Overcoming the bottleneck of iron loss computation in electrical machine design

• Applicant: Professor Dr.-Ing. Simon Steentjes
• Subject Area: Electrical Energy Systems, Power Management, Power Electronics, Electrical Machines and Drives
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 569667762

Iron losses pose a major challenge in the design of electrical machines and drives, significantly impacting energy efficiency. Despite their critical importance, the industry still lacks precise tools for accurately estimating these losses due to the complexity of both modeling and measurement. The increasing use of power electronics and the push for high-power-density drives have further intensified the problem, as higher operating frequencies and additional harmonics dramatically increase iron losses. For example, in a 0.50-mm thick lamination, losses escalate from 1.05 W/kg at 1T and 50 Hz to 24 W/kg at 1 T and 400 Hz. Two fundamental challenges have hindered progress in accurate iron loss modeling. First, magnetic hysteresis is inherently complex and poorly modeled, with existing models struggling to address anisotropy, rotational losses, and seamless integration into finite element simulations. Second, iron loss computation requires capturing both microstructural and macroscopic effects, traditionally a computationally prohibitive task. Recent advances in Machine Learning offer a breakthrough solution. Properly trained Neural Networks can efficiently encapsulate the intricate multiscale physics of iron losses, enabling fast and accurate simulations. This research project aims to enhance existing hysteresis models and develop AI-driven tools for precise iron loss estimation. By bridging the gap between accuracy and computational efficiency, these innovations will empower engineers to optimize iron losses, reduce energy consumption, and improve overall efficiency—marking a significant step forward in sustainable electrical engineering.

• DFG Programme: Research Grants
• International Connection: Belgium
• Major Instrumentation: Rotational Power Loss Tester Stator Test Bench System
• Instrumentation Group: 0150 Geräte zur Messung der magnetischen Materialeigenschaften
• Partner Organisation: Fonds National de la Recherche Scientifique - FNRS
• Cooperation Partner: Professor Dr.-Ing. Christophe Geuzaine