UAV group charging based on demand for UAV service

• US 10,580,311 B2
• Filed: 10/26/2017
• Issued: 03/03/2020
• Est. Priority Date: 10/26/2017
• Status: Active Grant

• Current Assignee: Wing Aviation LLC (Alphabet Inc.)
• Original Assignee: Wing Aviation LLC (Alphabet Inc.)
• Inventors: Schmalzried, Jim, Blake, Jesse
• Primary Examiner(s): Memula, Suresh
• Application Number:
  US15/794,925
• Publication Number:
  US 20190126769A1
• Time in Patent Office: 859 Days
• Field of Search: 320109
• CPC Class Codes: B60L 2200/10 Air crafts
• CPC Class Codes: B60L 2200/40 Working vehicles
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2260/32 Auto pilot mode
• CPC Class Codes: B60L 53/305 Communication interfaces
• CPC Class Codes: B60L 53/36 by positioning the vehicle
• CPC Class Codes: B60L 53/64 Optimising energy costs, e.g. responding to electricity rates
• CPC Class Codes: B60L 53/65 involving identification of vehicles or their battery types
• CPC Class Codes: B60L 53/67 Controlling two or more charging stations
• CPC Class Codes: B60L 53/68 Off-site monitoring or control, e.g. remote control
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B64C 39/024 of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64U 10/14 with four distinct rotor axes, e.g. quadcopters
• CPC Class Codes: B64U 10/20 Vertical take-off and landing [VTOL] aircraft flying platforms B64U10/13; helicopters B64U10/17
• CPC Class Codes: B64U 2101/60 for transporting passengers; for transporting goods other than weapons
• CPC Class Codes: B64U 2101/64 for parcel delivery or retrieval
• CPC Class Codes: B64U 2101/67 the UAVs comprising tethers for lowering the goods
• CPC Class Codes: B64U 50/19 using electrically powered motors
• CPC Class Codes: B64U 50/37 Charging when not in flight
• CPC Class Codes: G05D 1/104 : involving a plurality of aircrafts, e.g. formation flying traffic control systems for aircraft G08G5/00
• CPC Class Codes: G06Q 10/04 : Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/0631 : Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06316 : Sequencing of tasks or work
• CPC Class Codes: G06Q 10/08 : Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G08G 5/0069 : specially adapted for an unmanned aircraft
• CPC Class Codes: H02J 7/0013 : acting upon several batteries simultaneously or sequentially H02J7/1423 takes precedence
• CPC Class Codes: Y02P 90/60 : Electric or hybrid propulsion means for production processes
• 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 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/12 : Electric charging stations
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles
• CPC Class Codes: Y02T 90/167 : Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: Y04S 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Abstract

Example embodiments can help to more efficiently charge unmanned aerial vehicles (UAVs) in a plurality of UAVs that provide delivery services. An example method includes: determining demand data indicating demand for item-transport services by the plurality of UAVs during a period of time; determining battery state information for the plurality of UAVs, wherein the battery state information is based at least in part on individual battery state information for each of two or more of the UAVs; based at least in part on (a) the demand data for item-transport services by the plurality of UAVs, and (b) the battery state information for the fleet of UAVs, determining respective charge-rate profiles for one or more of the UAVs; and sending instructions to cause respective batteries of the one or more of the UAVs to be charged according to the respectively determined charge-rate profiles.

Claims

1. A method comprising:

determining, by a control system for a plurality of unmanned aerial vehicles (UAVs) that are available to provide item-transport service a first geographic area, UAV demand data indicating demand for item-transport services by the plurality of UAVs during a period of time in the first geographic area, wherein the demand data indicates demand for deployment of UAVs from a charging hub to pick up items from one or more source locations and transport the items to one or more target locations in the first geographic area;

determining, by the control system, battery state information for the plurality of UAVs that are available to provide item-transport service in the first geographic area, wherein the battery state information is based at least in part on individual battery state information for each of two or more of the UAVs;

based at least in part on (a) the demand data for item-transport services by the plurality of UAVs, and (b) the battery state information for the plurality of UAVs, determining respective charge-rate profiles for one or more of the UAVs; and

sending instructions to cause respective batteries of the one or more of the UAVs to be charged according to the respectively determined charge-rate profiles.

2. The method of claim 1, wherein the demand data indicates the demand during a given period of time.

3. The method of claim 1, wherein the plurality of UAVs serve the first geographic area, wherein a plurality of UAV charging hubs are distributed throughout the first geographic area, and wherein the demand data is indicative of demand on per-charging-hub basis.

4. The method of claim 1, wherein determining the demand data comprises:

determining expected tasks for the plurality of UAVs during the period of time; and

determining group-wide power requirements corresponding to the expected tasks.

