TY - JOUR
T1 - Reinforcement Learning Based Efficiency Optimization Scheme for the DAB DC-DC Converter with Triple-Phase-Shift Modulation
AU - Tang, Yuanhong
AU - Hu, Weihao
AU - Xiao, Jian
AU - Chen, Zhangyong
AU - Huang, Qi
AU - Chen, Zhe
AU - Blaabjerg, Frede
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - DAB DC-DC converter
KW - power efficiency
KW - optimization
KW - Reinforcement Learning (RL)
KW - Q-learning
U2 - 10.1109/TIE.2020.3007113
DO - 10.1109/TIE.2020.3007113
M3 - Journal article
SN - 0278-0046
VL - 68
SP - 7350
EP - 7361
JO - I E E E Transactions on Industrial Electronics
JF - I E E E Transactions on Industrial Electronics
IS - 8
M1 - 9138774
ER -