2022
DOI: 10.1039/d1ee03224k
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Machine learning-assisted design of flow fields for redox flow batteries

Abstract: Flow fields are a crucial component of redox flow batteries (RFBs). Conventional flow fields, designed by trial-and-error approaches and limited human intuition, are difficult to optimize, thus limiting the performance...

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Cited by 54 publications
(25 citation statements)
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“…Furthermore, we demonstrate that the selection of an appropriate electrode microstructure and flow field geometry can enhance species transport through a better electrolyte distribution, and as a result achieve higher electrochemical performance. Thus, to maximize the overall output, the optimization of flow field designs and electrode microstructure must be performed in tandem [47], [70], [71].…”
Section: Performance Trade-offs -mentioning
confidence: 99%
“…Furthermore, we demonstrate that the selection of an appropriate electrode microstructure and flow field geometry can enhance species transport through a better electrolyte distribution, and as a result achieve higher electrochemical performance. Thus, to maximize the overall output, the optimization of flow field designs and electrode microstructure must be performed in tandem [47], [70], [71].…”
Section: Performance Trade-offs -mentioning
confidence: 99%
“…However, these studies have been developed numerically, and their findings have only been experimentally tested by few authors. Wan et al used manufacturing methods based on machining the channels on the graphite plates, 30 whereas Trogadas et al manufactured their lung-inspired flow fields for polymer electrolyte fuel cells using 3D printing via direct metal laser sintering, achieving the defined geometry with a resolution of 33 μm. 19 Previous research leveraged interdigitated flow fields as the base pattern for optimization studies.…”
Section: ■ Introductionmentioning
confidence: 99%
“…Based on this work, Lin et al used a similar methodology to design 3D flow fields in all-vanadium RFBs with full three-dimensional geometry variation, which resulted in significantly reduced power losses by evolving 3D geometries from the standard interdigitated flow field to optimized patterns in the three spatial dimensions. In an alternative effort, Wan et al used machine learning methods to identify eight potential flow field designs which can achieve superior limiting currents (with an increase of 22%), resulting in improvements up to 11% of the battery energy efficiency compared with the serpentine flow field . In both the non-heuristic and heuristic studies, , the potential and relevance of fractal and hierarchical features in flow fields were identified and exploided.…”
Section: Introductionmentioning
confidence: 99%
“…Based on this work, Lin et al used a similar methodology to design 3D flow fields in all-vanadium RFBs with full three-dimensional geometry variation [29], which resulted in significantly reduced power losses by evolving 3D geometries from the standard interdigitated flow field to optimized patterns in the three spatial dimensions. In an alternative effort, Wan et al used machine learning methods to identify eight potential flow field designs which can achieve superior limiting currents with an increase of 22% and improve up to 11% the battery energy efficiency compared with the serpentine flow field [30]. In both the non-heuristic and heuristic studies [19], [20], the potential and relevance of fractal and hierarchy features in flow fields was identified and leveraged.…”
Section: Introductionmentioning
confidence: 99%
“…However, these studies have been developed numerically, and their findings have only been experimentally tested by few authors. Wan et al used manufacturing methods based on machining the channels on the graphite plates [30], whereas Trogadas et al…”
Section: Introductionmentioning
confidence: 99%