Integrated Numerical and Experimental Data-Driven Parameter Identification of Electrode Properties in All-Vanadium Redox Flow Batteries

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally light simulation framework, which is used to generate a dataset of 16800 electrode property combinations under four different cell voltages to track the impact of different structural parameters on the cell current density. These structure-performance relationships are quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs.

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Data-Driven Electrode Parameter Identification for Vanadium Redox Flow Batteries Through Experimental and Numerical Methods

10.26434/chemrxiv.11936124.v4 ◽

2020 ◽

Author(s):

Ziqiang Cheng ◽

Kevin M. Tenny ◽

Alberto Pizzolato ◽

Antoni Forner-Cuenca ◽

Vittorio Verda ◽

...

Keyword(s):

Parameter Identification ◽

Rank Correlation ◽

Cell Voltage ◽

Cell Polarization ◽

Vanadium Redox Flow Battery ◽

Data Driven ◽

Redox Flow Battery ◽

Flow Battery ◽

Kinetic Rate Constant ◽

Vanadium Redox

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volumetric area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery (RFB) stacks.

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Data-Driven Electrode Parameter Identification for Vanadium Redox Flow Batteries Through Experimental and Numerical Methods

10.26434/chemrxiv.11936124.v3 ◽

2020 ◽

Author(s):

Ziqiang Cheng ◽

Kevin M. Tenny ◽

Alberto Pizzolato ◽

Antoni Forner-Cuenca ◽

Vittorio Verda ◽

...

Keyword(s):

Parameter Identification ◽

Rank Correlation ◽

Cell Voltage ◽

Cell Polarization ◽

Vanadium Redox Flow Battery ◽

Data Driven ◽

Redox Flow Battery ◽

Flow Battery ◽

Kinetic Rate Constant ◽

Vanadium Redox

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volumetric specific surface area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery (RFB) stacks.

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Data-Driven Electrode Parameter Identification for Vanadium Redox Flow Batteries Through Experimental and Numerical Methods

10.26434/chemrxiv.11936124.v2 ◽

2020 ◽

Author(s):

Ziqiang Cheng ◽

Kevin M. Tenny ◽

Alberto Pizzolato ◽

Antoni Forner-Cuenca ◽

Vittorio Verda ◽

...

Keyword(s):

Parameter Identification ◽

Rank Correlation ◽

Cell Voltage ◽

Cell Polarization ◽

Vanadium Redox Flow Battery ◽

Data Driven ◽

Redox Flow Battery ◽

Flow Battery ◽

Kinetic Rate Constant ◽

Vanadium Redox

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volume-specific surface area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery (RFB) stacks.

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Data-Driven Electrode Parameter Identification for Vanadium Redox Flow Batteries Through Experimental and Numerical Methods

10.26434/chemrxiv.11936124 ◽

2020 ◽

Author(s):

Ziqiang Cheng ◽

Kevin M. Tenny ◽

Alberto Pizzolato ◽

Antoni Forner-Cuenca ◽

Vittorio Verda ◽

...

Keyword(s):

Parameter Identification ◽

Rank Correlation ◽

Cell Voltage ◽

Cell Polarization ◽

Vanadium Redox Flow Battery ◽

Data Driven ◽

Redox Flow Battery ◽

Flow Battery ◽

Kinetic Rate Constant ◽

Vanadium Redox

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volumetric area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery (RFB) stacks.

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Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

Energies ◽

10.3390/en12153005 ◽

2019 ◽

Vol 12 (15) ◽

pp. 3005 ◽

Cited By ~ 1

Author(s):

Meng ◽

Xiong ◽

Lim

Keyword(s):

Parameter Identification ◽

Real Time ◽

Vanadium Redox Flow Battery ◽

Redox Flow Battery ◽

Flow Battery ◽

Model Parameter ◽

Model Based ◽

Model Parameter Identification ◽

Vanadium Redox ◽

Capacity Decay

The safe, efficient and durable utilization of a vanadium redox flow battery (VRB) requires accurate monitoring of its state of charge (SOC) and capacity decay. This paper focuses on the unbiased model parameter identification and model-based monitoring of both the SOC and capacity decay of a VRB. Specifically, a first-order resistor-capacitance (RC) model was used to simulate the dynamics of the VRB. A recursive total least squares (RTLS) method was exploited to attenuate the impact of external disturbances and accurately track the change of model parameters in realtime. The RTLS-based identification method was further integrated with an H-infinity filter (HIF)-based state estimator to monitor the SOC and capacity decay of the VRB in real-time. Experiments were carried out to validate the proposed method. The results suggested that the proposed method can achieve unbiased model parameter identification when unexpected noises corrupt the current and voltage measurements. SOC and capacity decay can also be estimated accurately in real-time without requiring additional open-circuit cells.

