scholarly journals Methodology for predicting long-term fuel cell performance from short-term testing. Quarterly technical progress report No. 1, June 6-September 5, 1979

1979 ◽  
Author(s):  
M Hooper ◽  
H Maru ◽  
D Patel ◽  
C Ware
Keyword(s):  
Fuel Cell ◽  
Technical Progress ◽  
Progress Report ◽  
Cell Performance ◽  
Short Term ◽  
Fuel Cell Performance

Performance of Proton Exchange Membrane in the Presence of Mg2+

2014 ◽  
Vol 11 (4) ◽  
Author(s):  
Guo Li ◽  
Jinzhu Tan ◽  
Jianming Gong ◽  
Xiaowei Zhang ◽  
Yanchao Xin ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Carbon Dioxide Emissions ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cell ◽  
Proton Exchange ◽  
Cell Performance ◽  
Electrical Performance ◽  
Fuel Cell Performance ◽  
Exchange Membrane

Proton exchange membrane (PEM) fuel cell is regarded as one of the potential renewable energy which may provide a possible long-term solution to reduce carbon dioxide emissions, reduce fossil fuel dependency and increase energy efficiency. Even though great progress has been made, long-term stability and durability is still an issue. The contamination ion plays an important role on the electrical performance of PEM fuel cell. This paper investigates the effect of Mg2+ contamination on PEM fuel cell performance as a function of Mg2+ concentration. Two levels of Mg2+ concentration was chose. From the experimental results, it can be obtained that a significant drop in fuel cell performance occurred when Mg2+ was injected into the anode fuel stream. The voltage and power density of fuel cell decreased larger and larger with increase of Mg2+ concentration over time. The Mg2+ mainly caused the concentration polarization loss from the anode catalyst to the membrane in fuel cell.

scholarly journals A capillary water retention effect to improve medium-temperature fuel cell performance

2013 ◽  
Vol 31 ◽  
pp. 120-124 ◽  
Author(s):  
So Young Lee ◽  
Dong Won Shin ◽  
Chenyi Wang ◽  
Kang Hyuck Lee ◽  
Michael D. Guiver ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Water Retention ◽  
Cell Performance ◽  
Capillary Water ◽  
Medium Temperature ◽  
Fuel Cell Performance

scholarly journals Effect of Pt and Ionomer Distribution on Polymer Electrolyte Fuel Cell Performance and Durability

2021 ◽  
Vol 4 (3) ◽  
pp. 2307-2317
Author(s):  
Aki Kobayashi ◽  
Takahiro Fujii ◽  
Chie Harada ◽  
Eiichi Yasumoto ◽  
Kenyu Takeda ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Polymer Electrolyte ◽  
Cell Performance ◽  
Polymer Electrolyte Fuel Cell ◽  
Fuel Cell Performance

scholarly journals An Artificial Intelligence Solution for Predicting Short-Term Degradation Behaviors of Proton Exchange Membrane Fuel Cell

2021 ◽  
Vol 11 (14) ◽  
pp. 6348
Author(s):  
Zijun Yang ◽  
Bowen Wang ◽  
Xia Sheng ◽  
Yupeng Wang ◽  
Qiang Ren ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Pem Fuel Cell ◽  
Activation Function ◽  
Proton Exchange ◽  
Cell Performance ◽  
Short Term ◽  
Degradation Behaviors ◽  
Hidden Layer ◽  
Exchange Membrane

The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. Highly efficient prediction of the short-term degradation behaviors of the PEM fuel cell has great significance. In this paper, we propose a data-driven degradation prediction method based on multivariate polynomial regression (MPR) and artificial neural network (ANN). This method first predicts the initial value of cell performance, and then the cell performance variations over time are predicted to describe the degradation behaviors of the PEM fuel cell. Two cases of degradation data, the PEM fuel cell in the DEA and anode recirculation modes, are employed to train the model and demonstrate the validation of the proposed method. The results show that the mean relative errors predicted by the proposed method are much smaller than those by only using the ANN or MPR. The predictive performance of the two-hidden-layer ANN is significantly better than that of the one-hidden-layer ANN. The performance curves predicted by using the sigmoid activation function are smoother and more realistic than that by using rectified linear unit (ReLU) activation function.

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