This work discusses a road-tyre friction estimator considering combined tyre slip. The friction estimator design is motivated by its importance in vehicle dynamics control as accurate friction estimation can improve performance and safety. The estimator uses tyre force measurements from Load Sensing Bearing (LSB) technology and does not rely on parameterized tyre model. The tyre force measurements benefit the estimator mainly because of the uncertainties and nonlinearities of the tyre force characteristics. The proposed estimator uses tyre slip and tyre force representations where the longitudinal and lateral tyre slips and forces are combined into a single tyre slip and tyre force values. This representation makes the method effective during pure longitudinal dynamics, pure lateral dynamics and for combined slip. In addition, individual tyre-road friction estimation is possible with the proposed estimator and a computationally inexpensive algorithm, suitable for real-time implementation, is used to estimate the friction. The estimator is studied in simulation during pure braking, pure cornering and for combined slip. Further, the estimator is simulated in closed loop with a yaw rate controller to study whether the estimator improves vehicle safety. Finally the estimator is validated using test data from several maneuvers performed on a test vehicle instrumented with LSB technology.

This paper investigates the potential of load based vehicle sideslip estimation. Different techniques to measure tyre forces have been presented over the years; so far no technique has made it to the market. This paper considers a new technology based on load sensing bearings, which provides tyre force measurements. Based on the features of the sensor, a vehicle sideslip angle estimator is designed, analyzed and tested. The paper shows that direct tyre force sensing has mainly two advantages over traditional model-based estimators: primarily, it avoids the use of tyre models, which are heavily affected by uncertainties and modeling errors and secondarily, providing measurements on the road plane, it is less prone to errors introduced by roll and pitch dynamics. Extensive simulation tests along with a detailed analysis of experimental tests performed on an instrumented vehicle prove that the load based estimation outperforms the kinematic model-based benchmark yielding a root mean square error of 0.15°.