Loss Prediction of Vehicle Permanent Magnet Synchronous Motor Based on Deep Learning
Tóm tắt
Based on the deep learning BP neural network algorithm, we establish the electromagnetic torque and loss prediction analysis model of permanent magnet synchronous motor to provide new design ideas and methods for optimizing motor structure design. In this paper, four-rotor structure parameters which are Rib, Air Gap, Magnet Thickness and Magnet Width, motor electromagnetic torque, and motor losses of the “V” type Interior Permanent Magnet Synchronous Motor are selected as the research object. The BP neural network structure prediction model with 2 visible layers and 2 hidden layers was built by 256 groups of sample data calculated by Maxwell transient electromagnetic simulation. 226 out of 256 randomly selected data samples were used to train the prediction model, and 30 groups were used to test the accuracy and generalization ability of the prediction model. and the prediction results data were compared with the deep learning prediction model through finite element simulation data. The results show that the BP neural network small-sample data prediction model has high prediction accuracy in the loss prediction of the vehicle permanent magnet synchronous motor, and verifies the feasibility of the motor torque and loss prediction model based on the deep learning algorithm.
Tài liệu tham khảo
Renyuan T (2015) Modern permanent magnet machines-theory and design. China Machine Press, Beijing
Fengge Z, Du Guanghui WT et al (2016) Review on development and design of high-speed machines. Trans China Electrotech Soc 31(07):1–18
Yamazaki K, Ishigami H (2009) Rotor-shape optimization of interior-permanent-magnet motors to reduce harmonic iron losses. IEEE Trans Ind Electron 57(1):61–69
Chi Q, Zhang Y, Cao Z et al (2017) Analysis of rotational loss property in an electrical steel sheet and correction of loss model. Proc CSEE 37(08):2418–2426
Wang J, Wang F, Kong X (2008) Design and analysis of electromagnetic properties for high speed PM generator. Proc CSEE 20:105–110
Shen J, Li P, Hao H et al (2013) Study on electromagnetic losses in high-speed permanent magnet brushless machines-the state of the art. Proc CSEE 33(03):62-74+14
He B, Zhang Q, Chen S et al (2018) Iron loss and permanent magnet loss analysis based on inverter power supply PMSM. Small Spec Electr Mach 46(05):35–38
Chen X, Wang J, Sen B, Lazari P, Sun T (2015) A high-fidelity and computationally efficient model for interior permanent-magnet machines considering the magnetic saturation, spatial harmonics, and iron loss effect. IEEE Trans Ind Electron 62(7):4044–4055
Hu Y (2017) Research on interior permanent magnet synchronous machines for EV applications. Nanjing University of Aeronautics and Astronautics, Nanjing
Tang S (2016) Optimization and research of rotor eddy-current loss in electric vehicles. Shanghai Dianji University, Shanghai
Wang W (2020) Research on improved simulated annealing algorithm for optimal design of variable frequency induction motor. Harbin University of Science and Technology, Harbin
Jin Y, Wang A, Wang T et al (2019) Multi-objective optimization design of nuclear canned induction motor based on particle swarm optimization. Electr Mach Control Appl 46(09):53–57
Zhang Y, Xu Y (2018) Design of rotor structure of permanent magnet brushless motor for EV based on genetic algorithm and response surface method. Micromotors 51(11):6–12
Lu Y, Miao H, Zeng C (2019) Multi-objective optimization of surface-mounted and interior permanent magnet synchronous motor based on Taguchi method. Micromotors 52(09):1-5+24
Jin L, Wang F, Yang Q et al (2018) Typical deep learning model and training method for performance analysis of permanent magnet synchronous motor. Trans China Electrotech Soc 033(0z1):41–48
Dong X, Wang Y, Yang L (2019) A survey of magnetic pole design for high performance permanent magnet synchronous motors. Micromotors 52(11):97–100
Tong W, Wang Y, Jia J et al (2018) Calculation and experimental research on the rotor loss of interior permanent magnet synchronous motors with converter supply. Trans China Electrotech Soc 33(24):5811–5820
Liu W (2018) Research on thermal performance under multi-operating conditions and cooling water-jacket of water-cooled permanent magnet synchronous motor. Beijing Jiao Tong University, Beijing
He H, Liu Y, Ba L et al (2021) Nonlinear dynamic model of active magnetic bearing in flywheel system based on BP neural network. Proc CSEE 17:1–15
Zhu H, Yan L, Diao X (2020) Displacement sensorless operation control of bearingless permanent magnet synchronous motor based on BP neural network left inverse. Proc CSEE 40(11):3673–3681