自动变速器换档规律确定方法的研究

自动换档规律是车辆自动变速的核心。在自动变速器的开发过程中，提出了确定换档规律的新方法，即动态驱动力曲线法、油门法和车速法，以此制定出自动变速器的动力性与经济性换档规律。这些方法丰富了车辆自动操纵理论。该换档规律在实际跑车中得到了验证。     

汽车机械式自动变速器换档规律的动态评价方法研究

本文根据机械式自动变速器的换档特性,应用计算机仿真技术,通过建立换档规律的动态评价系统,分析了不同换档规律对AMT车辆燃料经济性的影响,其分析结果与试验结果有很好的一致性。所建立的动态评价系统和相应的分析方法对进一步研究机械式自动变速器换档规律的动态特性及其对汽车性能的影响有一定的参考价值。     

在底盘测功机上进行自动变速器换档规律检验的功能开发

本文介绍在汽车底盘测功机上进行轿车液力自动变速器换档规律检验的功能开发，包括信号的变换，试验数据采集和处理，控制逻辑框图和检验实例。     

AMT车辆坡道行驶综合控制换档规律的研究

分析AMT车辆坡道行驶时容易出现的意外升档和换档循环的原因;提出坡道工况的模糊识别方法,制定坡道行驶综合控制换档规律,不仅解决了意外升档和换档循环的问题,而且实现了坡道换档性能的最优。通过仿真分析,验证了该方法的有效性。     

AMT自动换档变速器在城市客车上的应用

介绍一种正在兴起的变速技术———AMT(Automated Mechanical Transmission)自动换档变速器,阐述其发展过程、工作原理、性能特点及其在城市客车上的应用前景。     

基于等效坡度的自动手动变速器换档规律研究

针对自动变速器在复杂路面情况下很难进行较好的路面识别,导致换挡规律很难适应路面状况的问题,文章从汽车动力学的角度,对其进行建模分析;提出了等效坡度概念,提取各种复杂路面共性因素,并进行换档规律研究;利用车辆动力学模型及发动机特性及整车传动系统特性,分析了车辆在特殊路面上行驶规律;分析得出了特殊路面的共性因素,即路面阻力...     

上海别克4T65E型自动变速器换档冲击的维修措施

检查动力控制模块pCM中是否存在故障代码上海别克4T65E型自动变速器换档冲击的维修措施@汤杰$南京公交教育培训中心!210009对于4T65E型自动变速器的上海别克汽车在车辆行驶过程中产生换档冲击的故障,建议参考图1所示的流程进行故障区分。     

微观交通流模型在车辆自动驾驶仿真中的应用

仿真测试是缩短自动驾驶安全测试周期、降低实车测试成本的重要方法,而交通流模型可以为车辆自动驾驶测试提供数字化的交通环境,有利于加速车辆功能和性能的开发及验证。介绍了自动驾驶仿真测试涉及的关键技术,针对共性问题开发了一套具有交互性的微观交通流模型和对应的评价指标体系;分析交通流模型和自动驾驶仿真软件间的数据链路,通过联合仿真的形式实现微观交通流模型在自动驾驶车辆仿真测试中的应用。     

电液自动换档技术在叉车上的应用

随着驾驶员对工业车辆操纵舒适性要求的日益提高,电液控制自动换档技术在叉车上应用日趋广泛,为了进一步提高自动换档的舒适性和可靠性,有必要对该技术的原理及其应用作一系统的分析。     

ZL50装载机自动换挡电子控制单元的开发研究

开发了ZL50装载机液力传动变速箱自动换挡操纵系统及其电子控制单元(ECU)。针对装载机作业过程中换挡操纵的复杂性,该系统采用了人机协同配合的多模式换挡控制方式;完成了控制软件设计、硬件电路及可靠性设计,并对研制的自动换挡操纵系统进行了台架试验和环境试验,验证了系统功能的实用性和工作可靠性。试验结果表明:自动换挡操纵系统可按驾驶员选择的控制方式工作,实现自动选挡功能及换挡过程的平稳性控制。     

ZL50型轮式装载机传动系统维修

介绍了ZL50型轮式装载机传动系统维修。包括离合器打滑引起油温过高、变速器换挡故障以及变速器常见故障。离合器打滑,使离合器片之间非正常摩擦产生大量的热,从而使油温过高。引起离合器打滑的原因主要有离合器片分离不彻底、过度磨损、翘曲变形、油压过低等。     

Adaptive shift scheduling strategy introducing neural network in automatic transmission

Conventional automatic transmissions, which have fixed shift patterns, sometimes show inconvenient shifting patterns, especially in uphill and downhill driving. In order to improve the driveability for these road conditions, Mitsubishi developed an adaptive shift logic called “Fuzzy shift” in 1992. Since then, further evolutional shift scheduling strategies has been developed to cover more extensive road conditions. This paper introduces neural network, learning and continuous variable shift patterns control incorporating this strategy.     

