This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the stateof-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.

The main requirements of magnetic components for power electronics applications are high power density and low power losses, driven by the need for more compact and more efficient power converters. Metal powder materials are a common choice for high-power and high-frequency inductors subject to a large magnetic field bias, since they feature high saturation flux density and low magnetic permeability (i.e., a “distributed” air gap), allowing for the adoption of un-gapped cores. Despite this, under high values of magnetomotive force (i.e., deep core magnetic saturation), the insertion of a concentrated air gap can lead to higher core inductance factor values with respect to an un-gapped configuration. In this context, this paper proposes a straightforward procedure to maximize the inductance factor of metal powder magnetic cores by identifying the optimal air gap length for a specified design operating point. In particular, the procedure completely relies on information available in the core manufacturer’s datasheet and does not require experimental characterization of the core itself, dramatically simplifying the inductor design procedure. The proposed methodology is theoretically described and then experimentally validated on an XFlux®60 core from Magnetics.

In this paper, we propose a method for the identification of the differential inductance of saturable ferrite inductors adopted in DC–DC converters, considering the influence of the operating temperature. The inductor temperature rise is caused mainly by its losses, neglecting the heating contribution by the other components forming the converter layout. When the ohmic losses caused by the average current represent the principal portion of the inductor power losses, the steady-state temperature of the component can be related to the average current value. Under this assumption, usual for saturable inductors in DC–DC converters, the presented experimental setup and characterization method allow identifying a DC thermal steady-state differential inductance profile of a ferrite inductor. The curve is obtained from experimental measurements of the inductor voltage and current waveforms, at different average current values, that lead the component to operate from the linear region of the magnetization curve up to the saturation. The obtained inductance profile can be adopted to simulate the current waveform of a saturable inductor in a DC–DC converter, providing accurate results under a wide range of switching frequency, input voltage, duty cycle, and output current values.

In this paper, we propose an optimal design procedure for magnetically shielded rooms. Focusing on multi-layer ferromagnetic structures, where inner layers operate at very low magnetic field, we propose an identification method of the magnetic material characteristic in the Rayleigh region. A numerical model to simulate the shielding efficiency of a multi-layer ferromagnetic structure is presented and experimentally tested on different geometries and layer configurations. The fixed point iterative method is adopted to handle the nonlinearity of the magnetic material. In conclusion, the optimization of the design parameters of a MSR is discussed, using the Vector Immune System algorithm to minimize the magnetic field inside the room and the cost of the structure. The results highlight that a linear magnetic characteristic for the material is sufficient to identify the suitable geometry of the shield, but the nonlinear model in the Rayleigh region is of fundamental importance to determine a realistic shielding factor.

LED driver application asks for a high power level operating at a high switching frequency. This feature is achievable by adopting gallium nitride transistors (eGaN FETs) in the DC-DC converter. In the paper, a Synchronous Buck Converter (SBC) with high current density GaN FETs is investigated and modelled to design a high brightness LED driver. At first, the GaN FET model is described and implemented in LTspice simulation software. Then, the synchronous Buck operation is considered to investigate the proposed model in actual application. The experimental waveforms of a half-bridge evaluation board in SBC configuration and a comparison with the simulation results are presented and discussed to validate the modelling approach.