中国机械工程 ›› 2013, Vol. 24 ›› Issue (20): 2826-2831.

• 车辆工程 • 上一篇    下一篇

电动轮自卸车动力总成悬置系统分析与优化

尹庆1;谷正气1,2;陶坚1;伍文广1;徐亚1   

  1. 1.湖南大学汽车车身先进设计制造国家重点实验室,长沙,410082
    2.湖南工业大学,株洲,412008
  • 出版日期:2013-10-25 发布日期:2013-10-25
  • 基金资助:
    国家高技术研究发展计划(863计划)资助项目(SS2012AA041809);国家自然科学基金资助项目(50975083);湖南省科技重大专项计划资助项目(2010FJ1003) 
    National High-tech R&D Program of China (863 Program) (No. SS2012AA041809);
    National Natural Science Foundation of China(No. 50975083);
    Hunan Provincial Science and Technology Major Project ( No. 2010FJ1003)

Analysis and Optimization Design of Powertrain Mounting System of Electric Drive Truck

Yin Qing1;Gu Zhengqi1,2;Tao Jian1;Wu Wenguang1;Xu Ya1   

  1. 1.State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University,Changsha,410082
    2.Hunan University of Technology,Zhuzhou,Hunan,412008
  • Online:2013-10-25 Published:2013-10-25
  • Supported by:
     
    National High-tech R&D Program of China (863 Program) (No. SS2012AA041809);
    National Natural Science Foundation of China(No. 50975083);
    Hunan Provincial Science and Technology Major Project ( No. 2010FJ1003)

摘要:

针对电动轮自卸车动力总成结构特点,根据能量解耦理论和最优化理论,建立了电动轮自卸车动力总成悬置系统六自由度动力学模型,以其六自由度解耦率最大为优化目标,采用优化拉丁方法,分析出影响解耦的主要参数,结合遗传算法、模拟退火法对电动轮自卸车动力总成悬置系统进行了优化设计。然后将优化结果视为满足正态分布的随机变量,运用Monte Carlo方法对其进行稳健性分析,以考察设计值的变化对目标函数的影响。结果表明,运用此优化方法对悬置系统进行优化能有效提高解耦率,优化结果稳定可靠,基本满足稳健性要求。

关键词: 电动轮自卸车, 动力总成, 悬置系统, 遗传算法, 模拟退火算法, 稳健性

Abstract:

Based on the energy decoupling theory and the optimization theory,a 6 DOFs model was constructed concerning the characteristics of electric drive truck powertrain
structure.The optimization of the mounting system was conducted by using genetic algorithms and simulated annealing algorithm for the purpose of maximizing decoupling rate of 6 DOFs mounting system.Before optimization,the main parameters affecting the results were analyzed by using Latin cube algorithm.Then the optimization solutions were considered as random variable with normal distribution feature, and a robustness analysis of the mounting system  was made by using Monte Carlo method to observe the effects of the design value changes on the objective function.The results show that this optimization method can enhance the decoupling rate of powertrain mount effectively,and the results are reliable,which can meet the robustness requirements basically.

Key words: electric drive truck, powertrain, mounting system, genetic algorithm, simulated annealing algorithm, robustness

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