pressure). This paper seeks to address this research gap. To that end, we delve into feature parameters, extracting underlying patterns of feature evolution to construct a feature exploration-based framework for predicting PEMFC performance degradation. Initially, Time-Varying Filtered Empirical Mode Decomposition (TVF-EMD) is employed to decompose each feature variable, isolating the core information of the features. Furthermore, to reduce the computational burden caused by redundant features, Uniform Manifold Approximation and Projection (UMAP) is used to perform dimensionality reduction on the decomposed feature subsequences, ensuring the efficiency and representativeness of the extracted features. Finally, to overcome the shortcomings of long short-term memory networks (LSTM) in terms of convergence and the effective use of time series information, we introduce an innovative Mogrifier LSTM (M-LSTM) model to enhance the accuracy of PEMFC performance degradation predictions. The experimental and validation results demonstrate that the extraction of feature parameters significantly enhances the predictive precision of the baseline model. Relative to the compared models, there was an improvement of at least 16.859% in Root Mean Square Error (RMSE), a minimum increase of 23.333% in Mean Absolute Error (MAE), and a boost of no less than 0.913% in the coefficient of determination (R 2 ). Simultaneously, the proposed predictive framework demonstrates good stability and accuracy in predicting PEMFC performance degradation. • Developed a novel feature processing framework for predicting the performance degradation of PEMFC. • Employed TVF-EMD in conjunction with UMAP to extract evolving trends and cyclical patterns of feature parameters. • Introduced an innovative M-LSTM network to enhance the interaction between historical information and current inputs. • Feature processing framework improves single-model performance degradation predictions [ABSTRACT FROM AUTHOR]

The thermal management of proton exchange membrane fuel cell crucially impacts the performance and durability of fuel cell vehicle systems. Optimized temperature control is critical to the water balance and electrochemical reaction inside the fuel cell stack under dynamic operating conditions. This work develops a dynamic thermal management system model with uncertain parameters for the dual coolant circulation fuel cell system to simulate unforeseeable disturbances in real systems. With the proposed model, the tube-based robust model predictive control (tube-RMPC) is innovatively applied to optimize dynamic thermal management. This strategy can identify the optimal trajectories of thermostat angle, heater power, water pump speed, and cooling fan speed, thus maximizing the temperature dynamic response speed while minimizing the stack temperature difference fluctuations in the presence of unforeseeable disturbances. The result shows that during the load ascending and descending operations, the temperature dynamic response time is 8 and 15 s, with temperature fluctuations below 1 °C. The root mean square error for stack temperature is reduced by 10.4% and 24.4% compared to the closed-loop MPC. The fuel cell system exhibits excellent temperature dynamic response capability using the tube-RMPC method. • Tube-RMPC is applied in fuel cell vehicle systems to effectively suppress stack temperature fluctuations. • Robust dynamic thermal management strategy is proposed to determine optimal action trajectories of critical TMS components. • Proposed method controls temperatures at different stack locations precisely under rapid load changes. • Robustness of tube based MPC under unpredictable disturbances is validated. [ABSTRACT FROM AUTHOR]

Proton exchange membrane (PEM) fuel cells require high-purity hydrogen. Carbon monoxide (CO) is a common impurity in hydrogen that significantly reduces the performance of PEM fuel cells. Currently, there are two main methods to eliminate the effect of CO: direct removal of CO and increasing the CO tolerance of PEM fuel cells. Each method has its advantages and disadvantages. It is important to consider the specific application when choosing between these methods. This review summarizes the mechanism of CO poisoning and its effect on the operation and durability of PEM fuel cells, as well as hydrogen purification and CO-tolerant techniques. Then analyzes the advantages and disadvantages of commonly used techniques and predicts future research directions for PEM fuel cells using impure hydrogen containing CO, aiming to expand the application scenarios, and promote the large-scale application of PEM fuel cells. • Mechanisms of CO poisoning on PEM fuel cells are summarized. • Impacts on potential oscillation and durability are reviewed. • Various hydrogen purifications techniques are compared. • CO-tolerant techniques for PEM fuel cells are discussed. • Futher directions for research on eliminating CO effect are presented. [ABSTRACT FROM AUTHOR]

