RL-ANN-Based Minimum-Current-Stress Scheme for the Dual-Active-Bridge Converter With Triple-Phase-Shift Control

Aiming to reduce the current stress and improve the power efficiency of the dual-active-bridge (DAB) converter, this article proposes a reinforcement learning (RL) + artificial neural network (ANN)-based minimum-current-stress scheme. In the first stage, Q-learning as a typical algorithm of the RL method is adopted for offline training. The aim of the first stage is to solve the optimized control strategy based on the triple-phase-shift (TPS) control. More specifically, the zero-voltage-switching (ZVS) constraints and each effective operation mode are taken into consideration during the training process of the Q-learning algorithm. Therefore, the minimum-current-stress scheme while maintaining the soft switching can be obtained after the first stage. In the second stage, the training results of the Q-learning algorithm are used to train an ANN, in order to reduce the computational time and memory allocation. After that, the trained agent of the ANN, which likes an implicit function, can provide optimal phase-shift-angle online in real time under the entire continuous operation range. Finally, the detailed simulation and experimental results are given to demonstrate the effectiveness of the proposed optimized scheme.