Design of Nonlinear Model Predictive Control for Planar Direct Internal Reforming Solid Oxide Fuel Cell Stacks

The paper proposes an optimization-based nonlinear model predictive control (NMPC) strategy for the operation of a planar direct internal reforming solid oxide fuel cell (DIR-SOFC). The strategy focuses on regulating the inlet fuel and air flowrates to meet power demands, maintain the design temperature, and adhere to other operational constraints. A state estimator and optimizer are employed. Control and prediction horizons, as well as the time step lengths are carefully selected to balance estimation accuracy, stability, and computational cost. The obtained results demonstrate the effectiveness of NMPC in managing large-scale energy supply systems. Comparative analyses with varying tuning weights demonstrate the importance of appropriate weight selection regarding control performance and the robustness of NMPC. Both the dynamic modeling of the planar DIR-SOFC and the NMPC framework are implemented in the MATLAB/Simulink environment.