A cooperative strategy-based differential evolution algorithm for robust PEM fuel cell parameter estimation
Proton exchange membrane fuel cells (PEMFCs) are powered by hydrogen energy, which is valued for its renewable, safe, and efficient characteristics, and are therefore critical in sustainable electricity generation through hydrogen electrochemical conversion. Parameter estimation in PEMFCs is a chall...
Gespeichert in:
| Veröffentlicht in: | Ionics 2025, Vol.31 (1), p.703-741 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 741 |
|---|---|
| container_issue | 1 |
| container_start_page | 703 |
| container_title | Ionics |
| container_volume | 31 |
| creator | Jangir, Pradeep Arpita Agrawal, Sunilkumar P. Pandya, Sundaram B. Parmar, Anil Kumar, Sumit Tejani, Ghanshyam G. Abualigah, Laith |
| description | Proton exchange membrane fuel cells (PEMFCs) are powered by hydrogen energy, which is valued for its renewable, safe, and efficient characteristics, and are therefore critical in sustainable electricity generation through hydrogen electrochemical conversion. Parameter estimation in PEMFCs is a challenging but critical task, since accurate modeling is directly related to cell performance optimization and reliable energy output under different operational conditions. To improve parameter estimation accuracy, a cooperative strategy-based differential evolution (CS-DE) algorithm was developed to minimize the sum of squared errors (SSE) between experimental and simulated PEMFC voltage data for multiple BCS 500-W PEM, BCS 250-W PEM, Nedstack PS6 PEM, 500W SR-12 PEM, H-12 PEM, and HORIZON 500W PEMFC models. The CS-DE algorithm was benchmarked against standard differential evolution (DE) and other conventional methods on six commercial PEMFC types, resulting in a 15% reduction in SSE and an average improvement of 12% in estimation accuracy. These results demonstrate the robustness and adaptability of CS-DE for complex PEMFC modeling tasks. |
| doi_str_mv | 10.1007/s11581-024-05963-x |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3158667162</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3158667162</sourcerecordid><originalsourceid>FETCH-LOGICAL-c200t-753c30a5e9f19558849e7fb557d42cc849312363414ffba18d530f0dffe95423</originalsourceid><addsrcrecordid>eNp9kM1LxDAQxYMouH78A54CnqOTpknao4hfoOhh7yFtJ2vXblOTVPS_N-sK3jzNDLz35vEj5IzDBQfQl5FzWXEGRclA1kqwzz2y4JUqGGgF-2QBdamZhlIfkqMY1wBK8UIvyNsVbb2fMNjUfyCNKS-4-mKNjdjRrncOA46ptwPFDz_MqfcjtcPKhz69bqjzgQbfzDHRl5sn6mYcaIvDQCcb7AYTBoox9Ru79Z2QA2eHiKe_85gsb2-W1_fs8fnu4frqkbUFQGJailaAlVg7XktZVWWN2jVS6q4s2jafghdCiZKXzjWWV50U4KDLVWtZFuKYnO9ip-Df5_zerP0cxvzRiExJKc3VVlXsVG3wMQZ0Zgq5Z_gyHMyWqdkxNZmp-WFqPrNJ7Ewxi8cVhr_of1zfU1t7sA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3158667162</pqid></control><display><type>article</type><title>A cooperative strategy-based differential evolution algorithm for robust PEM fuel cell parameter estimation</title><source>SpringerLink Journals - AutoHoldings</source><creator>Jangir, Pradeep ; Arpita ; Agrawal, Sunilkumar P. ; Pandya, Sundaram B. ; Parmar, Anil ; Kumar, Sumit ; Tejani, Ghanshyam G. ; Abualigah, Laith</creator><creatorcontrib>Jangir, Pradeep ; Arpita ; Agrawal, Sunilkumar P. ; Pandya, Sundaram B. ; Parmar, Anil ; Kumar, Sumit ; Tejani, Ghanshyam G. ; Abualigah, Laith</creatorcontrib><description>Proton exchange membrane fuel cells (PEMFCs) are powered by hydrogen energy, which is valued for its renewable, safe, and efficient characteristics, and are therefore critical in sustainable electricity generation through hydrogen electrochemical conversion. Parameter estimation in PEMFCs is a challenging but critical task, since accurate modeling is directly related to cell performance optimization and reliable energy output under different operational conditions. To improve parameter estimation accuracy, a cooperative strategy-based differential evolution (CS-DE) algorithm was developed to minimize the sum of squared errors (SSE) between experimental and simulated PEMFC voltage data for multiple BCS 500-W PEM, BCS 250-W PEM, Nedstack PS6 PEM, 500W SR-12 PEM, H-12 PEM, and HORIZON 500W PEMFC models. The CS-DE algorithm was benchmarked against standard differential evolution (DE) and other conventional methods on six commercial PEMFC types, resulting in a 15% reduction in SSE and an average improvement of 12% in estimation accuracy. These results demonstrate the robustness and adaptability of CS-DE for complex PEMFC modeling tasks.