Nonlinear model predictive control based on the moving horizon state estimation for the solid oxide fuel cell

As a nonlinear power generation device, the solid oxide fuel cell (SOFC) often operates under small window of operating conditions due to the constraints stemming from the environmental and safety considerations. The nonlinear model predictive control (NMPC) appears to be well suited control algorithm for this application. NMPC is a closed-loop feedback control scheme that predicts the open-loop optimal input based on the measurements and the setting trajectory. This work aims to develop a closed-loop feedback control strategy based on the NMPC controller for a planar SOFC. The current density, fuel and air molar flow rates are chosen as manipulated variables to control the output power, fuel utilization and temperature. The mole fraction and temperature of the exit gases are set as state variables, which can be estimated from the moving horizon estimation (MHE) method. The validation here is referred to robustness and stability of the controller, a typical case study has been conducted with the power output changes under constant fuel utilization and temperature. Simulation results show that the noise of the output is successfully filtered by the MHE. The NMPC controller works satisfactorily following the setting output trajectory.