An analytical review of intelligent optimization design techniques for electric machines

Electrical machines are essential parts of today's industrial, transportation, aerospace, and renewable energy systems, and due to demands for greater performance, efficiency and reliability, their design has become an increasingly complex process. The design of electrical machines is a multidisciplinary problem, where electromagnetic, thermal, mechanical, and manufacturing aspects are all taken into consideration, and advanced modeling and optimization methods are needed to solve the complex nonlinear design problem. This paper reviews in detail recent advances in the methodology of electrical machine design, such as analytical modeling, finite element analysis (FEA), surrogate models (SMs), digital twin (DT) technologies, and AI-assisted design frameworks, and assesses them from a critical perspective. In addition, deterministic, stochastic and multi-objective optimization algorithms are comparatively analyzed based on their characteristics, advantages, limitations, and applicability to various design requirements. Instead of superiority, the review establishes the importance of the effectiveness of the modeling-optimizing techniques not only because of their design objectives or the requirements of practical engineering work, but also due to computational efficiency and the level of modeling accuracy. Recent developments show a definite shift towards hybrid optimization frameworks that combine physics-based modeling, FEA, data-driven methods, and intelligent optimization algorithms (IOAs) to boost convergence, computational efficiency, and design robustness. Lastly, the review highlights current research challenges and prospects, focusing on the evolving paradigm of intelligent multidisciplinary design for future electrical machines.