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The intention of this work is to illustrate a method used to overcome limitations of tire models developed during an evaluation study of an Empirical Dynamic™ (ED) damper model. A quarter vehicle test system was built to support the evaluation, and a model of the test system was also developed in ADAMS™. In the model, the damper was represented by a polynomial spline function and by an ED model separately. Vehicle level comparisons between the physical measurements and the model predictions were conducted. The actuator displacement signal from the physical test was used to drive the virtual test system. Spindle acceleration, spindle force, and other signals were collected for comparison. The tire model was identified as a significant source of error and as a result, the direct vehicle level correlation study did not illustrate any advantage of the ED damper model over a spline damper model.

Recent studies have shown that complex vehicle components such as shock absorbers, rubber bushings, and engine mounts can be accurately modeled by combining laboratory measurements with neural network technology. These nonlinear dynamic blackbox models (also known as Empirical Dynamics1 models) make it possible to predict nonlinear and hysteretic component behavior over wide ranges of amplitude and frequency. The models can handle realistic input waveforms as well as multiple inputs and multiple outputs. These techniques have now been applied to rolling pneumatic tires, to enable high accuracy predictions of tire and vehicle handling behavior. Models that predict high amplitude force components (three forces and three moments) using up to four randomly-varying inputs (radial deflection, slip angle, and camber angle, and slip ratio) have been successfully generated, using data obtained from MTS Flat-Trac III tire test equipment.