Electric vehicle mobility modeling and energy resources scheduling

• US 10,023,062 B2
• Filed: 01/10/2012
• Issued: 07/17/2018
• Est. Priority Date: 01/10/2012
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Kang, Taehyen
• Primary Examiner(s): Cook, Brian S
• Application Number:
  US13/347,520
• Publication Number:
  US 20130179135A1
• Time in Patent Office: 2,380 Days
• CPC Class Codes: B60L 2240/62 Vehicle position
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 53/63 in response to network capacity
• CPC Class Codes: B60L 55/00 Arrangements for supplying energy stored within a vehicle to a power network, i.e. vehicle-to-grid [V2G] arrangements
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• 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: 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]

Abstract

A device includes a processor configured to execute one or more instructions stored in a memory to receive an electrical vehicle (EV) location profile having information regarding movement of an EV between a first and a second charging location of an electrical network. The processor is also configured to apply an EV mobility model to the EV location profile to determine an optimized schedule for the EV to charge or discharge a storage cell of the EV at the first or second charging locations during a scheduling period. The EV mobility model comprises constraints that describe interactions of the EV with the electrical network.

Claims

1. A distributed energy resource aggregator system, comprising:

an electrical vehicle (EV) comprising a storage cell; and

an EV mobility modeler comprising a mobility modeler processor configured to execute one or more instructions stored in a memory that when executed by the mobility modeler processor cause the EV mobility modeler to;

receive an EV location profile comprising information regarding anticipated movement of the EV between a first and a second charging location of an electrical network during a scheduling period, the scheduling period defined by an amount of time during which various power injection and consumption activities of the EV at multiple charging locations is anticipated to occur and wherein at an end of the scheduling period a predetermined amount of stored energy is required to exist in the storage cell of the EV;

apply an EV mobility model of the EV defining the predetermined amount of stored energy required to exist in the storage cell of the EV at the end of the scheduling period to the received EV location profile by reducing distance between power generation and consumption within the electrical network in determining an optimized schedule of whether an EV will increase or decrease stored energy in the storage cell of the EV at the first charging location during the scheduling period and whether the EV will increase or decrease the stored energy in the storage cell of the EV at the second charging location during the scheduling period to contribute to the predetermined final amount of stored energy in the storage cell of the EV at the end of the scheduling period, wherein the predetermined amount of stored energy in the storage cell of the EV is an initial amount of stored energy in the storage cell of the EV at a start of the scheduling period and a sum of all power injections and consumptions throughout the scheduling period and the optimized schedule minimizes at least one of power costs and power transmission losses throughout the scheduling period; and

transmit the optimized schedule to a first charging station device at the first charging location and a second charging station device at the second charging location, wherein the EV is physically coupled to the first charging station device at the first charging location at a first time of the scheduling period, the first charging station device controls alteration of the stored energy in the storage cell of the EV according to the optimized schedule transmitted to the first charging station device ensuring the predetermined amount of stored energy is in the storage cell at the end of the scheduling period, wherein the EV is physically coupled to the second charging station device at the second charging location at a second time of the scheduling period, the second charging station device controls alteration of the stored energy in the storage cell of the EV according to the optimized schedule transmitted to the second charging station device ensuring the predetermined amount of stored energy is in the storage cell at the end of the scheduling period.

2. The distributed energy resource aggregator system of claim 1, wherein the EV mobility model comprises constraints that describe interactions of the EV with the electrical network, wherein the constraints comprise at least one of a stored energy constraint, a power capacity constraint, a location power constraint.

3. The distributed energy resource aggregator system of claim 2, wherein the stored energy constraint defines a minimum energy storage limit and a maximum energy storage limit of the EV, and wherein the power capacity constraint defines a minimum power injection or consumption limit and a maximum power injection or consumption limit of the EV.

4. The distributed energy resource aggregator system of claim 2, wherein the stored energy constraint defines at least one of a stored energy of the EV using a previous stored energy of the EV, a charging or discharging efficiency of the EV, a time interval for charging or discharging the EV, an amount of energy consumed in moving the EV the between the first and second charging locations.

5. The distributed energy resource aggregator system of claim 2, wherein the location power constraint is defined such that the EV can charge or discharge the storage cell of the EV at the first charging location only when the EV is physically located at the first location, and the EV can charge or discharge the storage cell of the EV at the second location only when the EV is physically located at the second charging location.

6. The distributed energy resource aggregator system of claim 1, wherein the predetermined amount of stored energy in the storage cell of the EV is based, at least in part, on a value provided by an operator of the EV.

7. The distributed energy resource aggregator system of claim 1, wherein the device comprises a network interface configured to receive the EV location profile from at least one of a second processor, from the EV.

8. The distributed energy resource aggregator system of claim 1, wherein the EV location profile comprises a position for the first and second charging locations, a distance between the first and second charging locations, a travel time between the first and second charging locations for the EV, global positioning system (GPS) data correlating to the movement of the EV between the first and second charging locations, or any combination thereof.

9. The distributed energy resource aggregator system of claim 1, where the optimized schedule comprises a schedule for the EV to receive or provide power at the first or second charging locations that minimizes power costs based, at least in part, on a cost of power at a time of day.