</description><identifier>ISSN: 0947-7047</identifier><identifier>EISSN: 1862-0760</identifier><identifier>DOI: 10.1007/s11581-024-05963-x</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Algorithms ; Chemistry ; Chemistry and Materials Science ; Condensed Matter Physics ; Electrochemistry ; Energy Storage ; Evolutionary algorithms ; Evolutionary computation ; Modelling ; Optical and Electronic Materials ; Parameter estimation ; Parameter robustness ; Proton exchange membrane fuel cells ; Renewable and Green Energy ; Task complexity</subject><ispartof>Ionics, 2025, Vol.31 (1), p.703-741</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>Copyright Springer Nature B.V. 2025</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c200t-753c30a5e9f19558849e7fb557d42cc849312363414ffba18d530f0dffe95423</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11581-024-05963-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11581-024-05963-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Jangir, Pradeep</creatorcontrib><creatorcontrib>Arpita</creatorcontrib><creatorcontrib>Agrawal, Sunilkumar P.</creatorcontrib><creatorcontrib>Pandya, Sundaram B.</creatorcontrib><creatorcontrib>Parmar, Anil</creatorcontrib><creatorcontrib>Kumar, Sumit</creatorcontrib><creatorcontrib>Tejani, Ghanshyam G.</creatorcontrib><creatorcontrib>Abualigah, Laith</creatorcontrib><title>A cooperative strategy-based differential evolution algorithm for robust PEM fuel cell parameter estimation</title><title>Ionics</title><addtitle>Ionics</addtitle><description>Proton exchange membrane fuel cells (PEMFCs) are powered by hydrogen energy, which is valued for its renewable, safe, and efficient characteristics, and are therefore critical in sustainable electricity generation through hydrogen electrochemical conversion. Parameter estimation in PEMFCs is a challenging but critical task, since accurate modeling is directly related to cell performance optimization and reliable energy output under different operational conditions. To improve parameter estimation accuracy, a cooperative strategy-based differential evolution (CS-DE) algorithm was developed to minimize the sum of squared errors (SSE) between experimental and simulated PEMFC voltage data for multiple BCS 500-W PEM, BCS 250-W PEM, Nedstack PS6 PEM, 500W SR-12 PEM, H-12 PEM, and HORIZON 500W PEMFC models. The CS-DE algorithm was benchmarked against standard differential evolution (DE) and other conventional methods on six commercial PEMFC types, resulting in a 15% reduction in SSE and an average improvement of 12% in estimation accuracy. These results demonstrate the robustness and adaptability of CS-DE for complex PEMFC modeling tasks.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Condensed Matter Physics</subject><subject>Electrochemistry</subject><subject>Energy Storage</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Modelling</subject><subject>Optical and Electronic Materials</subject><subject>Parameter estimation</subject><subject>Parameter robustness</subject><subject>Proton exchange membrane fuel cells</subject><subject>Renewable and Green Energy</subject><subject>Task complexity</subject><issn>0947-7047</issn><issn>1862-0760</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><recordid>eNp9kM1LxDAQxYMouH78A54CnqOTpknao4hfoOhh7yFtJ2vXblOTVPS_N-sK3jzNDLz35vEj5IzDBQfQl5FzWXEGRclA1kqwzz2y4JUqGGgF-2QBdamZhlIfkqMY1wBK8UIvyNsVbb2fMNjUfyCNKS-4-mKNjdjRrncOA46ptwPFDz_MqfcjtcPKhz69bqjzgQbfzDHRl5sn6mYcaIvDQCcb7AYTBoox9Ru79Z2QA2eHiKe_85gsb2-W1_fs8fnu4frqkbUFQGJailaAlVg7XktZVWWN2jVS6q4s2jafghdCiZKXzjWWV50U4KDLVWtZFuKYnO9ip-Df5_zerP0cxvzRiExJKc3VVlXsVG3wMQZ0Zgq5Z_gyHMyWqdkxNZmp-WFqPrNJ7Ewxi8cVhr_of1zfU1t7sA</recordid><startdate>2025</startdate><enddate>2025</enddate><creator>Jangir, Pradeep</creator><creator>Arpita</creator><creator>Agrawal, Sunilkumar P.</creator><creator>Pandya, Sundaram B.</creator><creator>Parmar, Anil</creator><creator>Kumar, Sumit</creator><creator>Tejani, Ghanshyam G.