Title :
A dynamic model of engine using neural network description
Author :
Yin, Xiaofeng ; Ge, Anlin
Author_Institution :
Coll. of Automobile Eng., Jilin Univ., Chuangchun, China
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
Keywords :
identification; internal combustion engines; mechanical engineering computing; neural nets; Santana 2000 EFI engine; automatic shift schedule; automotive engines; curve approximation; determinant factors; dynamic conditions; dynamic engine characteristics; dynamic fuel consumption model; dynamic model; dynamic test; dynamic torque model; engine operation; inner complex chemical changes; inner complex physical changes; mechanism modeling; neural network description; neural network identification; optimal match; powertrain system; test data; Automatic testing; Automotive engineering; Chemicals; Dynamic scheduling; Engines; Neural networks; Optimal matching; Power system modeling; System testing; Vehicle dynamics;
Conference_Titel :
Vehicle Electronics Conference, 2001. IVEC 2001. Proceedings of the IEEE International
Conference_Location :
Tottori
Print_ISBN :
0-7803-7229-8
DOI :
10.1109/IVEC.2001.961735
