Analytical Modeling of Air-gap Magnetic Field and Multi-objective Optimization of Interior Permanent Magnet Synchronous Motor for Electric Vehicles

To improve the analytical calculation accuracy and optimization efficiency of the air-gap magnetic field of an interior permanent magnet synchronous motor (IPMSM) for electric vehicles, a parametric analytical model of the air-gap magnetic field considering the saturation effect of rotor core magnetic bridges was established using a hybrid analytical method. First, an analytical model of the open-circuit air-gap magnetic field of an IPMSM was established using the subdomain method combined with the equivalent magnetic circuit method. A virtual magnetic field analytical model of the rotor magnetic bridge was established by the same method. An armature reaction magnetic field analytical model considering the saturation effect of the rotor magnetic bridge was obtained, and an analytical model of the load air-gap magnetic field of the IPMSM was established by the superposition principle. The accuracy of the analytical model was verified using the finite element method and a torque test. Compared with the experimental results, the analytical calculation error is less than 5%. The calculation time is reduced by more than 90% compared with the finite element simulation. Based on the established analytical model, the optimization variables are the pole arc width of the permanent magnet, width of the stator slot, and thickness of the magnetic bridge at the end of the rotor. Taking the radial force wave of specific order frequency, torque, and efficiency as the optimization objectives, a multivariable and multi-objective optimization of an IPMSM prototype for electric vehicles was carried out using the non-dominated sorting genetic algorithm with elite strategy (NSGA-II). After optimization, the radial force wave of the specific-order frequency of the motor reduces by 9.2%, the maximum torque increases by 2.49%, the maximum efficiency increases by 0.69%, and the area of the high-efficiency zone increases by approximately 54.46%. The results show that the proposed method solves the common difficulties of analytical modeling of IPMSM with strong nonlinearity and high saturation and considerably improves motor multi-objective optimization efficiency. This research can provide a reference method for the optimization design of an IPMSM for electric vehicles. © 2023 Xi'an Highway University. All rights reserved.