Proton Exchange Membrane Fuel Cell Model Research Articles

Modeling proton exchange membrane fuel cells with fiber-based microporous layers

Microporous layers (MPLs) play a crucial role in improving water management in proton exchange membrane fuel cells (PEMFCs). Highly tunable electrospun carbon fibers offer a promising candidate for MPLs to facilitate two-phase water and gas transport in PEMFCs. In this work, we present a two-phase PEMFC model to investigate the mass transport characteristics with MPLs made of nano-/micro-fibers. Simulations were validated by the reported experimental results. It is revealed that the fiber-based MPLs (fMPLs) reduce the liquid water saturation at the cathode side due to the higher permeability, thus significantly reducing the oxygen transport resistance and resulting in superior cell performance than conventional MPLs (cMPLs) do. Moreover, PEMFCs with fMPLs outperform those with cMPLs under a wide range of operating temperatures from 40 to 80 °C. In addition, our parametric study results suggest that fMPLs with a high porosity (> 0.5), a large fiber diameter (> 2 µm), and a large contact angle (> 135°) can effectively boost water drainage and gas transport, thereby considerably enhancing the PEMFC performance. This work provides insights into the two-phase transport behavior in PEMFCs with fMPLs, paving the way for design and development of novel MPLs for high-performance PEMFCs.

Model parameters identification of the PEMFCs using an improved design of Crow Search Algorithm

There is an increasing trend for fuel cell systems applications in electricity generation systems instead of traditional power generation systems because of their advantages such as high efficiency and almost no environmental pollution, desirable dynamic response, and reliability. Due to this reason, herein, a new method has been presented for optimum identification of the model of the proton exchange membrane fuel cell (PEMFC) model. The major concept is to lessen the sum of squared error (SSE) amount of the observed output voltage and the output voltage of the PEMFC stack by an improved version of Crow Search optimizer (ICSO). To validate the suggested technique, it is applied to two studied cases and the achievements are put in comparison with several newest optimizers, which are Genetic algorithm (GA), Grasshopper Optimizer (GHO), and Salp Swarm Optimizer (SSO). The achievements show that the suggested ICSO gives a better superiority to the other comparative algorithms for optimum estimation of the PEMFC model.

Simulation study on magnetic field distribution of PEMFC

As a promising next-generation energy source, proton exchange membrane fuel cell (PEMFC) still suffers from durability and reliability issues, where PEMFC performance will decay during its operation. In this study, a three-dimensional, multi-component and multi-physics PEMFC model is developed to investigate the effect of PEMFC performance degradation on its external magnetic field. By comparing simulation results and experimental data, the capability of the developed model in simulating magnetic field due to PEMFC current is demonstrated. With developed model, different PEMFC degradation mechanisms, including flooding, dehydration, PEMFC aging are simulated, and the amplitudes and distributions of magnetic field under different mechanisms are investigated. Moreover, considering local defects may happen in practical PEMFC systems, its influence on magnetic field distribution is also studied. From the results, the correlation between PEMFC performance degradation and its magnetic field distribution is clarified, which will be beneficial for researches utilizing PEMFC magnetic field for analyzing PEMFC performance variation.

Extracting Unknown Parameters of Proton Exchange Membrane Fuel Cells Using Quantum Encoded Pathfinder Algorithm

Proton exchange membrane fuel cell (PEMFC) is one of the most widely used fuel cell types. Accurate modeling of PEMFC can better facilitate the research of PEMFC and guide designers to design FC products that meet people’s needs. The modeling problem of PEMFC can be transformed into a parametric optimization problem. In order to improve the exploration capability of the pathfinder algorithm, the concept of quantum computing is introduced and a new quantum coded pathfinder optimization algorithm (QPFA) is proposed. QPFA was applied to the extraction of parameters of NedStackPS6, BCS500W and 250W FC, and these models of PEMFC have been applied for commercial use. The experimental results were compared with seven recently proposed metaheuristics and recently published literature, showing the accuracy and high precision of QPFA in extracting PEMFC parameters.

