Empowering Electrical Machine Performance: The Convergence of Artificial Intelligence and Motor Design

Abstract

The integration of Artificial Intelligence (AI) in electrical machine design and maintenance has opened new pathways for achieving enhanced performance, efficiency, and sustainability across various applications. This review explores the current landscape of AI-driven methodologies for motor design, predictive maintenance, and control optimization, analyzing the impact of these technologies on electrical machine capabilities. The study identifies prevalent AI approaches-including machine learning, optimization algorithms, and neural networks-detailing their application, benefits, and limitations in improving motor efficiency and reliability. Despite the advancements, challenges remain, particularly in terms of model complexity, interpretability, and economic feasibility. Moreover, the societal and environmental implications of AI-driven innovations in electrical engineering are considered, with a focus on aligning future research with sustainable development goals. This paper provides a comprehensive overview of the state of AI in electrical machine applications, emphasizing the potential of AI to transform the field while addressing the barriers that must be overcome for widespread implementation.