Proton Exchange Membrane Fuel Cell Model Research Articles

An event-triggered and dimension learning scheme WOA for PEMFC modeling and parameter identification

Aiming at the high dimension and complexity of parameters identification problem, a whale optimization algorithm based on event-triggered and dimension learning scheme (EDWOA) is presented, which is specifically designed for the proton exchange membrane fuel cell (PEMFC) model. Drawing inspiration from group optimization strategies, a novel dimensional learning method is introduced to enhance the dynamic search capabilities of the algorithm. To assess the efficacy of the proposed algorithm, benchmark function testing was conducted, and its fitness surpassed common heuristic algorithms on over 15 objective functions. The results clearly indicate that the EDWOA algorithm outperforms its counterparts in terms of global search performance. Its ability to navigate complex search spaces sets it apart from other algorithms. Finally, the proposed EDWOA algorithm is successfully applied to parameter identification in the PEMFC model. Through a comparative analysis with existing research findings, it was found that the identified PEMFC model exhibited a notable enhancement in fitness, ranging from 0.02 to 0.93. This underscores the effectiveness of the EDWOA algorithm in improving the performance and dynamic output of PEMFC models.

Numerical Study on Effect of Flow Field Configuration on Air-Breathing Proton Exchange Membrane Fuel Stacks

Air-breathing proton exchange membrane fuel cells (PEMFCs) show enormous potential in small and portable applications because of their brief construction time without the need for gas supply, humidification and cooling devices. In the current work, a 3D multiphase model of single air-breathing PEMFCs is developed by considering the contact resistance between the gas diffusion layer and bipolar plate and the anisotropic thermal conduction and electric conductive in the through-plane and in-plane directions. The 3D model presents good grid independence and agreement with the experimental polarization curve. The single PEMFC with the best open area ratio of 55% achieves the maximum peak power density of 179.3 mW cm−2. For the fuel cell stack with 10 single fuel cells, the application of the anode window flow field is beneficial to improve the stack peak power density compared to the anode serpentine flow field. The developed model is capable of providing assistance in designing high-performance air-breathing PEMFC stacks.

Coupled optimization of auxiliary channels and porosity gradient of GDL for PEMFC

In this study, we proposed a novel under-rib auxiliary channel and developed a systematic methodology combining computational fluid dynamics (CFD), an artificial neural network (ANN), and a particle swarm optimization (PSO) algorithm for the coupled optimization of the auxiliary-channel structure and gas diffusion layer (GDL) porosity gradient in a proton exchange membrane fuel cell (PEMFC). First, we developed a three-dimensional multiphase numerical model of a non-isothermal parallel flow-field PEMFC. ANN was then applied to obtain a mapping relationship between auxiliary channels structure, GDL porosity gradient and output performance of PEMFC with finite CFD data. Finally, PSO was employed to perform an optimization search to determine the optimal parameter pairing, resulting in a 16.7 % increase in maximum power density and a 20.6 % increase in the current operating range. In addition, the optimized PEMFC exhibited superior mass transfer and water removal capabilities, where the average oxygen concentration in the intermediate section of cathode GDL was increased by 33.3 % and the maximum liquid water saturation was reduced by 23.5 %. Moreover, the coupled optimization of the auxiliary channels and the GDL porosity gradient can increase power density by at least 8.7 % and current density by 9.1 % compared to single component optimization.

Extracting Accurate Parameters from a Proton Exchange Membrane Fuel Cell Model Using the Differential Evolution Ameliorated Meta-Heuristics Algorithm

