OPTIMIZING BATTERY USE FOR KNOWN FUTURE LOAD

• US 20130346762A1
• Filed: 06/22/2012
• Published: 12/26/2013
• Est. Priority Date: 06/22/2012
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: HODGES, Stephen, CHANDRA, Ranveer, MEINERSHAGEN, Julia Louise, SCOTT, James
• Granted Patent: US 9,285,851 B2
• Time in Patent Office: Days
• US Class Current: 713/300
• CPC Class Codes: G06F 1/263 Arrangements for using multiple switchable power supplies, e.g. battery and AC G06F1/30 takes precedence
• CPC Class Codes: G06F 1/3206 Monitoring of events, devices or parameters that trigger a change in power modality
• CPC Class Codes: G06F 1/3212 Monitoring battery levels, e.g. power saving mode being initiated when battery voltage goes below a certain level
• CPC Class Codes: G06F 1/3234 Power saving characterised by the action undertaken
• CPC Class Codes: H01M 10/4257 Smart batteries, e.g. electronic circuits inside the housing of the cells or batteries
• CPC Class Codes: H01M 10/441 for several batteries or cells simultaneously or sequentially
• CPC Class Codes: H01M 10/482 for several batteries or cells simultaneously or sequentially
• 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: H02J 7/0068 Battery or charger load switching, e.g. concurrent charging and load supply H02J7/0013 takes precedence
• CPC Class Codes: Y02E 60/10 Energy storage using batteries

Abstract

Methods for optimizing battery use for a known future load are described. In an embodiment, one or more battery cells are selected from a set of battery cells to provide power to a computing-based device. The battery cells are selected based on discharge profile data for each battery cell and both the current power requirement and a known future power requirement of the computing-based device. The known future power requirement is calculated based on information available to the operating system running on the computing-based device. In some examples, one or more battery cells may also be selected for charging when a power source is available and these cells may be selected based on charge profile data and the known future power requirement. The selection of the battery cells may also be made in order to satisfy a battery optimization goal, which may be defined by a user.

Claims

1. A method of controlling a computing-based device, the computing-based device arranged to run an operating system and comprising a plurality of battery cells, the method comprising:

accessing a discharging profile for each of the plurality of battery cells;

determining, based on data available to the operating system, a current load and a known future load; and

using the discharging profile, the current load and the known future load to adaptively control which of the plurality of battery cells are used to provide power to the computing-based device.

2. A method according to claim 1, further comprising:

accessing a charging profile for each of the plurality of battery cells; and

using the charging profile and the known future load to adaptively control which of the plurality of battery cells are charged.

3. A method according to claim 2, further comprising:

using the charging profile to adaptively control charging of a battery cell to accelerate charging for a period of time.

4. A method according to claim 1, wherein using the discharging profile, the current load and the known future load to adaptively control which of the plurality of battery cells are used to provide power to the computing-based device comprises:

using the discharging profile, the current load and the known future load to adaptively control which of the plurality of battery cells are used to provide power to the computing-based device in order to satisfy a battery optimization goal.

5. A method according to claim 4, further comprising:

receiving a user input defining the battery optimization goal.

6. A method according to claim 4, wherein the battery optimization goal comprises reducing a cost of replacement of one or more of the battery cells.

7. A method according to claim 1, further comprising:

learning the discharging profile for each of the plurality of battery cells; and

storing the learned discharging profiles.

8. A method according to claim 1, further comprising:

monitoring operation of a battery cell; and

using information obtained from monitoring the battery cell to update stored profile information for the battery cell.

9. A method according to claim 1, implemented on a device remote from the computing-based device and further comprising:

remotely accessing the data available to the operating system running on the computing-based device.

10. A computing-based device arranged to run an operating system, the device comprising:

an adaptive battery control module;

a plurality of battery cells;

a switching element connected to each of the plurality of battery cells;

a memory arranged to store battery cell profile data for the plurality of battery cells; and

a processor,

wherein the adaptive battery control module is arranged to calculate a current load and a known future load based on information available from the operating system and to use the calculated loads and the stored battery cell profile data to control the switching element such that a selected subset of the battery cells are connected to provide power to the computing-based device.

11. A computing-based device according to claim 10, further comprising a sensing element arranged to detect an alternative power source.

12. A computing-based device according to claim 10, further comprising a smart battery module, the smart battery module comprising the processor, battery cells, switching element, memory and adaptive battery control module.

13. A computing-based device according to claim 10, wherein the adaptive battery control module is arranged to control the switching element such that a selected subset of the battery cells are connected to provide power to the computing-based device to satisfy a battery optimization goal.

14. A computing-based device according to claim 13, wherein the battery optimization goal is user-defined and further comprising an input/output controller arranged to receive a user input identifying the battery optimization goal.

15. A computing-based device according to claim 10, wherein the plurality of battery cells comprise at least one battery cell which uses a first battery technology and at least one battery cell which uses a second battery technology.

16. A computing-based device according to claim 15, wherein the at least one battery cell which uses a first battery technology comprises at least one supercapacitor.

17. A computing-based device according to claim 10, wherein the battery cell profile data comprises charging profiles for each battery cell or a charging model for each battery cell and the device further comprises a charging interface and wherein the adaptive battery control module is further arranged to use the calculated known future load and the stored battery cell profile data to control the switching element such that a second selected subset of the battery cells are connected to the charging interface.

18. A computing-based device according to claim 17, wherein the adaptive battery control module is further arranged to control the operation of the charging interface to provide accelerated charging to the second selected subset of the battery cells.

19. A computing-based device according to claim 10, wherein the computing-based device is a smart phone or tablet computer.

20. One or more tangible device-readable media with device-executable instructions that, when executed by a computing system arranged to run an operating system and comprising a plurality of heterogeneous battery cells, direct the computing system to perform steps comprising:

accessing a charging profile and a discharging profile for each of the plurality of heterogeneous battery cells;

receiving a user input identifying a user-defined battery optimization goal;

determining, based on data available to the operating system, a current load and a known future load; and

using the discharging profile and the known future load to adaptively control which of the plurality of heterogeneous battery cells are used to provide power to the computing-based device to satisfy the user-defined battery optimization goal.