INTELLIGENT CONTEXT BASED BATTERY CHARGING

• US 20150123595A1
• Filed: 05/13/2014
• Published: 05/07/2015
• Est. Priority Date: 11/04/2013
• Status: Abandoned Application

• Current Assignee: Qualcomm, Inc.
• Original Assignee: XIAM Technologies Limited (Qualcomm, Inc.)
• Inventors: O'DONOGHUE, Hugh, HUSSAIN, Abid, WHALE, Peter Charles, MORKAN, William Kevin, HAWAWINI, Shadi
• Application Number:
  US14/276,856
• Publication Number:
  US 20150123595A1
• Time in Patent Office: Days
• US Class Current: 320/107
• CPC Class Codes: H01M 10/4257 Smart batteries, e.g. electronic circuits inside the housing of the cells or batteries
• CPC Class Codes: H02J 7/00 Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• CPC Class Codes: H02J 7/00712 the cycle being controlled or terminated in response to electric parameters
• CPC Class Codes: H02J 7/007184 in response to battery voltage gradient
• CPC Class Codes: H02J 7/04 Regulation of charging current or voltage
• CPC Class Codes: Y02E 60/10 Energy storage using batteries

Abstract

Aspects disclosed include systems and methods for context based battery charging. In one aspect, context information about usage patterns of an electronic device is used to customize charging a rechargeable battery. In one aspect, a predictive engine accesses context information and generates a predicted charge duration. A charging application customizes charging parameters in a battery charger based on the predicted charge duration. In some aspects, the charging application may generate suggestions to a user to improve battery charging.

Claims

1. A method comprising:

accessing, by an electronic device, context information describing one or more usage patterns of the electronic device;

predicting, by the electronic device, a charging duration based on the context information;

determining, by the electronic device, charging parameters based on the charging duration, wherein the charging parameters are used to charge a battery of the electronic device; and

configuring a battery charger with the charging parameters to charge the battery.

2. The method of claim 1, wherein said predicting comprises generating a model establishing relations between data elements of the context information and the charging duration.

3. The method of claim 2, wherein said predicting further comprises:

storing the context information as charge history data; and

comparing the charge history data to a current context information to predict said charging duration.

4. The method of claim 2, wherein the model is generated dynamically.

5. The method of claim 2, wherein the model classifies past context information and current context elements into a discrete number of charging durations.

6. The method of claim 1, wherein the charging parameters comprise a charge current and a float voltage.

7. The method of claim 1, wherein the context information comprises measured parameters and prescriptive parameters, the method further comprising receiving the charging duration, the measured parameters, and the prescriptive parameters in a charging application and mapping the charging duration to the charging parameters based on the measured parameters and the prescriptive parameters.

8. The method of claim 1, wherein the context information comprises a charge status, charge time, a location, a charge source, and a battery level.

9. An electronic device comprising:

a battery charger;

a battery;

one or more processors; and

a non-transitory computer readable medium having stored thereon one or more instructions, which when executed by the one or more processors, causes the one or more processors to;

access context information describing one or more usage patterns of the electronic device;

predict a charging duration based on the context information;

determine charging parameters based on the charging duration, wherein the charging parameters are used to charge the battery of the electronic device; and

configure the battery charger with the charging parameters to charge the battery.

10. The electronic device of claim 9, wherein said predict comprises one or more instructions to cause the one or more processors to:

generate a model to establish relations between data elements of the context information and the charging duration.

11. The electronic device of claim 10, wherein said predict further comprises one or more instructions to cause the one or more processors to:

store the context information as charge history data; and

compare the charge history data to a current context information to predict said charging duration.

12. The electronic device of claim 10, wherein the model is generated dynamically.

13. The electronic device of claim 10, wherein the model classifies past context information and current context elements into a discrete number of charging durations.

14. The electronic device of claim 9, wherein the charging parameters comprise a charge current and a float voltage.

15. The electronic device of claim 9, wherein the context information comprises measured parameters and prescriptive parameters, the one or more instructions further comprising one or more instructions to cause the one or more processors to:

receive the charging duration, the measured parameters, and the prescriptive parameters in a charging application; and

map the charging duration to the charging parameters based on the measured parameters and the prescriptive parameters.

16. The electronic device of claim 9, wherein the context information comprises a charge status, charge time, a location, a charge source, and a battery level.

17. A non-transitory computer readable medium having stored thereon one or more instructions, which when executed by one or more processor, causes the one or more processors to:

access context information describing one or more usage patterns of the electronic device;

predict a charging duration based on the context information;

determine charging parameters based on the charging duration, wherein the charging parameters are used to charge the battery of the electronic device; and

configure the battery charger with the charging parameters to charge the battery.

18. The non-transitory computer readable medium of claim 17, wherein said predict comprises one or more instructions to cause the one or more processors to generate a model establishing relations between data elements of the context information and the charging duration.

19. The non-transitory computer readable medium of claim 18, wherein said predict further comprises one or more instructions to cause the one or more processors to:

store the context information as charge history data; and

compare the charge history data to a current context information to predict said charging duration.

20. The non-transitory computer readable medium of claim 18, wherein the model is generated dynamically.

21. The non-transitory computer readable medium of claim 18, wherein the model classifies past context information and current context elements into a discrete number of charging durations.

22. The non-transitory computer readable medium of claim 17, wherein the charging parameters comprise a charge current and a float voltage.

23. The non-transitory computer readable medium of claim 17, wherein the context information comprises measured parameters and prescriptive parameters, one or more instructions further comprising one or more instructions to cause the one or more processors to:

receive the charging duration, the measured parameters, and the prescriptive parameters in a charging application; and

map the charging duration to the charging parameters based on the measured parameters and the prescriptive parameters.

24. The non-transitory computer readable medium of claim 17, wherein the context information comprises a charge status, charge time, a location, a charge source, and a battery level.