A Real Case: How to Combine Polarization Curve and EIS Techniques to Identify Problematic Cells in a Commercial PEM Stack

Nowadays, the mobility sector is assessing different technologies to substitute the internal combustion engines in order to reduce its CO2 emissions; one of these possible alternatives is the Polymer Electrolyte Membrane (PEM) fuel cell. So, the development of non-destructive diagnostic tools that could identify defective cells and/or any malfunctioning behavior and can be easily embarked on in any vehicle will expand the durability of PEM fuel cells, improve their performance, and enable them to carry out predictive maintenance. In this research, we use an in-house developed methodology that combines the polarization curve and electrochemical impedance spectroscopy (EIS) techniques to characterize different cells of a commercial PEM stack, identifying malfunctioning ones.

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Publication Details

Journal
Fuels
Published
2024-09-02
DOI
https://doi.org/10.3390/fuels5030026
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.12
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article

A Real Case: How to Combine Polarization Curve and EIS Techniques to Identify Problematic Cells in a Commercial PEM Stack

Pilar Argumosa, Jesús Maellas, Guillermo Gómez
1 citations
Fuels
Machine Learning in Materials Science
0.12
article

A Real Case: How to Combine Polarization Curve and EIS Techniques to Identify Problematic Cells in a Commercial PEM Stack

Pilar Argumosa, Jesús Maellas, Guillermo Gómez
article en
1 citations

Abstract

Nowadays, the mobility sector is assessing different technologies to substitute the internal combustion engines in order to reduce its CO2 emissions; one of these possible alternatives is the Polymer Electrolyte Membrane (PEM) fuel cell. So, the development of non-destructive diagnostic tools that could identify defective cells and/or any malfunctioning behavior and can be easily embarked on in any vehicle will expand the durability of PEM fuel cells, improve their performance, and enable them to carry out predictive maintenance. In this research, we use an in-house developed methodology that combines the polarization curve and electrochemical impedance spectroscopy (EIS) techniques to characterize different cells of a commercial PEM stack, identifying malfunctioning ones.

FuelsVol. 5(3)
Instituto Nacional de Técnica Aeroespacial (ES)
Affordable and clean energy
Openalex Percentile: Top 60%
Machine Learning in Materials Science
0.12
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A Real Case: How to Combine Polarization Curve and EIS Techniques to Identify Problematic Cells in a Commercial PEM Stack — Pilar Argumosa, Jesús Maellas, et al. · Fuels (2024) | TGRS Research Map | TGRS