Electric vehicle distributed intelligence

Patent metadata

Field	Value
Publication number	US-10126796-B2
Application number	US-201715706061-A
Country	US
Kind code	B2
Filing date	Sep 15, 2017
Priority date	Feb 13, 2012
Publication date	Nov 13, 2018
Grant date	Nov 13, 2018

Abstract

The present disclosure provides system and methods for electric vehicle distributed intelligence. A system may determine scheduled charging times and scheduled charging locations to charge electric vehicles. The scheduled charging locations may correspond to charging stations geographical distributed throughout a power grid. The system may receive parameters related to power grid components configured to affect distribution of power. The system may forecast a power demand for charging the electric vehicles at the scheduled charging times and scheduled charging locations. The system may determine that a power demand to charge the electric vehicles is greater or less than power made available by the power grid components at the charging stations. The system may cause power to be reallocated among the power grid components in response to determination that the power demand to charge the electric vehicles is greater or less than power made available by the power grid components.

First claim

The invention claimed is:

1. A method, comprising:
receiving, by a computing device, profile parameters communicated from a mobile device, the profile parameters associated with a profile for respective electric vehicles;
determining, by the computing device, based on the profile parameters, scheduled charging times and scheduled charging locations to charge the electric vehicles, the scheduled charging locations corresponding to charging stations geographically distributed throughout a power grid;
receiving, by the computing device, parameters at least partially derived from sensor data provided by sensors configured to monitor power grid components, the power grid components configured to affect distribution of power to at least one of the charging stations, the parameters indicative of power made available by the power grid components;
forecasting, by the computing device, a power demand for charging the electric vehicles at the scheduled charging times and scheduled charging locations;
determining, by the computing device, that the power demand to charge the electric vehicles is greater or less than power made available by the power grid components at the charging stations; and
causing, by the computing device, power to be reallocated among the power grid components in response to determination that the power demand to charge the electric vehicles is greater or less than power made available by the power grid components.

2. The method of claim 1 , wherein causing power to be reallocated further comprises:
determining a time at which the power demand will vary from power available by the power grid; and
causing the power to be reallocated before the time.

3. The method of claim 1 , wherein the power grid components comprise at least one of a generator, a substation, a feeder circuit, or a transformer.

4. The method of claim 1 , further comprising:
receiving a customer preference parameter for charging an electric vehicle at a predetermined time or a predetermined location;
determining that charging the electric vehicle did not occur at a predetermined time;
adjusting the customer preference parameter to generate an adjusted parameter in response to determination that the charging did not occur at the predetermined time; and
causing the electric vehicle to be recharged in accordance with the adjusted parameter.

5. The method of claim 4 , wherein adjusting the customer preference parameter comprises at least one of decreasing a charging duration parameter or decreasing a charging speed parameter.

6. The method of claim 1 , further comprising
maintaining respective vehicle profiles corresponding to the electric vehicles;
receiving, from the charging stations, power usage information corresponding to the electric vehicles; and
associating the power usage information with the respective vehicles profiles,
wherein the step of forecasting further comprises forecasting a power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations based on the power usage information corresponding to the electric vehicles.

7. The method of claim 1 , further comprising:
receiving a customer preference for charging an electric vehicle, the customer preference comprising a scheduled time for charging the electric vehicle or a scheduled location for charging the electric vehicle; and
communicating, in response to determination the power demand to charge the electric vehicles is greater or less than power available by the power grid components, an alternative scheduled time for charging the electric vehicle or an alternative scheduled location for charging the electric vehicle.

8. A non-transitory storage medium comprising a plurality of instructions executable by a processor, the instructions comprising:
instructions executable by the processor to receive, from a server in communication with a plurality of mobile devices, profile parameters communicated by the mobile devices, the profile parameters associated with respective profiles for electric vehicles;
instructions executable by the processor to determine, based on the profile parameters, scheduled charging times and scheduled charging locations to charge the electric vehicles, the scheduled charging locations corresponding to charging stations geographical distributed throughout a power grid;
instructions executable by the processor to receive parameters at least partially derived from sensor data provided by sensors configured to monitor power grid components, the power grid components configured to distribute power to at least one of the charging stations, the parameters indicative of availability of the power grid components to supply power;
instructions executable by the processor to determine that an expected power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations varies from the power made available by the power grid components at the charging stations; and
instructions executable by the processor to send a command to reallocate power made available by the power grid components in response to determination that the expected power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations varies from the power made available by the power grid components, wherein the command reallocates power from a first group of power grid components that provide power to an electric vehicle charging station to a second group of power grid components that provide power to the electric vehicle charging station.

9. The non-transitory storage medium of claim 8 , further comprising:
instructions executable by the processor to determine a peak time that the expected power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations exceeds power made available by the power grid; and
instructions executable by the processor to send the command before the peak time.

10. The non-transitory storage medium of claim 8 , wherein the instructions executable by the processor to determine scheduled charging times and scheduled charging locations further comprises:
instructions executable by the processor to communicate, to a remote device, a suggested time and a suggested location corresponding to a charging station; and
instructions executable by the processor to receive, from the remote device, selection information indicative of a selected time and a selected location.

11. The non-transitory storage medium of claim 8 , further comprising:
instructions executable by the processor to send the command to a substation configured to affect a supply of power to the charging station.

12. The non-transitory storage medium of claim 8 , further comprising
instructions executable by the processor to receive a customer preference, the customer preference comprising a first parameter for charging an electric vehicle;
instructions executable to identify a second parameter for charging the electric vehicle; and
causing the electric vehicle to be recharged in accordance with the second parameter instead of the first parameter in response to determination that the expected power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations varies from power available by the power grid components.

13. The non-transitory storage medium of claim 12 , wherein the first parameter comprises a first rate of charging the electric vehicle and the second parameter comprises a second rate of charging the electric vehicle.

14. The non-transitory storage medium of claim 8 , further comprisin

Assignees

• Accenture Global Services Ltd

Inventors

• Dorn John Z
• Malcolm Wade P

Classifications

• H02J3/322

  the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging · CPC title

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• H02J3/003

  Load forecast, e.g. methods or systems for forecasting future load demand · CPC title

• G06Q50/06

  Energy or water supply · CPC title

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title