Modeling Frequency-Dependent Losses in Ferrite Cores

Modeling Frequency-Dependent Losses in Ferrite Cores
Modeling Frequency-Dependent Losses
in Ferrite Cores
Peter R. Wilson, Member, IEEE, J. Neil Ross, and Andrew D. Brown, Senior Member, IEEE
Abstract—We suggest a practical approach for modeling frequency-dependent losses in ferrite cores for circuit simulation. Previous work has concentrated on the effect of eddy-current losses on
the shape of the – loop, but in this paper we look at the problem
from the perspective of energy loss and propose a different network for accurately modeling power loss in ferrite cores. In power
applications, the energy loss across the frequency range can have a
profound effect on the efficiency of the system, and a simple ladder
network in the magnetic domain is not always adequate for this
task. Simulations and measurements demonstrate the difference
ladder network models both in the
in this approach from the
small-signal and large-signal contexts.
Fig. 1. Basic mixed-domain transformer model.
Index Terms—Circuit simulation, energy loss, Jiles–Atherton,
magnetic component modeling.
A. Background
HE accurate prediction of the losses in magnetic cores
is crucial for a number of applications, especially power
electronics and in the use of ferrite materials to absorb unwanted
harmonics. In the first case, the frequency dependence of the ferrite material has a significant bearing on the design of the magnetic component and the resulting performance of the system as
a whole. For ferrite beads and other filter devices, it is the behavior of the lossy material that determines how effective the
material will be at removing unwanted signals. In both cases, it
is important to be able to predict losses across the desired frequency range if possible using computer simulations that are
fast and accurate. It is important for the model to be practically
useful that it is able to be included within standard electronic
circuit simulation.
B. Modeling Core Loss in Magnetic Components
The approaches used for modeling core loss partly depend
on whether the model is linear or nonlinear and whether the
frequency is high enough to cause eddy-current or other frequency-dependent effects. The total core loss consists of two
parts, the basic low frequency core hysteresis loss and the higher
frequency eddy-current or other frequency-dependent losses. In
the low frequency case, the core behavior may be implemented
using a linear or a nonlinear model. Cherry [1], Laithwaite [2],
and Carpenter [3] show how electromagnetic components may
be implemented using equivalent circuit elements in either, or
Manuscript received July 22, 2003; revised February 19, 2004.
The authors are with the Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, U.K. (e-mail:
[email protected]).
Digital Object Identifier 10.1109/TMAG.2004.826910
Fig. 2. Energy loss in B –H loop showing the transfer of energy into and out
of the core with changing H .
both, the electrical and magnetic domains. For example, a magnetic reluctance can be modeled as a resistive element in the
magnetic domain.
Taking a simple transformer as an example (Fig. 1), a model
may easily be developed that has models for each winding of the
transformer connected to a magnetic model of the core. This is
an example of a “mixed-domain” model for circuit simulation.
The core reluctance may be modeled as a simple linear element—this corresponds to a perfect lossless core. In practice, of
course, there usually needs to be an accurate model of the –
loop of the material which may also vary with frequency. The
area inside the – loop corresponds to the energy lost in the
core, as shown in Fig. 2.
If a linearized model is required (useful for frequency domain
analysis), the average core loss can be implemented as a resistor
in the electrical domain, or as an inductor in the magnetic domain as shown in Fig. 3. In the magnetic domain, the energy loss
per cycle may be found by integrating, over for a complete
cycle and multiplying by the core volume.
0018-9464/04$20.00 © 2004 IEEE
Fig. 4. Zones and equivalent core loss circuit model—where each RL circuit
model corresponds to a physical zone in the magnetic material. (a) Physical
zones. (b) Equivalent circuit model.
Fig. 3. Core loss implemented in the electrical and magnetic domains.
When a nonlinear model of hysteresis is required, the ubiquitous Jiles–Atherton (JA) [10]–[12], Preisach [13] or Chan and
Vladirimescu [14] models are all in common use.
In each case, the nonlinear – curve is characterized at a
specific frequency and temperature and used under those conditions. Frequency-dependent (otherwise known as rate-dependent) models which model the change in – loop with frequency also exist, such as the Hodgdon model [16]–[18] or Carpenter’s differential equation approach [15].
C. Modeling High Frequency (Eddy-Current) Losses
In conducting core materials, currents are induced which flow
in loops. These eddy currents act against the externally applied
magnetic field, causing decreased flux and increased losses as
the frequency is increased. Konrad [4] discusses eddy currents
generally in some detail and Zhu, Hui, and Ramsden [5]–[8]
propose methods of implementing the effects of eddy currents
in a simulation model of a magnetic core. The basic modeling
concept is to treat the magnetic material as a series of zones. This
approach is also described in detail by Brown et al. [9] to model
magnetic components for sensor applications. The currents in
these zones approximate the eddy-current behavior as the frequency increases. Each zone is modeled as a resistance–inductance ( ) element, in a network in the magnetic domain, as
shown in Fig. 4.