Due to the coupling of multiple physical fields inside proton exchange membrane fuel cell (PEMFC) and the complex and changeable external application scenarios, the faults of PEMFC system greatly affects its engineering application. The paper proposes an ensemble learning-based fault diagnosis method for PEMFC systems, utilizing five distinct algorithms to develop an integrated ensemble classifier for fault detection. The original sample dataset collected from fuel cells undergoes a process of deep feature extraction to obtain a composite feature sample dataset, which is then inputted into an ensemble classifier to establish a fault diagnosis model. The experimental data of four kinds of faults of 80 W fuel cell test bench, EC fuel cell system and 50 W water-cooled fuel cell test bench are analyzed by using this classification model. The results show that the accuracy and generalization of the ensemble learning algorithm for different fault diagnosis of PEMFC system in different application environments are verified. • Adopt multiple classification algorithms for fault modeling and diagnosis. • Good applicability to different faults is shown in different test scenarios. • Compared with a single algorithm, it has higher fault identification accuracy. [ABSTRACT FROM AUTHOR]

The operating temperature and thermal distribution play a significant role in the appropriate performance of the proton exchange membrane fuel cell (PEMFC). A suitable cell cooling method can guarantee uniform temperature distribution and preservation of membrane water content under harsh conditions in a PEMFC. The compact structure of PEM fuel cells and the size of the cooling system impose a great challenge to achieve a proper design of cooling plate. In this numerical study, four designs of the multi-stage Tesla valve with straight and reverse configuration are proposed to be employed as a cooling channel in which three different hybrid nanofluids such as TiO 2 –Cu, Al 2 O 3 –CuO, and Ag–MgO dispersed in water and Ethylene glycol flow inside the channels. A validated numerical model is developed using a commercial computational fluid dynamics (CFD) code to investigate the effect of inlet temperatures and mass flow rates of cooling fluid and volume fraction of nanoparticles on heat transfer coefficient, pressure drop, index of uniform temperature (IUT), and performance evaluation criteria (PEC). The results demonstrated that the reverse configuration of the Tesla valve (CASE E) experienced outstanding uniform thermal distribution while the heat transfer coefficient increased by 15% in this case compared to the straight-line cooling channel. The Ag–MgO hybrid nanoparticles showed better thermal performance than the two other nanoparticles with a 4% reduction in temperature difference in the cooling plate. • A well-validated numerical model developed to evaluate the thermal performance of PEMFC. • Four novel designs of Tesla valve configuration were presented to achieve uniform temperature distribution. • Three hybrid nanofluids were employed to enhance the heat transfer rate in the cooling plate. • The reverse Tesla valve geometry showed the maximum Nusselt number, lowest IUT and highest PEC. • The maximum reduction of temperature difference and maximum temperature achieved by Ag–MgO. [ABSTRACT FROM AUTHOR]

The aim of this study is to understand the influence of the membrane electrode assembly (or MEA) interfaces properties on the mechanical behavior of PEMFCs, using the development of a numerical model. The in-plane elastic orthotropic properties and nonlinear out-of-plane properties of the gas diffusion layers will be considered in the numerical model. Numerical results suggest that it is possible to limit the plastic deformation of the PEM by controlling its assembly mode, as well as the properties at the MEA interfaces, considered as bonded or free. Different recommendations are then proposed to increase the durability of PEMFCs by limiting the amplitude of mechanical strains or stresses in their core. [Display omitted] [ABSTRACT FROM AUTHOR]

