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. 2019 Aug 26;24(17):3097.
doi: 10.3390/molecules24173097.

Optimization of Membrane Electrode Assembly of PEM Fuel Cell by Response Surface Method

Rohit K S S Vuppala  1   2 , Benitta A Chedir  1 , Lishuai Jiang  3 , Lianjun Chen  3 , Muhammad Aziz  4 , Agus P Sasmito  5
Affiliations

Optimization of Membrane Electrode Assembly of PEM Fuel Cell by Response Surface Method

Rohit K S S Vuppala et al. Molecules. .

Abstract

The membrane electrode assembly (MEA) plays an important role in the proton exchange membrane fuel cell (PEMFC) performance. Typically, the structure comprises of a polymer electrolyte membrane sandwiched by agglomerate catalyst layers at the anode and cathode. Optimization of various parameters in the design of MEA is, thus, essential for reducing cost and material usage, while improving cell performance. In this paper, optimization of MEA is performed using a validated two-phase PEMFC numerical model. Key MEA parameters affecting the performance of a single PEMFC are determined from sensitivity analysis and are optimized using the response surface method (RSM). The optimization is carried out at two different operating voltages. The results show that membrane thickness and membrane protonic conductivity coefficient are the most significant parameters influencing cell performance. Notably, at higher voltage (0.8 V per cell), the current density can be improved by up to 40% while, at a lower voltage (0.6 V per cell), the current density may be doubled. The results presented can be of importance for fuel cell engineers to improve the stack performance and expedite the commercialization.

Keywords: PEM fuel cell; computational fuel cell dynamics; membrane electrode assembly (MEA); response surface method.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic view of a single PEMFC: (a) components of PEMFC, (b) computational domains with boundaries: I—insulation/wall, II—anode inlet, III—cathode inlet, and IV—outlets.
Figure 2
Figure 2
Polarization curves: [formula image] is the experimental potential, [formula image] is the iR-corrected experimental potential, [formula image] is the experimental power density, [— · ] is the predicted potential, [ — ] is the predicted iR-corrected potential, and [··· ] is the predicted power density.
Figure 3
Figure 3
Local current density distribution. Predicted (lines) and experimental (points) for current densities: 1.3 Acm−2 (formula image), 1.1 A cm−2 (formula image), 0.9 A cm−2 (formula image), 0.7 A cm−2 (formula image), 0.5 A cm−2 (formula image), 0.4 A cm−2 (formula image), 0.3 A cm−2 (formula image), 0.2 A cm−2 (formula image), 0.1 A cm−2 (formula image), and 0.05 A cm−2 (formula image).
Figure 4
Figure 4
Experimental (points) and predicted (line) polarization curve for case b.
Figure 5
Figure 5
Flow diagram of response surface methodology.
Figure 6
Figure 6
Goodness of fit for response surface generated at: (a) 0.8 V and (b) 0.6 V.
Figure 7
Figure 7
Local sensitivity analysis at 0.6 V presented in: (a) bar plot and (b) sensitivity curve.
Figure 8
Figure 8
Local sensitivity analysis at 0.8 V presented in: (a) the bar plot and the (b) sensitivity curve.
Figure 9
Figure 9
Three-dimensional response surface plot at 0.6 V.
Figure 10
Figure 10
Three-dimensional response surface plot at 0.8 V.
See this image and copyright information in PMC

References

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