A DOD-SOH balancing control method for dynamic reconfigurable battery systems based on DQN algorithm

Xu Yang, Pei Liu, Fang Liu,Zhicheng Liu, Daoqi Wang,Jin Zhu,Tongzhen Wei

Frontiers in Energy Research(2023)

引用 0|浏览3
暂无评分
摘要
This article presents a DOD-SOH equalization method for a DRB system based on the Deep DQN algorithm. The proposed method utilizes DQN to learn the operational processes of the system. By integrating the advantages of DRB with SOH equalization theory and the DQN algorithm from the perspective of DOD, our method significantly improve battery performance and ensure cell balancing. To begin with, we present a dynamic reconfigurable battery system with a simple topological structure and outline its switching control process. Additionly, we provide an analysis of the SOH balancing principle and elaborate on the control process of DQN algorithm. Finally, subsequent simulations are carried out, and the simulation results demonstrate outstanding performances in reducing the variance of SOHs, which indicates an enhancement in the level of SOH balancing as well.
更多
查看译文
关键词
state of health,state of charge,deep reinforcement learning,deep Q-network,dynamic reconfigurable battery,battery imbalance
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要