Re-adhesion control for high-speed electric railway with parallel motor control system

Hwang, Don-Ha; Kim, Moon-Sup; Park, Doh-Young; Kim, Yong Joo; Kim, Dong‐Hee

doi:10.1109/isie.2001.931635

Cited by 5 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rapid development of electric traction control, several adhesion utilization control algorithms have been proposed to improve the adhesion utilization performance of railway vehicle in recent years, such as re-adhesion control [7,8] and slip control [6,[9][10][11][12]. The former limits the slip velocity to an acceptable value after the slip phenomenon has already occurred, while the latter controls the slip velocity in a proper range around the maximum value of the adhesion-slip characteristic.…”

Section: Introductionmentioning

confidence: 99%

Research on the Identification of Nonlinear Wheel–Rail Adhesion Characteristics Model Parameters in Electric Traction System Based on the Improved TLBO Algorithm

Gan,

Zhao,

Wei

et al. 2024

Electronics

View full text Add to dashboard Cite

The wheel–rail adhesion is one of the key factors limiting the traction performance of railway vehicles. To meet the adhesion optimization needs and rapidly obtain wheel–rail creep characteristics under specific operating conditions, an engineering identification method for wheel–rail adhesion characteristics based on a nonlinear model is proposed. The proposed method, built upon the traditional Teaching-Learning-Based Optimization (TLBO) algorithm, has been adapted to the specific nature of nonlinear wheel–rail adhesion model parameters identification, enhancing both the search speed in the early stages and the search accuracy in the later stages of the algorithm. The proposed identification algorithm is validated using experimental data from the South African 22E dual-flow locomotive. The validation results demonstrate that the proposed identification algorithm can obtain a nonlinear wheel–rail adhesion characteristics model with an average adhesion coefficient error of around 0.01 within 50 iteration steps. These validation results indicate promising prospects for the engineering practice of the proposed algorithm.

show abstract