A Review on Artificial Intelligence Assisted Design and Optimization of Electric Motors

As performance requirements for electric motors continue to escalate, traditional design and optimization methodologies increasingly encounter bottlenecks, including high computational complexity, inadequate optimization efficiency, and constrained global search capabilities. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques as its important subsets, offers novel paradigms for motor development by seamlessly integrating intelligent algorithms with advanced simulation technologies. This article reviews the recent developments of design and optimization methods for electric motors, with a particular focus on the application of AI. First, the core challenges in the electric motor design and optimization process are analyzed. Subsequently, the progressive integration of AI across four key stages, including parametric modeling, performance analysis, multi-objective optimization, and design guidance is comprehensively reviewed. Through an in-depth analysis of multiple practical cases, the significant advantages of AI in accelerating design iterations are demonstrated. Furthermore, future technological breakthrough directions are discussed, particularly the potential of advanced architectures like Transformer models to address complex topological optimizations in permanent magnet motors for new energy vehicles. Overall, this article not only offers a comprehensive summary and technical references for the field of motor design and optimization, but also provides strong support for related engineering practices with AI methods.