A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation,...

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Bibliographic Details
Published in:International journal of hydrogen energy Vol. 46; no. 2; pp. 2555 - 2564
Main Authors: Meraghni, Safa, Terrissa, Labib Sadek, Yue, Meiling, Ma, Jian, Jemei, Samir, Zerhouni, Noureddine
Format: Journal Article
Language:English
Published: Elsevier Ltd 06.01.2021
Elsevier
Subjects:
Automatic
Computer Science
Data Structures and Algorithms
Deep learning
Digital twin
Electric power
Engineering Sciences
Fluid mechanics
Mechanics
Physics
Prognostics
Proton exchange membrane fuel cell
Remaining useful life
Thermics
Deep learning
Proton exchange membrane fuel cell
Digital twin
Remaining useful life
Prognostics
ISSN:0360-3199, 1879-3487
Online Access:Get full text
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Summary:Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data. •A data-driven digital twin prognostics method is proposed for PEMFCs.•A data-driven model of PEMFC is established on the digital side.•The degradation behaviour is captured via stacked autoencoders.•High prognostics accuracy is achieved with limited measurement data.
ISSN:0360-3199
1879-3487
DOI:10.1016/j.ijhydene.2020.10.108