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Measured vehicle loads, taken during durability events, are commonly used to drive in-lab vehicle subsystem validation testing. The use of measured loads can be problematic due to (a) off-nominal characteristics of the test vehicle, (b) post-test changes to vehicle tuning - bushings, springs, and shocks for example, (c) scheduling, timing and weather requirements, (d) modification of vehicle characteristics by the inclusion of transducers and (e) the cost of executing tests. A general process for supplementing and rationalizing measured vehicle data through the use of correlated multi-body dynamic simulations is presented. Difficulties in modeling tires and other components, as well as difficulties in model correlation for abusive load events are also discussed.

Automotive engineering development processes are growing more dependent on the use of multi-body dynamic (MBD) models for generating vehicle loads that at one time could only be measured using physical hardware. A certain technique combines these two approaches using a minimal set of physical measurements to excite a vehicle MBD model for predicting loads at various vehicle interfaces. This approach eliminates the use of a tire model, often the roadblock in MBD-based loads prediction simulations. However, for various reasons, the direct application of loads to a model can lead to problems with the simulation. Alternatively, the model can be artificially constrained but this also has its disadvantages. The purpose of this paper is to present a loads prediction technique that relaxes the use of artificial boundary conditions for applications involving the input of measurements to an MBD model.