Monitoring system of VRLA battery capacitance

• US 6,928,371 B1
• Filed: 02/08/2001
• Issued: 08/09/2005
• Est. Priority Date: 02/08/2000
• Status: Expired due to Fees

• Current Assignee: Paul T. Roshau
• Original Assignee: Paul T. Roshau
• Inventors: Roshau, Paul T.
• Primary Examiner(s): ASSOUAD, PATRICK J
• Application Number:
  US09/779,107
• Time in Patent Office: 1,643 Days
• Field of Search: 320/116, 320/118, 320/119, 320/120, 320/132, 320/134, 320/135, 320/136, 702/63, 706/906, 706/907, 706/908, 706/911, 706/914, 706/915
• US Class Current: 702/63
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/379 for lead-acid batteries
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements
• CPC Class Codes: H01M 10/121 Valve regulated lead acid batteries [VRLA]
• CPC Class Codes: H01M 10/4207 for several batteries or cells simultaneously or sequentially
• CPC Class Codes: H01M 10/4285 Testing apparatus
• CPC Class Codes: Y02E 60/10 Energy storage using batteries
• CPC Class Codes: Y02P 70/50 Manufacturing or production processes characterised by the final manufactured product

Abstract

A method and apparatus for monitoring the capacity of a valve regulated lead acid battery comprising at least one battery monitor connected to the valve regulated lead acid battery; a centralized system connecting the battery monitor through an industry standard data system to a central office; and an alarm connected to the centralized system; wherein, a short-term discharge test is performed on the battery using the battery monitor which provides input parameters for a neural network and fuzzy logic network used in combination with a prediction algorithm to calculate the predicted capacity; and, wherein, the alarm is activated when said predicted capacity falls below eighty percent, when an individual cell voltage is reduced to 1.95 volts or less, or when a system failure occurs

Claims

1. A method of monitoring the capacity of a valve regulated lead acid battery, comprising:

connecting at least one battery monitor to said valve regulated lead acid battery;

connecting said monitor to a centralized system through an industry standard data system that collects and transfers data from the field to a central office;

providing said centralized system with an alarm;

performing a short-term discharge test on said battery using said battery monitor; and

obtaining a predicted capacity using a neural network and fuzzy logic network in combination with a prediction algorithm;

training said neural network for a specific battery type in a lab by determining the actual capacity of a battery, inputting parameters with the actual capacity used as a target, and programming the set of coefficients yielded by said neural network in said battery monitor;

wherein, said discharge test provides input parameters for said networks which in combination with said prediction algorithm calculate said predicted capacity, and wherein, said alarm is activated when said predicted capacity falls below a predetermined percentage, when an individual cell voltage is reduced to about 1.95 volts or less, or when a system failure occurs.

2. A method as in claim 1, wherein said battery monitor consists of hardware for monitoring the voltages of each battery cell and currents that are flowing into and out of said battery.

3. A method as in claim 1, wherein said battery monitor contains a serial port enabling data to be downloaded onto a network, computer, or printer.

4. A method as in claim 3, further comprising a real time clock.

5. A method as in claim 4, wherein said real time clock is automatically verified and updated through said network.

6. A method as in claim 1, wherein said discharge test is a four hour discharge test.

7. A method as in claim 1, wherein said discharge test collects specific data parameters including the cell age, open circuit voltage, voltage after one hour of discharge, voltage after three hours of discharge, and voltage after four hours of discharge.

8. A method as in claim 7, wherein said parameters are used to derive three additional data points including the slope of the discharge curve, the delta between voltages at three and four hours, and the proximity to two volts of the four hour voltage.

9. A method as in claim 1, wherein said discharge test is performed on a repeated basis.

10. A method as in claim 1, wherein said actual capacity is determined by the following formula:

(time to discharge to 1.75V×

24 hour discharge rate)/(24×

24-hour discharge rate in amps).

11. A method as in claim 1, wherein said neural network is trained only once.

12. A method as in claim 1, wherein said fuzzy logic network comprises:

dividing total battery capacity into capacity spans of ten percent;

associating a voltage range with each of said ten percent spans, wherein said voltage range was previously determined from said 24-hour rate discharge lab tests;

determining within which of said capacity spans said voltage ranges fall by comparing each of said open, one, three, and four hour cell voltages with the known base lines for the specific battery type;

determining the strength value of the cell'"'"'s voltage within a particular span;

determining the capacity range with the most positive strength value;

adjusting the range that is immediately above said capacity range with most positive strength value by adding the strength value of said range to the lowest capacity value of said range to yield the upper capacity limit;

adjusting the range that is immediately below said capacity range with most positive strength value by subtracting the strength value of said range from the highest capacity value of said range to yield the lower capacity limit;

wherein, said difference between said upper capacity limit and said lower capacity limit yields the fuzzy logic capacity range span.

