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Development of Advanced Models for Analysis and Simulation of Fuel Cells

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A5: Hydrogen Energy".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 33011

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Special Issue Editors

Prof. Dr. Tomaž Katrašnik

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Guest Editor

Faculty of Mechanical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: internal combustion engines; fuel cells; degradation mechanisms; batteries; hybrid powertrains; combustion; alternative fuels; combined heat and power; thermodynamics; numerical modeling of systems and components; observer models

Prof. Dr. Viktor Hacker

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Guest Editor

Institute of Chemical Engineering and Environmental Technology, Graz University of Technology, Stremayrgasse 9, 8010 Graz, Austria
Interests: fuel cells; degradation mechanisms; electrode development; accelerated stress tests; fuel cell monitoring; hydrogen production; hydrogen purification; electrolysis

Special Issue Information

Dear Colleagues,

Guest Editors are inviting submissions to a Special Issue of Energies on the subject area of “Development of Advanced Models for Analysis and Simulation of Fuel Cells”. To address the requirements on shorter product development cycles and reduced development costs, while striving to approach engineering limits in power density, efficiency, service life and safety, it is necessary to rely on advanced simulation models in the development process of fuel cells, their components, and fuel-cell-based systems. Likewise, simulation models are indispensable for the analysis of fuel cells and interpreting highly complex intertwined processes inherent to this type of electrochemical devices. Advanced models are further key elements of precise online monitoring units, which are crucial to ensure highly efficient, durable, and safe operation of fuel-cell-based systems.

To provide an answer to these challenges, this Special Issue will focus on advanced models for the analysis and simulation of fuel cells that cover all types of fuel cells and all application areas of models from detailed material modeling over fuel cell performance and degradation modeling to models applied in analysis, control, and fault diagnostic applications. Thus, this Special Issue covers the entire spectrum of applications of advanced models comprising: concept layout; material, component, and system design; component and system testing and analysis; as well as online monitoring, fault diagnostics, and predictive maintenance. Topics of interest for publication include but are not limited to:

All types of fuel cells, their components, and fuel-cell-based systems;
Atomistic models;
Mesoscopic models;
Continuum models;
Multiscale models;
Scale bridging approaches and methodologies;
Reduced dimensionality models;
Models for interpreting EIS spectra;
Observer (SoX), online monitoring and control oriented models;
Advanced model parametrization tools;
Model based fault diagnostics;
Model based predictive maintenance.

Prof. Dr. Tomaž Katrašnik
Prof. Dr. Viktor Hacker
Guest Editors

Manuscript Submission Information

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Keywords

Fuel cell
Modeling
Atomistic models
Mesoscopic models
Continuum models
Online monitoring
Model parametrization
Fault diagnostics
Predictive maintenance

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Published Papers (11 papers)

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Research

18 pages, 25717 KiB

Open AccessArticle

Biomass Potential for Producing Power via Green Hydrogen

by Nestor Sanchez, David Rodríguez-Fontalvo, Bernay Cifuentes, Nelly M. Cantillo, Miguel Ángel Uribe Laverde and Martha Cobo

Energies 2021, 14(24), 8366; https://doi.org/10.3390/en14248366 - 11 Dec 2021

Cited by 8 | Viewed by 4179

Abstract

Hydrogen (H₂) has become an important energy vector for mitigating the effects of climate change since it can be obtained from renewable sources and can be fed to fuel cells for producing power. Bioethanol can become a green H₂ source via Ethanol Steam Reforming (ESR) but several variables influence the power production in the fuel cell. Herein, we explored and optimized the main variables that affect this power production. The process includes biomass fermentation, bioethanol purification, H₂ production via ESR, syngas cleaning by a CO-removal reactor, and power production in a high temperature proton exchange membrane fuel cell (HT-PEMFC). Among the explored variables, the steam-to-ethanol molar ratio (S/E) employed in the ESR has the strongest influence on power production, process efficiency, and energy consumption. This effect is followed by other variables such as the inlet ethanol concentration and the ESR temperature. Although the CO-removal reactor did not show a significant effect on power production, it is key to increase the voltage on the fuel cell and consequently the power production. Optimization was carried out by the response surface methodology (RSM) and showed a maximum power of 0.07 kWh kg⁻¹ of bioethanol with an efficiency of 17%, when ESR temperature is 700 °C. These values can be reached from different bioethanol sources as the S/E and CO-removal temperature are changed accordingly with the inlet ethanol concentration. Because there is a linear correlation between S/E and ethanol concentration, it is possible to select a proper S/E and CO-removal temperature to maximize the power generation in the HT-PEMFC via ESR. This study serves as a starting point to diversify the sources for producing H₂ and moving towards a H₂-economy. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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16 pages, 3233 KiB

