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Load data representing severe customer usage is required during the chassis development process. One area of current research is the use of road profiles for predicting chassis loads. The most direct method of predicting these loads is to run dynamic simulations of the vehicle using numerous road profiles as the excitation. This onerous task may be avoided, and a greatly reduced number of simulations would be required, if roads having similar characteristics can be grouped. Currently, road profiles are characterized by their spectral content. It has been noted by several researches, however, that road profiles are generally nonstationary signals that contain significant transient events and are not well described in the spectral domain. The objective of this work, then, is to develop a method by which the characteristics of the road can be captured by describing these constitutive transient events.

A high fidelity seat suspension model, which can be used for ride quality predictions, is developed in this work. The coil-spring seat suspension model includes unique nonlinear forms for the stiffness and damping characteristics. This is the first paper to consider the nonlinear geometric effects of the suspension, derive the coil-spring suspension model from physical principles, and compare theoretical and experimental results. A simplified nonlinear form is achieved via an admissible function describing the vertical suspension deflection as a function of the lateral position. This simplified nonlinear form is compared to experimental data and demonstrated to have exceptional fidelity.