Optimization and Analysis of the Quarter Car Passive Suspension Using Taguchi, Genetic Algorithm, and Simulated Annealing Approaches

Abstract

The suspension system design has been one of the most challenging tasks for engineers due to the importance of its role in reducing the road vibrations transmitted to the vehicle, which have a harmful effect on the human body. This research aims to use and compare different optimization techniques used to design the passive suspension parameters, including the spring stiffness (Ks), damping coefficient (Cs), and tire stiffness (Kt), to minimize body accelerations and subsequently enhance ride comfort for vehicles. The quarter car is modelled as a two-degrees-of-freedom system by using MATLAB/Simulink. Different optimization techniques were introduced and used, such as Taguchi, Genetic Algorithms (GA), and Simulated Annealing (SA), to design the passive suspension parameters. The results showed that the optimal design parameters for suspension systems were obtained using GA and SA methods, which reduced the value of the root mean square of vertical vibration by approximately 44% and the peak of acceleration by approximately 60% compared to the original values. The Taguchi approach reduces the value of the root mean square by approximately 32% and the peak of the acceleration by approximately 28% compared to the original values.