FRACTIONAL DEPLETION ESTIMATION FOR BATTERY CONDITION METRICS

• US 20170036561A1
• Filed: 10/18/2016
• Published: 02/09/2017
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Crown Equipment Corporation
• Original Assignee: Crown Equipment Corporation
• Inventors: Harman, John R.
• Granted Patent: US 10,556,514 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60L 2200/36 Vehicles designed to transport cargo, e.g. trucks
• CPC Class Codes: B60L 2200/42 Fork lift trucks
• CPC Class Codes: B60L 2240/545 Temperature
• CPC Class Codes: B60L 2240/547 Voltage
• CPC Class Codes: B60L 2240/549 Current
• CPC Class Codes: B60L 2240/70 Interactions with external data bases, e.g. traffic centres
• CPC Class Codes: B60L 2240/80 Time limits
• CPC Class Codes: B60L 2250/10 by alarm
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 3/04 Cutting off the power supply under fault conditions protective devices and circuit arrangements in general H01H; H02H
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60L 58/16 responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables
• CPC Class Codes: G01R 31/371 with remote indication, e.g. on external chargers
• CPC Class Codes: G01R 31/382 Arrangements for monitoring battery or accumulator variables, e.g. SoC
• CPC Class Codes: G01R 31/3832 without measurement of battery voltage
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/085 : using electronic data carriers
• CPC Class Codes: H01M 10/425 : Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing printed circuits H05K1/00
• CPC Class Codes: H01M 10/48 : Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte constructional details of current conducting connections for detecting conditions inside cells or batteries, e.g. details of voltage sensing terminals, H01M50/569
• CPC Class Codes: H01M 2010/4271 : Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
• CPC Class Codes: H01M 2010/4278 : Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller
• CPC Class Codes: H01M 2220/20 : Batteries in motive systems, e.g. vehicle, ship, plane
• CPC Class Codes: Y02E 60/10 : Energy storage using batteries
• CPC Class Codes: Y02P 90/60 : Electric or hybrid propulsion means for production processes
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles

Abstract

Evaluation of a battery state comprises transforming a time based history of the load on the battery into a spectral representation of that history in a load domain, e.g., the current domain. The method also comprises comparing the spectral representation to an expected battery capability for the load represented by each line in the spectra and calculating the fraction of the expected capability used at each spectral line. The method still further comprises aggregating the calculated fractions into a total fraction that represents the estimated fraction of the expected battery capability associated with that particular time history.

Claims

1. A computer-implemented process of evaluating a battery state comprising:

collecting, by a battery monitor that is coupled to an industrial vehicle battery, samples that represent a measure of a magnitude of a current discharged from the industrial vehicle battery during use of a corresponding industrial vehicle, wherein the battery monitor has at least one sensor to sample battery current;

sorting, by a processor, collected samples into corresponding bins based upon the magnitude of the collected current samples, wherein the corresponding bins are stored in memory that is accessible by the processor;

creating, by the processor, a battery use estimate based upon a number of samples sorted into the corresponding bins;

determining, by the processor, for each of the corresponding bins, a fractional depletion contribution of the industrial vehicle battery by computing a quotient where the quotient is computed by dividing a lifetime expected estimate for that bin by the battery use estimate for that bin;

generating, by the processor, a depletion estimate associated with the battery state based upon an accumulation of the fractional depletion contributions; and

outputting a measure of the battery state based upon the generated depletion estimate.

2. The method of claim 1, wherein:

outputting a measure of the battery state based upon the generated depletion estimate comprises outputting a prediction of how much of a battery capacity has been depleted based upon the evaluation of the depletion estimate.

3. The method of claim 1, wherein:

outputting a measure of the battery state based upon the generated depletion estimate comprises outputting a prediction of how much of a battery capacity is remaining based upon the evaluation of the depletion estimate.

4. The method of claim 1, wherein:

outputting a measure of the battery state based upon the generated depletion estimate comprises outputting a prediction of an interval until an occurrence of an event of interest related to the battery state, based upon the evaluation of the depletion estimate.

5. The method of claim 1, wherein:

collecting, by a battery monitor that is coupled to an industrial vehicle battery, samples, comprises collecting the samples over a load history; and

sorting, by a processor, collected samples comprises accumulating each sample of the load history that represent a measure of a magnitude of a current discharged from the industrial vehicle battery into a corresponding bin based upon the value of the current sample, where each bin stores only samples accumulated during the load history.

6. The method of claim 5, wherein:

outputting a measure of the battery state based upon the generated depletion estimate comprises outputting a prediction of an accumulated depletion in a capacity related to the battery state, based upon the depletion estimate for the load history.

7. The method of claim 6, wherein:

outputting a prediction of an accumulated depletion in a capacity related to the battery state further comprises;

outputting a prediction of the accumulated depletion in the capacity based upon the depletion estimate for the load history and fractional depletion estimates of previously collected and aggregated load histories.

8. The method of claim 1, wherein:

determining, by the processor, for each of the corresponding bins, a fractional depletion contribution of the industrial vehicle battery comprises;

identifying a curve that represents a battery characteristic as a function of current; and

comparing each battery use estimate with an associated point on the curve, and computing therefrom, a fractional depletion estimate that estimates a fraction of the expected characteristic of the battery depleted by the current samples in the corresponding bin.

