A Deep Q-Network based optimized modulation scheme for Dual-Active-Bridge converter to reduce the RMS current

Yuanhong Tang, Weihao Hu, Jian Xiao, Zhengdong Lu, Zhou Liu, Zhe Chen, Frede Blaabjerg

Research output: Contribution to journalConference article in Journalpeer-review

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Abstract

In order to reduce the conduction losses of the Dual-Active-Bridge (DAB) converter, this paper proposes an optimized modulation scheme based on deep reinforcement learning (DRL). Owing to the Extended-Phase-Shift (EPS) modulation based Deep Q-Network (DQN) algorithm, the optimal phase-shift-angles can be defined, which reduces the root-mean-square (RMS) current tremendously. Moreover, the zero-voltage-switching (ZVS) performance can be guaranteed for the whole operation conditions. A 200 W prototype of the DAB converter is built and tested to prove the effectiveness of the proposed optimized modulation scheme. Experimental results demonstrates that the proposed optimized modulation scheme can obtain lower RMS current and higher operation efficiency in comparison to other three modulations.
Original languageEnglish
JournalEnergy Reports
Volume6
Issue number9
Pages (from-to)1192-1198
Number of pages7
DOIs
Publication statusPublished - Dec 2020
Event7th International Conference on Power and Energy Systems Engineering (CPESE 2020) - Fukuoka, Japan
Duration: 26 Sep 202029 Sep 2020

Conference

Conference7th International Conference on Power and Energy Systems Engineering (CPESE 2020)
CountryJapan
CityFukuoka
Period26/09/202029/09/2020

Keywords

  • DAB converter
  • RMS current
  • deep reinforcement learning
  • deep Q-network
  • ZVS

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