汽车悬架参数的多目标多标准决策优化

Multi-objective and Multi-criteria Decision Optimization of Automobile Suspension Parameters

为了改善汽车行驶的舒适性、安全性并减小轮胎动载对路面的破坏,以某载货汽车的四自由度悬架系统为研究对象,将车速分别为50、80和100 km/h在A、B和C级路面行驶的车身垂直加速度、前后轮胎动载荷的统计均方根值作为目标,用多目标遗传算法和多标准决策方法进行了优化。结果表明:车身垂直加速度均方根值平均减小了40%,前后轮动载荷均方根值平均减小了30%,性能有较大改善,并且采用后期多标准决策方法,可以同时求得满足不同设计需求的最优解,较传统多目标优化方法更为实用有效。

For improving automobile ride comfort, safety and decreasing tire dynamic load to road, the four degree of freedom suspension model was established. MOGA （multi-object genetic algorithm） and multi-criteria decision method were applied to multi-objective optimization of suspension parameters, with root-mean-square values of vertical acceleration of body, front and rear tire load at 50, 80 and 100 km/h on A, B and C level road as objective functions. The results show that on average, root-meansquare value of vertical acceleration of body reduces by 40 %, root-mean-square value of dynamic load of front and rear wheel reduces by 30%, and the performance gets better. This method, making decision after searching the Pareto solutions, can help the designer get the best answer under different demand. Compared with the traditional multi-objective optimization method, it is more applicable and effective.