The electrochemical proton exchange membrane fuel cell (PEMFC) is an electrical generator that utilizes a chemical reaction mechanism to produce electricity, serving as a sustainable and environmentally friendly energy source. To thoroughly analyze and develop the features and performance of a PEMFC, it is essential to use a precise model that incorporates exact parameters to effectively suit the polarization curve. In addition, parameter extraction plays a crucial role in the simulation analysis, evaluation, optimum control, and fault detection of the proton exchange membrane fuel cell (PEMFC) system. Despite the development of many algorithms for parameter extraction in PEMFC, obtaining accurate and trustworthy results rapidly remains a challenge. This study presents a hybridized algorithm, namely differential evolution ameliorated (DEA) for reliably estimating PEMFC model parameters. To evaluate the proposed DEA-based parameter identification, a comparison analysis with previously published methods is conducted using MATLAB/SimulinkTM (R2016b, MathWorks, Natick, MA, USA) in terms of system correctness and convergence process. The proposed DEA algorithm is tested to extract the parameters of two PEMFC models: SR-12 500 W and 250 W. The sum of the squared errors (SSE) between the experimental and the obtained voltage data is defined as an objective function. The simulation results prove that the suggested DEA algorithm is capable of identifying the optimal PEMFC parameters rapidly and accurately in comparison with other optimization algorithms.

Airfoil flow field for proton exchange membrane fuel cells enhancing mass transfer with low pressure drop

Optimal configuration of bipolar plate (BP) embedding flow field is of great significance in improving the power density and durability of proton exchange membrane fuel cells (PEMFCs). Inspired by the airfoil in aircraft dividing the airflow into two streams causing pressure difference, a novel airfoil flow field (AFF) is designed to enhance the mass transfer capacity in channel and across the rib between two adjacent channels, which hardly increases the pressure drop in channel, unlike many baffle designs. The performance improvement achieved by implementing AFF in PEMFC is compared with common parallel flow field (PFF) and the wave flow field (WFF) by experimental test with respect to polarization curve and electrochemical loss and numerical simulation via a three-dimensional (3D) PEMFC model. The experimental results indicate that the novel AFF exhibits best fuel cell performance at various operation conditions (e.g. relative humidity, air stoichiometric ratio) mainly benefitted from the enhanced oxygen transfer, which contributes to the improvement of maximum power density by 8.85 % and 14.37 % compared to PFF and WFF, respectively. The modeling results indicate that the pressure drop increase in AFF is negligible compared with PFF, and is even slightly lower than WFF, indicating high practicability. Moreover, the utilization of AFF also helps the uniformity of oxygen concentration, current density and temperature in PEMFC.

A Critical Review of Proton Exchange Membrane Fuel Cells Matter Transports and Voltage Polarisation for Modelling

Abstract Technologies based on the use of hydrogen are promising for future energy requirements in a more sustainable world. Consequently, modelling fuel cells is crucial, for instance, to optimize their control to achieve excellent performance, to test new materials and configurations on a limited budget, or to consider their degradation for improved lifespan. To develop such models, a comprehensive study is required, encompassing both well-established and the latest governing laws on matter transport and voltage polarisation for Proton Exchange Membrane Fuel Cells (PEMFCs). Recent articles often rely on outdated or inappropriate equations, lacking clear explanations regarding their background. Indeed, inconsistent understanding of theoretical and experimental choices or model requirements hinders comprehension and contributes to the misuse of these equations. Additionally, specific researches are needed to construct more accurate models. This study aims to offer a comprehensive understanding of the current state-of-the-art in PEMFC modeling. It clarifies the corresponding governing equations, their usage conditions, and assumptions, thus serving as a foundation for future developments. The presented laws and equations are applicable in most multi-dimensional, dynamic, and two-phase PEMFC models.

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

Proton Exchange Membrane Fuel Cells (PEMFCs) are promising sources of clean and renewable energy, but their performance and efficiency depend on an accurate modeling and identification of their system parameters. However, existing methods for PEMFC modeling suffer from drawbacks, such as slow convergence, high computational cost, and low accuracy. To address these challenges, this research work proposes an enhanced approach that combines a modified version of the SqueezeNet model, a deep learning architecture that reduces the number of parameters and computations, and a new optimization algorithm called the Modified Transient Search Optimization (MTSO) Algorithm, which improves the exploration and exploitation abilities of the search process. The proposed approach is applied to model the output voltage of the PEMFC under different operating conditions, and the results are compared with empirical data and two other state-of-the-art methods: Gated Recurrent Unit and Improved Manta Ray Foraging Optimization (GRU/IMRFO) and Grey Neural Network Model integrated with Particle Swarm Optimization (GNNM/PSO). The comparison shows that the proposed approach achieves the lowest Sum of Squared Errors (SSE) and the highest accuracy, demonstrating its superiority and effectiveness in PEMFC modeling. The proposed approach can facilitate the optimal design, control, and monitoring of PEMFC systems in various applications.

