Multi-fidelity surrogate-based optimal design of road vehicle suspension systems

Ride comfort is a relevant performance for road vehicles. The suspension system can filter vibration caused by the uneven road to improve ride comfort. Optimization of the road vehicle suspension system has been extensively studied. As detailed models require significant computational effort, it becomes increasingly important to develop an efficient optimization framework. In this work, a multi-fidelity surrogate-based optimization framework based on the Approximate Normal Constraint method and Extended Kernel Regression surrogate modeling method is proposed and applied. An analytical model and a multi-body model of the suspension system are used as the low-fidelity and high-fidelity models, respectively. Compared with other well-known methods, the proposed method can provide good accuracy and high efficiency. In addition, the proposed method is applied to different types of vehicle suspension optimization problems and shows good robustness and efficiency.