@inproceedings{dd86423a40f543459d09d1df53b10950,
title = "Artificial Intelligence Assisted Parametric Design by Splitting Inductance in Dual Active Bridge Converter",
abstract = "There is abundant research about achieving zero-voltage switching (ZVS) of dual active bridge (DAB) converters, among which the splitting of interfacing inductance and placing on both sides of the transformer is an effective method for extending the ZVS region for all the switching devices. However, the traditional analytical model can hardly imitate the proposed converter precisely under the high switching frequency (i.e.>1MHz) due to the complex converter model with the consideration of the parasitic components. Thus, the converter system can be regarded as a gray-box model. Consequently, artificial intelligence (AI) techniques can be utilized for the targeted optimization inside this gray-box. In this case, a genetic algorithm is employed in the DAB converter parametric design with an explicit fitness desire. The methodology of implementing AI techniques into converter parametric design is introduced and verified with a 1 MHz Gallium Nitride (GaN) based DAB converter prototype.",
keywords = "Artificial intelligence, Dual active bridge, Genetic algorithm, Gray-box model, Splitting inductance tuning method, Zero-voltage switching",
author = "Chang Wang and Yudi Xiao and Gabriel Zsurzsan and Zhe Zhang",
year = "2021",
doi = "10.1109/ECCE-Asia49820.2021.9479386",
language = "English",
isbn = "978-1-7281-6345-1",
series = "Proceedings of the Energy Conversion Congress and Exposition - Asia, Ecce Asia 2021",
pages = "554--561",
booktitle = "Proceedings of IEEE 12th Energy Conversion Congress & Exposition - Asia",
publisher = "IEEE",
address = "United States",
note = "2021 IEEE 12<sup>th</sup> Energy Conversion Congress and Exposition – Asia, ECCE Asia 2021 ; Conference date: 24-05-2021 Through 27-05-2021",
}