CROWD SOURCING TO PREDICT VEHICLE ENERGY CONSUMPTION

• US 20160025508A1
• Filed: 07/25/2014
• Published: 01/28/2016
• Est. Priority Date: 07/25/2014
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Meyer, Jason, McGee, Ryan Abraham, Tseng, Fling Finn, Kristinsson, Johannes Geir
• Granted Patent: US 10,801,848 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3407 specially adapted for specific applications
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects

Abstract

An example method of crowd-sourcing energy consumption data for vehicles includes controlling a vehicle in response to a predicted energy consumption that is continually updated based on a difference between a previous predicted energy consumption and a previous actual base energy consumption. Another example method of routing a vehicle includes changing a route for a vehicle in response to a predicted energy consumption for the vehicle when travelling the route, the predicted energy consumption based on a difference between a previous predicted energy consumption and a previous actual base energy consumption.

Claims

1. A method of crowd-sourcing energy consumption data for vehicles, comprising:

controlling a vehicle in response to a predicted energy consumption that is continually updated based on a difference between a previous predicted energy consumption and a previous actual base energy consumption.

2. The method of claim 1, wherein the predicted energy consumption is additionally based on an energy consumption model.

3. The method of claim 2, further comprising updating the energy consumption model in response to the difference.

4. The method of claim 1, wherein the controlling includes selecting a route for the vehicle to travel from a starting position to a destination different than the starting position, the route selected from a plurality of possible routes.

5. The method of claim 4, further comprising calculating a current location of the vehicle and using the current location as the starting position for the route.

6. The method of claim 5, further comprising calculating a current road segment based on the current location of the vehicle.

7. The method of claim 6, further comprising controlling the vehicle in response to identifying information about the vehicle, time, location, a road segment, or some combination of these.

8. The method of claim 1, wherein the controlling of the vehicle is further in response to at least one of a characteristic of the vehicle or a characteristic of a driver of the vehicle.

9. A method of routing a vehicle, comprising:

changing a route for a vehicle in response to a predicted energy consumption for the vehicle when travelling the route, the predicted energy consumption based on a difference between a previous predicted energy consumption and a previous actual base energy consumption.

10. The method of claim 9, further comprising dividing the route into a set of road segments, and performing the changing step for each road segment in the set of road segments.

11. The method of claim 9, wherein the changing includes calculating a relevancy factor based on a first set of data entries corresponding to roads along the route and a second set of data entries corresponding to roads similar to the roads along the route.

12. The method of claim 11, wherein the calculating is with respect to one or more characteristics of the roads along the route.

13. The method of claim 12, wherein the characteristics include at least one of a geographical location, speed limit, number of lanes, road classification, number of traffic lights or stop signs.

14. The method of claim 11, wherein calculating the relevancy factor is with respect to a time since the data entries were collected.

15. The method of claim 11, wherein a predetermined number of data entries with the highest relevancy factor are used in the predicting step.

16. The method of claim 9, further comprising changing the route to the most energy-efficient route.

17. A system for crowd-sourcing information to predict vehicle energy consumption, comprising:

a server in communication with a crowd of vehicles; and

a database on the server, wherein at least one of the server and database collect data corresponding to vehicle energy consumption from the crowd of vehicles, update the data in the database, and provide data corresponding to vehicle energy consumption to the crowd of vehicles.

18. The system of claim 17, wherein the vehicles in the crowd are further in communication with one another.

19. The system of claim 17, wherein the vehicles are in communication with the server via a mobile device.