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Numerical Simulation and Experiment on the Electrolyte Flow Distribution for All Vanadium Redox Flow Battery

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.236-238.604 ◽

2011 ◽

Vol 236-238 ◽

pp. 604-607 ◽

Cited By ~ 20

Author(s):

Jin Qing Chen ◽

Bao Guo Wang ◽

Hong Ling Lv

Keyword(s):

Flow Field ◽

Flow Distribution ◽

Parallel Flow ◽

Vanadium Redox Flow Battery ◽

Redox Flow Battery ◽

Flow Battery ◽

Electrolyte Flow ◽

Cfd Method ◽

The Impact ◽

Vanadium Redox

The electrolyte flow states of all vanadium redox flow battery (VRB) have a direct effect on the battery performance and life. To reveal the electrolyte distribution in the battery, the computation fluid dynamics (CFD) method was used to simulate a parallel flow field. A hydraulics experiment and a battery performance experiment were carried out to confirm the simulated results. The results show that the predicted information agreed well with the experimental results. The electrolyte has a concentrated distribution in the central region of the parallel flow field and the disturbed flow and then vortex flow areas mainly appear in the inlet and outlet regions. The higher flux of electrolyte is helpful to uniform the distributions and to reduce the impact of flow irregularity on the battery performance. The battery with the flow field generates a power density of 15.9 mW∙cm-2, and the coulombic, voltage and energy efficiency is up to 90.5%, 74.0% and 67.2% at a current density of 20 mA·cm-2.

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Evaluation of the automated hematology analyzer Sysmex XT-2000iV™ compared to the ADVIA® 2120 for its use in dogs, cats, and horses. Part II

Journal of Veterinary Diagnostic Investigation ◽

10.1177/1040638711436243 ◽

2012 ◽

Vol 24 (1) ◽

pp. 74-89 ◽

Cited By ~ 41

Author(s):

Natali Bauer ◽

Julia Nakagawa ◽

Cathrin Dunker ◽

Klaus Failing ◽

Andreas Moritz

Keyword(s):

Rank Correlation ◽

Correlation Coefficients ◽

Differential Count ◽

Mean Corpuscular Hemoglobin ◽

Cell Counts ◽

Hematology Analyzer ◽

Automated Hematology Analyzer ◽

The Impact ◽

And Storage ◽

Edta Blood

The automated laser-based hematology analyzer Sysmex XT-2000 iV™ provides a 5-part differential count and specific cytograms that are of great interest for large veterinary laboratories. The aim of the study was to validate the Sysmex XT-2000 iV compared to the laser-based hematology analyzer ADVIA® 2120 and manual differential in dogs, cats, and horses as well as the impact of anticoagulant (heparin, ethylenediamine tetra-acetic acid [EDTA], and citrate) and storage at 22°C and 4°C. Consecutive fresh K3–EDTA blood samples from 216 cats, 314 dogs, and 174 horses were included. The impact of anticoagulant and sample storage was assessed in specimens obtained from an additional 9 cats, 10 dogs, and 10 horses. Agreement between both analyzers was excellent to good except for monocytes and canine reticulocytes. Spearman rank correlation coefficients ( rs) between Sysmex XT-2000 iV and manual differential were good to fair and ranged from 0.91 (cat lymphocytes) to 0.44 (cat monocytes). Hematocrit value (Hct), mean corpuscular hemoglobin (MCH), MCH concentration (MCHC; all: P < 0.001), and mean corpuscular volume (MCV; P < 0.01) were higher in canine citrated blood compared to heparin and EDTA. In cats, lymphocytes and monocytes were lower in heparinized blood compared to EDTA ( P < 0.05), whereas in horses no significant effect was seen. Regarding storage time and temperature, white and red blood cell counts, hemoglobin, and MCH were stable. Hct, MCV, and MCHC were influenced by erythrocyte swelling. Differential count remained stable for 24 hr (22°C) and nearly 72 hr (4°C) except for monocytes. The overall performance of the Sysmex XT-2000 iV was excellent and compared favorably with that of the ADVIA 2120. A special strength was the excellent detection of feline eosinophils.