Three-dimensional multi-field digital twin technology for proton exchange membrane fuel cells

In times of the commercialization process of proton exchange membrane fuel cells (PEMFCs), a full knowledge of in-situ state in PEMFCs is of critical significance to the in-situ operational process and the evaluation of material stage and potential damage. The conventional experimental observation and in-situ prediction models can only obtain very limited information while the computational fluid dynamics approach takes too long time to get the detailed information. To reach a full knowledge of PEMFC real-time state, a novel 3D multi-physics digital twin model for PEMFCs is proposed based on the proper orthogonal decomposition (POD) method. In the model, firstly, for one kind of PEMFC, 139 ex-situ snapshots are designed and simulated based on the three-dimensional two-phase non-isothermal numerical model with the assumption of liquid pressure continuity in the whole membrane electrode assembly. Then the modes of each field in snapshots are extracted by singular value decomposition method using Jacobi algorithm. Finally, the coefficients in the POD prediction equation are obtained by using the multivariate adaptive regression splines. The digital twin results of voltage, temperature, membrane water content and liquid water saturation fields are exhibited and analyzed. Results suggest that for the studied PEMFC, the digital twin technique can capture the global values and the local distribution characteristics of each above physical fields well in 0.913 s. The mean global deviations of the above four fields of 20 groups of random conditions within wide current density and operational condition ranges are 5.7 %, 1.3 %, 8.9 % and 12.0 % respectively. Even though the practical results can only be applied for the studied PEMFC, the proposed methodology has its general application range.

Study on the output performance of the proton exchange membrane fuel cells using print circuit board

This paper presents a proton exchange membrane fuel cell (PEMFC) using anode and cathode printed circuit boards (PCBs) to load the membrane electrode assembly (MEA). A three-dimensional CFD-electrochemical model of the PCB-based PEMFC is developed and used to calculate the internal species, charge, and water distribution. The effects of the shape of the inlet opening, the opening ratio and the relative humidity of the air on the output performance of the PEMFC are investigated. To validate the simulation model, the output performance of the PEMFC is tested under the same operating conditions. The results show that circular and rounded rectangular openings show better PEMFC output performance than U-shaped openings, and the output performance of PEMFC is improved as the opening ratio of the inlet openings and the relative humidity of air increase. The results will provide specific reference values for the design of PCB-based planar PEMFC stacks.

Optimal parameter extraction of proton exchange membrane fuel cell using Henry gas solubility optimization

The proton exchange membrane fuel cell (PEMFC) transforms chemical energy directly into electricity without polluting the environment or creating noise. The solid electrolyte, excellent reliability, speedy start-up, and lightweight portability distinguish PEMFC. It is difficult to predict various parameters of PEMFC optimally. Numerous metaheuristic models have been established, yet significant inaccuracy arises due to a lack of accurate estimation. So, this present study uses a new optimization approach called Henry’s gas solubility optimization (HGSO) to optimally estimate the parameters in the PEMFC model faster and more accurately. To create a balance between exploiting and inquiry in the search area and avoid local minima, the HGSO algorithm resembles gas behavior. The algorithm’s performance is assessed using ten benchmark functions, and the results are compared to five other well-known methods. Furthermore, three alternative PEMFC models are used for validation: the SR-12 modular PEMFC stack, the BCS-500 W FC stack, and the NedSstack PS-6 PEMFC stack. The results are compared to actual data. HGSO was able to provide high-quality solutions, faster convergence, shorter processing time, and less errors when compared to other algorithms studied.

