TY - JOUR
T1 - Enhancing reliability and lifespan of PEM fuel cells through neural network-based fault detection and classification
AU - Dhimish, Mahmoud
AU - Zhao, Xing
N1 - © 2023 Hydrogen Energy Publications LLC. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.
PY - 2023/5/12
Y1 - 2023/5/12
N2 - 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.
AB - 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.
U2 - 10.1016/j.ijhydene.2023.01.064
DO - 10.1016/j.ijhydene.2023.01.064
M3 - Article
SN - 0360-3199
VL - 48
SP - 15612
EP - 15625
JO - International Journal of Hydrogen Energy
JF - International Journal of Hydrogen Energy
IS - 41
ER -