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Energy management strategy of hybrid energy storage system based on fuzzy control for ships
[State-of-charge balancing of lithium-ion batteries with state-of-health awareness capability]

Author

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  • Wenqing Hu
  • Qianming Shang
  • Xiangrui Bian
  • Renjie Zhu

Abstract

Lithium-ion batteries have high-energy density, but they cannot respond quickly to power fluctuations; supercapacitors (SCs) can quickly respond to power fluctuations, but their energy density is low. The hybrid energy storage system (HESS) that uses both lithium-ion batteries and SCs can take into account the advantages of both, making the system perform better; however, the energy distribution between lithium-ion batteries and SC is difficult. This paper takes ships as the research object, analyzes the power changes of ships during operation, and finds that high-power fluctuations are always only a minority. This paper uses a fuzzy control strategy, based on the actual operating conditions of the ship, except that the ship’s power fluctuation is very small; the SC will provide energy for the ship to make full use of the SC instead of waiting for the arrival of high-power fluctuations. This paper builds an experimental platform and uses the Arbin tester to simulate the operation of the ship. The experimental data show that the SC can have enough time to adjust its state of charge to deal with the power fluctuations at the next moment even if it keeps charging and discharging. The research results of this paper have certain significance for the energy distribution and capacity configuration of HESS.

Suggested Citation

  • Wenqing Hu & Qianming Shang & Xiangrui Bian & Renjie Zhu, 2022. "Energy management strategy of hybrid energy storage system based on fuzzy control for ships [State-of-charge balancing of lithium-ion batteries with state-of-health awareness capability]," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 673-684.
    • Handle: RePEc:oup:ijlctc:v:17:y:2022:i::p:673-84.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctab094
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    References listed on IDEAS

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    1. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
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    Cited by:

    1. Xin Peng & Hui Chen & Cong Guan, 2023. "Energy Management Optimization of Fuel Cell Hybrid Ship Based on Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 16(3), pages 1-15, January.
    2. Tang, Ruoli & Zhang, Shihan & Zhang, Shangyu & Lai, Jingang & Zhang, Yan, 2023. "Semi-online parameter identification methodology for maritime power lithium batteries," Applied Energy, Elsevier, vol. 339(C).

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