Solid oxide fuel cell stack temperature estimation with data-based modeling: Designed experiments and parameter identification

Antti Pohjoranta, Matias Halinen, Jari Pennanen, Jari Kiviaho

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)

Abstract

Data-based modeling is utilized for the dynamic estimation of the temperature inside a solid oxide fuel cell (SOFC) stack. Experiment design and implementation, data pretreatment, model parameter identi- fication and application of the obtained model for the estimation and prediction of the SOFC stack maximum and minimum temperatures are covered. Experiments are carried out on a complete 10 kW SOFC system to obtain data for model development. An ARX-type (autoregressive with extra input) polynomial inputeoutput model is identified from the data and Kalman filtering is utilized to obtain an accurate estimator for the internal stack temperatures. Prediction capabilities of the model are demonstrated and using the modeling approach for SOFC system monitoring is discussed.
Original languageEnglish
Pages (from-to)464-473
JournalJournal of Power Sources
Volume277
DOIs
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed

Fingerprint

parameter identification
solid oxide fuel cells
Solid oxide fuel cells (SOFC)
Identification (control systems)
Experiments
Temperature
temperature
experiment design
predictions
estimators
pretreatment
polynomials
Monitoring

Keywords

  • SOFC temperature estimation
  • ARX modeling
  • experimental design
  • Kalman filtering
  • solid oxide fuel cell

Cite this

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title = "Solid oxide fuel cell stack temperature estimation with data-based modeling: Designed experiments and parameter identification",
abstract = "Data-based modeling is utilized for the dynamic estimation of the temperature inside a solid oxide fuel cell (SOFC) stack. Experiment design and implementation, data pretreatment, model parameter identi- fication and application of the obtained model for the estimation and prediction of the SOFC stack maximum and minimum temperatures are covered. Experiments are carried out on a complete 10 kW SOFC system to obtain data for model development. An ARX-type (autoregressive with extra input) polynomial inputeoutput model is identified from the data and Kalman filtering is utilized to obtain an accurate estimator for the internal stack temperatures. Prediction capabilities of the model are demonstrated and using the modeling approach for SOFC system monitoring is discussed.",
keywords = "SOFC temperature estimation, ARX modeling, experimental design, Kalman filtering, solid oxide fuel cell",
author = "Antti Pohjoranta and Matias Halinen and Jari Pennanen and Jari Kiviaho",
note = "Project code: 78850",
year = "2015",
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language = "English",
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pages = "464--473",
journal = "Journal of Power Sources",
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Solid oxide fuel cell stack temperature estimation with data-based modeling : Designed experiments and parameter identification. / Pohjoranta, Antti; Halinen, Matias; Pennanen, Jari; Kiviaho, Jari.

In: Journal of Power Sources, Vol. 277, 2015, p. 464-473.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Solid oxide fuel cell stack temperature estimation with data-based modeling

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AU - Pohjoranta, Antti

AU - Halinen, Matias

AU - Pennanen, Jari

AU - Kiviaho, Jari

N1 - Project code: 78850

PY - 2015

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N2 - Data-based modeling is utilized for the dynamic estimation of the temperature inside a solid oxide fuel cell (SOFC) stack. Experiment design and implementation, data pretreatment, model parameter identi- fication and application of the obtained model for the estimation and prediction of the SOFC stack maximum and minimum temperatures are covered. Experiments are carried out on a complete 10 kW SOFC system to obtain data for model development. An ARX-type (autoregressive with extra input) polynomial inputeoutput model is identified from the data and Kalman filtering is utilized to obtain an accurate estimator for the internal stack temperatures. Prediction capabilities of the model are demonstrated and using the modeling approach for SOFC system monitoring is discussed.

AB - Data-based modeling is utilized for the dynamic estimation of the temperature inside a solid oxide fuel cell (SOFC) stack. Experiment design and implementation, data pretreatment, model parameter identi- fication and application of the obtained model for the estimation and prediction of the SOFC stack maximum and minimum temperatures are covered. Experiments are carried out on a complete 10 kW SOFC system to obtain data for model development. An ARX-type (autoregressive with extra input) polynomial inputeoutput model is identified from the data and Kalman filtering is utilized to obtain an accurate estimator for the internal stack temperatures. Prediction capabilities of the model are demonstrated and using the modeling approach for SOFC system monitoring is discussed.

KW - SOFC temperature estimation

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KW - experimental design

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JF - Journal of Power Sources

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