摘 要：本文针对某轻型商用车麦弗逊前悬架在平行轮跳工况下前束角变化非线性显著的问题，提出一种基于响应面法(RSM)与改进粒子群算法(MPSO)的协同优化方法。首先通过中心复合设计安排试验方案，并运用多体动力学仿真获取样本数据；在此基础上，以转向拉杆内外硬点的空间坐标为设计变量，构建了前束角变化量、外倾角变化量及上跳行程前束角变化梯度的预测模型，并对模型进行了显著性分析；随后利用该预测模型，结合改进粒子群算法对悬架性能进行优化求解，最终从Pareto解集中筛选出最优方案。优化结果表明：前束角变化量由0.724°降低至0.493°,上跳行程前束角变化梯度由-8.78(°)/m优化为-4.11(°)/m,前束角变化曲线趋于平缓且变化范围显著收窄。本研究为悬架系统的设计与性能提升提供了有效的解决方案，对工程实践具有指导意义。
关键词：RSM；MPSO；麦弗逊悬架；前束角；硬点优化；中心复合设计
中图分类号：U463.33 文献标志码：A DOI：10.15917/j.cnki.1006-3331.2025.06.004
Analysis and Optimization of Suspension Layout for a Light Commercial Vehicle Based on Response Surface Methodology
HU Bin,ZHOU Yunmao
Abstract: This research addresses the significant nonlinear variation in the toe angle during parallel wheel travel of a MacPherson front suspension in a light commercial vehicle.It proposes a collaborative optimization method that integrates response surface methodology(RSM)and modified particle swarm optimization(MPSO).First,the method defines the experimental plan using a central composite design and obtains sample data through multi-body dynamics simulations.Subsequently,it utilizes the spatial coordinates of the inner and outer hardpoints of the tie rod as design variables to establish predictive models for the variation in toe angle,camber angle,and the gradient of toe angle change during jounce travel.Then,the method performs a significance analysis on these models.Next,it employs these predictive models with the MP-SO algorithm to optimize suspension performance,ultimately selecting the optimal solution from the Pareto solution set.The optimization results demonstrate that the toe angle variation decreases from 0.724°to0.493°,and the toe angle change gradient during jounce travel improves from-8.78(°)/m to-4.11(°)/m.Consequently,the toe angle change curve becomes smoother,and its variation range narrows significantly.This research provides an effective solution for suspension system design and performance enhancement,offering valuable guidance for engineering practice.
Key words: RSM; MPSO; MacPherson suspension; toe angle; hardpoint optimization; central composite design
