Integrated optimization for mechanical elastic wheel and suspension based on an improved artificial fish swarm algorithm

Abstract

This paper aims to provide a baseline for the future optimum design of the Mechanical Elastic Wheel (MEW), and improve the ride comfort of the vehicle equipped with the MEW. A new factor δ is introduced, which is the ratio of the length of hinge groups to the thickness of elastic wheel. The influence of δ on the MEW radial stiffness is studied based on finite element analyses and experimental verification. The stiffness characteristics with δ are fitted by means of artificial neural network. A quarter-car model is established, combining the MEW and the hydro-pneumatic suspension. A multi-objective optimization is proposed, where an improved Pareto Artificial Fish Swarm Algorithm is applied to attain the appropriate parameters of both suspension and MEW. The results after optimization illustrate that the root mean square value of body vertical acceleration, suspension working space and dynamic tyre load are reduced by 43.88%, 24.38% and 46.46% respectively. In addition, the power spectral densities of three indexes are all dropped. This method proposed by this paper not only offers an optimal matching between the MEW and the hydro-pneumatic suspension, but also has the reference value for improving vehicle ride comfort.