Abstract
Fuel cells are a pivotal technology in the changes towards sustainable and clean energy methods due to their high energy performance and environmentally aligning functioning. This investigation carried out an overall computational analysis to investigate the influences of these crucial factors on the efficiency of a single fuel cell. This research work measures the influence of parameters on key performance variables like polarization curves, electric potential and current density output. The results show that higher inlet pressures and mass flow rates significantly enhance reactant transport, thereby decreasing concentration losses and improving polarized current outcomes. GDL porosity and electrode exchange coefficients are found to play a significant role in enhancing reactant distribution and electrochemical reaction kinetics leading to good utilization of fuel and higher cell performance. Conversely, higher inlet temperatures negatively impact efficiency due to rises in thermal stresses and reduced reactant concentrations at critical reaction zone. Furthermore, the research identifies optimal ranges for these parameters, offering actionable insights for improving fuel cell design and operation. These results contribute to the broader efforts in advancing fuel cell technologies paving the way for their effective deployment in clean energy applications. This study underscores the importance of integrating computational analysis into the optimization of high-performance and durable fuel cells for the energy demands of the future.
1 Introduction
Fuel cells play a vital role in sustainable and clean energy solutions giving optimum efficiency and lower environmental effects. Proton Exchange Membrane Fuel Cells (PEMFCs) performance is depending on different design and operational parameters such as inlet temperature, inlet pressure, inlet mass flow rate, gas diffusion layer (GDL) porosity, and electrode exchange coefficients, etc. Present developmental studies in fuel cell research have concentrated on optimizing these parameters to improve efficiency, durability, and cost-effectiveness.
1.1 Background studies
Nowadays many researchers have implemented new novel approaches to enhancing fuel cell performance.
Hierarchical multiple-input, multiple-output (MIMO) control technique combined with a reference governor, primarily improving airflow dynamics and energy efficiency in fuel cell airpath systems [1].
In the same way, tapered parallel flow field for cathode-side transport in PEMFCs, discussing optimum reactant distribution by multi-objective optimization method [2].
Graphene-coated Nafion® membranes possessing enhanced proton conductivity and chemical inertness resulting in more efficiency and lifespan [3].
Gaussian Process Model Predictive Control to control voltage variations in PEMFCs at dynamic load environments indicating the effectiveness of optimized control techniques [4].
Indicating material degradation, high-temperature PEMFCs finding innovative techniques to protect material wear at higher temperatures [5].
An economic factor also plays a significant role in fuel cell optimization and cost-performance trade-offs by mentioning that reduced usage of platinum electrode lowers manufacturing expenses [6].
Computational simulation on designed open-cathode fuel cells emphasizing the significance of airflow patterns in protecting reactant deviation and maintaining performance [7].
Further, optimized additive manufacturing to improve end plate designs, lowering weight and increasing thermal dispersion [8].
On the other hand, numerical simulations are performed in the system to enhance the water management strategies and uniform distribution of reactant [9].
In the same way, optimized material coatings to improve the lifespan of high-temperature PEMFCs [10].
The influence of humidity on proton conductivity to finding maximum hydration levels to protect membrane drying and enhance efficiency [11].
Instead of these developments, still some problems remain in the advancement of operating conditions, transport of reactant and improvement of lifespan.
Several research gives knowledge into isolated parameters, yet an overall simulation of the influences among many factors is lacking.
1.2 Research problems and objectives
Considering these research gaps, this research conducts a numerical simulation of a single fuel cell: structured variation of inlet conditions and material characteristics to analyze their influence on key performance parameters such as polarization curves, electric potential, and current density output. The significance of this research accommodates several influencing parameters to improve efficiency and durability. The main objectives of this work are listed below.
To investigate the influence of inlet pressure, temperature, and mass flow rate on reactant transport and electrochemical performance.
To calculate the effects of GDL porosity and electrode exchange coefficients on reactant dispersion and reaction kinetics.
To calculate maximum operating conditions that increase fuel consumption and reduce concentration losses.
By providing an overall numerical evaluation, this work delivers significant information about fuel cell design and optimization strategy to assist with their wider application in clean energy.
