Online estimation of internal stack temperatures in solid oxide fuel cell power generating units

B. Dolenc (Corresponding Author), D. Vrečko, Ɖ. Juričić, A. Pohjoranta, C. Pianese

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

Abstract

Thermal stress is one of the main factors affecting the degradation rate of solid oxide fuel cell (SOFC) stacks. In order to mitigate the possibility of fatal thermal stress, stack temperatures and the corresponding thermal gradients need to be continuously controlled during operation. Due to the fact that in future commercial applications the use of temperature sensors embedded within the stack is impractical, the use of estimators appears to be a viable option. In this paper we present an efficient and consistent approach to data-driven design of the estimator for maximum and minimum stack temperatures intended (i) to be of high precision, (ii) to be simple to implement on conventional platforms like programmable logic controllers, and (iii) to maintain reliability in spite of degradation processes. By careful application of subspace identification, supported by physical arguments, we derive a simple estimator structure capable of producing estimates with 3% error irrespective of the evolving stack degradation. The degradation drift is handled without any explicit modelling. The approach is experimentally validated on a 10 kW SOFC system.
Original languageEnglish
Pages (from-to)251-260
Number of pages10
JournalJournal of Power Sources
Volume336
DOIs
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

Fingerprint

solid oxide fuel cells
Solid oxide fuel cells (SOFC)
Degradation
estimators
degradation
Thermal stress
thermal stresses
Temperature
temperature
Temperature sensors
Programmable logic controllers
Thermal gradients
temperature sensors
logic
controllers
platforms
gradients
estimates

Keywords

  • Data-driven estimator
  • Degradation compensation
  • Inner stack temperatures
  • Model structure identification
  • Solid oxide fuel cell (SOFC) systems
  • Bacteriology
  • Degradation
  • Estimation
  • Fuel cells
  • Programmable logic controllers
  • Thermal stress
  • Commercial applications
  • Data driven
  • Data-driven design
  • Degradation process
  • On-line estimation
  • Stack temperature
  • Structure identification
  • Subspace identification
  • Solid oxide fuel cells (SOFC)

Cite this

Dolenc, B. ; Vrečko, D. ; Juričić, Ɖ. ; Pohjoranta, A. ; Pianese, C. / Online estimation of internal stack temperatures in solid oxide fuel cell power generating units. In: Journal of Power Sources. 2016 ; Vol. 336. pp. 251-260.
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title = "Online estimation of internal stack temperatures in solid oxide fuel cell power generating units",
abstract = "Thermal stress is one of the main factors affecting the degradation rate of solid oxide fuel cell (SOFC) stacks. In order to mitigate the possibility of fatal thermal stress, stack temperatures and the corresponding thermal gradients need to be continuously controlled during operation. Due to the fact that in future commercial applications the use of temperature sensors embedded within the stack is impractical, the use of estimators appears to be a viable option. In this paper we present an efficient and consistent approach to data-driven design of the estimator for maximum and minimum stack temperatures intended (i) to be of high precision, (ii) to be simple to implement on conventional platforms like programmable logic controllers, and (iii) to maintain reliability in spite of degradation processes. By careful application of subspace identification, supported by physical arguments, we derive a simple estimator structure capable of producing estimates with 3{\%} error irrespective of the evolving stack degradation. The degradation drift is handled without any explicit modelling. The approach is experimentally validated on a 10 kW SOFC system.",
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Online estimation of internal stack temperatures in solid oxide fuel cell power generating units. / Dolenc, B. (Corresponding Author); Vrečko, D.; Juričić, Ɖ.; Pohjoranta, A.; Pianese, C.

In: Journal of Power Sources, Vol. 336, 2016, p. 251-260.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Online estimation of internal stack temperatures in solid oxide fuel cell power generating units

AU - Dolenc, B.

AU - Vrečko, D.

AU - Juričić, Ɖ.

AU - Pohjoranta, A.

AU - Pianese, C.

N1 - Cited By :3 Export Date: 18 December 2017 CODEN: JPSOD

PY - 2016

Y1 - 2016

N2 - Thermal stress is one of the main factors affecting the degradation rate of solid oxide fuel cell (SOFC) stacks. In order to mitigate the possibility of fatal thermal stress, stack temperatures and the corresponding thermal gradients need to be continuously controlled during operation. Due to the fact that in future commercial applications the use of temperature sensors embedded within the stack is impractical, the use of estimators appears to be a viable option. In this paper we present an efficient and consistent approach to data-driven design of the estimator for maximum and minimum stack temperatures intended (i) to be of high precision, (ii) to be simple to implement on conventional platforms like programmable logic controllers, and (iii) to maintain reliability in spite of degradation processes. By careful application of subspace identification, supported by physical arguments, we derive a simple estimator structure capable of producing estimates with 3% error irrespective of the evolving stack degradation. The degradation drift is handled without any explicit modelling. The approach is experimentally validated on a 10 kW SOFC system.

AB - Thermal stress is one of the main factors affecting the degradation rate of solid oxide fuel cell (SOFC) stacks. In order to mitigate the possibility of fatal thermal stress, stack temperatures and the corresponding thermal gradients need to be continuously controlled during operation. Due to the fact that in future commercial applications the use of temperature sensors embedded within the stack is impractical, the use of estimators appears to be a viable option. In this paper we present an efficient and consistent approach to data-driven design of the estimator for maximum and minimum stack temperatures intended (i) to be of high precision, (ii) to be simple to implement on conventional platforms like programmable logic controllers, and (iii) to maintain reliability in spite of degradation processes. By careful application of subspace identification, supported by physical arguments, we derive a simple estimator structure capable of producing estimates with 3% error irrespective of the evolving stack degradation. The degradation drift is handled without any explicit modelling. The approach is experimentally validated on a 10 kW SOFC system.

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KW - On-line estimation

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KW - Subspace identification

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