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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22450

Title: Full vehicle and tyre identification using unscented and extended identifying Kalman Filters
Authors: Best, Matt C.
Bogdanski, Karol
Keywords: Topics / vehicle dynamics
Tyre model
Modeling and simulation
State estimation
System identification
Issue Date: 2016
Publisher: © Taylor and Francis
Citation: BEST, M.C. and BOGDANSKI, K., 2016. full vehicle and tyre identification using unscented and extended identifying Kalman Filters. Presented at the 13th International Symposium on Advanced Vehicle Control, Munich, Germany, 13-16th Sept.
Abstract: This paper considers identification of all significant vehicle handling and driveline dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle CAN buses. The method extends previous work using augmented Kalman Filter state estimators to concentrate wholly on parameter identification, and it compares Extended and Unscented Kalman filter algorithms. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The method is suited to applications of system identification, but also in on-line model predictive controllers or estimators. It can also operate in real-time, so the model could be continuously identified to maintain accuracy with each new journey.
Description: This paper is in closed access until 12 months after publication.
Sponsor: This work is supported by Jaguar Land Rover and the UK-EPSRC grant EP/K014102/1 as part of the jointly funded Programme for Simulation Innovation (PSi).
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/22450
Publisher Link: http://www.avec16.com/index.php
Appears in Collections:Closed Access (Aeronautical and Automotive Engineering)

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