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This paper discusses an approach to construct a high fidelity surrogate tire model using a two-phase optimization-based algorithm that draws on data generated by off-line nonlinear ABAQUS tire simulations. It subsequently describes the process of Simulink-based interfacing of the resulting surrogate model to a full ADAMS vehicle model to enable accurate and expeditious durability studies. The two-phase surrogate model construction relies on an identification method that draws on the Instantaneous Center Manifold (ICM) theory. In the proposed method, a generally forced non-autonomous nonlinear structural system is represented as a sequence of harmonically excited autonomous nonlinear systems. The close-form solution of each of these systems is produced using the ICM theory. The first phase of the surrogate model construction uses an optimal Orthogonal Matching Pursuit (OMP) algorithm to unify all ICMs used to approximate the reaction force of the tire at its spindle.

This study outlines an approach for speeding up the simulation of the dynamic response of vehicle models that include hysteretic nonlinear tire components. The method proposed replaces the hysteretic nonlinear tire model with a surrogate model that emulates the dynamic response of the actual tire. The approach is demonstrated via a dynamic simulation of a quarter vehicle model. In the proposed methodology, training information generated with a reduced number of harmonic excitations is used to construct the tire hysteretic force emulator using a Neural Network (NN) element. The proposed approach has two stages: a learning stage, followed by an embedding of the learned model into the quarter car model. The learning related main challenge stems from the attempt to capture with the NN element the behavior of a hysteretic element whose response depends on its loading history.