Supervised Parameter Estimation for Road Vehicles, Mitigating Powertrain Induced Uncertainty

Robert Wragge-Morley, Guido Herrmann, Phil Barber

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

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

Funding

Manuscript received August 22, 2018; revised December 15, 2018, April 3, 2019, and August 3, 2019; accepted August 28, 2019. Date of publication January 27, 2020; date of current version July 16, 2020. This work and the Ph.D. of R. Wragge-Morley were supported by Jaguar Land Rover. The experiments for this work were partially carried out at the FlandersDrive and partially supported by E-VECTOORC Research Consortium FP7/2007-2013 under Grant Agreement no. 284708. The review of this article was coordinated by Prof. H. Chaoui. (Corresponding author: Robert Wragge-Morley.) R. Wragge-Morley is with the Powertrain and Vehicle Research Centre, Department of Mechanical Engineering, University of Bath, BA2 7AY Somerset, U.K. (e-mail: [email protected]). The authors would like to acknowledge the financial and in kind support of Jaguar Land Rover research in this project. Additionally the authors would like to thank FlandersDrive and the E-VECTOORC research consortium for facilitating some of the on-track testing of estimation algorithms.

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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