Model Predictive Control for Inter-Submodule State-of-Charge Balancing in Cascaded H-Bridge Converter-Based Battery Energy Storage Systems

被引:10
|
作者
Liang, Gaowen [1 ]
Rodriguez, Ezequiel [1 ]
Farivar, Glen G. [2 ]
Nunes, Enrique [3 ]
Konstantinou, Georgios [4 ]
Townsend, Christopher D. [5 ]
Leyva, Ramon [6 ]
Pou, Josep [3 ]
机构
[1] Nanyang Technol Univ, Energy Res Inst, Singapore 639798, Singapore
[2] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia
[3] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
[4] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia
[5] Univ Western Australia, Dept Elect Elect & Comp Engn, Crawley, WA 6009, Australia
[6] Univ Rovira i Virgili, Dept Elect Elect & Automat Engn, Tarragona 43007, Spain
基金
新加坡国家研究基金会;
关键词
Battery energy storage; cascaded H-bridge; model predictive control; optimization; state-of-charge;
D O I
10.1109/TIE.2023.3290249
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the operation of battery energy storage systems based on the cascaded H-bridge converter, it is beneficial to balance the state of charge of batteries in different submodules within the converter phase-arm. This is achieved by distributing the active power among the submodules. Although multiple methods have been proposed for this purpose, they face the challenge of rendering optimal active power distributions that maximize balancing speed while meeting power constraints in the battery energy storage system. To overcome this challenge, a model predictive control scheme is developed in this paper. The proposed method is remarkably robust against parametric uncertainties (battery voltage, capacity, etc.), as evidenced by its ability to tolerate a substantial 50% uncertainty in the parameters, resulting in a mere 0.05% steady-state error. Furthermore, because the predictive control can be executed at a low frequency, the computational burden is comparable to other existing methods.
引用
收藏
页码:5777 / 5786
页数:10
相关论文
共 38 条
  • [31] Modular Model Predictive Control Algorithm Based on Minimum Current Error for Single-phase Cascaded H-bridge Rectifier
    Wu X.
    Yu L.
    Yang H.
    Xiong C.
    Feng X.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2020, 40 (19): : 6284 - 6293
  • [32] Hierarchical single-objective variable double voltage vector model predictive control with low computational burden for cascaded H-bridge multilevel converter
    Han, Jingang
    Li, Xiangyu
    Tang, Tianhao
    INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, 2024, : 1118 - 1134
  • [33] Low-Complexity Two-Voltage-Based Model Predictive Control for a Single-Phase Cascaded H-Bridge Inverter
    Qi, Chen
    Chen, Xiyou
    Tu, Pengfei
    Wang, Peng
    2017 IEEE 3RD INTERNATIONAL FUTURE ENERGY ELECTRONICS CONFERENCE AND ECCE ASIA (IFEEC 2017-ECCE ASIA), 2017, : 1440 - 1444
  • [34] A Fully Filter-Based Decentralized Control With State of Charge Balancing Strategy for Battery Energy Storage Systems in Autonomous DC Microgrid Applications
    Lin, Xin
    Zamora, Ramon
    Baguley, Craig A.
    IEEE ACCESS, 2021, 9 : 15028 - 15040
  • [35] Finite control set model predictive control for static synchronous compensator based on hybrid cascaded H-bridge and neutral point clamped multilevel inverter
    Monfared, Kourosh Khalaj
    Miremad, Armin
    Iman-Eini, Hossein
    Neyshabouri, Yousef
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2021, 31 (02)
  • [36] Model Predictive Control Based Advanced Switching Strategy for H-Bridge Converter Used in SMES Applications to Obtain Even Loss Sharing
    Chowdhury, Md. Razon
    Chowdhury, Sumon
    Rahman, Md. Ashib
    Islam, Md. Rabiul
    Mahmud, M. A. Parvez
    Kouzani, Abbas Z.
    IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, 2021, 31 (08)
  • [37] LQG Current Control Strategy for a CHB Converter-Based Second-Life Battery Energy Storage System without Steady-State Error
    Poblete, Pablo
    Aguilera, Ricardo P.
    Alcaide, Abraham M.
    Cuzmar, Rodrigo H.
    Siwakoti, Yam P.
    Lu, Dylan Dah-Chuan
    2024 IEEE 34TH AUSTRALASIAN UNIVERSITIES POWER ENGINEERING CONFERENCE, AUPEC 2024, 2024,
  • [38] Dispatching of Wind/Battery Energy Storage Hybrid Systems Using Inner Point Method-Based Model Predictive Control
    Yang, Deyou
    Wen, Jiaxin
    Chan, Ka-wing
    Cai, Guowei
    ENERGIES, 2016, 9 (08):