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.

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 ALTinduced 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.

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.

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 practicalvalue, especially under variable and non-uniform rail conditions.

This study aims to implement condition monitoring for urban rail train permanent magnet synchronous motors and inverter systems. Through the construction of a digital twin model, it performs fault diagnosis of potential system failures, enabling rapid fault localization and protection.

This research begins with a brief introduction to the structure and classification of permanent magnet synchronous motors (PMSMs), followed by a detailed analysis of their mathematical model. Subsequently, it thoroughly investigates the working principle of three-phase two-level inverters and the distribution of space voltage vectors. Based on the analysis of the main circuit topology, a digital twin model matching the external characteristics of the physical circuit is established using the model predictive control method, achieving accurate system simulation. Furthermore, through theoretical analysis and simulation verification of phase current characteristics under inverter switch tube faults, general patterns of phase currents under fault conditions are summarized. The established digital twin model is then employed to validate these patterns, confirming the model’s effectiveness in fault diagnosis.

This study proposes a fault diagnosis method based on digital twins. Experimental and simulation results demonstrate that the established digital twin model can accurately simulate the external characteristics of the actual physical circuit, validating its effectiveness in inverter fault diagnosis. This approach offers practical value for condition monitoring in actual urban rail train systems.

The study innovatively starts from a mathematical model and simulates the actual physical model through a virtual model, requiring only external characteristics to achieve system fault diagnosis, thereby enhancing diagnostic efficiency.

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.

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.

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.

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.

This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.

In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton’s law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.

In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.

The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.