基于强化学习的双主动半桥直流变换器的效率优化方案

doi:10.19595/j.cnki.1000-6753.tces.L90399

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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.

Key words： Dual-phase-shift control dual-active-half-bridge DC-DC converter reinforcement learning Q-learning efficiency

Received: 11 July 2020

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TM46

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	Hu Guang
	Hu Weihao
	Tang Yuanhong
	Huang Qi
	Chen Zhe

Cite this article:

Hu Guang,Hu Weihao,Tang Yuanhong等. Efficiency Optimization Scheme of Dual-Active-Half-Bridge DC-DC Converter Based on Reinforcement Learning[J]. Transactions of China Electrotechnical Society, 2022, 37(zk1): 225-234.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90399 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/Izk1/225

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