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Fuel Consumption Modelling of a TFSI Gasoline Engine with Embedded Prior Knowledge

Zhang, Hongyang; Koch, Sergej; Han, Yu; Toedter, Olaf ORCID iD icon; Kubach, Heiko; Koch, Thomas

Abstract (englisch):

As an important means of engine development and optimization, modelbuilding plays an increasingly important role in reducing carbon dioxide emissions of the internal combustion engines (ICEs). However, due to the non-linearity and high dimension of the engine system, a large amount of data is required to obtain high model accuracy. Therefore, a modelling approach combining the experimental data and prior knowledge was proposed in this study. With this method, an artificial neural network (ANN) model simulating the engine brake specific fuel consumption (BSFC) was established. With mean square error (MSE) and Kullback-Leibler divergence (KLD) serving as the fitness functions, the 86 experimental samples and constructed physical models were used to optimize the ANN weights through genetic algorithms. To improve the performance of the model, model-based feature selection method constructed by generalized regression neural network (GRNN) is introduced reducing the input dimension from 8 to 4. Subsequently, different fitness functions and features were applied to construct the models. Through the comparison of the models, the ANN model trained with MSE + KLD and selected features (ANNM+L,S) obtained the best comprehensive performance. ... mehr

Zugehörige Institution(en) am KIT Institut für Kolbenmaschinen (IFKM)
Institut für Technische Informatik (ITEC)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 06.04.2021
Sprache Englisch
Identifikator ISSN: 0148-7191
KITopen-ID: 1000131220
Erschienen in SAE WCX Digital Summit
Veranstaltung WCX Digital Summit (2021), Online, 13.04.2021 – 15.04.2021
Verlag SAE International
Serie Technical Paper ; 2021-01-0633
Schlagwörter Neural networks; Carbon dioxide; Scale models; Ignition timing; Fuel consumption
Nachgewiesen in Scopus
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