Fractional depletion estimation for battery condition metrics

• US 9,488,697 B2
• Filed: 03/13/2014
• Issued: 11/08/2016
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Crown Equipment Corporation
• Original Assignee: Crown Equipment Corporation
• Inventors: Harman, John R.
• Primary Examiner(s): Dunn, Drew A
• Assistant Examiner(s): Robbins, Jerry D
• Application Number:
  US14/208,214
• Publication Number:
  US 20140266227A1
• Time in Patent Office: 971 Days
• Field of Search: 324/427
• 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 method of evaluating a battery state comprising:

collecting, by a battery monitor that is coupled to an industrial vehicle battery, samples recorded by the battery monitor during use of an industrial vehicle, wherein the battery monitor has a processor coupled to at least one sensor to sample battery current, the samples are recorded at a predetermined sampling interval, and the samples represent a measure of a magnitude of a current discharged from a battery powering the industrial vehicle, thus creating a time-based history of a load on the battery during operation of the industrial vehicle;

sorting, by a processor of the industrial vehicle, each collected sample into a corresponding bin, where each bin is associated with a value based on the magnitude of the current samples sorted into that bin, and each bin is stored in memory on the industrial vehicle that is accessible by the processor of the industrial vehicle;

creating, by the processor of the industrial vehicle, a battery use estimate for each bin by multiplying a number of samples sorted into the bin by the value of the bin, and the sampling interval;

determining, by the processor on the industrial vehicle, for each bin, a fractional depletion contribution of the 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 on the industrial vehicle, a depletion estimate associated with the state of the battery 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 further comprising:

identifying the state of the battery by evaluating the depletion estimate and 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 further comprising:

identifying the state of the battery by evaluating the depletion estimate and 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 further comprising:

identifying the state of the battery by evaluating the depletion estimate and 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 recorded by the battery monitor during use of an industrial vehicle comprises;

collecting the current samples over a load history; and

sorting, by a processor of the industrial vehicle, each collected sample into a corresponding bin comprises;

accumulating each current sample of the load history 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 1 further comprising:

predicting 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:

predicting an accumulated depletion in a capacity related to the battery state further comprises;

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

8. The method of claim 1, wherein:

determining, by the processor on the industrial vehicle, for each bin, a fractional depletion contribution of the 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 the 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 of the industrial vehicle, a battery use estimate for each bin by multiplying a number of samples sorted into the bin by the value of the bin, and the sampling interval 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 a depletion estimate associated with the state of the battery based upon an accumulation of the fractional depletion contributions 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.