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Modelica based hybrid-dimensional dynamic modeling, multi-objective optimization and thermodynamic analysis of cross-flow SOFC system

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  • Xia, Lei
  • Khosravi, Ali
  • Han, Minfang
  • Sun, Li

Abstract

The cross-flow configuration has obvious advantages for the fabrication of solid oxide fuel cell (SOFC) stacks, but results in a complex distribution of variables within the cell. This study introduces a cross-flow SOFC system designed to enhance unreacted hydrogen recovery and air recycling. The optimization framework formed by combining system simulation, Artificial Neural Network (ANN) surrogate model and Non-dominated Sorting Genetic Algorithm-Ⅱ (NSGA-Ⅱ) is applied to improve the system's comprehensive performance. Then, the steady-state performance and the dynamic responses of the system are analyzed. The results show that the ANN model has high prediction precision with coefficients of determination greater than 0.9999, and the optimization framework can implement a fast-global optimization of the system. The SOFC power and efficiency of the optimized system are 80.2024 kW and 61.97 % respectively, and the fuel utilization of the system is 99.92 %. The maximum temperature gradient of SOFC is less than 10 K/cm and the standard deviation of the temperature distribution is 18.8664 K. The SOFC temperature of the optimal system increases and then decreases along the hydrogen flow direction. The step change of current and air flow causes different dynamic responses of the system, especially significant differences in SOFC voltage, and system efficiency.

Suggested Citation

  • Xia, Lei & Khosravi, Ali & Han, Minfang & Sun, Li, 2025. "Modelica based hybrid-dimensional dynamic modeling, multi-objective optimization and thermodynamic analysis of cross-flow SOFC system," Renewable Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:renene:v:241:y:2025:i:c:s0960148125000345
    DOI: 10.1016/j.renene.2025.122372
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    References listed on IDEAS

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