Vertical Suspension Optimization for a High-Speed Train with PSO Intelligent Method

Abstract

Intelligent methods and algorithms have promoted the development of the intelligent transportation system in many ways. In the rail transportation, the vertical performance of a high-speed train suspension system has a great impact on the riding comfort of the train. Based on the intelligent optimization method of particle swarm optimization (PSO) algorithm, different inerter-spring-damper (ISD) suspension layouts are proposed for better riding comfort. A 10-degree-of-freedom (10-DOF) vertical dynamic model of a high-speed train is established, and the new suspension layouts are applied to the primary and secondary suspension of the train at the same time. Optimizations are carried out for the suspension parameters of the high-speed train. Performances of different suspension layouts at different running speeds are analysed and compared. The best layout for suspension is concluded. What is more, the virtual prototype simulation and analysis of a high-speed train with consideration of nonlinear inerters are carried out. Friction of a rack-pinion inerter is simulated in the virtual prototype simulation. And the influence of nonlinearity is discussed compared with the ideal suspensions. All the results can represent a guidance for future train suspension design and help the intelligent rail transportation system to be more comfortable.