Effects of various operating conditions and optimal ionomer-gradient distribution on temperature-driven water transport in cathode catalyst layer of PEMFC
Abstract
Temperature distribution affects water transport by phase-change-induced (PCI) flow in the cathode catalyst layer, where water generates and heat releases. To elucidate the role of PCI flow in water removal, a 1-D, non-isothermal, two-phase model coupled with an agglomerate submodel is established and a dimensionless parameter, Ts , evaluating the relation between the capillary-driven flow and PCI flow is also employed. The effects of operating temperature and inlet relative humidity (RH) on the Ts value are investigated in detail. The results show that raising the operating temperature (from 333.15 K to 363.15 K) contributes to increased Ts and extension of the PCI flow dominating area. Compared with other RH cases, Case-D has the highest Ts due to the adsorption/desorption process between equilibrium water content and dissolved water content as well as the changes of phase-change heat in the cathode catalyst layer. But the widest PCI flow dominating area emerges at Case-C. Lastly, the well-designed ionomer-gradient distribution is identified by the Particle Swarm Optimization algorithm, namely reduction along the through-plane direction in the cathode catalyst layer with three sub-layers, which in turn increases the local porosity. As a result, the overall mass transport capability is enhanced, as reflected by the decrease of Damkohler number and increase of effectiveness factor, thus promoting the output performance. Also, this optimization facilitates the PCI flow and expands the PCI flow dominating area. Overall, it is realizable to avoid water flooding by reasonably adjusting the operating conditions as well as optimizing the catalyst layer gradient structure.
- Publication:
-
Chemical Engineering Journal
- Pub Date:
- January 2023
- DOI:
- 10.1016/j.cej.2022.138924
- Bibcode:
- 2023ChEnJ.45138924X
- Keywords:
-
- PEMFC;
- Cathode catalyst layer;
- Phase-change-induced flow;
- Ionomer-gradient distribution;
- Particle swarm optimization