Multi-physical Field Twinning Model Of Permanent Magnet Synchronous Motor Based On Multi-objective Optimization

With the implementation of the national "double carbon" strategy,the trend of new energy vehicles replacing fuel cars has become irreversible.In the new energy vehicles based on the three cores of motor,electric control and battery,the more efficient,stable and quiet motor has become a hot spot for research and development by major manufacturers.Permanent magnet synchronous motors(PMSM)are one of the best options for new energy vehicles due to their high efficiency,small size,low noise,and large power and torque output.However,the multi-physical field performance of high efficiency,low vibration and low noise of the vehicle-mounted PMSM is difficult to be fully optimized,and it is easy to lose both,and the state detection of the multi-physical field of the motor is still in the initial stage,which can only be achieved by adding sensors inside the motor,and the installation of sensors is very inconvenient and there are problems of falling off,degradation and space occupation.With the development of digital twin technology there are new solutions to this problem.The following work is carried out in this dissertation with a 1.5k W PMSM as the research object:(1)The electromagnetic performance of four common types of 1V,2V,V1 and 11 magnets of PMSM under no-load and on-load conditions are studied and compared.A2 D electromagnetic analysis model,a quarter 3D finite element model,a housing model and an air domain model of the four types of magnets of the motor were constructed and finite element simulations were performed by material assignment and meshing operations.Finally,the 1V type magnet with high efficiency and high torque was selected as the rotor magnet of the PMSM.(2)To solve the deficiencies in vibration noise of 1V type magnets,a multi-physics field multi-objective optimization method for PMSM considering motor vibration noise is proposed in this dissertation.Using the main dimensional parameters of the motor as the optimization object,82 sets of data sampled by Latin hypercube are trained and optimized.The important parameters such as efficiency,permanent magnet temperature,sound pressure level,maximum stress,maximum torque and torque pulsation,which affect the performance and comfort of the motor,are made to reach 96.6051%,79.72°C,74.153 d B,24.513 MPa,8.9165 N/m and 11.64%,respectively,and the performance of all aspects of the motor is improved comprehensively compared with that before optimization.(3)In this dissertation,a twin model of PMSM with motor speed as input and multi-physics field performance index as output is constructed.By monitoring the motor speed variation,the maximum torque,maximum permanent magnet temperature,maximum vibration acceleration and maximum sound pressure level of the motor are monitored,replacing the traditional physical sensors for motor condition monitoring.The temperature rise and vibration experimental platform of the motor is also built,and the accuracy of the finite element model and the twin model is verified by comparing the data results of the twin model and the physical entity under different working conditions.