Continuously Variable Multi-Permeability Inductor for Improving the Efficiency of High-Frequency DC–DC Converter

Liu, Junchang; Mei, Yunhui; Lu, Shengchang; Li, Xin; Lü, Gongxuan

doi:10.1109/tpel.2019.2907770

Cited by 15 publications

(10 citation statements)

References 30 publications

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“…Equating (15) and 16 The horizontal region of the winding Ph (top or bottom) can be computed using the same approach, however the energy integral in (16) is integrated from rci to rco, to the height of the coil which is assumed to vary linearly from hri to hro, as shown in Fig. 2 The coil permeances are used to both to compute the leakage flux linkage and proximity effect losses.…”

Section: B Coil Leakage Permeancementioning

confidence: 99%

“…Recent developments in magnetic material treatment has enabled a continuous spatial permeability tuning around the core [13]- [15]. In [15], the concept employed in [11] is taken IEEE POWER ELECTRONICS REGULAR PAPER/LETTER/CORRESPONDENCE one step further to obtain a continuous permeability profile around a toroid through gradient sintering. By leveraging the linear relationship of the sintering temperature of a ferrite with its resultant permeability, a continuous spatial tuning can be achieved [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Nascimento

Moon

Byerly

et al. 2021

IEEE Trans. Power Electron.

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Spatially tuning core permeability of an electromagnetic device enables superior performance. A permeability profile can be heuristically selected to improve the flux distribution in a device with a given geometry, but in order to fully leverage the capacity of spatial dependent permeability engineering, the geometry and the permeability should be optimized simultaneously. The work herein presented sets forth a multi-physics design optimization paradigm that includes the permeability profile tuning in the context of both inductor and converter design. This approach enables the determination of Pareto optimal fronts consisting of a set of optimal solutions against competing objectives (e.g. mass and loss) under imposed constraints. To this end, computationally efficient analytical solutions of the heat transfer and electromagnetic formulations are derived for toroidal inductors, which are validated with Finite Element Analysis based simulations. The software implemented in Matlab 2018b is available online as an attachment to this paper.

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Section: B Coil Leakage Permeancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Nascimento

Moon

Byerly

et al. 2021

IEEE Trans. Power Electron.

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“…Toroidal cores are widely applied as transformers and inductors due to their simple geometry [3], [4]; however, one major drawback for the toroidal cores is that the magnetic flux is not uniformly distributed; this leads to premature saturation of the inner ring. Recent research has developed toroidal cores with spatially tuned permeability to improve the flux distribution in magnetic components [5], [6], [7]. In [5], a continuously variable multipermeability NiZnCu ferrite core is proposed to balance the flux distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has developed toroidal cores with spatially tuned permeability to improve the flux distribution in magnetic components [5], [6], [7]. In [5], a continuously variable multipermeability NiZnCu ferrite core is proposed to balance the flux distribution. Such a core is constructed by applying a temperature gradient during the sintering process.…”

Section: Introductionmentioning

confidence: 99%

Toroidal Nanocrystalline Powder Core With Trapezoidal Cross Section

Shillaber

Luo

et al. 2023

IEEE Trans. Magn.

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This article introduces a toroidal nanocrystalline powder core with a trapezoidal cross section. The nonuniform magnetic flux distribution of toroidal cores with different cross-sectional geometries is discussed, the inductance equations are derived, and the geometrical optimization factor is given in order to retain the highest possible inductance value while minimizing material usage. A ten-turn coil is wound on the core, with the measured inductance values showing a good match with both finite element method (FEM) and mathematical calculation. The near-field electromagnetic interference (EMI) radiation in close proximity to the inductor is investigated by both FEM and experimental validation, and the toroidal core with a trapezoidal cross section has shown reduced EMI. By adopting a trapezoidal cross section for the toroidal core, a 30% reduction in material usage and lower EMI are achieved, albeit with a compromise of 14% in inductance.

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“…However, this approach does not solve the fitting problem, given that these graphs have no applicable fitting expressions. Moreover, this approach is still limited by the frequency dependency of the original Steinmetz parameters [1] and the dependency on the geometries of individual components (e.g., gap losses [3], ununiform flux distribution [20] and fringing flux effect [21]). To consider the shape of waveforms, an improved generalized Steinmetz equation (iGSE) is proposed in [1], but it cannot solve the non-linear frequency-dependent Steinmetz parameters or the DC bias effect.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Aided Loss Maps for Inductors and Transformers

Wang

Yuan

2022

IEEE Open J. Power Electron.

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The non-linear property of magnetics poses challenges for their loss modelling in power electronics due to lacking full physical models. As a practical approach for their loss estimation, the manufacturers can pre-measure the losses in standalone rigs and distribute the "loss maps" as interpolated look-up tables/curves for the end users. However, with more factors discovered that impact the losses, e.g., DC bias and load conditions, the dimensions of the loss maps cannot be solved by conventional surface/curve fitting methods. This paper addresses this problem by applying the Artificial Neural Network (ANN). For both inductors and transformers, Neural Network-aided loss maps (NNALMs) are designed and evaluated with comparisons against conventional loss maps to reveal the limitations of the latter caused by physically intercoupled input variables. The NNALMs not only show superior accuracy throughout the whole datasets, but also enable the loss maps to expand the dimensions to account for more factors (e.g., load conditions in transformers) and generate multiple outputs (e.g., both the winding loss and core loss). The ANN-aided loss maps can be distributed as digitized datasheets of standardized magnetics, enabling rapid, accurate and user-friendly loss estimations for power electronics engineers.

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Continuously Variable Multi-Permeability Inductor for Improving the Efficiency of High-Frequency DC–DC Converter

Cited by 15 publications

References 30 publications

Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Multiobjective Optimization Paradigm for Toroidal Inductors With Spatially Tuned Permeability

Toroidal Nanocrystalline Powder Core With Trapezoidal Cross Section

Artificial Neural Network Aided Loss Maps for Inductors and Transformers

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