Multidomain Device-Level Fuel-Cell Modeling and Real-Time Hardware Emulation for Marine Research Vessel Power System

Marine research vessels (MRVs) are redesigned and refurbished to meet higher energy conversion efficiency and modular integration schemes while conforming to stronger environmental regulations. The proton exchange membrane fuel cell (PEMFC) is currently regarded as a potential power source for marine transportation applications due to its advantages of stability, sustainability, and zero emissions. This paper proposes a hierarchical scheme for the real-time hardware emulation of the MRV’s power system and a comprehensive multi-domain model for PEMFCs. The PEMFC model is presented in the electrochemical, hydration, and thermal domains by ordinary differential equations considering the interactions and dynamics of each domain. Meanwhile, the multi-domain PEMFC model considers the implications of the fluctuating supply of the onboard hydrogen circulation system. Moreover, the dynamics of the lithium-ion battery stacks are represented by an equivalent circuit model which considers heat flux phenomena. In the case study, the DC-AC grid of the MRV’s power system and electric propulsion system is configured using extensive electrification technology with an average model. The real-time hardware emulation is conducted on the Xilinx® UltraScale+™ VCU118 FPGA platform to execute the device-level and system-level behavior transients of the MRV. The results of the real-time hardware emulation have been validated against the full-scale hybrid MRV power system emulation over a wide operating range.

Moth flame-random search optimization of a zero-dimensional model of a proton exchange membrane fuel cell

Modelling of proton exchange membrane fuel cell (PEMFC) is important for better understanding, simulation, and design of high-efficiency fuel cell systems. PEMFC models are often multivariate with several nonlinear elements. Metaheuristic algorithms that are successful in solving nonlinear optimization problems are good candidates for this purpose. This study proposes a new metaheuristic algorithm called MFORS that uses the advantages of the moth-flame optimization algorithm in global search and the non-deterministic properties of the random search algorithm to identify the optimal parameters of the PEMFC model. The sum of squared errors between the estimated and measured voltage is a quality of fit criterion. To show the effectiveness of the proposed algorithm, two case studies of zero-dimensional models of SR-12 Modular PEM Generator and Ballard Mark V fuel cell are considered. The sum of squared errors for the parameter estimated of electrical PEMFCs by the proposed MFORS algorithm is compared with recent works. The results showed that by the MFORS algorithm, the minimum sum of absolute errors of the actual stack voltage and the simulated stack voltage in the two PEMFC are 0.095037 and 0.018019, compared with the second-best algorithm results giving 0.09681 and 0.8092, respectively.

Effects of Inhomogeneous Gas Diffusion Layer Properties on the Transportation Phenomenon and Performances of Proton-Exchange Membrane Fuel Cells.

The encapsulation of the proton-exchange membrane fuel cell (PEMFC) is an essential step of fuel cell stack assembly. The selection of the assembly pressure is very important to the stack performance and life. Based on that, this paper presents a method to describe the internal physical deformation of the gas diffusion layer (GDL) after inhomogeneous pressure by using user-defined functions (UDFs). The results show that the transmission difference caused by the nonuniform GDL deformation can be clearly seen by the UDFs method, and there is an obvious transition of GDL at the interface of the channel and the rib. A three-dimensional single-channel PEMFC model is established, and an optimal assembly pressure range is obtained, between 1.5 and 2.0 MPa. The maximum thermal stress inside the cell occurs in the middle of the membrane electrode assembly and decreases as the assembly pressure increases. Furthermore, the influence of rib-channel ratios is discussed. Compared to the fuel cells with ratios of 2:1, 2:3, and 1:2, the one with 1:1 exhibits the maximum current density and the highest power density.