Proton-exchange membrane (PEM) fuel cells are increasingly used in the automotive sector. A crucial point for estimating the performance of such systems is open-circuit voltage (OCV) losses, among which the most influential are mixed potential, hydrogen crossover, and internal short circuits. These losses are often overlooked in the modeling of such electrochemical cells, leading to an inaccurate estimation of the real voltage that is calculated starting from the Nernst Equation. An innovative method is presented to estimate the losses based on the division of the membrane into two domains: solid and aqueous. The influence of the macro-parameters (temperature, pressure, and RH) was analyzed for each phenomenon and was linked to the membrane water content. For low levels of PEM hydration, internal short circuits were of the same order of magnitude as hydrogen crossover. The OCV model accuracy was assessed on a commercial stack, used on a vehicle prototype competing in the Shell Eco-Marathon challenge. The data of interest were obtained through laboratory tests and subsequent disassembly of the stack. A PEM thickness of 127 μm was measured corresponding to Nafion 115. For further validation, the model results were compared with data in the literature. [ABSTRACT FROM AUTHOR]

Polymer electrolyte membrane fuel cells (PEMFCs) have attracted much attention as highly efficient, eco-friendly energy conversion devices. However, carbon-supported Pt (Pt/C) catalysts for PEMFCs still have several problems, such as low long-term stability, to be widely commercialized in fuel cell applications. To address the stability issues of Pt/C such as the dissolution, detachment, and agglomeration of Pt nanoparticles under harsh operating conditions, we design an interesting fabrication process to produce a highly active and durable Pt catalyst by introducing a robust carbon shell on the Pt surface. Furthermore, this approach provides insights into how to regulate the carbon shell layer for fuel cell applications. Through the application of an appropriate amount of H2 gas during heat treatment, the carbon shell pores, which are integral to the structure, can be systematically modulated to facilitate oxygen adsorption for the oxygen reduction reaction. Simultaneously, the carbon shell functions as a protective barrier, preventing catalyst degradation. In this regard, we investigate an in-depth analysis of the effects of critical parameters including H2 content and the flow rate of H2/N2 mixed gas during heat treatment to prepare better catalysts. [ABSTRACT FROM AUTHOR]

The Proton Exchange Membrane Fuel Cell (PEMFC) has found widespread use for regulated output voltage applications because of its quick response time and mobility. A different form of hydrogen is employed in fuel cell-based electric vehicles for smart transportation for the reduction of global warming and the development of smart cities. To properly manage the operation of Fuel Cells (FCs), there is a need for accurate modeling. One of the most common challenges is finding the exact values of unknown parameters in the PEMFC. In the current study, a new method called Dandelion Optimizer (DO) is used for parameter identification. DO is used to estimate the parameters of the PEMFC based on Current-Voltage (I-V) characteristics. The Ballard Mark V and BCS 500-W PEMFC stacks use the DO method to identify unknown parameters. The performance of the DO algorithm is compared to that of other optimization techniques and the Sum of Squared Errors (SSE) is used to represent the objective function of the current optimization problem. In contrast to traditional and other efficient techniques, the simulation results proposed by the DO algorithm have excellent accuracy in extracting the PEMFC optimal parameters. [ABSTRACT FROM AUTHOR]

The practical application of non‐precious Fe─N─C catalysts in proton exchange membrane fuel cells (PEMFCs) continues to remain one of the major challenges due to their relatively poor oxygen reduction reaction (ORR) performance in acid. In this work, a fast and facilely performance enhancement strategy is first proposed for various Fe─N─C catalysts by supplying different direct‐current voltages to achieve a rapid solid‐state activation at room temperature. The voltage‐activated state‐of‐the‐art Fe─N─C catalyst has demonstrated a peak power density of 1.1 W cm−2 for PEMFC and remarkably increased long‐term durability for ORR. The substantially improved performance can be attributed to the formation of highly active ketone‐decorated (edge‐hosted) FeN4 sites and substantially boosts coupled proton−electron transfer (CPET) by improving the conductivity of the as‐synthesized Fe─N─C with an interpenetrating network structure. The interconnected micro‐nodes within the interpenetrating network are the main active regions for the ORR, evidenced by an optimized structural model based on the above typical morphology. Therefore, this finding provides an innovative and facile idea for solving the activity and stability deficiency for promising Fe─N─C for commercial PEMFC. [ABSTRACT FROM AUTHOR]

