Dynamic modelling of PEM fuel cell of power electric bicycle system
Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and veh...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kheirandish, Azadeh [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs - Dedhia, Kavita ELSEVIER, 2018, official journal of the International Association for Hydrogen Energy, New York, NY [u.a.] |
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| Übergeordnetes Werk: |
volume:41 ; year:2016 ; number:22 ; day:15 ; month:06 ; pages:9585-9594 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.ijhydene.2016.02.046 |
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| 520 | |a Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. | ||
| 520 | |a Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. | ||
| 650 | 7 | |a Regression models |2 Elsevier | |
| 650 | 7 | |a Artificial neural networks |2 Elsevier | |
| 650 | 7 | |a Modelling |2 Elsevier | |
| 650 | 7 | |a Proton exchange membrane fuel cell |2 Elsevier | |
| 700 | 1 | |a Motlagh, Farid |4 oth | |
| 700 | 1 | |a Shafiabady, Niusha |4 oth | |
| 700 | 1 | |a Dahari, Mahidzal |4 oth | |
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10.1016/j.ijhydene.2016.02.046 doi GBVA2016012000025.pica (DE-627)ELV014123444 (ELSEVIER)S0360-3199(16)30120-3 DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Dynamic modelling of PEM fuel cell of power electric bicycle system 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Regression models Elsevier Artificial neural networks Elsevier Modelling Elsevier Proton exchange membrane fuel cell Elsevier Motlagh, Farid oth Shafiabady, Niusha oth Dahari, Mahidzal oth Enthalten in Elsevier Dedhia, Kavita ELSEVIER External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs 2018 official journal of the International Association for Hydrogen Energy New York, NY [u.a.] (DE-627)ELV000127019 volume:41 year:2016 number:22 day:15 month:06 pages:9585-9594 extent:10 https://doi.org/10.1016/j.ijhydene.2016.02.046 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 22 15 0615 9585-9594 10 045F 660 |
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10.1016/j.ijhydene.2016.02.046 doi GBVA2016012000025.pica (DE-627)ELV014123444 (ELSEVIER)S0360-3199(16)30120-3 DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Dynamic modelling of PEM fuel cell of power electric bicycle system 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Regression models Elsevier Artificial neural networks Elsevier Modelling Elsevier Proton exchange membrane fuel cell Elsevier Motlagh, Farid oth Shafiabady, Niusha oth Dahari, Mahidzal oth Enthalten in Elsevier Dedhia, Kavita ELSEVIER External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs 2018 official journal of the International Association for Hydrogen Energy New York, NY [u.a.] (DE-627)ELV000127019 volume:41 year:2016 number:22 day:15 month:06 pages:9585-9594 extent:10 https://doi.org/10.1016/j.ijhydene.2016.02.046 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 22 15 0615 9585-9594 10 045F 660 |
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10.1016/j.ijhydene.2016.02.046 doi GBVA2016012000025.pica (DE-627)ELV014123444 (ELSEVIER)S0360-3199(16)30120-3 DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Dynamic modelling of PEM fuel cell of power electric bicycle system 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Regression models Elsevier Artificial neural networks Elsevier Modelling Elsevier Proton exchange membrane fuel cell Elsevier Motlagh, Farid oth Shafiabady, Niusha oth Dahari, Mahidzal oth Enthalten in Elsevier Dedhia, Kavita ELSEVIER External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs 2018 official journal of the International Association for Hydrogen Energy New York, NY [u.a.] (DE-627)ELV000127019 volume:41 year:2016 number:22 day:15 month:06 pages:9585-9594 extent:10 https://doi.org/10.1016/j.ijhydene.2016.02.046 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 22 15 0615 9585-9594 10 045F 660 |
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10.1016/j.ijhydene.2016.02.046 doi GBVA2016012000025.pica (DE-627)ELV014123444 (ELSEVIER)S0360-3199(16)30120-3 DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Dynamic modelling of PEM fuel cell of power electric bicycle system 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Regression models Elsevier Artificial neural networks Elsevier Modelling Elsevier Proton exchange membrane fuel cell Elsevier Motlagh, Farid oth Shafiabady, Niusha oth Dahari, Mahidzal oth Enthalten in Elsevier Dedhia, Kavita ELSEVIER External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs 2018 official journal of the International Association for Hydrogen Energy New York, NY [u.a.] (DE-627)ELV000127019 volume:41 year:2016 number:22 day:15 month:06 pages:9585-9594 extent:10 https://doi.org/10.1016/j.ijhydene.2016.02.046 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 22 15 0615 9585-9594 10 045F 660 |
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10.1016/j.ijhydene.2016.02.046 doi GBVA2016012000025.pica (DE-627)ELV014123444 (ELSEVIER)S0360-3199(16)30120-3 DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Dynamic modelling of PEM fuel cell of power electric bicycle system 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. Regression models Elsevier Artificial neural networks Elsevier Modelling Elsevier Proton exchange membrane fuel cell Elsevier Motlagh, Farid oth Shafiabady, Niusha oth Dahari, Mahidzal oth Enthalten in Elsevier Dedhia, Kavita ELSEVIER External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs 2018 official journal of the International Association for Hydrogen Energy New York, NY [u.a.] (DE-627)ELV000127019 volume:41 year:2016 number:22 day:15 month:06 pages:9585-9594 extent:10 https://doi.org/10.1016/j.ijhydene.2016.02.046 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 22 15 0615 9585-9594 10 045F 660 |
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Enthalten in External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs New York, NY [u.a.] volume:41 year:2016 number:22 day:15 month:06 pages:9585-9594 extent:10 |
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Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. |
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Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. |
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Fuel cells eliminate pollution caused by burning fossil fuels; hence, a proton exchange membrane fuel cell (PEMFC) is one of the promising technological advances for the future of the transportation industry. The key existing challenges for fuel cell commercialization are performance, design and vehicle efficiency. Since the analytical model expressing fuel cells' characteristics is not accurate in comparison with the real system's performance a robust and dynamic model for fuel cells is of great importance. This study aims to introduce an optimized model for PEMFC using an electric bicycle that consists of a 250 W fuel cell, battery pack, DC/DC convertor, electric motor and electric control unit (ECU). In the first phase of this multi-fold study, the analytical model of PEMFC's efficiency has been compared with the experimental results obtained from the electric bicycle. The result of this phase showed an overall system efficiency of 35.4% and a maximum fuel cell efficiency of 63%. This confirms that fuel cell performance is least efficient when functioning under maximum output power conditions. In the second phase of this research, the collected data was used for developing linear and nonlinear regression models. The resulting model was compared with an artificial neural network used for the same purpose, and their prediction efficiencies compared. Results show that neural network modelling improves accuracy and provides promising performance for the electric bicycle system. |
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