Optimization approaches in electric machine design: insights from a bibliometric analysis

Abstract

Optimizing the design of electric machines is a complex task due to the large number of interrelated geometric and physical parameters that influence performance, efficiency, cost, and sustainability. Numerous deterministic and stochastic optimization techniques have been developed to address these challenges, each presenting distinct advantages and limitations. Deterministic approaches, though computationally efficient, often converge to local minima, while stochastic methods provide broader search capabilities at the expense of higher computational effort. Despite the growing application of optimization in electric machine design, a comprehensive bibliometric overview of this research area is lacking. This study aims to fill that gap by conducting a bibliometric analysis of optimization methods applied to electric machine design. Using data retrieved from the Web of Science (WoS) and analyzed through co-citation mapping with VOSviewer, 246 relevant articles were examined, resulting in a focused sample of 73 key studies. The analysis identifies the most frequently optimized machine types, the main optimization objectives, and the predominant methodologies employed in recent years. By addressing these dimensions, this work provides a conceptual reference framework to guide both academic researchers and industry professionals in selecting appropriate optimization strategies. The results also highlight emerging trends and future research opportunities, contributing to a deeper understanding of the evolution and intellectual structure of optimization in electric machine design.