Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

Abstract

This paper 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, dc bias), mechanical properties (e.g., pressure, 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 paper, 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 datadriven methods.

Core Loss Modeling: Review and a New Approach Based on the Concept of Magnetic-flux resistance and the Square Law

“…The ISE and the IGSE are two straightforward models to apply, however, the ISE performs much better in terms of accuracy as previously stated. This result was reported in some papers such as [14] and [34] and in the following we present more comparison results using the loss data available in the MagNet database [4] and we highlight their major issues to be solved in future works. Fig.…”

“…They are measured under a symmetric triangular flux waveform (50% duty cycle) and under the following temperatures: 25, 59, 70 and 90 ºC. More details about the used measurement technique and the test set-up can be found in [4].…”

“…The sources affecting the core loss go beyond the three former issues to include many other effects such as temperature, stress, material permeability, core size and geometry. A detailed discussion of these effects and their complexity can be found in [4].…”

MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation

“…Figure 2 compares multiple measured B-H loops for N87 ferrite material as an example, where the material characteristics differ significantly under different conditions. These various factors are quantified in [10], and in real-world applications, they often coexist and change concurrently, which renders the modeling of magnetic materials extremely difficult. To develop a neural network model that is capable of predicting the hysteresis loop under different operating conditions, an encoder-projector-decoder architecture is proposed in this paper.…”

“…Conventional models for power magnetics, e.g., the Steinmetz Equation, the iGSE [8], and the Jiles-Atherton model [9], are established based on empirical simplifications or physical approximations, which limit their modeling accuracy. The magnetic core material behavior is highly complex [10]. The limited complexity of these models limits their capability of capturing sophisticated waveform, temperature, and dc-bias determined impact.…”