Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm

This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for...
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Autor*in:	Yuan, Zhi [verfasserIn] Wang, Weiqing [verfasserIn] Wang, Haiyun [verfasserIn] Ashourian, Mohsen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Übergeordnetes Werk:	Enthalten in: Energy reports - Amsterdam [u.a.] : Elsevier, 2015, 6(2020) vom: Nov., Seite 1572-1580
Übergeordnetes Werk:	volume:6 ; year:2020 ; month:11 ; pages:1572-1580

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.egyr.2020.06.011

Katalog-ID:	172858731X

Internformat


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publishDate	2020
allfields	10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell
spelling	10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell
allfields_unstemmed	10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell
allfieldsGer	10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell
allfieldsSound	10.1016/j.egyr.2020.06.011 doi 10419/244146 hdl (DE-627)172858731X (DE-599)KXP172858731X DE-627 ger DE-627 rda eng Yuan, Zhi verfasserin (DE-588)1207951595 (DE-627)1694291065 aut Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Wang, Weiqing verfasserin (DE-588)1207951846 (DE-627)1694291766 aut Wang, Haiyun verfasserin (DE-588)1207951951 (DE-627)1694291871 aut Ashourian, Mohsen verfasserin aut Enthalten in Energy reports Amsterdam [u.a.] : Elsevier, 2015 6(2020) vom: Nov., Seite 1572-1580 Online-Ressource (DE-627)820689033 (DE-600)2814795-9 (DE-576)427950821 2352-4847 nnns volume:6 year:2020 month:11 pages:1572-1580 https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf Verlag kostenfrei https://doi.org/10.1016/j.egyr.2020.06.011 Resolving-System kostenfrei http://hdl.handle.net/10419/244146 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 6 2020 11 1572-1580 26 01 0206 374597865X x1z 02-09-20 2403 01 DE-LFER 3758605504 00 --%%-- --%%-- n --%%-- l01 18-09-20 2403 01 DE-LFER https://doi.org/10.1016/j.egyr.2020.06.011 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf 26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell
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source	Enthalten in Energy reports 6(2020) vom: Nov., Seite 1572-1580 volume:6 year:2020 month:11 pages:1572-1580
sourceStr	Enthalten in Energy reports 6(2020) vom: Nov., Seite 1572-1580 volume:6 year:2020 month:11 pages:1572-1580
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sw_local_iln_str_mv	26:Convolutional neural network DE-206:Convolutional neural network 26:Deer hunting optimization algorithm DE-206:Deer hunting optimization algorithm 26:Parameter identification DE-206:Parameter identification 26:Proton exchange membrane fuel cell DE-206:Proton exchange membrane fuel cell
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authorswithroles_txt_mv	Yuan, Zhi @@aut@@ Wang, Weiqing @@aut@@ Wang, Haiyun @@aut@@ Ashourian, Mohsen @@aut@@
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spellingShingle	Yuan, Zhi 26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm
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topic_title	26 00 DE-206 56 Convolutional neural network 26 00 DE-206 56 Deer hunting optimization algorithm 26 00 DE-206 56 Parameter identification 26 00 DE-206 56 Proton exchange membrane fuel cell Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian
topic	26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell
topic_unstemmed	26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell
topic_browse	26 Convolutional neural network 26 Deer hunting optimization algorithm 26 Parameter identification 26 Proton exchange membrane fuel cell
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title	Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm
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title_full	Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm Zhi Yuan, Weiqing Wang, Haiyun Wang, Mohsen Ashourian
author_sort	Yuan, Zhi
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title_sort	parameter identification of pemfc based on convolutional neural network optimized by balanced deer hunting optimization algorithm
title_auth	Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm
abstract	This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.
abstractGer	This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.
abstract_unstemmed	This paper proposes a new optimal method for the parameter identification of a proton exchange membrane fuel cell (PEMFC) for increasing the model accuracy. In this research, a new improved version based on deer hunting optimization algorithm (DHOA) is applied to the Convolutional neural network for the PEMFC parameters identification purpose. Indeed, the method is implemented to develop the method performance for estimating the PEMFC model parameters. The method is then validated based on 4 operational conditions. Experimental results declared that utilizing the proposed method gives a prediction with higher accuracy for the parameters of the PEMFC model.
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title_short	Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm
url	https://www.sciencedirect.com/science/article/pii/S2352484720303152/pdfft?md5=0b0eea0dd552c369d26769663ebee941&pid=1-s2.0-S2352484720303152-main.pdf https://doi.org/10.1016/j.egyr.2020.06.011 http://hdl.handle.net/10419/244146
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GND_str_mv	Zhi, Yuan Yuan Zhi Yuan, Zhi Weiqing, Wang Wang Weiqing Wang, Weiqing Haiyun, Wang Wang Haiyun Wang, Haiyun
GND_txt_mv	Zhi, Yuan Yuan Zhi Yuan, Zhi Weiqing, Wang Wang Weiqing Wang, Weiqing Haiyun, Wang Wang Haiyun Wang, Haiyun
GND_txtF_mv	Zhi, Yuan Yuan Zhi Yuan, Zhi Weiqing, Wang Wang Weiqing Wang, Weiqing Haiyun, Wang Wang Haiyun Wang, Haiyun
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doi_str	10.1016/j.egyr.2020.06.011
callnumber-a	--%%--
up_date	2024-07-05T02:29:27.182Z
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Parameter identification of PEMFC based on Convolutional neural network optimized by balanced deer hunting optimization algorithm

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