Search

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. [ABSTRACT FROM AUTHOR]

The friction coefficient at the wheel–rail interface is crucial for the traction, braking and guidance of the railway vehicle. Real-time knowledge of the friction coefficient could reduce wheel–rail damages as well as improve the vehicle running performance. The friction coefficient is very difficult to be directly measured; hence an indirect measurement method using an unscented Kalman filter was proposed in this paper. A re-adhesion controller was also developed. The method was assessed in a Simpack-Simulink co-simulation environment. The friction coefficient estimation method was found to be accurate, and the re-adhesion controller improved the vehicle braking performance as well as reduced the wheel–rail damage. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]