Study on Intelligent Shift Control Strategy of Automobile Based on Genetic-Fuzzy Algorithm

In order to improve automobile transfer efficiency, we design a fuzzy control strategy based on genetic algorithm. Acceleration is taken as a control parameter of automatic transmission upshifting or downshifting, namely that only when acceleration is positive, automobile can be upshifted, only when...

Full description

Saved in:
Bibliographic Details
Published in2008 3rd International Conference on Innovative Computing Information and Control p. 402
Main Authors Shijing Wu, Enyong Zhu, Qunli Li, Jing Xie, Xiao Peng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Acceleration
Automobiles
Engines
Gears
Genetic algorithms
Torque
Wheels
Online AccessGet full text
DOI10.1109/ICICIC.2008.522

Cover

More Information
Summary:In order to improve automobile transfer efficiency, we design a fuzzy control strategy based on genetic algorithm. Acceleration is taken as a control parameter of automatic transmission upshifting or downshifting, namely that only when acceleration is positive, automobile can be upshifted, only when acceleration is negative or zero, automobile can be downshifted. Two different fuzzy controllers are respectively used to control upshifting and downshifting. Fuzzy controllers are designed according shift maps obtained from experimental. Genetic algorithm is used to optimize the fuzzy controllers and the simulation model is built to achieve the control strategy. The results of simulation show: automobile with this shift strategy can effectively avoid shift cycle even if it is running in complicated driving conditions, this shift strategy improves automobile power and economy performance and the optimization by genetic algorithm improves the design efficiency of controllers and the shift quality.
DOI:10.1109/ICICIC.2008.522