Physically Based Model-Predictive Control for SOFC Stacks and Systems

Abstract

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.

On governing equations and closure relations for the multiscale modeling of concentration polarization in solid-oxide fuel cells: mass transfer and concentration-induced voltage losses.

“…Other types of data-based modes have been developed for SOFCs. Firstly, there are those which are combined with ad hoc physical models and thus take into consideration more details of chemical and thermal aspects, requiring the solution of a set of nonlinear ODEs [ 242,243,244] . In the most common situation, these physics-based models are zero-dimensional or quasi-1D models of fuel cell system components [ 245,246,247,248] .…”