• CN:11-2187/TH
  • ISSN:0577-6686

›› 2009, Vol. 45 ›› Issue (10): 278-283.

• 论文 • 上一篇    下一篇

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基于遗传算法的ECAS系统中三级阻尼匹配优化设计

江洪;李仲兴;周文涛;周孔亢   

  1. 江苏大学机械工程学院;江苏大学汽车与交通工程学院
  • 发布日期:2009-10-15

Optimum Match Design of Tri-grade Adjustable Damper in ECAS System Based on Genetic Algorithm

JIANG Hong;LI Zhongxing;ZHOU Wentao;ZHOU Kongkang   

  1. School of Mechanical Engineering, Jiangsu University School of Automobile and Traffic Engineering, Jiangsu University
  • Published:2009-10-15

摘要: 以阻尼有级可调的电子控制空气悬架系统为研究对象,提出一种阻尼和弹簧刚度与车辆不同运行工况的匹配方法。该方法分析空气弹簧的刚度特性,拟合了空气弹簧的有效面积随弹簧高度变化的多项式;在考虑空气弹簧非线性的情况下,建立双质量非线性动力学模型,以提高平顺性为目标,以控制动挠度与动载荷为约束条件,采用遗传算法优化,分析路面状况、车速、簧上质量对阻尼值优化的影响,设计阻尼的优化策略;根据优化结果确定三挡阻尼的方案,通过建立1/2整车车辆非线性动力学模型,考察三挡阻尼值控制方案的有效性。分析结果表明:该匹配方法可有效地提高车辆的平顺性,实现了阻尼值与弹簧刚度及车辆不同运行工况下的匹配。

关键词: 方均根值, 平顺性, 悬架优化, 遗传算法

Abstract: The matching between damping and spring stiffness of the electronically controlled air suspension with stepped adjustable damping under different working conditions is presented. The stiffness characteristic of air spring is analyzed and the curve between the height and effective area is fitted with polynomials. Considering the nonlinearity of the air spring, two-mass nonlinear dynamic model is established. The improvement of ride comfort is taken as the target, and the suspension working space and dynamic load are taken as constraint conditions. The genetic algorithm is adopted, and the influence of road surface, vehicle speed and on-spring mass on damping is analyzed. Then optimization strategy for damping is designed and the control method of three stepped adjustable damping is determined according to optimization. By establishing the nonlinear dynamic system model of half-vehicle, the validity of the control method of three stepped adjustable damping is investagted. Finally, the result of analysis indicates that the ride comfort can be improved effectively through matching between damping coefficient and spring stiffness under different working conditions.

Key words: Genetic algorithm, Ride comfort, RMS, Suspension optimization

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