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Hybrid Model Predictive Control Strategy of Supercapacitor Energy Storage System Based on Double Active Bridge

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  • Lujun Wang

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Jiong Guo

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Chen Xu

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Tiezhou Wu

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China)

  • Huipin Lin

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

In order to solve the problem of which the dynamic response of a supercapacitor (SC) is limited due to the mismatch dynamic characteristics between the DC/DC converter and supercapacitor in an energy storage system, this paper proposes a hybrid model predictive control strategy based on a dual active bridge (DAB). The hybrid model predictive control model considers the supercapacitor and DAB in a unified way, including the equivalent series resistance and capacitance parameters of the SC. The method can obtain a large charging and discharging current of the SC, thereby not only improving the overall response speed of the system, but also expanding the actual capacity utilization range of the SC. The simulation results show that compared with the model prediction method of the dual active bridge converter, the proposed control method can effectively improve the overall response speed of the system, which can be improved by at least 0.4 ms. In addition, the proposed method increases the actual upper limit of the SC voltage, reduces the actual lower limit of the SC voltage, and then expands the actual capacity utilization range of the SC by 18.63%. The proposed method has good application prospects in improving the dynamic response performance of energy storage systems.

Suggested Citation

  • Lujun Wang & Jiong Guo & Chen Xu & Tiezhou Wu & Huipin Lin, 2019. "Hybrid Model Predictive Control Strategy of Supercapacitor Energy Storage System Based on Double Active Bridge," Energies, MDPI, vol. 12(11), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2134-:d:236985
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    References listed on IDEAS

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    1. Jingyu Liu & Lei Zhang, 2016. "Strategy Design of Hybrid Energy Storage System for Smoothing Wind Power Fluctuations," Energies, MDPI, vol. 9(12), pages 1-17, November.
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    Cited by:

    1. Huafeng Xia & Feiyan Chen, 2020. "Filtering-Based Parameter Identification Methods for Multivariable Stochastic Systems," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    2. Run Min & Dian Lyu & Shuai Cheng & Yingshui Sun & Linkai Li, 2019. "Linearized Discrete Charge Balance Control with Simplified Algorithm for DCM Buck Converter," Energies, MDPI, vol. 12(16), pages 1-17, August.
    3. Aissa Benhammou & Mohammed Amine Hartani & Hamza Tedjini & Hegazy Rezk & Mujahed Al-Dhaifallah, 2023. "Improvement of Autonomy, Efficiency, and Stress of Fuel Cell Hybrid Electric Vehicle System Using Robust Controller," Sustainability, MDPI, vol. 15(7), pages 1-21, March.

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