Electrical Model of a Membraneless Micro Redox Flow Battery-Fluid Dynamics Influence

被引:6
作者
De Quiros, Alberto Bernaldo [1 ]
Quintero, Alberto E. [2 ]
Frances, Airan [3 ]
Maurice, Ange A.
Uceda, Javier
机构
[1] Univ Politecn Madrid, Ctr Elect Ind, Madrid 28040, Spain
[2] Micro Electrochem Technol SL, Res & Dev Dept, Leganes 28919, Spain
[3] Univ Carlos III Madrid, Dept Ingn Term & Fluidos, Leganes 28911, Spain
关键词
Batteries; Inductors; Redox; Computer architecture; Integrated circuit modeling; Electrolytes; Microprocessors; Battery efficiency; electric equivalent model; grey box identification; microfluidics; redox flow battery; MICROFLUIDIC FUEL-CELL; ENERGY-STORAGE; CIRCUIT MODEL; MASS-TRANSFER; PERFORMANCE; PROSPECTS; SYSTEM;
D O I
10.1109/ACCESS.2023.3273927
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Membraneless micro redox flow batteries are an incipient technology that has been shown to extend some properties of traditional redox flow batteries. Due to their microfluidic scale and the absence of membrane, the fluid dynamics operation is critical in the electrical response. In this work, an electrical model is established to evaluate the influence on three battery performance metrics: steady-state power, power transient dynamics, and mixing and self-discharge losses. First, an equivalent electrical circuit, derived from a state-of-the-art regular battery equivalent circuit, is defined by studying the influence of flow changes on its impedances and source, aggregating it as a variable. Then, empirical data are used to demonstrate the proposed equations defining the variation of the electrical response relative to fluid dynamics, and their parameters are identified with grey box methods. The steady-state power model incorporates the interphase position, extending conventionally used redox flow batteries expressions, such as Faraday textasciiacute s Law and Nersnt textasciiacute s equation, for the membraneless analysis. A transient response model is built, which becomes effectively relevant in intermittent power applications (such as many renewable energy storage ones). Finally, mixing and self-discharge losses are evaluated with the variation state of charge at the outputs of the cell, using spectrophotometry measurements, and compared with flowmeter mixing values. This demonstrates that flow-rate values can provide a precise quantification of these losses. The electrical model with dependent parameters from the three fluid dynamics analyses can be used to evaluate the performance of micro membraneless redox flow batteries and their response to fluidic operation.
引用
收藏
页码:46132 / 46143
页数:12
相关论文
共 46 条
[1]   A Review on Vanadium Redox Flow Battery Storage Systems for Large-Scale Power Systems Application [J].
Aluko, Anuoluwapo ;
Knight, Andy .
IEEE ACCESS, 2023, 11 :13773-13793
[2]   Prospects of recently developed membraneless cell designs for redox flow batteries [J].
Bamgbopa, Musbaudeen O. ;
Almheiri, Saif ;
Sun, Hong .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 70 :506-518
[3]   Machine Learning Coupled Multi-Scale Modeling for Redox Flow Batteries [J].
Bao, Jie ;
Murugesan, Vijayakumar ;
Kamp, Carl Justin ;
Shao, Yuyan ;
Yan, Litao ;
Wang, Wei .
ADVANCED THEORY AND SIMULATIONS, 2020, 3 (02)
[4]   Design and experimental validation of a generalised electrical equivalent model of Vanadium Redox Flow Battery for interfacing with renewable energy sources [J].
Bhattacharjee, Ankur ;
Saha, Hiranmay .
JOURNAL OF ENERGY STORAGE, 2017, 13 :220-232
[5]   Multiphysics and Energetic Modeling of a Vanadium Redox Flow Battery [J].
Blanc, Christian ;
Rufer, Alfred .
2008 IEEE INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY TECHNOLOGIES (ICSET), VOLS 1 AND 2, 2008, :696-701
[6]  
Bohlin TP., 2006, ADV IND CON
[7]   Estimating the State-of-Charge of Lithium-Ion Battery Using an H-Infinity Observer Based on Electrochemical Impedance Model [J].
Chen, Ning ;
Zhang, Peng ;
Dai, Jiayang ;
Gui, Weihua .
IEEE ACCESS, 2020, 8 :26872-26884
[8]   Microfluidic fuel cell based on laminar flow [J].
Choban, ER ;
Markoski, LJ ;
Wieckowski, A ;
Kenis, PJA .
JOURNAL OF POWER SOURCES, 2004, 128 (01) :54-60
[9]   Vanadium Redox Flow Battery State of Charge Estimation Using a Concentration Model and a Sliding Mode Observer [J].
Clemente, Alejandro ;
Montiel, Manuel ;
Barreras, Felix ;
Lozano, Antonio ;
Costa-Castello, Ramon .
IEEE ACCESS, 2021, 9 :72368-72376
[10]   Reinforcement Learning for Dynamic Microfluidic Control [J].
Dressler, Oliver J. ;
Howes, Philip D. ;
Choo, Jaebum ;
deMello, Andrew J. .
ACS OMEGA, 2018, 3 (08) :10084-10091