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Dynamic Modeling of Fuel Cells

  • Randall S. Gemmen
Part of the Fuel Cells and Hydrogen Energy book series (FCHY)

The analysis of fuel cells can be divided into two areas, steady state modeling and dynamicmodeling. Prior chapters largely focused on the fundamental principles and present practices formodeling the steady state performance of fuel cells and fuel cell systems. For these situations, stable conditions exist at all points in the system, and process performance parameters, such as efficiency, are precisely described. This chapter opens the door to the study of the dynamic behavior of fuel cells and fuel cell stacks. (Full system dynamic analysis, where both stacks and other balance of plant dynamics are modeled, is considered separately in Chapter 8. In this chapter, important capacitive elements in the fuel cell process are identified and modeled. While some inductive-type behavior has been noted within the literature (see Section 9.2.1), little advancement has been made to develop physical models to support such transient behavior for the cell per se; therefore, our attention is focused primarily on capacitive behavior only. These capacitive elements control the rate at which process parameters change due to changes in other coupled process parameters. For example, thermal capacitance controls the rate of cell temperature change due to an imbalance between internal energy sources and boundary energy transfers.

The chapter begins with a presentation of available evidence of cell and system transient behavior. Based on this evidence, parameters that have been proposed to account for various capacitive behaviors within the cell and fuel cell system are presented. A brief discussion for how such transient behavior affects the safe operation of a fuel cell and system is then given. Models are then developed for predicting the behavior of the cell and system, and, in the final section of this chapter, examples of their application are given.

Keywords

Fuel Cell Solid Oxide Fuel Cell Cell Voltage Fuel Cell System Internal Heat Generation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media B.V 2008

Authors and Affiliations

  • Randall S. Gemmen
    • 1
  1. 1.U.S. Department of EnergyNational Energy Technology LaboratoryMorgantownUSA

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