2 Model development
2.1 Fuel cell geometry and simplifications
The computational model of the fuel cell was developed using ANSYS Fluent 2021 R2 leveraging its Fuel Cell Module to simulate electrochemical reactions, multi-species transport, and porous media behaviour. The fuel cell consists of an anode, cathode, gas flow channels, catalyst layers and a structure of PEMFC, as shown in Fig. 1 and dimentions are listed in Table 1.
Structure of fuel cell
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
2.2 To simplify the computational complexity certain assumptions were made
Steady-State Analysis: The study assumes steady-state operation, neglecting transient effects.
Isothermal Conditions: The fuel cell temperature was assumed constant to focus on mass transport and electrochemical performance.
Ideal Gas Behaviour: The reactant gases (H2 and O2) were modelled as ideal gases to simplify thermodynamic calculations.
Dimensions of fuel cell components
| S. No | Fuel cell components | Dimension |
| 1 | Anode current collector | 10 × 2 × 3 mm |
| 2 | Anode flow channel | 10 × 1 × 1 mm |
| 3 | Anode diffusion section | 10 × 2 × 0.025 mm |
| 4 | Anode catalytic section | 10 × 2 × 0.015 mm |
| 5 | Membrane layer | 10 × 2 × 0.05 mm |
| 6 | Cathode catalytic section | 10 × 2 × 0.015 mm |
| 7 | Cathode diffusion section | 10 × 2 × 0.025 mm |
| 8 | Cathode flow channel | 10 × 1 × 1 mm |
| 9 | Cathode current collector | 10 × 2 × 3 mm |
3 Boundary conditions
3.1 Meshing and grid independence study
A structured mesh was generated in ANSYS Meshing, ensuring accurate resolution of thin catalyst and membrane layers. Polarization plot of PEM fuel cell is shown in Fig. 2: current density magnitude (A cm−2) for various mesh elements in a computational simulation. The curves indicate the outcome from different element numbers, indicating how mesh refinement influences accuracy of the simulation outcome. The coarsest mesh (124,546 elements) deviates appropriately, while finer meshes (923,800 and 1,184,934 elements) converge very closely. This shows that the solution is mesh-independent, that is, further refinement will not notably change the simulation outcome. This plot ensures that the numerical model is reliable and computationally efficient.
Effects of mesh refinement on polarization curve of fuel cell
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The mesh consisted of 923,800 elements and 1,033,743 nodes, with the following quality metrics: Skewness: 0.9, Orthogonality: 0.75. Additionally, various mesh counts in particular sections such as the catalyst layers and the gas diffusion layer (GDL) possess different effects on numerical validation [12].
3.2 Boundary conditions and solver settings
Pressure-Based Solver is used to solve the numerical model by considering boundary conditions as follows:
Anode Inlet: Hydrogen mass flow rate and species composition specified.
Cathode Inlet: Air (21% O2, 79% N2) with mass flow rate and pressure conditions.
Anode/Cathode Outlets: Pressure outlets with backflow constraints.
Membrane: Proton conductivity defined as a function of hydration and temperature.
Catalyst Layers: Source terms for hydrogen oxidation (HOR) and oxygen reduction reactions (ORR).
Gas Diffusion Layer (GDL): Modelled as porous media with specified permeability and porosity values.
3.3 Convergence criteria and solution validation
The convergence was monitored using residual values set to 1 × 10−6 for continuity, momentum, species transport, and energy equations. The polarization curve was tracked to ensure solution stability. The simulation results were validated against experimental data, comparing polarization curves and current density distributions. A validation illustrates the agreement between numerical and experimental results in Fig. 3. Comparison of polarization curve between the numerical analysis and experimental work [13] depicts the voltage variation at different current densities magnitude. The numerical outcome indicating a slightly lesser voltage than the experimental outcome shows minor deviations due to modelling assumptions and simulation. Such variations are generally noted in electrochemical work and can be attributed to different mass transport effects and electrode kinetics. Comprehensively the numerical outcome closely follows the experimental result evaluating its reliability for further analysis.
Comparison of polarization curve
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
4 Governing equations for fuel cell modelling
Fuel cell modelling in ANSYS Fluent involves solving a set of governing equations that describe fluid flow, species transport, electrochemical reactions and energy conservation. These equations are tailored to the specific components of the fuel cell.
