Dynamic SOFC Temperature Estimation with Designed Experiments and Time-Series Model Identification

Antti Pohjoranta, Halinen, Jari Pennanen, Jari Kiviaho

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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 languageEnglish
Title of host publicationProceedings of the 11th European SOFC & SOE Forum
Place of PublicationLuzern-Adligenswil
PublisherEuropean Fuel Cell Forum AG
PagesA0952-A0961
ISBN (Print)978-3-905592-16-0
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
EventThe 11th European SOFC & SOE Forum - Lucerne, Switzerland
Duration: 1 Jul 20144 Jul 2014

Conference

ConferenceThe 11th European SOFC & SOE Forum
CountrySwitzerland
CityLucerne
Period1/07/144/07/14

Keywords

  • SOFC temperature estimation
  • ARX
  • SOFC modeling
  • Kalman filter

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    Pohjoranta, A., Halinen, Pennanen, J., & Kiviaho, J. (2014). Dynamic SOFC Temperature Estimation with Designed Experiments and Time-Series Model Identification. In Proceedings of the 11th European SOFC & SOE Forum (pp. A0952-A0961). European Fuel Cell Forum AG.