Ride Optimization of Van Truck Based on Genetic Algorithm

doi:10.3901/JME.2017.20.121

Abstract

Abstract: The multi-body vehicle dynamics model of a is truck is built in RecurDyn. By using discrete beam method, the multi-body dynamics model of leaf spring in vehicle model is established. The contact and friction force between the interacting spring leafs has been effectively simulates by defining the user subroutine translational forces. Based on meta-model optimization algorithm, the parameters of the two leaf spring dynamics model can be effectively identified. Based on AR model, a road roughness reconstruction application program is developed which can output the road file to be used in RecurDyn. The ride comfort of the van transporter physical prototype is tested. Data of the simulation and the experiment are analyzed to verify correctness of full vehicle model. DOE method is used to analyze the influence of suspension parameters and cab suspension parameters on the ride comfort, which provides the basis for the selection of variable scope. A method to optimize the ride comfort of vehicle model is proposed. In this method, Genetic algorithm is realized by integrating the AForge.Genetic component with ProcessNet and using batch mode. After optimization, the total weighted RMS acceleration value which tested on the floor of driver's foot decreased by 9.35% and the ride comfort of the van is improved.

Key words: genetic algorithm, multi-body dynamics, parameter identification, ride comfort, van truck

CLC Number:

U463

LU Jianhui, ZHOU Kongkang, HOU Yongtao, YU Kai, JI Jiaqi. Ride Optimization of Van Truck Based on Genetic Algorithm[J]. Journal of Mechanical Engineering, 2017, 53(20): 121-130.

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