Data-based modeling and estimation of vehicle crash processes in frontal fixed-barrier crashes

As a complex process, vehicle crash is challenging to be described and estimated mathematically. Although different mathematical models are developed, it is still difficult to balance the complexity of models and the performance of estimation. The aim of this work is to propose a novel scheme to model and estimate the processes of vehicle-barrier frontal crashes. In this work, a piecewise model structure is predefined to represent the accelerations of vehicle in frontal crashes. Each segment in the model is corresponding to the energy absorbing component in the crashworthiness structure. With the help of Ensemble Empirical Mode Decomposition (EEMD), a robust scheme is proposed for parameter identification. By adjusting the model structure and parameters according to the initial velocity, crash processes in different conditions are estimated effectively. The estimation results exhibit good agreement with finite element (FE) simulations in three different cases. It is shown that, the proposed model keeps low complexity. Furthermore, the structure information of vehicle is involved in improving the accuracy and ability of crash estimation.