Neural Network based Diagnostic of PEM Fuel Cell

Neural Network based Diagnostic of PEM Fuel Cell

This paper focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation. Towards this aim, the artificial intelligence technology is proposed. More specifically, a neural networ...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of new materials for electrochemical systems 2020-12, Vol.23 (4), p.225-234
Hauptverfasser: Kahia, Hichem, Aicha, Saadi, Herbadji, Djamel, Herbadji, Abderrahmane, Bedda, Said
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 234
container_issue 4
container_start_page 225
container_title Journal of new materials for electrochemical systems
container_volume 23
creator Kahia, Hichem
Aicha, Saadi
Herbadji, Djamel
Herbadji, Abderrahmane
Bedda, Said
description This paper focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation. Towards this aim, the artificial intelligence technology is proposed. More specifically, a neural network model is used to enable monitoring the influence of the humidity content of the fuel cell membrane, through employing electrochemical impedance spectroscopy method (EIS analysis). This technique allows analyzing and diagnosing PEM fuel cell failure modes (flooding & drying). The benefit of this work is summed up in the demonstration of the existence in a simple way that helps to define the state of health of the PEMFC.
doi_str_mv 10.14447/jnmes.v23i4.a02
format Article
fullrecord <record><control><sourceid>crossref</sourceid><recordid>TN_cdi_crossref_primary_10_14447_jnmes_v23i4_a02</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_14447_jnmes_v23i4_a02</sourcerecordid><originalsourceid>FETCH-LOGICAL-c285t-5dab94d3fad186dd7c3d90044af98d0ecbe1d815cb13419eabe6c570a704480a3</originalsourceid><addsrcrecordid>eNotzzFPwzAUBGALgURUujP6DyS8ZzuJPaLSAlIpDDBbjv2CUtIG2SmIf08UuOWW00kfY9cIBSql6pv98UCp-BKyU4UDccYyIYzIESt9zjJUGnKhhLhky5T2MEWL0qDIGOzoFF3PdzR-D_GDNy5R4Hedez8Oaew8H1r-sn7imxP1fEV9f8UuWtcnWv73gr1t1q-rh3z7fP-4ut3mXuhyzMvgGqOCbF1AXYVQexkMgFKuNToA-YYwaCx9g1KhIddQ5csaXD1tNDi5YPD36-OQUqTWfsbu4OKPRbAz2s5oO6PthJa_8KhLkQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Neural Network based Diagnostic of PEM Fuel Cell</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Kahia, Hichem ; Aicha, Saadi ; Herbadji, Djamel ; Herbadji, Abderrahmane ; Bedda, Said</creator><creatorcontrib>Kahia, Hichem ; Aicha, Saadi ; Herbadji, Djamel ; Herbadji, Abderrahmane ; Bedda, Said</creatorcontrib><description>This paper focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation. Towards this aim, the artificial intelligence technology is proposed. More specifically, a neural network model is used to enable monitoring the influence of the humidity content of the fuel cell membrane, through employing electrochemical impedance spectroscopy method (EIS analysis). This technique allows analyzing and diagnosing PEM fuel cell failure modes (flooding &amp; drying). The benefit of this work is summed up in the demonstration of the existence in a simple way that helps to define the state of health of the PEMFC.</description><identifier>ISSN: 1480-2422</identifier><identifier>EISSN: 2292-1168</identifier><identifier>DOI: 10.14447/jnmes.v23i4.a02</identifier><language>eng</language><ispartof>Journal of new materials for electrochemical systems, 2020-12, Vol.23 (4), p.225-234</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c285t-5dab94d3fad186dd7c3d90044af98d0ecbe1d815cb13419eabe6c570a704480a3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Kahia, Hichem</creatorcontrib><creatorcontrib>Aicha, Saadi</creatorcontrib><creatorcontrib>Herbadji, Djamel</creatorcontrib><creatorcontrib>Herbadji, Abderrahmane</creatorcontrib><creatorcontrib>Bedda, Said</creatorcontrib><title>Neural Network based Diagnostic of PEM Fuel Cell</title><title>Journal of new materials for electrochemical systems</title><description>This paper focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation. Towards this aim, the artificial intelligence technology is proposed. More specifically, a neural network model is used to enable monitoring the influence of the humidity content of the fuel cell membrane, through employing electrochemical impedance spectroscopy method (EIS analysis). This technique allows analyzing and diagnosing PEM fuel cell failure modes (flooding &amp; drying). The benefit of this work is summed up in the demonstration of the existence in a simple way that helps to define the state of health of the PEMFC.