The following considerations are assumed for fuel cell analysis in ANSYS Fluent module:
The analysis is assuming steady-state conditions.
Gases (hydrogen, oxygen, air) are treated as ideal property changes.
The chemical reactions occur only at the anode and cathode.
The no-slip boundary condition is applied because of fluid velocity is nil at solid region.
Electrochemical reactions are analyzed by using the Butler-Volmer method.
Electrodes are designed as porous region with flow resistance made by Darcy's Law.
Water transport may be analyzed or omitted.
The electrolyte is considered ideal with no complex ion transport models.
4.1 Mass conservation (continuity equation)
ρ - Fluid density,
- Velocity vector, and Sm - Source term accounting for species generation or consumption due to electrochemical reactions.
4.2 Momentum conservation (Navier-Stokes equation)
P - Pressure,
μ - Dynamic viscosity,
- Gravitational acceleration, and - External forces, such as those induced by electrochemical reactions [2].
4.3 Species transport equation
Yi - Mass fraction of species I,
- Diffusion coefficient, and Ri - Reaction rate [4].
4.4 Energy conservation equation
E - Total energy,
k - Thermal conductivity,
T - Temperature, and
SE - Heat sources from reactions [5].
4.5 Proton transport in the membrane
4.6 Electrochemical reaction kinetics (Tafel equation)
j - Current density,
j0 - Exchange current density,
α - Charge transfer coefficient,
η - Over potential,
F - Faraday constant,
R - Universal gas constant, and
T - Temperature [10].
4.7 Water transport in the membrane
Jwater - Water flux,
Dw - Water diffusion coefficient,
Cwater - Water concentration, and
- Electro-osmotic drag coefficient [9].
These equations form the foundation for simulating fluid flow, species transport, heat transfer, and electrochemical processes in fuel cells. By incorporating appropriate source terms, boundary conditions, and material properties, ANSYS Fluent solves these equations to predict the performance of fuel cells under various operating conditions.
5 Result and discussion
The temperature distribution in a fuel cell under varying operating conditions simulated using ANSYS and contour is shown in Fig. 4(a)–(c). The plot 4(a) indicates the significant role of inlet temperature in defining the thermal behaviour of the fuel cell. The red regions, which denote higher temperatures, typically correspond to areas where reaction rates are highest or where thermal dissipation is less efficient. Conversely, the cooler blue regions are indicative of areas with lower reaction intensity or better heat management. The Fig. 4(b) contour reveals the non-uniformity in heat generation and dissipation with red zones indicating hotspots potentially caused by localized areas of high reaction activity or inadequate cooling. Managing these hotspots is necessary to assure the durability and performance of the fuel cell, as excessive temperatures can degrade the membrane and other critical sections. In Fig. 4(c), the influence of an inlet pressure of 5 bar on the temperature distribution is analyzed. Higher pressures typically enhance the delivery and utilization of reactants leading to increased reaction rates and consequently higher heat generation. However, elevated pressure also improves reactant flow and diffusion potentially enhancing thermal dissipation.
(a) Temperature variation at inlet temperature 323.15 K, (b) Temperature variation at reference electrode current 0.95, (c) Temperature variation at inlet pressure 5 bar
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The capillary pressure distribution in a fuel cell under varying operating conditions is shown in the contour of Fig. 4(a)–(c). Figure 5(a) illustrates that capillary pressure arises due to the location of water inside the porous structure of the fuel cell. The red regions represent higher capillary pressure, which may indicate areas of water build-up, while the blue regions signify lower capillary pressure, where water removal is more efficient. Managing this balance is vital to avoid flooding, which can block reactant flow paths and reduce fuel cell efficiency. In Fig. 5(b) the contour shows how the elevated pressure influences capillary forces potentially leading to a redistribution of liquid water in the porous media. Higher capillary pressure in specific regions (depicted in red) might indicate zones prone to flooding while lower pressure zones (in blue) suggest better water management. This highlights the need to optimize operating pressure to maintain effective hydration of the membrane without causing performance issues due to water accumulation. In Fig. 5(c) the contour reveals the distribution of capillary forces caused by the interaction of water generation and removal. Higher capillary pressure regions reflect areas where water removal is insufficient possibly due to inadequate gas flow or uneven porous structure leading to localized flooding. Conversely, lower pressure zones indicate effective water management, ensuring smooth operation.
