INFORMATION SYSTEMS AND METHODS FOR DEPLOYMENT OF CHARGING INFRASTRUCTURE IN SUPPORT OF ELECTRIC VEHICLES

• US 20120203726A1
• Filed: 02/09/2012
• Published: 08/09/2012
• Est. Priority Date: 02/09/2011
• Status: Active Grant

• Current Assignee: Northwestern University
• Original Assignee: Northwestern University
• Inventors: Klabjan, Diego
• Granted Patent: US 10,467,556 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: B60L 2260/50 by future state prediction
• CPC Class Codes: B60L 2260/54 Energy consumption estimation
• 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: G06N 5/00 Computing arrangements using knowledge-based models
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• 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 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

Certain examples provide systems and methods to identify placement for an electric charging station infrastructure. Certain examples provide systems and methods to generate a deployment plan for one or more electric vehicle charging stations. An example method includes gathering data for a specified geographic area and forecasting a demand for electric vehicles for the specified area. The example method includes modeling driving patterns in the specified area using available data and improving a charging infrastructure model based on the driving pattern and demand forecast information for the specified area. The example method includes generating and providing a recommendation regarding an electric vehicle charging infrastructure and deployment strategy for the specified area based on the improved charging infrastructure model.

Claims

1. A method to generate a deployment plan for one or more electric vehicle charging stations, the method comprising:

gathering data for a specified geographic area;

forecasting a demand for electric vehicles for the specified area;

modeling driving patterns in the specified area using available data;

improving a charging infrastructure model based on the driving pattern and demand forecast information for the specified area; and

generating and providing a recommendation regarding an electric vehicle charging infrastructure and deployment strategy for the specified area based on the improved charging infrastructure model.

2. The method of claim 1, wherein the specified area comprises a selected zip code.

3. The method of claim 1, wherein modeling further comprises modeling driving patterns in the specified area based on at least one of daily trips originating at the zip code and daily trips arriving at the zip code.

4. The method of claim 3, wherein modeling further comprises applying a regression model to the daily trips information, the regression model calibrated based on survey data.

5. The method of claim 1, wherein improving the charging infrastructure model comprises locally optimizing the charging infrastructure model based on address.

6. The method of claim 1, wherein forecasting further comprises:

deriving driving routes and usage within the specified area;

applying a mathematical programming model to provide a number of charging stations to be deployed in the specified area; and

selecting, using the mathematical programming model, locations of charging stations for the specified area.

7. The method of claim 1, further comprising revising the forecast.

8. A computer readable storage medium including computer program code to be executed by a processor, the computer program code, when executed, to implement a system to generate a deployment plan for one or more electric vehicle charging stations, the system comprising:

predictive analytics to receive a demand model constructed for electric vehicle demand in a specified area and information regarding location, driver habits and available power information to generate an electric vehicle charging station location plan for the specified area; and

an optimizer to optimize the electric vehicle charging station location plan for the specified area based on driving pattern and electric vehicle demand forecast information for the specified area, the optimizer to provide a deployment strategy for charging station locations in the specified area.

9. The computer readable storage medium of claim 8, wherein the predictive analytics forecast demand for electric vehicles in the specified area, forecasting comprising:

deriving driving routes and usage within the specified area;

applying a mathematical programming model to provide a number of charging stations to be deployed in the specified area; and

selecting, using the mathematical programming model, locations of charging stations for the specified area.

10. The computer readable storage medium of claim 8, wherein the specified area comprises a selected zip code.

11. The computer readable storage medium of claim 8, wherein the driving patterns are to be modeled by applying a regression model to daily trip information, the regression model calibrated based on survey data.

12. The computer readable storage medium of claim 8, wherein the optimizer locally optimizes the electric vehicle charging station location plan based on address.

13. The computer readable storage medium of claim 8, wherein the electric vehicle demand forecast information comprises:

a high level framework of proportionally assigning a total production capacity to the specified area,

solar residential installations and media messages organized as indicators of electric vehicle adoption, and

prioritized indicators of electric vehicle usage.

14. A method of forecasting electric vehicle usage in a defined geographic area, the method comprising;

using an adoption forecasting model to forecast an electric vehicle allocation at a national level;

determining an electric vehicle allocation at a state level using the electric vehicle allocation at the national level and one or more factors; and

forecasting an electric vehicle allocation at a zip code level based on the electric vehicle allocation at the state level and one or more factors.

15. The method of claim 14, wherein the adoption forecasting model comprises a Bass diffusion model.

16. The method of claim 14, wherein a plurality of factors including hybrid vehicle sales, solar panels, and online messages are input into the adoption forecasting model to determine a national level electric vehicle allocation.

17. The method of claim 14, wherein the one or more factors used to determine the state level electric vehicle allocation includes hybrid vehicle sales, solar panel installations, and social media messages.

18. The method of claim 14, wherein each of a plurality of zip codes is forecast based at least in part on commute distance.

19. The method of claim 14, wherein forecasting further comprises;

generating a weighted forecast of activity in a selected zip code;

calibrating a forecast analysis with respect to one or more of indicators to provide a calibrated weighted forecast;

calculating a proportion of an overall forecast based on the weighted forecast value for the selected zip code;

allocating total electric vehicle sales to one or more states based on an average commute distance in each state and then allocating total electric vehicle sales at the zip code level.

20. The method of claim 14, wherein the predictive analytics forecast demand for electric vehicles in the specified area, forecasting comprising:

deriving driving routes and usage within the specified area;

applying a mathematical programming model to provide a number of charging stations to be deployed in the specified area; and

selecting, using the mathematical programming model, locations of charging stations for the specified area.