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A Novel Multi-Agent Model-Free Control for State-of-Charge Balancing Between Distributed Battery Energy Storage Systems

Hong, Yujin, Xu, Dezhi, Yang, Weilin, Jiang, Bin, Yan, Xinggang (2020) A Novel Multi-Agent Model-Free Control for State-of-Charge Balancing Between Distributed Battery Energy Storage Systems. IEEE Transactions on Emerging Topics in Computational Intelligence, . E-ISSN 2471-285X. (doi:10.1109/TETCI.2020.2978434) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:81700)

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http://dx.doi.org/10.1109/TETCI.2020.2978434

Abstract

This article proposes a novel state of charge (SoC) balancing control strategy based on multi-agent control between distributed the battery energy storage systems (BESSs) in super-UPS. The proposed control strategy has plug and play capability. Batteries with different capacities are considered in the control system. The battery capacity degradation under long term operation is further taken into account. In addition, a model-free iPI control strategy is designed for primary current control to achieve the tracking response of BESS to the reference current. Firstly, the distributed voltage and per-unit current dynamic consensus algorithm is introduced to maintain the DC bus voltage and achieve the battery current sharing. Then, a SoC balancing algorithm based on multi-agent control strategy is proposed. Furthermore, an adaptive capacity estimation algorithm is proposed considering the battery capacity degradation. The efficiency of the proposed control strategy is demonstrated in MATLAB/Simulink.

Item Type: Article
DOI/Identification number: 10.1109/TETCI.2020.2978434
Uncontrolled keywords: Multi-agent system, model-free control, iPI control, battery energy storage systems, super-UPS, SoC balancing
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Xinggang Yan
Date Deposited: 13 Jun 2020 13:10 UTC
Last Modified: 04 Mar 2024 17:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/81700 (The current URI for this page, for reference purposes)

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