Investigation of soft-switching techniques for Power Electronics Building Blocks (PEBB)

Author(s):  
Heping Dai ◽  
Kun Xing ◽  
F.C. Lee
Author(s):  
He Song ◽  
Jun Wang ◽  
Yue Xu ◽  
Joshua Stewart ◽  
Slavko Mocevic ◽  
...  

Author(s):  
Abdul-Hakeem Mohammed Dobi ◽  
Mohd Rodhi Sahid ◽  
Tole Sutikno

Application of soft switching in DC-DC converter has achieved a remarkable success in power electronics technology in terms of reduction in switching losses, improve in power density, minimization of electromagnetic interference (EMI) and reduction in the volume of DC-DC converters. Quite a number of soft switching techniques had been reported in the past four decades. This paper aims at providing a review of various soft switching techniques, based on topology, the location of the resonant network, performance characteristics, and principles of operation. In addition, converters area of application, advantages as well as limitations are also highlighted.


Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 271
Author(s):  
André Andreta ◽  
Luiz Fernando Lavado Villa ◽  
Yves Lembeye ◽  
Jean Christophe Crebier

This work proposes a methodology for designing power electronic converters called “Automatic Design for Manufacturing” (ADFM). This methodology proposes creating Power Converter Arrays (PCAs) using standardized converter cells. The approach is greatly inspired by the microelectronics integrated circuit design flow, power electronics building blocks, and multicell converters. To achieve the desired voltage/current specifications, the PCA conversion stage is made from the assembly of several Conversion-Standard Cells (CSCs) in series and/or parallel. The ADFM uses data-based models to simulate the behavior of a PCA with very little computational effort. These models require a special characterization approach to maximize the amount of knowledge while minimizing the amount of data. This approach consists of establishing an experiment plan to select the relevant measurements that contain the most information about the PCA technology, building an experimental setup that is capable of acquiring data automatically and using statistical learning to train models that can yield precise predictions. This work performed over 210 h of tests in nine different PCAs in order to gather data to the statistical models. The models predict the efficiency and converter temperature of several PCAs, and the accuracy is compared with real measurements. Finally, the models are employed to compare the performance of PCAs in a specific battery charging application.


2010 ◽  
Vol 10 (3) ◽  
pp. 262-269 ◽  
Author(s):  
Young-Min Park ◽  
Han-Seong Ryu ◽  
Hyun-Won Lee ◽  
Myung-Gil Jung ◽  
Se-Hyun Lee

2021 ◽  
Author(s):  
Dehong Xu ◽  
Rui Li ◽  
Ning He ◽  
Jinyi Deng ◽  
Yuying Wu

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