Reduced-Order Modeling in Nonlinear Driveline Systems

Abstract

This thesis presents and investigates different approaches for constructing reducedorder BEV driveline models in state-space form using a data-driven approach. Existing Multi-Body Dynamic (MBD) models of the full driveline are often computationally demanding, while reduced models of sub-parts may lack a clear connection to the full-scale model and to one another. In order to improve simulation times while maintaining consistency with the more general MBD model, local Linear Time-Invariant (LTI) models are identified around selected operating points using subspace system identification methods and data generated from the larger MBD model. However, system internal periodic disturbances related to driveline rotation complicate the system identification as it contaminates the externally forced response. Two methods addressing this are investigated: Removing the frequency components associated with these disturbances from the data, or augmenting the model with additional input signals with those frequencies. Both methods improve the model quality by reducing the effect of the periodic disturbances. The model constructed by including additional input signals is also shown to more accurately reproduce the amplitude and frequency of the periodic disturbance. In order to model gyroscopic effects, such as resonance frequencies depending on the operating speed, several local LTI models are identified at different operating points. The local models are combined by either linear interpolation of the matrix elements, fitting a basis function to the matrix elements or by interpolating the outputs of each model. All three methods result in models with similar input-output accuracy, but differ in their requirements when creating the local models. In particular, methods based on output interpolation do not require system internal consistency between local models, unlike methods that rely on the local models sharing a common state-space realization. The disturbance-modeling approach based on additional input signals is also extended to the Linear Parametric Variant (LPV) framework, enabling the resulting models to capture both the speed-dependent system dynamics and the periodic disturbances associated with driveline rotation.