A method of online traction parameter identification and mapping

Alexander Kobelski, Pavel Osinenko, Stefan Streif

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Fuel consumption of heavy-duty vehicles such as tractors, bulldozers etc. is comparably high due to their scope of operation. The operation settings are usually fixed and not tuned to the environmental factors, such as ground conditions. Yet exactly the ground-to-propelling-unit properties are decisive in energy efficiency. Optimizing the latter would require a means of identifying those properties. This is the central matter of the current study. More specifically, the goal is to estimate the ground conditions from the available measurements, such as drive train signals, and to establish a map of those. The ground condition parameters are estimated using an adaptive unscented Kalman filter. A case study is provided with the actual and estimated ground condition maps. Such a mapping can be seen as a crucial milestone in optimal operation control of heavy-duty vehicles.

Original languageEnglish
Pages (from-to)13933-13938
Number of pages6
JournalIFAC-PapersOnLine
Volume53
Issue number2
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event21st IFAC World Congress 2020 - Berlin, Germany
Duration: 12 Jul 202017 Jul 2020

Keywords

  • Data storage
  • Dynamic modelling
  • Identification algorithms
  • Kalman filters
  • Traction control
  • Vehicle Dynamics

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