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Machine Learning Coupled Multi‐Scale Modeling for Redox Flow Batteries
Author(s) -
Bao Jie,
Murugesan Vijayakumar,
Kamp Carl Justin,
Shao Yuyan,
Yan Litao,
Wang Wei
Publication year - 2020
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201900167
Subject(s) - battery (electricity) , artificial neural network , solver , scale (ratio) , flow battery , reduction (mathematics) , redox , computer science , flow (mathematics) , materials science , process engineering , biological system , simulation , power (physics) , artificial intelligence , mechanics , engineering , mathematics , thermodynamics , physics , geometry , quantum mechanics , biology , metallurgy , programming language
The framework of a multi‐scale model that couples a deep neural network, a widely used machine learning approach, with a partial differential equation solver and provides understanding of the relationship between the pore‐scale electrode structure reaction and device‐scale electrochemical reaction uniformity within a redox flow battery is introduced. A deep neural network is trained and validated using 128 pore‐scale simulations that provide a quantitative relationship between battery operating conditions and uniformity of the surface reaction for the pore‐scale sample. Using the framework, information about surface reaction uniformity at the pore level to combined uniformity at the device level is upscaled. The information obtained using the framework and deep neural network against the experimental measurements is also validated. Based on the multi‐scale model results, a time‐varying optimization of electrolyte inlet velocity is established, which leads to a significant reduction in pump power consumption for targeted surface reaction uniformity but little reduction in electric power output for discharging. The multi‐scale model coupled with the deep neural network approach establishes the critical link between the micro‐structure of a flow‐battery component and its performance at the device scale, thereby providing rationale for further operational or material optimization.