PPMG-based composite electrolytes were fabricated via the solution method using the polyvinyl alcohol and polyvinylpyrrolidone blend reinforced with various contents of sulfonated inorganic filler. Sulfuric acid was employed as the sulfonating agent to functionalize the external surface of the inorganic filler, i.e., graphene oxide. The proton conductivities of the newly prepared proton exchange membranes (PEMs) were increased by increasing the temperature and content of sulfonated graphene oxide (SGO), i.e., ranging from 0.025 S/cm to 0.060 S/cm. The induction of the optimum level of SGO is determined to be an excellent route to enhance ionic conductivity. The single-cell performance test was conducted by sandwiching the newly prepared PEMs between an anode (0.2 mg/cm2 Pt/Ru) and a cathode (0.2 mg/cm2 Pt) to prepare membrane electrode assemblies, followed by hot pressing under a pressure of approximately 100 kg/cm2 at 60 °C for 5–10 min. The highest power densities achieved with PPMG PEMs were 14.9 and 35.60 mW/cm2 at 25 °C and 70 °C, respectively, at ambient pressure with 100% relative humidity. Results showed that the newly prepared PEMs exhibit good electrochemical performance. The results indicated that the prepared composite membrane with 6 wt% filler can be used as an alternative membrane for applications of high-performance proton exchange membrane fuel cell. [ABSTRACT FROM AUTHOR]

The flow field structure plays the key roles in the operating reliability and power output of proton exchange membrane fuel cell (PEMFC). This study investigates and compares two typical structural parameters of PEMFC with the trapezoidal flow channel (TFC) and the trapezoidal flow channel with block (TFCB). A three-dimensional (3-D) multiphase TFC model is first developed, and then the multi-objective optimization is performed by using the trained artificial neural network (ANN) surrogate model and non-dominated sorting genetic algorithm (NSGA-II). Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is used to investigate the optimized structural parameters of TFC. The results show the net power output and the oxygen uniformity index of the optimized TFC are increased by 19.77% and 21.92% compared with the straight flow channel (SFC). Furthermore, it is also found using block in the trapezoidal flow channel (TFCB) can increase the performance of PEMFC, and it exhibits a 25.10% improvement for the net power output and 27.88% for oxygen uniformity index, respectively. [ABSTRACT FROM AUTHOR]

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In a proton exchange membrane fuel cell (FC), the gas diffusion layer (GDL) is identified as the component that is most affected by mechanical compression. In this article, a particular focus is provided on the methods to measure the three main electrical parameters—contact resistance, through‐plane resistance, and in‐plane resistance—of the GDL under compression. A nonlinear decrease of these resistances under compression is typically observed. In particular, an important decrease is observed from 0 to 2 MPa, then a lower one above 2 MPa. The smallest contact and in‐plane resistances are measured for the graphitized straight carbon papers analyzing GDL resistances under compression gives a first approach to explaining ohmic losses in FCs as a large part of these losses is related to the GDL. This review would be helpful for researchers in better understanding ohmic losses and establishing a database of main GDL electrical resistances and their variations according to several operating parameters. These data could be used in design models to optimize GDL properties. [ABSTRACT FROM AUTHOR]

Abstract Understanding the ice nucleation mechanism in the catalyst layers (CLs) of proton exchange membrane (PEM) fuel cells and inhibiting icing by designing the CLs can optimize the cold start strategies, which can enhance the performance of PEM fuel cells. Herein, mitigating the structural matching and templating effects by adjusting the surface morphology and wettability can inhibit icing on the platinum (Pt) catalyst surface effectively. The Pt(211) surface can inhibit icing because the atomic spacing of (211) crystalline surface is much larger than the characteristic distance of ice crystal, thereby mitigating the structural matching effects. A water overlayer on the Pt surface induced by the strong attraction of Pt can act as a template for ice layers and plays an important role in the icing process. Buckling of water overlayer due to the larger atomic spacing of (211) crystalline surface mitigates the templating effect and inhibits icing. Moreover, the water overlayer on the hydrophobic Pt(211) surface with fewer water molecules also mitigates the templating effect, which makes ice nucleation more difficult than homogeneous nucleation. These findings reveal the ice nucleation mechanisms on the Pt catalyst surface from the molecular level and are valuable for catalyst designs to inhibit icing in CL.

