Development and Implementation of a Warp Chassis Model 2011-01-2172
Vehicle dynamics models often assume that the suspension attachment locations on the chassis are fixed and do not deform. However, this is not always a good assumption as the chassis can deform causing the suspension-to-chassis attachment locations to move in response to the forces transmitted through the suspension. When the suspension attachment points move, the kinematic and compliance properties of the suspensions can be affected. This can create a feedback situation where the chassis deformation affects the suspension response which affects the chassis loading.
When developing a vehicle dynamics model, the chassis is usually modeled as either a rigid member, as a series of lumped masses, or as a multi-body structure. The progression from rigid body to lumped mass to multi-body chassis models generally improves the resulting simulations as the estimation of suspension behavior improves. However, the increases in the quality of the simulations require additional chassis data and the simulation costs increase with the complexity of the chassis model.
The warp model is a fourth chassis modeling option where the vehicle chassis is modeled as a pair of parallel frames with a cross member that can deform torsionally. The single degree of freedom in the model allows one frame rail to rotate relative to the other such that the deformation shape resembles a helix albeit with minimal twist. This results in a chassis deformation shape that is similar to the deformations seen in an actual vehicle chassis. This modeling approach is relatively inexpensive and can be reasonably accurate in describing the true chassis deformation. The location of the cross member in the model is not constrained and there need not be a physical cross member at the location of the modeled cross member.
In this paper a new procedure for developing the warp model and its implementation in a vehicle dynamics simulation is outlined. The warp model development presented here differs from prior methods in an attempt to better show the requirements for implementing the model.