Method and system for charging of electric vehicles

• US 9,148,027 B2
• Filed: 07/30/2012
• Issued: 09/29/2015
• Est. Priority Date: 07/30/2012
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Shane, Conner Brooks, Fittro, Roger Lewis, Mathews, Harry Kirk Jr.
• Primary Examiner(s): Alam, Mohammed
• Application Number:
  US13/561,393
• Publication Number:
  US 20140028254A1
• Time in Patent Office: 1,156 Days
• Field of Search: 320/109
• US Class Current: 1/1
• CPC Class Codes: B60L 2200/10 Air crafts
• CPC Class Codes: B60L 2200/18 Buses
• CPC Class Codes: B60L 2200/32 Waterborne vessels
• CPC Class Codes: B60L 2200/36 Vehicles designed to transport cargo, e.g. trucks
• CPC Class Codes: B60L 2200/44 Industrial trucks or floor conveyors
• CPC Class Codes: B60L 2240/662 Temperature
• CPC Class Codes: B60L 2240/667 Precipitation
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2240/80 Time limits
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 2260/50 by future state prediction
• CPC Class Codes: B60L 2260/52 drive range estimation, e.g. of estimation of available travel distance
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• CPC Class Codes: B60L 2260/58 Departure time prediction
• CPC Class Codes: B60L 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: B60L 53/305 Communication interfaces
• 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: B60L 53/665 Methods related to measuring, billing or payment
• CPC Class Codes: B60L 53/67 : Controlling two or more charging stations
• CPC Class Codes: B60L 53/68 : Off-site monitoring or control, e.g. remote control
• CPC Class Codes: B60L 58/12 : responding to state of charge [SoC]
• CPC Class Codes: H02J 13/00017 : using optical fiber
• CPC Class Codes: H02J 13/00024 : by means of mobile telephony
• CPC Class Codes: H02J 13/00026 : involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
• CPC Class Codes: H02J 2310/48 : for electric vehicles [EV] or hybrid vehicles [HEV]
• CPC Class Codes: H02J 2310/64 : The condition being economic, e.g. tariff based load management
• CPC Class Codes: H02J 3/14 : by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H02J 7/0013 : acting upon several batteries simultaneously or sequentially H02J7/1423 takes precedence
• CPC Class Codes: Y02B 70/3225 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 : Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 60/00 : Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y02P 90/60 : Electric or hybrid propulsion means for production processes
• 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 20/222 : Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 30/12 : Remote or cooperative charging
• CPC Class Codes: Y04S 30/14 : Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing
• CPC Class Codes: Y04S 40/124 : using wired telecommunication networks or data transmission busses
• CPC Class Codes: Y04S 40/126 : using wireless data transmission

Abstract

A method for charging plurality of electric vehicles is provided. The method includes determining predicted usage information for the plurality of electric vehicles based on past information for the plurality of vehicles. The predicted usage information, among other information, includes a first total energy needed to be provided to the plurality of vehicles to reach a respective desired state of charge set point. The method further includes optimizing power to be provided to the plurality of vehicles to generate an optimal power requirement plan based on a first cost incurred to charge the vehicles with the first total energy. Furthermore, the method includes scheduling charging of the plurality of vehicles through at least one charging station according to the optimal power requirement plan and a priority associated with each of the plurality of vehicles.

Claims

1. A computer implemented method for controlling charging of a plurality of electric vehicles, the method comprising:

determining predicted usage information for the plurality of electric vehicles based on past information for each vehicle in the plurality of vehicles, wherein the predicted usage information comprises a first total energy needed to be provided to the plurality of vehicles to reach a respective desired state of charge set point;

optimizing power to be provided to the plurality of vehicles to generate an optimal power requirement plan, based on a first cost incurred to charge the plurality of vehicles with the first total energy, wherein generating the optimal power requirement plan comprises;

determining a second amount of energy required by each of the plurality of vehicles to cover at least a predicted distance to be covered by each of the plurality of vehicles;

iteratively adjusting a third amount of energy from the first total energy to be provided to each of the plurality of vehicles such that the third amount is greater than or equal to the second amount of energy and a second cost incurred to provide each of the plurality of vehicles with the third amount of energy is less than the first cost; and

charging the plurality of vehicles through at least one charging station according to the optimal power requirement plan and a priority associated with each of the plurality of vehicles.

