Massimiliano Pinna
Model-based slip and friction estimation algorithms for ABS performance enhancement.
Rel. Massimiliana Carello, Henrique De Carvalho Pinheiro, Matteo De Carlo. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2026
Abstract
This thesis presents a model-based methodology for the real-time estimation of tyre longitudinal slip and tyre‑road friction characteristics to support advanced anti‑lock braking strategies. The proposed framework integrates a kinematics‑based extended Kalman filter for vehicle state and tyre force reconstruction with a compact, physics‑informed tyre model and dedicated online identification procedures for slip stiffness and friction parameters. A key contribution is the tyre model developed in this thesis, which extends the Dugoff formulation by introducing separate static and dynamic friction coefficients together with a sliding-speed-dependent friction law, enabling a physically consistent peak in the longitudinal force-slip characteristic while preserving the simplicity required for onboard implementation.
The estimation process first identifies a load‑dependent slip stiffness law using an adaptive fitting routine, then employs a regression strategy to estimate friction parameters from the reconstructed state and force signals, allowing the recovery of peak grip and optimal slip ratio in real time
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