Method and System For Intelligent Fuel Monitoring and Real Time Planning

• US 20110301806A1
• Filed: 06/03/2010
• Published: 12/08/2011
• Est. Priority Date: 06/03/2010
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Messier, Daniel John, Mackenzie, Patricia Denise, Salvo, Joseph James, Bouqata, Bouchra
• Application Number:
  US12/792,882
• Publication Number:
  US 20110301806A1
• Time in Patent Office: Days
• US Class Current: 701/33
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3476 using point of interest [POI] information, e.g. a route passing visible POIs
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles

Abstract

A method and system are provided for intelligent fuel monitoring and real-time dynamic route planning for a vehicle. The system includes one or more sensor devices monitoring a vehicle status; and a processing system coupled to the one or more sensor devices, the processing system having a memory and a processor, wherein the processor is programmed to: receive a plurality of data such as: vehicle status sensor data, points of interest data, environmental data, user data, route data. The processor then learns driver behavior from the plurality of data that influences selection of driving routes; processes the plurality of data for a current route; determines an optimal route based on the plurality of data processed for the current route and the learned driver behavior; and outputs the optimal route.

Claims

1. An intelligent fuel monitoring and dynamic routing system for vehicles, the system comprising:

one or more sensor devices monitoring a vehicle status; and

a processing system coupled to the one or more sensor devices, the processing system having a memory and a processor, wherein the processor is programmed to;

(a) receive a plurality of data comprising;

(i) vehicle status data;

(ii) points of interest data;

(iii) environmental data;

(iv) user data;

(v) route data;

(b) learn driver behavior from the plurality of data that influences selection of driving routes;

(c) process the plurality of data for a current route;

(d) determine an optimal route based on the plurality of data processed for the current route and the learned driver behavior; and

(e) output the optimal route.

2. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein vehicle status data comprise one or more of fuel level data, vehicle fluid level data, vehicle speed data, vehicle temperature data, outside temperature data, and vehicle location data.

3. The intelligent fuel monitoring and dynamic routing system of claim 2 wherein the one or more sensor devices monitor the vehicle for the vehicle status data.

4. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein points of interest data comprises information about intermediate stopping points including one or more of locations for points of interest, hours of operation at the points of interest, prices for goods and services at the points of interest, goods and services offered at the points of interest, and fuel prices at the points of interest.

5. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein environmental data comprises environmental data that affects a current driving route and at least one alternate driving route comprising one or more of current weather conditions for the current route, forecasted weather conditions for the current route, traffic for the current route, speed limits for the current route, tolls for the current route, current weather conditions for at least one alternate route, forecasted weather conditions for at least one alternate route, traffic for at least one alternate route, speed limits for at least one alternate route, tolls for at least one alternate route.

6. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein user data comprises user inputs of preferences and thresholds comprising one or more inputs for minimum fuel level, minimum vehicle speed, maximum vehicle speed, speed limits for routes, toll for routes, vehicle temperature, outside temperature, fuel price, goods and services offered at points of interest, hours of operation of points of interest, and location of points of interest.

7. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein route data comprises one or more driving routes including a current route and at least one alternate route.

8. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein learned driver behavior from the plurality of data that influences selection of driving routes comprises one or more driver behaviors learned from analyzing a history of the plurality of data.

9. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein the memory of the processing device has stored locally thereon current routing information.

10. The intelligent fuel monitoring and dynamic routing system of claim 1 wherein determining the optimal route comprises applying a weighted determination system to the plurality of data.

11. The intelligent fuel monitoring and dynamic routing system of claim 1 further comprising a display for displaying the optimal route.

12. The intelligent fuel monitoring and dynamic routing system of claim 1 further comprising a communication device for receiving one or more of the plurality of data from one or more remote sources communicating wirelessly with the processing system.

13. The intelligent fuel monitoring and dynamic routing system of claim 12 wherein the remote source comprises a backend system wherein communication comprises pushing, pulling, periodic, or continuous data transmission.

14. The intelligent fuel monitoring and dynamic routing system of claim 12 wherein route data comprises road maps for navigation.

15. A method of intelligent fuel monitoring and dynamic routing for vehicles, the method comprising:

(a) receiving and storing a plurality of data comprising;

(i) vehicle status data;

(ii) location data;

(iii) environmental data;

(iv) user data;

(v) route data;

(b) learning by a processing device driver behavior from the plurality of data that influences selection of driving routes;

(c) processing by the processing device the plurality of data for a current route;

(d) determining by the processing device an optimal route based on the plurality of data processed for the current route and the learned driver behavior; and

(e) outputting to an output device the optimal route.

16. The method intelligent fuel monitoring and dynamic routing for vehicles of claim 15 wherein route data comprises one or more driving routes including a current route and at least one alternate route.

17. The method intelligent fuel monitoring and dynamic routing for vehicles of claim 15 wherein learned driver behavior from the plurality of data that influences selection of driving routes comprises one or more driver behaviors learned from analyzing a history of the plurality of data.

18. The method intelligent fuel monitoring and dynamic routing for vehicles of claim 15 wherein determining the optimal route comprises applying a weighted determination system to the plurality of data.

19. The method intelligent fuel monitoring and dynamic routing for vehicles of claim 15 further comprising communicating wirelessly with the processing system via a communication device for receiving one or more of the plurality of data from one or more remote sources.

20. A computer readable medium for intelligent fuel monitoring and dynamic routing for vehicles, including code devices for:

(a) receiving and storing a plurality of data comprising;

(i) vehicle status data;

(ii) location data;

(iii) environmental data;

(iv) user data;

(v) route data;

(b) learning by a processing device driver behavior from the plurality of data that influences selection of driving routes;

(c) processing by the processing device the plurality of data for a current route;

(d) determining by the processing device an optimal route based on the plurality of data processed for the current route and the learned driver behavior; and

(e) outputting to an output device the optimal route.