A multipoint voltage-monitoring method for fuel cell inconsistency analysis
- Authors
- Hu, Zunyan; Xu, Liangfei; Li, Jianqiu; Gan, Quanquan; Xu, Xin; Ouyang, Minggao; Song, Ziyou; Kim, Jinyong
- Issue Date
- Dec-2018
- Publisher
- Pergamon Press Ltd.
- Keywords
- Cell consistency; Degradation analysis; Fuel cell; Fuel cell diagnosis; Fuel cell modeling; Fuel cell uniformity
- Citation
- Energy Conversion and Management, v.177, pp 572 - 581
- Pages
- 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- Energy Conversion and Management
- Volume
- 177
- Start Page
- 572
- End Page
- 581
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115200
- DOI
- 10.1016/j.enconman.2018.09.077
- ISSN
- 0196-8904
1879-2227
- Abstract
- Traditionally, only one set voltage monitor was used for fuel cell diagnosis and bipolar plate was regarded as the equipotential body. This study presents a novel analysis about this classic method, and proves that only one-set voltage monitor cannot obtain the real health status of every cell. Experimental results point out that the voltage of each cell along the cell gas channel is variable. The current and voltage redistribution phenomenon, caused by the cell interaction, was observed in the fuel cell stack with graphite bipolar plate. Thus, a one-dimensional model has been proposed to explain the mechanism of the current and voltage redistribution with cell interaction. Simulation result shows that, as long as the abnormal cell exists, the current and voltage distribution of the whole stack is significantly affected. Finally, a multipoint voltage-monitoring method has been developed for fuel cell consistency analysis. New method was applied in a specially designed four-cell stack to validate the model analysis. Additionally, the proposed method was used to diagnose anode starvation, which cannot be easily detected using only the one-set voltage monitor for the anode inlet. All experiments and modeling works prove that the multipoint voltage-monitoring method is effective for fuel cell consistency analysis. © 2018 Elsevier Ltd
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