Open Access
113- https://doi.org/10.1080/00423114.2020.1714672
Vehicle state and tyre force estimation: demonstrations and guidelines
- Jan 16, 2020
- Vehicle System Dynamics
- Marco Viehweger +10 more
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.
- # Tyre Force Estimation
- # Extended Kalman Filter Scheme
- # Linear Tyre Model
- # Extended Kalman Filter
- # Vehicle State Estimation
- # Vehicle Tyre
- # Tyre Model
- # Techniques For State Estimation
- # Vehicle State
- # Key Properties
