Abstract
| 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 |
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Keywords
- control
- fule cell
- fuel cell system
- mathematical modeling
- SOFC
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Application of multivariable regression model for SOFC stack temperature estimation in system environment. / Halinen, M.; Pohjoranta, A. (Corresponding Author); Pennanen, J.; Kiviaho, J.
In: Fuel Cells, Vol. 15, No. 5, 2015, p. 749-756.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Application of multivariable regression model for SOFC stack temperature estimation in system environment
AU - Halinen, M.
AU - Pohjoranta, A.
AU - Pennanen, J.
AU - Kiviaho, J.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - control
KW - fule cell
KW - fuel cell system
KW - mathematical modeling
KW - SOFC
UR - https://onlinelibrary.wiley.com/toc/16156854/2015/15/5
U2 - 10.1002/fuce.201500009
DO - 10.1002/fuce.201500009
M3 - Article
VL - 15
SP - 749
EP - 756
JO - Fuel Cells
JF - Fuel Cells
SN - 1615-6846
IS - 5
ER -