(a) Capillary pressure variation at inlet temperature 303.15 K, (b) Capillary pressure variation at inlet pressure 4 bar, (c) Capillary pressure variation at reference electrode current 0.55
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
5.1 Inlet temperature
The changes of current density magnitude (A cm−2) across various regions of a single fuel cell under different working temperatures (313.15 K, 323.15 K, 333.15 K, and 343.15 K) are shown in Fig. 6. The figure shows the significant variations in current density across all regions. The anode and cathode regions show maximum peaks in current density signifying capability of electrochemical reactions in these regions. These sharp peaks are more significantly located at high temperatures (e.g., 343.15 K, represented by the pink colour), which shows the required enhancement in reaction kinetics and ion transport at higher temperatures. On the other hand, the ADL, ACL, membrane, CCL, and CDL zones display a relatively flat line indicating less current density in these regions. This minimal reaction shows that these zones primarily function as support structures for gas diffusion ion transport and separation rather than being zones of active electrochemical reactions.
Current density magnitude variation with various inlet temperatures
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The relationship between inlet temperature and polarized current output in a single fuel cell is shown in Fig. 7. Green stars indicate the data value obtained from the numerical analysis and the blue line indicates the linear regression fit representing a consistent trend. The graph shows a clear linear decrease in polarized current output as the inlet temperature increases. At lower temperatures, the current output is high near to 1.075 (A cm−2) but it decreases uniformly to around 0.900 (A cm−2) at higher temperatures. The fuel cell's electrochemical performance may be adversely affected by the raising temperatures. The linear regression indicates the predictability of this trend and emphasizes the significance of optimizing the working temperature to balance performance and durability in the fuel cell system.
Polarized current output with various inlet temperatures
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The variation of capillary pressure (Pa) under various working temperatures (303.15 K, 323.15 K, and 333.15 K) is shown in Fig. 8. At the anode region, capillary pressure is indicating high, approximately 600 Pa at 333.15 K. This steep pressure difference indicates the presence of water vapour accumulation or transport challenges at the anode zone. Moving through the ADL and ACL zone the capillary pressure rapidly decreases, which shows a lesser effect of liquid water transport in these regions. Across the membrane the pressure remains less consistent with its role as a proton-conducting and water-managing barrier. At the cathode region, there is a sharp rise in capillary pressure specifically in the CCL zone, which is highly pronounced at elevated temperatures. This proves that water management issues could be focused in the cathode particularly at higher temperatures due to higher electrochemical reaction and high-water production. In the CDL zone, the capillary pressure is uniform and constant but a temperature-dependent trend is indicated, where higher temperatures correspond to slightly higher-pressure point.
Capillary pressure variation with different inlet temperatures
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The polarization curve is shown in Fig. 9 and the colour bar on the right represents the inlet temperature in Kelvin (from 303.15 to 350 K) with darker shades indicating lower temperatures and brighter shades representing higher temperatures. The polarization curve shows a characteristic linear decrease in voltage as the current density increases. This behaviour of fuel cells occurs due to activation losses, ohmic losses and concentration losses. The voltage is comparatively higher at lower current densities (e.g., below 0.4 A cm−2) at about 0.7 V, indicating effective electrochemical processes with minimal losses. The current density magnitude is increasing linearly with voltage drops due to combined effect of resistive losses within the membrane and diffusion limitations at electrode. The influence of different inlet temperatures on the polarization curve is represented in the color-coded data value. At higher temperatures, the fuel cell exhibits better performance because of the linearly increasing voltage with constant current density magnitude.
Polarization curve for various inlet temperatures
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
5.2 Mass flow rate
The correlation of inlet mass flow rate (kg s−1) and the polarized current output (A cm−2) for a fuel cell is shown in Fig. 10. The numerical simulation data are represented by green stars and the blue line indicates the linear regression. The plot shows a direct linear relationship between the inlet mass flow rate and the polarized current output. As the mass flow rate is increasing the current output also rises. This trend indicates that increasing the inlet mass flow rate improves the supply of reactants (such as hydrogen and oxygen) to the fuel cell, thereby enhancing electrochemical reactions and overall performance. This relationship is critical for enhancing fuel cell operation as it indicates the significance of ensuring a required reactant flow rate to reach higher current outcomes.
