VP10-02 Electromagnetic Heat Flow Coupling Optimization of High Speed Permanent Magnet Motor Based on Deep Neural Network
Creators: xiaoyi luo
Publisher: name: IEEE
Year: 2025

Optimizating of electromagnetic thermal fluid couplings is commonly used in design of high-speed permanent magnet synchronous motors[1-2].However, a clear disadvantage of conventional design methods is their time-cost issue when applied throughout the optimization process.Less computing is an advantage of the agent model,which can result in significant optimization time savings.However, common agent models with large construction costs and low global agent precision are the response surface model, radial basis function, and kriging model[3].Deep learning can be built at a minimal cost and offers excellent global prediction and agent accuracy[4].In order to lower the computational cost of the electromagnetic heat flow coupling optimization of high speed permanent magnet motors, this article using deep learning. In this paper, the optimization of high speed permanent magnet motor is 24 slot 4 pole.The Fig.1(a) shows the optimization variables of high speed permanent magnet motor.And the electromagnetic heat flow coupling process as Fig.1(b) show. Then through the deep neural network is used to estimate the loss,torque,efficiency,maximum winding temperature and maximum rotor permanent magnet temperature of the high-speed motor. Aiming at the neural network's accuracy of global predictions,this paper propose optimizing the number of layers and nodes.Aiming at the problem of sample size of training neural network,a method of increasing sample points based on local sensitivity analysis is proposed in this paper.Through this method the global proxy precision of the deep network is improved,and reduce computation. The Figure.2 shows the comparison between the predicted value of neural network and the calculated value of finite element for different design objectives.Next,NSGA-2 is used to optimize efficiency and torque by using deep neural network prediction model to maximize efficiency and torque.The Figure.2(i) shows the preliminary optimization result,the torque is 44.72 N●m,the efficiency is 96.89%.At last, an optimization procedure was verified through the creation and testing of a prototype.The full paper will present the detailed calculation procedure and analysis.References: ○ N. Uzhegov, E. Kurvinen and J. Nerg, J. Pyrhönen, IEEE Transactions on Industrial Electronics., vol. 63, pp. 784-795(2016). ○ M. K. Islam, K. N. Tasnim and S. Choi, IEEE Access., vol. 10, pp. 106305-106323(2022). ○ H. Sasaki,K. Yamamura, IEEE Transactions on Magnetics., vol. 60,pp. 1-4(2024). ○ M. N. Al-Andoli, K. S. Sim, S. C. Tan and P. Y. Goh ,IEEE Access., vol. 11, pp. 76330-76346(2023).
Optimizating of electromagnetic thermal fluid couplings is commonly used in design of high-speed permanent magnet synchronous motors[1-2].However, a clear disadvantage of conventional design methods is their time-cost issue when applied throughout the optimization process.Less computing is an advantage of the agent model,which can result in significant optimization time savings.However, common agent models with large construction costs and low global agent precision are the response surface model, radial basis function, and kriging model[3].Deep learning can be built at a minimal cost and offers excellent global prediction and agent accuracy[4].In order to lower the computational cost of the electromagnetic heat flow coupling optimization of high speed permanent magnet motors, this article using deep learning. In this paper, the optimization of high speed permanent magnet motor is 24 slot 4 pole.The Fig.1(a) shows the optimization variables of high speed permanent magnet motor.And the electromagnetic heat flow coupling process as Fig.1(b) show. Then through the deep neural network is used to estimate the loss,torque,efficiency,maximum winding temperature and maximum rotor permanent magnet temperature of the high-speed motor. Aiming at the neural network's accuracy of global predictions,this paper propose optimizing the number of layers and nodes.Aiming at the problem of sample size of training neural network,a method of increasing sample points based on local sensitivity analysis is proposed in this paper.Through this method the global proxy precision of the deep network is improved,and reduce computation. The Figure.2 shows the comparison between the predicted value of neural network and the calculated value of finite element for different design objectives.Next,NSGA-2 is used to optimize efficiency and torque by using deep neural network prediction model to maximize efficiency and torque.The Figure.2(i) shows the preliminary optimization result,the torque is 44.72 N●m,the efficiency is 96.89%.At last, an optimization procedure was verified through the creation and testing of a prototype.The full paper will present the detailed calculation procedure and analysis.References: ○ N. Uzhegov, E. Kurvinen and J. Nerg, J. Pyrhönen, IEEE Transactions on Industrial Electronics., vol. 63, pp. 784-795(2016). ○ M. K. Islam, K. N. Tasnim and S. Choi, IEEE Access., vol. 10, pp. 106305-106323(2022). ○ H. Sasaki,K. Yamamura, IEEE Transactions on Magnetics., vol. 60,pp. 1-4(2024). ○ M. N. Al-Andoli, K. S. Sim, S. C. Tan and P. Y. Goh ,IEEE Access., vol. 11, pp. 76330-76346(2023).