Systems and methods using artificial intelligence for routing electric vehicles

• US 10,288,439 B2
• Filed: 02/22/2017
• Issued: 05/14/2019
• Est. Priority Date: 02/22/2017
• Status: Active Grant

• Current Assignee: Robert D. Pedersen
• Original Assignee: Robert D. Pedersen
• Inventors: Pedersen, Robert D.
• Primary Examiner(s): Dager, Jonathan M
• Application Number:
  US15/439,673
• Publication Number:
  US 20180238698A1
• Time in Patent Office: 811 Days
• Field of Search: None
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/64 Road conditions
• CPC Class Codes: B60L 2240/66 Ambient conditions
• CPC Class Codes: B60L 2240/68 Traffic data
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2250/10 by alarm
• CPC Class Codes: B60L 2250/16 by display
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60L 58/16 responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• CPC Class Codes: G01C 21/343 Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047
• CPC Class Codes: G01C 21/3469 Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3476 using point of interest [POI] information, e.g. a route passing visible POIs
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0217 in accordance with energy consumption, time reduction or distance reduction criteria
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS
• CPC Class Codes: G05D 1/0285 using signals transmitted via a public communication network, e.g. GSM network
• CPC Class Codes: G05D 1/228 Command input arrangements located on-board unmanned vehicles
• CPC Class Codes: G05D 1/247 using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
• CPC Class Codes: G05D 1/248 generated by satellites, e.g. GPS
• CPC Class Codes: G05D 1/644 : Optimisation of travel parameters, e.g. of energy consumption, journey time or distance
• CPC Class Codes: G06N 5/048 : Fuzzy inferencing
• CPC Class Codes: G08G 1/096811 : where the route is computed offboard
• CPC Class Codes: G08G 1/096833 : where different aspects are considered when computing the route
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries
• 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 30/12 : Remote or cooperative charging

Abstract

The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations. Expert system algorithms operating on combinations of expert defined parameter subsets for route selection are disclosed. Specific fuzzy logic methods are also disclosed based on defined potential route parameters with fuzzy logic determination of crisp numerical values for multiple potential routes and comparison of those crisp numerical values for selection of a particular route. Application of the present invention systems and methods to autonomous or driver-less EVs is also disclosed.

Claims

1. A method for routing an Electric Vehicle (EV) from a current position to a destination wherein said method comprises one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and route selection optimization control, said method further comprising:

a step of storing in electronic memory of said one or more specifically programmed computer machines artificial intelligence expert system software program code for battery energy management and route selection optimization control, said software program code including;

battery energy and route selection optimization parameter definitions including range of parameter values and subsets of those defined ranges;

expert system propositional logic statements defining relationships between said battery energy parameters and route selection optimization parameters based on parameter membership in said subset ranges;

a step of storing in in electronic memory of said one or more specifically programmed computer machines one or more of the following;

EV descriptive information,

EV energy requirements,

EV battery specification information, and

EV current position and the location of the destination of said EV;

a step of monitoring and storing in electronic memory of said one or more specifically programmed computer machines the status of said EV stored battery energy;

a step of executing said program code of said one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and route selection optimization control comprising;

a step of comparing current EV stored battery energy to one or more defined thresholds;

a step of transmitting information from said EV to one more cloud or remote computer/data processing systems when said battery energy is less than a selected threshold, wherein said transmitted information comprises one or more of the following;

EV descriptive information,

EV energy requirements,

EV battery specification information,

EV stored battery energy status, and

EV current GPS position and the EV destination address location;

a step of said EV receiving artificial intelligence expert system route selection optimization information from said one or more cloud or remote computer/data processing systems for potential routes of travel, wherein said received route selection optimization information comprises;

information regarding potential routes of travel for said EV to reach one or more battery charging or replacement stations and, after charging or replacement, to continue on to said destination;

information regarding one or more route selection optimization parameters for each of said potential routes;

a step of artificial intelligence expert system evaluation of a potential route of travel by one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and route selection optimization control based at least in part on route selection optimization parameter membership in defined parameter subsets and artificial intelligence expert system propositional logic statements; and

,

a step of artificial intelligence expert system selection of a particular route of travel by one or more specifically programmed computer machine with artificial intelligence expert system battery energy management and route selection optimization control based at least in part on comparisons of results from said individual route evaluations of potential routes of travel based on said received information.

