Enhancing reliability and lifespan of PEM fuel cells through neural network-based fault detection and classification

Mahmoud Dhimish*, Xing Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In order to maximise fuel cell reliability of operation and useful life span, an accurate online health assessment of the fuel cell system is essential. Existing algorithms for fault detection in fuel cell systems are based on sensing elements, control methods, and statistical/probabilistic models. In this paper, an artificial neural network (ANN) will be developed to detect and classify faults in proton-exchange membrane (PEM) fuel cell systems. As the ANN model developed within the PEM system relies on the input and output current and voltage, additional sensing devices are not required within the system. Based on an experimental setup using a 3-kW fuel cell system, it was found that the proposed model was able to detect faults associated with the reduction/increase of fuel pressure, H2 consumption rate, and voltage regulation changes in the dc-dc converter with >90% accuracy. In the proposed model, historical data is required to train and validate the ANN algorithm, but after this is complete, no human intervention is required afterward.
Original languageEnglish
Pages (from-to)15612-15625
Number of pages14
JournalInternational Journal of Hydrogen Energy
Volume48
Issue number41
Early online date22 Apr 2023
DOIs
Publication statusPublished - 12 May 2023

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