### Abstract

The applicability of multivariable linear regression
(MLR) models to estimate the maximum temperature inside a
SOFC stack is investigated experimentally. The
experiments were carried out with a complete 10 kW SOFC
system. The behavior of the maximum temperature measured
inside a SOFC stack with respect to four independent
input variables (stack current, air flow, air inlet
temperature and fuel flow) is examined following the
design of experiments methodology, and MLR models are
created based on the retrieved data. The practical
feasibility of the MLR estimate is investigated
experimentally with the 10 kW system by evaluating the
accuracy of the estimate in two test cases: (i) a system
load change where the stack temperature is regulated by a
closed-loop controller using the MLR estimate and (ii)
during operator-imposed disturbances in the fuel system
(a variation in the methane conversion in the fuel
pre-reformer). Finally, the performance of the MLR
estimate is evaluated with another, 64-cell stack
operated at higher current density.

Original language | English |
---|---|

Pages (from-to) | 749-756 |

Journal | Fuel Cells |

Volume | 15 |

Issue number | 5 |

DOIs | |

Publication status | Published - 2015 |

MoE publication type | A1 Journal article-refereed |

Event | The 11th European SOFC & SOE Forum - Lucerne, Switzerland Duration: 1 Jul 2014 → 4 Jul 2014 |

### Keywords

- control
- fule cell
- fuel cell system
- mathematical modeling
- SOFC

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## Cite this

Halinen, M., Pohjoranta, A., Pennanen, J., & Kiviaho, J. (2015). Application of multivariable regression model for SOFC stack temperature estimation in system environment.

*Fuel Cells*,*15*(5), 749-756. https://doi.org/10.1002/fuce.201500009