A comprehensive design approach for a three-winding planar transformer
quarta-feira, 11 de outubro de 2023
A comprehensive design approach for a three-winding planar transformer Shenli Zou1 Chanaka Singhabahu2 Jianfei Chen2 Alireza Khaligh2
A comprehensive design approach for a three-winding planar transformer
Shenli Zou1 Chanaka Singhabahu2 Jianfei Chen2 Alireza Khaligh2
1Electric Power Conversion, Rivian Automotive,
Inc, USA
2Maryland Power Electronics Laboratory (MPEL),
Department of Electrical and Computer
Engineering, Institute for Systems Research,
University of Maryland, College Park, Maryland,
USA
Abstract
In this paper, a new three-winding planar transformer design with the integrated leakage inductor is proposed for a triple-active-bridge converter. It enables two output voltage levels: a high voltage (HV) output port and a low voltage (LV) output port. The primary and secondary windings are split unevenly in both side legs while the tertiary winding is connected in parallel. The unique winding configuration enables: (i) enhanced efficiency with low volume; and (ii) suppressed parasitic capacitances. Detailed transformer reluctance and loss models are developed in the design process. The core geometry is optimized using a reluctance-model-based mathematical computation. Moreover, comprehensive high-fidelity simulations are conducted to analyse the trade-offs among parasitic capacitances, losses, and inductances. The customized core and the non-overlapping winding boards are assembled, characterized, and tested under various power flow conditions.
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domingo, 8 de outubro de 2023
Full-SiC Integrated Power Module Based on Planar Packaging Technology for High Efficiency Power Converters in Aircraft Applications O. Raab, M. Guacci, A. Griffo, K. Kriegel, M. Heller, J. Wang, D. Bortis, M. Schulz, J. W. Kolar
Proceedings of the 11th International Conference on Integrated Power Electronics Systems (CIPS 2020), Berlin, Germany, March 24-26, 2020
Full-SiC Integrated Power Module Based on Planar Packaging Technology for High
Efficiency Power Converters in Aircraft Applications
O. Raab,
M. Guacci,
A. Griffo,
K. Kriegel,
M. Heller,
J. Wang,
D. Bortis,
M. Schulz,
J. W. Kolar
Full-SiC Integrated Power Module based on Planar Packaging Technology for High Efficiency Power Converters in Aircraft Applications
Oliver Raaba
, Mattia Guaccib
, Antonio Griffoc
, Kai Kriegela
, Morris Hellerb
, Jiabin Wangc
, Dominik Bortisb
, Martin
Schulza
, and Johann W. Kolarb
aSiemens AG, Corporate Technology, Munich, Germany
bPower Electronic Systems Laboratory, ETH Zurich, Zurich, Switzerland
cDepartment of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, UK
Abstract
Compact, light-weight, efficient and reliable power converters are fundamental for the future of More Electrical Aircraft
(MEA). Core elements supporting the electrification of the aerospace industry are power modules (PMs) employing
exclusively SiC MOSFETs. In order to fully exploit the high switching speeds enabled by SiC, and to address the
challenges arising from the parallelization of power devices, novel PM concepts must be investigated. In this paper,
highly symmetrical layouts, low inductance planar interconnection technologies, and integrated buffer capacitors are
explored to realize a high efficiency, fast-switching, and reliable full-SiC PM for MEA applications. A comprehensive
assessment of a number of performance metrics against state-of-the-art full-SiC PMs demonstrates the benefits of the
proposed design approach and manufacturing technologies. Moreover, by integrating temperature and current sensors,
intelligent functions, which are crucial for the safe application of power electronics in MEA, are added to the developed
PM. In this context, the use of MOSFETs’ Temperature Sensitive Electrical Parameters for online junction temperature
estimation is demonstrated, allowing for non-invasive, i.e. without the need for dedicated sensors, thermal monitoring.
Additionally, a highly compact gate driver, reducing the overall system volume and complexity, is designed and integrated
in the housing of the PM. Finally, switching waveforms are measured during operation of the PM at 500V and 200A,
proving the performance improvement enabled by the low inductance layout, the integrated snubber, and the gate driver.
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sexta-feira, 6 de outubro de 2023
The MEGACube 166kW/20kHz Medium-Frequency Transformer
The MEGACube 166kW/20kHz Medium-Frequency Transformer
ABSTRACT
High power DC-DC conversion constitutes the key enabling technology for the implementation of solid-statetransformers.
Within these high-power DC-DC converters, the Medium Frequency (MF) transformer is one of the
main components, as its task is to provide the primary to secondary isolation and the step-up ratio between the
different voltage levels. Several options for the construction of this MF transformer have been reported with
different considered core materials, winding arrangements, isolation concepts and thermal management, whereby the
main realizations will be revised in this paper. Thereafter, the details of a 166kW/20kHz MF transformer will be
presented together with the designed test-bench utilized for the continuous testing of the transformer.
Design and Experimental Analysis of a Medium-Frequency Transformer for Solid-State Transformer Applications M. Leibl, G. Ortiz, J. W. Kolar
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 5, NO. 1, MARCH 2017
Design and Experimental Analysis of a
Medium-Frequency Transformer for
Solid-State Transformer Applications
Michael Leibl, Member, IEEE, Gabriel Ortiz, Member, IEEE, and Johann W. Kolar, Fellow, IEEE
Abstract— Within a solid-state transformer, the isolated dc–dc
converter and in particular its medium-frequency transformer
are one of the critical components, as it provides the required
isolation between primary and secondary sides and the voltage
conversion typically necessary for the operation of the system.
