Optimization of Vehicle-to-Vehicle Frontal Crash Model based on Measured Data using Genetic Algorithm
Journal article, Peer reviewed
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Original versionMunyazikwiye, B. B., Karimi, H. R. & Robbersmyr, K. G. (2017). Optimization of Vehicle-to-Vehicle Frontal Crash Model based on Measured Data using Genetic Algorithm. IEEE Access, 5, 3131-3138. doi: 10.1109/ACCESS.2017.2671357
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.