基于改进多目标粒子群算法的商用车悬架系统优化

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (15) : 216-224.

论文

范政武1，王铁1，王永红2，石晋宏1

作者信息 +

Commercial vehicle suspension system optimization based on improved multi-objective particle swarm optimization algorithm

FAN Zhengwu1,WANG Tie1,WANG Yonghong2,SHI Jinhong1

Author information +

文章历史 +

摘要

为改善商用车辆的平顺性、安全性并减小轮胎动载对路面的破坏，以某6×4牵引列车为研究对象，建立12自由度振动力学模型。以车辆平顺性和轮胎动载为优化目标，选择前后悬架弹簧刚度、减振器阻尼系数为优化参数，设计了一种基于改进多目标粒子群优化算法MDPL-MOPSO的悬架系统多目标优化策略。悬架系统优化前后的结果对比表明，本文提出的改进多目标粒子群优化策略在改善车辆平顺性的同时，兼顾了轮胎动载对路面的损坏，具有较好的优化效果，对重型商用车在开发阶段平顺性和安全性优化评估具有一定的指导意义。

Abstract

In order to improve ride comfort and safety of a vehicle and reduce road surface damage due to tire dynamic load, a 6 × 4 tractor combination vehicle was taken as a study object to establish its 12-DOF vibration mechanical model. The vehicle’s ride comfort and tire dynamic load were taken as optimization objectives, spring rigidities of its front and rear suspensions and damping coefficient of its damper were taken as parameters to be optimized. Here, a multi-objective optimization strategy for a vehicle’s suspension system based on an improved multi-objective particle swarm optimization (PSO) algorithm called the minimum distance of point to line multi-object particle swarm optimization (MDPL-MOPSO) was designed. The results before and after a suspension system was optimized were compared. The comparison showed that the improved multi-objective particle swarm optimization strategy improves the vehicle ride comfort, and reduces damage of road surface due to tire dynamic load, it has a better optimization effect, and it provided a guide for optimization evaluation of commercial vehicles’ ride comfort and safety in their development stage.

导出引用

范政武1，王铁1，王永红2，石晋宏1. 基于改进多目标粒子群算法的商用车悬架系统优化[J]. 振动与冲击, 2018, 37(15): 216-224

FAN Zhengwu1,WANG Tie1,WANG Yonghong2,SHI Jinhong1. Commercial vehicle suspension system optimization based on improved multi-objective particle swarm optimization algorithm[J]. Journal of Vibration and Shock, 2018, 37(15): 216-224

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