Vehicle state and tyre force estimation: demonstrations and guidelines

Abstract

This paper presents an in-depth analysis of the application of different techniques for vehicle state and tyre force estimation using the same experimental data and vehicle models, except for the tyre models. Four schemes are demonstrated: (i) an Extended Kalman Filter (EKF) scheme using a linear tyre model with stochastically adapted cornering stiffness, (ii) an EKF scheme using a Neural Network (NN) data-driven linear tyre model, (iii) a tyre model-less Suboptimal-Second Order Sliding Mode (S-SOSM) scheme, and (iv) a Kinematic Model (KM) scheme integrated in an EKF. The estimation accuracy of each method is discussed. Moreover, guidelines for each method provide potential users with valuable insight into key properties and points of attention.

Keywords:

• Virtual sensing
• state estimation
• sliding mode observer
• neural network
• Kalman filtering
• quaternion
• sideslip angle
• cog velocities
• tyre forces

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The European Commission is gratefully acknowledged for their support of the Marie Skłodowska-Curie research and innovation programme ITEAM (Interdisciplinary Training Network in Multi-Actuated Ground Vehicles https://iteam-project.net/) (GA 675999). The Research Fund KU Leuven is gratefully acknowledged for its support. The research of F. Naets is funded by a grant from the Research Foundation Flanders (FWO). This research was partially supported by Flanders Make, the strategic research centre for the manufacturing industry.