基于帕莱托的汽车悬架参数多目标优化

Multi-objective Optimization of Automobile Suspension Parameters Based on Pareto

为了提高汽车平顺性,减少轮胎对路面的动载,以某1/2载货汽车的弹簧刚度和阻尼系数为设计参数,以最大动挠度为约束条件,以车身垂直加速度、前后轮胎动载荷的均方根值为目标函数,运用多目标遗传算法求得三目标的帕莱托解集,经过后期决策得到不同要求下的最优解。结果表明:优化后的悬架弹簧刚度减少而阻尼系数增大,前悬比后悬变化小,性能有大幅度改善,而且采用先寻优后决策的求解模式,能有效弱化先验知识不足的影响,避免局部最优问题,较传统多目标优化方法更为实用有效。

For improving automobile ride comfort and decreasing tire dynamic load to road, with spring stiffness and damper coefficient as design parameters, maximum of suspension displacement as constraint, root-mean-square value of vertical acceleration of body,front and rear tire load as three objective functions,MOGA（Multi-object Genetic Algorithm） is applied to obtain the Pareto results of mull-objective optimization of suspension system, and the best answer is chosen according to design requirements. The results show that the coefficients of spring stiffness decrease, the coefficients of damper increase,the change of front suspension parameters is less than that of rear parameters and the performance gets better. The pattern,making decision after searching optimum solutions, is more applicable and effective and can weak designer＇ s transcendental information deficiency problem and avoid the main problem of simplified multi-objective optimization.