Supervised Parameter Estimation for Road Vehicles, Mitigating Powertrain Induced Uncertainty

Robert Wragge-Morley, Guido Herrmann, Phil Barber

Research output: Contribution to journalArticle

Abstract

This paper demonstrates a real world case study of a new method for robust simultaneous estimation of two parameters using multiple data sources. The method is used to simultaneously estimate vehicle mass and road gradient. No additional sensors are required beyond those that would normally be found on a vehicle controller network. The estimation algorithm combines components driven by observer state error and also directly by the parameter error using a sliding-mode inspired regressor structure. The algorithm incorporates a novel information fusion method that is integral to the regressor structure and a supervised data-rejection system to limit estimation activity in periods of recognised error-promoting activity. The estimation method has been demonstrated in real time on a modified production passenger car platform. It has been shown to be effective at robustly predicting road gradient and offering more reliable and stable prediction of vehicle mass than existing estimation methods employed in the same multi-parameter estimation context. The estimator allows prediction of vehicle mass whose limiting factor is the bandwidth and accuracy of the available driveline torque information, not the algorithm itself, allowing the identification of a 150 kg change in mass on a 2000 kg vehicle in this case study.

Original languageEnglish
Article number8970523
Pages (from-to)7000-7013
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Volume69
Issue number7
DOIs
Publication statusPublished - 31 Jul 2020

Keywords

  • Control engineering
  • land vehicles
  • parameter estimation
  • supervisory control
  • system identification

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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