In this paper, a mathematical model for vehicle-to-vehicle frontal crash is developed. The experimental data are taken from the National Highway Traffic Safety Administration. To model the crash scenario, the two vehicles are represented by two masses moving in opposite directions. The front structures of the vehicles are modeled by Kelvin elements, consisting of springs and dampers in parallel, and estimated as piecewise linear functions of displacements and velocities, respectively. To estimate and optimize the model parameters, a genetic algorithm approach is proposed. Finally, it is observed that the developed model can accurately reproduce the real kinematic results from the crash test.

Optimization of vehicle-to-vehicle frontal crash model based on measured data using genetic algorithm

Karimi, Hamid Reza;
2017

Abstract

In this paper, a mathematical model for vehicle-to-vehicle frontal crash is developed. The experimental data are taken from the National Highway Traffic Safety Administration. To model the crash scenario, the two vehicles are represented by two masses moving in opposite directions. The front structures of the vehicles are modeled by Kelvin elements, consisting of springs and dampers in parallel, and estimated as piecewise linear functions of displacements and velocities, respectively. To estimate and optimize the model parameters, a genetic algorithm approach is proposed. Finally, it is observed that the developed model can accurately reproduce the real kinematic results from the crash test.
IEEE ACCESS
genetic algorithm; Modeling; parameters estimation; vehicle-to-vehicle crash; Computer Science (all); Materials Science (all); Engineering (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1036430
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