Analytical modeling for redox flow battery design

Chen, Yunxiang; Xu, Zhijie; Wang, Chao; Bao, Jie; Koeppel, Brian J.; Yan, Litao; Gao, Peiyuan; Wang, Wei

doi:10.1016/j.jpowsour.2020.228817

Cited by 33 publications

(27 citation statements)

References 42 publications

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“…In this section we conduct CoPhIK simulations as described above, except the input parameters to the Gaussian random variables in Eqs. ( 30)-( 33) are taken from literature [8] instead of [21]. The rest of the data remains the same.…”

Section: A Sensitivity To Choice Of Initial Parametersmentioning

confidence: 99%

“…The two half reactions in the RFB are given by FIG. 7: Examples of the 0d model with the parameters from [8,9,21].…”

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

confidence: 99%

“…The coefficients k 1 and k 2 are the reference rate constants for reactions (20) and ( 21 [8], [9], and [21].…”

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

confidence: 99%

“…For details see [8,22]. While most of the parameters in the 0d model can be directly measured or estimated based on the battery design and material properties, the parameters k 1 , k 2 , σ e , and S denoting the reference rates for reactions (20) and ( 21), the electrode conductivity, and the specific surface area, must be calibrated [8,9,21]. Table II presents values of these parameters from the calibration studies in [8,9,21].…”

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

confidence: 99%

“…7. For all considered experiments, the parameters in [9] underestimate E, while the parameters in [8] overestimate E. The parameters from [21] were obtained from an experiment in a cell similar to the one used in the experiments considered in this work, and so they provide the best agreement in Fig. 7 and serve as the starting point for the MC simulations.…”

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

confidence: 99%

“…The two half reactions in the RFB are given by FIG. 7: Examples of the 0d model with the parameters from [8,9,21].…”

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

confidence: 99%

“…The coefficients k 1 and k 2 are the reference rate constants for reactions (20) and ( 21 [8], [9], and [21].…”

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

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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Physics-informed CoKriging model of a redox flow battery

Howard¹,

Tartakovsky²

2021

Preprint

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A Review of Capacity Decay Studies of All‐vanadium Redox Flow Batteries: Mechanism and State Estimation

Wang,

Mu,

Wang

et al. 2024

ChemSusChem

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As a promising large‐scale energy storage technology, all‐vanadium redox flow battery has garnered considerable attention. However, the issue of capacity decay significantly hinders its further development, and thus the problem remains to be systematically sorted out and further explored. This review provides comprehensive insights into the multiple factors contributing to capacity decay, encompassing vanadium cross‐over, self‐discharge reactions, water molecules migration, gas evolution reactions, and vanadium precipitation. Subsequently, it analyzes the impact of various battery parameters on capacity. Based on this foundation, the article expounds upon the significance of battery internal state estimation technology. Additionally, the review also summarizes domestic and international mathematical models utilized for simulating capacity decay, serving as a valuable reference for future research endeavors. Finally, through the comparison of traditional experimental methods and mathematical modeling methods, this article offers effective guidance for the future development direction of battery state monitoring. This review generally overview the problems related to the capacity attenuation of all‐vanadium flow batteries, which is of great significance for understanding the mechanism behind capacity decay and state monitoring technology of all‐vanadium redox flow battery.

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