Hydrogen energy, due to its clean and efficient nature, has shown great potential during the current transition period in the shipbuilding industry. However, the application of hydrogen energy in ship energy systems is influenced by variations in operational load and the integration of new energy sources during actual navigation. To address these issues, this paper focuses on optimizing and scheduling the operation of ships under various navigation conditions, considering the distributed nature of hydrogen energy. System simulations were conducted to model the photovoltaic (PV), proton exchange membrane fuel cells (PEMFCs), lithium batteries (LIBs), electrolytic cells (ECs), and energy storage modules of yacht energy systems. Component boundaries and objective functions were set, and two cases (excess photovoltaic state and constant power state) were designed to optimize and regulate the energy balance of hydrogen-powered yachts, enhancing their comprehensive utilization of renewable energy. By comparing the changes in ship energy under the two cases, it was concluded that case 1 ensures the maximum utilization of renewable energy. When photovoltaic power generation is insufficient, the PEMFC and LIB in the system provide the required power to achieve a supply-demand balance. Moreover, when PV power generation is sufficient, hydrogen energy is used to store renewable energy. The optimization method designed in this study can, to some extent, maximize the application of renewable energy in new energy yachts, ensuring the efficiency of the comprehensive energy system of new energy yachts, reducing emissions, and improving the sustainability and economic efficiency of the ships.

In a fuel cell, flow channels are crucial components responsible for various essential functions that enable the system to operate effectively. The design of a directly coupled flow channel in a Proton Exchange Membrane Fuel Cell (PEMFC) system, assuming deterministic parameters, has been extensively studied. However, this deterministic approach neglects the inherent uncertainties in system performance during real-life operation, resulting in potentially unreliable and suboptimal performance. To address this issue, we propose a reliability-based design optimization (RBDO) of the PEMFC’s channel structure, considering uncertainties in operating parameters. This paper presents a numerical model of the PEMFC in COMSOL, deterministic designs, reliability-based designs and a global sensitivity analysis on the PEMFC cell’s potential output and average water activity on the membrane. Although the RBDO approach shows a reduction in cell efficiency compared to the deterministic design, it significantly improves reliability, with increases from 60.92% to 95.10% for cell potential and from 79.31% to 96.85% for water activity.

This study is presented to examine the performance of a newly proposed metaheuristic algorithm within discrete and continuous search spaces. Therefore, the multithresholding image segmentation problem and parameter estimation problem of both the proton exchange membrane fuel cell (PEMFC) and photovoltaic (PV) models, which have different search spaces, are used to test and verify this algorithm. The traditional techniques could not find approximate solutions for those problems in a reasonable amount of time, so researchers have used metaheuristic algorithms to overcome those shortcomings. However, the majority of metaheuristic algorithms still suffer from slow convergence speed and stagnation into local minima problems, which makes them unsuitable for tackling these optimization problems. Therefore, this study proposes an improved nutcracker optimization algorithm (INOA) for better solving those problems in an acceptable amount of time. INOA is based on improving the performance of the standard algorithm using a newly proposed convergence improvement strategy that aims to improve the convergence speed and prevent stagnation in local minima. This algorithm is first applied to estimating the unknown parameters of the single-diode, double-diode, and triple-diode models for a PV module and a solar cell. Second, four PEMFC modules are used to further observe INOA's performance for the continuous optimization challenge. Finally, the performance of INOA is investigated for solving the multi-thresholding image segmentation problem to test its effectiveness in a discrete search space. Several test images with different threshold levels were used to validate its effectiveness, stability, and scalability. Comparison to several rival optimizers using various performance indicators, such as convergence curve, standard deviation, average fitness value, and Wilcoxon rank-sum test, demonstrates that INOA is an effective alternative for solving both discrete and continuous optimization problems. Quantitively, INOA could solve those problems better than the other rival optimizers, with improvement rates for final results ranging between 0.8355 % and 3.34 % for discrete problems and 4.97 % and 99.9 % for continuous problems.

The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.