Intelligent diagnosis of proton exchange membrane fuel cell water states based on flooding-specificity experiment and deep learning method

Yuqi Zhang, Yu Li, Caizhi Zhang, Yunzi Yang, Xingzi Yu, Tong Niu, Lei Wang and Gucheng Wang

Renewable Energy, 2024, vol. 222, issue C

Abstract: Flood-related malfunctions stand out as a primary impediment, constraining the effective and consistent functioning of proton exchange membrane fuel cells (PEMFC). This study firstly confirmed the correlation between health characteristic and PEMFC watering based on flooding-specificity experiment of PEMFC. In addition, a new index of iterative power drop was calculated, which could reflect the effect of the set operation condition on the stack water states timely. Moreover, this study took into account that not only normal state and flooded state, but also the mutual transformation stages between the two have the monitoring significance. Finally, a data-driven method was deployed to further delineate the three-classification diagnosis of the water states inside the stack and the diagnostic accuracy had been reached to 99.5 %. The proposed new index and water states definition method could open up new ideas for improving the durability and hydrogen consumption economy of PEMFC.

Keywords: Flooding diagnosis; Proton exchange membrane fuel cell (PEMFC); Flooding-specificity experiment; Iterative power drop; Three-classification diagnosis (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148124000314
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:222:y:2024:i:c:s0960148124000314

DOI: 10.1016/j.renene.2024.119966

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().