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161.
地铁车辆车轮踏面异常磨耗原因初探 总被引:1,自引:0,他引:1
运营速度80 km/h常规城轨车辆的基础制动方式基本采用踏面制动+合成闸瓦,文章针对城轨车辆合成闸瓦对车轮踏面磨耗的影响、制动力分配方式对踏面磨耗的影响、闸瓦与车轮的匹配及热负荷计算等进行了分析研究,探讨了造成地铁车辆踏面异常磨耗的原因。 相似文献
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分析了国内外目前所应用的列车再生制动能量吸收装置方案的优缺点,提出了城市轨道交通设置列车再生制动能量吸收装置的建议方案。 相似文献
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介绍了日本制动系统的技术发展现状、主要成果,以及待解决的课题,展望了将来的技术动向,也对合成闸瓦及新一代制动技术的开发动向进行了阐述。 相似文献
168.
Yintao Wei Christian Oertel Yahui Liu Xuebing Li 《Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility》2016,54(4):463-473
ABSTRACTIt is well known that the tyre steering torque is highly dependent on the tyre rolling speed. In limited cases, i.e. parking manoeuvre, the steering torque approaches the maximum. With the increasing tyre speed, the steering torque decreased rapidly. Accurate modelling of the speed-dependent behaviour for the tyre steering torque is a key factor to calibrate the electric power steering (EPS) system and tune the handling performance of vehicles. However, no satisfactory theoretical model can be found in the existing literature to explain this phenomenon. This paper proposes a new theoretical framework to model this important tyre behaviour, which includes three key factors: (1) tyre three-dimensional transient rolling kinematics with turn-slip; (2) dynamical force and moment generation; and (3) the mixed Lagrange–Euler method for contact deformation solving. A nonlinear finite-element code has been developed to implement the proposed approach. It can be found that the main mechanism for the speed-dependent steering torque is due to turn-slip-related kinematics. This paper provides a theory to explain the complex mechanism of the tyre steering torque generation, which helps to understand the speed-dependent tyre steering torque, tyre road feeling and EPS calibration. 相似文献
169.
K. T. R. Van Ende D. Schaare J. Kaste F. Küçükay R. Henze F. K. Kallmeyer 《Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility》2016,54(10):1362-1383
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. 相似文献
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