METHOD FOR LOAD BALANCING OF CHARGING STATIONS FOR MOBILE LOADS WITHIN A CHARGING STATIONS NETWORK AND A CHARGING STATIONS NETWORK

• US 20170036560A1
• Filed: 04/22/2014
• Published: 02/09/2017
• Est. Priority Date: 04/22/2014
• Status: Abandoned Application

• Current Assignee: Nec Laboratories Europe GMBH (NEC Corporation)
• Original Assignee: NEC Europe Ltd (NEC Corporation)
• Inventors: Kurpatov, Roman, Schuelke, Anett, Maslekar, Nitin
• Application Number:
  US15/304,074
• Publication Number:
  US 20170036560A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60L 15/2045 for optimising the use of energy
• 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 53/64 Optimising energy costs, e.g. responding to electricity rates
• CPC Class Codes: B60L 53/66 Data transfer between charging stations and vehicles
• 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: G05F 1/66 Regulating electric power
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y02T 10/64 Electric machine technologies in electromobility
• 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 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

Abstract

A method for load balancing of charging stations for mobile loads within a charging stations network includes performing, based on a prediction of a charging demand of the mobile loads, a distribution of an energy-power-range limitation (ΔE, ΔP)_{p, lim} for each of the charging stations p under consideration of a definable optimization parameter, wherein p=1, . . . , n and wherein n and p are integers. Under consideration of the distribution, an adaptation and/or selection of at least one transportation parameter of a mobile load is performed so as to at least partially fulfill the energy-power-range limitation (ΔE, ΔP)_{p, lim} for each of the charging stations p or for a definable number of the charging stations p.

Claims

1. A method for load balancing of charging stations for mobile loads within a charging stations network, the method comprising:

based on a prediction of a charging demand of the mobile loads, performing a distribution of an energy-power-range limitation (Δ

E, Δ

P)_{k, lim} for each of the charging station p under consideration of a definable optimization parameter, wherein p=1, . . . , n and wherein n and p are integers, and

under consideration of the distribution, performing at least one of an adaptation or a selection of at least one transportation parameter of at least one of the mobile loads so as to at least partially fulfill the energy-puwer-range limitation (Δ

E, Δ

P)_{p, lim} for each of the charging stations p or for a definable number of the charging stations p.

2. The method according to claim 1, wherein the at least one of the adaptation or the selection of the at least one transportation parameter results in a modification of the prediction of the charging demand of at least one of the mobile loads.

3. The method according to claim 1, wherein the optimization parameter is defined so as to find a low cost distribution or a lowest cost distribution.

4. The method according to claim 1, wherein at least one of the mobile loads is transformed into at least one time tolerant and capacity tolerant mobile load.

5. The method according to claim 1, wherein the energy-power-range limitation (Δ

E, Δ

P)_{p, lim} is equally distributed for each of the charging stations p.

6. The method according to claim 1, wherein the energy-power-range limitation (Δ

E, Δ

P)_{p, lim} is distributed for each of the charging stations p under consideration of a factor in a form of at least one of a past balancing potential, economical strength within a network grid or a strategic location.

7. The method according to claim 1, wherein the prediction of the charging demand is performed depending on at least one transportation parameter.

8. The method according to claim 1, wherein the charging demand regarding energy and power to be provided to one of the mobile loads is a function of time and location.

9. The method according to claim 1, wherein the charging demand considers at least one of a route requirement, route requirements of different routes, a location, a direction, a time condition, a travel time, an estimated arrival time, ETA, a speed, a driving pattern, a current charging need, a predicted charging need, a charging time, a charging mode, a charging level or a battery level.

10. The method according to claim 1, wherein the at least one transportation parameter is at least one of a user preference, a route, a route guidance, a routing information, a distance, a direction, a charging time, a travel time, a speed, a waiting time or a break.

11. The method according to claim 1, wherein the at least one transportation parameter is received from an. intelligent transport system or service, ITS.

12. The method according to claim 1, wherein the method is performed in a reactive manner on or after cause of a load exceeding or having exceeded a definable threshold.

13. The method according to claim 1, wherein the method is performed dynamically.

14. The method according to claim 1, wherein, during the at least one of the adaptation or selection, at least one of a user preference, a traffic condition or a weather condition is considered.

15. The method according to claim 1, wherein, during the at least one of the adaptation or selection, an intelligent transport system or service, ITS, or data from the ITS is exploited.

16. The method according to claim 1, wherein the energy-power-range limitation (Δ

E, Δ

P)_{p, lim} for each of the charging stations p is adapted under consideration of at least one of a user interaction/feedback, a user preference or a real-time traffic condition.

17. The method according to claim 1, wherein the at least one of the adaptation or the selection is performed by a de-centralized management scheme.

18. The method according to claim 1, wherein the at least one of the adaptation or the selection is performed by neighbored charging stations in a bi-lateral manner.

19. A charging stations network comprising:

means for load balancing of charging stations for mobile loads,

means for distributing an energy-power-range limitation (Δ

E, Δ

P)_{k, lim} for each of the charging stations p based on a prediction of a charging demand of the mobile loads and under consideration of a definable optimization parameter, wherein p=1, . . . , n and wherein n and p are integers, and

means for at least one of adapting or selecting of at least one transportation parameter of at least one of the mobile loads under consideration of the distribution so as to at least partially fulfill the energy-power-range limitation (Δ

E, Δ

P)_{p, lim} for each of the charging stations p or for a definable number of the charging stations p.

20. The charging stations network according to claim 19, wherein the balancing means, the distributing means or the adapting and/or selecting means comprises at least one of a communication system, route guidance or online route guidance, charging demand predictor, Energy Management System, EMS, of charging station or EMS control center.