A comprehensive optimization procedure is required to find a
transformer design that maximizes power density and efficiency
within the available degrees of freedom while complying with
material limits, such as temperature, flux density, and dielectric
strength as well as outer dimension limits. This paper presents
an optimization routine and its underlying loss and thermal
models, which are used to design a 166 kW/20 kHz transformer
prototype achieving 99.4% efficiency at a power density of
44 kW/dm3. Extensive measurements are performed on the
constructed prototype in order to measure core and winding
losses and to investigate the current distribution within the litz
wire and the flux sharing between the cores.
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quarta-feira, 4 de outubro de 2023
Fast Charging, State of Charge Estimation, and Remaining Useful Life Prediction of Lithium-Ion Battery for Smart Battery Management System = 스마트 배터리 관리 시스템을 위한 리튬 이온 배터리의 급속충전, 충전 상태 및 잔여 수명 예측
Dissertation for the degree of Doctor of Philosophy Fast Charging, State of Charge Estimation, and Remaining Useful Life Prediction of Lithium-Ion Battery for Smart Battery Management System]
BY Muhammad Umair Ali-February 2020
Department of Electrical and Computer Engineering
The Graduate School
Pusan National University
ABSTRACT
Due to the escalation in environmental pollution and energy prices, electric vehicles
(EVs) have widely explored in the past few years. Battery electric vehicles (BEVs), plug-in
hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs) are the different
variants of EVs. These EVs consist of energy storage and the motor system as the auxiliary
or primary energy source (FCEVs and PHEVs) or the sole energy source (BEVs). The
lithium-ion (Li-ion) batteries are preferred as an energy storage system because of its longlife
cycle, reliability, high energy density, low toxicity, low self-discharge rate, high power
density, and high efficiency. In EVs, a smart battery management system (BMS) is one of
the essential components; it not only measures the states of battery accurately but also
ensures safe charging/discharging operation and prolongs the battery life. The issues of
accurate estimation of the state of charge (SOC), remaining useful life prediction (RUL),
and reduction in charging time of the Li-ion battery is still a bottleneck for the
commercialization of EVs because the Li-ion battery is a highly time-variant, non-linear,
and complex electrochemical system. In this dissertation, a novel fuzzy logic and
temperature feedback-based method, Lagrange multiplier approach, and partial discharge
data (PDD) based support vector machine (SVM) model are presented for reduction of the
charging time, SOC estimation, and RUL prediction of the Li-ion battery, respectively.
This dissertation comprises of four studies, each of which constitutes a step towards a
smarter BMS for EV applications. The first study proposes an efficient, real-time, fastcharging
methodology of Li-ion batteries. Fuzzy logic was adopted to drive the charging
current trajectory for series-connected Li-ion batteries. The voltage and temperature of the
cells were fed to the controller to find the optimal charge current value within the safe
temperature limit. A temperature control unit was also implemented to evade the effects of
fast charging on the aging mechanism. The proposed method of charging also protects the
battery from overvoltage and overheating. Extensive testing and comprehensive analysis
were conducted to examine the proposed charging scheme. The results show that the
proposed charging strategy favors a full battery recharging in 9.76% less time than the
conventional constant-current–constant-voltage (CC/CV) method. The methodology
charges the battery at a 99.26% SOC without significant degradation. The entire scheme
was implemented in real-time, using Arduino interfaced with MATLABTM Simulink. This
decrease in charging time assists in the fast charging of cell phones and notebooks and the
large-scale deployment of EVs.
The second work presents a new online data-driven battery model identification
method, where the battery parameters are updated using the Lagrange multiplier method. A
battery model with unknown battery parameters was formulated in such a way that the
terminal voltage at an instant time step is a linear combination of the voltages and load
current. A cost function was defined to determine the optimal values of the unknown
parameters with different data points measured experimentally. The constraints were added
in the modified cost function using the Lagrange multiplier method, and the optimal value
of the update vector was determined using the gradient approach. An adaptive open-circuit
voltage (OCV) and SOC estimator was designed for the Li-ion battery. The experimental
results showed that the proposed estimator is highly accurate and robust. The proposed
method effectively tracks the time-varying parameters of a battery with high accuracy.
During the SOC estimation, the maximum noted error was 1.28%. The convergence speed
of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the
high accuracy and robustness, the proposed method can be used in the design of a smarter
BMS for real-time applications.
In the third work, the sensitivity analysis is performed for the first and second-order
RC autoregressive exogenous (ARX) battery model to check the influence of voltage and
current transducer measurement uncertainty. The sensitivity analysis is performed under
the following conditions: Current sensor precision of ±5 mA, ±50 mA, ±100 mA, and ±500
mA and voltage sensor precision of ±1 mV, ±2.5 mV, ±5 mV, and ±10mV. The comparative
analysis of both models under the perturbed environment has been carried out. The effects
of the sensor’s sensitivity on the different battery structures and complexity are also
analyzed. Results show that the voltage and current sensor sensitivity has a significant
influence on SOC estimation. This research outcome assists the researcher in selecting the
optimal value of sensor accuracy to accurately estimate the SOC of the Li-ion battery for a
smarter BMS.
In the last work, a novel partial discharge data (PDD) based support vector machine
(SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical
features from the voltage and temperature of PDD to train the SVM models. The
classification and regression attributes of SVM are utilized to classify and predict accurate
RUL. The different ranges of PDD were analyzed to find the optimal range for training the
SVM model. The SVM model trained with optimal PDD features classifies the RUL into
six different classes for gross estimation, and the support vector regression is used to
estimate the accurate value of the last class. The classification and predictive performance
of SVM model trained using the full discharge data and PDD are compared. Results show
that the SVM classification and regression model trained with PDD features can accurately
predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be
utilized for online RUL estimation in Li-ion battery BMS.
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