Modeling and control of tubular solid-oxide fuel cell systems: II. Nonlinear model reduction and model predictive control

Abstract This paper describes a systematic method for developing model-based controllers for solid-oxide fuel cell (SOFC) systems. To enhance the system efficiency and to avoid possible damages, the system must be controlled within specific operating conditions, while satisfying a load requirement. Model predictive control (MPC) is a natural choice for control implementation. However, to implement MPC, a low-order model is needed that captures the dominant dynamic behavior over the operating range. A linear parameter varying (LPV) model structure is developed and applied to obtain a control-oriented dynamic model of the SOFC stack. This approach effectively reduces a detailed physical model to a form that is compatible with MPC. The LPV structure includes nonlinear scheduling functions that blend the dynamics of locally linear models to represent nonlinear dynamic behavior over large operating ranges. Alternative scheduling variables are evaluated, with cell current being shown to be an appropriate choice. Using the reduced-order model, an MPC controller is designed that can respond to the load requirement over a wide range of operation changes while maintaining input–output variables within specified constraints. To validate the approach, the LPV-based MPC controller is applied to the high-order physical model.

[1]  R. G. Colclaser,et al.  Transient modeling and simulation of a tubular solid oxide fuel cell , 1999 .

[2]  J. R. McDonald,et al.  An integrated SOFC plant dynamic model for power systems simulation , 2000 .

[3]  Anna G. Stefanopoulou,et al.  Control of Fuel Cell Power Systems: Principles, Modeling, Analysis and Feedback Design , 2004 .

[4]  Jaime Arriagada,et al.  Artificial neural network simulator for SOFC performance prediction , 2002 .

[5]  Jie Yang,et al.  Nonlinear model predictive control of SOFC based on a Hammerstein model , 2008 .

[6]  Tyrone L. Vincent,et al.  Modeling and control of tubular solid-oxide fuel cell systems. I: Physical models and linear model reduction , 2011 .

[7]  Biao Huang,et al.  Data-driven predictive control for solid oxide fuel cells , 2007 .

[8]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[9]  Robert J. Kee,et al.  Solid Oxide Fuel Cells: Operating Principles, Current Challenges, and the Role of Syngas , 2008 .

[10]  Tyrone L. Vincent,et al.  Identification of Structured Nonlinear Systems , 2008, IEEE Transactions on Automatic Control.

[11]  Tyrone L. Vincent,et al.  Physically Based Model-Predictive Control for SOFC Stacks and Systems , 2009 .

[12]  Haralambos Sarimveis,et al.  Operational optimization and real-time control of fuel-cell systems , 2009 .

[13]  Jie Yang,et al.  Predictive control of solid oxide fuel cell based on an improved Takagi-Sugeno fuzzy model , 2009 .

[14]  Michael A. Danzer,et al.  Model-based control of cathode pressure and oxygen excess ratio of a PEM fuel cell system , 2008 .

[15]  Roger A. Dougal,et al.  Multiple model predictive control for a hybrid proton exchange membrane fuel cell system , 2009 .

[16]  Anna G. Stefanopoulou,et al.  Control of Fuel Cell Power Systems , 2004 .

[17]  Jie Yang,et al.  Control-oriented thermal management of solid oxide fuel cells based on a modified Takagi-Sugeno fuzzy model , 2009 .

[18]  Francisco Jurado,et al.  Predictive control of solid oxide fuel cells using fuzzy Hammerstein models , 2006 .

[19]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[20]  Weirong Chen,et al.  Nonlinear robust control of proton exchange membrane fuel cell by state feedback exact linearization , 2009 .

[21]  Robert J. Kee,et al.  Solid-oxide fuel cells with hydrocarbon fuels , 2005 .