6533b7d4fe1ef96bd1262a4a

RESEARCH PRODUCT

Optimization of Vehicle-to-Vehicle Frontal Crash Model Based on Measured Data Using Genetic Algorithm

Bernard B. MunyazikwiyeHamid Reza KarimiKjell G. Robbersmyr

subject

0209 industrial biotechnologyGeneral Computer ScienceComputer scienceCrash02 engineering and technologyVehicle-to-vehicleDamperComputer Science::RoboticsEngineering (all)020901 industrial engineering & automation0203 mechanical engineeringControl theoryparameters estimationGenetic algorithmgenetic algorithmGeneral Materials ScienceSimulationvehicle-to-vehicle crashComputer Science (all)ModelingGeneral EngineeringCrash test020303 mechanical engineering & transportsMaterials Science (all)lcsh:Electrical engineering. Electronics. Nuclear engineeringgenetic algorithm; Modeling; parameters estimation; vehicle-to-vehicle crash; Computer Science (all); Materials Science (all); Engineering (all)lcsh:TK1-9971

description

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. nivå1

yearjournalcountryeditionlanguage
2017-01-01IEEE Access
https://doi.org/10.1109/access.2017.2671357