Electric vehicle distributed intelligence

• US 10,126,796 B2
• Filed: 09/15/2017
• Issued: 11/13/2018
• Est. Priority Date: 02/13/2012
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Dorn, John Z., Malcolm, Wade P.
• Primary Examiner(s): Hamilton, Lalita M
• Application Number:
  US15/706,061
• Publication Number:
  US 20180018007A1
• Time in Patent Office: 424 Days
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 53/62 in response to charging parameters, e.g. current, voltage or electrical charge
• CPC Class Codes: B60L 53/63 in response to network capacity
• CPC Class Codes: B60L 53/65 involving identification of vehicles or their battery types
• CPC Class Codes: G06F 1/26 Power supply means, e.g. regulation thereof for memories G11C
• CPC Class Codes: G06Q 10/04 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 Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: G08G 1/20 Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/322 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 Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y02P 80/10 Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• 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 10/72 Electric energy management in electromobility
• 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 10/126 : the energy generation units being or involving electric vehicles [EV] or hybrid vehicles [HEV], i.e. power aggregation of EV or HEV, vehicle to grid arrangements [V2G]
• CPC Class Codes: Y04S 10/50 : Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
• CPC Class Codes: Y04S 40/20 : Information technology specific aspects, e.g. CAD, simulation, modelling, system security
• CPC Class Codes: Y04S 50/14 : Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

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.

Claims

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 comprising

instructions executable by the processor to maintain respective vehicle profiles corresponding to the electric vehicles;

instructions executable by the processor to receive, from the charging stations, power usage information corresponding to the electric vehicles;

instructions executable by the processor to associate the power usage information with the respective vehicles profiles; and

instructions executable by the processor to forecast the expected power demand to charge the electric vehicles based on the power usage information associated with the respective vehicle profiles.

15. A system, comprising

a processor, the processor configured to:

receive a customer parameter communicated over a network from a remote device, the customer parameter related to charging an electric vehicle;

determine, based on the customer parameter, a scheduled charging time and a scheduled charging location to charge the electric vehicle, the scheduled charging location corresponding to a charging station geographical distributed in a power grid;

receive parameters at least partially derived from sensor data communicated by sensors configured to monitor power grid components that affect distribution of power to the charging station, the sensor data indicative of power availability via the power grid components;

determine that a power demand to charge electric vehicles at scheduled charging times and scheduled charging locations varies from power availability via the power grid components; and

communicate a command to reallocate power among the power grid components in response to determination that the power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations varies from power availability via the power grid components, wherein the command reallocates power from a first substation that does not provide power to the electric vehicle charging station to a second substation that provides power to the electric vehicle charging station.

16. The system of claim 15, wherein the processor is further configured to:

determine a peak time that a power demand to support charging the electric vehicles at the scheduled charging times and scheduled charging locations exceeds power availability via the power grid; and

communicate the command before the peak time.

17. The system of claim 15, wherein the processor is further configured to:

forecast that the power demand to charge the electric vehicles exceeds the power availability via the power grid components at the scheduled location;

identify a parameter of a customer profile that affects charging the electric vehicle; and

modify the parameter of the customer profile to generate a modified parameter configured to decrease the power demand to charge the electric vehicle via the power grid components at the scheduled location.

18. The system of claim 15, wherein the customer parameter comprises a first parameter related to charging the electric vehicle, the processor is further configured to:

determine a second parameter for charging the electric vehicle, the second parameter determined in response to the determination that power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations varies from power availability via the power grid components; and

communicate a command that causes the electric vehicle to be recharged in accordance with the second parameter instead of the first parameter.

19. The system of claim 15, wherein the processor is further configured to:

receive, from the charging stations, power usage information corresponding to the electric vehicles;

associate the power usage information with respective vehicles profiles corresponding to the electric vehicles; and

forecast the power demand to charge the electric vehicles based on the power usage information associated with the respective vehicle profiles.

20. The system of claim 15, the processor is further configured to:

determine a shortage of available power for electric vehicle charging in response to determination that the power demand to charge the electric vehicles at the scheduled charging times and scheduled charging locations varies from power availability via the power grid components; and

perform a demand response in the power grid that compensates for the shortage of available power.