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Hybrid Kinematic-Dynamic Sideslip and Friction Estimation.
- Source :
-
Journal of Dynamic Systems, Measurement, & Control . May2023, Vol. 145 Issue 5, p1-10. 10p. - Publication Year :
- 2023
-
Abstract
- Vehicle sideslip and tyre/road friction are crucial variables for advanced vehicle stability control systems. Estimation is required since direct measurement through sensors is costly and unreliable. In this paper, we develop and validate a sideslip estimator robust to unknown road grip conditions. Particularly, the paper addresses the problem of rapid tyre/road friction adaptation when sudden road condition variations happen. The algorithm is based on a hybrid kinematic-dynamic closed-loop observer augmented with a tyre/road friction classifier that reinitializes the states of the estimator when a change of friction is detected. Extensive experiments on a four wheel drive electric vehicle carried out on different roads quantitatively validate the approach. The architecture guarantees accurate estimation on dry and wet asphalt and snow terrain with a maximum sideslip estimation error lower than 1.5 deg. The classifier correctly recognizes 87% of the friction changes; wrongly classifies 2% of the friction changes while it is unable to detect the change in 11% of the cases. The missed detections are due to the fact that the algorithm requires a certain level of vehicle excitation to detect a change of friction. The average classification time is 1.6 s. The tests also indicate the advantages of the friction classifiers on the sideslip estimation error. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FRICTION
*ELECTRIC vehicles
Subjects
Details
- Language :
- English
- ISSN :
- 00220434
- Volume :
- 145
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Dynamic Systems, Measurement, & Control
- Publication Type :
- Academic Journal
- Accession number :
- 175664280
- Full Text :
- https://doi.org/10.1115/1.4062159