5. The method of claim 1, wherein determining the one or more charge-rate profiles comprises:

determining expected tasks for the plurality of UAVs during the period of time; and

determining a schedule of operation for the plurality of UAVs to collectively carry out the expected tasks.

6. The method of claim 1, wherein determining the battery state information for the plurality of UAVs comprises:

for each of one or more of the UAVs, determining one or more of the following information items;

a current battery level of the UAV, a number of charge cycles experienced by a battery of the UAV, an expected number of remaining charge cycles for the battery of the UAV, and a remaining useful life for the battery of the UAV.

7. The method of claim 1, wherein the demand data comprises power-consumption data for a set of UAV item-transport tasks, and wherein determining the charge-rate profiles comprises:

for each UAV from a subset of the plurality of UAVs, applying a cost function to evaluate cost of charging the UAV such that the UAV can perform one or more tasks from the set of tasks during the period of time; and

based on the cost evaluation for the subset of UAVs, selecting a set of one or more of the UAVs that are collectively capable of performing the set of tasks during the period of time.

8. The method of claim 1, wherein determining the demand data comprises:

when the demand data indicates a lower demand level during the time period, then assigning a lower charge-rate to one or more selected UAVs from the plurality of UAVs; and

when the demand data indicates a higher demand level during the time period, then assigning a higher charge-rate to the one or more selected UAVs.

9. The method of claim 1, further comprising:

based at least in part on the demand data, determining a cost per unit of UAV downtime during the period of time.

10. The method of claim 9, wherein determining the cost per unit of UAV downtime comprises:

determining a set of tasks for performance by the plurality of UAVs during the period of time;

determining a cumulative power consumption requirement for the plurality of UAVs to carry out the set of tasks; and

based at least in part on the cumulative power consumption requirement, determining a cost per unit of UAV downtime during the period of time, wherein time spent by a given UAV results in downtime for the given UAV.

11. A control system for a plurality of unmanned aerial vehicles (UAVs) comprising:

at least one communication interface for communicating with a plurality of charging hubs, wherein each charging hub is capable of charging one or more of a plurality of UAVs that are available to provide item-transport service a first geographic area; and

a computing system operable to;

determine UAV demand data indicating demand for item-transport services by the plurality of UAVs in the first geographic area, wherein the demand data indicates demand for deployment of UAVs from a charging hub to pick up items from one or more source locations and transport the items to one or more target locations in the first geographic area;

determine battery state information for the plurality of UAVs that are available to provide item-transport service in the first geographic area, wherein the battery state information is based at least in part on individual battery state information for each of two or more of the UAVs;

based at least in part on (a) the demand data for item-transport services by the plurality of UAVs, and (b) the battery state information for the plurality of UAVs, determine a respective charge-rate profile for each of one or more of the UAVs; and

send, via the at least one communication interface, instructions to at least one charging hub to cause respective batteries of the one or more of the UAVs to be charged according to the respectively determined charge-rate profiles.

12. The control system of claim 11, wherein the demand data indicates the demand during a given period of time.

13. The control system of claim 11, wherein a plurality of UAV charging hubs are distributed throughout the first geographic area, and wherein the demand data is indicative of demand on per-charging-hub basis.

14. The control system of claim 11, wherein, in order to determine the demand data the computing system is configured to:

determine expected tasks for the plurality of UAVs during a period of time; and

determine fleet-wide power requirements, for the plurality of UAVs, corresponding to the expected tasks.

15. The control system of claim 11, wherein, in order to determine the demand data the computing system is configured to:

determine expected tasks for the plurality of UAVs during a period of time; and

determine a schedule of operation for one or more of the plurality of UAVs to collectively carry out the expected tasks.

16. The control system of claim 11, wherein, in order to determine the battery state information for the plurality of UAVs, the computing system is configured to:

for each of one or more of the UAVs, determine one or more of the following information items;

a current battery level of the UAV, a number of charge cycles experienced by a battery of the UAV, an expected number of remaining charge cycles for the battery of the UAV, and a remaining useful life for the battery of the UAV.

17. The control system of claim 11:

wherein the demand data comprises power-consumption data for a set of UAV item-transport tasks, and

wherein, in order to determine the charge-rate profiles, the computing system is configured to;

for each UAV from the plurality of UAVs, apply a cost function to evaluate cost of charging the UAV such that the UAV can perform one or more tasks from the set of tasks during a period of time; and

based on the cost evaluations for the plurality of UAVs, select a set of one or more of the plurality UAVs that are collectively capable of performing the set of tasks during the period of time.

18. The control system of claim 11, wherein, in order to determine the charge-rate profiles, the computing system is configured to:

when the demand data indicates a lower demand level during a time period, then assign a lower charge-rate to one or more selected UAVs from the plurality of UAVs; and

when the demand data indicates a higher demand level during the time period, then assign a higher charge-rate to the one or more selected UAVs.