Technological and Engineering design of a megawatt proton exchange membrane fuel cell system

For the large-scale application of Proton Exchange Membrane Fuel Cell (PEMFC) in the generation, multi-stack PEMFCs are usually adopted to meet high-power needs. The connection method among stacks, layout parameters, and operating parameters can greatly influence the performance and uniformity of system. A megawatt (MW) class PEMFC system with 20 stacks is proposed and modeled with a flow network model, a thermal model, and a PEMFC model. The temperature and voltage uniformity index (TUF and VUF) are defined to evaluate the system uniformity among stacks. The effect of several operating parameters, such as current density and hydrogen inlet pressure, and layout parameters such as spacing distance and the diameter of the pipe, on the stack voltage, system electricity efficiency, VUF and TUF are quantitatively compared. The results show that as the current density increases, the consistency of the system output decreases. The stack with the shortest gas transmission distance shows the best performance and the frictional loss is the main factor influencing the pressure differences among the inlet pressure of stacks. Under the inlet pressure of 2.5 atm, the voltage difference among stacks is within 0.15 V. From the perspective of overall cost and system output, a pipe diameter of 25 mm is more suitable for the design of the intake pipe in this system.

Experimental and Numerical Analysis of Low Temperature Proton Exchange Membrane Fuel Cell (PEMFC) with Different Fuel Flow Rate in Improving Fuel Cell Performance

The PEM fuel cell was examined using numerical simulation in varied circumstances. To restore the fuel cell performance, a 3D-based PEMFC model was designed employing COMSOL Multiphysics 5.1. The analysis validity was confirmed using the V-I curves derived from data analysis in varied operational circumstances. The continuity, momentum, species transport and charge equations were used to represent the cell transport phenomenon. The flow of permeable medium in the gas diffusion layer was defined by employing Brinkman equations. V-I curves were obtained using the Butler-Volmer equations. According to findings, the current supply in the cathode catalyst layer achieves an optimum one, functioning as mass transport, ionic and charge transport resistances. It indicates optimum current supply in the cell holds a feature of highest oxygen deprivation on the channel's output side.

A unified catalyst layer design classification criterion on proton exchange membrane fuel cell performance based on a modified agglomerate model

The main goal of catalyst layer (CL) optimization is decreasing the platinum in proton exchange membrane fuel cells (PEMFCs) without a deterioration in performance; this, however, is hindered by a lack of precise knowledge regarding the cell performance at various CL design criterions. In this study, a modified CL agglomerate model is developed to consider the coupled local oxygen and ionic transport and subsequently incorporated into a multi-phase, non-isothermal PEMFC model. A general classification criterion for CL design is proposed, which is classified into four CL modes: dynamic volume fraction mode, constant ionomer volume fraction mode, constant carbon volume fraction mode, and constant pore volume fraction mode. The respective contribution of local oxygen and ionic transport under four CL design modes are elucidated. It is found that the oxygen concentration at Pt surface is determined by oxygen transport resistance and local current density, and their impacts are synergetic. For fixed platinum/carbon and ionomer/carbon ratios, an increase in Pt loading results in a decrease in the ohmic loss by improving the ionic transport capacity. On the condition of fixing the volume fraction of each CL composition, the ionic conduction essentially remains unchanged as the Pt loading, whereas the oxygen transfer is enhanced. There exists a threshold value, beyond which the dominant loss shifts from concentration loss to activation loss with an increase in Pt loading. An increase in the ionomer and carbon volume fractions help improve the local membrane hydration, whereas a high porosity facilitates the oxygen concentration at Pt surface. This study is expected to provide deeper insights into CL design and performance optimization for PEMFCs.

Simultaneous fault diagnosis of proton exchange membrane fuel cell systems based on an Incremental Multi-label Classification Network

