Author(s)

Shuai Qian¹, Lingshuang Kong^2*, Jing He³

¹College of Railway Transportation, Hunan University of Technology, Zhuzhou, China.
²College of Electronic Information and Electrical Engineering, Changsha University, Changsha, China.
³College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, China.

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

Heavy-Duty Train, Kiencke Model, Quadratic Programming, Time-Varying Forgetting Factor, Granger Causality Test

Qian, S. , Kong, L. and He, J. (2024) Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains. Journal of Transportation Technologies, 14, 53-63. doi: 10.4236/jtts.2024.141004.