Suspension parameter optimal design to enhance stability and wheel wear in high-speed trains

Abstract

Ensuring running stability and minimising wheel tread wear are critical factors for enhancing the dynamic performance of high-speed trains. As an efficient method for improving vehicle performances, previous studies on suspension parameter optimisation have not fully considered these two factors. It is essential to pay attention to stability at extreme wheel-rail contact states, and the wheel wear evolution needs to be revealed instead of investigating the wear performance only through an index like wear number. This research aims to investigate the stability/wear Pareto optimisation of bogie suspension parameters for a high-speed passenger train. Stability-related indices are defined as the lateral ride comfort index at low equivalent conicity and the bogie frame acceleration at high conicity. To represent the long-term wheel wear performance, a contact spreading index is proposed and combined with the wear number index. The key suspension parameters are optimised through the genetic algorithm NSGA-II. The obtained Pareto front clearly reveals the relationship between the objective functions. To meet different performance requirements, four distinct matching rules of the suspension parameters are summarised by employing clustering analysis. Finally, the applicability of the parameter matching rules is examined by analysing stability at different equivalent conicities and wheel wear evolution.

KEYWORDS:

• Suspension parameter design
• running stability
• wear evolution
• high-speed train
• Pareto optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The material in this paper is based on work supported by the National Natural Science Foundation of China [grant number U2268211], the Natural Science Foundation of Sichuan Province [grant numbers 2022NSFSC0034; 2022NSFSC1901], and Independent Research and Development Project from State Key Laboratory of Traction Power [grant number [2022TPL_Q02].