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Modelling of Engine Cooling System with a New Modelling Approach Based on Dynamic Neural Network

Zhang, Hongyang; Weyhing, Thomas ORCID iD icon; Fan, Xiuyang; Blesinger, Georg; Toedter, Olaf ORCID iD icon; Koch, Thomas

Abstract (englisch):

Thermal management has always played a significant role in reducing emissions and improving the fuel efficiency of the internal combustion engines (ICEs). With a momentous influence on the thermal behavior of the engines, the cooling system has a considerable impact on ICE performance.
In this scenario, a method based on artificial neural network (ANN) of the cooling system was proposed in this work. Specific modeling methods were adopted for the various operating conditions and flow circuits of the cooling system.
To describe these varied dynamic characteristics, four ANN sub-models were established to simulate the system at different temperature stages. As a closed-loop system, the temperature of the cooling system can be regarded as a result of all the experienced operating points. Therefore, integral parameters describing the trajectory of the system were selected as the input of the ANNs. The training data was segmented into multiple segmentations and parallel training was utilized. With this training method, each segmented data can be regarded as a brand-new learning content since a new trajectory is generated due to the initialization of the segmented data. ... mehr

DOI: 10.4271/2021-01-0203
Zitationen: 2
Zugehörige Institution(en) am KIT Institut für Kolbenmaschinen (IFKM)
Institut für Technische Informatik (ITEC)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 04.2021
Sprache Englisch
Identifikator ISSN: 0148-7191
KITopen-ID: 1000131191
Erschienen in WCX Digital Summit, April 13 - 15, 2021
Veranstaltung WCX Digital Summit (2021), Online, 13.04.2021 – 15.04.2021
Verlag SAE International
Seiten 2021-01-0203
Serie SAE Technical Paper Series
Bemerkung zur Veröffentlichung Technical Paper 2021-01-0203
Vorab online veröffentlicht am 06.04.2021
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