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One popular complement to track testing that successful race teams use to better understand their vehicle’s behavior is dynamic shaker rig testing, such as 7-post and 8-post testing. Compared to track testing, rig testing is more repeatable, costs less, and can be conducted around the clock. While rig testing certainly is an attractive option, an extensive number of tests may be required to find the best setup. To make better use of rig test time, more efficient testing methods are needed. One method to expedite rig testing is to use rig test data to perform system identification and generate a model of the experiment, which may then be applied to identify potential gains for further rig study. This study develops a system identification method for use in rig testing, using data generated from a known physical model. The results show that this method can be used to accurately predict sensor response during an 8-post test for different shock selections.

The primary purpose of this paper is to evaluate how various time-domain system identification techniques, which have been successfully used for different dynamic systems, can be applied for identifying heavy truck dynamics. System identification is the process by which a model is constructed from prior knowledge of a system and a series of experimental data. The parameters obtained from the identification process can be used for developing or improving the mathematical representation of a physical system. In contrast to lighter vehicles, heavy trucks have considerably more flexible frames. The frame can exhibit beaming dynamics in a frequency range that is within the range of interest for evaluating the ride and handling aspects of the truck. Understanding the dynamic contributions of the truck frame is essential for improving the ride characteristics of a vehicle. This understanding is also needed for designing new frame configurations for the existing or new production trucks.