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Ni agglomeration and performance degradation of solid oxide fuel cell: A model-based quantitative study and microstructure optimization

Author

Listed:
  • Fu, Quanrong
  • Tian, Chunyu
  • Hun, Lianming
  • Wang, Xin
  • Li, Zhiyi
  • Liu, Zhijun
  • Wei, Wei

Abstract

Nickel agglomeration poses a noteworthy impediment to the commercialization of SOFCs. A comprehensive coupled degradation model is established, encompassing a Ni-particle coarsening model, microstructural parameters, effective mesoscopic parameters, and various transport processes (mass/momentum/heat/charge/multi-species transports), and chemical/electrochemical reactions. The initial anode microstructural parameters are optimized by the comprehensive model and response surface method (RSM). Single-factor analysis shows that within the initial Ni-particle diameter (dNi(t=0)) range of 0.6–0.9 μm, a particle size ratio (R) of 1.0–1.5 between YSZ-particle and Ni-particle, and a solid-phase volume fraction of Ni-particle (ψNi) ranging from 0.35 to 0.55, SOFC demonstrates high power density and a reduced degradation rate. Using the single-factor results, RSM is employed for -anode microstructure optimization, specifying: dNi(t=0) = 0.7 μm, R = 1.0, and ψNi = 0.49. Correspondingly, the average power density at 0.6 V (PD‾0.6V) reaches 2828.5 W/m2, with a degradation rate (Vde) off 0.441 %/1000 h. In comparison to original microstructure parameters (dNi(t=0) = 0.6 μm, R = 1.0, ψNi = 0.4), the optimal SOFC exhibits a remarkable enhancement in electrical performance and durability, with an 11.9 % increase in PD‾0.6V and a 69 % reduction in Vde. The integration of the comprehensive model and RSM presents a promising strategy for predicting performance, refining operation condition, and optimizing electrode microstructure for long-term operating SOFC.

Suggested Citation

  • Fu, Quanrong & Tian, Chunyu & Hun, Lianming & Wang, Xin & Li, Zhiyi & Liu, Zhijun & Wei, Wei, 2024. "Ni agglomeration and performance degradation of solid oxide fuel cell: A model-based quantitative study and microstructure optimization," Energy, Elsevier, vol. 289(C).
  • Handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223033911
    DOI: 10.1016/j.energy.2023.129997
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