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Neural network-aided 4-DF global efficiency optimal control for the DAB converter based on the comprehensive loss model

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  • Zhang, Hao
  • Tong, Xiangqian
  • Yin, Jun
  • Blaabjerg, Frede

Abstract

—Intending to enhance the efficiency of dual-active-bridge (DAB) converters, this paper presents a four degree-of-freedom (DF) efficiency optimization method based on the comprehensive loss model. Besides the traditional triple-phase-shift modulation, the switching frequency is added as another control DF to minimize the core loss of the high-frequency transformer (HFT). Considering the nonlinear characteristics of the switching devices and the HFT, the comprehensive loss model is established first to reflect the converter's overall loss directly. Then the algorithm-based efficiency optimization solution is proposed to investigate the 4-DF modulation variables, which are subject to the minimal overall loss of the DAB converter in a given operating condition. A neural network (NN) is then adopted to model the mapping relationship between the ideal 4-DF variables and the working conditions. On this basis, a closed-loop controller that combines the traditional PI regulator and the NN module is suggested to realize both the output target control and the efficiency optimization. Finally, the performance of the proposed control strategy is fully verified through a 1.2 kW experimental prototype. The experimental results show that the converter's efficiency is significantly improved with the NN-aided 4-DF control strategy.

Suggested Citation

  • Zhang, Hao & Tong, Xiangqian & Yin, Jun & Blaabjerg, Frede, 2023. "Neural network-aided 4-DF global efficiency optimal control for the DAB converter based on the comprehensive loss model," Energy, Elsevier, vol. 262(PA).
  • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023301
    DOI: 10.1016/j.energy.2022.125448
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    References listed on IDEAS

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    1. McIlwaine, Neil & Foley, Aoife M. & Morrow, D. John & Al Kez, Dlzar & Zhang, Chongyu & Lu, Xi & Best, Robert J., 2021. "A state-of-the-art techno-economic review of distributed and embedded energy storage for energy systems," Energy, Elsevier, vol. 229(C).
    2. Yuan, Zhi & Wang, Weiqing & Wang, Haiyun & Mizzi, Scott, 2020. "Combination of cuckoo search and wavelet neural network for midterm building energy forecast," Energy, Elsevier, vol. 202(C).
    3. Yilmaz, Unal & Turksoy, Omer & Teke, Ahmet, 2019. "Intelligent control of high energy efficient two-stage battery charger topology for electric vehicles," Energy, Elsevier, vol. 186(C).
    4. Alirahmi, Seyed Mojtaba & Mousavi, Seyedeh Fateme & Ahmadi, Pouria & Arabkoohsar, Ahmad, 2021. "Soft computing analysis of a compressed air energy storage and SOFC system via different artificial neural network architecture and tri-objective grey wolf optimization," Energy, Elsevier, vol. 236(C).
    5. Zhang, Bowei & Guo, Simao & Jin, Hui, 2022. "Production forecast analysis of BP neural network based on Yimin lignite supercritical water gasification experiment results," Energy, Elsevier, vol. 246(C).
    6. Lin, Chun-Cheng & Wu, Yi-Fang & Liu, Wan-Yu, 2021. "Optimal sharing energy of a complex of houses through energy trading in the Internet of energy," Energy, Elsevier, vol. 220(C).
    7. Karthikeyan, V. & Gupta, Rajesh, 2017. "Light-load efficiency improvement by extending ZVS range in DAB-bidirectional DC-DC converter for energy storage applications," Energy, Elsevier, vol. 130(C), pages 15-21.
    8. Kadri, Ameni & Marzougui, Hajer & Aouiti, Abdelkrim & Bacha, Faouzi, 2020. "Energy management and control strategy for a DFIG wind turbine/fuel cell hybrid system with super capacitor storage system," Energy, Elsevier, vol. 192(C).
    Full references (including those not matched with items on IDEAS)

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