Polarization current output with various inlet mass flow rates
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The variation in the hydrogen mass fraction along the normalized length of a fuel cell for different inlet mass flow rates is shown in Fig. 11. At high inlet mass flow rates (e.g., 9 × 10−7) a bigger hydrogen mass fraction is maintained throughout the span of the fuel cell compared to low mass flow rates (e.g., 2 × 10−8). This is because more flow rates supply higher hydrogen to the reaction zone ensuring good availability for the electrochemical process. The slope decreases vary with differences in the mass flow rate. Low mass flow rates yield steady declines indicating more important hydrogen depletion along the fuel cell span due to consistent inlet supply. On the other hand, higher mass flow rates indicate a gentler decrease showing that required hydrogen is available to sustain the reaction over the entire span of the fuel cell.
Variation of hydrogen mass fraction along with length of fuel cell
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The changes of oxygen mass fraction along the normalized span of a fuel cell for various inlet mass flow rates are shown in Fig. 12. The legend indicating the specific inlet mass flow rates (kg s−1) with various colour lines for each mass flow rate: 3 × 10−7, 9 × 10−7 8 × 10−8 and 2 × 10−8. The oxygen mass fraction declines linearly along the span of the fuel cell for all mass flow rates, showing oxygen consumption during the electrochemical reactions at the cathode. More inlet mass flow rates (e.g., 9 × 10−7) result in higher oxygen mass fractions across the cell span compared to lower mass flow rates (e.g., 2 × 10−8). This plot indicates the significance of optimizing the inlet mass flow rate to ensure sufficient oxygen delivery, which are needed for maintaining high reaction efficiency and preventing performance losses due to oxygen depletion.
Variation of oxygen mass fraction along with length of fuel cell
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The relationship between electric potential and current density magnitude (A cm−2) for a fuel cell with the inlet mass flow rate is presented as a colour gradient in Fig. 13. The colour bar on the right shows the inlet mass flow rate (kg s−1) with yellow indicating higher mass flow rates and purple indicating lower mass flow rates. The curve depicts the characteristic polarization behaviour of a fuel cell, which can be divided into distinct zones:
Activation Loss Zone: At low current densities magnitude the curve drops steadily from approximately 0.7 V representing activation losses. These losses occur due to the energy required to drive the electrochemical reactions.
Ohmic Loss Zone: As the current density magnitude increases (nearly 0.3–0.5 A cm−2) the curve flattens showing a lesser voltage drop. This indicates ohmic losses primarily due to resistance in the electrolyte and electrodes.
Concentration Loss Zone: At higher current densities magnitude (above 0.5 A cm−2) the curve declines more steadily again showing concentration losses caused by reactant depletion at the electrodes.
Polarization curve for different inlet mass flow rates
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
This plot emphasizes the significance of optimizing the inlet mass flow rate to balance reactant supply and energy efficiency. Higher flow rates mitigate concentration losses and improve overall performance especially at higher current densities where reactant starvation is more likely to occur.
5.3 Reference electric potential
The relationship between voltage and current density magnitude (A cm−2) for a fuel cell with a colour bar indicating the reference electric potential (in volts) is shown in Fig. 14. The colour bar ranges from 0.25 V (green) to 0.95 V (blue), indicating variations in electric potential across operating conditions. The curve follows the typical polarization behaviour of a fuel cell characterized by three regions:
Activation Loss Region: At low current densities magnitude (below 1.0 A cm−2), the voltage drops steadily from 0.9 V due to activation losses, associated with electrochemical reactions.
Ohmic Loss Region: The resistive losses in the fuel cell components, such as the electrolyte and electrodes are responsible for the linear and mild voltage drops shown in the curve between 1.0 and 1.5 A m−2.
Concentration Loss Region: At high current density magnitude (above 1.5 A m−2), the voltage declines sharply due to reactant depletion at reaction zones.
