Physics-informed CoKriging model of a redox flow battery

Howard, Amanda A.; Tartakovsky, Alexandre M.

doi:10.1016/j.jpowsour.2022.231668

Cited by 9 publications

(2 citation statements)

References 51 publications

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“…15) and S/GO-DA-1, 16 73% EE can be retained at 160 mA cm −2 current density, while the present SPEEK/NF-1 : 1 reaches 80.0% EE. This result reects that the cooperative UiO-66-NH 2 / s-g-C 3 N 4 complexes contribute excellent vanadium resistance and selectivity, 58,59 thus proving good cell efficiency. The satisfactory VFB performance also elucidates that the addition of 2D UiO-66-NH 2 /s-g-C 3 N 4 nanollers with controllable functionality is a promising method to optimize the SPEEK membrane structure.…”

Section: Vfb Performancementioning

confidence: 83%

A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH₂ and sulfonated graphitic carbon nitride for vanadium flow batteries

Zhao,

Wang,

et al. 2024

J. Mater. Chem. A

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Section: Vfb Performancementioning

confidence: 83%

A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH₂ and sulfonated graphitic carbon nitride for vanadium flow batteries

Zhao,

Wang,

et al. 2024

J. Mater. Chem. A

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“…Machine learning (ML) algorithms, including data-driven and physics-informed neural network models, − have significantly advanced RFB development. They enable characterization of molecular properties and reaction kinetics and prediction of cell performance across various scales. − Despite these advancements, predicting AORFB performance remains a challenging area due to the lack of well-curated ASO redox-active material data sets.…”

mentioning

confidence: 99%

Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance

Fu,

Howard,

Zeng

et al. 2024

ACS Energy Lett.

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Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness, and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitates efficient machine learning surrogates for predicting battery performance. The physics-guided continual learning (PGCL) method proposed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Using an AORFB database with a thousand potential materials generated by a 780 cm 2 interdigitated cell model, PGCL incorporates AORFB physics to optimize the continual learning task formation and training strategies to retain previously learned battery material knowledge. The trained PGCL demonstrates its capability in assessing emerging ASO materials within the established parameter space when evaluated with the dihydroxyphenazine isomers.

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Machine‐Learning‐Based Accurate Prediction of Vanadium Redox Flow Battery Temperature Rise Under Different Charge–Discharge Conditions

Anirudh Narayan,

Johar,

Kalra

et al. 2024

Energy Storage

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Accurate prediction of battery temperature rise is very essential for designing efficient thermal management scheme. In this paper, machine learning (ML)‐based prediction of vanadium redox flow battery (VRFB) thermal behavior during charge–discharge operation has been demonstrated for the first time. Considering different currents with a specified electrolyte flow rate, the temperature of a kW scale VRFB system is studied through experiments. Three different ML algorithms; linear regression (LR), support vector regression (SVR), and extreme gradient boost (XGBoost) have been used for prediction. The training and validation of ML algorithms have been done by the practical dataset of a 1 kW 6 kWh VRFB storage under 40 , 45, 50, and 60 A charge–discharge currents and 10 L min−1 of flow rate. A comparative analysis among ML algorithms is done by performance metrics such as correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE). XGBoost shows the highest R2 value of around 0.99, which indicates its higher prediction accuracy compared to other ML algorithms used. The ML‐based prediction results obtained in this work can be very useful for controlling the VRFB temperature rise during operation and act as an indicator toward further development of an optimized thermal management system.

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Physics-informed CoKriging model of a redox flow battery

Cited by 9 publications

References 51 publications

A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH₂ and sulfonated graphitic carbon nitride for vanadium flow batteries

A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH₂ and sulfonated graphitic carbon nitride for vanadium flow batteries

Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance

Machine‐Learning‐Based Accurate Prediction of Vanadium Redox Flow Battery Temperature Rise Under Different Charge–Discharge Conditions

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Physics-informed CoKriging model of a redox flow battery

Cited by 9 publications

References 51 publications

A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH2 and sulfonated graphitic carbon nitride for vanadium flow batteries

A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH2 and sulfonated graphitic carbon nitride for vanadium flow batteries

Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance

Machine‐Learning‐Based Accurate Prediction of Vanadium Redox Flow Battery Temperature Rise Under Different Charge–Discharge Conditions

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Product

Resources

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A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH₂ and sulfonated graphitic carbon nitride for vanadium flow batteries

A highly selective and high-performance sulfonated poly(ether ether ketone)-based hybrid membrane enabled by complexed UiO-66-NH₂ and sulfonated graphitic carbon nitride for vanadium flow batteries