For each zone, the eddy-current loop can be considered to
be a single turn winding around the cross section. In each cross
section, the resistor represents the reluctance of the core material
in the magnetic domain and is calculated using
Fig. 5. Behavioral model to implement frequency-dependent hysteresis.
reluctances, simply by replacing the linear models with a
suitable nonlinear model with the correct physical dimensions
of the lamination.
D. Behavioral Modeling of High Frequency Losses
Another approach to implementing the eddy-current behavior
described in the previous section is to modify the – loop
behavior by modifying the applied magnetic field strength
prior to calculating . As the frequency of the applied magnetic field strength increases, a low-pass filter function
causes the apparent magnetic field strength
to decrease as illustrated in Fig. 5. The effect of this is to widen the – loop.
The advantage of this type of approach (as implemented in the
Saber simulator) is the simplicity of implementation, as opposed
to the complex network of individual nonlinear core models required in the network method. This gives a resulting increase in
simulation speed and reliability due to the reduced number of
equations and nonlinearities to be solved.
where is the magnetic path length, is the cross-sectional area
of the zone, and is the relative permeability of the core material. The magnetic-domain inductance representing the core loss
for the lamination is calculated using
is the height of the zone multiplied by the magnetic
path length, is the length around the eddy-current loop, and
is the conductivity of the magnetic core material.
Using this approach, the number of zones can be controlled
components derived
for the required accuracy, and the
easily. This technique can also be used with nonlinear core
The drawback for a model such as the
ladder approach
for predicting high frequency losses is that, in electrical terms,
it only has real poles. In order to model many soft ferrite materials, a more complex network is required. Even using a ladder
network of the form described previously, the effect is only to
add more poles at a higher frequency. It does not help model the
more complex characteristics of many commercial soft ferrites
such as Philips 3E5 or 3F3 material, where the complex permeability of the material (and the permeance of the core) varies
with frequency as illustrated in Fig. 6.
The concept behind the proposed model is to design a network that represents the small-signal variation in permeability
but that can also be used with a nonlinear reluctance model
to predict the variation in loss with frequency. In order to acnetwork is modified to include a
complish this, the basic
Fig. 6.
Frequency variation of permeability.
Fig. 7.
Modified model structure.
Fig. 8. The 3E5 complex permeability curves.
“magnetic” capacitor. The modified model structure is shown
in Fig. 7.
The concept of a “magnetic capacitor” is not physically realistic (a capacitor in the magnetic domain would be an energy
source!), but in combination with the standard loss component
it gives a second-order response with frequency for the reluctance. The resulting small-signal behavior in the frequency domain may now represent the behavior shown in Fig. 6. Note
that the relative permeability characteristic is a second-order response rather than the first-order response achievable using a
ladder network.
The complex permeance of the network may be written as in
is the reluctance (the “magnetic resistance”), is the “magis the “magnetic capacitance.” The
netic inductance,” and
requirement that the network is a net energy loss imposes the
condition that the imaginary part of the complex permeance is
negative. This requires that is greater than . The reluctance
is found directly from the low frequency perof the core
meability. In order to choose the values of and , appropriate
Fig. 9. The 3F3 complex permeability curves.
values of and must be chosen. This may be done by reference to published data for the complex permeability. The rolloff
frequency is primarily determined by , with the slope of the
curves being determined by .
To illustrate the approach two materials, 3E5 and 3F3 were
chosen that have different permeability characteristics. In each
case, the magnetic and parameters were derived from the
Ferroxcube data sheet curves for complex permeability and simulations carried out in the frequency domain to calculate the
model’s response across a similar frequency range. Fig. 8 shows
the response for 3E5 and Fig. 9 shows the same analysis for 3F3.
It is interesting to note that even for this basic lumped model
with the simplest network, that the complex permeability is reasonably accurate across the majority of the frequency range. If
greater accuracy is required at the higher end of the frequency
range, then extra network components could be added as required, but it then becomes more difficult to characterize the
In order to provide an accurate nonlinear model for time-domain circuit simulation, the linear magnetic reluctance model
can be replaced with a nonlinear model of hysteresis such as
that of Jiles–Atherton. A strength of this approach is that the
linear model can be used to empirically define the loss terms (
Fig. 10.
Fig. 11.
Frequency-dependent core model simulation results.
Fig. 12.
B –H curves (measured and simulated) at 50 kHz.
Fig. 13.
B –H curves (measured and simulated) at 100 kHz.
B –H curves (measured and simulated) at 10 kHz.
and ) and then the core model can be changed into the nonlinear core model required for large-signal analysis.
Using the Jiles–Atherton model of hysteresis in the place
of the linear core model used previously, large-signal time-domain simulations were carried out over the frequency range
10–100 kHz. The resulting – curves for the modified complete core model are shown in Fig. 10 (Philips 3F3 material).
