Low grade heat recovery for power generation through electrochemical route: Vanadium Redox Flow Battery, a case study

Eapen, D.; Choudhury, Suman Roy; Rengaswamy, Raghunathan

doi:10.1016/j.apsusc.2018.02.025

Cited by 33 publications

(30 citation statements)

References 14 publications

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“…Comparing with Table I, there is no significant difference between using the input parameters in this work and the input parameters from [8] for the 2 error, and results are similar for the ∞ error. Very similar results are seen when using the parameters from [9]. The top row shows the resulting charge-discharge curve, the bottom row shows the relative errors.…”

Section: A Sensitivity To Choice Of Initial Parameterssupporting

confidence: 64%

“…The 0d model of an RFB was originally proposed in [8,22] and modified by [9] to give a fast estimate of redox-flow battery performance. The 0d model gives the cell voltage, E, due to an applied current density, j app , in the form…”

Section: B Monte-carlo Estimates Of the Mean And Covariance Of E Give...mentioning

confidence: 99%

“…This control-oriented model is derived for a unit cell RFB system and contains several fitting parameters. These parameters are determined by fitting the 0d model to experiments and vary widely between experiments even for the same battery design [8,9]. The dependence of parameters on initial and operating conditions reduces the predictive ability of the 0d model.…”

mentioning

confidence: 99%

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Physics-informed CoKriging model of a redox flow battery

Howard¹,

Tartakovsky²

2021

Preprint

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Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently, however, there is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB. In the multifidelity model, we use the Physicsinformed CoKriging (CoPhIK) machine learning method that is trained on experimental data and constrained by the so-called "zero-dimensional" physics-based model. Here we demonstrate that the model shows good agreement with experimental results and significant improvements over existing zero-dimensional models. We show that the proposed model is robust as it is not sensitive to the input parameters in the zero-dimensional model. We also show that only a small amount of highfidelity experimental datasets are needed for accurate predictions for the range of considered input parameters, which include current density, flow rate, and initial concentrations.

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Section: A Sensitivity To Choice Of Initial Parameterssupporting

confidence: 64%

Section: B Monte-carlo Estimates Of the Mean And Covariance Of E Give...mentioning

confidence: 99%

mentioning

confidence: 99%

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Physics-informed CoKriging model of a redox flow battery

Howard¹,

Tartakovsky²

2021

Preprint

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“…The governing equations for device‐scale modeling are the same as the ones for pore‐scale modeling (Equations –) for the overpotential (η) and Faradaic current density ( j ). Equation is used to calculate the device‐scale open circuit voltage ( E ocv ) for the VRFB

E_{normalocv} = E_{p}^{0} - E_{n}^{0} + \frac{R T}{F} l n (\frac{{\bar{c}}_{2} {\bar{c}}_{5} {({\bar{c}}_{H_{p}})}^{3}}{{\bar{c}}_{3} {\bar{c}}_{4} {\bar{c}}_{H_{2} O_{p}} {\bar{c}}_{H_{n}}})

…”

Section: Methodsmentioning

confidence: 99%

“…Equation is used to estimate the ohmic loss of the VRFB

η_{normalohm} = (\frac{w_{m}}{σ_{m}} + \frac{2 w_{e}}{σ_{e} ε^{1.5}} + \frac{2 w_{c}}{σ_{c}}) J

where

w_{m}

w_{e}

, and

w_{c}

are the thickness ( x direction in Figure ) of the membrane, electrode, and current collector,

σ_{m}

σ_{e}

, and

σ_{c}

are the conductivity, ε is the electrode porosity, and J is the discharge current density.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Coupled Multi‐Scale Modeling for Redox Flow Batteries

Bao

Vijayakumar

Kamp

et al. 2019

Advcd Theory and Sims

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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.

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Liquid-based electrochemical systems for the conversion of heat to electricity

Feng

Cheng

et al. 2022

Low-Grade Thermal Energy Harvesting

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Low grade heat recovery for power generation through electrochemical route: Vanadium Redox Flow Battery, a case study

Cited by 33 publications

References 14 publications

Physics-informed CoKriging model of a redox flow battery

Physics-informed CoKriging model of a redox flow battery

Machine Learning Coupled Multi‐Scale Modeling for Redox Flow Batteries

Liquid-based electrochemical systems for the conversion of heat to electricity

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