Open AccessArticle

Numerical Investigations on the Damage Behaviour of a Reconstructed Anode for Solid Oxide Fuel Cell Application

by Katharina Steier, Vinzenz Guski and Siegfried Schmauder

Energies 2021, 14(23), 8082; https://doi.org/10.3390/en14238082 - 2 Dec 2021

Cited by 3 | Viewed by 1632

Abstract

This paper addresses the damage behaviour of a nickel/yttria-stabilised zirconia (Ni-YSZ) anode, in order to understand microstructural degradation processes of Solid Oxide Fuel Cells (SOFCs) during long-term operation. Numerical investigations are carried out to analyse the failure mechanisms in detail. For this purpose, finite element (FE) models are generated from focused ion beam-scanning electron microscopy 3D image data, representing the anode microstructure with varying phase compositions. A brittle model and a ductile material model were assigned to the YSZ phase and the nickel phase, respectively. The porosity is found to affect the strength of the microstructure significantly, leading to low compressive strength results. A high Ni content generally increases the toughness of the overall structure. However, the orientation and the geometry of the nickel phase is essential. When the Ni phase is aligned parallel to the loading direction, a supporting effect on the microstructure is observed, resulting in a significant high toughness. On the contrary, a rapid failure of the sample occurs when the Ni phase is oriented perpendicular to the loading direction. Two main failure mechanisms are identified: (i) cracking at the Ni/YSZ interface and (ii) cracking of struts at the location of the smallest diameter. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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20 pages, 4695 KiB

Open AccessArticle

Simulation-Assisted Determination of the Start-Up Time of a Polymer Electrolyte Fuel Cell

by Merit Bodner, Željko Penga, Walter Ladreiter, Mathias Heidinger and Viktor Hacker

Energies 2021, 14(23), 7929; https://doi.org/10.3390/en14237929 - 26 Nov 2021

Cited by 2 | Viewed by 2286

Abstract

Fuel starvation is a major cause of anode corrosion in low temperature polymer electrolyte fuel cells. The fuel cell start-up is a critical step, as hydrogen may not yet be evenly distributed in the active area, leading to local starvation. The present work investigates the hydrogen distribution and risk for starvation during start-up and after nitrogen purge by extending an existing computational fluid dynamic model to capture transient behavior. The results of the numerical model are compared with detailed experimental analysis on a 25 cm² triple serpentine flow field with good agreement in all aspects and a required time step size of 1 s. This is two to three orders of magnitude larger than the time steps used by other works, resulting in reasonably quick calculation times (e.g., 3 min calculation time for 1 s of experimental testing time using a 2 million element mesh). Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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20 pages, 6958 KiB

Open AccessArticle

Physically Motivated Water Modeling in Control-Oriented Polymer Electrolyte Membrane Fuel Cell Stack Models

by Zhang Peng Du, Andraž Kravos, Christoph Steindl, Tomaž Katrašnik, Stefan Jakubek and Christoph Hametner

