This article applies machine learning to power magnetics modeling. We first introduce an open-source database—MagNet—which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500 000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and $B$ – $H$ loops. The neural network allows multiple factors that may influence the magnetic characteristics to be modeled in a unified framework, where the nonlinear behaviors are captured with high accuracy and high generality. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics, paving the way for “neural networks as datasheets” to assist power magnetics design. Transfer learning is applied to the training of neural network models to further reduce the data size requirement while maintaining sufficient model accuracy.

This article motivates the development of sophisticated data-driven models for power magnetic material characteristics. Core losses and hysteresis loops are critical information in the design process of power magnetics, yet the physics behind them is not fully understood or directly applicable. Both losses and hysteresis loops change for each magnetic material and depend heavily on electrical operating conditions (e.g., waveform, frequency, amplitude, and dc bias), mechanical properties (e.g., pressure and vibration), temperature, and geometry of the magnetic components, and in a nonlinear and coupled fashion. Understanding the complex and intertwined relationship these factors have on core loss is important for the development of accurate models and their applicability and limitations. Existing studies on power magnetics are usually developed based on a small amount of data and do not reveal the full magnetic behavior across a wide range of operating conditions. In this article, based on a recently developed large-scale open-source database— MagNet —the core losses and hysteresis loops of Mn–Zn ferrites are analyzed over a wide range of amplitudes, frequencies, waveform shapes, dc bias levels, and temperatures, to quantify the complexity of modeling magnetic core losses, amplitude permeability, and hysteresis loops and provide guidelines for modeling power magnetics with data-driven methods.

This paper presents the MagNet-AI platform as an online platform to demonstrate the “Neural Network as Datasheet” concept for $B$ – $H$ loop modeling and material recommendation of power magnetics across wide operation range. Instead of directly presenting the measured characteristics of magnetic core materials as time sequences, we employ a neural network to capture the $B$ – $H$ loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures and dc-bias. LSTM and Transformer based neural network models are developed, verified, and compared. The neural network can be used to rapidly predict hysteresis loops and core losses under different operating conditions, compare materials, and recommend materials for design. The neural network model is also proved effective in reconstructing the raw measurement while accurately maintaining the magnetic characteristics, enabling rapid material evaluation and comparison.

Highly efficient, hybrid DC/DC topology, consisted of a standard interleaved boost followed by a multilevel RSCC (Resonant Switched Capacitor Converter), that employs 900V SiC (Silicon-Carbide) devices, for use in emerging, two-stage, grid connected 1500V PV (Photo-Voltaic) systems is presented in this paper. Operation principle of this topology, controllability of the output voltage and power distribution among the stages in function of the input voltage is described. Partial processing of the input energy and full ZVS (Zero Voltage Switching) transitions in all the switches, over the wide range of input voltages and output power, make this topology more efficient in the comparison with the solutions presented in literature. Simplified model of Coss for prediction of ZVS conditions in high voltage applications is presented. This model appears sufficiently accurate, yet is also simple enough to be included in the optimization program without increasing the time consumption of calculations. Results of multivariable optimization (pareto front) for the presented topology are provided. Previousely described theoretical analysis is fully experimentally confirmed by building and measuring a 10kW prototype of 8.26kW/kg of specific power and 404.6cm3 of volume occupied only by the power stage components. Euro efficiency of 99.48% in nominal input/output voltage conditions is achieved.

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.

In this paper we review some of the fundamental limits of dc-dc power converters described in 1966 and 1969 and generate an averaged power model that is useful to perform high-level comparison of power converters and architectures. The model is extended to 3 port power converters, which is used to calculate the minimum possible internal power to be processed by the components of the power stage of energy buffered converters. Finally, a single stage inverter/rectifier with energy storage capability that meets the fundamental limit is synthesized.