English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

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

MPS-Authors
/persons/resource/persons86309

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

/persons/resource/persons86397

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

/persons/resource/persons86481

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

/persons/resource/persons86359

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;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-9AEB-A
Abstract
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]