Energies 2021, 14(22), 7693; https://doi.org/10.3390/en14227693 - 17 Nov 2021

Cited by 6 | Viewed by 2822

Abstract

Polymer electrolyte membrane fuel cells (PEMFCs) are prone to membrane dehydration and liquid water flooding, negatively impacting their performance and lifetime. Therefore, PEMFCs require appropriate water management, which makes accurate water modeling indispensable. Unfortunately, available control-oriented models only replicate individual water-related aspects or use oversimplistic approximations. This paper resolves this challenge by proposing, for the first time, a control-oriented PEMFC stack model focusing on physically motivated water modeling, which covers phase change, liquid water removal, membrane water uptake, and water flooding effects on the electrochemical reaction. Parametrizing the resulting model with measurement data yielded the fitted model. The parameterized model delivers valuable insight into the water mechanisms, which were thoroughly analyzed. In summary, the proposed model enables the derivation of advanced control strategies for efficient water management and mitigation of the degradation phenomena of PEMFCs. Additionally, the model provides the required accuracy for control applications while maintaining the necessary computational efficiency. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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18 pages, 3761 KiB

Open AccessArticle

The Influence Catalyst Layer Thickness on Resistance Contributions of PEMFC Determined by Electrochemical Impedance Spectroscopy

by Maximilian Grandi, Kurt Mayer, Matija Gatalo, Gregor Kapun, Francisco Ruiz-Zepeda, Bernhard Marius, Miran Gaberšček and Viktor Hacker

Energies 2021, 14(21), 7299; https://doi.org/10.3390/en14217299 - 4 Nov 2021

Cited by 12 | Viewed by 3608

Abstract

Electrochemical impedance spectroscopy is an important tool for fuel-cell analysis and monitoring. This study focuses on the low-AC frequencies (2–0.1 Hz) to show that the thickness of the catalyst layer significantly influences the overall resistance of the cell. By combining known models, a new equivalent circuit model was generated. The new model is able to simulate the impedance signal in the complete frequency spectrum of 10⁵–10⁻² Hz, usually used in experimental work on polymer electrolyte fuel cells (PEMFCs). The model was compared with experimental data and to an older model from the literature for verification. The electrochemical impedance spectra recorded on different MEAs with cathode catalyst layer thicknesses of approx. 5 and 12 µm show the appearance of a third semicircle in the low-frequency region that scales with current density. It has been shown that the ohmic resistance contribution (R_mt) of this third semicircle increases with the catalyst layer’s thickness. Furthermore, the electrolyte resistance is shown to decrease with increasing catalyst-layer thickness. The cause of this phenomenon was identified to be increased water retention by thicker catalyst layers. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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26 pages, 2614 KiB

Open AccessArticle

Design and Modeling of Metallic Bipolar Plates for a Fuel Cell Range Extender

by Uwe Reimer, Ekaterina Nikitsina, Holger Janßen, Martin Müller, Dieter Froning, Steven B. Beale and Werner Lehnert

Energies 2021, 14(17), 5484; https://doi.org/10.3390/en14175484 - 2 Sep 2021

Cited by 2 | Viewed by 4014

Abstract

Fuel cells, designed for mobile applications, should feature compact and low-weight designs. This study describes a design process that fulfills the specific needs of target applications and the production process. The key challenge for this type of metallic bipolar plate is that the combination of two plates creates three flow fields, namely an anode side, a cathode side, and a coolant. This illustrates the fact that each cell constitutes an electrochemical converter with an integrated heat exchanger. The final arrangement is comprised of plates with parallel and separate serpentine channel configurations. The anode and cathode sides are optimized for operation under dry conditions. The final plate offers an almost perfect distribution of coolant flow over the active area. The high quality of this distribution is almost independent of the coolant mass flow, even if one of the six inlet channels is blocked. The software employed (OpenFOAM and SALOME) is freely available and can be used with templates. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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23 pages, 2093 KiB

Open AccessArticle

Integration of Classical Mathematical Modeling with an Artificial Neural Network for the Problems with Limited Dataset

