Machine learning-enabled performance prediction and optimization for iron–chromium redox flow batteries

Niu, Yingchun; Heydari, Ali; Qiu, Wei; Guo, Chao; Liu, Yinping; Xu, Chunming; Zhou, Tianhang; Xu, Quan

doi:10.1039/d3nr06578b

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Screening active metal-acetylacetonates [59] DFT Identified two promising complexes Predicting and optimizing performance for FeÀ Cr RFBs [60] ML Proposed a data-driven optimization methodology Predicting potentials of phenazine derivaties [61] DFT-assisted ML Evaluated four ML models using DFT analyses…”

Section: Application Description Methods Achievementmentioning

confidence: 99%

“…[7c,40] In the field of RFB research, Niu et al proposed a data-driven optimization methodology based on active learning to predict battery performance more accuratelye. [60] Battery life prediction methods in this context are generally categorized into two types: physical models and data-driven approaches. [120] Physical models explore the internal dynamics of batteries, examining physical and chemical transformations.…”

Section: Analysing Rfb Cycling Performance With Time-dependent Featuresmentioning

confidence: 99%

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Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

Tang,

Leung,

et al. 2024

ChemElectroChem

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This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials.

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Section: Application Description Methods Achievementmentioning

confidence: 99%

Section: Analysing Rfb Cycling Performance With Time-dependent Featuresmentioning

confidence: 99%

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

Tang,

Leung,

et al. 2024

ChemElectroChem

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The Effect of Bismuth on the Performance of a Single‐Cell Iron–Chromium Redox Flow Battery

Mans,

van der Westhuizen,

Krieg

2024

Adv Energy and Sustain Res

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This study examines the need for bismuth as a catalyst for the Cr2+/Cr3+ redox couple in an iron–chrome redox flow battery (ICRFB) using 1) open‐circuit voltage (OCV) periods to understand the impact of bismuth and the mechanism of hydrogen production with and without electrolyte flow, and 2) charge/discharge cycles to evaluate how bismuth influences ICRFB performance. The OCV study finds that the capacity decay in the ICRFB cycling is not solely due to the hydrogen evolution reaction, suggesting an alternative oxidation reaction is involved, likely catalyzed by metallic carbides like bismuth carbide. Both the OCV and the ICRFB confirm that the presence of bismuth negatively influences the battery performance due to increased H2 production. Further research is ongoing to validate the decay mechanism and confirm these results on a larger scale.

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Machine-learning assisted analysis on coupled fluid-dynamics and electrochemical processes in interdigitated channel for iron-chromium flow batteries

Zhou,

Liu,

Yuan

et al. 2024

Chemical Engineering Journal

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Machine learning-enabled performance prediction and optimization for iron–chromium redox flow batteries

Cited by 6 publications

References 32 publications

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

The Effect of Bismuth on the Performance of a Single‐Cell Iron–Chromium Redox Flow Battery

Machine-learning assisted analysis on coupled fluid-dynamics and electrochemical processes in interdigitated channel for iron-chromium flow batteries

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