Intelligent coil control for vehicle wireless power transfer (WPT)

• US 10,343,536 B2
• Filed: 10/23/2017
• Issued: 07/09/2019
• Est. Priority Date: 08/07/2017
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Moghe, Ashok Krishnaji, Apostolopoulos, John George
• Primary Examiner(s): Lin, Sun J
• Application Number:
  US15/790,308
• Publication Number:
  US 20190039463A1
• Time in Patent Office: 624 Days
• Field of Search: 320108
• CPC Class Codes: B60L 53/12 Inductive energy transfer
• CPC Class Codes: B60L 53/122 Circuits or methods for driving the primary coil, e.g. supplying electric power to the coil
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles

Abstract

In some embodiments, a device in a network receives vehicle characteristic data regarding a set of vehicles. The device identifies, based on the received vehicle characteristic data regarding the set of vehicles, a particular ground-based charging coil with which charging coils of the set of vehicles are expected to be in a close proximity. The device determines, based on the received vehicle characteristic data regarding the set of vehicles, a set of power levels and times at which the particular ground-based charging coil is to be powered. The device dynamically controls the particular ground-based charging coil to be powered at the determined set of power levels and times.

Claims

1. A method comprising:

receiving, at a device in a network, vehicle characteristic data regarding a set of vehicles;

identifying, by the device and based on the received vehicle characteristic data regarding the set of vehicles, a particular ground-based charging coil with which charging coils of the set of vehicles are expected to be in a close proximity;

determining, by the device and based on the received vehicle characteristic data regarding the set of vehicles, a set of power levels and times at which the particular ground-based charging coil is to be powered; and

dynamically controlling, by the device, the particular ground-based charging coil to be powered at the determined set of power levels and times.

2. The method as in claim 1, further comprising:

receiving, at the device, feedback data regarding a power transferred from the ground-based charging coil to a particular one of the vehicles;

updating, by the device, a machine learning-based power transfer model for the particular vehicle using the received feedback data; and

using, by the device, the machine learning-based power transfer model for the particular vehicle to determine a power level for a second ground-based charging coil that is to charge the particular vehicle.

3. The method as in claim 1, wherein the vehicle characteristic data is indicative of different makes and models of the set of vehicles, and wherein the set of power levels for the particular ground-based charging coil are determined based in part on the different makes and models of the set of vehicles.

4. The method as in claim 1, wherein the determining a set of power levels and times at which the particular ground-based charging coil is to be powered comprises:

estimating, by the device, a power level of the particular ground-based charging coil that would result after charging a first vehicle of the set of vehicles; and

determining, by the device, a difference between the estimated power level of the particular ground-based charging coil and a power level associated with a second vehicle of the set of vehicles.

5. The method as in claim 4, wherein the determining a set of power levels and times at which the particular ground-based charging coil is to be powered comprises further:

determining, by the device and based on the determined difference, that the particular ground-based charging coil should not be supplied power at a time at which the charging coil of the second vehicle is expected to be in a close proximity to the particular ground-based charging coil.

6. The method as in claim 1, wherein the vehicle characteristic data is indicative of different battery ages of the set of vehicles, and wherein the set of power levels for the particular ground-based charging coil arc determined based in part on the different battery ages of the set of vehicles.

7. The method as in claim 1, wherein the set of power levels and times are determined further based on a road condition or weather condition affecting the particular ground-based charging coil.

8. The method as in claim 1, wherein the particular ground-based charging coil is embedded in a drive-thru, and wherein the set of power levels and times are determined further based on an input received via an ordering kiosk of the drive-thru, a license plate reader, a camera, or a mobile device associated with one vehicle of the set of vehicles.

9. The method as in claim 1, wherein the determining a set of power levels and times at which the particular ground-based charging coil is to be powered comprises:

determining, by the device, a distance between a particular vehicle of the set of vehicles and the particular ground-based charging coil.

10. An apparatus, comprising:

one or more network interfaces to communicate with a network;

a processor coupled to the network interfaces and configured to execute one or more processes; and

a memory configured to store a process executable by the processor, the process when executed configured to;

receive vehicle characteristic data regarding a set of vehicles;

identify, based on the received vehicle characteristic data regarding the set of vehicles, a particular ground-based charging coil with which charging coils of the set of vehicles are expected to be in a close proximity;

determine, based on the received vehicle characteristic data regarding the set of vehicles, a set of power levels and times at which the particular ground based charging coil is to be powered; and

dynamically control the particular ground-based charging coil to be powered at the determined set of power levels and times.

11. The apparatus as in claim 10, wherein the process when executed is further configured to:

receive feedback data regarding a power transferred from the particular ground-based charging coil to a particular vehicle of the set of vehicles;

update a machine learning-based power transfer model for the particular vehicle using the received feedback data; and

use the machine learning-based power transfer model for the particular vehicle to determine a power level for a second ground-based charging coil that is to charge the particular vehicle.

12. The apparatus as in claim 10, wherein the vehicle characteristic data is indicative of different makes and models of the set of vehicles, and wherein the set of power levels for the particular ground-based charging coil are determined based in part on the different makes and models of the set of vehicles.

13. The apparatus as in claim 10, wherein the apparatus determines the set of power levels and times at which the particular ground-based charging coil is to be powered by:

estimating a power level of the particular ground-based charging coil that would result after charging a first vehicle of the set of vehicles; and

determining a difference between the estimated power level of the particular ground-based charging coil and a power level associated with a second vehicle of the set of vehicles.

14. The apparatus as in claim 13, wherein the apparatus determines the set of power levels and times at which the particular ground-based charging coil is to be powered further by:

determining, based on the determined difference, that the particular ground-based charging coil should not be supplied power at a time at which the charging coil of the second vehicle is expected to be in a close proximity to the particular ground-based charging coil.

15. The apparatus as in claim 10, wherein the vehicle characteristic data is indicative of different battery ages of the set of vehicles, and wherein the set of power levels for the particular ground-based charging coil arc determined based in part on the different battery ages of the set of vehicles.

16. The apparatus as in claim 10, wherein the vehicle characteristic data for one vehicle of the set of vehicles comprises data from a license plate reader, a camera, or a vehicle navigation system.

17. The apparatus as in claim 10, wherein the particular ground-based charging coil is embedded in a drive-thru, and wherein the set of power levels and times is determined further based on an input received via an ordering kiosk of the drive-thru or a mobile device associated with one vehicle of the set of vehicles.

18. The apparatus as in claim 10, wherein the apparatus determines the set of power levels and times at which the particular ground-based charging coil is to be powered by:

determining a distance between a particular vehicle of the set of vehicles and the particular ground-based charging coil.

19. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:

receiving, at the device, vehicle characteristic data regarding a set of vehicles;

identifying, by the device and based on the received vehicle characteristic data regarding the set of vehicles, a particular ground-based charging coil with which charging coils of the set of vehicles are expected to be in a close proximity;

determining, by the device and based on the received vehicle characteristic data regarding the set of vehicles, a set of power levels and times at which the particular ground-based charging coil is to be powered; and

dynamically controlling, by the device, the particular ground-based charging coil to be powered at the determined set of power levels and times.

20. The computer-readable medium as in claim 19, wherein the process further comprises:

receiving, at the device, feedback data regarding a power transferred from the particular ground-based charging coil to a particular vehicle of the set of vehicles;

updating, by the device, a machine learning-based power transfer model for the particular vehicle using the received feedback data; and

using, by the device, the machine learning-based power transfer model for the particular vehicle to determine a power level for a second ground-based charging coil that is to charge the particular vehicle.