by Szymon Buchaniec, Marek Gnatowski and Grzegorz Brus

Energies 2021, 14(16), 5127; https://doi.org/10.3390/en14165127 - 19 Aug 2021

Cited by 13 | Viewed by 2454

Abstract

One of the most common problems in science is to investigate a function describing a system. When the estimate is made based on a classical mathematical model (white-box), the function is obtained throughout solving a differential equation. Alternatively, the prediction can be made by an artificial neural network (black-box) based on trends found in past data. Both approaches have their advantages and disadvantages. Mathematical models were seen as more trustworthy as their prediction is based on the laws of physics expressed in the form of mathematical equations. However, the majority of existing mathematical models include different empirical parameters, and both approaches inherit inevitable experimental errors. Simultaneously, the approximation of neural networks can reproduce the solution exceptionally well if fed sufficient data. The difference is that an artificial neural network requires big data to build its accurate approximation, whereas a typical mathematical model needs several data points to estimate an empirical constant. Therefore, the common problem that developers meet is the inaccuracy of mathematical models and artificial neural networks. Another common challenge is the mathematical models’ computational complexity or lack of data for a sufficient precision of the artificial neural networks. Here we analyze a grey-box solution in which an artificial neural network predicts just a part of the mathematical model, and its weights are adjusted based on the mathematical model’s output using the evolutionary approach to avoid overfitting. The performance of the grey-box model is statistically compared to a Dense Neural Network on benchmarking functions. With the use of Shaffer procedure, it was shown that the grey-box approach performs exceptionally well when the overall complexity of a problem is properly distributed with the mathematical model and the Artificial Neural Network. The obtained calculation results indicate that such an approach could increase precision and limit the dataset required for learning. To show the applicability of the presented approach, it was employed in modeling of the electrochemical reaction in the Solid Oxide Fuel Cell’s anode. Implementation of a grey-box model improved the prediction in comparison to the typically used methodology. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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16 pages, 1370 KiB

Open AccessArticle

Identifiability Analysis of Degradation Model Parameters from Transient CO₂ Release in Low-Temperature PEM Fuel Cell under Various AST Protocols

by Andraž Kravos, Ambrož Kregar, Kurt Mayer, Viktor Hacker and Tomaž Katrašnik

Energies 2021, 14(14), 4380; https://doi.org/10.3390/en14144380 - 20 Jul 2021

Cited by 8 | Viewed by 2960

Abstract

The detrimental effects of the catalyst degradation on the overall envisaged lifetime of low-temperature proton-exchange membrane fuel cells (LT-PEMFCs) represent a significant challenge towards further lowering platinum loadings and simultaneously achieving a long cycle life. The elaborated physically based modeling of the degradation processes is thus an invaluable step in elucidating causal interaction between fuel cell design, its operating conditions, and degradation phenomena. However, many parameters need to be determined based on experimental data to ensure plausible simulation results of the catalyst degradation models, which proves to be challenging with the in situ measurements. To fill this knowledge gap, this paper demonstrates the application of a mechanistically based PEMFC modeling framework, comprising real-time capable fuel cell performance, and platinum and carbon support degradation models, to model transient CO₂ release rates in the LT-PEMFCs with the consistent calibration of reaction rate parameters under multiple different accelerated stress tests at once. The results confirm the credibility of the physical and chemical modeling basis of the proposed modeling framework, as well as its prediction and extrapolation capabilities. This is confirmed by an increase of only 29% of root mean square deviations values when using a model calibrated on all three data sets at once in comparison to a model calibrated on only one data set. Furthermore, the unique identifiability and interconnection of individual model calibration parameters are determined via Fisher information matrix analysis. This analysis enables optimal reduction of the set of calibration parameters, which results in the speed up of both the calibration process and the general simulation time while retaining the full extrapolation capabilities of the framework. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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20 pages, 3587 KiB

Open AccessArticle

Health-Conscious Optimization of Long-Term Operation for Hybrid PEMFC Ship Propulsion Systems

