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

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.

Keywords

• DAB converter
• RMS current
• Deep reinforcement learning
• Deep Q-Network
• ZVS