China Mechanical Engineering

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Evaluation Method of Handling Force Feedback Comfort of Vehicle Gear Lever

Liu Mingzhou;Zhang Miao;Hu Jing;Liu Zhengqiong;Chen Ziang   

  1. Hefei University of Technology, Hefei,230009
  • Online:2016-08-10 Published:2016-08-10
  • Supported by:

汽车换挡杆操纵力反馈舒适度测评方法

刘明周;张淼;扈静;刘正琼;陈子昂   

  1. 合肥工业大学,合肥,230009
  • 基金资助:
    国家自然科学基金资助项目(51375134)

Abstract: A method for evaluating handling comfort of vehicle gear laver was proposed based on its force feedback characteristics. The method combined the objective test with the subjective evaluation, to compensate for the inadequacy of subjective evaluation only. The variation of handling force feedback with the displacement of the gear lever and the driver's individual differences were analyzed. The relative force feedback maxima and relative handling stiffness were extracted as objective indicators for the processes of gear selection, in-gear and out-of-gear. In addition, back propagation (BP) neural network was improved by particle swarm optimization (PSO), thereby an evaluation model was established, which described the mapping relationships between objective indicators and subjective scores. Finally, the model was trained and tested by 48 groups of sample data obtained from the handling experiments, which took the manual transmission used widely in normal car for examples. The results show that the evaluation with proposed  method is accurate and stable, so that it can provide guidance for optimization design of vehicle transmissions.

Key words: manual transmission, gear lever, force feedback, handling comfort, PSO-BP neural network

摘要: 基于汽车换挡杆的操纵力反馈特性,提出主客观结合的换挡杆操控舒适性测评方法,以弥补主观评价的不足。分析汽车换挡杆操纵力反馈随操纵位移的变化特征以及驾驶者的个体差异,分别针对选挡、进挡和退挡过程提取相对力反馈极值和相对操纵刚度作为客观测评指标;利用粒子群优化算法(PSO)对前向反馈(BP)神经网络进行改进,从而建立面向换挡杆操纵力反馈舒适性的PSO-BP神经网络测评模型,用以表征客观指标集与主观评分之间的映射关系;最后,以普通轿车上应用较多的手动变速器为例,利用在操控试验中获取的48组样本数据对模型进行训练和检验。结果表明,应用该方法取得了较为准确、稳定的测评效果,能够为汽车变速器操控舒适性优化设计提供指导。

关键词: 手动变速器, 换挡杆, 力反馈, 操控舒适性, PSO-BP神经网络

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