Juan Ignacio Videla
Telemark University College, Norway
Bernt Lie
Telemark University College, Norway
Ladda ner artikel
Ingår i: The 48th Scandinavian Conference on Simulation and Modeling (SIMS 2007); 30-31 October; 2007; Göteborg (Särö)
Linköping Electronic Conference Proceedings 27:14, s. 117-125
Publicerad: 2007-12-21
ISBN:
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
The state/parameter estimation problem is studied for a small-scale ICE CHP model. Three main groups of estimators with significant performance and com- plexity differences are analyzed: the Extended Kalman Filter (EKF) as an extension of the classical Kalman Filter; the generalized unscented Kalman filter (UKF) that uses the unscented transformation; and particle filtering like the particle filter with resampling (PFr) and the Ensemble Kalman Filter (EnKF)
The internal combustion engine is modeled as a mean-value engine model connected with a static generator model and the heat recovery circuit is modeled with two lumped heat exchanger models; one for the coolant circuit and the other for the exhaust gases. The coolant circuit is connected with the engine through a lumped inner engine thermal model.
Experimental data sets are artificially generated to test the di¤erent estimators. Dynamic parameters of the mean-value engine model are identify when the CHP model is simulated in open loop. Additionally; relevant heat transfer coe¢ cients of the heat recovery circuit are monitored when the model is simulated in closed loop.
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