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Cooperative maximum adhesion tracking control for multi-motor electric locomotives
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Leiting Zhao, Yongxiang Wang, Kan Liu, Liran Li, Jingyuan Zhan, Qingliang Liu
Railway Sciences | 2025, 4(1) : 22 - 36
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Railway Sciences | 2025, 4(1): 22-36
Research paper
Cooperative maximum adhesion tracking control for multi-motor electric locomotives
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Leiting Zhao, Yongxiang Wang, Kan Liu, Liran Li, Jingyuan Zhan, Qingliang Liu
Affiliations
  • AC Transmission Department, China Academy of Railway Sciences Corporation Limited, Beijing, China
  • Locomotive and Car Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China
  • AC Transmission Department, China Academy of Railway Sciences Corporation Limited, Beijing, China
  • School of Information Science and Technology, Beijing University of Technology, Beijing, China
Published: 2025-02-10 doi: 10.1108/RS-11-2024-0049
Outline
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Purpose

This study aims to propose a cooperative adhesion control method for trains with multiple motors electric locomotives. The method is intended to optimize the output torque of each motor, maximize the utilization of train adhesion within the total torque command, reduce the train skidding/sliding phenomenon and achieve optimal adhesion utilization for each axle, thus realizing the optimal allocation of the multi-motor electric locomotives.

Design/methodology/approach

In this study, a model predictive control (MPC)-based cooperative maximum adhesion tracking control method for multi-motor electric locomotives is presented. Firstly, train traction system with multiple motors is constructed in accordance with Newton's second law. These equations include the train dynamics equations, the axle dynamics equations, and the wheel-rail adhesion coefficient equations. Then, a new MPC-based multi-axle adhesion co-optimization method is put forward. This method calculates the optimal output torque through real-time iteration based on the known reference slip speed to achieve multi-axle co-optimization under different circumstances.

Findings

This paper presents a MPC system designed for the cooperative control of multi-axle adhesion. The results indicate that the proposed control system is able to optimize the adhesion of multiple axles under numerous different conditions and achieve the optimal power distribution based on the reduction of train skidding/sliding.

Originality/value

This study presents a novel cooperative adhesion tracking control scheme. It is designed for multi-motor electric locomotives, which has rarely been studied before. And simulations are carried out in different conditions, including variable surfaces and motor failing.

Cooperative control  /  Adhesion control  /  Model predictive control  /  Slip prevent
Leiting Zhao, Yongxiang Wang, Kan Liu, Liran Li, Jingyuan Zhan, Qingliang Liu. Cooperative maximum adhesion tracking control for multi-motor electric locomotives[J]. Railway Sciences, 2025 , 4 (1) : 22 -36 . DOI: 10.1108/RS-11-2024-0049
  • Scientific Research Projects of China Association of Metros(CAMET-KY-2022039)
  • State Key Laboratory of Traction and Control System of EMU and Locomotive(2023YJ386)
Year 2025 volume 4 Issue 1
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Article Info
doi: 10.1108/RS-11-2024-0049
  • Receive Date:2024-11-25
  • Online Date:2026-06-11
  • Published:2025-02-10
Article Data
Affiliations
History
  • Received:2024-11-25
  • Revised:2024-11-27
  • Accepted:2024-11-27
Funding
Scientific Research Projects of China Association of Metros(CAMET-KY-2022039)
State Key Laboratory of Traction and Control System of EMU and Locomotive(2023YJ386)
Affiliations
    AC Transmission Department, China Academy of Railway Sciences Corporation Limited, Beijing, China
    Locomotive and Car Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China
    AC Transmission Department, China Academy of Railway Sciences Corporation Limited, Beijing, China
    School of Information Science and Technology, Beijing University of Technology, Beijing, China

Corresponding:

Kan Liu can be contacted at:
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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