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

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, H 2 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. [Display omitted] • An ANN-based algorithm for identifying faults in PEM fuel cells. • Voltage regulation of dc-dc converters, fuel pressure, and hydrogen consumption rate can be detected. • A 3-kW fuel cell system was used to demonstrate the effectiveness of the ANN model. • The detection accuracy rate is always over 90% for all seven types of faults considered. • Performance of the ANN algorithm is compared with that of classical methods. [ABSTRACT FROM AUTHOR]