In a groundbreaking study, researchers have unveiled the origins of energy loss in electric motors, a revelation that could significantly enhance the efficiency of electric vehicles. This discovery centers on magnetic hysteresis loss, or iron loss, a critical factor that accounts for approximately 30% of total energy loss in motors, thereby contributing to carbon dioxide emissions and posing environmental challenges.
Magnetic hysteresis loss occurs when the magnetic field within the motor core, composed of soft magnetic materials, is repeatedly reversed due to the changing flow of current in the windings. This reversal forces magnetic domains to change their magnetization direction, a process that is not perfectly efficient, resulting in energy loss. Despite decades of research, the precise origins of iron loss in these materials have remained elusive.
Unraveling the Mystery of Iron Loss
The breakthrough comes from a research team at Tokyo University of Science (TUS), led by Professor Masato Kotsugi, who, along with Mr. Michiki Taniwaki, has developed a novel approach using the extended-Ginzburg–Landau (ex-GL) framework. This method successfully traces the origin of iron loss to the magnetic domain structure of soft magnetic materials like nonoriented electrical steel (NOES).
“The Ginzburg–Landau (GL) free energy was a useful concept for analyzing magnetization reversal in homogeneous systems,” explains Prof. Kotsugi. “Recent progress in data science has enabled the ex-GL model, which can be used to analyze heterogeneous systems.”
“Our approach enabled us to extract information that would otherwise have been difficult to obtain with only visual inspection,” remarks Prof. Kotsugi.
Advanced Techniques in Magnetic Analysis
The research team employed persistent homology (PH), a mathematical tool for multiscale analysis of topological features in data, to quantify the complexity of magnetic domains from microstructure images of NOES. They then applied principal component analysis (PCA) to extract essential features hidden in the complex PH data, identifying two key features: PC1, representing magnetization, and PC2, representing magnetic domain walls.
Using these features, the team constructed an extended energy landscape with the ex-GL framework, mapping changes in the magnetic domain structure as a graph. This allowed for a comprehensive correlation analysis between the features and physical parameters, revealing factors that influence energy loss during magnetization reversal.
“The competition between the promoting and resisting factors automatically identifies the location of magnetic domain wall pinning, a key phenomenon responsible for energy loss in soft magnetic materials,” notes Prof. Kotsugi.
Implications for Sustainable Energy and Future Innovations
This innovative method provides automated, precise, data-driven insights into the mechanisms and locations of energy loss, paving the way for more efficient and environmentally friendly electric vehicles. The study, published in the journal Scientific Reports on July 15, 2025, aligns with the United Nations’ sustainable development goals, promoting affordable and clean energy, industrialization, innovation, and infrastructure, while combating climate change.
By identifying the origins of energy loss in soft magnetic materials, this research not only enhances the efficiency of electric motors but also contributes to the broader goal of a sustainable future. The findings promise to revolutionize the design and production of electric vehicles, making them greener and more efficient, and setting a new standard for the industry.
As the world continues to shift towards renewable energy and sustainable practices, the implications of this study are far-reaching. It represents a significant step forward in understanding and mitigating the factors that contribute to energy loss, ultimately leading to advancements in technology and environmental conservation.
