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

Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan and Qinghe Yao ()
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Junyi Chen: School of Aeronautics and Astronautics, Sun Yat-sen University, 135, Xingang Xi Road, Guangzhou 510275, China
Huijun Ran: School of Aeronautics and Astronautics, Sun Yat-sen University, 135, Xingang Xi Road, Guangzhou 510275, China
Ziyang Chen: School of Aeronautics and Astronautics, Sun Yat-sen University, 135, Xingang Xi Road, Guangzhou 510275, China
Trevor Hocksun Kwan: School of Advanced Energy, Sun Yat-sen University, No.66, Gongchang Road, Guangming District, Shenzhen 518107, China
Qinghe Yao: School of Aeronautics and Astronautics, Sun Yat-sen University, 135, Xingang Xi Road, Guangzhou 510275, China

Energies, 2025, vol. 18, issue 10, 1-17

Abstract: 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 prediction framework integrating three novel contributions: (1) a sensor fusion strategy leveraging existing thermal/electrochemical measurements (voltage, current, temperature, humidity, and pressure) without requiring embedded stack sensors; (2) a real-time sliding window mechanism enabling dynamic prediction updates every 1 s under variable load conditions; and (3) a modified CNN-based Bi-LSTM parallel model with attention mechanism (ConvBLSTM-PMwA) architecture featuring multi-input multi-output (MIMO) capability for simultaneous flooding/air-starvation detection. Through comparative analysis of different neural architectures using experimental datasets, the optimized ConvBLSTM-PMwA achieved 96.49% accuracy in predicting dual faults 64.63 s pre-occurrence, outperforming conventional LSTM models in both temporal resolution and long-term forecast reliability.

Keywords: 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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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