Abstract
This paper presents the development of ARX-type
(autoregressive with extra input) time-series models for
the dynamic estimation and prediction of the SOFC stack
maximum temperature. Experiment design aspects, model
identification as well as filtering are discussed, and
practical results obtained on a 10 kW SOFC system are
presented. Data-based time-series models, whose
parameters are identified directly from system
measurements provide an alternative modeling approach
compared to models based on physical first principles.
Although physical models are very useful during the
system design, they often become nonlinear and complex by
structure, meaning that their application to control
development or in embedded system software can be
impractical. Often at least significant model
simplification is required. Time-series models can be
directly created as linear discrete-time models, which
means that their utilization in control design as well as
deployment into a modern embedded computational
environment is straightforward.
Original language | English |
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Title of host publication | Proceedings of the 11th European SOFC & SOE Forum |
Place of Publication | Luzern-Adligenswil |
Publisher | European Fuel Cell Forum AG |
Pages | A0952-A0961 |
ISBN (Print) | 978-3-905592-16-0 |
Publication status | Published - 2014 |
MoE publication type | A4 Article in a conference publication |
Event | The 11th European SOFC & SOE Forum - Lucerne, Switzerland Duration: 1 Jul 2014 → 4 Jul 2014 |
Conference
Conference | The 11th European SOFC & SOE Forum |
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Country/Territory | Switzerland |
City | Lucerne |
Period | 1/07/14 → 4/07/14 |
Keywords
- SOFC temperature estimation
- ARX
- SOFC modeling
- Kalman filter