Search Results

Mechanical and thermal properties of the rubber compounds of a tire play an important role in the overall performance of the tire when it is in contact with the terrain. Although there are many studies conducted on the properties of the rubber compounds of the tire to improve some of the tire characteristics such as the wear of the tread, there is a limited number of studies that focused on the performance of the tire when it is in contact with ice. This study is a part of a more comprehensive project looking into tire-ice performance and modeling. A significant part of this study is the experimental investigation of the effect of rubber compounds on tire performance in contact with ice. For this, four tires have been selected for testing. Three of them are completely identical in all tire parameters (such as tire dimensions), except for the rubber compounds. Several tests were conducted for the chosen tires in three modes: free rolling, braking, and traction.

This paper presents a six degree of freedom full vehicle model simulating the testing of heavy truck suspensions to evaluate the ride comfort and stability using actual characteristics of gas charged single tube shock absorbers. The model is developed using one of the commercial multi-body dynamics software packages, ADAMS. The model incorporates all sources of compliance: stiffness and damping with linear and non-linear characteristics. The front and the rear springs and dampers representing the suspension system were attached between the axles and the vehicle body. The front and the rear axles were attached to a wheel spindle assembly, which in turn was attached to the irregular drum wheel, simulating the road profile irregularities. As a result of the drum rotation, sudden vertical movements were induced in the vehicle suspension, due to the bumps and rebounds, thus simulating the road profile.

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.