基于BP神经网络的中间轴制动器控制策略研究  

Research on Control Strategy of Intermediate Shaft Brake Based on BP Neural Network

作　　者：王鑫 刘丹 石钊卓 潘建斌 邓晶哲 WANG Xin;LIU Dan;SHI Zhaozhuo;PAN Jianbin;DENG Jingzhe(Shaanxi Fast Automotive Transmission Engineering Research Institute,Xi'an 710119,China)

机构地区：[1]陕西法士特汽车传动工程研究院,陕西西安710119

出　　处：《汽车实用技术》2024年第17期74-77,154,共5页Automobile Applied Technology

摘　　要：中间制动器是机械自动变速箱(AMT)的一种关键部件,主要用于车辆在换挡过程中的变速,实现更加顺畅的驾驶体验,反向传播(BP)神经网络是一种通过自适应方式调节参数和优化权重的代理数学模型,机械自动变速箱在换挡过程中存在动力中断的现象,动力中断时间长短会影响整车行驶舒适性。文章通过分析换挡控制策略及制动器工作时间影响相关参数,发现该控制过程存在较多容易产生影响的非线性因素,因此,提出利用BP神经网络建立预测模型预测中间轴制动时间,对中间轴制动进行更精准灵敏的控制,从而达到更好的变速效果。The middle shaft brake is a key component of the automated manual transmission(AMT),which is mainly used for gear shifting during the driving process to achieve a smoother driving experience.Back propagation(BP)neural network is a proxy mathematical model that can adaptively adjust parameters and optimize weights.During gear shifting of an AMT,power interruption may occur,and the duration of the power interruption affects the driving comfort of the vehicle.This article analyzes the gear shifting control strategy and the related parameters affected by the middle shaft brake working time.It is found that there are nonlinear relationships and multiple influencing factors in this control process.Therefore,it is proposed to use BP neural network to establish a prediction model to predict the middle shaft brake time,and achieve more precise and sensitive control of the middle shaft brake,thereby achieving better shifting effect.

关 键 词：AMT 中间轴制动器 BP神经网络 

分 类 号：U462[机械工程—车辆工程]