2. The method as recited in claim 1, wherein determining the predicted usage information comprises receiving input from an operator with respect to the past information for each vehicle from the plurality of vehicles.

3. The method as recited in claim 1 wherein the predicted usage information comprises at least one of energy consumed by each of the plurality of vehicles between a defined time period, state of charge of each of the plurality of vehicles after the defined time period, the distance to be covered by each of the plurality of vehicles, an average distance covered by each of the plurality of vehicles between the defined time period, a maximum distance covered by each of the plurality of vehicles between the defined time period, cost of power after the defined time period, a time that each of the plurality of vehicles is at the charging station, a total amount of time required to provide the first total energy to the plurality of vehicles, and an amount of power available with the charging station for charging the plurality of vehicles.

4. The method as recited in claim 1, wherein assigning the priority to each of the plurality of vehicles comprises:

calculating a comparison metric between an amount of time required to charge the vehicle by the third amount of energy, and time available with the at least one charging station to charge the plurality of vehicles; and

assigning a first priority to a vehicle with the highest comparison metric among the plurality of vehicles.

5. The method as recited in claim 4, wherein the comparison metric is a ratio of the time required to charge the vehicle by the third amount of energy and the time available with the at least one charging station to charge the plurality of vehicles.

6. The method as recited in claim 5 further comprises excluding vehicles with the comparison metric greater than 1 from assigning the priority.

7. The method as recited in claim 4, wherein the comparison metric is a difference between the time required to charge the vehicle by the third amount of energy and the time available with the at least one charging station to charge the plurality of vehicles.

8. The method as recited in claim 7 further comprises excluding vehicles with the comparison metric greater than 0 from assigning the priority.

9. The method as recited in claim 1, further comprising determining an estimated state of charge of each vehicle based on an energy storage model.

10. The method as recited in claim 9, further comprising updating the optimal power requirement plan based on the predicted usage information and the estimated state of charge of each vehicle.

11. The method as recited in claim 1, further comprising transmitting charging commands to the at least one charging station in accordance with the optimal power requirement plan.

12. A system to charge a plurality of vehicles, the system comprising:

at least one charging station to charge the plurality of vehicles by providing power; and

a processing sub-system communicably coupled to the charging station, wherein the processing sub-system further comprises;

a prediction module to generate predicted usage information for the plurality of electric vehicles based on past information for each vehicle in the plurality of vehicles, wherein the predicted usage information comprises a first total energy needed to be provided to the plurality of vehicles to reach a respective desired state of charge set point;

an optimization module to optimize the first total energy to be provided to the plurality of vehicles to generate an optimal power requirement plan, based on a first cost incurred to charge the plurality of vehicles by the first total energy, wherein the optimization module is configured to;

determine a second amount of energy required by each of the plurality of vehicles to cover a predicted distance to be covered by each of the plurality of vehicles;

iteratively adjust a third amount of energy to be provided to each of the plurality of vehicles such that the third amount is greater than or equal to the second amount of energy and a second cost incurred to provide each of the plurality of vehicles with the third amount of energy is less than the first cost; and

a controller to control a schedule of charging the plurality of vehicles based on the optimal power requirement plan and a priority associated with each of the plurality of vehicles.

13. The system as recited in claim 12 further comprises a user interface to provide past information to the prediction module to generate the predicted usage information.

14. The system as recited in claim 12 further comprises a weather data repository communicably coupled with the prediction module.

15. The system as recited in claim 12 further comprises power source information repository communicably coupled with the prediction module, wherein the power source information repository stores information related to a power source providing power to the at least one charging station.

