基于遗传算法的汽车平顺性时域仿真与优化

Genetic algorithm based vehicle ride comfort time-domain simulation and optimization

首先,建立汽车五自由度振动系统的力学模型,利用牛顿第二定律建立动力学方程,并基于IFFT法进行路面不平度的时域模拟。然后,通过ADAMS进行对比验证平顺性模型。最后,以悬架刚度阻尼为设计变量,以悬架动挠度和轮胎相对动载荷为约束函数,以座椅中心加权加速度均方根值为目标函数,建立其优化模型,利用Matlab优化工具箱中的遗传算法函数进行优化。优化结果表明,遗传算法具有很强的全局寻优能力,优化结果大大地改善了平顺性指标,提高了汽车的性能。

The mechanical model of vehicle＇s five degrees of freedom vibration system was established. The kinetic equa- tion was established with Newton＇s second law. The road roughness time-domain simulation was performed based on IFFT （in- verse fast Fourier transform） method. The ride comfort model was verified with ADAMS. And its optimization model was estab- lished by taking the suspension stiffness and damping as the design variables, suspension dynamic deflection and relative dyna- mic load of vehicle tire as the constraint function, and root-mean-square value of seat center weighed acceleration as the objec- tive function. The model was optimized with genetic algorithm function in Matlab optimization toolbox. The optimization resuhs show that the genetic algorithm has good global optimization ability, the optimization result improves the indicator of ride com- fort and the vehicle＇s performance.