Estimation of road adhesion coefficient based on Sage-Husa adaptive filtering improved square root cubature Kalman filter algorithm
by Quanwei Wang; Xiaobin Fan; Shuwen He; Zipeng Huang; Mingxin Chen
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 31, No. 5, 2024

Abstract: Due to the influence of various factors, real-time and stability analysis of road adhesion coefficient is extremely complex. This paper proposes a road adhesion coefficient estimation algorithm based on Sage-Husa adaptive filtering improved square root cubature Kalman filter based on the experimental vehicle driven by in-wheel motor. Based on the Dugoff tyre model, an eight-degree-of-freedom vehicle dynamics model is established. Then, using the vehicle state parameters, the Sage-Husa adaptive filter combined with the square root cubature Kalman filter algorithm is used to estimate the road adhesion coefficient. Through the simulation comparison with traditional unscented Kalman filter algorithm, combined with real vehicle road experiment, the results show that the estimation accuracy of the in-wheel motor drive electric vehicle based on improved square root cubature Kalman filter algorithm based on the Sage-Husa adaptive filter is significantly improved. The algorithm can meet estimation accuracy and real-time requirements of road adhesion coefficient.

Online publication date: Thu, 12-Sep-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Heavy Vehicle Systems (IJHVS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com