16. The system as recited in claim 12, wherein the predicted usage information comprises at least one of energy consumed by each of the plurality of vehicles between a defined time period, state of charge of each of the plurality of vehicles after the defined time period, the distance to be covered by each of the plurality of vehicles, an average distance covered by each of the plurality of vehicles between the defined time period, a maximum distance covered by each of the plurality of vehicles between the defined time period, cost of power after the defined time period, a time that each of the plurality of vehicles is at the charging station, a total amount of time required to provide the first total energy to the plurality of vehicles, and an amount of power available with the charging station for charging the plurality of vehicles.

17. The system as recited in claim 12, wherein the controller further comprises a priority assignment module to assign priority of charging to each of the plurality of vehicles.

18. The system as recited in claim 17, wherein the priority assignment module is configured to:

calculate a comparison metric between an amount of time required to charge the vehicle by the third amount of energy, and time available with the at least one charging station to charge the plurality of vehicles; and

assign a first priority to a vehicle with the highest comparison metric among the plurality of vehicles.

19. The system as recited in claim 12, wherein the at least one charging station is configured to transmit actual arrival time of a vehicle from the plurality of vehicles, when the vehicle is connected to the at least one charging station.

20. The system as recited in claim 19, wherein the controller further comprises an energy storage estimator configured to estimate a current state of charge of the vehicle based on an energy storage mechanism model.

21. The system as recited in claim 20, wherein the optimization module is further configured to:

receive the estimated state of charge of the vehicle from the energy storage estimator; and

update the optimum power requirement plan for the vehicle according to the estimated state of charge.

22. The system as recited in claim 12, wherein the controller further comprises a scheduler configured to transmit charging commands to the at least one charging station in accordance with the optimal power requirement plan.

23. A non-transitory computer readable media embodying computer executable instructions, which when executed by a processor cause the computer to perform a method of optimally charging a plurality of vehicles, the computer readable media comprising:

program instructions to generate predicted usage information for each vehicle of the plurality of vehicles, wherein the predicted usage information is generated using past information for each of the plurality of vehicles, and the predicted usage information comprises a first total energy needed to be provided to the plurality of vehicles to reach a respective desired state of charge set point;

program instructions to optimize the first total power to be provided to the plurality of vehicles to generate an optimal power requirement plan, based on a first cost incurred to charge the plurality of vehicles by the first total energy, wherein program instructions to generate an optimal power requirement plan comprise;

program instructions to determine a second amount of energy required by each of the plurality of vehicles to cover a predicted distance to be covered by each of the plurality of vehicles;

program instructions to iteratively adjust a third amount of energy to be provided to each of the plurality of vehicles such that the third amount is greater than or equal to the second amount of energy and a second cost incurred to provide each of the plurality of vehicles with the third amount of energy is less than the first cost; and

program instruction to control a schedule of charging the plurality of vehicles through at least one charging station according to the optimal power requirement plan and a priority associated with each of the plurality of vehicles.

24. A computer implemented method for controlling charging of a plurality of electric vehicles, the method comprising:

determining predicted usage information for the plurality of electric vehicles based on past information for each vehicle in the plurality of vehicles, wherein the predicted usage information comprises a first total energy needed to be provided to the plurality of vehicles to reach a respective desired state of charge set point and at least one of energy consumed by each of the plurality of vehicles between a defined time period, state of charge of each of the plurality of vehicles after the defined time period, the distance to be covered by each of the plurality of vehicles, an average distance covered by each of the plurality of vehicles between the defined time period, a maximum distance covered by each of the plurality of vehicles between the defined time period, cost of power after the defined time period, a time that each of the plurality of vehicles is at the charging station, a total amount of time required to provide the first total energy to the plurality of vehicles, and an amount of power available with the charging station for charging the plurality of vehicles;

optimizing power to be provided to the plurality of vehicles to generate an optimal power requirement plan, based on a first cost incurred to charge the plurality of vehicles with the first total energy; and

charging the plurality of vehicles through at least one charging station according to the optimal power requirement plan and a priority associated with each of the plurality of vehicles.