DocumentCode :
1938036
Title :
Estimation of engine torque based on improved BP neural network
Author :
Wang, Xudong ; Wu, Xiaogang ; Jing, Jimin ; Yu, Tengwei
Author_Institution :
Sch. of Electr. & Electron. Eng., Harbin Univ. of Sci. & Technol., Harbin, China
fYear :
2009
fDate :
7-10 Sept. 2009
Firstpage :
1679
Lastpage :
1683
Abstract :
Aiming at the mass-energy power assembly control system in HEVs, a method is designed to estimate the engine torque, which is based on improved BP neural network. Based on the experiment results in engine dynamometer, and strong nonlinear characteristic of the engine is taken into account, traditional BP neural network error function is improved, and it is trained by optimal stopping, as a result over-fitting will be avoided. The engine torque output model is established with MATLAB, and it has high estimated accuracy and nice generalization ability. After all, validity of the algorithm mentioned above is verified by experiments.
Keywords :
backpropagation; hybrid electric vehicles; neural nets; torque; BP neural network; engine torque; hybrid electric vehicle; mass-energy power assembly control system; Control systems; Design engineering; Electronic mail; Engines; Equations; Mathematical model; Neural networks; Neurons; Power engineering and energy; Torque control; estimation; hybrid electric vehicle; neural network; optimal stopping rule; torque;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Vehicle Power and Propulsion Conference, 2009. VPPC '09. IEEE
Conference_Location :
Dearborn, MI
Print_ISBN :
978-1-4244-2600-3
Electronic_ISBN :
978-1-4244-2601-0
Type :
conf
DOI :
10.1109/VPPC.2009.5289684
Filename :
5289684
Link To Document :
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