State of health estimation and remaining useful life prediction of solid oxide fuel cell stack

B. Dolenc, P. Boskoski, M. Stepancic, A. Pohjoranta, D. Juricic

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)

Abstract

Having concurrent information regarding the state of health (SoH) of an operating solid oxide fuel cell (SOFC) stack can actively improve its overall management. Firstly, operating the SOFC by taking into account current health conditions, and its anticipated trend, can be beneficial to the total life span of the stack. Furthermore, such information can be of great importance to the maintenance staff, e.g. unplanned shutdowns can be avoided. Relatively little work has been done in the field of remaining useful life (RUL) prediction of SOFCs. The majority of work employs stack/cell voltage as a direct link for RUL predictions. This paper proposes an integrated approach for SoH estimation based on stack's Ohmic area specific resistance (ASR). Subsequently, a drift model that describes the ASR increase over time enables accurate RUL prediction. The approach consists of three steps. Firstly, an Unscented Kalman filter, based on a lumped stack model, estimates the current ASR value. Secondly, a drift model for describing the temporal evolution in ASR is recursively identified employing the linear Kalman filter. Finally, employing the identified drift model, Monte Carlo simulation is performed to predict future time evolution in ASR and so to obtain RUL. The developed approach is validated with experimental data from a 10 kW SOFC power system. The results confirm that ASR is a viable SoH indicator for the SOFC stack.
Original languageEnglish
Pages (from-to)993-1002
Number of pages10
JournalEnergy Conversion and Management
Volume148
DOIs
Publication statusPublished - 1 Jan 2017
MoE publication typeA1 Journal article-refereed

Fingerprint

Solid oxide fuel cells (SOFC)
Health
Kalman filters
Electric potential

Keywords

  • area specific resistance (ASR)
  • degradation
  • drift model identification
  • non-linear estimation
  • remaining useful life prediction (RUL)
  • solid oxide fuel cell (SOFC) stack

Cite this

Dolenc, B. ; Boskoski, P. ; Stepancic, M. ; Pohjoranta, A. ; Juricic, D. / State of health estimation and remaining useful life prediction of solid oxide fuel cell stack. In: Energy Conversion and Management. 2017 ; Vol. 148. pp. 993-1002.
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abstract = "Having concurrent information regarding the state of health (SoH) of an operating solid oxide fuel cell (SOFC) stack can actively improve its overall management. Firstly, operating the SOFC by taking into account current health conditions, and its anticipated trend, can be beneficial to the total life span of the stack. Furthermore, such information can be of great importance to the maintenance staff, e.g. unplanned shutdowns can be avoided. Relatively little work has been done in the field of remaining useful life (RUL) prediction of SOFCs. The majority of work employs stack/cell voltage as a direct link for RUL predictions. This paper proposes an integrated approach for SoH estimation based on stack's Ohmic area specific resistance (ASR). Subsequently, a drift model that describes the ASR increase over time enables accurate RUL prediction. The approach consists of three steps. Firstly, an Unscented Kalman filter, based on a lumped stack model, estimates the current ASR value. Secondly, a drift model for describing the temporal evolution in ASR is recursively identified employing the linear Kalman filter. Finally, employing the identified drift model, Monte Carlo simulation is performed to predict future time evolution in ASR and so to obtain RUL. The developed approach is validated with experimental data from a 10 kW SOFC power system. The results confirm that ASR is a viable SoH indicator for the SOFC stack.",
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State of health estimation and remaining useful life prediction of solid oxide fuel cell stack. / Dolenc, B.; Boskoski, P.; Stepancic, M.; Pohjoranta, A.; Juricic, D.

In: Energy Conversion and Management, Vol. 148, 01.01.2017, p. 993-1002.

Research output: Contribution to journalArticleScientificpeer-review

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