by Chiara Dall’Armi, Davide Pivetta and Rodolfo Taccani

Energies 2021, 14(13), 3813; https://doi.org/10.3390/en14133813 - 24 Jun 2021

Cited by 14 | Viewed by 2766

Abstract

The need to decarbonize the shipping sector is leading to a growing interest in fuel cell-based propulsion systems. While Polymer Electrolyte Membrane Fuel Cells (PEMFC) represent one of the most promising and mature technologies for onboard implementation, they are still prone to remarkable degradation. The same problem is also affecting Lithium-ion batteries (LIB), which are usually coupled with PEMFC in hybrid powertrains. By including the combined degradation effects in an optimization strategy, the best compromise between costs and PEMFC/LIB lifetime could be determined. However, this is still a challenging yet crucial aspect, rarely addressed in the literature and rarely yet explored. To fill this gap, a health-conscious optimization is here proposed for the long-term minimization of costs and PEMFC/LIB degradation. Results show that a holistic multi-objective optimization allows a 185% increase of PEMFC/LIB lifetime with respect to a fuel-consumption-minimization-only approach. With the progressive ageing of PEMFC/LIB, the hybrid propulsion system modifies the energy management strategy to limit the increase of the daily operation cost. Comparing the optimization results at the beginning and the end of the plant lifetime, daily operation costs are increased by 73% and hydrogen consumption by 29%. The proposed methodology is believed to be a useful tool, able to give insights into the effective costs involved in the long-term operation of this new type of propulsion system. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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17 pages, 8921 KiB

Open AccessArticle

Model-Predictive-Control-Based Reference Governor for Fuel Cells in Automotive Application Compared with Performance from a Real Vehicle

by Martin Vrlić, Daniel Ritzberger and Stefan Jakubek

Energies 2021, 14(8), 2206; https://doi.org/10.3390/en14082206 - 15 Apr 2021

Cited by 8 | Viewed by 2509

Abstract

In this paper, a real-time capable reference governor superordinate model predictive controller (RG-MPC) is developed for fuel cell (FC) control suitable for automotive application. The RG-MPC provides reference trajectories for the subordinate proportional-integral (PI) controllers, which act directly on the FC system. Antiwindup and decoupling schemes, which are common problems in multivariable PI control, are unnecessary, given that the RG-MPC can inherently consider constraints and multivariable systems. The PI dynamics are incorporated into the prediction model used for control, ensuring the feasibility of the provided references for the PI controllers. The successive linearization technique is used in the RG-MPC to cope with the model’s nonlinear nature in real-time. The concept has been illustrated in a simulation scenario featuring efficient and safe power control of an FC stack in automotive application using real driving data obtained from an in-house-built FC vehicle. This work is the first step towards upgrading an existing, PI-based control scheme without the necessity of completely rebuilding the interface. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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15 pages, 1295 KiB

Open AccessFeature PaperArticle

Sensitivity Based Order Reduction of a Chemical Membrane Degradation Model for Low-Temperature Proton Exchange Membrane Fuel Cells

by Ambrož Kregar, Philipp Frühwirt, Daniel Ritzberger, Stefan Jakubek, Tomaž Katrašnik and Georg Gescheidt

Energies 2020, 13(21), 5611; https://doi.org/10.3390/en13215611 - 27 Oct 2020

Cited by 5 | Viewed by 2118

Abstract

The chemical degradation of the perfluorinated sulfonic acid (PFSA) ion-exchange membrane as a result of an attack from a radical species, originating as a by-product of the oxygen reduction reaction, represents a significant limiting factor in a wider adoption of low-temperature proton exchange membrane fuel cells (LT-PEMFCs). The efficient mathematical modeling of these processes is therefore a crucial step in the further development of proton exchange membrane fuel cells. Starting with an extensive kinetic modeling framework, describing the whole range of chemical processes leading to the membrane degradation, we use the mathematical method of sensitivity analysis to systematically reduce the number of both chemical species and reactions needed to efficiently and accurately describe the chemical degradation of the membrane. The analysis suggests the elimination of chemical reactions among the radical species, which is supported by the physicochemical consideration of the modeled reactions, while the degradation of Nafion backbone can be significantly simplified by lumping several individual species concentrations. The resulting reduced model features only 12 species coupled by 8 chemical reactions, compared to 19 species coupled by 23 reactions in the original model. The time complexity of the model, analyzed on the basis of its stiffness, however, is not significantly improved in the process. Nevertheless, the significant reduction in the model system size and number of parameters represents an important step in the development of a computationally efficient coupled model of various fuel cell degradation processes. Additionally, the demonstrated application of sensitivity analysis method shows a great potential for further use in the optimization of models of operation and degradation of fuel cell components. Full article

(This article belongs to the Special Issue Development of Advanced Models for Analysis and Simulation of Fuel Cells)

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