Damage Prediction and Crashworthiness Optimization of FOBEVs in Positive Crashes for Battery Electric Vehicles

The Mobile Progressive Deformable Barrier (MPDB) is a standardized automotive crash scenario that comprehensively evaluates the safety of battery-electric vehicles (BEVs) in a crash. In an accident, the deformation pattern of the Front of Battery Electric Vehicle (FOBEV) structure, the efficiency of energy absorption, the acceleration pulse, and the degree of intrusion into the passenger compartment combine to affect the safety of the driver and passengers. In order to simulate and calculate the damage state of FOBEV in MPDB more efficiently and to construct a collision damage dataset in the entire velocity domain, a FOBEV equivalent model is proposed. The acceleration pulses from numerical simulations and impact tests were compared to verify the model’s validity. On this basis, the prediction accuracies of the Support Vector Machine model (SVM), Gaussian Process Regression model (GPR), and BP neural network model (BP) in FOBEV collision events are compared and analyzed, and BP is taken as the most suitable model and further improved. Taking a BEV under development as an example, the application of the accident damage prediction method based on the FOBEV equivalent model in the optimal design of BEV crashworthiness is illustrated. The results show that the constructed FOBEV equivalent model exhibits high consistency in the impact test. The accuracy of the improved Tent-SSA BP model increased by 34.85%. The neural network prediction technique with multiple input parameters is used to study the crash damage of FOBEVs over the entire speed range, revealing the relationship between the parameters of FOBEVs on the crashworthiness of BEVs in highly nonlinearly varying crashes.