2. The method of claim 1 wherein said steps of artificial intelligence expert system evaluation and selection of a particular route of travel are executed by one or more specifically programmed computer machines located in the EV with artificial intelligence expert system battery energy management and route selection optimization control.

3. The method of claim 1 wherein said steps of artificial intelligence expert system evaluation and selection of a particular route of travel are executed by one or more specifically programmed cloud based or remote computer/data processing systems with artificial intelligence expert system battery energy management and route selection optimization control.

4. The method of claim 1 wherein said transmitted EV descriptive information comprises one or more of the following:

vehicle type;

vehicle loaded weight; and

,

vehicle energy requirement history.

5. The method of claim 1 wherein said transmitted EV battery specification information comprises one or more of the following:

battery type;

battery capacity;

battery charging requirements;

battery age; and

,

battery charging time.

6. The method of claim 1 wherein said route selection optimization parameters define for each EV potential route of travel the expected total travel time from the EV current location to the destination including intermediate battery charging or replacement times and the total expected energy required to travel from the current position to the desired destination.

7. The method of claim 6 wherein said EV total travel time for each potential route includes route roadway considerations including at least one of roadway conditions, traffic congestion, weather conditions and/or emergency traffic considerations.

8. The method of claim 7 wherein said EV route selection optimization information further includes consideration of actual or probable requests for route including battery charging or replacement station usage from other EVs traveling within a defined radius or distance from said EV position.

9. The method of claim 1 wherein said EV is a self-driving vehicle.

10. The method of claim 9 wherein said system software program code for battery energy management and route selection optimization control comprises expert system artificial intelligence code with no required driver input for route decision making.

11. A method for routing an Electric Vehicle (EV) from a current position to a destination wherein the said method comprises one or more specifically programmed computer machines with artificial intelligence expert system fuzzy logic battery energy management and route selection optimization control, said method comprising:

a step of storing in electronic memory of said one or more specifically programmed computer machines artificial intelligence expert system fuzzy logic software program code for battery energy management and route selection optimization control;

a step of storing in in electronic memory of said one or more specifically programmed computer machines EV descriptive information, EV energy requirements, EV battery specification information, the current position of said EV and the location of the destination of said EV;

a step of monitoring and storing in electronic memory of said one or more specifically programmed computer machines status of said EV stored battery energy;

a step of executing said program code of said one or more specifically programmed computer machines with artificial intelligence expert system fuzzy logic battery energy management and route selection optimization control comprising;

a step of comparing current EV stored battery energy to defined thresholds to estimate sufficiency of said stored energy to reach said destination;

a step of transmitting information from said EV to one more cloud or remote computer/data processing systems when said battery energy is less than a selected threshold, wherein said transmitted information comprises;

EV descriptive information,

EV battery specification information,

EV energy requirements,

EV stored battery energy status, and

EV current position and the destination location;

a step of receiving artificial intelligence expert system fuzzy logic derived route selection optimization information for potential routes of travel from said one or more cloud or remote computer/data processing system, wherein said received route guidance information comprises;

information regarding potential routes of travel for said EV to reach one or more battery charging or replacement stations and, after battery charging or replacement, to continue on to said destination;

information regarding one or more route selection optimization parameters for each of said potential routes;

a step of artificial intelligence expert system fuzzy logic selection of a particular route of travel based at least in part on said received information.

