基于NSGA-Ⅱ算法的麦弗逊悬架多目标优化  

Multi-objective optimization of Macpherson suspension based on NSGA-Ⅱ algorithm

作　　者：何志刚[1] 陈阳[1] 盘朝奉[1,2] 

机构地区：[1]江苏大学汽车与交通工程学院,江苏镇江212013 [2]江苏大学汽车工程研究院,江苏镇江212013

出　　处：《广西大学学报（自然科学版）》2016年第6期1807-1814,共8页Journal of Guangxi University（Natural Science Edition）

基　　金：国家自然科学基金资助项目(51105178);江苏省自然科学基金资助项目(BK2011489)

摘　　要：为了减小悬架定位参数在轮跳过程中的变化量和变化趋势,以改善整车的操纵稳定性,减小轮胎的磨损,搭建了悬架硬点优化设计的平台。利用多目标/多参数优化软件Isight,联合Adams/Car,在工程可行性约束条件下,应用改进型非支配排序遗传算法NSGA-Ⅱ对悬架系统进行寻优计算,得到Pareto最优解集。优化结果表明,悬架定位参数在车轮跳动过程中的变化量有不同程度的减小;前束角和外倾角变化最明显,变化范围由-1.11°-1.22°降低到-0.2°-0.31°,减小了78.4%,外倾角由-1.12°-1.71°降低到-0.56°-1.27°,减小了35.1%;主销后倾和主销内倾角变化范围分别减小了3.1%和4.1%,有效地降低了轮胎磨损,使整车的操纵稳定性能有所改善。In order to reduce the variation and trend of the alignment parameters of suspension in the process of wheel jumping and to enhance the vehicle handling stability,a platform is established for the optimization design of suspension hard points. The platform is processed as follow： By using multi-objective and multi-parameter optimization software Isight and Adams / Car,the improved nondominated sorting genetic algorithm NSGA-Ⅱ is applied on the suspension system for optimization calculation to obtain the Pareto optimal solution set under the engineering feasibility constraints. The optimization results show that the variation and trend of the alignment parameters of suspension are reduced distinctly in the process of wheel jumping; The variation of toe and camber changes obviously,the toe is ranged from-1. 11° - 1. 22° to-0. 2° - 0. 31°,reduced by 78. 4%,and the camber is ranged from-1. 12° - 1. 71° to-0. 56° - 1. 27°,reduced by 35. 1%. Besides,the variation range ofcaster and kingpin inclination is reduced by 3. 1%,4. 1% respectively,which can effectively reduce the tire wear,and improve the vehicle handling stability.

关 键 词：麦弗逊悬架 灵敏度分析 硬点 多目标优化 NSGA-Ⅱ算法 

分 类 号：U463[机械工程—车辆工程]