By Topic

Optimization of Fuzzy Controller Based on Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Shichun Yang ; Sch. of Transp. Sci. & Eng., Beihang Univ., Beijing, China ; Ming Li ; Bin Xu ; Bin Guo
more authors

The power required to drive the Hybrid electric generated by combination of internal combustion engine and electric motor. To make the power train of the hybrid electric vehicle as efficient as possible, proper management of the different energy elements is essential. This task is completed by the hybrid electric vehicle control strategy. A genetic-fuzzy control strategy is proposed for Hybrid electric vehicle in this paper. The genetic-fuzzy controller is a fuzzy logic controller that is tuned by a genetic algorithm. The objective of optimization is to decrease fuel consumption and emissions in two different test cycles NEDC and UDDS, the results demonstrate that compared with fuzzy logic control strategy, genetic-fuzzy control strategy can get better control effects. The effectiveness of this approach can reduce fuel consumption and emissions without sacrificing vehicle performance.

Published in:

Intelligent System Design and Engineering Application (ISDEA), 2010 International Conference on  (Volume:2 )

Date of Conference:

13-14 Oct. 2010