Managing a fleet of vehicles

Patent metadata

Field	Value
Publication number	US-11959759-B2
Application number	US-202117495173-A
Country	US
Kind code	B2
Filing date	Oct 6, 2021
Priority date	Mar 15, 2018
Publication date	Apr 16, 2024
Grant date	Apr 16, 2024

Abstract

Aspects of the disclosure relate to determining a next vehicle task for a vehicle of a fleet. Vehicle data from the vehicle, charger data about at least one charger, and demand data may be received and used to determine the next vehicle task. The vehicle may be directed to the next vehicle task. Determining a next vehicle task may further be based on predictions made using the vehicle data, the charger data, and the demand data. Heuristics may also be used in determining a next vehicle task.

First claim

The invention claimed is:

1. A method comprising:
receiving, by one or more processors of one or more server computing devices, vehicle data including a vehicle charge status and a vehicle charge capacity;
receiving, by the one or more processors, charger data for each charger of a plurality of chargers configured to charge vehicles of a fleet of rechargeable vehicles;
determining, by the one or more processors based on the vehicle data and the charger data, one charger of the plurality of chargers to perform charging;
controlling, by the one or more processors, an autonomous driving computing system of a vehicle of the fleet for which the vehicle charge status reaches a threshold minimum charge level to maneuver to a location of the determined one charger;
determining, by the one or more processors, a threshold maximum charge level based on the vehicle charge status, the vehicle charge capacity and a charger speed indicated by the charger data for the determined one charger; and
controlling, by the one or more processors, the vehicle to perform charging at the determined one charger up to the determined threshold maximum charge level.

2. The method of claim 1 , wherein areas serviced by the fleet of vehicles are segmented into a plurality of zones, the method further comprising limiting a number of vehicles of the fleet using one of the chargers in a particular zone at a given time in order to avoid exceeding a threshold number of vehicles using the one charger.

3. The method of claim 1 , further comprising:
automatically adjusting, by the one or more processors, target charging levels of the vehicles in response to a real-time change in demand for services provided by the fleet of vehicles.

4. The method of claim 1 , wherein the charger data indicates at least one of a type of each charger, a location of each charger, a current availability of each charger or the charger speed of each charger.

5. The method of claim 1 , further comprising:
predicting, by the one or more processors, a future time when the determined one charger will become available based on at least one of a type of the determined one charger or the charger speed.

6. The method of claim 5 , wherein the future time is predicted based on how long the determined one charger will take to complete charging of a vehicle immediately prior to the future time.

7. The method of claim 1 , further comprising:
receiving, by the one or more processors, demand data including at least one of current trip demand or predicted future trip demand for trips by the vehicles of the fleet.

8. The method of claim 7 , further comprising storing in memory the charger data, the vehicle data and the demand data.

9. The method of claim 7 , further comprising:
determining, by the one or more processors, an extent to recharge the vehicle based on whether the vehicle charge status exceeds a threshold maximum level; and
adjusting, by the one or more processors, the threshold maximum level when the current trip demand is above a threshold peak demand,
wherein determining the extent to recharge the vehicle is further based on whether the vehicle charge status exceeds the adjusted threshold maximum level.

10. The method according to claim 1 , further comprising:
predicting a likelihood that an energy cost for recharging the vehicle at a first time will be lower than an energy cost for recharging the vehicle at a second time,
wherein each vehicle of the fleet of vehicles is controlled to perform charging based on the likelihood that the energy cost for recharging the vehicle at the first time will be lower than the energy cost for recharging the vehicle at the second time.

11. The method according to claim 1 , further comprising:
determining an energy consumption rate of the vehicle; and
predicting a future vehicle charge status after a trip based on the energy consumption rate,
wherein controlling the autonomous driving computing system of the vehicle includes controlling the autonomous driving computing system of the vehicle to recharge after the trip when the predicted future vehicle charge status after the trip is below a threshold minimum level.

12. The method according to claim 11 , wherein the energy consumption rate for the trip is determined further based on weather conditions.

13. The method according to claim 1 , further comprising:
determining, by the one or more processors, one or more time windows with peak energy costs during which recharging of the vehicle is not performed.

14. A system comprising:
a memory; and
one or more server computing devices, each server computing device of the one or more server computing devices including one or more processors coupled to the memory and configured to:
receive vehicle data including a vehicle charge status and a vehicle charge capacity;
receive charger data for each charger of a plurality of chargers configured to charge vehicles of a fleet of rechargeable vehicles;
determine, based on the vehicle data and the charger data, one charger of the plurality of chargers to perform charging;
control an autonomous driving computing system of a vehicle of the fleet for which the vehicle charge status reaches a threshold minimum charge level to maneuver to a location of the determined one charger;
determine a threshold maximum charge level based on the vehicle charge status, the vehicle charge capacity and a charger speed indicated by the charger data for the determined one charger; and
control the vehicle to perform charging at the determined one charger up to the determined threshold maximum charge level.

15. The system of claim 14 , wherein areas serviced by the fleet of vehicles are segmented into a plurality of zones, and the one or more server computing devices are configured to limit a number of vehicles of the fleet using one of the chargers in a particular zone at a given time in order to avoid exceeding a threshold number of vehicles using the one charger.

16. The system of claim 14 , wherein the one or more processors are further configured to automatically adjust target charging levels of the vehicles in response to a real-time change in demand for services provided by the fleet of vehicles, wherein the determination of the one charger is further based on the adjusted target charging levels.

17. The system of claim 14 , wherein the one or more processors are further configured to receive demand data including at least one of current trip demand or predicted future trip demand for trips by the vehicles of the fleet, wherein the determination of the one charger is further based on the demand data.

18. The system of claim 17 , wherein the memory is configured to store the charger data, the vehicle data and the demand data.

19. The system of claim 14 , wherein the charger data indicates at least one of a type of each charger, a location of each charger, a current availability of each charger or the charger speed of each charger.

20. The system of claim 14 , wherein the one or more processors are further configured to predict a future time when the determined one charger will become available based on at least one of a type of the determined one charger or the speed of the determined one charger.

21. The system of claim 20 , wherein the future time is predicted based on how long the determined one charger will take to complete charging of a vehicle immediately prior to the future time.

Assignees

• Waymo Llc

Inventors

• Ludwick Christopher Kennedy
• Chatham Andrew Hughes
• Mcnaughton Matthew Paul
• Johnson Charles Bigelow

Classifications

• G01C21/3469 Primary

  Fuel consumption; Energy use; Emission aspects · CPC title

• B60L58/12

  responding to state of charge [SoC] · CPC title

• G01C21/343

  Calculating itineraries (travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047) · CPC title

• G05D1/0291

  Fleet control (monitoring fleets in traffic control systems for road vehicles G08G1/127, G08G1/127) · CPC title

• G07C5/008

  communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title