Braking distance prediction for vehicle consist in low-speed on-sight operation: a Monte Carlo approach

被引:0
|
作者
Raphael Pfaff
机构
[1] FH Aachen University of Applied Sciences,
来源
关键词
Freight rail; Shunting; Braking curves; Brake set-up; Driver assistance system; Automatic train operation;
D O I
暂无
中图分类号
学科分类号
摘要
The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive (e.g. in the case of depot operations) or highly inefficient (e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for low-speed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.
引用
收藏
页码:135 / 144
页数:9
相关论文
共 16 条
  • [1] Braking distance prediction for vehicle consist in low-speed on-sight operation: a Monte Carlo approach
    Pfaff, Raphael
    [J]. RAILWAY ENGINEERING SCIENCE, 2023, 31 (02) : 135 - 144
  • [2] Braking distance prediction for vehicle consist in low-speed on-sight operation: a Monte Carlo approach
    Raphael Pfaff
    [J]. Railway Engineering Science, 2023, (02) : 135 - 144
  • [3] Braking distance prediction for vehicle consist in low-speed on-sight operation: a Monte Carlo approach
    Raphael Pfaff
    [J]. Railway Engineering Science, 2023, 31 (02) : 135 - 144
  • [4] Influence of braking force in low-speed vehicle collisions
    Mastandrea, M
    Vangi, D
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2005, 219 (D2) : 151 - 164
  • [5] The design of Vehicle Low-speed intelligent braking system
    Bao, Fanbiao
    Huang, Baoshan
    [J]. PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MECHATRONICS, ROBOTICS AND AUTOMATION (ICMRA 2015), 2015, 15 : 1397 - 1400
  • [6] A modified direct simulation Monte Carlo method for low-speed microflows
    Pan, LS
    Liu, GR
    Khoo, BC
    Song, B
    [J]. JOURNAL OF MICROMECHANICS AND MICROENGINEERING, 2000, 10 (01) : 21 - 27
  • [7] Autonomous Electric Vehicle Route Optimization Considering Regenerative Braking Dynamic Low-Speed Boundary
    Mohammadi, Masoud
    Fajri, Poria
    Sabzehgar, Reza
    Harirchi, Farshad
    [J]. ALGORITHMS, 2023, 16 (06)
  • [8] Human-Machine Redundant Braking System for Aftermarket Low-Speed Electric Vehicle: Design and Validation
    Sun, Shulei
    Qu, Wei
    Huang, Xiaorong
    Tian, Guoying
    Deng, Pengyi
    Liu, Kunfan
    Tang, Yan
    Chen, Liang
    Wei, Chongfeng
    [J]. PROCESSES, 2023, 11 (07)
  • [9] New approach for the low-speed operation of PMSM drives without rotational position sensors
    Kim, JS
    Sul, SK
    [J]. IEEE TRANSACTIONS ON POWER ELECTRONICS, 1996, 11 (03) : 512 - 519
  • [10] Variance-reduced Monte Carlo solutions of the Boltzmann equation for low-speed gas flows: A discontinuous Galerkin formulation
    Baker, Lowell L.
    Hadjiconstantinou, Nicolas G.
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, 2008, 58 (04) : 381 - 402