12. The method of claim 11 wherein said steps of artificial intelligence expert system fuzzy logic evaluation and selection of a particular route of travel are executed by one or more specifically programmed computer machines located in the EV with artificial intelligence expert system fuzzy logic battery energy management and route selection optimization control.

13. The method of claim 12 wherein said steps of artificial intelligence expert system fuzzy logic evaluation and selection of a particular route of travel are executed by one or more specifically programmed cloud based or remote computer/data processing system computer machines with artificial intelligence expert system fuzzy logic battery energy management and route selection optimization control.

14. The method of claim 11 wherein said route evaluation criteria includes relative predictions of considered route travel parameters including energy required by said EV to travel to said destination and travel time of said EV to said destination.

15. The method of claim 14, wherein said relative predictions comprise defined fuzzy sets with possible overlapping parameter ranges and further wherein said artificial intelligence expert system fuzzy logic decisions are based on calculation of a degree of membership in defined fuzzy sets for particular considered route evaluation parameters.

16. The method of claim 15 further comprising a step of defuzzifying multiple fuzzy logic degree of membership results to derive crisp numerical route selection indices values for particular routes considered.

17. The method of claim 16 further comprising selecting a particular recommended route of travel from among multiple such potential routes by comparing said derived crisp numerical route selection indices values for considered routes.

18. A method for routing a driverless Electric Vehicle (EV) from a current position to a destination wherein said method comprises one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and route selection optimization control, said method further comprising:

a step of storing in electronic memory of said one or more specifically programmed computer machines artificial intelligence expert system software program code for battery energy management and route selection optimization control, said software program code including;

battery energy and route selection optimization parameter definitions including range of parameter values and subsets of those defined ranges

expert system propositional logic statements defining relationships between said battery energy parameters and route selection optimization parameters based on parameter membership in said subset ranges;

a step of storing in in electronic memory of said one or more specifically programmed computer machines one or more of the following;

EV descriptive information,

EV energy requirements,

EV battery specification information, and

EV current position and the location of the destination of said EV;

a step of monitoring and storing in electronic memory of said one or more specifically programmed computer machines the status of said EV stored battery energy;

a step of executing said program code of said one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and route selection optimization control comprising;

a step of comparing current EV stored battery energy to one or more defined thresholds;

a step of transmitting information from said EV to one more cloud or remote computer/data processing systems when said battery energy is less than a selected threshold, wherein said transmitted information comprises one or more of the following;

EV descriptive information,

EV energy requirements,

EV battery specification information,

EV stored battery energy status, and

EV current GPS position and the EV destination address location;

a step of said EV receiving artificial intelligence expert system derived route selection optimization information from said one or more cloud or remote computer/data processing systems for potential routes of travel, wherein said received route selection optimization information comprises;

information regarding potential routes of travel for said EV to reach one or more battery charging or replacement stations and, after charging or replacement, to continue on to said destination;

information regarding one or more route selection optimization parameters for each of said potential routes;

a step of artificial intelligence expert system evaluation of a particular route of travel by one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and route selection optimization control based at least in part on said received information, route selection optimization parameter membership in defined parameter subsets and artificial intelligence expert system propositional logic statements; and

,

a step of artificial intelligence expert system selection of a particular route of travel by one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route selection optimization control based at least in part on comparisons of results from said individual route evaluations of potential routes of travel based on said received information,

whereby said driverless EV is guided along an artificial intelligence expert system selected route chosen from one or more other potential routes based on defined route selection optimization criteria without requiring additional EV driver input or control actions.

19. The method of claim 18 wherein said steps of artificial intelligence expert system evaluation and selection of a particular route of travel are executed by one or more specifically programmed computer machines located in the EV with artificial intelligence expert system battery energy management and route selection optimization control.

20. The method of claim 18 wherein said steps of artificial intelligence expert system evaluation and selection of a particular route of travel are executed by one or more specifically programmed cloud based or remote computer/data processing system computer machines with artificial intelligence expert system battery energy management and route selection optimization control.