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

21 Citations (Scopus)


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
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed



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

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