Model reduction in vehicle dynamics using importance analysis

Previous work by the authors developed a novel model reduction method, namely, importance analysis, that offered a unique set of properties: concurrent dynamic and kinematic reduction, applicability to nonlinear systems, preservation of realization, and trajectory dependence. This paper investigates the utility of importance analysis as a model reduction tool within the context of vehicle dynamics. To this end, a high-fidelity model of a High Mobility Multipurpose Wheeled Vehicle (HMMWV) is considered, and this model is reduced for three different scenarios. Reduction is achieved in both dynamics and kinematics while preserving the original definition and interpretation of state variables and parameters. Furthermore, the resulting reduced models are very different in terms of complexity, containing only what is necessary for their respective scenarios, and providing important insight and computational savings. The conclusion is that importance analysis can be an invaluable reduction tool in vehicle dynamics, offering the aforementioned unique set of properties.