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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.

Tire-pavement interaction noise (TPIN) is a dominant source for passenger cars and trucks above 40 km/h and 70 km/h, respectively. TPIN is mainly generated from the interaction between the tire and the pavement. In this paper, twenty-two passenger car radial (PCR) tires of the same size (16 in. radius) but with different tread patterns were tested on a non-porous asphalt pavement. For each tire, the noise data were collected using an on-board sound intensity (OBSI) system at five speeds in the range from 45 to 65 mph (from 72 to 105 km/h). The OBSI system used an optical sensor to record a once-per-revolution signal to monitor the vehicle speed. This signal was also used to perform order tracking analysis to break down the total tire noise into two components: tread pattern-related noise and non-tread pattern-related noise.

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.