Deep Transfer Learning-Based Performance Prediction Considering 3-D Flux in Outer Rotor Interior Permanent Magnet Synchronous Motors

Abstract

Accurate performance prediction in the design phase of permanent magnet synchronous motors (PMSMs) is essential for optimizing efficiency and functionality. While 2-D finite element analysis (FEA) is commonly used due to its low computational cost, it overlooks important 3-D flux components such as axial leakage flux (ALF) and fringing flux (FF) that affect motor performance. Although 3-D FEA can account for these flux components, it is computationally expensive and impractical for rapid design iterations. To address this challenge, we propose a performance prediction method for interior permanent magnet synchronous motors (IPMSMs) that incorporates 3-D flux effects while reducing computational time. This method uses deep transfer learning (DTL) to transfer knowledge from a large 2-D FEA dataset to a smaller, computationally costly 3-D FEA dataset. The model is trained in 2-D FEA data and fine-tuned with 3-D FEA data to predict motor performance accurately, considering design variables such as stator diameter, axial length, and rotor design. The method is validated through 3-D FEA simulations and experimental testing, showing that it reduces computational time and accurately predicts motor characteristics compared to traditional 3-D FEA approaches.