Efficiency Optimization Scheme of Dual-Active-Half-Bridge DC-DC Converter Based on Reinforcement Learning
Hu Guang1, Hu Weihao1, Tang Yuanhong1, Huang Qi1, Chen Zhe2
1. University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Power System Wide Area Measurement and Control of the Key Laboratory of Sichuan Province Chengdu 611731 China; 2. Department of Energy Technology Aalborg University Aalborg DK-9110 Denmark
Abstract：Aiming to optimize the modulation efficiency of the dual-active-half-bridge (DAHB) DC-DC converter under traditional dual-phase-shift (DPS) modulation in the light load region with large power loss, a DPS modulation efficiency optimization scheme (QDPS) based on reinforcement learning (RL) is proposed in this paper. Using the Q-learning algorithm as a typical RL algorithm, the scheme obtains the optimized modulation strategy QDPS through offline training of the agent, which can provide the optimal phase shift angle for the DAHB DC-DC converter and effectively reduce the power loss of the converter in the light load region. Compared with the existing single-phase-shift and dual-phase-shift control strategy, the QDPS phase shift control strategy can effectively increase the operating efficiency and improve the performance of the converter. Finally, Matlab/Simulink simulation verifies the effectiveness of the proposed optimization scheme.
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2022, Vol. 37 
