Multiscale modeling of degradation of full solid oxide fuel cell stacks

Limiting the degradation of solid oxide fuel cells is an important challenge for their widespread use and commercialization. The computational expense of long-term simulation of a full stack with conventional models is immense. In this study, we present a multiscale three-dimensional model of a degrading full stack of solid oxide cells, where we integrate degradation phenomena of nickel particle coarsening in the anode electrode, chromium poisoning of the cathode electrode, and oxidation of the interconnect into a multiscale model of the stack. This approach makes this type of simulation computationally feasible, and 38 thousand hours of the stack operation can be simulated in 1 h and 15 min on a high-end workstation. Hereby one can start to explore the optimum operating conditions for a range of parameters. The model is validated with experimental data from an 18-cell Ju spacing diaeresis lich Mark-F stack experiment and predicts common trends reported in the literature for evolutions of the stack performance, degradation phenomena, and the related model variables. Moreover, it captures how different regimes in the full stack degrades at different rates and how the various degradation phenomena interact over time. The model is used to investigate the effects of galvanostatic and potentiostatic operation modes, operating conditions, and flow configurations on the long-term performance of the stack. Results demonstrate, as expected, that potentiostatic operation mode, moderate temperature, lower load current, and counter-flow configuration improve the long-term performance of the stack. (c) 2021 The Author(s). Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).

Automated summary

The study presents a multiscale three-dimensional model for simulating the degradation of solid oxide fuel cell stacks, integrating various degradation phenomena to explore optimal operating conditions, validating with experimental data, and investigating the effects of operation modes, conditions, and configurations on the long-term performance of the stack. Results demonstrate that certain factors such as potentiostatic operation mode and flow configuration can improve the long-term performance of the stack.