Fault diagnosis plays an important role in the operation of proton exchange membrane fuel cell (PEMFC) systems. In some certain working conditions, multiple faults can occur simultaneously. And, to the best of our knowledge, very few studies have yet to design an algorithm specifically for simultaneous fault diagnosis in PEMFC systems. Therefore, a novel simultaneous fault diagnosis algorithm, based on multi-label classifier chain named Incremental Multi-label Classification Network (IMCN), is proposed. To develop and optimize IMCN, a PEMFC model is constructed based on the commercial software AVL CURISE M to simulate data when simultaneous multiple faults occur. To further verify the generalization performance and practical effect of IMCN, a bench experiment using a high-power PEMFC system is conducted, which has similar boundary conditions as the boundary conditions embedded in simulation model. And, whether in experiment or simulation, corresponding verification methods are adopted to verify the success of simultaneous multiple faults embedding. Experimental data testing shows that, the subset accuracy, Hamming loss, Jaccard index, precision and recall of IMCN reaches 0.973, 0.029, 0.921, 0.961 and 0.956 respectively (better than Multi-Label MLP classifier, Label powerset MLP classifier, etc.), and the proposed simultaneous fault diagnosis method has achieved excellent results.

Investigation of Ship Vibration Effects on the Gas Distribution and Output Voltage of a Proton Exchange Membrane Fuel Cell.

The effects of ship vibrations on the mass transfer and performance of proton exchange membrane fuel cells (PEMFCs) have many vague aspects. Hence, the method of loading ship vibrations with the sliding mesh is first proposed in this paper. Furthermore, this method is adopted to form the solution of the simulation study of PEMFC performance under ship vibrations. In the framework of computational fluid dynamics, the ship vibration is loaded into the three-dimensional PEMFC model and successfully solved. Then, a series of special cases are performed to explore the gas distribution and output features of PEMFCs in ship environments with different vibrations. Finally, the results show that the ship vibrations have vectorial effects on the gas transfer in the PEMFC. Additionally, vibrations perpendicular to the channel evidently affect the gas transfer. Moreover, while the vibrations are parallel to the channel, they remain nearly normal. Relatively, a more significant influence on the output voltage of the PEMFC will occur under smaller frequencies and/or greater amplitudes of ship vibrations.

Numerical study on water transfer characteristics under joint effect of placement orientation and flow channel size for PEMFC with dead-ended anode

Proton exchange membrane fuel cell (PEMFC) with dead-ended anode is of outstanding advantages of high hydrogen utilization and simple hydrogen system. Because of its unique operation mode, the effect of gravity on water and gas transportation in PEMFC with dead-ended anode cannot be ignored. A two-phase model of PEMFC with dead-ended anode was developed, and the effects of placement orientation and cross section sizes of flow channel on water transport characteristics were analyzed. The results show that the flow channel with smaller cross section (0.5mm × 0.5 mm) showed better initial performance, but its voltage drop rate was faster due to liquid water was easier to be accumulated in the channel. Conversely, the liquid water volume fraction was smaller with a larger flow channel size. Moreover, the volume fraction of liquid water at the end of the channel was significantly greater than that in other areas when it was placed vertically. According to the results, the flow channel placed vertically combined with a larger cross section size (2mm × 2 mm) could effectively transferred liquid water to the end of the flow channel. Thus, it can effectively reduce the damage to PEMFC caused by water accumulation.

A two-level power management strategy in a DC-coupled hybrid microgrid powered by fuel cell and energy storage systems with model predictive controlled interface converter

This paper intends to present a DC-coupled hybrid microgrid, including proton exchange membrane fuel cell (PEMFC), battery bank, and supercapacitor, which is suitable for applications such as distributed power generation and the power system of vessels. In the proposed configuration, the PEMFC serves as the main energy source, and supercapacitor and battery bank are as energy storage systems. A comprehensive model of the 6 kW PEMFC, including the dynamic model along with the electrical model, is presented. Three DC-DC converters, consisting of an inter-leaved boost with voltage multiplier converter (IBVM) connected to the fuel cell and two bi-directional converters connected to the storage devices are employed. Additionally, an AC-DC converter is utilized to ensure stable AC voltage supply, proper power flow, and DC-link voltage control. Moreover, this paper provides a two-level power management strategy (PMS) to control and optimally distribute power between the components of the hybrid microgrid. The proposed strategy is divided into device-level and system-level controls. At the device-level control, a decentral-ized model predictive control strategy (MPC) without using any proportional-integral-derivative (PID) controllers is proposed to control the different modules of the hybrid microgrid. Also, at the system-level control, a rule-based strategy is developed to optimally distribute power between power sources and ensure stable operation under different operation modes. The simulation results in Matlab/Simulink environment are given to verify the effectiveness of the PEMFC model, the chosen converters, the proposed control methods, and the proposed hybrid microgrid.