Hybrid fuel cell-supercapacitor system: modeling and energy management using Proteus

The increasing adoption of electric vehicles (EVs) presents a promising solution for achieving sustainable transportation and reducing carbon emissions. To keep pace with technological advancements in the vehicular industry, this paper proposes the development of a hybrid energy storage system (HESS) and an energy management strategy (EMS) for EVs, implemented using Proteus Spice Ver 8. The HESS consists of a proton exchange membrane fuel cell (PEMFC) as the primary source and a supercapacitor (SC) as the secondary source. The EMS, integrated into an electronic board based on the STM32, utilizes a low-pass filter algorithm to distribute energy between the sources. The accuracy of the proposed PEMFC and SC models is validated by comparing Proteus simulation results with experimental tests conducted on the Bahia didactic bench and Maxwell SC bench, respectively. To optimize energy efficiency, simulations of the HESS system involve adjusting the hybridization rate through changes in the cutoff frequency. The analysis compares the state-of-charge (SOC) of the SC and the voltage efficiency of the fuel cell (FC), across different frequencies to optimize overall system performance. The results highlight that the chosen strategy satisfies the energy demand while preserving the FC’s dynamic performance and optimizing its utilization to the maximum.

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

Influence of crack formation at the catalyst layer and membrane interface on the performance of PEMFC

ABSTRACT 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.

Water saturation distribution in a PEMFC at the cathode side using OpenFOAM

Water management is a critical issue for achieving the optimum performance of proton exchange membrane fuel cells (PEMFC) since water flooding limits the flow of reactants to catalyst zones and produces electrode regions with higher electric resistance. We used a 3D non-isothermal multiphase model of a single PEMFC to investigate the liquid water saturation distribution at the cathode side. We used an open-source toolbox to solve finite volume method calculations and implement an effective diffusion model based on fiber layers. A parametric study of the condensation rate and saturation at 0.4, 0.2, and 0.1 V illustrates the distribution of liquid water saturation and the flooding zones across the electrode components. The comparison shows that the flooding zones are located at the land ribs. Also, increased water saturation and higher oxygen consumption produce a reactant restriction in the porous media and impact the global performance of the fuel cell.

Investigating and quantifying the effects of catalyst layer gradients, operating conditions, and their interactions on PEMFC performance through global sensitivity analysis

A gradient cathode catalyst layer (CCL) with promoted mass transfer and Pt utilization could enhance the output performance of proton exchange membrane fuel cells (PEMFCs). Since operating conditions affect reaction and mass transfer rate inside the PEMFC, the gradient CCL can only exert its full potential within a narrow operating range. Assessing the impact of gradients, operating conditions, and their interactions on output performance is vital to maximizing the benefits of gradient CCLs. Therefore, a global sensitivity analysis was conducted using Sobol's approach based on a one-dimensional, two-phase, non-isothermal PEMFC model. Five parameters, including Pt gradient (α), ionomer gradient (β), temperature (T), anode relative humidity (RHa), and cathode relative humidity (RHc), were selected as model inputs. The results show that RHc has the most significant impact on output performance among the operating parameters. The advantage provided by the gradient design is presented in mass transport-constrained situations. The impact of α on current density is more significant than β. Meanwhile, regarding the interaction effect of operating conditions and gradients, α and RHc provide the most significant pairwise interaction effect. Finally, the mechanisms underlying these interactions were analyzed, and some options were proposed to maximize the benefits of gradient CCLs.

A Multiscale Modeling Approach to Identify and Quantify the Transport Limitations in Proton Exchange Membrane Fuel Cells

