Load allocation for multi-battery devices

Patent metadata

Field	Value
Publication number	US-10263421-B2
Application number	US-201715650666-A
Country	US
Kind code	B2
Filing date	Jul 14, 2017
Priority date	Feb 26, 2015
Publication date	Apr 16, 2019
Grant date	Apr 16, 2019

Abstract

This document describes techniques and apparatuses of load allocation for multi-battery devices. In some embodiments, these techniques and apparatuses determine an amount of load power that a multi-battery device consumes to operate. Respective efficiencies at which the device's multiple batteries are capable of providing power are also determined. A respective portion of load power is then drawn from each of the batteries based on their respective efficiencies.

First claim

What is claimed is:

1. A computer-implemented method comprising:
drawing, from a first battery cell of a device, load power to support a workload of the device;
determining that an efficiency at which the first battery cell is capable of powering a future workload is not optimal; and
charging, via a second battery cell of the device, the first battery cell effective to improve the efficiency at which the first battery is capable of powering the future workload.

2. The computer-implemented method of claim 1 , wherein the workload is allocated by a sequential algorithm configured to draw the load power from the first battery cell and the second battery cell sequentially.

3. The computer-implemented method of claim 1 , wherein the workload is allocated by a least-resistance algorithm configured to allocate the load power based on instantaneous power level of a load and instantaneous respective internal resistances of the first battery cell and the second battery cell.

4. The computer-implemented method of claim 1 , wherein the workload is allocated by a threshold algorithm configured to draw the load power from the first battery cell and the second battery cell based on thresholds for state of charge or internal resistance for the respective battery cells.

5. The computer-implemented method of claim 1 , wherein the workload is a low-powered workload for a predicted sleep time or a standby time for the device.

6. The computer-implemented method of claim 1 , wherein the future workload is a high-powered workload, and wherein the determining is based on a previous discharge of the first battery cell.

7. The computer-implemented method of claim 1 , wherein the determining occurs responsive to an unexpected user interaction with the device.

8. The computer-implemented method of claim 1 , wherein the charging the first battery cell increases the first battery cell's state of charge and decreases the first battery cell's internal resistance.

9. A computing device comprising:
multiple batteries configured to provide power to enable operation of the computing device;
switching circuitry configured to enable the power to be drawn from each of the multiple batteries;
sensing circuitry configured to measure load power consumed by the computing device to operate; and
a load manager configured to perform operations comprising:
drawing, from a first battery cell of the multiple battery cells, load power to support a workload of the computing device;
determining that an efficiency at which the first battery cell is capable of powering a future workload is not optimal; and
charging, via a second battery cell of the multiple battery cells, the first battery effective to improve the efficiency at which the first battery cell is capable of powering the future workload.

10. The computing device as described in claim 9 , wherein the workload is a low-powered workload for a predicted sleep time or a standby time for the computing device.

11. The computing device as described in claim 9 , wherein the future workload is a high-powered workload, and wherein the determining is based on a previous discharge of the first battery cell.

12. The computing device as described in claim 9 , wherein the determining occurs responsive to an unexpected user interaction with the computing device.

13. The computing device as described in claim 9 , wherein the charging the first battery cell increases the first battery cell's state of charge and decreases the first battery cell's internal resistance.

14. The computing device as described in claim 9 , wherein the efficiency at which the first battery cell is capable of providing power is determined based on one or more of the first battery cell's state-of-charge, internal resistance, age, cycle count, temperature, chemistry, circuit topology, or capacity.

15. A system comprising:
a first battery and a second battery configured to provide power to enable operation of the computing device;
switching circuitry configured to enable the power to be drawn from each of the first battery and the second battery;
sensing circuitry configured to measure load power consumed by the system to operate; and
a load allocator configured to perform operations comprising:
determining, via an algorithm and based on respective efficiencies of the first battery and the second battery, an allocation of load power being consumed by the system to the first battery and the second battery to maximize an efficiency at which the first battery and the second battery power the system;
drawing, from the first battery, load power to support a workload of the system;
determining, via the algorithm and based on the efficiency of the first battery, that an efficiency at which the first battery is capable of powering a future workload is not optimal; and
charging, via the second battery, the first battery effective to improve the efficiency at which the first battery is capable of powering the future workload.

16. The system as described in claim 15 , wherein the algorithm includes one or a variable-weighted algorithm, a sequential algorithm, a least-resistance algorithm, or a threshold algorithm.

17. The system as described in claim 16 , wherein:
the sequential algorithm is configured to allocate the load power such that the load power is drawn sequentially from the first battery and the second battery;
the least-resistance algorithm is configured to allocate the load power based on an instantaneous power level of the load and an instantaneous respective internal resistance of the first battery and the second battery; or
the threshold algorithm is configured to allocate the load power based on a predefined threshold for a state-of-charge or internal resistance of the first battery and the second battery.

18. The system as described in claim 16 , wherein the allocation of load power is determined based on a hybrid algorithm that includes use of the variable-weighted algorithm, the sequential algorithm, or the least-resistance algorithm based on the threshold algorithm.

19. The system as described in claim 15 , wherein the workload is a low-powered workload for a predicted sleep time or a standby time for the system, and wherein the future workload is a high-powered workload.

20. The system as described in claim 15 , wherein the charging the first battery increases the first battery's state of charge and decreases the first battery's internal resistance.

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Huang Bojun
• Moscibroda Thomas
• Chandra Ranveer
• Hodges Stephen E
• Meinershagen Julia L
• Priyantha Nissanka Arachchige Bodhi
• Badam Anirudh
• Hu Pan
• Ferrese Anthony John
• Skiani Evangelia

Classifications

• H02J7/36

  Arrangements using end-cell switching · CPC title

• H01M10/441

  for several batteries or cells simultaneously or sequentially · CPC title

• H02J7/855 Primary

  with circuits adapted for supplying loads from the battery · CPC title

• H02J7/585

  Sequential battery discharge in systems with a plurality of batteries · CPC title

• H02J7/50

  acting upon multiple batteries simultaneously or sequentially · CPC title