SYSTEM AND METHODS FOR PREDICTING ENERGY REQUIREMENTS OF A PLURALITY OF ELECTRIC ENERGY VEHICLES

• US 20140025364A1
• Filed: 11/24/2011
• Published: 01/23/2014
• Est. Priority Date: 12/16/2010
• Status: Abandoned Application

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Inventors: Loewel, Thomas, Sund, Matthias, Ballentin, Ralf
• Application Number:
  US13/978,926
• Publication Number:
  US 20140025364A1
• Time in Patent Office: Days
• US Class Current: 703/18
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/625 by GSM
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• 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 3/12 Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 50/50 using propulsion power supplied by batteries or fuel cells
• 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: 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: 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 30/14 Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Abstract

A System for predicting energy need of a plurality of electric-energy vehicles across a determined management area.

A processor in the system aggregates, from each electric-energy vehicle within the management area, positional information and energy-need information of each electric vehicle.

The processor is adapted to process the positional information and the energy-need information by way of a cognitive method, to determine predictions of the movement of the vehicle and endurance of the battery.

The processor is further adapted to combine this determined information to generate a predicted energy distribution model over said management area, indicative of future energy needs for said electric-energy vehicles.

Claims

1. A system for predicting energy need of a plurality of electric-energy vehicles across a determined management area, said system comprising:

a plurality of electric-energy vehicles (12) located within said management area, each comprising battery means (22), geolocation means, and a first processor means (25);

a energy management apparatus (20) comprising second processor means (27) in communication with each of said electric vehicles;

wherein the first or second processor means (25;

27) is adapted to receive from each electric-energy vehicle within said management area, positional information and energy-need information,

said first or second processor having received positional information being further adapted to determine, from the positional information and by way of a cognitive method, information pertaining to the movement of the vehicle for which the information is related, associated with probability factors of each possible movement;

said first or second processor having received energy-need information being further adapted to determine, by way of a cognitive method, information pertaining to the energy endurance of the battery means of the vehicle for which the information is related, associated with probability factors of each possible endurance; and

said second processor being adapted to combine the information pertaining to the movement of the vehicle and the information pertaining to the energy need of the battery means of the vehicle to generate a predicted energy distribution model over said management area, indicative of future energy needs for said electric-energy vehicles.

2. The system according to claim 1, wherein the second processor means receives from each electric-energy vehicle within said management area, positional information and energy-need information.

3. The system according to claim 2, wherein the second processor means further receives a unique vehicle identifier with reception of all received information.

4. The system according to claim 1, wherein energy need information comprises information as to the energy required to fill the battery means to full capacity.

5. The system according to claim 1, wherein the movement information determined by first or second processing means compares historical movements of said electric vehicle.

6. The system according to claim 1, wherein the movement information determined by first or second processing means further comprises cartographic information and traffic affluence information, and determines movement information by analyzing traffic patterns at intersection points of routes contained in the cartographic information.

7. The system according to claim 1, wherein the geolocation means determines a position information from a communication relay device, such as a cell-phone antenna or a base station.

8. A method adapted to be executed on a processor (25) of an electric-energy vehicle, said processor being in communication with battery means and geolocation means of said vehicle, said method comprising the steps of:

receiving, from said geolocation means, information pertaining to the position of the vehicle;

receiving, from said battery means, information pertaining to the energy-need of the vehicle;

determining, from the positional information and by way of a cognitive method, information pertaining to the movement of the vehicle, associated with probability factors of each possible movement;

determining, from the energy-need information and by way of a cognitive method, information pertaining to the endurance of the battery means, associated with probability factors of each possible endurance;

generating a predicted energy distribution model over the management area from said movement information and said endurance information.

10. The method of claim 8, further comprising the steps of managing energy needs of a plurality of electric-energy vehicles across a determined management area:

receiving, from each electric-energy vehicle, positional information, and energy-need information,

determining, based on historical positional information for each electric vehicle (12), movement information for each electric vehicle, associated with probability factors of each movement prediction;

determining, based on historical energy-use information for each electric vehicle (12), energy endurance information for each electric vehicle, preferably associated with probability factors of each endurance prediction;

generating a predicted-energy-distribution model over said determined management area from said movement information and energy endurance information.

16. The method of claim 10, wherein the method is used in the planning of electric-vehicle infrastructure.

9. (canceled)

11. (canceled)

12. (canceled)

13. (canceled)

14. A computer program product embodied on a non-transitory computer readable medium capable of being executed in a processor, said computer program product comprising instructions that, when the program is loaded and executed within said processor, carry out the following steps:

receiving, from said geolocation means, information pertaining to the position of the vehicle;

receiving, from said battery means, information pertaining to the energy-need of the vehicle;

determining, from the positional information and by way of a cognitive method, information pertaining to the movement of the vehicle, associated with probability factors of each possible movement;

determining, from the energy-need information and by way of a cognitive method, information pertaining to the endurance of the battery means, associated with probability factors of each possible endurance;

generating a predicted energy distribution model over the management area from said movement information and said endurance information.

15. The computer program product according to claim 14, further comprising the steps of managing energy needs of a plurality of electric-energy vehicles across a determined management area:

receiving, from each electric-energy vehicle, positional information, and energy-need information,

determining, based on historical positional information for each electric vehicle (12), movement information for each electric vehicle, associated with probability factors of each movement prediction;

determining, based on historical energy-use information for each electric vehicle (12), energy endurance information for each electric vehicle, preferably associated with probability factors of each endurance prediction;

generating a predicted-energy-distribution model over said determined management area from said movement information and energy endurance information.

17. The computer program product of claim 15, wherein the computer program product is used in the planning of electric-vehicle infrastructure.