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Model predictive control of a one-dimensional methane reforming solid oxide fuel cell system based on Modelica/SIMULINK co-simulation

Author

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  • Xia, Lei
  • Zhang, Miao
  • Han, Minfang
  • Sun, Li

Abstract

The electrical response rate of solid oxide fuel cell (SOFC) systems is much higher than the thermal and gas response rates, posing challenges for load tracking and temperature control. To address these issues, a one-dimensional (1D) co-flow methane reforming SOFC (MR-SOFC) system mechanism model is developed. A low-order state-space model is obtained by the subspace identification method (SIM), and the Kalman filter-based model predictive control (KF-MPC) is developed in SIMULINK. Finally, a Modelica/SIMULINK co-simulation platform is proposed to analyze the control performance of KF-MPC. The results show that the prediction model has high prediction accuracy. During the load step change, the settling time (tst) of KF-MPC is less than 50s, much lower than that of PIDs. The integral of absolute error (IAE) for system net power out (Pnet) under KF-MPC is 349.08, improving the control accuracy by 21.16 % over PID2 (i.e., 442.77). Meanwhile, the mean square error (RMSE) for SOFC inlet temperature (Tin) under KF-MPC is only 0.0788, reducing the tracking error by 75.79 % compared to PID1 (i.e., 0.3255). The maximum temperature gradient (max|ΔTPEN|) remains within the constraints in the KF-MPC system. The high air flow in the KF-MPC system leads to a more uniform temperature distribution of SOFC.

Suggested Citation

  • Xia, Lei & Zhang, Miao & Han, Minfang & Sun, Li, 2026. "Model predictive control of a one-dimensional methane reforming solid oxide fuel cell system based on Modelica/SIMULINK co-simulation," Renewable Energy, Elsevier, vol. 256(PB).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pb:s0960148125016180
    DOI: 10.1016/j.renene.2025.123954
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    References listed on IDEAS

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    1. Tawfiq, Kotb B. & Zeineldin, Hatem & Al-Durra, Ahmed & El-Saadany, Ehab F., 2026. "Hydrogen fuel cell-inverter system for grid-connected applications: LCL filter design and optimized control strategies for power quality and standard compliance," Renewable Energy, Elsevier, vol. 260(C).

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