Rail surface identification and adhesion control based on dynamic adhesion characteristics
Abstract Railroad transportation is an integral part of the transportation sector, especially high-speed railroad, which is a key link in the railroad system. In particular, high-speed railways serve as a crucial component of the railway system, and significant achievements have been made in their c...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
|
| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-07218-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849685912563220480 |
|---|---|
| author | Wen Liu Hongfeng Qi Jingchun Huang Yiyuan Chen Sheng He Haoxiang Feng |
| author_facet | Wen Liu Hongfeng Qi Jingchun Huang Yiyuan Chen Sheng He Haoxiang Feng |
| author_sort | Wen Liu |
| collection | DOAJ |
| description | Abstract Railroad transportation is an integral part of the transportation sector, especially high-speed railroad, which is a key link in the railroad system. In particular, high-speed railways serve as a crucial component of the railway system, and significant achievements have been made in their construction and operation in recent years. However, as the speed of high-speed trains continues to increase, the use of wheel-rail adhesion is also being challenged. Therefore, to achieve stable tracking of the optimal adhesion state while adopting the feedback dynamic adhesion characteristic model, this study utilizes a machine learning algorithm to perform classification training on discrete points of the simulated adhesion characteristic model under various rail conditions. Subsequently, the trained model is integrated into the vehicle simulation model for real-time identification of current rail surface conditions, and the output value from this module is fed into the subsequent optimal adhesion search algorithm. By dynamically adjusting the initial search step size and its change coefficient, in conjunction with torque controller action, precise operation of the vehicle at the optimal adhesion point can be achieved. Finally, validation is conducted using a semi-physical simulation platform based on ModelinTech real-time simulator. |
| format | Article |
| id | doaj-art-5f3e4bb4fca84862b17a55965fa1766e |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-5f3e4bb4fca84862b17a55965fa1766e2025-08-20T03:22:54ZengSpringerDiscover Applied Sciences3004-92612025-06-017711810.1007/s42452-025-07218-4Rail surface identification and adhesion control based on dynamic adhesion characteristicsWen Liu0Hongfeng Qi1Jingchun Huang2Yiyuan Chen3Sheng He4Haoxiang Feng5Technology Research Department, CRRC Industrial Institute Corporation LIMITEDTechnology Research Department, CRRC Industrial Institute Corporation LIMITEDSchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversityAbstract Railroad transportation is an integral part of the transportation sector, especially high-speed railroad, which is a key link in the railroad system. In particular, high-speed railways serve as a crucial component of the railway system, and significant achievements have been made in their construction and operation in recent years. However, as the speed of high-speed trains continues to increase, the use of wheel-rail adhesion is also being challenged. Therefore, to achieve stable tracking of the optimal adhesion state while adopting the feedback dynamic adhesion characteristic model, this study utilizes a machine learning algorithm to perform classification training on discrete points of the simulated adhesion characteristic model under various rail conditions. Subsequently, the trained model is integrated into the vehicle simulation model for real-time identification of current rail surface conditions, and the output value from this module is fed into the subsequent optimal adhesion search algorithm. By dynamically adjusting the initial search step size and its change coefficient, in conjunction with torque controller action, precise operation of the vehicle at the optimal adhesion point can be achieved. Finally, validation is conducted using a semi-physical simulation platform based on ModelinTech real-time simulator.https://doi.org/10.1007/s42452-025-07218-4Adhesion controlDynamic adhesion characteristic modelLongitudinal dynamicsTrack identification |
| spellingShingle | Wen Liu Hongfeng Qi Jingchun Huang Yiyuan Chen Sheng He Haoxiang Feng Rail surface identification and adhesion control based on dynamic adhesion characteristics Discover Applied Sciences Adhesion control Dynamic adhesion characteristic model Longitudinal dynamics Track identification |
| title | Rail surface identification and adhesion control based on dynamic adhesion characteristics |
| title_full | Rail surface identification and adhesion control based on dynamic adhesion characteristics |
| title_fullStr | Rail surface identification and adhesion control based on dynamic adhesion characteristics |
| title_full_unstemmed | Rail surface identification and adhesion control based on dynamic adhesion characteristics |
| title_short | Rail surface identification and adhesion control based on dynamic adhesion characteristics |
| title_sort | rail surface identification and adhesion control based on dynamic adhesion characteristics |
| topic | Adhesion control Dynamic adhesion characteristic model Longitudinal dynamics Track identification |
| url | https://doi.org/10.1007/s42452-025-07218-4 |
| work_keys_str_mv | AT wenliu railsurfaceidentificationandadhesioncontrolbasedondynamicadhesioncharacteristics AT hongfengqi railsurfaceidentificationandadhesioncontrolbasedondynamicadhesioncharacteristics AT jingchunhuang railsurfaceidentificationandadhesioncontrolbasedondynamicadhesioncharacteristics AT yiyuanchen railsurfaceidentificationandadhesioncontrolbasedondynamicadhesioncharacteristics AT shenghe railsurfaceidentificationandadhesioncontrolbasedondynamicadhesioncharacteristics AT haoxiangfeng railsurfaceidentificationandadhesioncontrolbasedondynamicadhesioncharacteristics |