摘要
利用ADAMS/Car模块建立了某车麦弗逊式前悬架系统模型,为优化该悬架结构,运用ADAMS/Insight对悬架参数进行了灵敏度分析,根据分析结果和相关参数的设计要求,运用遗传算法对该悬架系统进行优化。在ADAMS/Car中分别对优化前后的悬架进行双轮同向激振仿真试验,对比前轮定位参数的变化量和变化范围。结果表明,前轮主要定位参数的变化量减小,改善了悬架的运动学特性,达到了优化目的,为汽车悬架的设计提供了依据。
A MacPherson front suspension system model was established based on the ADAMS/Car modulus. In order to optimize the suspension structure, the sensitivity analysis of the suspension parameters was performed using ADAMS/Insight. According to the analysis results and design requirements of the relative parameters, the genetic algorithm was used to optimize the suspension system. Two-wheel in-line excitation simulation tests were performed on the suspension before and after optimization in the ADAMS/Car. The variation of front wheel alignment parameters and its range were finally compared with each other. The results showed that the main location parameter of front wheel was reduced and the motion characteristics of the suspension was improved, which achieved the optimization target. The study provided the technical basis for automotive suspension design.
出处
《机械设计》
CSCD
北大核心
2017年第1期15-19,共5页
Journal of Machine Design
基金
国家科技支撑计划资助项目(2015BAF07B04)
关键词
遗传算法
麦弗逊悬架
硬点坐标优化
genetic algorithm
MacPherson suspension
optimization of hard point coordinate
