Modeling of commercial proton exchange membrane fuel cell using support vector machine
A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-...
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: |
8 |
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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:26 ; day:13 ; month:07 ; pages:11351-11358 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.ijhydene.2016.04.043 |
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ELV014125455 |
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| 520 | |a A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. | ||
| 520 | |a A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. | ||
| 650 | 7 | |a Energy-saving |2 Elsevier | |
| 650 | 7 | |a PEMFC |2 Elsevier | |
| 650 | 7 | |a Fuel cells |2 Elsevier | |
| 650 | 7 | |a Prediction |2 Elsevier | |
| 650 | 7 | |a Modelling |2 Elsevier | |
| 650 | 7 | |a Support vector machine |2 Elsevier | |
| 700 | 1 | |a Shafiabady, Niusha |4 oth | |
| 700 | 1 | |a Dahari, Mahidzal |4 oth | |
| 700 | 1 | |a Kazemi, Mohammad Saeed |4 oth | |
| 700 | 1 | |a Isa, Dino |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Dedhia, Kavita ELSEVIER |t External auditory canal: Inferior, posterior-inferior, and anterior canal wall overhangs |d 2018 |d official journal of the International Association for Hydrogen Energy |g New York, NY [u.a.] |w (DE-627)ELV000127019 |
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10.1016/j.ijhydene.2016.04.043 doi GBVA2016012000025.pica (DE-627)ELV014125455 (ELSEVIER)S0360-3199(16)30667-X DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Modeling of commercial proton exchange membrane fuel cell using support vector machine 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. Energy-saving Elsevier PEMFC Elsevier Fuel cells Elsevier Prediction Elsevier Modelling Elsevier Support vector machine Elsevier Shafiabady, Niusha oth Dahari, Mahidzal oth Kazemi, Mohammad Saeed oth Isa, Dino 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:26 day:13 month:07 pages:11351-11358 extent:8 https://doi.org/10.1016/j.ijhydene.2016.04.043 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 26 13 0713 11351-11358 8 045F 660 |
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10.1016/j.ijhydene.2016.04.043 doi GBVA2016012000025.pica (DE-627)ELV014125455 (ELSEVIER)S0360-3199(16)30667-X DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Modeling of commercial proton exchange membrane fuel cell using support vector machine 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. Energy-saving Elsevier PEMFC Elsevier Fuel cells Elsevier Prediction Elsevier Modelling Elsevier Support vector machine Elsevier Shafiabady, Niusha oth Dahari, Mahidzal oth Kazemi, Mohammad Saeed oth Isa, Dino 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:26 day:13 month:07 pages:11351-11358 extent:8 https://doi.org/10.1016/j.ijhydene.2016.04.043 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 26 13 0713 11351-11358 8 045F 660 |
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10.1016/j.ijhydene.2016.04.043 doi GBVA2016012000025.pica (DE-627)ELV014125455 (ELSEVIER)S0360-3199(16)30667-X DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Modeling of commercial proton exchange membrane fuel cell using support vector machine 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. Energy-saving Elsevier PEMFC Elsevier Fuel cells Elsevier Prediction Elsevier Modelling Elsevier Support vector machine Elsevier Shafiabady, Niusha oth Dahari, Mahidzal oth Kazemi, Mohammad Saeed oth Isa, Dino 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:26 day:13 month:07 pages:11351-11358 extent:8 https://doi.org/10.1016/j.ijhydene.2016.04.043 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 26 13 0713 11351-11358 8 045F 660 |
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10.1016/j.ijhydene.2016.04.043 doi GBVA2016012000025.pica (DE-627)ELV014125455 (ELSEVIER)S0360-3199(16)30667-X DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Modeling of commercial proton exchange membrane fuel cell using support vector machine 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. Energy-saving Elsevier PEMFC Elsevier Fuel cells Elsevier Prediction Elsevier Modelling Elsevier Support vector machine Elsevier Shafiabady, Niusha oth Dahari, Mahidzal oth Kazemi, Mohammad Saeed oth Isa, Dino 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:26 day:13 month:07 pages:11351-11358 extent:8 https://doi.org/10.1016/j.ijhydene.2016.04.043 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 26 13 0713 11351-11358 8 045F 660 |
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10.1016/j.ijhydene.2016.04.043 doi GBVA2016012000025.pica (DE-627)ELV014125455 (ELSEVIER)S0360-3199(16)30667-X DE-627 ger DE-627 rakwb eng 660 620 660 DE-600 620 DE-600 610 VZ 44.94 bkl Kheirandish, Azadeh verfasserin aut Modeling of commercial proton exchange membrane fuel cell using support vector machine 2016transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. Energy-saving Elsevier PEMFC Elsevier Fuel cells Elsevier Prediction Elsevier Modelling Elsevier Support vector machine Elsevier Shafiabady, Niusha oth Dahari, Mahidzal oth Kazemi, Mohammad Saeed oth Isa, Dino 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:26 day:13 month:07 pages:11351-11358 extent:8 https://doi.org/10.1016/j.ijhydene.2016.04.043 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 41 2016 26 13 0713 11351-11358 8 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:26 day:13 month:07 pages:11351-11358 extent:8 |
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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:26 day:13 month:07 pages:11351-11358 extent:8 |
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Modeling of commercial proton exchange membrane fuel cell using support vector machine |
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A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. |
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A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. |
| abstract_unstemmed |
A method for predicting the performance of a proton exchange membrane fuel cell (PEMFC) system of a commercially available electrical bicycle using a support vector machine (SVM) is presented in this paper. The main advantage of the results obtained from this study is facilitating the use of carbon-free fuels instead of carbon-based ones and consequently reducing the energy consumption. Because such cells are nonlinear, multivariable systems that are difficult to model through traditional methods hence SVMs, which are powerful tools for predicting PEMFC performance, are used. Experimental data obtained from a 250 W PEMFC were used to predict parameters to describe the V–I, P–I, and efficiency–power curves, and then, the data was applied to predict overall PEMFC performance. To evaluate the functionality of suggested model, this method has been compared with multi-layer perceptron (MLP) artificial neural network model. It has been demonstrated that, the error of SVM model is much smaller than MLP, and the proposed approach has better performance to predict the PEM fuel cell curve for the electrical bicycle. It was shown that the coefficient of determination in the SVM prediction model for power–current curve is approximately 99%, which is 97% for MLP model that makes the proposed black box SVM PEMFC model suitable for monitoring and simulating fuel cell performance in the electrical bicycle that is beneficial for its variety of energy saving applications. |
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