</description><issn>1480-2422</issn><issn>2292-1168</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNotzzFPwzAUBGALgURUujP6DyS8ZzuJPaLSAlIpDDBbjv2CUtIG2SmIf08UuOWW00kfY9cIBSql6pv98UCp-BKyU4UDccYyIYzIESt9zjJUGnKhhLhky5T2MEWL0qDIGOzoFF3PdzR-D_GDNy5R4Hedez8Oaew8H1r-sn7imxP1fEV9f8UuWtcnWv73gr1t1q-rh3z7fP-4ut3mXuhyzMvgGqOCbF1AXYVQexkMgFKuNToA-YYwaCx9g1KhIddQ5csaXD1tNDi5YPD36-OQUqTWfsbu4OKPRbAz2s5oO6PthJa_8KhLkQ</recordid><startdate>20201231</startdate><enddate>20201231</enddate><creator>Kahia, Hichem</creator><creator>Aicha, Saadi</creator><creator>Herbadji, Djamel</creator><creator>Herbadji, Abderrahmane</creator><creator>Bedda, Said</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20201231</creationdate><title>Neural Network based Diagnostic of PEM Fuel Cell</title><author>Kahia, Hichem ; Aicha, Saadi ; Herbadji, Djamel ; Herbadji, Abderrahmane ; Bedda, Said</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c285t-5dab94d3fad186dd7c3d90044af98d0ecbe1d815cb13419eabe6c570a704480a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kahia, Hichem</creatorcontrib><creatorcontrib>Aicha, Saadi</creatorcontrib><creatorcontrib>Herbadji, Djamel</creatorcontrib><creatorcontrib>Herbadji, Abderrahmane</creatorcontrib><creatorcontrib>Bedda, Said</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of new materials for electrochemical systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kahia, Hichem</au><au>Aicha, Saadi</au><au>Herbadji, Djamel</au><au>Herbadji, Abderrahmane</au><au>Bedda, Said</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural Network based Diagnostic of PEM Fuel Cell</atitle><jtitle>Journal of new materials for electrochemical systems</jtitle><date>2020-12-31</date><risdate>2020</risdate><volume>23</volume><issue>4</issue><spage>225</spage><epage>234</epage><pages>225-234</pages><issn>1480-2422</issn><eissn>2292-1168</eissn><abstract>This paper focuses in finding a suitable, effective, and easy to use method, to avoid the frequent mistakes that are presented by the poor flow of water inside the fuel cell during its operation. Towards this aim, the artificial intelligence technology is proposed. More specifically, a neural network model is used to enable monitoring the influence of the humidity content of the fuel cell membrane, through employing electrochemical impedance spectroscopy method (EIS analysis). This technique allows analyzing and diagnosing PEM fuel cell failure modes (flooding &amp; drying). The benefit of this work is summed up in the demonstration of the existence in a simple way that helps to define the state of health of the PEMFC.</abstract><doi>10.14447/jnmes.v23i4.a02</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1480-2422
ispartof Journal of new materials for electrochemical systems, 2020-12, Vol.23 (4), p.225-234
issn 1480-2422
2292-1168
language eng
recordid cdi_crossref_primary_10_14447_jnmes_v23i4_a02
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
title Neural Network based Diagnostic of PEM Fuel Cell
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T06%3A34%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Neural%20Network%20based%20Diagnostic%20of%20PEM%20Fuel%20Cell&rft.jtitle=Journal%20of%20new%20materials%20for%20electrochemical%20systems&rft.au=Kahia,%20Hichem&rft.date=2020-12-31&rft.volume=23&rft.issue=4&rft.spage=225&rft.epage=234&rft.pages=225-234&rft.issn=1480-2422&rft.eissn=2292-1168&rft_id=info:doi/10.14447/jnmes.v23i4.a02&rft_dat=%3Ccrossref%3E10_14447_jnmes_v23i4_a02%3C/crossref%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true