Segmented Estimation of Road Adhesion Coefficient Based on Multimodal Vehicle Dynamics Fusion in a Large Steering Angle Range
Haobin Jiang, Tonghui Shen, Bin Tang, Kun Yang
TL;DR
This paper introduces a new method to accurately estimate road surface friction across a wide range of steering angles using a combination of advanced algorithms and vehicle dynamics models.
Contribution
The novel contribution is a segmented estimation strategy that combines multimodal vehicle dynamics fusion to improve road adhesion coefficient estimation under large steering angles.
Findings
The proposed method achieves a relative error of less than 10% in road surface friction coefficient estimation across different steering angles.
The segmented approach effectively reduces the impact of tire nonlinearities on estimation accuracy.
Verification through simulation and hardware experiments confirms the method's effectiveness for vehicle dynamics control.
Abstract
Real-time estimation of the road surface friction coefficient is crucial for vehicle dynamics control. Under large steering angles, the accuracy of existing road surface friction coefficient estimation methods is unsatisfactory due to the nonlinear characteristics of the tire. This paper proposes a segmented estimation method for the road adhesion coefficient, which considers different steering angle ranges and utilizes multimodal vehicle dynamics fusion. The method is designed to accurately estimate the road adhesion coefficient across the full steering angle range of the steer-by-wire system. When the front wheel angle is small (less than 2.8°), an improved Unscented Kalman Filter (AUKF) algorithm is used to estimate the road surface friction coefficient. When the front wheel angle is large (greater than 3.2°), a rack force expansion state observer is constructed using the dynamics…
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Taxonomy
TopicsVehicle Dynamics and Control Systems · Soil Mechanics and Vehicle Dynamics · Mechanical Engineering and Vibrations Research