Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles

Modelling the dynamic behaviour of heavy vehicles, such as buses or trucks, can be very useful for driving simulation and training, autonomous driving, crash analysis, etc. However, dynamic modelling of a vehicle is a difficult task because there are many subsystems and signals that affect its behaviour. In addition, it might be hard to combine data because available signals come at different rates, or even some samples might be missed due to disturbances or communication issues. In this paper, the authors propose a non-invasive data acquisition hardware/software setup to carry out several experiments with an urban bus, in order to collect data from one of the internal communication networks and other embedded systems. Subsequently, non-conventional sampling data fusion using a Kalman filter has been implemented to fuse data gathered from different sources, connected through a wireless network (the vehicle’s internal CAN bus messages, IMU, GPS, and other sensors placed in pedals). The authors results show that the proposed combination of experimental data gathering and multi-rate filtering algorithm allows useful signal estimation for vehicle identification and modelling, even when data samples are missing.

• Record URL:
  • https://doi.org/10.3390/s19163515
• Record URL:
  • https://www.mdpi.com/1424-8220/19/16/3515
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
• Supplemental Notes:
  • © 2019 Vicent Girbés et al.
• Authors:
  • Girbés, Vicent
  • Hernández, Daniel
  • Armesto, Leopoldo
  • Dols, Juan F
  • Sala, Antonio
• Publication Date: 2019-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 3515
• Serial:
  • Sensors
  • Volume: 19
  • Issue Number: 16
  • Publisher: MDPI AG
  • ISSN: 1424-8220
  • Serial URL: http://www.mdpi.com/journal/sensors
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Buses (Electricity); Computer network protocols; Data fusion; Estimating; Heavy duty vehicles; Kalman filtering; Motor vehicle dynamics; Sensors
• Identifier Terms: SAE J1939
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01720896
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 29 2019 9:32AM