The reduction of platinum loading is essential for lowering the cost of Proton Exchange Membrane Fuel Cells (PEMFCs). However, low platinum loadings typically lead to performance losses due to reduced electrochemical active surface area as well as additional local transport losses within the catalyst layer. Experimentally, limiting current analysis provides a helpful tool to investigate the oxygen transport limitations. In this method the limiting current (obtained at cell voltages of about 0.2 V) is measured for different oxygen concentrations and total pressures. The oxygen transport resistance is then calculated from the ratio of oxygen concentration and limiting current. The pressure variation allows separating the pressure dependent and pressure independent parts of this resistance, which are related to transport in the channels, molecular diffusion in the Gas Diffusion Layer (GDL), Knudsen diffusion through the Micro Porous Layer (MPL) and cathode catalyst layer (CCL) plus local transport losses within the CCL, respectively. However, the method does not allow to easily break down the contributions of each cell component and processes to the total resistance, which requires the variation of the components[1].Therefore, within the project FURTHER-FC[2] we develop a multiscale modeling approach for interpreting the experimentally observed transport losses and to quantify the contributions of the different cell components based on their microstructure. On the sub-µm scale a Lattice Boltzmann model is developed to describe the transport within the CCL on the agglomerate scale, taking into account the transport through the ionomer film. This model is used to parametrize the effective local transport resistance[3] for calculation of the reaction kinetics in the cell model. For the GDL/MPL Direct Numerical Simulation (DNS) applied to the real microstructures[4] is used to obtain the effective diffusion coefficients, which are then applied in the cell model. The 2D PEMFC model considers the multicomponent transport, i.e., free flow in the gas channels and transport through the porous media according to Darcy’s law, charge transport, water transport in the ionomer, energy transport as well as the electrochemical reactions. Model validation is performed with limiting current analysis on MEAs with different platinum loading under various pressures and relative humidity. By analyzing the local concentrations within the cell, the model is then used to quantify the contributions of each cell component to the overall oxygen transport resistance. The project FURTHER-FC [2] has received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking (now Clean Hydrogen Partnership) under Grant Agreement No 875025. This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation program, Hydrogen Europe and Hydrogen Europe Research. [1] Daniel R. Baker et al, J. Electrochem. Soc. (2009) 156 B991[2] https://further-fc.eu/[3] T. Jahnke and A. Baricci, J. Electrochem. Soc. 169 (2022) 094514[4] M. Ahmed-Maloum et al., Journal of Power Sources 561 (2023) 232735 Figure 1

Modelling Analysis of MEA Dynamic Operation Under Real-World Automotive Driving Cycle

In last years, Proton Exchange Membrane Fuel Cells (PEMFC) have received an increasing interest as a candidate for light and heavy-duty vehicle application1. To guarantee an efficient and durable operation of PEMFC, the understanding of local conditions, experienced by MEA, during dynamic operation is highly important. Effective numerical modelling can help to gain insights on the local heterogeneity linked to water and thermal management of the cell.A 1 + 1D multiphase dynamic non isothermal PEMFC model, implemented into MATLAB Simulink environment, was used to investigate experimental data obtained on a segmented cell hardware 2. Particular attention was paid to properly model the mass transport and kinetic related phenomena in catalyst layer(CL), as the local oxygen transport resistance through the ionomer thin film3 and the platinum oxide formation/reduction reaction, and their mutual interaction. Exploiting experimental data, obtained from tests on the segmented cell hardware, it was possible to give a robust validation to the 1+1D model, both at global and local level, thanks to the spatially-resolved information available. By simulating real-world driving cycle (DLC) protocol, developed within the ID-FAST project4, drying-up periods of ionomer contained in catalyst layers and in membrane, as well as flooding periods of porous layers and channels, were identified and analyzed during low-load and high-load operation.The performance model was coupled with a catalyst durability model, such to investigate the effect of ageing on dynamic performance of PEMFC. An innovative semi-empirical electrochemical surface area (ECSA) loss model was developed, based on the platinum dissolution mechanism, and validated initially on accelerated stress tests on different operating conditions5. The simulated load profile and local operating conditions in CLs were processed by a proper algorithm, to convert a driving cycle into a combination of elementary steps and to associate a loss factor for the ECSA to each of the identified elements, thus to estimate the retained local and global ECSA. Exploiting experimental database made up of 1000 operating hours performing ID-FAST DLC, the effect of local operating conditions on PEMFC degradation and performance was furtherly comprehended, focusing mainly on the effect of reactant humidification. The developed model framework succeeded in estimating performance loss during driving cycle operation, as visible in Figure 1(a-b). Simulated and experimental ECSA decay over time is shown in Figure 1(c), demonstrating good model prediction capability. The obtained results showed the potentiality of the model to capture complex two-phase and non-isothermal transport phenomena both in the along channel and through-MEA direction, consistently with a large set of experimental data.Experimental data on single cell were collected under ID-FAST project (Grant Agreement No 779565, Joint Undertaking, EU Horizon 2020).References D. A. Cullen et al., Nat. Energy, 6, 462–474 (2021) https://www.nature.com/articles/s41560-021-00775-z.E. Colombo, A. Baricci, A. Bisello, L. Guetaz, and A. Casalegno, J. Power Sources, 553, 232246 (2023) https://doi.org/10.1016/j.jpowsour.2022.232246.T. Suzuki, H. Yamada, K. Tsusaka, and Y. Morimoto, J. Electrochem. Soc., 165, F166–F172 (2018) https://iopscience.iop.org/article/10.1149/2.0471803jes.F. Wilhelm et al., ID-FAST -D4.3 – Analysis of coupling between mechanisms and definition of combined ASTs, p. 49, (2021) https://www.id-fast.eu/uploads/media/ID-FAST_D4-3_Analysis_of_coupling_between_mechanisms_and_definition_of_combined_ASTs_OK.pdf.A. Kneer and N. Wagner, J. Electrochem. Soc., 166, F120–F127 (2019) https://iopscience.iop.org/article/10.1149/2.0641902jes. Figure 1

