System and method for condition monitoring of redox flow batteries using data analytics

Patent metadata

Field	Value
Publication number	US-2017279140-A1
Application number	US-201615080127-A
Country	US
Kind code	A1
Filing date	Mar 24, 2016
Priority date	Mar 24, 2016
Publication date	Sep 28, 2017
Grant date	—

Abstract

One embodiment provides a method for predicting maintenance of a redox flow battery, the method including: receiving, from a plurality of sensors, data regarding characteristics of the redox flow battery; weighting, using a processor, each of the characteristics to form an estimated state parameter for the redox flow battery; and determining, using the processor, a maintenance action for the redox flow battery using the estimated state parameter. Other aspects are described and claimed.

First claim

What is claimed is:

1 . A method for predicting maintenance of a redox flow battery, the method comprising:
receiving, from a plurality of sensors, data regarding characteristics of the redox flow battery; weighting, using a processor, each of the characteristics to form an estimated state parameter for the redox flow battery; and determining, using the processor, a maintenance action for the redox flow battery using the estimated state parameter.

2 . The method of claim 1 , wherein the estimated state parameter comprises state of charge of the redox flow battery.

3 . The method of claim 2 , comprising receiving operational history data for the redox flow battery;
wherein the determining takes into account the operation history data.

4 . The method of claim 2 , comprising receiving predicted operational load data for the redox flow battery;
wherein the determining takes into account the predicted operational load data.

5 . The method of claim 2 , wherein the data regarding characteristics of the redox flow battery are selected from the group consisting of: temperature data, current data, voltage data, conductivity data, potentiometeric data, and optical data.

6 . The method of claim 5 , comprising:
training a regression model for estimating the state of charge using a conductivity characteristic of the redox flow battery; training the regression model for estimating the state of charge using a potentiometeric characteristic of the redox flow battery; training a threshold function that estimates an electrolyte color change with respect to the state of charge of the redox flow battery; and utilizing temperature buckets to identify which of the plurality of sensors provides a most accurate estimate of the state of charge of the redox flow battery for a given temperature.

7 . The method of claim 6 , wherein the weighting comprises:
measuring a current temperature of the redox flow battery using a temperature sensor; identifying a sensor of the plurality of sensors that provides the most accurate estimate of the state of charge at the current temperature; determining if the estimate of the state of charge is within a predetermined range associated with electrolyte color change; and responsive to determining if the estimate of the state of charge is within the predetermined range, performing a confirming measurement with an optical sensor.

8 . The method of claim 1 , wherein the maintenance action is selected from the group consisting of electrolyte remixing, electrode replacement, electrode cleaning, adding a reductant, adding inert gas, electrolyte refilling, tank replacement, pump replacement, leak fixing, and electrolyte modification.

9 . The method of claim 1 , wherein the redox flow battery is a vanadium redox flow battery.

10 . An apparatus for predicting maintenance of a redox flow battery, the apparatus comprising:
at least one processor; and a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor, the computer readable program code comprising: computer readable program code that receives, from a plurality of sensors, data regarding characteristics of the redox flow battery; computer readable program code that weights each of the characteristics to form an estimated state parameter for the redox flow battery; and computer readable program code that determines a maintenance action for the redox flow battery using the estimated state parameter.

11 . A computer program product for predicting maintenance of a redox flow battery, the computer program product comprising:
a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code being executable by a processor and comprising: computer readable program code that receives, from a plurality of sensors, data regarding characteristics of the redox flow battery; computer readable program code that weights each of the characteristics to form an estimated state parameter for the redox flow battery; and computer readable program code that determines a maintenance action for the redox flow battery using the estimated state parameter.

12 . The computer program product of claim 11 , wherein the estimated state parameter comprises state of charge of the redox flow battery.

13 . The computer program product of claim 12 , comprising computer readable program code that receives operational history data for the redox flow battery;
wherein the computer readable program code that determines a maintenance action takes into account the operation history data.

14 . The computer program product of claim 12 , comprising computer readable program code that receives predicted operational load data for the redox flow battery;
wherein the computer readable program code that determines a maintenance action takes into account the predicted operational load data.

15 . The computer program product of claim 12 , wherein the data regarding characteristics of the redox flow battery are selected from the group consisting of: temperature data, current data, voltage data, conductivity data, potentiometeric data, and optical data.

16 . The computer program product of claim 15 , comprising:
computer readable program code that trains a regression model for estimating the state of charge using a conductivity characteristic of the redox flow battery; computer readable program code that trains the regression model for estimating the state of charge using a potentiometeric characteristic of the redox flow battery; computer readable program code that trains a threshold function that estimates an electrolyte color change with respect to the state of charge of the redox flow battery; and computer readable program code that utilizes temperature buckets to identify which of the plurality of sensors provides a most accurate estimate of the state of charge of the redox flow battery for a given temperature.

17 . The computer program product of claim 16 , wherein the computer readable program code that weights comprises:
computer readable program code that measures a current temperature of the redox flow battery using a temperature sensor; computer readable program code that identifies a sensor of the plurality of sensors that provides the most accurate estimate of the state of charge at the current temperature; computer readable program code that determines if the estimate of the state of charge is within a predetermined range associated with electrolyte color change; and computer readable program code that, responsive to determining the estimate of the state of charge is within the predetermined range, performs a confirming measurement with an optical sensor.

18 . The computer program product of claim 11 , wherein the maintenance action is selected from the group consisting of electrolyte remixing, electrode replacement, electrode cleaning, adding a reductant, adding inert gas, electrolyte refilling, tank replacement, pump replacement, leak fixing, and electrolyte modification.

19 . The computer program product of claim 11 , wherein the redox flow battery is a vanadium redox flow battery.

20 . A system for predicting maintenance of a redox flow battery, the method comprising:
a redox flow battery operatively coupled to a commercial power grid; and an apparatus comprising:
at least one processor; and
a computer readable storage medium having computer readable program code embodied ther

Assignees

• IBM

Inventors

• Choudhury Anamitra Roy
• Dechu Sampath
• Kumar Pratyush

Classifications

• H01M8/18 Primary

  Regenerative fuel cells, e.g. redox flow batteries or secondary fuel cells · CPC title

• H01M8/04477

  of the electrolyte · CPC title

• H01M8/20

  Indirect fuel cells, e.g. fuel cells with redox couple being irreversible (H01M8/18 takes precedence) · CPC title

• H01M2250/10

  Fuel cells in stationary systems, e.g. emergency power source in plant · CPC title

• H01M8/225

  Fuel cells in which the fuel is based on materials comprising particulate active material in the form of a suspension, a dispersion, a fluidised bed or a paste · CPC title