Physically Based Model-Predictive Control for SOFC Stacks and Systems

This paper discusses model-predictive controllers (MPC) that can incorporate physical knowledge of fuel-cell behavior into real-time multiple-input-multiple-output (MIMO) process-control strategies. The controller development begins with a high-fidelity, transient, physical model that represents the physical and chemical processes responsible for fuel-cell function. However, because such large nonlinear models cannot be solved in real time as part of the controller logic, linear reduced-order state-space models are required. The model reduction is accomplished via a process called system identification. The controller is designed to interpret sensors in the context of the reduced-order model and determine optimal actuation sequences that cause the system to follow a desired output trajectory. The process is demonstrated for a tubular SOFC stack that could be used for auxiliary-power unit (APU) applications.