A Machine Learning Accelerated Hierarchical 3D+1D Model for Proton Exchange Membrane Fuel Cells

Physics-based continuum models for proton exchange membrane fuel cells (PEMFCs) are an essential tool for fuel cell design and management. To date, many continuum models, ranging from 1D to 3D, have been developed for PEMFCs. Although computationally efficient, 1D models do not account for heterogeneity in flow fields, which negatively impact their accuracy. In contrast, 2D and 3D models are usually more representative of actual operating conditions but computationally intensive due to the coupled partial differential equations and large number of mesh elements involved. To overcome these issues, a hierarchical approach that combines a 2D/3D description of flow fields, gas diffusion layers (GDLs) and a simplified microporous layer (MPL)/catalyst layer (CL)/membrane sub-model has been proposed in the literature. However, studies based on this method often use a simplified or 0D MPL/CL/membrane sub-model, whose results may deviate from a full 1D description due to the neglected nonlinearity, especially at higher loads.In this study, we present a computationally efficient 3D+1D hierarchical model for PEMFCs accelerated by machine learning. The 3D model, which captures the two-phase flow in the gas channels and GDLs, is coupled with a full 1D description of the MPLs, membrane, CLs, and CL agglomerates by exchanging boundary values and fluxes, as shown in the figure. To avoid the high computing cost increase associated with the full 1D description, we develop a physics-informed neural network to replace the 1D sub-model for coupling with the 3D model, while maintaining the full description of fuel cell internal states. Large synthetic datasets are generated using the 1D model for training the neural network, ensuring the accuracy of the model. The proposed 3D+1D model is validated against experimentally obtained polarization curves and high frequency resistances under different relative humidities. The proposed model is then used to study the nonlinear distribution of the internal states along the thickness direction of the membrane electrode assembly as well as in the 3D flow field. The model is also highly effective in elucidating the dominant voltage loss factor under different operating conditions. Our developed model offers high accuracy at low computing cost under a wide range of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry and advance the water and thermal management of existing fuel cell designs. Figure 1

Parameterization and Validation of a 2-Dimensional, Transient, Two-Phase MEA Model Capable of Simulating Electrochemical Impedance Spectra

