考虑随机因素的汽车悬架参数多目标稳健优化

Multi-objective robust optimization of automobile suspension parameters considering random factors

为了稳健地提高汽车平顺性,减少轮胎对路面破坏。以某载货汽车的四自由度悬架模型为研究对象,同时考虑制造精度的影响,以车身垂直加速度、前后轮胎动载荷的均方根值的统计均值和方差为目标函数,采用蒙特卡罗抽样法和自适应多目标遗传算法对悬架参数进行稳健优化。结果表明:优化后的悬架弹簧刚度减少而阻尼系数增大,性能有大幅度改善,与传统的优化设计相比更符合实际情况,且具有更强的抗干扰能力。

For robustly improving automobile ride comfort and decreasing dynamic tire load on road,the four-degrees-of-freedom suspension model was established.Monte Carlo and self-adaptive multi-objective genetic algorithm(MOGA) were applied to solve the multi-objective robust optimization of suspension parameters,taking the statistic mean and standard deviation of root-mean-square value of the vertical acceleration of vehicle body and the dynamic load of front and rear tires as objective functions.The results show t...