Method and device for improving the service life of an equipment battery in a battery-operated machine in a machine-specific manner

Patent metadata

Field	Value
Publication number	US-11794602-B2
Application number	US-202117550624-A
Country	US
Kind code	B2
Filing date	Dec 14, 2021
Priority date	Dec 15, 2020
Publication date	Oct 24, 2023
Grant date	Oct 24, 2023

Abstract

The disclosure relates to a computer-implemented method for operating a system comprising a central processing unit, which is in communication with a multiplicity of battery-operated machines that each have an equipment battery, and for providing an action for extending the service life of the equipment battery of the machine in a machine-specific manner.

First claim

What is claimed is:

1. A method, which is computer-implemented, for operating a system including a central processor that is in communication with a plurality of battery-operated machines that each have an equipment battery, and for providing an action for extending a service life of the equipment batteries of the plurality of battery-operated machines in a machine-specific manner, the method comprising:
determining, for each respective equipment battery in a plurality of the equipment batteries, a respective history function of a state of health of the respective equipment battery depending on time progressions of operating variables that are one of continuously-provided and continuously-received, the respective history function indicating a progression of the state of health of the respective equipment battery;
providing operating attributes depending on the time progressions of the operating variables for each of the plurality of equipment batteries, the operating attributes being assigned to stress factors to indicate a stress load on the respective equipment battery;
determining a corresponding normalized time gradient of each of the determined history functions;
plotting operating attribute points in a corresponding attribute space for each of the operating attributes for each equipment battery of the plurality of equipment batteries, the attribute space including values of the corresponding operating attribute plotted against the corresponding normalized time gradient of the history function to form the plotted operating attribute points;
performing a cluster analysis by clustering the plotted operating attribute points into a plurality of clusters;
identifying a corresponding centroid for each cluster of the plurality of clusters, each centroid representing a value of the corresponding operating attribute and a value of the corresponding normalized time gradient;
identifying, for each respective operating attribute of the operating attributes, a respective cluster of the plurality of clusters for which the centroid indicates (i) a highest stress load based on the value of the corresponding operating attribute, and (ii) for which the value of the corresponding normalized time gradient indicates a highest degradation; and
signaling, for each respective operating attribute of the operating attributes, to machines of the plurality of battery-operated machines that are associated with the identified cluster a stress-reducing action assigned to the respective operating attribute.

2. The method according to claim 1 , the performing the cluster analysis further comprising:
performing the cluster analysis using one of (i) K-means++ and (ii) competitive learning.

3. The method according to claim 1 , the determining, for each respective equipment battery in the plurality of equipment batteries, the respective history function further comprising:
ascertaining state-of-health values of the respective equipment battery, depending on the operating variables, using a state-of-health model to determine data points, each of the data points assigning an ascertained state-of-health value to an ageing time-point; and
parameterizing the respective history function to fit the history function to the data points.

4. The method according to claim 1 further comprising:
determining the stress-reducing action to be signaled depending on a predefined assignment model and the identified respective cluster.

5. The method according to claim 1 , wherein the operating variables indicate a battery current, a battery voltage, a battery temperature, and a state of charge.

6. The method according to claim 1 , wherein the operating attributes, which indicate stress factors, include (i) a calendar ageing with a first state of charge and first temperature, (ii) a cyclic ageing corresponding to a first frequency of charging and discharging processes, (iii) and a frequency of charging processes with first amperages.

7. The method according to claim 1 , the plurality of battery-operated machines include at least one of (i) a motor vehicle, (ii) a pedelec, (iii) a flight device, (iv) a drone, (v) a power tool, (vi) a consumer electronics device, (vii) a mobile phone, (viii) an autonomous robot, and (ix) a household appliance.

8. The method according to claim 1 , wherein the method is carried out by a computer program that is executed by at least one data processing device.

9. The method according to claim 8 , wherein the computer program is stored on a non-transitory machine-readable storage medium.

10. The method according to claim 3 , wherein the respective history function is a linear history function.

11. The method according to claim 6 , wherein the operating attributes are correspondingly quantified to indicate by their value a load on at least one of the plurality of equipment batteries.

12. The method according to claim 1 , wherein the signaled stress-reducing action is to reduce fast charging operations of the respective equipment battery and to use overnight charging to charge the respective equipment battery.

13. The method according to claim 1 , wherein the signaled stress-reducing action is an automatic limit of a charge consumption during a charging process of the respective equipment battery.

14. The method according to claim 1 , wherein the signaled stress-reducing action is an automatic limit of a maximum discharging current of the respective equipment battery.

15. The method according to claim 1 , wherein the values of each corresponding operating attribute indicate an extent to which a usage characterized by the corresponding operating attribute has stressed the respective equipment battery.

16. A device of a system, which is in communication with a plurality of battery-operated machines that each have an equipment battery, for providing an action for extending a service life of the equipment batteries of the plurality of battery-operated machines in a machine-specific manner, the device being configured to:
determine, for each respective equipment battery in a plurality of the equipment batteries, a respective history function of a state of health of the respective equipment battery depending on time progressions of operating variables that are one of continuously-provided and continuously-received, the respective history function indicating a progression of the state of health of the respective equipment battery;
provide operating attributes depending on the time progressions of the operating variables for each of the plurality of equipment batteries, the operating attributes being assigned to stress factors to indicate a stress load on the respective equipment battery;
determine a corresponding normalized time gradient of each of the determined history functions;
plot operating attribute points in a corresponding attribute space for each of the operating attributes for each equipment battery of the plurality of equipment batteries, the attribute space including values of the corresponding operating attribute plotted against the corresponding normalized time gradient of the history function to form the plotted operating attribute points;
perform a cluster analysis by clustering the plotted operating attribute points into a plurality of clusters;
identify a corresponding centroid for each cluster of the plurality of clusters, each centroid representing a value of the corresponding operating attribute and a value of the corresponding normalized time gradient;
identify, for each respective operating attribute of the operating attributes, a respective cluster of the plurality of clusters for which the cen

Assignees

• Bosch Gmbh Robert

Inventors

• Kroener Christoph
• Simonis Christian
• Woll Christoph

Classifications

• H02J7/825

  Detection of fully charged condition · CPC title

• H02J7/84

  Control of state of health [SOH] · CPC title

• B60L53/66 Primary

  Data transfer between charging stations and vehicles · CPC title

• B60L58/13

  Maintaining the SoC within a determined range · CPC title

• H01M10/441

  for several batteries or cells simultaneously or sequentially · CPC title