Vehicle Driveline Benchmarking to Support Predictive CAE Modeling Development

Abstract

The development of predictive models requires several assumptions along with known system properties and boundary conditions to generate a correlated model. When a prototype product is available, modal analysis can be used to benchmark the current product and extract modal properties. The extracted values are often cross referenced with FEA solutions and utilized to feed forward into CAE models for data replication and future prediction. This study was used to perform modal testing on a full sized pickup truck driveline to build a one-dimensional lumped parameter model. The successful extraction of modal parameters was able to provide benchmark stiffness and damping estimates for use in CAE model updating to achieve better correlation with experimental vehicle data. The resulting lumped parameter changes reduce the number of model assumptions and allow for modification of stiffness design targets for new prototype driveshafts and/or additional driveline components.