4.8 Article

Exploration of the Polarization Curve for Proton-Exchange Membrane Fuel Cells

期刊

ACS APPLIED MATERIALS & INTERFACES
卷 13, 期 49, 页码 58838-58847

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsami.1c20289

关键词

polarization curve; proton-exchange membrane; Markov chain Monte Carlo; machine learning; extreme gradient boosting (XGB)

资金

  1. National Natural Science Foundation of China [21774128, U1832177, 51988102, 22173094, 22075276]
  2. CAS Key Research Program of Frontier Sciences [QYZDY-SSWSLH027]
  3. Network and Computing Center, Changchun Institute of Applied Chemistry

向作者/读者索取更多资源

The study reveals that the polarization curve is closely related to the performance of PEMFCs, with factors such as thermodynamic parameters and operational settings playing a role. The research indicates that PPD is positively correlated with ion-exchange capacity, operational temperature, and internal resistance, while negatively correlated with membrane thickness and catalyst loading.
The polarization curve is the most important profile to evaluate the performance of proton-exchange membrane fuel cells (PEMFCs). To explore the important thermodynamic parameters and their correlation with the composition, fabrication, and operational settings, a comprehensive data set consisting of 446 polarization curves from 191 perfluorosulfonate and 255 sulfonated hydrocarbon-based PEMs is collected. Then, a Markov chain Monte Carlo simulation within the Bayesian frame provides higher than 93% confidence to extract six important thermodynamic parameters including open-circuit potential, the transfer coefficient, the current loss, the reference exchange current density, the internal resistance, and the limiting current density. An extreme gradient boosting algorithm affords a mean determinative coefficient of 0.89 to predict the whole polarization curve and a confidence of 94% to predict the peak power density (PPD). Both approaches to explore the polarization curve for PEMFCs show good robustness in the blind test. Overall, the PPD is positively correlated with the ion-exchange capacity of the polymer, operational temperature, and humidity and is negatively affected by internal resistance, membrane thickness, and the loading of the catalyst. The flow rate of fuels can effectively enhance them, while the increase of catalyst loading or fuel concentration shows deleterious impacts.

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