Optimal Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using a Machine Learning-Based Surrogate Model
Song Guo, Xiangdong Su, Hang Zhao
Abstract
This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).
Topics & Concepts
MagnetElectric vehicleFinite element methodPermanent magnet synchronous motorComputer scienceRotor (electric)TorqueSortingSynchronous motorGenetic algorithmAutomotive engineeringSurrogate modelEngineeringMechanical engineeringAlgorithmPhysicsMachine learningStructural engineeringElectrical engineeringPower (physics)Quantum mechanicsThermodynamicsElectric Motor Design and AnalysisMagnetic Properties and ApplicationsNon-Destructive Testing Techniques