Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling
ABSTRACT Proton‐exchange membrane (PEM) water electrolysis is pivotal for green hydrogen production, necessitating accurate predictive models to manage their non‐linearities and expedite commercial deployment. Understanding degradation mechanisms through macro‐scale modeling and uncertainty quantifi...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-06-01
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| Series: | Electrochemical Science Advances |
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| Online Access: | https://doi.org/10.1002/elsa.70000 |
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| author | Violeta Karyofylli K. Ashoke Raman Linus Hammacher Yannik Danner Hans Kungl André Karl Eva Jodat Rüdiger‐A. Eichel |
| author_facet | Violeta Karyofylli K. Ashoke Raman Linus Hammacher Yannik Danner Hans Kungl André Karl Eva Jodat Rüdiger‐A. Eichel |
| author_sort | Violeta Karyofylli |
| collection | DOAJ |
| description | ABSTRACT Proton‐exchange membrane (PEM) water electrolysis is pivotal for green hydrogen production, necessitating accurate predictive models to manage their non‐linearities and expedite commercial deployment. Understanding degradation mechanisms through macro‐scale modeling and uncertainty quantification (UQ) is crucial for advancing this technology via efficiency enhancement and lifetime extension. This study primarily utilizes a one‐dimensional physics‐based model to elucidate the presence of electron transport within the PEM, another degradation phenomenon, besides gas crossover. This work also applies a machine learning (ML) algorithm, such as eXtreme Gradient Boosting (XGBoost), to model PEM electrolytic cell (PEMEC) operation based on a dataset generated from the previously mentioned physics‐based model. The ML model excels in predicting the polarization behavior. Based on this surrogate model, UQ and sensitivity analysis are finally employed to enlighten the dependence of PEMECs performance and Faradaic efficiency on the effective electronic conductivity of PEM, especially when electronic pathways exist within the membrane and operating at low current densities. |
| format | Article |
| id | doaj-art-b158cbf66759464db8ba2cea4419b6fe |
| institution | Kabale University |
| issn | 2698-5977 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Electrochemical Science Advances |
| spelling | doaj-art-b158cbf66759464db8ba2cea4419b6fe2025-08-20T03:50:17ZengWiley-VCHElectrochemical Science Advances2698-59772025-06-0153n/an/a10.1002/elsa.70000Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven ModelingVioleta Karyofylli0K. Ashoke Raman1Linus Hammacher2Yannik Danner3Hans Kungl4André Karl5Eva Jodat6Rüdiger‐A. Eichel7Institute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyInstitute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyInstitute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyInstitute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyInstitute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyInstitute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyInstitute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyInstitute of Energy Technologies Fundamental Electrochemistry (IET‐1) Forschungszentrum Jülich GmbH Jülich GermanyABSTRACT Proton‐exchange membrane (PEM) water electrolysis is pivotal for green hydrogen production, necessitating accurate predictive models to manage their non‐linearities and expedite commercial deployment. Understanding degradation mechanisms through macro‐scale modeling and uncertainty quantification (UQ) is crucial for advancing this technology via efficiency enhancement and lifetime extension. This study primarily utilizes a one‐dimensional physics‐based model to elucidate the presence of electron transport within the PEM, another degradation phenomenon, besides gas crossover. This work also applies a machine learning (ML) algorithm, such as eXtreme Gradient Boosting (XGBoost), to model PEM electrolytic cell (PEMEC) operation based on a dataset generated from the previously mentioned physics‐based model. The ML model excels in predicting the polarization behavior. Based on this surrogate model, UQ and sensitivity analysis are finally employed to enlighten the dependence of PEMECs performance and Faradaic efficiency on the effective electronic conductivity of PEM, especially when electronic pathways exist within the membrane and operating at low current densities.https://doi.org/10.1002/elsa.70000data‐drivendegradationmodelingparasitic currentphysics‐basedproton‐exchange membrane |
| spellingShingle | Violeta Karyofylli K. Ashoke Raman Linus Hammacher Yannik Danner Hans Kungl André Karl Eva Jodat Rüdiger‐A. Eichel Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling Electrochemical Science Advances data‐driven degradation modeling parasitic current physics‐based proton‐exchange membrane |
| title | Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling |
| title_full | Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling |
| title_fullStr | Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling |
| title_full_unstemmed | Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling |
| title_short | Elucidating Parasitic Currents in Proton‐Exchange‐Membrane Electrolytic Cells via Physics‐Based and Data‐Driven Modeling |
| title_sort | elucidating parasitic currents in proton exchange membrane electrolytic cells via physics based and data driven modeling |
| topic | data‐driven degradation modeling parasitic current physics‐based proton‐exchange membrane |
| url | https://doi.org/10.1002/elsa.70000 |
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