A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell

Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durabi...

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Vydané v:	Applied energy Ročník 313; s. 118835
Hlavní autori:	Benaggoune, Khaled, Yue, Meiling, Jemei, Samir, Zerhouni, Noureddine
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.05.2022 Elsevier
Predmet:	Automatic commercialization Computer Science Convolutional neural network Data Structures and Algorithms durability Electric power energy Engineering Sciences Fluid mechanics fuel cells markets Mechanics neural networks Physics prediction Prognostics Proton membrane exchange fuel cell Thermics Time series prediction uncertainty Proton membrane exchange fuel cell Time series prediction Convolutional neural network Prognostics
ISSN:	0306-2619, 1872-9118
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Abstract	Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful commercialization and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the first time applied for predicting the time-dependent fuel cell performance and is proved to be more efficient than other recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics. •A dilated CNN is applied for the multi-step-ahead fuel cell degradation prediction.•An attention block is combined to improve the prediction performance.•The prediction performance is compared with three other RNN prediction tools.•A conditional CNN is proposed to improve the long-term prognostics performance.
AbstractList	Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful commercialization and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the first time applied for predicting the time-dependent fuel cell performance and is proved to be more efficient than other recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics. Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful commercialization and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the first time applied for predicting the time-dependent fuel cell performance and is proved to be more efficient than other recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics. •A dilated CNN is applied for the multi-step-ahead fuel cell degradation prediction.•An attention block is combined to improve the prediction performance.•The prediction performance is compared with three other RNN prediction tools.•A conditional CNN is proposed to improve the long-term prognostics performance.
ArticleNumber	118835
Author	Benaggoune, Khaled Zerhouni, Noureddine Yue, Meiling Jemei, Samir
Author_xml	– sequence: 1 givenname: Khaled surname: Benaggoune fullname: Benaggoune, Khaled organization: FEMTO-ST Institute, Univ. Bourgogne Franche-Comté, CNRS, Besançon, France – sequence: 2 givenname: Meiling orcidid: 0000-0002-4624-4743 surname: Yue fullname: Yue, Meiling email: meiling.yue@femto-st.fr, yueml@bjtu.edu.cn organization: School of mechanical, electronic and control engineering, Beijing Jiaotong University, Beijing, China – sequence: 3 givenname: Samir surname: Jemei fullname: Jemei, Samir organization: FEMTO-ST Institute, FCLAB, Univ. Bourgogne Franche-Comté, CNRS, Belfort, France – sequence: 4 givenname: Noureddine surname: Zerhouni fullname: Zerhouni, Noureddine organization: FEMTO-ST Institute, Univ. Bourgogne Franche-Comté, CNRS, Besançon, France
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Keywords	Proton membrane exchange fuel cell Time series prediction Convolutional neural network Prognostics
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Snippet	Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells...
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SubjectTerms	Automatic commercialization Computer Science Convolutional neural network Data Structures and Algorithms durability Electric power energy Engineering Sciences Fluid mechanics fuel cells markets Mechanics neural networks Physics prediction Prognostics Proton membrane exchange fuel cell Thermics Time series prediction uncertainty
Title	A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell
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