基于可靠灰色粒子群算法的汽车悬架参数的多目标优化设计

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摘要 This study presents a multi-objective optimal design of automobile suspension systems to improve vehicle ride comfort and reduce tire-induced dynamic excitations on road surface simultaneously. In the optimal model, spring stiffness and the damper coefficient are considered design variables, whereas the maximum deflection of the suspension system is regarded as a constraint. Meanwhile, the root-mean-square values of the vertical acceleration of the vehicle body and the dynamic loadings of the front and rear tires are treated as objective functions. Multi-objective optimization is implemented using the gray particle swarm algorithm. Globally optimal solutions are obtained by introducing the variance of relevant sequence numbers into gray relevant theory. A half-car model is used to illustrate the proposed optimal model and solution method. Results show that the minimum acceleration of the vehicle body and the minimum dynamic loads exerted by tires on road surfaces can be achieved through the proposed multi-objective optimal design.

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	贾爱琴
	崔建锋
	陈建军
	高卫

关键词 ： automobile engineering, multi-objective optimization, gray particle swarm algorithm, reliable gray correlation degree, automotive suspension system

Abstract：This study presents a multi-objective optimal design of automobile suspension systems to improve vehicle ride comfort and reduce tire-induced dynamic excitations on road surface simultaneously. In the optimal model, spring stiffness and the damper coefficient are considered design variables, whereas the maximum deflection of the suspension system is regarded as a constraint. Meanwhile, the root-mean-square values of the vertical acceleration of the vehicle body and the dynamic loadings of the front and rear tires are treated as objective functions. Multi-objective optimization is implemented using the gray particle swarm algorithm. Globally optimal solutions are obtained by introducing the variance of relevant sequence numbers into gray relevant theory. A half-car model is used to illustrate the proposed optimal model and solution method. Results show that the minimum acceleration of the vehicle body and the minimum dynamic loads exerted by tires on road surfaces can be achieved through the proposed multi-objective optimal design.

Key words： automobile engineering multi-objective optimization gray particle swarm algorithm reliable gray correlation degree automotive suspension system

收稿日期: 2015-05-19

基金资助:Supported by the Science and Technology Research Projects of Henan Provincial Education Department (No.14B460030, No.14a590001); the General Science and Technology Projects of Zhengzhou (No.141PPTGG348); the National Natural Science Foundation of China (No.70971120); the Aeronautical Science Foundation of China(No.2012ZD55009); and the Foundation for the University Key Teacher by Henan Province (No.2014GGJS-104)

通讯作者: JIA Ai-qin, E-mail:aqjia1977@126.com E-mail: aqjia1977@126.com

引用本文:

贾爱琴, 崔建锋, 陈建军, 高卫. 基于可靠灰色粒子群算法的汽车悬架参数的多目标优化设计[J]. Journal of Highway and Transportation Research and Development, 2015, 9(3): 102-110.
JIA Ai-qin, CUI Jian-feng, CHEN Jian-jun, GAO Wei. Multi-objective Optimal Design of Vehicle Suspension Parameters Based on Reliable Gray Particle Swarm Optimization. Journal of Highway and Transportation Research and Development, 2015, 9(3): 102-110.

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http://manu27.magtech.com.cn/Jwk_gljtkj_en/CN/ 或 http://manu27.magtech.com.cn/Jwk_gljtkj_en/CN/Y2015/V9/I3/102

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