9. The method of claim 8, wherein:

creating, by the processor, a battery use estimate further comprises;

integrating across each bin to determine discharged amp-hours represented by the bin.

10. The method of claim 9, wherein:

identifying a curve that represents a battery characteristic comprises;

identifying a curve that characterizes battery lifetime amp-hours as a function of current; and

comparing each battery use estimate with an associated point on the curve comprises;

comparing the computed discharged amp-hours for each bin to an associated point on the curve identifying lifetime amp-hours for that bin.

11. The method of claim 10, wherein:

comparing the computed discharged amp-hours for each bin comprises;

computing a quotient for each bin based upon the computed discharged amp-hours for that bin and a magnitude associated with a point on the curve identifying lifetime amp-hours for that bin.

12. The method of claim 11, wherein:

generating, by the processor, a depletion estimate comprises;

accumulating each computed quotient to predict an amount of life of the battery used up by the load history.

13. The method of claim 1 further comprising:

predicting an accumulated depletion in a capacity related to the battery state, by performing the prediction off-line after the samples have been collected on an industrial vehicle and wirelessly transmitted to a remote server.

14. The method of claim 1 further comprising:

predicting an accumulated depletion in a capacity related to the battery state by performing the prediction on an industrial vehicle on the fly as samples are recorded by a processor of the materials handling vehicle.

15. The method of claim 1 further comprising:

clearing current samples from bins after being wirelessly transmitted to a remote server.

17. The system of claim 1, wherein the processor is programmed to:

output a measure of the battery state by executing code to output at least one of;

a prediction of how much of a battery capacity has been depleted based upon the evaluation of the depletion estimate;

a prediction of how much of a battery capacity is remaining based upon the evaluation of the depletion estimate; and

a prediction of an interval until an occurrence of an event of interest related to the battery state, based upon the evaluation of the depletion estimate.

18. The system of claim 1, wherein the processor is programmed to:

collect the samples over a load history;

sort, by a processor, collected samples by accumulating each sample of the load history that represent a measure of a magnitude of a current discharged from the industrial vehicle battery into a corresponding bin based upon the value of the current sample, where each bin stores only samples accumulated during the load history; and

output a prediction of an accumulated depletion in a capacity related to the battery state, based upon the depletion estimate for the load history.

19. The system of claim 18, wherein:

the processor outputs a prediction of the accumulated depletion in the capacity based upon the depletion estimate for the load history and fractional depletion estimates of previously collected and aggregated load histories.

20. The system of claim 1, wherein:

the processor determines, for each of the corresponding bins, a fractional depletion contribution of the industrial vehicle battery by executing code that;

identifies a curve that represents a battery characteristic as a function of current; and

compares each battery use estimate with an associated point on the curve, and computing therefrom, a fractional depletion estimate that estimates a fraction of the expected characteristic of the battery depleted by the current samples in the corresponding bin;

the processor creates a battery use estimate by executing code that;

integrates across each bin to determine discharged amp-hours represented by the bin.

21. The system of claim 20, wherein:

the processor executes code that identifies a curve by executing code that;

identifies a curve that characterizes battery lifetime amp-hours as a function of current; and

the processor executes code that compares each battery use estimate by executing code that;

compares the computed discharged amp-hours for each bin to an associated point on the curve identifying lifetime amp-hours for that bin by computing a quotient for each bin based upon the computed discharged amp-hours for that bin and a magnitude associated with a point on the curve identifying lifetime amp-hours for that bin.

22. The system of claim 21, wherein:

the processor generates a depletion estimate by executing code that;

accumulates each computed quotient to predict an amount of life of the battery used up by the load history.

23. The system of claim 1, wherein the processor is further programmed to:

predict an accumulated depletion in a capacity related to the battery state, by performing the prediction off-line after the samples have been collected on an industrial vehicle and wirelessly transmitted to a remote server.

24. The system of claim 1, wherein the processor is further programmed to:

predict an accumulated depletion in a capacity related to the battery state by performing the prediction on an industrial vehicle on the fly as samples are recorded by a processor of the materials handling vehicle.

25. The system of claim 1, wherein the processor is further programmed to:

clear current samples from bins after being wirelessly transmitted to a remote server.

16. A system for evaluating a battery state comprising:

a battery monitor that couples to an industrial vehicle battery to collect samples during use of an industrial vehicle, wherein the battery monitor has at least one sensor to sample battery current that represents a measure of a magnitude of a current discharged from a battery powering the industrial vehicle; and

a processor that couples to the industrial vehicle and the battery monitor, the processor further coupled to memory that includes program code that when executed, causes the processor to;

sort collected samples into corresponding bins based upon the magnitude of the collected current samples;

create a battery use estimate based upon a number of samples sorted into the corresponding bins;

determine for each of the corresponding bins, a fractional depletion contribution of the industrial vehicle battery by computing a quotient where the quotient is computed by dividing a lifetime expected estimate for that bin by the battery use estimate for that bin;

generate a depletion estimate associated with the battery state based upon an accumulation of the fractional depletion contributions; and

output a measure of the battery state based upon the generated depletion estimate.