چكيده به لاتين
New ideas for improving suspension performance and thus, improving the vehicle’s dynamic performance have always been of great interest to researchers. One of the effective methods that has attracted much of research, is the use of air springs. As air springs provide good ride comfort for passengers, and many auto companies use this type of spring in their production cars, therefore, a complete vehicle model with seven degrees of freedom, equipped with air suspension will be examined. The degrees of freedom of the model includes the vertical displacement of all wheels, vertical displacement of the sprung mass of the car body roll and pitch angles. Optimization parameters that have been considered in the project include orifice resistance, air spring volume, storage volume, a change in the coefficient of damping in front and rear dampers and the initial pressure of the air springs. Given that each of the air spring parameters has a direct impact on the others, these parameters are optimized in order to achieve optimal vehicle ride quality and handling. Therefore, in this thesis, the particle swarm optimization (PSO) algorithm is used to perform multi-objective optimization to improve these two factors together as much as possible. The results show that particle swarm algorithm compared with other methods of optimization works better and faster.
Keywords: Pneumatic suspension, Optimization, Particle Swarm Optimization, air spring
