Method for detection of plug-in electric vehicle charging via interrogation of smart meter data

• US 9,156,368 B2
• Filed: 11/11/2011
• Issued: 10/13/2015
• Est. Priority Date: 11/11/2011
• Status: Active Grant

• Current Assignee: San Diego Gas & Electric Company (Sempra Energy)
• Original Assignee: San Diego Gas & Electric Company (Sempra Energy)
• Inventors: Chen, Chris, Torre, William Vincent, Lyle, Charles, Colson, Benjamin L., Fan, Eric S., Kolluru, Praneet
• Primary Examiner(s): Assouad, Patrick
• Assistant Examiner(s): Zhang, Haidong
• Application Number:
  US13/294,976
• Publication Number:
  US 20120123709A1
• Time in Patent Office: 1,432 Days
• Field of Search: 702/60
• US Class Current: 1/1
• CPC Class Codes: B60L 53/665 Methods related to measuring, billing or payment
• 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 90/12 Electric charging stations
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• 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

A method and application that utilizes a signal processing algorithm is provided for detecting plug-in electric vehicle charging events through interrogation of smart meter data. The method is a form of non-invasive load monitoring that allows the utility to track plug-in electric vehicle charging events in real time or over extended periods. These parameters are suitably flexible so as to accommodate diverse electric vehicle technologies and a range of electric vehicle owners'"'"' charging habits. Application of the method facilitates the integration of electric vehicles into utilities'"'"' electrical grids, protection of the utilities'"'"' assets, geographic mapping of electric vehicle owners and charging events, managed charging through communication between the utility and the vehicle or owner, managed charging through the offer of time of use rates for electric vehicle owners, and an enabling technology to accompany other Smart Grid functions such as Vehicle-to-Grid distributed energy storage.

Claims

1. A non-transitory computer readable medium having computer executable program code embodied thereon, the computer executable program code configured to cause a computing device to:

monitor a residence over time for the presence of individual charging events;

detect an individual charging event at the residence and identify it as a plug-in electric vehicle charging event through signal processing analysis of interval consumption data for the residence based on a threshold level of total kWh consumption, a defined duration at which kWh consumption remains above this threshold, a leading edge increase in kWh consumption, and a lagging edge decrease in kWh consumption.

2. The non-transitory computer readable medium of claim 1, wherein the computer executable program code is further configured to cause the computing device to detect a beginning and an end of the charging event using a predetermined set of parameters.

3. The non-transitory computer readable medium of claim 1, wherein the computer executable program code is further configured to cause the computing device to detect a magnitude and a duration of the charging event.

4. The non-transitory computer readable medium of claim 2, wherein the predetermined set of parameters is selected to accommodate a particular model of plug-in electric vehicle.

5. The non-transitory computer readable medium of claim 2, wherein the predetermined set of parameters is selected to accommodate a particular model of plug-in electric vehicle.

6. The non-transitory computer readable medium of claim 2, wherein the predetermined set of parameters is selected to accommodate various modes including DC fast charging of charging and associated electric vehicle supply equipment.

7. The non-transitory computer readable medium of claim 2, wherein the predetermined set of parameters is selected to accommodate various modes including DC fast charging of charging and associated electric vehicle supply equipment.

8. The non-transitory computer readable medium of claim 2, wherein the computer executable program code is further configured to a plug-in electric vehicle charging event from another load.

9. The non-transitory computer readable medium of claim 3, wherein the computer executable program code is further configured to distinguish a plug-in electric vehicle charging event from another load.

10. The non-transitory computer readable medium of claim 2, wherein the computer executable program code is further configured to identify a plug-in electric vehicle charging event as occurring either on-peak or off-peak.

11. The non-transitory computer readable medium of claim 3, wherein the computer executable program code is further configured to identify a plug-in electric vehicle charging event as occurring either on-peak or off-peak.

12. The non-transitory computer readable medium of claim 2, wherein the computer executable program code is further configured to identify the residence with plug-in electric vehicle charging by a pattern of repeated charging.

13. The non-transitory computer readable medium of claim 3, wherein the computer executable program code is further configured to identify the residence with plug-in electric vehicle charging by a pattern of repeated charging.

14. The non-transitory computer readable medium of claim 2, wherein the computer executable program code is further configured to assign a charging rate to the residence with plug-in electric vehicle charging, wherein the charging rate is expressed as charging events per unit time.

15. The non-transitory computer readable medium of claim 3, wherein the computer executable program code is further configured to assign a charging rate to the residence with plug-in electric vehicle charging, wherein the charging rate is expressed as charging events per unit time.

16. The non-transitory computer readable medium of claim 2, wherein the computer executable program code is further configured to subject identified residences to probabilistic ranking to identify plug-in electric vehicle owners.

17. The non-transitory computer readable medium of claim 3, wherein the computer executable program code is further configured to subject identified residences to probabilistic ranking to identify plug-in electric vehicle owners.

18. The non-transitory computer readable medium of claim 6, wherein the computer executable program code is further configured to subject identified residences to probabilistic ranking to identify plug-in electric vehicle owners.

19. The non-transitory computer readable medium of claim 6, wherein the computer executable program code is further configured to apply database query language to archival data for retrospective analysis and to real time data to create situational awareness.

20. The non-transitory computer readable medium of claim 7, wherein the computer executable program code is further configured to convert resulting information to a geographic information system mapping of plug-in electric vehicle events and owners.