A porous-rib flow field for performance enhancement in proton exchange membrane fuel cells

Concentration loss under solid rib induced by oxygen starvation and water flooding in conventional solid-rib flow fields (sr-FFs) limits the cell performance. Herein, a porous-rib flow field (pr-FF) is proposed for proton exchange membrane fuel cells (PEMFCs) to improve the gas transport and water removal under the ribs. The multi-physical processes and cell performance are investigated via a three-dimensional multi-phase PEMFC model. Numerical results show that the limiting current density and peak power density for the pr-FF-based PEMFC are increased by 15% and 9% respectively, while the pressure drop is reduced by 38%. A higher oxygen concentration and a lower liquid saturation under the porous ribs are obtained, in addition to the improved uniformities of oxygen concentration and current density. The pr-FF-based PEMFC is suggested to operate under a fully humidified condition for yielding a higher performance, where the peak power density is increased by 14% corresponding to the increase in cathode inlet humidity from 0.5 to 1.0. More interesting, the porous-rib design can enhance the cell performance regardless of the porosity. The proposed pr-FF provides an alternative solution for performance enhancement of PEMFCs by enhancing under-rib mass transport without increasing pumping power.

Experimental and numerical study on improvement performance by wave parallel flow field in a proton exchange membrane fuel cell

• Numerical and experimental study of the PEMFC with a novel three-dimensional WPFF is investigated. • The PEMFC with WPFF improves the net power. • The WPFF with three-dimensional structure promote the transportation of oxygen to the catalytic layer. • The narrow region in WPFF enhances the gas flow velocity and improves the water management of PEMFC. The performance and operation stability of proton exchange membrane fuel cells (PEMFCs) are closely related to the transportation of reactants and water management in the membrane electrode assembly (MEA) and flow field. In this paper, a new three-dimensional wave parallel flow field (WPFF) in cathode was designed and analyzed throughout simulation studies and an experimental method. The experimental results show that the performance of PEMFC with WPFF outperforms that of PEMFC with straight parallel flow field (SPFF). Specifically, the peak power density increased by 13.45% for the PEMFC with WPFF as opposed to PEMFC with SPFF. In addition, the flow field with area of 11.56 cm 2 was formed by the assembly of transparent end plate used for cathode and the traditional graphite plate used for anode. To understand the mechanism of the novel flow field improving the performance of PEMFC, a model of PEMFC was proposed based on the geometry, operating conditions and MEA parameters. The thickness of gas diffusion layers (GDL), catalytic layers (CL) and proton exchange membrane were measured by scanning electron microscope. The simulation result shows that compared with SPFF, the WPFF based PEMFC promote the oxygen transfer from flow channel to the surface of CL through GDL, and it was beneficial to remove the liquid water in the flow channel and the MEA.

Optimization of maximum power density output for proton exchange membrane fuel cell based on a data-driven surrogate model

Operating conditions are of great significance for proton exchange membrane fuel cells (PEMFCs) and directly determine the output performance of PEMFC. In this paper, an optimization framework is proposed, which is combining a neural network data-driven surrogate model and a stochastic optimization algorithm to achieve multi-variable global optimization, and optimizes the operating conditions for enhancing the power density of the PEMFC. A numerical model of the PEMFC is constructed as the source of the database, and the database is used to train a data-driven surrogate model based on radial basis functions (RBF), which is a typical feed-forward neural network. Then the surrogate model is fed into a particle swarm optimization (PSO) algorithm to obtain the optimal solution for the best combination of operating conditions. Results show that the surrogate model can accurately predict the output voltage of the PEMFC model, where the squared correction factor (R-square) and the mean percentage error of the test set are 0.99638 and 4.4686% respectively. And the optimization framework using a combination of data-driven surrogate model and stochastic optimization algorithm can obtain the maximum power density of 0.6097 W cm−2, with a relative error of only 1.5321% to the PEMFC model results. The result shows that the framework can effectively handle the multivariate optimization of complex systems.