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Effect of Leakage Delay on Hopf Bifurcation in a Fractional BAM Neural Network

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127419500779 ◽

2019 ◽

Vol 29 (06) ◽

pp. 1950077 ◽

Cited By ~ 2

Author(s):

Jiazhe Lin ◽

Rui Xu ◽

Liangchen Li

Keyword(s):

Neural Network ◽

Hopf Bifurcation ◽

Experimental Studies ◽

Rank Correlation ◽

Correlation Coefficients ◽

Leakage Delay ◽

Scatter Plots ◽

Bam Neural Network ◽

Pass Through ◽

The Impact

Recently, experimental studies show that fractional calculus can depict the memory and hereditary attributes of neural networks more accurately. In this paper, we introduce temporal fractional derivatives into a six-neuron bidirectional associative memory (BAM) neural network with leakage delay. By selecting two different bifurcation parameters and analyzing corresponding characteristic equations, it is verified that the delayed fractional neural network generates Hopf bifurcation when the bifurcation parameters pass through some critical values. In order to measure how much is the impact of leakage delay on Hopf bifurcation, sensitivity analysis methods, such as scatter plots and partial rank correlation coefficients (PRCCs), are introduced to assess the sensitivity of bifurcation amplitudes to leakage delay. Numerical examples are carried out to illustrate the theoretical results and help us gain an insight into the effect of leakage delay.

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Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods

Applied Energy ◽

10.1016/j.apenergy.2020.115530 ◽

2020 ◽

Vol 279 ◽

pp. 115530

Author(s):

Ziqiang Cheng ◽

Kevin M. Tenny ◽

Alberto Pizzolato ◽

Antoni Forner-Cuenca ◽

Vittorio Verda ◽

...

Keyword(s):

Numerical Methods ◽

Parameter Identification ◽

Data Driven ◽

Redox Flow Batteries ◽

Flow Batteries ◽

Vanadium Redox

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The Effect of Pre-Analytical Conditions on Blood Metabolomics in Epidemiological Studies

Metabolites ◽

10.3390/metabo9040064 ◽

2019 ◽

Vol 9 (4) ◽

pp. 64 ◽

Cited By ~ 2

Author(s):

Diana Santos Ferreira ◽

Hannah Maple ◽

Matt Goodwin ◽

Judith Brand ◽

Vikki Yip ◽

...

Keyword(s):

Rank Correlation ◽

Correlation Coefficients ◽

Epidemiological Studies ◽

Processing Conditions ◽

Linear Regression Models ◽

Nmr Analysis ◽

Serum Samples ◽

Spearman’S Rank Correlation ◽

Epidemiology Studies ◽

The Impact

Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (n = 151 traits). Correlations and differences in mean of metabolite concentrations were compared between reference (pre-storage: 4 °C, 1.5 h; post-storage: no buffer addition delay or NMR analysis delay) and four pre-storage blood processing conditions, where samples were incubated at (i) 4 °C, 24 h; (ii) 4 °C, 48 h; (iii) 21 °C, 24 h; and (iv) 21 °C, 48 h, before centrifugation; and two post-storage sample processing conditions in which samples thawed overnight (i) then left for 24 h before addition of sodium buffer followed by immediate NMR analysis; and (ii) addition of sodium buffer, then left for 24 h before NMR profiling. We used multilevel linear regression models and Spearman’s rank correlation coefficients to analyse the data. Most metabolic traits had high rank correlation and minimal differences in mean concentrations between samples subjected to reference and the different conditions tested, that may commonly occur in studies. However, glycolysis metabolites, histidine, acetate and diacylglycerol concentrations may be compromised and this could bias results in association/causal analyses.

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