| This research attempts to improve automobile suspension systems by using nonlinear dashpot through dynamic analyses and optimization designs. The methodology presented here is to investigate the optimization process of a passive nonlinear automobile suspension system model. This investigation includes the analyses of a quarter car, a half car, and a full car model. The objective of this research is to produce a quantifiable application process in selecting the best optimal suspension set-up for a frequency selective damper used for road vehicles. Our goal is to use Genetic Algorithm (GA) as our optimization process as it is already relevant in today's research studies. The follow through for this research project is to use a more realistic approach in simulating the road excitations by applying a power spectral density method and using experimental data of different road profiles to generate the perspective road excitations. A conversion of the vehicle suspension setup is applied by converting the damper and spring to nonlinear in their behavior. This allows simulation of a more representative reaction of vehicles suspension system model. Our objective is to use ISO2631 standards to improve the passenger root mean square (rms) acceleration while improving vertical acceleration, suspension travel, jerk, pitch motion, and roll motion. In order to achieve these objectives applied some optimization constraints were applied. Validation of our design process is handled by comparing and contrasting the different responses of each model using Matlab numerical analysis and Simulink Simulation analysis. Preliminary results show that by using specially design nonlinear damping and stiffness of automobile suspension system can have notable improvements in reducing the rms vibration levels of an automobile. |