13. A method as in claim 12, wherein said strength value is determined as follows:

calculating the average voltage for each capacity range using the maximum and minimum voltage values;

dividing the cell test voltage (open, one, three, or four hour) by said average voltage;

subtracting the quotient of previous step from one and taking the absolute value of the difference;

performing this calculation for each of said cell test voltages—

open one, three, and four hour,

adding the calculated values for each of said cell test voltages and dividing the sum by seventy; and

subtracting the quotient of the previous step from one;

wherein, the value of said strength value can be positive or negative.

14. The method of claim 1, wherein said neural network comprises:

inputting parameters including open, one-hour, three-hour, and four-hour cell voltages and the age of said battery;

obtaining three more data points from said parameters including slope of the discharge curve, the delta between voltages at three and four hours, and the proximity to two volts of the four hour voltage;

outputting a number between zero and one;

wherein, said neural network performs calculations as any standard neural network using the coefficients determined from said training of said network using lab data; and

wherein, the output of said neural network is multiplied by the span of the capacity range obtained from the fuzzy logic network and adding this product to said lower capacity limit in order to determine the final capacity prediction.

15. An apparatus for monitoring the capacity of a valve regulated lead acid battery, comprising:

at least one battery monitor connected to said valve regulated lead acid battery, said battery monitor having a neural network trained in a lab for a specific battery type by determining the actual capacity of a battery, inputting parameters with the actual capacity used as a target, and programmed with a set of coefficients yielded by said neural network and a fuzzy logic network;

a prediction algorithm, said prediction algorithm using data produced by said neural network and fuzzy logic network,

a centralized system connecting said battery monitor through an industry standard data system to a central office;

an alarm connected to said centralized system.

16. An apparatus as in claim 15, wherein said battery monitor consists of hardware for monitoring the voltages of each battery cell and currents that are flowing into and out of said battery.

17. An apparatus as in claim 15, wherein said battery monitor contains a serial port enabling data to be downloaded onto a network, computer, or printer.

18. An apparatus as in claim 17, further comprising a real time clock.

19. An apparatus as in claim 18, wherein said real time clock is automatically verified and updated through said network.

20. A method of monitoring the capacity of a valve regulated lead acid battery, comprising:

connecting at least one battery monitor to said valve regulated lead acid battery;

connecting said monitor to a centralized system through an industry standard data system that collects and transfers data from the field to a central office;

providing said centralized system with an alarm;

performing a short-term discharge test on said battery; and

obtaining a predicted capacity using a neural network and fuzzy logic network in combination with a prediction algorithm;

wherein, said discharge test comprises a four hour discharge of said battery, performed at a discharge rate calculated from the amp-hour size of the battery and a 24-hour period, during which specific data parameters consisting of cell age, open circuit voltage, voltage after one hour of discharge, voltage after three hours of discharge, and voltage after four hours of discharge are logged and three additional parameters including three hour slope, the delta between the three and four hour voltages, and the proximity to two volts of the four hour voltage are determined; and

wherein said neural network is trained in a lab by determining the actual capacity of a battery using the following formula;

(time to discharge to 1.75V×

24 hour discharge rate)/(24×

24-hour discharge rate in amps),

inputting said parameters with the actual capacity used as a target, and programming the set of coefficients yielded by said neural network into an EEPROM which is inserted in said battery monitor; and

wherein, said fuzzy logic network comprises dividing total battery capacity into capacity spans of ten percent;

associating a voltage range with each of said ten percent spans, wherein said voltage range was previously determined from said 24 hour rate discharge lab tests;

determining within which of said capacity spans said voltage ranges fall by comparing each of said open, one, three, and four hour cell voltages with the known base lines for the specific battery type;

determining the strength value of the cell'"'"'s voltage within a particular span by the following steps;

calculating the average voltage for each capacity range using the maximum and minimum voltage values;

dividing the cell test voltage (open, one, three, or four hour) by said average voltage;

subtracting the quotient of previous step from one and taking the absolute value of said answer;

performing this calculation for each of cell test voltages (open one, three, and four hour);

adding the calculated values for each of said cells test voltages and dividing the sum by seventy; and

subtracting the quotient of the previous step from one;

determining the capacity range with the most positive strength value;

adjusting the range that is immediately above said capacity range with most positive strength value by adding the strength value of said range to the lowest capacity value of said range to yield the upper capacity limit; and

adjusting the range that is immediately below said capacity range with most positive strength value by subtracting the strength value of said range from the highest capacity value of said range to yield the lower capacity limit;

wherein, said difference between said upper capacity limit and said lower capacity limit yields the fuzzy logic capacity range span; and

wherein, said neural network comprises inputting parameters including open, one-hour, three-hour, and four-hour cell voltages and the age of said battery;

obtaining three more data points from said parameters including slope of the discharge curve, the delta between voltages at three and four hours, and the proximity to two volts of the four hour voltage; and

outputting a number between zero and one;

wherein, said neural network performs calculations as any standard neural net using the coefficients determined from said training of said network using lab data; and

wherein, the output of said neural network is multiplied by the span of the capacity range obtained from the fuzzy logic network and adding this product to said lower capacity limit in order to determine the final capacity prediction.