Proton exchange membrane fuel cells (PEMFC) are promising energy converters, offering both sustainability and efficiency. Achieving optimal performance, however, requires a deep understanding of the underlying cause-effect relationships within the functional layers. One effective approach for validating models that capture the complex physics of PEMFC is through differential cells, which reduce computational effort by allowing along-the-channel-effects to be discarded [1,2].In this study, we present a 2-dimensional, transient, non-isothermal PEMFC model that enables the disentanglement of loss contributions, facilitating effective material screening. Our model accounts for multiphase transport to provide insights into water management and mass transport. To ensure robust parameterization, we conducted a multitude of both ex-situ and in-situ experiments, reducing our reliance on often-contradictory literature data [3].We fitted our model to a wide range of polarization curves obtained under operating conditions spanning temperatures of 50-80 °C and relative humidities of 40-100 %. Notably, our model is able to simulate impedance spectra, which enables the disentanglement of processes with different time constants [4]. This approach provides a unique opportunity to study the electrochemical behavior and offers a more profound understanding of PEMFC performance limitations. The thorough parameterization process and validation against a broad range of operating conditions and impedance spectra render our model reliable and effective for industry professionals and researchers. We also highlight shortcomings and physics aspects that require further research to deepen insights and enable faster industrialization cycles.

Fuel-Cell Performance and Stability during Liquid-Water Removal Cycles

Water management is crucial for achieving high-performance proton-exchange-membrane fuel cells (PEMFCs), as it helps keep the electrolyte hydrated while avoiding performance degradation and cell shutdown due to liquid-water accumulation. According to ex-situ measurements [1-3], accumulation of liquid water in gas-diffusion layers (GDLs) of PEMFCs is a transient process that can be accompanied with oscillations in capillary pressure and saturation. To understand how liquid-water accumulation and drainage impact PEMFC performance hysteresis and stability, a transient cell-level model that accounts for electrode structure and composition and is computationally efficient is needed. A number of volume-averaged models that describe the electrode structure through pore-size distribution have been developed in the past [4-7], but they are steady-state and thus cannot predict dynamic PEMFC performance. Existing transient models have also not been used to analyze cyclic liquid-water accumulation [8-10].In this work, a transient two-phase 2D PEMFC model is developed in the open-source fuel-cell modeling software OpenFCST [11] and applied to analyze PEMFC performance hysteresis and stability during liquid-water accumulation and drainage cycles. The model accounts for the electrode structure through a mixed-wettability pore-size-distribution framework and incorporates a novel dynamic boundary condition to describe the experimentally observed cyclic liquid-water accumulation. Results of the numerical simulations are compared to transient current-density and resistance data at two polarization scan rates and during voltage steps measured with an in-house single-channel cell at multiple operating conditions. This work demonstrates how high breakthrough pressure and rapid liquid-water removal from GDLs may cause highly unstable fuel-cell operation with strong hysteresis and oscillations in the polarization curve (such as those in the figure below) that closely resemble experimental reports [12]. Numerical simulations also reveal the existence of a scan rate that maximizes polarization hysteresis due to a match between the time scale of GDL flooding and of a complete voltage sweep. Fast-scan polarization sweeps are shown most suitable for detecting catalyst-layer flooding that depends on its wettability and occurs within single seconds in contrast to GDL flooding that takes hundreds of seconds. Overall, this work brings more attention to the transient analysis of fuel-cell performance under wet conditions.Figure: Polarization-curve oscillations caused by cyclic liquid-water removal from the cathode GDL. Similar fluctuations have been experimentally observed in [12]. Transient graphs show the dynamics of current density and cathode GDL saturation. Operating conditions are 60 °C, 90% RH, 1.5 atm; scan rate is 0.5 mV/s. References J. T. Gostick et al., J. Electrochem. Soc. 157.4 (2010) C. Quesnel et al., J. Phys. Chem. C 119.40 (2015) D. Ziegler, B.Sc. thesis, Hochschule Mannheim / University of Alberta (2020) A. Z. Weber et al., J. Electrochem. Soc. 151.10 (2004) M. Eikerling, J. Electrochem. Soc. 153.3 (2006) V. Mulone and K. Karan, Int. J. Hydrog. Energy 38.1 (2013) Zhou et al., J. Electrochem. Soc. 164.6 (2017) R. J. Balliet and J. Newman, J. Electrochem. Soc. 158.8 (2011) I. V. Zenyuk et al., J. Electrochem. Soc. 163.7 (2016) A. Goshtasbi et al., J. Electrochem. Soc. 166.7 (2019) M. Secanell et al. ECS Transactions 64.3 (2014) C. Ziegler and D. Gerteisen, J. Power Sources 188.1 (2009) Figure 1