Starting Control with Constant Engine Speed for AMT Automotive Based on Neural Network

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Abstract:

Starting control with constant engine speed of AMT automotive is a clutch control means based on the target accelerator pedal opening. According to the different accelerator pedal opening, the clutch engagement speed will be different. The neural network is used to estimate the target accelerator pedal opening by the start of the initial accelerator pedal opening variation. Thus completing the engine target speed and clutch engagement speed is to make sure the AMT start fast and smoothly. The results showed that: through estimating the target accelerator pedal opening, this control strategy can shorten the starting time and improved control quality significantly.

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1637-1642

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June 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] LEI YU-LONG, GE AN-LING, LI YONG-JUN . Clutch Control Strategy in Starting Process[J]. Automotive Engineering, 2000, 4: 266-281.

Google Scholar

[2] SUN DONG-YE, QIN DA-TONG. Clutch starting control with a constant engine speed in part process for a car [J]. Chinese Journal of Mechanical Engineering, 2003, 39(11) : 108-111.

DOI: 10.3901/jme.2003.11.108

Google Scholar

[3] SUN DONG-YE ,QIN DA-TONG ,LIU ZHEN-JUN. Control Method on the Electronic Throttle of AMT Vehicle in Starting Process [J]. Automotive Engineering, 2009, 31(11): 1020-1024.

Google Scholar

[4] CHENG FANG-XIAO, LIU XU-DONG, LIN XIAO-MEI. Simulation Study of Optimal Matching for Vehicle Power Train[C]/2010 International Conference on Measuring Technology and Mechanotronics Automation, Changsha, 2010, Vol, III: 378-381.

DOI: 10.1109/icmtma.2010.626

Google Scholar

[5] M. Delogu, L. Pilo. Influence of throttle control system in vehicle-driveline dynamic and in car performance perception[C]/SAE Paper 2002-01-2157, (2002).

DOI: 10.4271/2002-01-2157

Google Scholar

[6] HU CHUN. Aplication of BP Neural Network to futures price Prediction[J]. Financial Finance and Tax, 2011. 01: 62-64.

Google Scholar

[7] ZHU Kai, WANG ZHENG-lin. Proficient in Matlab neural network [ M]. Beijing: Publishing House of electronics industry2010. 1.

Google Scholar

[8] LIU XIAO-GANG, CHU GUI-HONG. Aplication of BP Neural Network to Annual Run off Prediction [J]. Inner Mongolia Water Resources, 2011, 4: 76-78.

Google Scholar

[9] Chang Jun Zhu, Li Ping Wu, Sha Li. Flood Forecasting Research Based on the Chaotic BP Neural Network Model [J]. Key Engineering Materials , 2010, 6, (Volumes 439 - 440), 411-416.

DOI: 10.4028/www.scientific.net/kem.439-440.411

Google Scholar

[10] Yao Zhi Luo, Ruo Fei Tong , Study of BP Network for a Cylinder Shell's Support Identification[J]. Advanced Materials Research, 2011, Advanced Materials Research, 2011, 10: 2050-(2055).

DOI: 10.4028/www.scientific.net/amr.368-373.2050

Google Scholar