液力变矩器通用特性及其匹配方法的研究  

Research on General Characteristics of Hydraulic Torque Converter and Its Matching Method

作　　者：马惠臣 刘城[1,2] 李娟 李晋[3] MA Huichen;LIU Cheng;LI Juan;LI Jin(School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China;Chongqing Innovation Center of Beijing Institute of Technology,Chongqing 401147,China;China North Vehicle Research Institute,Beijing 100072,China)

机构地区：[1]北京理工大学机械与车辆学院,北京100081 [2]北京理工大学重庆创新中心,重庆401147 [3]中国北方车辆研究所,北京100072

出　　处：《北京理工大学学报》2021年第9期927-934,共8页Transactions of Beijing Institute of Technology

基　　金：国家自然科学基金资助项目(51805027);北京理工大学青年教师学术启动计划项目(2019CX04039);车辆传动重点实验室基金及稳定支持项目(6142213180407)。

摘　　要：利用系列泵轮转速及工况的试验数据,建立了一种基于液力变矩器通用特性的发动机与液力变矩器匹配模型.分析了传统匹配方法产生较大误差的来源,在有限试验数据基础上利用反向传播神经网络的拟合和泛化能力,确定了神经网络结构的隐含层节点数,建立了液力变矩器通用特性预测模型,并与传统经验修正模型进行对比.对比结果显示文中提出的方法使得特性预测精度有显著提升.在此基础上,结合发动机净外特性提出了发动机与液力变矩器通用特性匹配模型,该匹配模型考虑了泵轮转速对液力变矩器稳态性能的影响,更符合实际运行情况下发动机与液力变矩器共同工作特性,提高了液力变矩器与发动机的匹配精度.Based on the test data of a series of pump speeds and working conditions,a matching method based on the general characteristics of the hydraulic torque converter was proposed.This method was used to match engine with hydraulic torque converter.Firstly,error sources of the traditional matching method were analyzed.Secondly,this paper used the back propagation(BP)neural network,which is good at fitting and generalization,to establish a general characteristics prediction model of torque converter.The number of hidden layer nodes of neural network structure is determined.The comparison between the BP model and the traditional modification model indicated pronounced improvement with regard to performance prediction.Combined with the engine performance and the general characteristics matching model,it was found that the matching approach proposed in this paper was able to reflect the influence of pump speed on the matching conditions and improve the matching accuracy.

关 键 词：反向传播 神经网络 匹配方法 液力变矩器 通用特性 

分 类 号：U463.22[机械工程—车辆工程]