Abstract
Automotive engines frequently run under dynamic conditions. Due to the inner complex physical and chemical changes in the course of engine operation, the precision of model obtained by means of mechanism modeling or curve approximation is limited. To meet the requirement of making automatic shift schedule, in this paper the determinant factors of dynamic characteristics of an engine are analyzed, the dynamic test of the Santana 2000 EFI engine is completed, and the dynamic model of the engine based on neural network identification is built through the learning of a great deal of test data, which leads the optimal match of the cooperation between the engine and powertrain system into reality.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
