| Author Name | Affiliation | | Qin Shi | School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China | | Fei Zhang | School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
Anhui Domain Compute Co., Ltd, Hefei 230000, China | | Yikai Chen | School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China | | Zongpin Hu | School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China |
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| Abstract: |
| Selecting design variables and determining optimal hard-point coordinates are subjective in the traditional multiobjective optimization of geometric design of vehicle suspension, thereby usually resulting in poor overall suspension kinematic performance. To eliminate the subjectivity of selection, a method transferring multiobjective optimization function into a single-objective one through the integrated use of grey relational analysis (GRA) and improved entropy weight method (IEWM) is proposed. First, a comprehensive evaluation index of sensitivities was formulated to facilitate the objective selection of design variables by using GRA, in which IEWM was used to determine the weight of each subindex. Second, approximate models between the variations of the front wheel alignment parameters and the design variables were developed on the basis of support vector regression (SVR) and the fruit fly optimization algorithm (FOA). Subsequently, to eliminate the subjectivity and improve the computational efficiency of multiobjective optimization (MOO) of hard-point coordinates, the MOO functions were transformed into a single-objective optimization (SOO) function by using the GRA–IEWM method again. Finally, the SOO problem was solved by the self-adaptive differential evolution (jDE) algorithm. Simulation results indicate that the GRA-IEWM method outperforms the traditional multiobjective optimization method and the original coordinate scheme remarkably in terms of kinematic performance. |
| Key words: front wheel alignment parameters GRA IEWM self-adaptive differential evolution algorithm SVR |
| DOI：10.11916/j.issn.1005-9113.21020 |
| Clc Number:U462.2 |
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| Descriptions in Chinese: |
| 基于灰色关联度-改进熵权法的麦弗逊悬架运动学优化 石琴1，张飞1,2，陈一锴1，胡宗品1 (1. 合肥工业大学 汽车与交通工程学院，合肥 230009; 2. 安徽域驰智能科技有限公司，合肥 230000) 摘要：在传统的汽车悬架几何参数多目标优化研究中，从备选硬点坐标中选取设计变量，并从解集中确定最优硬点坐标，带有一定主观性，往往导致悬架的整体运动特性较差。为消除选择的主观性，提出一种基于灰色关联分析（GRA）和改进熵权法（IEWM）的方法，该方法将多目标优化函数转化为单目标优化函数。首先，建立灵敏度综合评价指标，利用GRA客观地选择设计变量，其中IEWM用于确定各子指标的权重。其次，基于支持向量回归（SVR）和果蝇优化算法（FOA），建立前轮定位参数变化与设计变量之间的近似模型。然后，为消除硬点坐标多目标优化的主观性，提高计算效率，采用GRA-IEWM方法将多目标优化函数转化为单目标优化函数。最后，采用自适应差分进化（jDE）算法求解单目标优化问题。仿真结果表明，提出的GRA-IEWM方法在改善悬架运动学特性上明显优于传统的多目标优化方法。 关键词：前轮定位参数；灰色关联分析；改进熵权法；自适应差分进化算法；支持向量回归 |
