Multiobjective Optimal Design of Vehicle Suspension Parameters Based on Reliable Gray Particle Swarm Optimization

Abstract

This study presents a multiobjective optimal design of automobile suspension systems to improve vehicle-ride comfort and reduce tire-induced dynamic excitations on road surface simultaneously. In the optimal model, spring stiffness and the damper coefficient are considered design variables, whereas the maximum deflection of the suspension system is regarded as a constraint. Meanwhile, the root-mean-square values of the vertical acceleration of the vehicle body and the dynamic loadings of the front and rear tires are treated as objective functions. Multiobjective optimization is implemented using the gray particle swarm algorithm. Globally optimal solutions are obtained by introducing the variance of relevant sequence numbers into gray relevant theory. A half-car model is used to illustrate the proposed optimal model and solution method. Results show that the minimum acceleration of the vehicle body and the minimum dynamic loads exerted by tires on road surfaces can be achieved through the proposed multiobjective optimal design.