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This paper is part of the European OWHEEL project. It proposes a method to improve the comfort of a vehicle by adaptively controlling the Camber and Toe angles of a rear suspension. The purpose is achieved through two actuators for each wheel, one that allows to change the Camber angle and the other the Toe angle. The control action is dynamically determined based on the error between the reference angle and the actual angles. The reference angles are not fixed over time but dynamically vary during the maneuver. The references vary with the aim of maintaining a Camber angle close to zero and a Toe angle that follows the trajectory of the vehicle during the curve. This improves the contact of the tire with the road. This solution allows the control system to be used flexibly for the different types of maneuvers that the vehicle could perform. An experimentally validated sports vehicle has been used to carry out the simulations. The original rear suspension is a Trailing-arm suspension.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.

Tire modeling plays an important role in the development of an Active Vehicle Safety System. As part of a larger project that aims at developing an integrated chassis control system, this study investigates the performance of a 19” all-season tire on ice for a sport utility vehicle. A design of experiment has been formulated to quantify the effect of operational parameters, specifically: wheel slip, normal load, and inflation pressure on the tire tractive performance. The experimental work was conducted on the Terramechanics Rig in the Advanced Vehicle Dynamics Laboratory at Virginia Tech. The paper investigates an approach for the parameterization of the Dugoff tire model based on the experimental data collected. Compared to other models, this model is attractive in terms of its simplicity, low number of parameters, and easy implementation for real-time applications.