Taking the 3E5 example, measurements were made of the
– curve using a simple toroid core (cross section 4.44 mm ,
effective length 22.9 mm) with four turns on the primary and
secondary and at frequencies of 10, 50, and 100 kHz. These
measurements were compared with a modified Jiles–Atherton
model characterized at 10 kHz using the methods described by
Wilson, Ross and Brown [19]–[22], with the resulting paramk,
. The parameters of the model are related to
physical properties of the core magnetic material and are briefly
summarized as follows:
• controls the irreversible loss;
• defines the anhysteretic behavior;
• controls the reversible/irreversible proportions;
• influences the internal effective field strength;
defines the saturation magnetization;
• ECrate controls the rate of loop closure.
The value of was estimated from a small signal analysis to
be 4 mH and estimated to be 10 pF (in the magnetic domain).
The resulting measured and simulated – curves are shown
in Figs. 11–13.
It can be seen that the model accurately predicts the change in
– loop shape as the frequency increases (the slight dc offset
in Fig. 13 is probably due to measurement error).
There is good agreement between measured and simulated results for major and symmetric minor loops. However, for asymmetric minor loops, the Jiles–Atherton model behavior is less
satisfactory. In these cases, the minor loop will relax rapidly to
a point such that the loop becomes symmetric about the anhysteretic and this behavior is not observed experimentally. Modifications to the original Jiles–Atherton model have been proposed
by Carpenter [25] and Jiles [26] that go some way to improving
the modeling of minor loops, but no totally satisfactory solution
The results of the simulations demonstrate that this simple
network model can accurately replicate the complex permeability characteristics observed in typical soft ferrites for power,
signal, or EMI applications. The model taken in conjunction
with a suitable hysteresis model can be used for linear (frequency-domain) or nonlinear (time-domain) analyses with no
modification of the basic loss terms. This provides a systematic
approach to the characterization of the ferrite material model
based on data-sheet information or empirical measurement.
The technique could easily be extended to encompass more
complex network models for increased accuracy, but given the
relatively wide variability of material tolerances in general for
soft ferrites, this approach is generally adequate for most applications. This approach offers a mechanism for the inclusion
of model parameters that may depend on environmental factors
such as temperature or stress.
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Peter R. Wilson (M’98) received the B.Eng. degree in electrical and electronic
engineering and the postgraduate diploma in digital systems engineering from
Heriot-Watt University, Edinburgh, U.K., in 1988 and 1992, respectively, the
M.B.A degree from the Edinburgh Business School in 1999, and the Ph.D. degree from the University of Southampton, Southampton, U.K., in 2002.
He worked in the Navigation Systems Division of Ferranti plc., Edinburgh,
from 1988 to 1990 on fire control computer systems, before moving in 1990 to
the Radar Systems Division of GEC-Marconi Avionics, Edinburgh. From 1990
to 1994, he worked on modeling and simulation of power supplies, signal processing systems, servo, and mixed technology systems. From 1994 to 1999, he
was a European Product Specialist with Analogy Inc., Swindon, U.K. During
this time, he developed a number of models, libraries, and modeling tools for
the Saber simulator, especially in the areas of power systems, magnetic components, and telecommunications. He is currently a Lecturer in the Department of
Electronics and Computer Science, University of Southampton, and has been
working in the Electronic Systems Design Group at the university since 1999.
His current research interests include modeling of magnetic components in electric circuits, power electronics, renewable energy systems, VHDL-AMS modeling and simulation, and the development of electronic design tools.
Dr. Wilson is a member of the IEE and a Chartered Engineer in the U.K.
J. Neil Ross received the B.Sc. degree in physics in 1970 and the Ph.D. degree
in 1974 for work on the physics of ion laser discharges, both from the University
of St. Andrews, U.K.
For 12 years, he worked at the Central Electricity Research Laboratories of
the CEGB undertaking research on the physics of high voltage breakdown and
optical fiber sensors for use in a high-voltage environment. He joined the University of Southampton, Southampton, U.K., in 1987 and has undertaken research in a variety of fields associated with instrumentation and measurement.
He is currently a Senior Lecturer in the Department of Electronics and Computer Science at the University of Southampton. His current research interests
include the modeling of magnetic components for communications, instrumentation, and power applications.
Andrew D. Brown (M’90–SM’96) was born in the U.K. in 1955. He received
the B.Sc.(Hons) degree in physical electronics and the Ph.D. degree in microelectronics from the University of Southampton, Southampton, U.K., in 1976
and 1981, respectively.
He was appointed Lecturer in Electronics at the University of Southampton
in 1981, Senior Lecturer in 1989, Reader in 1992, and was appointed to an established chair in 1998. He was a Visiting Scientist at IBM, Hursley Park, U.K.,
in 1983 and a Visiting Professor at Siemens NeuPerlach, Munich, Germany, in
1989. He is currently head of the Electronic System Design Group, Electronics
Department, University of Southampton. The group has interests in all aspects
of simulation, modeling, synthesis, and testing.
Prof. Brown is a Fellow of the IEE, a Chartered Engineer, and a European
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