Simulation of Heavy-Duty Vehicles for the Use in Digital Twins

被引:0
作者
Rodriguez, Alejandro Secades [1 ]
Volmer, Jasper [1 ]
Bagnara, Davide [1 ]
机构
[1] MCI, Management Ctr Innsbruck, Innsbruck, Austria
来源
MANAGING AND IMPLEMENTING THE DIGITAL TRANSFORMATION, ISIEA 2022 | 2022年 / 525卷
关键词
Digital Twin; Hydrostatic Drive train; Multi-Body Simulation; Co-simulation;
D O I
10.1007/978-3-031-14317-5_11
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The digitalization of vehicle drive trains has been an ongoing trend for the recent past. Not only is its intention to speed up the design process but also the monitoring of the ultimate product. The development of accurate plant models of the machines provides the manufacturer with ample information of the final product performance in field. In addition, it can result in a testing foundation of different vehicle control systems and their optimization, as well as the launching platform for further developments on the direction of assistive and, ultimately, automated driving. Furthermore, with the aid of real measurements and commands taken in field by involved heavy-duty vehicles manufacturers, its performance can be first assessed and validated, and finally utilized as a tool to monitor its behaviour in different operation conditions, prior to or during operation. Thus, in the present, the authors describe their proposed means and processes to obtain extensive digital multi-physical models of heavy-duty vehicles, with an up-to-now results validation and their designed coupling to a multi-body simulation environment.
引用
收藏
页码:127 / 138
页数:12
相关论文
共 16 条
  • [1] Becker M.G., 1962, THEORY LAND LOCOMOTI
  • [2] LIDAR-camera fusion for road detection using fully convolutional neural networks
    Caltagirone, Luca
    Bellone, Mauro
    Svensson, Lennart
    Wande, Mattias
    [J]. ROBOTICS AND AUTONOMOUS SYSTEMS, 2019, 111 : 125 - 131
  • [3] Gonzalez F.J., 2009, 7 EUROMECH SOLID MEC
  • [4] Deformable Terrain Model for the Real-Time Multibody Simulation of a Tractor With a Hydraulically Driven Front-Loader
    Jaiswal, Suraj
    Korkealaakso, Pasi
    Aman, Rafael
    Sopanen, Jussi
    Mikkola, Aki
    [J]. IEEE ACCESS, 2019, 7 : 172694 - 172708
  • [5] Modular simulation in multibody system dynamics
    Kübler, R
    Schiehlen, W
    [J]. MULTIBODY SYSTEM DYNAMICS, 2000, 4 (2-3) : 107 - 127
  • [6] Automated Excavator Based on Reinforcement Learning and Multibody System Dynamics
    Kurinov, Ilya
    Orzechowski, Grzegorz
    Hamalainen, Perttu
    Mikkola, Aki
    [J]. IEEE ACCESS, 2020, 8 (08): : 213998 - 214006
  • [7] Multi-body co-simulation of semi-active suspension systems
    Levesley, M. C.
    Ramli, R.
    Stembridge, N.
    Crolla, D. A.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART K-JOURNAL OF MULTI-BODY DYNAMICS, 2007, 221 (01) : 99 - 115
  • [8] AADS: Augmented autonomous driving simulation using data-driven algorithms
    Li, W.
    Pan, C. W.
    Zhang, R.
    Ren, J. P.
    Ma, Y. X.
    Fang, J.
    Yan, F. L.
    Geng, Q. C.
    Huang, X. Y.
    Gong, H. J.
    Xu, W. W.
    Wang, G. P.
    Manocha, D.
    Yang, R. G.
    [J]. SCIENCE ROBOTICS, 2019, 4 (28)
  • [9] MathWorks, 2019, SIMSC LANG GUID
  • [10] Merrit H., 1967, HYDRAULIC CONTROL SY