Reinforcement Learning Based Efficiency Optimization Scheme for the DAB DC-DC Converter With Triple-Phase-Shift Modulation

• Published in: IEEE transactions on industrial electronics (1982) Vol. 68; no. 8; pp. 7350 - 7361
• Main Authors: Tang, Yuanhong, Hu, Weihao, Xiao, Jian, Chen, Zhangyong, Huang, Qi, Chen, Zhe, Blaabjerg, Frede
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Conversion ratio; DC-DC power converters; Dual-active-bridge (DAB) dc–dc converter; Efficiency; Electric bridges; Electric converters; Energy conversion efficiency; Heuristic algorithms; Inductors; Machine learning; Magnetic losses; Maximum power; Modulation; Optimization; Phase modulation; Phase shift; power efficiency; Q-learning; reinforcement learning (RL)
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2020.3007113

Aim to improve the power efficiency of the dual-active-bridge (DAB) dc-dc converter, an efficiency optimization scheme with triple-phase-shift (TPS) modulation using reinforcement learning (RL) is proposed in this article. More specifically, the Q-learning algorithm, as a typical algorithm of the RL, is applied to train an agent offline to obtain an optimized modulation strategy, and then the trained agent provides control decisions online in a real-time manner for the DAB dc-dc converter according to the current operating environment. The main objective is to obtain the optimal phase-shift angles for the DAB dc-dc converter, which can achieve the maximum power efficiency by reducing the power losses. Moreover, all possible operation modes of the TPS modulation are considered during the offline training process of the Q-learning algorithm. Thus, the cumbersome process for selecting the optimal operation mode in the conventional schemes can be circumvented successfully. Based on these merits, the proposed efficiency optimization scheme using the RL can realize the excellent performances for the whole load conditions and voltage conversion ratios. Finally, a 1.2-KW prototyped is built, and the simulation and the experimental results demonstrate that the power efficiency can be improved by using the optimization scheme based on the RL.