Degradation prognosis for proton exchange membrane fuel cell based on hybrid transfer learning and intercell differences

Ma, Jian; Li, Xue; Zou, Xinyu; Yue, Meiling; Shang, Pengchao; Kang, Lan; Jemeï, Samir; Lu, Chen; Ding, Yu; Zerhouni, Noureddine; Cheng, Yujie

doi:10.1016/j.isatra.2020.06.005

Cited by 31 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LSTM gives prediction accuracy of 99.23%. In another work (Ma et al, 2020), a hybrid method containing the LSTM algorithm is used for RUL prediction. The voltage curves of five cells from the PEMFC aging experiment provided by (Morando et al, 2017) are applied to train the LSTM model.…”

Section: Application Of Machine Learning For Fuel Cellsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

109

View full text Add to dashboard Cite

Technology advancement demands energy storage devices (ESD) and systems (ESS) with better performance, longer life, higher reliability, and smarter management strategy. Designing such systems involve a trade-off among a large set of parameters, whereas advanced control strategies need to rely on the instantaneous status of many indicators. Machine learning can dramatically accelerate calculations, capture complex mechanisms to improve the prediction accuracy, and make optimized decisions based on comprehensive status information. The computational efficiency makes it applicable for real-time management. This paper reviews recent progresses in this emerging area, especially new concepts, approaches, and applications of machine learning technologies for commonly used energy storage devices (including batteries, capacitors/supercapacitors, fuel cells, other ESDs) and systems (including battery ESS, hybrid ESS, grid and microgrid-containing energy storage units, pumped-storage system, thermal ESS). The perspective on future directions is also discussed.

show abstract

Section: Application Of Machine Learning For Fuel Cellsmentioning

confidence: 99%

Machine learning toward advanced energy storage devices and systems

Gao

2021

iScience

109

View full text Add to dashboard Cite

show abstract

“…In recent years, three types of PEMFC prognostic strategies [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][24][25][26][27][28][29][30][31][32][33] have been proposed: model-based, data-based, and hybrid methods based on model and data fusion. Among them, the model-based strategy describes the degradation processes by constructing a physical model [6][7][8], which is beneficial to the prediction accuracy in the condition that the physical degradation model is sufficiently accurate.…”

Section: Introductionmentioning

confidence: 99%

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Wang

Outbib

et al. 2022

International Journal of Hydrogen Energy

View full text Add to dashboard Cite

“…Among recently published papers dealing with DL in the field of performance evaluation of PEMFCs including prognosis (i.e., RUL prediction), learning techniques, which have been derived from leading supervised learning tools such as convolutional neural networks (CNN) [ 10 , 11 , 12 , 13 ], long short-term memory (LSTM) [ 14 , 15 , 16 ], deep belief neural networks (DBNs), and autoencoders (AEs) [ 17 , 18 ] have been extensively investigated. CNNs are generally recommended when trying to separate between different data patterns and make sure they offer a quite enhanced representation.…”

Section: Introductionmentioning

confidence: 99%

Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis

Berghout

Benbouzid

Bentrcia

et al. 2022

Entropy

View full text Add to dashboard Cite

The green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial application since dynamic operating conditions, including physical deterioration, have a serious impact on the cell performance. Under these circumstances, prognosis and health management (PHM) plays an important role in prolonging durability and preventing damage propagation via the accurate planning of a condition-based maintenance (CBM) schedule. In this specific topic, health deterioration modeling with deep learning (DL) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. In this context, the present paper proposes an investigation of further deeper representations by exposing DL models themselves to recurrent expansion with multiple repeats. Such a recurrent expansion of DL (REDL) allows new, more meaningful representations to be explored by repeatedly using generated feature maps and responses to create new robust models. The proposed REDL, which is designed to be an adaptive learning algorithm, is tested on a PEMFC deterioration dataset and compared to its deep learning baseline version under time series analysis. Using multiple numeric and visual metrics, the results support the REDL learning scheme by showing promising performances.

show abstract

Degradation prognosis for proton exchange membrane fuel cell based on hybrid transfer learning and intercell differences

Cited by 31 publications

References 29 publications

Machine learning toward advanced energy storage devices and systems

Machine learning toward advanced energy storage devices and systems

A novel long short-term memory networks-based data-driven prognostic strategy for proton exchange membrane fuel cells

Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis

Contact Info

Product

Resources

About