Mine car suspension parameter optimisation based on improved particle swarm optimisation and approximation model

被引：0

作者：

Zhang J. ^{[1
]}

Li X. ^{[1
]}

Liu D. ^{[2
]}

机构：

[1] School of Mechanical Engineering, Beijing Institute of Technology, 5. Zhongguancun South Street, Haidian District, Beijing

[2] China Automotive Technology &research Center Co., Ltd., 68, Xianfengdong Road, Tianjin

来源：

International Journal of Vehicle Design | 2019年 / 80卷 / 01期

关键词：

Approximation model; Chaos particle swarm optimisation; Mine car; Multi-parameter optimisation; Particle swarm optimisation; Ride comfort; Suspension parameter optimisation;

D O I：

10.1504/IJVD.2019.105062

中图分类号：

学科分类号：

摘要：

A suspension parameter optimisation method is proposed in this paper to improve mine car ride comfort. The most influential parameters on vehicle ride comfort are chosen as optimisation variables by analysing parameter sensitivity using a 7-degrees-of-freedom vehicle model. A simplified regression model based on the response surface method accelerates the optimisation process. An improved chaos particle swarm optimisation (ICPSO) approach is proposed based on standard particle swarm optimisation to optimise suspension parameters in the regression model. The ideal match of suspension parameters is obtained. Simulation results show that improved suspension parameters can greatly ensure the weighted root mean square acceleration and tyre dynamic loads; additionally, suspension dynamic deflections are limited within an allowable range. Test results reveal that the suspension multi-parameter optimisation method based on ICPSO can improve vehicle ride comfort. Therefore, this method can be used to guide future research and development of suspension systems. © 2019 Inderscience Enterprises Ltd.

引用

页码：23 / 40

页数：17

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