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Models for Control Applications

  • Dario Marra
  • Cesare Pianese
  • Pierpaolo Polverino
  • Marco Sorrentino
Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

Real-world deployment of SOFC systems entails developing suitable control strategies, which particularly have to guarantee meeting electrical load demand, while limiting as much as possible thermal stresses for ceramic components. In this way, undesirable excessive degradation can be prevented and, in turn, longer lifetime can be achieved. Therefore, the main targets are to control the operating load and manage air and fuel inlet flows so as not to induce severe thermal gradients across fuel cell length, as well as to reduce temperature derivative during both cold-start and shutdown phases. Of course, such control goals are to be pursued taking into account the final application of the SOFC system, depending on which load demand fluctuations considerably vary (e.g., compared to stationary generation, transportation applications exhibit more fluctuating load demand). Therefore, depending on how much articulated is the designed SOFC system, which can particularly include hybridizing components (e.g., batteries and fly wheels) to enable limited power rate operation of the SOFC stack, different control levels must be developed to ensure desired control targets be appropriately met. The current chapter initially focuses on the analysis of the physical relationship between main control and controlled variables, depending on which the multilevel control structure can be appropriately defined. Then, specific analyses are presented and discussed to demonstrate the great potential offered by the model-based approach, to ensure appropriate control strategies be developed for on-field energy-efficient and safe operation of SOFC systems.

Keywords

Internal Combustion Engine Power Demand Load Demand Fuel Cell System Battery Pack 
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-Verlag London 2016

Authors and Affiliations

  • Dario Marra
    • 1
  • Cesare Pianese
    • 1
  • Pierpaolo Polverino
    • 1
  • Marco Sorrentino
    • 1
  1. 1.Department of Industrial EngineeringUniversity of SalernoFiscianoItaly

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