Polarization curve for various reference electric potentials
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
It is concluded that the higher current density magnitude corresponds to lower electric potentials due to reactant depletion.
The relationship between the reference electric potential and the polarized current output (A cm−2) for a fuel cell is shown in Fig. 15. The orange stars represent the actual data value points, while the blue line indicates a linear regression fit illustrating the trend. The plot shows an inverse linear relationship between the reference electric potential and the polarized current output. This pattern indicates that optimum reference potentials aligned to reduced current outcome likely due to raised energy losses or resistance inside the fuel cell system. The linear decline fit provides that the relationship between the two variables is consistent and within limits, which is useful information about the performance behaviours of the fuel cell system. The graph shows the significance of carrying an optimal reference electric potential to get optimum current outcomes and high fuel cell system performance. This information is more critical for designing and operating fuel cells systems by conditions that reduce energy losses while withholding stable performance of the system.
Polarization current output with various reference electric potentials
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
5.4 GDL porosity
The relationship between electric potential and current density magnitude (A cm−2) for a fuel cell under various Gas Diffusion Layer (GDL) porosities is shown in Fig. 16. Each colour line shows a specific GDL porosity value (ranging from 0.5 to 0.9). The graph provides the insights on the significance of GDL porosity on the fuel cell's system polarization behaviour. For all porosity ranges the electric potential declines steadily with increasing current density magnitude.
Lower Porosity (e.g., 0.5): The orange line shows the steadiest voltage decline with increasing current density magnitude. This indicates higher resistance and poorer reactant transport due to limited permeability of the GDL, which restricts the supply of oxygen and hydrogen to the reaction sites.
Higher Porosity (e.g., 0.9): The blue line indicates the smallest slope maintaining higher voltages at the same current density magnitude compared to lower GDL porosities. This suggests enhanced reactant accessibility and reduced mass transport resistance, enhancing fuel cell performance.
Polarization curve with various GDL porosities
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
5.5 Anode exchange coefficient
The relationship between electric potential and current density magnitude (A cm−2) for a fuel cell with the anode exchange coefficient represented as a colour gradient is shown in Fig. 17. The colour bar on the right ranges from 1.0 to 0.25 reflecting different values of the anode exchange coefficient. The polarization curve displays three distinct regions:
Activation Loss Region: At low current densities (below 0.2 A cm−2) the electric potential declines sharply indicating activation losses. These losses are associated with the energy needs to resolve activation barriers in the electrochemical reactions at the anode side.
Ohmic Loss Region: Ohmic losses are indicated by a curve that decreases between 0.2 and 0.4 A m−2, this loss is due to the resistive behaviours of electrolyte and electrode materials.
Concentration Loss Region: At optimum current density magnitudes (above 0.4 A cm−2) the curve steadily reflects concentration losses due to reactant depletion at the anode side.
Polarization curve for various electrode exchange coefficients
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
The fuel cell performance is increased with optimum exchange coefficient at low and medium current density due to enhanced reaction kinetics. Enhancing this variable is required for attaining efficient and stable fuel cell system operation.
The relationship between the electrode exchange coefficient and the polarized current density magnitude outcome (A/cm2) for a fuel cell system is shown in Fig. 18. The purple stars indicate the numerical simulation data values while the blue line indicates a linear regression fit representing the overall trend of the system. The plot demonstrates a promotional linear relationship between the electrode exchange coefficient and the polarized current density output. An electrode exchange coefficient progressively increases from 0.3 to 1.0, the polarized current density output increases from 0.0 A cm−2 to nearly 0.8 A cm−2. This pattern indicates that optimum exchange coefficients outcome in enhanced electrochemical reaction provides better fuel cell system performance. The linear regression fit indicates a significance of the electrode exchange coefficient in the polarization behaviour of the fuel cell for better ion movement. By modifying the material properties and aligning at the electrode material, it is more possible to attain higher current outcomes and maximize the system's performance.
