The Suspension System Research Based On Vehicle Ride Comfort

Ride comfort is one of the six most important performances of the car, and the performance of the suspension system affects the vehicle ride comfort directly. The traditional passive suspension has a simple structure, low cost, and easy manufacturing etc., but it is difficult to match and optimize the parameters of the suspension system. Meanwhile, vehicle can’t always be at the best driving state because the parameters of passive suspension are constant. The performance of active suspension can achieve optimal by controlling the actuator, thereby the vehicle ride comfort is improved greatly. However, the current active suspension has some defects, such as the high cost, high energy consumption, complex control system, and so on. Therefore, it is significant to develop a new active suspension of low-cost, energy-saving and easily controlled for the application of active suspension.Combined with the special fund project of automobile industry development in Jilin Province-Research on Inertia Regulation of Car Active Suspension(20112330), this paper has do some research on the parameters optimization of car passive suspension spring and damper and hydraulic active suspension based on the vehicle ride comfort. The main contents are as follows:(1) Established the dynamic model of suspension system. This paper analyzed the random road and the whole vehicle, and built the random road model and whole vehicle model. Those models have lay the foundation of the parameters optimizing of passive suspension and the simulation verifying of active suspension.(2) Optimized the spring stiffness and damping shock absorbers of automobile passive suspension. This paper has obtained the improved culture particle swarm algorithm of "double evolution, dual promote" by combining particle swarm algorithm and cultural algorithm to ensure the vehicle ride comfort. The parameters of passive suspension were optimized using improved culture particle swarm algorithm. The body centroid vertical acceleration rms, body pitch angle rms, body inclination rms, sum of four wheels dynamic load rms of optimized parameters have respectively decreased by 18.93%, 17.81%, 18.36% and 15.97% compared to the theoretical parameters.(3) Designed neural network controller for active suspension. For active suspension control system, this paper has designed LM-BP neural network controller and improved particle swarm neural network indirect adaptive controller based on neural network and done the simulation on the seven degrees of freedom vehicle model. Simulation results showed that the body centroid vertical accelerat ion rms, body pitch angle rms, body inclination rms have respectively decreased by 36.06%, 33.62%, 29.08% and 42.52%, 39.60%, 40.59% using LM-BP neural network and improved particle swarm neural network indirect adaptive control.Simulation and experimental prototype show that the suspension parameters of spring and damper are optimized by improved culture particle swarm algorithm can improve the automobile ride comfort effectively in state of passive suspension. The active suspension performance with LM-BP neural network and neural network indirect adaptive control using improved particle swarm algorithm is better than the performance of passive suspension. It also validated the effectiveness of active suspension system, and ensured the automobile ride comfort in the state of active suspension.