Suspension parameters matching of high-speed locomotive based on stability/comfort Pareto optimization

Abstract

Suspension parameters optimisation and matching is a very important topic in the design of railway vehicles, and a reasonable combination of suspension parameters is the most powerful guarantee for safe and stable operation. However, most of the existing research about that still belongs to single-objective optimisation and pays little attention to the matching relationship between different suspension parameters, which has plenty of limitations. Therefore, the lateral stability and ride comfort of a certain type of domestic high-speed locomotive are studied and optimised through the genetic algorithm NSGA-II, to find the optimal lateral dynamic performance. It can be found that there is an obvious contradiction between linear stabilities under low-conicity and high-conicity conditions, but a strong positive correlation between low-conicity stability and lateral ride comfort in a low-conicity condition. Besides, the matching relation of the six key suspension parameters is explored employing data analysis methods, such as global sensitivity and clustering analysis, and three types of parameter combinations are proposed. In addition, the speed robustness and rail cant robustness regarding the three types of parameter combinations are analyzed, and the lateral dynamic performance characteristics of each parameter combination type are summarised.