基于灰色关联度TOPSIS法的前悬架与转向系统优化*

doi:10.19562/j.chinasae.qcgc.2020.08.012

摘要/Abstract

摘要： 以改善轮胎磨损和制动点头性能为目标,本文中引入灰色关联度TOPSIS法对多设计变量进行筛选,利用多体动力学仿真软件建立前悬架和转向系统的仿真模型,分析其K&C特性,并结合试验验证其准确性。本文中基于对前悬架和转向系统的15个硬点坐标为变量的灵敏度分析,使用熵权法和主观赋权法确定各指标的权重,综合灰色关联度和TOPSIS法筛选出综合贡献度系数较大的6个硬点坐标为设计变量,从而构建优化设计模型。使用Isight软件结合NSGA-II算法,获得Pareto最优解集,最终确定悬架和转向系统硬点布置的优化方案。经过优化,性能相对于初始设计有着明显的改善,前束角、外倾角、车轮侧向和纵向位移随轮跳的变化率分别减小了48.9%、21.2%、26.6%和20.5%,阿克曼百分比增加了19.02%,且抗点头率由9.2%增加到30.4%,侧倾中心高度由136降为100.5 mm,在兼顾操稳性的同时,能有效改善轮胎磨损和提高制动点头性能。

关键词: 麦弗逊悬架, 转向系统, 轮胎磨损, 制动点头, 灰色关联度, 多目标优化

Abstract: In order to reduce tire wear and improve braking nose dive performance, a selection strategy of multiple design variables is proposed based on the grey correlation TOPSIS method. The simulation model of front suspension and steering system is established by using multi-body dynamics simulation software and its K&C characteristics are analyzed, with its accuracy verified by experiments. With the target of improvement of tire wear and braking nose dive performance, based on the sensitivity analysis of 15 hard point coordinates of the front suspension and steering system, the weight of each index is determined by entropy weight method and subjective weight method, and 6 hard point coordinates with high comprehensive contribution coefficient are selected as design variables by combining the grey correlation degree and TOPSIS method, so as to construct the optimization design model. By using Isight software and NSGA-II algorithm, the Pareto optimal solution set is obtained and the optimal scheme of hard point arrangement of the suspension and steering system is finally determined. After optimization, the performance is improved significantly compared with the initial design, with the change rate of toe angle, camber angle, lateral and longitudinal displacement with wheel slip reduced by 48.9%, 21.2%, 26.6% and 20.5% respectively, the Ackerman percentage increased by 19.02%, the anti-dive rate increased from 9.2% to 30.4%, and the height of roll center reduced from 136 to 100.5 mm, effectively reducing tire wear and improving braking nose dive performance while ensuring operation stability.

Key words: Macpherson suspension, steering system, tire wear, braking nose dive, grey correlation degree, multi-objective optimization

张志飞, 薛昊祥, 陈钊, 蒲弘杰, 徐中明, 贺岩松. 基于灰色关联度TOPSIS法的前悬架与转向系统优化^*[J]. 汽车工程, 2020, 42(8): 1082-1089.

Zhang Zhifei, Xue Haoxiang, Chen Zhao, Pu Hongjie, Xu Zhongming, He Yansong. Optimization of Front Suspension and Steering System Based on Grey Correlation TOPSIS Method[J]. Automotive Engineering, 2020, 42(8): 1082-1089.

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基于灰色关联度TOPSIS法的前悬架与转向系统优化^*

Optimization of Front Suspension and Steering System Based on Grey Correlation TOPSIS Method

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