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

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