Journal
VEHICLE SYSTEM DYNAMICS
Volume 59, Issue 5, Pages 675-702Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00423114.2020.1714672
Keywords
Virtual sensing; state estimation; sliding mode observer; neural network; Kalman filtering; quaternion; sideslip angle; cog velocities; tyre forces
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Funding
- Marie Sklodowska-Curie research and innovation programme ITEAM [GA 675999]
- Research Fund KU Leuven
- Research Foundation Flanders (FWO)
- FlandersMake
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This paper provides an in-depth analysis of different techniques for vehicle state and tyre force estimation using the same experimental data and vehicle models. Four schemes are demonstrated, with discussion on the estimation accuracy of each method and guidelines for potential users regarding key properties and points of attention.
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.
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