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State Estimation of a Molten Carbonate Fuel Cell by an Extended Kalman Filter

MPG-Autoren
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Grötsch,  M.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Mangold,  M.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Sheng,  M.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Kienle,  A.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Zitation

Grötsch, M., Mangold, M., Sheng, M., & Kienle, A. (2006). State Estimation of a Molten Carbonate Fuel Cell by an Extended Kalman Filter. In W. Marquardt, & C. Pantelides (Eds.), 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering (pp. 1161-1166). Amsterdam: Elsevier.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-9AEB-A
Zusammenfassung
Industrial fuel cell stacks only provide very limited measurement information. To overcome this deficit, a state estimator for a molten carbonate fuel cell system is developed in this contribution. The starting point of the work is a rigorous spatially distributed model of the system. From this model a reduced model is derived by using a Galerkin method and the Karhunen Loève decomposition technique. An extended Kalman filter with a continuous time simulator part and a discrete time corrector part is designed on the basis of the reduced model. The filter is tested in simulations and experimentally. © 2006 Elsevier B.V. All rights reserved. [accessed 2014 January 9th]