Hardware-in-loop simulation of electronic control system of locomotive diesel engine

被引:0
作者
Wang, Su-Jing [1 ]
Wang, Li-De [1 ]
Shen, Ping [1 ]
Nie, Xiao-Bo [1 ]
机构
[1] School of Electrical Engineering, Beijing Jiaotong University
来源
Tiedao Xuebao/Journal of the China Railway Society | 2009年 / 31卷 / 06期
关键词
Hardware-in-loop simulation; Locomotive diesel engine; Neural network model; Virtual instruments;
D O I
10.3969/j.issn.1001-8360.2009.06.004
中图分类号
学科分类号
摘要
In order to solve the problems such as high costs, long debugging periods and a large number of uncertain factors in the bench-test experiments of the locomotive diesel engine, we use the hardware-in-loop simulation to verify the basic function and control strategy of the electronic control unit. The debug process of ECU can be accomplished in the laboratory. Combining the neural network and physical models, we research the dynamic and static characters on the hardware platform of virtual instruments. The simulation results prove the hardware-in-loop system can reflect real diesel running correctly. This system reduces the uncertain factors in the bench-test.
引用
收藏
页码:21 / 25
页数:4
相关论文
共 7 条
  • [1] Kao M., Moskwa John J., Turbocharged diesel engine modeling for nonlinear engine control and state estimation, Proceedings of the 1993 ASME Winter Annual Meeting, pp. 135-146, (1993)
  • [2] Oprins H., Van der Veken G., Nicole C.C.S., Et al., On-chip liquid cooling with integrated pump technology, IEEE Transactions on Components and Packaging Technologies, 30, 2, pp. 209-217, (2007)
  • [3] Yamaguma M., Kodama T., Observation of propagating brush discharge on insulating film with grounded antistatic materials, IEEE Transactions on Industry Applications, 40, 2, pp. 451-456, (2004)
  • [4] Sans M., Global predictive and optimal control applied to automotive engine management, Proceedings of the 1998 SAE International Congress & Exposition, pp. 103-108, (1998)
  • [5] Zito G., Landau L.D., Narmax model identification of a variable geometry turbocharged diesel engine, Proceedings of the 2005 American Control Conference, pp. 1021-1026, (2005)
  • [6] Lo J.T., Statistical method of pruning neural networks, Proceedings of the International Joint Conference on Neural Networks, 3, 1, pp. 1678-1680, (1999)
  • [7] Yao S.-F., Xue D.-Q., Zhang Y.-B., Et al., Method of Hybird Programming with LabView and Matlab, Ordnance Industry Automation, 24, 6, pp. 111-112, (2005)