Electrochemical Impedance Spectroscopy as a Diagnostic Tool for High-Temperature PEM Fuel Cells

Wednesday, October 14, 2015: 15:00
211-B (Phoenix Convention Center)
F. Mack, R. Laukenmann (Karlsruhe Institute of Technology, Helmholtz Institute Ulm), S. Galbiati, J. A. Kerres (University of Stuttgart, Institute of Chem. Proc. Eng.), and R. Zeis (Karlsruhe Institute of Technology, Helmholtz Institute Ulm)
The high-temperature polymer electrolyte membrane fuel cell (HT-PEMFC) continues to attract great interest as an alternative power source for stationary and auxiliary applications. The operating temperature of HT-PEMFCs ranging from 140 °C to 200 °C offers several advantages compared with low-temperature (<100 °C) PEMFCs such as higher tolerance to impurities like carbon monoxide (CO) and simplified water management. HT-PEMFCs based on phosphoric acid doped polybenzimidazole (PBI) membranes are so far the most promising candidates for practical high-temperature operation. For commercialization, however, the performance and long-term stability of the high-temperature membrane electrode assemblies (MEAs) still need significant improvement. To achieve this goal, suitable analytical tools to evaluate single cells and their components are critical for the MEA developers.

One such basic tool is electrochemical impedance spectroscopy (EIS). The technique is highly versatile, and instruments are widely available in electrochemistry laboratories. Here we present the capability of EIS to analyse the performance of high-temperature MEAs for optimizing their composition and material selection. We investigated MEAs with various commercial catalysts, PTFE contents, acid doping levels, and acid base blend membranes. Polarization curves and impedance spectra were taken for all these individual cases. Depending on the operating condition and the MEA design, up to three semi-circles were observed in the HT-PEMFC Nyquist plot, which allows us to resolve the individual cell overpotentials due to membrane resistivity, hydrogen oxidation, oxygen reduction, and mass transport. Such rich information acquired with EIS helps reveal the underlying processes occurring in the MEA during fuel cell operation, which is essential for MEA design and optimization.