摘要
为减小悬架定位参数在车轮跳动过程中的变化量,以改善整车的操纵稳定性,减小轮胎的磨损,提出了一种考虑不确定性因素的前悬架不确定性多目标优化方法。首先,在Adams/Car中建立某车轿麦弗逊式前悬架模型,并运用Adams/Insight进行悬架设计硬点参数的灵敏度分析。然后,利用基于薄板样条插值的高维模型描述技术构建了设计变量和不确定变量与目标函数之间的近似模型。最后,针对该近似模型运用双层嵌套的改进非支配排序遗传算法和隔代遗传算法进行多目标不确定性优化与可靠性优化,得到Pareto最优解集。结果表明,优化后悬架主要定位参数在车轮跳动过程中的变化量有不同程度的减小,说明整车的操纵稳定性有所改善。
In order to reduce the change of alignment parameters of suspension in the process of wheel bouncing and hence improve the handling stability of vehicle and reduce tire wear, a multi-objective uncertainty optimization scheme for front suspension is proposed with consideration of the effects of uncertainty. Firstly a MacPherson front suspension model for a car is built with Adams/Car and a sensitivity analysis on the design hard-point pa- rameters of suspension is conducted by using Adams/Insight. Then an approximate model between design variables, uncertain variables and objective functions is constructed by utilizing the technique of high dimensional model repre- sentation based on thin plate spline interpolation. Finally double-nested IP-GA and modified NSGA-II are applied to perform multi-objective uncertainty optimization and reliability optimization on the approximate model built with Pa- reto optimal solution set obtained. The results show that after optimization the changes of most alignment parameters of suspension in the process of wheel bouncing are reduced to different extents, meaning certain improvement in vehicle handling stability.
出处
《汽车工程》
EI
CSCD
北大核心
2015年第6期707-713,共7页
Automotive Engineering
基金
教育部长江学者与创新团队发展计划项目(531105050037)
国家高技术研究发展计划(863计划)项目(2012AA111802)资助
关键词
悬架
不确定性优化
高维模型描述
多目标优化
可靠性优化
遗传算法
suspension
uncertainty optimization
high dimensional model representation
multi-objecfive optimization
reliability optimization
genetic algorithm
