Method and apparatus for predicting a state of health of a device battery in a battery-operated device

Patent metadata

Field	Value
Publication number	US-12196817-B2
Application number	US-202117537791-A
Country	US
Kind code	B2
Filing date	Nov 30, 2021
Priority date	Dec 2, 2020
Publication date	Jan 14, 2025
Grant date	Jan 14, 2025

Abstract

A method for determining a state of health trajectory of a device battery is based on state of health values of device batteries of an identical battery type. The method includes providing time characteristics of operating variables of a multiplicity of device batteries of battery-operated machines in a central processing unit, and determining one or more state of health values of one or more of the multiplicity of device batteries by evaluating a respective characteristic of the operating variables within an evaluation period. The one or more state of health values with the applicable aging times each indicate a data point for the relevant device battery. The method further includes eliminating data points from the determined data points to obtain a set of cleaned-up data points, and ascertaining the state of health trajectory with an accuracy statement for each trajectory point based on the set of cleaned-up data points.

First claim

What is claimed is:

1. A method, which is computer-implemented, for determining a state of health trajectory of a selected device battery based on state of health values of a plurality of device batteries of an identical battery type in battery-operated machines, the plurality of device batteries including the selected device battery and other device batteries, the method comprising:
providing, in a central processor, time characteristics of operating variables of the plurality of device batteries;
implementing, in the central processor, a state of charge model and adapting the state of charge model based on data points from the other device batteries of the plurality of device batteries;
determining state of health values of the selected device battery of the plurality of device batteries for determining the state of health trajectory by evaluating (i) the state of charge model, and (ii) a respective time characteristic of the operating variables within an evaluation period, each of the state of health values within applicable aging times indicating a data point for the selected device battery;
obtaining a set of cleaned-up data points by eliminating data points from the data points indicated by the state of health values, at least one of (i) using an outlier elimination according to criteria, and (ii) based on system and domain knowledge according to the criteria;
ascertaining the state of health trajectory of the selected device battery with an accuracy statement for each trajectory point based on the set of cleaned-up data points;
predicting an end of life time of the selected device battery based on the ascertained state of health trajectory; and
operating the selected device battery based on the predicted end of life time,
wherein the criteria for obtaining the set of cleaned-up data points and eliminating the data points are based on the data points from the other batteries of the plurality of device batteries,
wherein the state of health is a measure of an aging of the selected device battery, and
wherein the state of health trajectory is configured to indicate or predict the state of health of the selected device battery based on an input aging time.

2. The method according to claim 1 , further comprising:
predicting, based on the state of health trajectory, a time at which a determined state of health is reached.

3. The method according to claim 1 , wherein at least one of (i) the providing the time characteristics, (ii) the determining the state of health values, (iii) the obtaining the set of cleaned-up data points, and (iv) the ascertaining the state of health trajectory, is performed by the central processor, the central processor being a device-external central processor that is communicatively connected to at least some of the battery-operated machines.

4. The method according to claim 1 , further comprising:
before using the operating variables to ascertain data points with state of health values for a respective aging time, at least one of:
filtering the time characteristics of the operating variables; and
eliminating outlier values of the time characteristics of the operating variables using a predefined outlier elimination method.

5. The method according to claim 1 , the ascertaining the state of health trajectory further comprising:
modelling the state of health trajectory based on the cleaned-up data points using at least one regression model.

6. The method according to claim 5 , the ascertaining the state of health trajectory further comprising:
determining each respective trajectory point of the state of health trajectory by:
selecting a predefined time period for a respective aging time; and
one of (i) fitting and (ii) parameterizing a model function of at least one regression model to the cleaned-up data points within the predefined time period, a model value being added to the model function at the respective aging time of the respective trajectory point.

7. The method according to claim 6 , further comprising:
creating the model function using only those data points of the cleaned-up data points within the predefined time period that are situated within a confidence interval around the model function, the confidence interval being determined by a standard deviation that is obtained from the residuals of the cleaned-up data points based on a characteristic of the model function.

8. The method according to claim 6 , further comprising:
assigning, to each respective cleaned-up data point of the cleaned-up data points, a respective accuracy statement; and
assigning an accuracy statement to the state of health of a trajectory point based on the respective accuracy statement of each of the cleaned-up data points taken into consideration within the predefined time period by using an uncertainty quantification.

9. The method according to claim 8 , wherein, when determining a state of health based on remaining capacity, the accuracy statement indicates a greater accuracy for the determined state of health with a higher evaluated charge variance of one of (i) a supplied amount of charge and (ii) a draining amount of charge.

10. The method according to claim 1 , the ascertaining the state of health trajectory further comprising:
predicting the state of health trajectory using a nearest neighbor algorithm.

11. The method according to claim 1 , the obtaining the set of cleaned-up data points further comprising:
ascertaining new data points, within a predefined time period, the new data points being ascertained in a most recently evaluated evaluation period with evaluation times within an applicable time period;
fitting a parameterizable model function to the data points indicated by the state of health values and the new data points;
ascertaining a standard deviation by statically quantifying a deviation between (i) the model function and (ii) the data points indicated by the state of health values and the new data points; and
one of (i) adding the new data points to cleaned-up data points in response to the new data points being within a range indicated by the standard deviation, and (ii) rejecting the new data points in response to the new data points being outside the range indicated by the standard deviation.

12. The method according to claim 1 , determining the state of health values of the selected device battery further comprising:
evaluating the respective characteristic of the operating variables within the evaluation period by at least one of (i) using Coulomb counting to determine a state of health value based on a remaining capacity, and (ii) measuring a change in an internal resistance of the selected device battery to determine a state of health value based on the change of internal resistance.

13. The method according to claim 1 , wherein a computer program has instructions that are executed by at least one data processing device to cause the at least one data processing device to carry out the method.

14. The method according to claim 1 , wherein the battery-operated machines comprise electrically driveable motor vehicles.

15. The method according to claim 1 , wherein the evaluation period is one of a charging process and a discharging process.

16. The method according to claim 2 , wherein the determined state of health is one of (i) an end of life of the selected device battery, and (ii) a remaining life of the selected device battery.

17. The method according to claim 8 , the assigning, to each respective cleaned-up data point of the cleaned-up data points, the respective accuracy statement further comprisin

Assignees

• Bosch Gmbh Robert

Inventors

• Simonis Christian
• Caspar Maurice
• Schindler Stefan

Classifications

• B60L58/13

  Maintaining the SoC within a determined range · CPC title

• B60L58/16

  responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH] · CPC title

• B60L53/66

  Data transfer between charging stations and vehicles · CPC title

• G01R31/382

  Arrangements for monitoring battery or accumulator variables, e.g. SoC · CPC title

• G01R31/392 Primary

  Determining battery ageing or deterioration, e.g. state of health · CPC title