DocumentCode :
3768416
Title :
Multi-objective optimization of HEV transmission system parameters based on immune genetic algorithm
Author :
Guangxing Tan;Cong Lin;Yuhe Bai;Zan Chen
Author_Institution :
School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
fYear :
2015
Firstpage :
426
Lastpage :
431
Abstract :
In consideration of transmission system parameters impact on fuel economy and exhaust emissions of hybrid electric vehicle (HEV), a multi-objective optimization scheme, immune genetic algorithm, is proposed in this paper for optimization of both transmission system parameters and control parameters of HEV. Therefore we establish a multi-objective optimal model where we consider transmission system parameters as variables, minimizing fuel consumption and exhaust emissions (CO, HC and NOx) as optimization objectives, dynamic performance and balance in battery state of charge as constraint conditions. Meanwhile, we transform the multiple-objective functions into single-objective ones by weighting coefficients to realize optimization via immune genetic algorithm. Thus a combined optimization and simulation model is established by using real coding method and calling functions on ADVISOR background. Simulation results show that the proposed algorithm can effectively reduce fuel consumption and exhaust emissions of the vehicle.
Keywords :
"Optimization","Vehicles","Acceleration","Fuels","Power system dynamics","Gears","Vehicle dynamics"
Publisher :
ieee
Conference_Titel :
Communication Problem-Solving (ICCP), 2015 IEEE International Conference on
Print_ISBN :
978-1-4673-6543-7
Type :
conf
DOI :
10.1109/ICCPS.2015.7454193
Filename :
7454193
Link To Document :
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