Cooperative traction control of multi-motor electric locomotives considering axle load transfer

Abstract

Purpose-This study aims to propose a cooperative adhesion control method for multi-motor electric locomotives that explicitly considers axle load transfer (ALT). The method is intended to optimize the output torque of each motor, maximize the utilization of available wheel-rail adhesion within the total torque command, mitigate wheel skidding and sliding phenomena, and achieve optimal torque allocation across all axles.

Design/methodology/approach-An advanced cooperative maximum adhesion tracking control strategy is developed using Model Predictive Control (MPC). First, a comprehensive multi-agent dynamic model of the locomotive traction system is constructed based on Newton’s second law, which incorporates longitudinal train dynamics, individual axle rotational dynamics, nonlinear wheel-rail adhesion characteristics, and dynamic ALT-induced load redistribution. Then, a novel MPC-based multi-axle co-optimization method is presented. This controller calculates the optimal output torque through real-time iteration based on a reference slip speed, ensuring coordinated torque allocation under strict physical constraints imposed by the traction control unit.

Findings-Simulation studies conducted under dry, wet, and mixed rail surface conditions indicate that the proposed MPC system effectively compensates for ALT. The results demonstrate that explicitly embedding ALT into the control framework allows the system to adaptively redistribute motor torques according to real-time axle loads. This guarantees stable slip regulation and significantly improves overall traction performance and power distribution compared to conventional strategies that ignore ALT.

Originality/value-This study introduces a novel cooperative adhesion tracking control scheme that uniquely integrates axle load transfer into a multi-agent MPC for multi-motor electric locomotives-a complex configuration rarely addressed in previous papers. This approach resolves the critical issues of torque imbalance, lightly loaded axle slip, and heavily loaded axle under-utilization, offering significant theoretical and practical value, especially under variable and non-uniform rail conditions.

Keywords

Cooperative control / Multi-motor electric locomotive / Axle load transfer / Model predictive control

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