ZL50F装载机故障原因分析

分析了装载机变速箱、变矩器泄漏、磨损和油温过高的原因,介绍了其危害,并针对每一种故障均提出了相应的修复措施.     

Modeling retroreflectivity degradation of traffic signs using artificial neural networks

Traffic signs are vital for communicating guidance, rules, warning, and other highway agency information for safe and efficient navigation through transportation networks. Signs must be clearly detectable, readable, and understandable to fulfill their intended purpose. Poor sign visibility, particularly during nighttime, is the leading cause of fatalities worldwide. Sign retroreflectivity is one of the key measures to evaluate sign visibility conditions. It gradually deteriorates over time with sign aging, exposure, and other environmental conditions necessitating periodic sign maintenance or, ultimately, replacement when the sign retro values fall below the minimum prescribed standards. In literature, studies have mostly used traditional statistical regression models to model sign retroreflectivity as a function of available explanatory variables. Further, these studies have proposed separate retro degradation prediction models for different sign sheeting grades and colors that limit their applications for other scenarios. To fill the research gap, this study compared the performance of the linear regression method with three different architecture of the neural network namely, Feed-Forward Neural Network (FFNN), Cascade Forward Neuran Network (CFNN), and Elman neural networkNeural Network (ELMNN) for signs retro prediction with an aim to optimize sign maintenance and replacement activities and to enhance road safety. All the Neural Network models were employed with varying combinations of training algorithms, activation functions, and model parameters. Sign retro data for 539 in-service signs along selected sections of two expressways (M-1 and M-2) near the capital city of Islamabad in Pakistan were collected through portable handheld retroreflectometer GR3. Data on other sign attributes like sign ages (0, 2, 5, and 10 years), sign orientation, observation angle, sign sheeting brand, grade, and color were also acquired. Feature-based sensitivity analysis was conducted to identify the relative importance and ranking of input predictor variables. Model prediction results expressed in terms of various statistical evaluation metrics root mean square error (RMSE), mean absolute percent error (MAPE), RMSE-observation standard deviation satio (RSR), coefficient of determination (R²), Willmott's index of agreement (WIA), Nash-Sutcliffe efficiency (NSEC), and percent bias (PBIAS) showed that all the NN models outperformed the regression technique. Comparing the NN models, ELMNN architecture with 21 neurons in the hidden layer for ‘tansig’ activation function and ‘trainlm’ training algorithm yielded better retro-prediction performance. Feature sensitivity analysis revealed that variables sign age, sheeting brand, color, and observation angle were the most sensitive variables in predicting the retro output. Findings of this study can guide the transport agencies and decision-makers for effective policy implications and sign management practices.     

神经网络模型在高速公路软基沉降预测中的应用

借助人工神经网络模型 ,建立了可依据现场量测信息对软基路堤沉降量随时间而发展的过程进行动态预报的分析方法。其要点是 :建立公路软基沉降预测的神经网络结构 ,并将前期沉降观测值作为样本 ,通过神经网络结构的训练寻求沉降及其主要影响因素的内在关系 ,据以预测后期沉降量     

自动变速器换挡冲击故障诊断

本对装有自动变速器的汽车在使用过程中产生换挡冲击的原因进行了较为全面的分析，介绍了故障的排除方法。     

50 kW发电机自动电压调节器

主要描述了50kW中频无刷同步发电机自动调节电压的基本原理和主要参数以及调整方法。     

Practicability study on the suitability of artificial,neural networks for the approximation of unknown steering torques

For steer-by-wire systems, the steering feedback must be generated artificially due to the system characteristics. Classical control concepts require operating-point driven optimisations as well as increased calibration efforts in order to adequately simulate the steering torque in all driving states. Artificial neural networks (ANNs) are an innovative control concept; they are capable of learning arbitrary non-linear correlations without complex knowledge of physical dependencies. The present study investigates the suitability of neural networks for approximating unknown steering torques. To ensure robust processing of arbitrary data, network training with a sufficient volume of training data is required, that represents the relation between the input and target values in a wide range. The data were recorded in the course of various test drives. In this research, a variety of network topologies were trained, analysed and evaluated. Though the fundamental suitability of ANNs for the present control task was demonstrated.