Vehicle sideslip angle estimation for a heavy-duty vehicle via Extended Kalman Filter using a Rational tyre model

DI BIASE, Feliciano, LENZO, Basilio and TIMPONE, Francesco (2020). Vehicle sideslip angle estimation for a heavy-duty vehicle via Extended Kalman Filter using a Rational tyre model. IEEE Access, p. 1.

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Vehicle sideslip angle is a key state for lateral vehicle dynamics, but measuring it is expensive and unpractical. Still, knowledge of this state would be really valuable for vehicle safety systems aimed at enhancing vehicle safety, to help to reduce worldwide fatal car accidents. This has motivated the research community to investigate techniques to estimate vehicle sideslip angle, which is still a challenging problem. One of the major issues is the need for accurate tyre model parameters, which are difficult to characterise and subject to change during vehicle operation. This paper proposes a new method for estimating vehicle sideslip angle using an Extended Kalman Filter. The main novelties are: i) the tyre behaviour is described using a Rational tyre model whose parameters are estimated and updated online to account for their variation due to e.g. tyre wear and environmental conditions affecting the tyre behaviour; ii) the proposed technique is compared with two other methods available in the literature by means of experimental tests on a heavy-duty vehicle. Results show that: i) the proposed method effectively estimates vehicle sideslip angle with an error limited to 0.5 deg in standard driving conditions, and less than 1 deg for a high-speed run; ii) the tyre parameters are successfully updated online, contributing to outclassing estimation methods based on tyre models that are either excessively simple or with non-varying parameters.

Item Type: Article
Uncontrolled Keywords: 08 Information and Computing Sciences; 09 Engineering; 10 Technology
Identification Number:
Page Range: p. 1
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 04 Aug 2020 09:47
Last Modified: 17 Mar 2021 23:32

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