</creator><creator>Abualigah, Laith</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2025</creationdate><title>A cooperative strategy-based differential evolution algorithm for robust PEM fuel cell parameter estimation</title><author>Jangir, Pradeep ; Arpita ; Agrawal, Sunilkumar P. ; Pandya, Sundaram B. ; Parmar, Anil ; Kumar, Sumit ; Tejani, Ghanshyam G. ; Abualigah, Laith</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c200t-753c30a5e9f19558849e7fb557d42cc849312363414ffba18d530f0dffe95423</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2025</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Condensed Matter Physics</topic><topic>Electrochemistry</topic><topic>Energy Storage</topic><topic>Evolutionary algorithms</topic><topic>Evolutionary computation</topic><topic>Modelling</topic><topic>Optical and Electronic Materials</topic><topic>Parameter estimation</topic><topic>Parameter robustness</topic><topic>Proton exchange membrane fuel cells</topic><topic>Renewable and Green Energy</topic><topic>Task complexity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jangir, Pradeep</creatorcontrib><creatorcontrib>Arpita</creatorcontrib><creatorcontrib>Agrawal, Sunilkumar P.</creatorcontrib><creatorcontrib>Pandya, Sundaram B.</creatorcontrib><creatorcontrib>Parmar, Anil</creatorcontrib><creatorcontrib>Kumar, Sumit</creatorcontrib><creatorcontrib>Tejani, Ghanshyam G.</creatorcontrib><creatorcontrib>Abualigah, Laith</creatorcontrib><collection>CrossRef</collection><jtitle>Ionics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jangir, Pradeep</au><au>Arpita</au><au>Agrawal, Sunilkumar P.</au><au>Pandya, Sundaram B.</au><au>Parmar, Anil</au><au>Kumar, Sumit</au><au>Tejani, Ghanshyam G.</au><au>Abualigah, Laith</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A cooperative strategy-based differential evolution algorithm for robust PEM fuel cell parameter estimation</atitle><jtitle>Ionics</jtitle><stitle>Ionics</stitle><date>2025</date><risdate>2025</risdate><volume>31</volume><issue>1</issue><spage>703</spage><epage>741</epage><pages>703-741</pages><issn>0947-7047</issn><eissn>1862-0760</eissn><abstract>Proton exchange membrane fuel cells (PEMFCs) are powered by hydrogen energy, which is valued for its renewable, safe, and efficient characteristics, and are therefore critical in sustainable electricity generation through hydrogen electrochemical conversion. Parameter estimation in PEMFCs is a challenging but critical task, since accurate modeling is directly related to cell performance optimization and reliable energy output under different operational conditions. To improve parameter estimation accuracy, a cooperative strategy-based differential evolution (CS-DE) algorithm was developed to minimize the sum of squared errors (SSE) between experimental and simulated PEMFC voltage data for multiple BCS 500-W PEM, BCS 250-W PEM, Nedstack PS6 PEM, 500W SR-12 PEM, H-12 PEM, and HORIZON 500W PEMFC models. The CS-DE algorithm was benchmarked against standard differential evolution (DE) and other conventional methods on six commercial PEMFC types, resulting in a 15% reduction in SSE and an average improvement of 12% in estimation accuracy. These results demonstrate the robustness and adaptability of CS-DE for complex PEMFC modeling tasks.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s11581-024-05963-x</doi><tpages>39</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0947-7047 |
| ispartof | Ionics, 2025, Vol.31 (1), p.703-741 |
| issn | 0947-7047 1862-0760 |
| language | eng |
| recordid | cdi_proquest_journals_3158667162 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Accuracy Algorithms Chemistry Chemistry and Materials Science Condensed Matter Physics Electrochemistry Energy Storage Evolutionary algorithms Evolutionary computation Modelling Optical and Electronic Materials Parameter estimation Parameter robustness Proton exchange membrane fuel cells Renewable and Green Energy Task complexity |
| title | A cooperative strategy-based differential evolution algorithm for robust PEM fuel cell parameter estimation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-07T22%3A31%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20cooperative%20strategy-based%20differential%20evolution%20algorithm%20for%20robust%20PEM%20fuel%20cell%20parameter%20estimation&rft.jtitle=Ionics&rft.au=Jangir,%20Pradeep&rft.date=2025&rft.volume=31&rft.issue=1&rft.spage=703&rft.epage=741&rft.pages=703-741&rft.issn=0947-7047&rft.eissn=1862-0760&rft_id=info:doi/10.1007/s11581-024-05963-x&rft_dat=%3Cproquest_cross%3E3158667162%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3158667162&rft_id=info:pmid/&rfr_iscdi=true |