{"title":"Power Sharing Control Strategy of ISOP LLC-DAB Hybrid Bidirectional Converters Based on Multiagent Consensus Theory","authors":"Yuefeng Liao;Zhenkun Yang;Duo Yang;Xiang Li;Guo Xu;Jing Liang","doi":"10.1109/TIE.2025.3555036","DOIUrl":null,"url":null,"abstract":"In applications such as dc microgrids and energy storage systems that are characterized by high power and input voltage, input-series output-parallel (ISOP) dc-dc bidirectional converters are widely adopted to intermediate power conversion. The LLC-DAB hybrid bidirectional converter is a single-stage converter, which is composed of LLC resonant circuit and dual active bridge (DAB) circuit. It combines the advantages of LLC and DAB, making it highly suitable for use as a submodule (SM). However, differences in input impedance among these SMs can lead to power imbalance during operation, affecting the performance of the system. To address this problem, a novel power sharing control strategy based on multiagent consensus theory is proposed in this article. This method treats each SM as an agent. By exchanging information with its neighbors, each SM can regulate its power in real- time to achieve power sharing. Additionally, an adaptive dynamic compensator is proposed to compensate for control processes in real-time, thereby reducing settling time of the system. Compared to traditional power sharing strategies, the proposed method not only ensures power sharing among SMs but also improves the dynamic performance of the system. Finally, the effectiveness of the proposed method is demonstrated through an experimental prototype.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 10","pages":"10290-10300"},"PeriodicalIF":7.2000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10948487/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In applications such as dc microgrids and energy storage systems that are characterized by high power and input voltage, input-series output-parallel (ISOP) dc-dc bidirectional converters are widely adopted to intermediate power conversion. The LLC-DAB hybrid bidirectional converter is a single-stage converter, which is composed of LLC resonant circuit and dual active bridge (DAB) circuit. It combines the advantages of LLC and DAB, making it highly suitable for use as a submodule (SM). However, differences in input impedance among these SMs can lead to power imbalance during operation, affecting the performance of the system. To address this problem, a novel power sharing control strategy based on multiagent consensus theory is proposed in this article. This method treats each SM as an agent. By exchanging information with its neighbors, each SM can regulate its power in real- time to achieve power sharing. Additionally, an adaptive dynamic compensator is proposed to compensate for control processes in real-time, thereby reducing settling time of the system. Compared to traditional power sharing strategies, the proposed method not only ensures power sharing among SMs but also improves the dynamic performance of the system. Finally, the effectiveness of the proposed method is demonstrated through an experimental prototype.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.