Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

2nd International Conference on Automation, Control and Robotics Engineering

工业技术;计算机科学;无线电电子学

Zhu, Tianjun^1,3 ; Li, Bin^2 ; Zong, Changfu^1 ; Wu, Yang^3

Department of Electronic Information and Electrical Engineering, ZhaoQing University, ZhaoQing

526061, China^1

CONCAVE Research Center, Department of Mechanical and Industrial Engineering, Concordia University, Montreal

QC

H3G 1M8, Canada^2

College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan

056038, China^3

关键词: Component parameters;    Genetic optimization algorithm;    Hybrid electric vehicle (HEV);    Maximum Efficiency;    Optimization design;    Power system optimization;    Powertrain components;    Series hybrid electric vehicles;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/235/1/012013/pdf
DOI  :  10.1088/1757-899X/235/1/012013

来源: IOP

【 摘 要 】

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.