Effect of freeze–thaw cycles on membrane electrode assembly of proton exchange membrane fuel cells and its fault diagnosis method

In low‐temperature environment, the residual water in the membrane electrode assembly (MEA) will freeze after the operation of proton exchange membrane fuel cells, which will cause damage to the MEA. In this paper, the effect of freeze–thaw cycles on MEA was studied. Six sets of MEA samples with 0,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Fuel cells (Weinheim an der Bergstrasse, Germany) Germany), 2024-04, Vol.24 (2), p.78-89
Hauptverfasser: Zhang, Ruixuan, Chen, Tao, Zhang, Rufeng, Gan, Zhongyu
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In low‐temperature environment, the residual water in the membrane electrode assembly (MEA) will freeze after the operation of proton exchange membrane fuel cells, which will cause damage to the MEA. In this paper, the effect of freeze–thaw cycles on MEA was studied. Six sets of MEA samples with 0, 20, 40, 60, 80, and 100 times freeze–thaw cycles were set up, and the damage on MEAs is analyzed by polarization curves, electrochemical impedance spectra, cyclic voltammetry curves, and scanning electron microscope. It was found that the freeze–thaw cycles caused degradation on MEA, and the ohmic resistance of MEA increases with the number of cycles increases before the 60 freeze–thaw cycles, and after 60 freeze–thaw cycles, a gap appeared between the proton exchange membrane (PEM) and the catalyst layer, which led to more water entering the PEM and the ohmic resistance of MEA decreased. Besides, according to the data analysis, the experimental samples are divided into three categories (normal MEA, lightly damaged MEA, and seriously damaged MEA). A classifier model combining inception network and light gradient boosting machine (LGBM) was established, and it was found that the combined model was better than inception–dense and LGBM for classification, reaching 96.89%.
ISSN:1615-6846
1615-6854
DOI:10.1002/fuce.202300134