10. A distributed energy resource aggregating method for an electrical network, comprising:

receiving, at a computer processor of an electric vehicle (EV) mobility modeler computer system residing in a distributed energy resources aggregator, an EV mobility model comprising constraints that define interactions of an EV with an electrical network, the constraints comprising a scheduling period, the scheduling period defined by an amount of time during which various power injection and consumption activities of the EV at multiple charging locations is anticipated to occur and at an end of the scheduling period a predetermined amount of stored energy is required to exist in a storage cell of the EV;

receiving, at the computer processor, an EV location profile, the EV location profile comprising information regarding anticipated movement of the EV between a first and a second charging location of the electrical network during the scheduling period, the information regarding anticipated movement of the EV between the first and the second charging location comprising positions of the first charging location and the second charging location of the electrical network, a travel distance, and a travel time for the EV to move between the first and the second charging locations;

applying the received EV mobility model of the EV defining the predetermined amount of stored energy required to exist in the storage cell of the EV at the end of the scheduling period to the received EV location profile;

generating, via the computer processor disposed external to the EV, and by reducing distance between power generation and consumption within the electrical network, an optimized schedule of whether the EV will increase or decrease stored energy in a storage cell of the EV at the first charging location during a scheduling period and whether the EV a will increase or decrease the stored energy in the storage cell of the EV at the second charging location during the scheduling period to contribute to the predetermined amount of stored energy in the storage cell of the EV at the end of the scheduling period, wherein the predetermined amount of stored energy in the storage cell of the EV is an initial amount of stored energy in the storage cell of the EV at a start of the scheduling period and a sum of all power injections and consumptions throughout the scheduling period and the optimized schedule minimizes at least one of power costs and power transmission losses throughout the scheduling period; and

transmitting the optimized schedule to a first charging station device at the first charging location and a second charging station device at the second charging location, wherein the EV is physically coupled to the first charging station device at the first charging location at a first time of the scheduling period, the first charging station device controls alteration of the stored energy in the storage cell of the EV according to the optimized schedule transmitted to the first charging station device ensuring the predetermined amount of stored energy is in the storage cell at the end of the scheduling period, wherein the EV is physically coupled to the second charging station device at the second charging location at a second time of the scheduling period, the second charging station device controls alteration of the stored energy in the storage cell of the EV according to the optimized schedule transmitted to the second charging station device ensuring the predetermined amount of stored energy is in the storage cell at the end of the scheduling period.

11. The distributed energy resource aggregating method for an electrical network of claim 10, wherein the constraints comprise a stored energy constraint that defines a minimum energy storage limit of the EV and a maximum energy storage limit of the EV and a power capacity limit constraint that defines a minimum power injection or consumption limit and a maximum power injection or consumption limit of the EV.

12. The distributed energy resource aggregating method for an electrical network of claim 10, wherein the constraints comprise at least one of a stored energy constraint defining a stored energy of the EV based, at least in part, on a previous stored energy of the EV, a charging or discharging efficiency of the EV, a time interval for either charging or discharging the EV.

13. The distributed energy resource aggregating method for an electrical network of claim 10, comprising transmitting the optimized schedule to a remote system for use when scheduling the charging or discharging of the storage cell of the EV.

14. A charging station device located at one of a first charging location or a second charging location of the electrical network, comprising:

a charging interface that physically couples a storage cell an electrical vehicle (EV) to an electrical grid via the charging station device at one of the first charging location or the second charging location and that facilitates power to pass between the storage cell of the EV and the electrical grid via the charging station device, wherein the charging station device is co-located with the EV at one of the first charging location or the second charging location and wherein the charging device is external to the EV;

a network interface that receives instructions for the charging station device to either supply power to or receive power from the storage cell of the EV when it is coupled to the charging interface, wherein the instructions are generated based on an output from an EV mobility modeler external to the charging station device comprising a mobility modeler processor configured to executing one or more instructions stored in a memory that when executed by the mobility modeler processor cause the EV mobility modeler to;

receive an EV location profile comprising information regarding anticipated movement of the EV between the first and the second charging location of the electrical network during a scheduling period, the scheduling period defined by an amount of time during which various power injection and consumption activities of the EV at multiple charging locations is anticipated to occur and at an end of the scheduling period a predetermined amount of stored energy is required to exist in the storage cell of the EV; and

applying an EV mobility model of the EV defining the predetermined amount of stored energy required to exist in the storage cell of the EV at the end of the scheduling period to the received EV location profile and by reducing distance between power generation and consumption within the electrical network, determining an optimized schedule of whether the EV will increase or decrease stored energy in the storage cell of the EV when coupled to the charging station device and contributing to the predetermined amount of stored energy in the storage cell of the EV at the end of a scheduling period, wherein the predetermined final amount of stored energy is an initial amount of stored energy of the storage cell of the EV at a start of the scheduling period and a sum of all power injections and consumptions throughout the scheduling period, and wherein the optimized schedule minimizes at least one of power costs and power transmission losses throughout the scheduling period; and

a processor that uses the instructions to instruct the charging interface when to supply power to or receive power from the storage cell of the EV during a power transfer between the storage cell of the EV and the electrical grid in accordance with the optimized schedule controlling charging and discharging of the storage cell of the EV at the charging station during the scheduling period ensuring the predetermined final amount of stored energy in the storage cell of the EV at the end of the scheduling period.

15. The charging station device of claim 14, wherein the EV location profile comprises at least one of positions of a plurality of charging locations, distances between the plurality of charging locations, travel times of the EV between the plurality of charging locations, global positioning system (GPS) data correlating to the movement of the EV between the plurality of charging locations.

16. The charging station device of claim 14, wherein the EV mobility model comprises at least one of constraints that describe interactions of the EV with the electrical grid, wherein the constraints comprise a stored energy constraint, an EV daily energy constraint, a power capacity constraint, a location power constraint.