Research on multi-objective optimization design method for automobile suspension based on distributed algorithm

Abstract

With the rapid development of the automobile industry, the ride comfort and maneuverability of automobiles in the process of driving have been paid more and more attention. In order to solve the problem of mutual coupling of automobile suspension smoothness and maneuvering stability, the research constructed a multi-objective optimization model of automobile suspension. The model takes the automobile suspension smoothness and handling stability as the objective function, and the particle swarm algorithm is designed in a distributed manner to obtain a distributed discrete particle swarm algorithm to solve the multi-objective optimization model. After adopting this multi-objective optimization method, the root mean square value of the acceleration in the vertical direction of the body and the maximum body roll angle when the car is steering and driving are reduced by 4.65% and 9.27%, which effectively improves the smoothness of the vehicle and the stability of the high-speed vehicle maneuvering, and provides a reference opinion for the tuning and matching of the suspension stiffness, the stiffness of the transverse stabilizer bar and the damping, and provides a certain basis for the designer to choose the appropriate suspension parameters.