Bald Eagle Search Algorithm for Parameter Identification of Proton Exchange Membrane Fuel Cell

A precise and reliable proton exchange membrane fuel cell (PEMFC) parameter identification performs an essential function in simulation analysis, optimal control, and performance research of actual PEMFC systems. Unfortunately, achieving an accurate, efficient, and stable parameter identification can sometimes be problematic for traditional optimization methods, owing to its strong coupling, inherent nonlinear, and multi-variable characteristics. Therefore, an advanced bald eagle search (BES) algorithm is designed to dependably identify the unknown parameters of the electrochemical PEMFC model in this work. For evaluating and analyzing the overall optimization performance of the BES comprehensively, it is compared with the genetic algorithm (GA) based on MATLAB under three cases. According to the simulation results, the optimum root mean square error (RMSE) achieved by BES is 96.27% less than that achieved by GA in parameter identification, which fully indicates that the precision, accuracy, and stability of the optimization results can be remarkably improved via the application of BES.

The influence of degradation effects in proton exchange membrane fuel cells on life cycle assessment modelling and environmental impact indicators

Although proton exchange membrane fuel cell (PEMFC) systems are expected to have lower environmental impacts in the operational phase, compared to conventional energy conversion systems, there are still certain economic, operational, and environmental setbacks. Durability under a wide range of operating conditions presents a challenge because degradation processes affect the PEMFC efficiency. Typically, life cycle assessment (LCA) of PEMFC systems do not include performance degradation. Thus, a novel semi-empirical PEMFC model is developed, which includes degradation effects caused by different operational regimes (dynamic and steady-state). The model is integrated into LCA through life cycle inventory (LCI) to achieve a more realistic and accurate evaluation of environmental impacts. Verification of the model clearly showed that the use of existing LCI models underestimates the environmental impacts. This is especially evident when green hydrogen is used in PEMFC operational phase, where manufacturing phase and maintenance (stack replacements) become more influential. Input parameters of the model can be modified to reflect technological improvements (e.g. platinum loading or durability) and evaluate the effects of future scenarios.

Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer

On the bases of meta-heuristic optimizers and experimental datasets, the parameter extraction of the proton exchange membrane fuel cells (PEMFCs) model to reach accurate current/voltage (I/V) curves remain an active research area during these last years. In this paper, an improved hybridized optimizer is developed to accurately estimate the PEMFC model parameters namely artificial bee colony differential evolution optimizer (ABCDE). In the developed ABCDE, the double execution of the mutation strategy allows enhancing the exploitation phase and avoiding to get stuck into the local minima. To assess the proposed ABCDE based parameter's identification, a comparative study with the recently published techniques including shuffled complex evolution, artificial ecosystem-based optimizer, and enhanced Lévy flight bat algorithm is performed using five typical PEMFCs modules. In this context, the reached sum of squared errors (SSE) and the standard deviations (STD) are very competitive among the challenging methodologies. ABCDE reaches the best SSE values within interesting overall STD and CPU run time less than 3e−15 and 0.225 s, respectively, for the five cases under study. It can be confirmed that the cropped SSE values and the STD among other challenging methodologies are very competitive with the best convergence speed. The ABCDE reaches 0.011697781, 2.07916558, 0.85360752, 9.6536060e−02, and 1.42098181379214e−04 for BCS 500W, NedStack PS6, Ballard Mark V, Horizon H-12, and Modular SR-12; respectively. In addition to that, the comparison results indicate that the proposed ABCDE is successfully utilized to characterize the PEMFC's model reliably and rapidly.