Thermo-electrochemical modeling of thermally regenerative flow batteries

Yuhao Cai, Xin Qian, Ruihang Su, Xiongjie Jia, Jinhui Ying, Tianshou Zhao and Haoran Jiang

Applied Energy, 2024, vol. 355, issue C, No S0306261923015684

Abstract: Thermally regenerative flow batteries are promising for harvesting the ubiquitous low-grade heat energy. Efforts have been made to improve the performance of this type of battery by focusing mainly on thermodynamics perspectives, but ignoring the mass transfer and electrochemical kinetics of the battery. In this work, a thermo-electrochemical model is developed for analyzing effects of the heat transfer irreversibility, overpotential losses, mass transfer, and temperature-dependent resistances on the energy conversion, based on which a five-dimensional performance evaluation framework is proposed for the first time. Results show that the Nernstian loss, concentration loss, and ohmic loss are the main limiting steps for thermo-electricity conversion, and a trade-off between different performance indices exists. To realize the synergistic improvement of the thermo-electrochemical performance, a multi-objective optimization scheme based on a genetic algorithm is developed, and a power density of 13.22 μW cm−2, thermal efficiency of 8.17%, output-voltage efficiency of 48.17%, exergy efficiency of 46.47% and ecological coefficient of performance of 1.23 is achieved, which is among the highest performance in the open literature. The methods and results reported here pave a new way for the design and optimization of thermally regenerative flow batteries.

Keywords: Low-grade heat; Thermally regenerative flow batteries; Thermo-electrochemical modeling; Genetic algorithm; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2023.122204

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