Application of Time-Domain Identification Techniques for Evaluating Heavy Truck Dynamics

Abstract

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. The tools for such evaluations, however, are often limited to grossly estimating the frame dynamics in a model or resorting to an experimental evaluation of the entire truck. Although such methods work well in most cases, they stand to be expensive and limited to vehicles that have a physical existence. System identification methods would allow one to determine the parameters of a component of a vehicle without its actual installation in the vehicle. The parameters, then, can be used in models of the vehicle, for evaluating the dynamic response of the vehicle with the component-such as the truck frame-installed in it. The results documented in this paper will show that system identification methods can be applied effectively for heavy truck frame parameter estimation. A series of laboratory tests are performed on a Class 8 truck and their results are used for performing system identification on the frame dynamics, which results in truck frame mass, stiffness, and damping parameters. Applying various parametric and non-parametric system identification methods, which have been suggested in the past for other dynamic systems, to this specific application allows us to determine the methods that work best for trucks.