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A 3D modelling study on all vanadium redox flow battery at various operating temperatures

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Listed:
  • He, Qijiao
  • Li, Zheng
  • Zhao, Dongqi
  • Yu, Jie
  • Tan, Peng
  • Guo, Meiting
  • Liao, Tianjun
  • Zhao, Tianshou
  • Ni, Meng

Abstract

To understand whether the optimization of the operating/electrode structural parameters are temperature dependent, a 3D numerical model is developed and validated to gain insight into the impact of practical operating temperature (273.15 K–323.15 K) on vanadium redox flow battery (VRFB) performance, in which the property parameters are from published experimental data. The operating temperature is found significantly influence the optimal design of VRFBs. Increasing the inlet flow rate and state of charge (SOC), decreasing the electrode porosity and fibre diameter can all improve the battery performance with interdigitated flow channels, and the improvement increases with increasing temperature. In contrast, decreasing the fibre diameter or porosity increases the flow resistance and costs higher pump consumption, which is more pronounced at a lower temperature due to higher electrolyte viscosity. The effect of electrode thickness is also different at various temperatures. The gradient porosity electrode is applied in VRFB with interdigitated flow channels. The electrochemical performance of VRFB with gradient electrode (porosity increases from 0.8 at channel side to 0.93 at membrane side) performs similarly with the VRFB with 0.8 porosity electrode, while the pressure drop is reduced by 40% at all temperature. This model provides a deep understanding of effects of a wide range of working temperature on the optimization of operating/electrode parameters and on the VRFBs’ performance.

Suggested Citation

  • He, Qijiao & Li, Zheng & Zhao, Dongqi & Yu, Jie & Tan, Peng & Guo, Meiting & Liao, Tianjun & Zhao, Tianshou & Ni, Meng, 2023. "A 3D modelling study on all vanadium redox flow battery at various operating temperatures," Energy, Elsevier, vol. 282(C).
  • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023289
    DOI: 10.1016/j.energy.2023.128934
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    References listed on IDEAS

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    1. Li, Zheng & Zhang, Hao & Xu, Haoran & Xuan, Jin, 2021. "Advancing the multiscale understanding on solid oxide electrolysis cells via modelling approaches: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    2. Chen, Wei & Kang, Jialun & Shu, Qing & Zhang, Yunsong, 2019. "Analysis of storage capacity and energy conversion on the performance of gradient and double-layered porous electrode in all-vanadium redox flow batteries," Energy, Elsevier, vol. 180(C), pages 341-355.
    3. Zhou, X.L. & Zhao, T.S. & An, L. & Zeng, Y.K. & Yan, X.H., 2015. "A vanadium redox flow battery model incorporating the effect of ion concentrations on ion mobility," Applied Energy, Elsevier, vol. 158(C), pages 157-166.
    4. Oh, Kyeongmin & Yoo, Haneul & Ko, Johan & Won, Seongyeon & Ju, Hyunchul, 2015. "Three-dimensional, transient, nonisothermal model of all-vanadium redox flow batteries," Energy, Elsevier, vol. 81(C), pages 3-14.
    5. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
    6. Zheng, Qiong & Zhang, Huamin & Xing, Feng & Ma, Xiangkun & Li, Xianfeng & Ning, Guiling, 2014. "A three-dimensional model for thermal analysis in a vanadium flow battery," Applied Energy, Elsevier, vol. 113(C), pages 1675-1685.
    7. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Sun, Jie & Zheng, Menglian & Yang, Zhongshu & Yu, Zitao, 2019. "Flow field design pathways from lab-scale toward large-scale flow batteries," Energy, Elsevier, vol. 173(C), pages 637-646.
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