EV route optimization through crowdsourcing

• US 9,285,240 B2
• Filed: 07/29/2014
• Issued: 03/15/2016
• Est. Priority Date: 08/07/2013
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Yenamandra, Rao Sanyasi, Kalla, Naveen
• Primary Examiner(s): Butler, Rodney
• Assistant Examiner(s): Sweeney, Brian P
• Application Number:
  US14/445,920
• Publication Number:
  US 20150045985A1
• Time in Patent Office: 595 Days
• Field of Search: 701/1, 903/903, 903/906, 903/907
• US Class Current: 1/1
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3679 Retrieval, searching and output of POI information, e.g. hotels, restaurants, shops, filling stations, parking facilities G01C21/3611 takes precedence
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence

Abstract

The present disclosure involves determining charging equipment availability through crowdsourcing. Information is gathered from a plurality of electrically powered vehicle in a geographical region. The charging stations in the geographical region are displayed. A request is received, from a user, to determine occupancy of the electrical charging equipment at one or more of the charging stations during a specified future time window. A calculation is performed based on the information gathered from the electrically powered vehicles. The calculation calculates a probability of the electrical charging equipment at the one or more charging stations being unoccupied during the specified future time window. As a part of the calculation, a weighted probability is assigned to each charging station of being visited by at least one of the electrically powered vehicles. The probability is communicated to the driver.

Claims

1. A system for determining charging equipment availability through crowdsourcing, comprising:

a computer memory storage device configured to store executable computer programming instructions; and

a computer processor operatively coupled to the computer memory storage module, wherein the computer processor module is configured to execute the computer programming instructions to perform the following steps;

gathering information from a plurality of electrically powered vehicles in a geographical region, wherein the gathering of the information comprises gathering behavioral pattern of drivers of the electrically powered vehicles;

displaying a plurality of charging stations in the geographical region, wherein the charging stations each provide electrical charging equipment configured to charge the electrically powered vehicles;

receiving, from a user, a request to obtain an occupancy status of the electrical charging equipment at one or more of the charging stations during a specified future time window;

calculating, based on the information gathered from the plurality of electrically powered vehicles, a likelihood of the electrical charging equipment at the one or more charging stations being unoccupied during the specified future time window, wherein the calculating comprises assigning a weighted probability to each charging station of being visited by at least one of the electrically powered vehicles, and wherein the calculating is performed such that the weighted probability is a function of the behavioral pattern of the drivers; and

communicating the likelihood to the user.

2. The system of claim 1, wherein the user is a driver of one of the electrically powered vehicles, and wherein computer programming instructions, when executed, further perform recommending a route to the driver based on the calculating.

3. The system of claim 1, wherein the gathering of the information comprises gathering one or more of the following types of information from each of the electrically powered vehicles:

a start timestamp, a GPS location, a state of a battery charge, and a battery threshold, and a current driving speed.

4. The system of claim 1, wherein the displaying comprises displaying virtual representations of the plurality of charging stations via an interface of a mobile electronic device, and wherein the communicating comprises displaying the likelihood as a percentage number next to the virtual representations of the one or more charging stations.

5. The system of claim 1, wherein the communicating comprises updating the likelihood in real time.

6. The system of claim 1, wherein the assigning of the weighted probability comprises assigning a more weighted probability to one of the charging stations located farther away from the user than one of the charging stations located closer to the user.

7. A method of determining charging equipment availability through crowdsourcing, the method comprising;

gathering information from a plurality of electrically powered vehicles in a geographical region, wherein the gathering of the information comprises gathering behavioral pattern of drivers of the electrically powered vehicles;

displaying a plurality of charging stations in the geographical region, wherein the charging stations each provide electrical charging equipment configured to charge the electrically powered vehicles;

receiving, from a user, a request to obtain an occupancy status of the electrical charging equipment at one or more of the charging stations during a specified future time window;

calculating, based on the information gathered from the plurality of electrically powered vehicles, a likelihood of the electrical charging equipment at the one or more charging stations being unoccupied during the specified future time window, wherein the calculating comprises assigning a weighted probability to each charging station of being visited by at least one of the electrically powered vehicles, and wherein the calculating is performed such that the weighted probability is a function of the behavioral pattern of the drivers; and

communicating the likelihood to the user;

wherein at least one of the gathering, the displaying, the receiving, the calculating, and the communicating is performed using one or more electronic processors.

8. The method of claim 7, wherein the displaying and the communicating are performed at least in part via a mobile computing device of the user or at least in part via an integrated electronic display of an electrically powered vehicle of the user.

9. The method of claim 7, wherein the assigning of the weighted probability comprises assigning a more weighted probability to one of the charging stations located farther away from the user than one of the charging stations located closer to the user.

10. The method of claim 7, wherein the user is a driver of one of the electrically powered vehicles.

11. The method of claim 10, further comprising:

recommending a route to the driver based on the calculating.

12. The method of claim 7, wherein the gathering of the information comprises gathering one or more of the following types of information from each of the electrically powered vehicles:

a start timestamp, a GPS location, a state of a battery charge, and a battery threshold, and a current driving speed.

13. The method of claim 7, wherein the displaying comprises displaying virtual representations of the plurality of charging stations via an interface of a mobile electronic device, and wherein the communicating comprises displaying the likelihood as a percentage number next to the virtual representations of the one or more charging stations.

14. The method of claim 7, wherein the communicating comprises updating the likelihood in real time.

15. A non-transitory computer readable medium comprising executable instructions that when executed by a processor, causes the processor to perform the steps of:

gathering information from a plurality of electrically powered vehicles in a geographical region, wherein the gathering of the information comprises gathering behavioral pattern of drivers of the electrically powered vehicles;

displaying a plurality of charging stations in the geographical region, wherein the charging stations each provide electrical charging equipment configured to charge the electrically powered vehicles;

receiving, from a user, a request to obtain an occupancy status of the electrical charging equipment at one or more of the charging stations during a specified future time window;

calculating, based on the information gathered from the plurality of electrically powered vehicles, a likelihood of the electrical charging equipment at the one or more charging stations being unoccupied during the specified future time window, wherein the calculating comprises assigning a weighted probability to each charging station of being visited by at least one of the electrically powered vehicles, and wherein the calculating is performed such that the weighted probability is a function of the behavioral pattern of the drivers; and

communicating the likelihood to the user.

16. The non-transitory computer readable medium of claim 15, wherein the user is a driver of one of the electrically powered vehicles, and the steps further comprise:

recommending a route to the driver based on the calculating.

17. The non-transitory computer readable medium of claim 15, wherein the gathering of the information comprises gathering one or more of the following types of information from each of the electrically powered vehicles:

a start timestamp, a GPS location, a state of a battery charge, and a battery threshold, and a current driving speed.

18. The non-transitory computer readable medium of claim 15, wherein the displaying comprises displaying virtual representations of the plurality of charging stations via an interface of a mobile electronic device, and wherein the communicating comprises displaying the likelihood as a percentage number next to the virtual representations of the one or more charging stations.

19. The non-transitory computer readable medium of claim 15, wherein the communicating comprises updating the likelihood in real time.

20. The non-transitory computer readable medium of claim 15, wherein the assigning of the weighted probability comprises assigning a more weighted probability to one of the charging stations located farther away from the user than one of the charging stations located closer to the user.