Optimization of stator ventilation structure of high‐speed railway traction motor based on the genetic algorithm - Junci Cao; Hua Yan; Wenlong Li; Dong Li; Yu Wang (IET Electric Power Applications)
Abstract Aiming at the problems of high power density and difficult heat dissipation of high‐speed train traction motors, this study takes a 600 kW asynchronous traction motor as a research object and optimises the motor cooling structure through the fluid‐structure coupled heat transfer analysis and genetic algorithm. Based on the principles of fluid dynamics and heat transfer, the motor's fluid flow and heat transfer laws are calculated and analysed, and the calculation accuracy is verified by the experimental data. Based on exploring the influence of the stator ventilation aperture and the number on motor fluid and temperature, aiming to reduce the maximum temperature of each motor component, a bivariate multi‐objective mathematical model of stator ventilation aperture and number is established, and the optimal solution is found with the help of the genetic algorithm. Finally, the optimisation effect is verified by comparing the motor temperature before and after optimisation.