Magnetic Properties and Hysteresis Modeling in Electrical Steel Systems

The performance of modern electrical machines and power conversion systems hinges on the magnetic behaviour of electrical steel, a soft ferromagnetic alloy engineered for minimal core losses and high permeability. Its microstructural features—grain orientation, domain structure and dislocation networks—govern magnetisation processes and energy dissipation. Magnetic hysteresis, the looped lag between applied field and resulting flux density, determines iron losses across operating frequencies and flux amplitudes. Accurate hysteresis models are vital for predicting machine efficiency, thermal performance and noise. Recent efforts integrate multiscale constitutive frameworks that capture magneto-mechanical coupling, texture effects and manufacturing-induced stresses, with data-driven algorithms that emulate nonlinear loop evolution. These hybrid approaches account for local damage from cutting, punching and mechanical loading, offering rapid and precise loss estimation. The convergence of advanced simulation tools, high-resolution imaging and machine-learning surrogates is enabling real-time design optimisation, reduced material consumption and enhanced power density in sustainable electrical machines.

Research from Nature Portfolio

Recent studies have probed the micromagnetic response of non-oriented electrical steel under ultra-small mechanical loads. By performing sub-micron indentations within single grains at varying strain rates, researchers have revealed that even negligible deformations can disturb magnetic textures through time-dependent dislocation dynamics. Advanced imaging techniques, including magnetic force microscopy and transmission Kikuchi diffraction, have elucidated the interplay between local stress fields and domain configurations. These insights highlight the necessity of accounting for nano-scale manufacturing effects when modelling hysteresis and iron losses in next-generation electric machines.

Magnetic Properties and Hysteresis Modeling in Electrical Steel Systems publication trend

The graph below shows the total number of articles in magnetic properties and hysteresis modeling in electrical steel systems across all publications each year (not limited to Nature Index journals).

Technical terms

Electrical steel: A soft ferromagnetic alloy optimised for low core losses and high permeability in transformers and motors.

Magnetic hysteresis: The looped magnetisation response of a material due to lag between changes in applied field and magnetic flux density.

Magneto-mechanical coupling: The interaction whereby mechanical stress alters magnetic properties and magnetisation induces strain.

Preisach model: A phenomenological representation of hysteresis as the weighted superposition of elementary bistable operators.

Neural network: A computational framework that learns to approximate nonlinear relationships, applied here to predict hysteresis loops efficiently.

References

1. Effect of sub-micron deformations at opposing strain rates on the micromagnetic behaviour of non-oriented electrical steel. Nature Communications (2024).
2. Deep neural networks for the efficient simulation of macro-scale hysteresis processes with generic excitation waveforms. Engineering Applications of Artificial Intelligence (2023).
3. Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design. IEEE Open Journal of Power Electronics (2020).
4. A multiscale model for magneto-elastic behaviour including hysteresis effects. Archive of Applied Mechanics (2014).
5. Impact of cut edges on magnetization curves and iron losses in e-machines for automotive traction. World Electric Vehicle Journal (2010).
6. Rotational Single Sheet Tester for Multiaxial Magneto-Mechanical Effects in Steel Sheets. IEEE Transactions on Magnetics (2019).
7. Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model. Materials (2020).