Life prediction of membrane electrode assembly through load and potential cycling accelerated degradation testing in polymer electrolyte membrane fuel cells
- Authors
- Choi, Sung Ryul; Lim, Munwon; Kim, Dong Yeong; An, Won Young; Lee, Sung Won; Choi, Sungyong; Bae, Suk Joo; Yim, Sung-Dae; Park, Jun-Young
- Issue Date
- May-2022
- Publisher
- Elsevier
- Keywords
- Accelerated degradation test; Degradation model; Load cycling test; Polymer electrolyte membrane fuel cell; Potential cycling test
- Citation
- International Journal of Hydrogen Energy, v.47, no.39, pp 17379 - 17392
- Pages
- 14
- Indexed
- SCIE
SCOPUS
- Journal Title
- International Journal of Hydrogen Energy
- Volume
- 47
- Number
- 39
- Start Page
- 17379
- End Page
- 17392
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/138670
- DOI
- 10.1016/j.ijhydene.2022.03.222
- ISSN
- 0360-3199
1879-3487
- Abstract
- Accelerated degradation tests (ADTs) are commonly used to assess the durability of membrane electrode assembly (MEA) components consisting of polymer electrolyte membrane fuel cells (PEMFC) under harsh stress conditions, estimating their lifetime in actual use condition and uncovering their vital degradation mechanisms. ADTs apply mechanically, chemically, or thermally combined stressors to efficiently investigate the durability of MEAs. However, combined stressors for ADTs might cause biased lifetime prediction because major deterioration mechanisms of MEA components are mixed with each other. This work proposes a method to accurately predict the lifetime of MEA through empirical modeling of its performance degradation through ADTs under potential cycle (carbon corrosion) and load cycle tests (electrocatalysts). To simulate operation modes of fuel cell electric vehicles, MEAs are tested under continuous on-off cycle testing (24 h operating and 1 h break) for 5000 h. Degradation patterns of MEAs are first modeled by the empirical model. The relationship between ADTs (potential and load cycle) and continuous on-off condition is then closely examined to accurately predict MEA lifetime under actual operation environments. The proposed idea has a potential to resolve critical durability issues of MEAs by identifying intermingling effects from other constituents.
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