Variation of polarized current output with various electrode exchange coefficients
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
5.6 Inlet pressure
The correlation between inlet pressure and polarized current density output (A cm−2) for a fuel cell system shows in Fig. 19. The red stars show the actual simulation data values, while the blue line represents a linear regression fit line aligning with the trend. The plot directly shows that the linear relationship between inlet fluid pressure and polarized current magnitude. As the inlet fluid pressure increases from 2 bars to 5 bars the polarized current magnitude rises from nearly 0.7 A cm−2 to 1.1 A cm−2. These changes show that higher inlet fluid pressures improve the reactant supply, enhancing reaction rates and raising the fuel cell's efficiency. This relationship indicates the significance of enhancing inlet fluid pressure in fuel cell operations. Therefore, maintaining a maximum balance is needed for increasing fuel cell performance and durability.
Variation polarized current output with different inlet pressures
Citation: International Review of Applied Sciences and Engineering 2025; 10.1556/1848.2025.01011
6 Conclusion
This research study comprehensively analyzed the effects of crucial working and design parameters on the efficiency of a single fuel cell by using a numerical approach. The investigation indicates the interdependence of variables such as inlet fluid pressure, mass flow rate, temperature, porosity of gas diffusion layer (GDL) and electrode exchange coefficients on significant performance metrics including polarization condition, current density output, and reactant usage. By complete numerical simulations, the work provided critical knowledge into enhancing these factors to increase fuel cell performance and durability.
The numerical simulation results show that increasing the inlet fluid pressure from 4 bars to 5 bars led to an important enhancement in reactant transport, decreasing concentration losses and increasing the overall current density magnitude output.
Similarly, more mass flow rates, specifically a rise from 8E-8 kg s−1 to 9E-7 kg s−1 resulted in increased reactant availability at the catalyst zone, thereby improving the electrochemical behaviour rates and overall efficiency. The relationship between these factors and performance followed an assumed trend.
The concern of GDL porosity was also found to be crucial. A more porous medium facilitated optimum reactant distribution, decreasing mass transport limits and maintaining saturated performance across the fuel cell. This work revealed that an increase in porosity of GDL from 0.5 to 0.75 significantly improved reactant transport and stabilized operation.
Similarly, the anode exchange coefficient established a profound effect on reaction transport, where rises from 0.3 to 1.0 led to a specific decline in activation losses, thereby enhancing the overall electrochemical performance.
Conversely, higher inlet temperatures provided negative effects on fuel cell efficiency. Raising the inlet temperature from 303.15 K to 323.15 K exhibited in less reactant concentrations and optimum thermal stresses, reason for a decline in polarized current magnitude from 1.075 A cm−2 to nearly 0.900 A cm−2. This indicates the significance of maintaining a maximum thermal balance to reduce efficiency losses.
The reference electric potential was also one of the key factors influencing fuel cell efficiency. This work demonstrated that an increase in reference electric potential from 0.55 V to 0.95 V led to a significant decrease in polarized current output from 1.75 A cm−2 to nearly 0.0 A cm−2. This inverse relationship suggests the need of improving reference potential values to minimize energy losses and maintain system performance.
The knowledge gained from this research work contributes to the overall understanding of fuel cell performance enhancement. The outcome delivers a framework for fuel cell manufacturers to refine material selection, structural design and operational strategies.
6.1 Recommendations for future research
Advanced Material Development: Investigate high-performance materials for GDLs, membranes, and catalysts to enhance conductivity, durability, and reactant transport properties.
Dynamic Operating Conditions: Analyze fuel cell efficiency under fluctuating temperatures, pressures, and load demands to develop robust and adaptive fuel cell systems.
Catalyst Optimization: Introduce non-precious metal catalysts to decrease costs while maintaining reaction efficiency and stability over long span operation.
Water Management Strategies: Develop enhanced water management techniques to prevent flooding and drying ensuring enhanced fuel cell hydration and reactant transport.
Degradation Mechanisms: Study long-term degradation of material mechanisms in detail including thermal stress, catalyst poisoning and material fatigue to improve fuel cell lifespan. Computational and experimental research work can help identify crucial factors contributing to performance loss over time.
AI and Machine Learning: Adopting artificial intelligence (AI) and machine learning techniques for factors enhancement and performance prediction. This technology can analyze bigger datasets and identify non-linear relationship among parameters enabling more efficient fuel cell design and operation.
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