Project 240 - Research on the Dynamics of Drifting

Recent developments in drift research was presented at the 2006 SAE World Congress in Detroit, MI, April 3-6, 2006. The paper summarizes the variations in tire force and moment equilibrium at various drifting angles.

SAE Paper 2006-01-1019 (click to download PDF manuscript)

Abstract
Driving at large angles of sideslip does not necessarily indicate terminal loss of control, rather, it is the fundamental objective of the sport of drifting. Drift racing challenges drivers to navigate a course in a sustained sideslip by exploiting coupled nonlinearities in the tire force response. The current study explores some of the physical parameters affecting drift motion, both in simulation and experiment. Combined-slip tire models are used to develop nonlinear models of a drifting vehicle in order to illustrate the conditions necessary for stability. Experimental drift testing is conducted to observe the dynamics featured in the track data. An accelerometer array on the test vehicle measures the acceleration vector field in order to estimate the vehicle states throughout the drift testing. Neural networks are used to identify the patterns in the accelerations that correspond to sideslip excursions during drifts. These estimates combined with computations of angular acceleration, yaw rate, and lateral acceleration build a framework for identifying the dynamics in terms of physical parameters and stability and control derivatives. The research developments are intended to support a future study quantifying the effects of vehicle configuration changes on drift capability as related to performance potential and handling qualities.





						Project_240 is an experimental study of the drifting characteristics of my Nissan 240SX. The research has been ongoing since early 2005, with significant progress in instrumentation, experimental approach, and post-processed visualization. The main focus of the research is studying the use of distributed inertial sensing combined with pattern recognition techniques to adequately describe the vehicle motion during drift maneuvers. The problem is non-trivial, considering that drifting involves highly nonlinear relationships between tire force, sideslip, and longitudinal slip. Thus, the simple linear models that may be used to describe cars incurring only small amounts of drift cannot express the motion that we know and love.