Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Attention Mechanism

Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Attention Mechanism

Proton exchange membrane fuel cell (PEMFC) fault diagnosis faces two critical limitations: conventional offline methods lack real-time predictive capability, while existing prediction approaches are confined to single fault types. To address these gaps, this study proposes an online multi-fault pred...

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Bibliographic Details
Main Authors: Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Energies
Subjects:
polymer electrolyte membrane fuel cells (PEMFC)
fault pre-diagnosis
CNN-based Bi-LSTM parallel model with attention mechanism (ConvBLSTM-PMwA)
long short-term memory (LSTM)
load-varying conditions
fault parameter
Online Access:https://www.mdpi.com/1996-1073/18/10/2669
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