The transient thermal performance of rolling bearings affects the mechanical performance and system safety of traction motors. Most of the traditional empirical formulas used in temperature analysis have been simplified and cannot be completely applied to the calculation of heat generation and convection heat transfer coefficients. Based on the comparative analysis of finite element transient temperature and experimental data, this paper proposes a correction method of mathematical model and derives an accurate calculation formula for the heat generation and lubricant convection heat transfer coefficient of ball bearings applicable for the non-driving end in the traction motor of a high-speed EMU (Electric Multiple Unit). The accuracy of the results has been verified by durability experiment data. In addition, with changes in speed, radial load and other factors taken into account, we have analyzed the influence of these time-varying factors on ball bearing temperature, as well as the temperature distribution law of each component in a grease-lubricated bearing, in a bid to lay a foundation for follow-up research on the heat transfer laws of traction motors and rolling bearings.

The performance of an induction motor, a core component of the high-speed train, is of critical importance to the safety of the train. As a result of limited space in the train and the motor’s high-power density, motor heating poses a grave threat to its service life and operational safety. In this study, a 600-kW high-speed train induction motor model was established and its temperature field calculated by the finite volume method. To optimize the heat dissipation and further reduce the motor temperature rise, a variable cross-section stator ventilation hole was proposed and its influence on the wind velocity and temperature rise comprehensively studied. After comparing the cooling effect of the linear and nonlinear variable cross-section of the ventilation hole, the influence of the cross-sectional diameter and the radial position of the ventilation hole on the temperature rise was explored. Finally, the most favorable scheme for motor heat dissipation was determined.

Accurate temperature prediction is critical for ensuring mechanical stability and operational safety during complex operating conditions and long-term operation of rolling bearings. This study proposes a digital twin (DT) system for bearing thermal analysis and a digital twin-driven virtual–real hybrid framework, achieving thermal prediction from low-risk behaviors (low-speed/light-load) to high-risk behaviors (high-speed/heavy-load). To address the time-varying and ambiguous parameters, an efficient Nutcracker Optimization Algorithm (NOA)-based identification mechanism is introduced to dynamically calibrate the virtual thermal model, overcoming the limitations of static modeling and data isolation inherent in conventional thermal analysis methods. The Euclidean distance and uncertainty analysis between real temperature and predicted temperature demonstrate the highly reliable predictive ability of the proposed framework in terms of bearing thermal, especially under variable speed conditions. The proposed framework has certain guiding significance for enhancing thermal safety and fault early-warning capabilities of bearings during long-term operation.

Thermal management in forced air-cooling asynchronous traction motor (ATM) is currently bottlenecked by the limitations of traditional axial ventilation architecture and independent cooling design for the stator and rotor, which restrict further escalations in cooling efficacy and lightweight. This paper proposes a collaborative dual-stage strategy to optimize stator and rotor axial ventilation ducts (AVDs) simultaneously. By spatially decoupling the design parameters of both components, the strategy facilitates an optimization transition from uniform cross-section (UC) to variable cross-section (VC) topologies from a global multi-physics perspective. This dimensionality reduction framework effectively alleviates computational overhead in computational fluid dynamics (CFD) simulations and reduces surrogate model training costs. At Stage 1, the circumferential parameters of the stator and rotor UC-AVDs are optimized via a multi-objective approach that combines stratified hybrid sampling, multiple surrogate models, the NSGA-II algorithm and TOPSIS decision-making to trade off hydraulic and thermal performance. Stage 2 subsequently leverages orthogonal design and ANOVA to maximize the performance evaluation criterion (PEC) by seeking optimal axial parameters of stator VC-AVDs. The results demonstrate that the proposed approach achieves a 14.1°C reduction in the stator winding hotspots alongside an 8.44% decrease in total pressure drop. These findings indicate a significant optimization of the internal flow field, offering a robust solution for cooling enhancement in high-power traction motors.