Systems and methods using artificial intelligence for routing electric vehicles
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.
1. An artificial intelligence (AI) Electric Vehicle (EV) route optimization method for an EV comprising:
an electronic, specifically programmed, communication computer AI system performing EV route optimization for travel of said EV from a designated origin location or EV present location to an EV designated destination location with intermediate stops at intervening battery charging stations to maintain battery charge levels;
storing in memory one or more EV attribute parameters comprising EV operational status parameters, EV location parameters, or EV battery status parameters;
derivation of EV potential route condition parameters for said EV based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
storing expert defined propositional logic inference rules specifying multiple multidimensional conditional relationships between two or more of said EV attribute parameters and EV potential route condition parameters, and storing expert defined individual EV attribute parameter and EV potential route condition parameter degree of danger value ranges;
AI evaluation and assignment of expert defined value ranges to selected of said EV attribute parameters and selected of said EV potential route condition parameters and wherein said expert defined value ranges depend on individual parameter importance to EV route optimization;
storing expert defined propositional logic inference rules defining multiple range dependent conditional relationships between two or more interrelated multidimensional parameters comprising selected said EV attribute parameters and selected said EV potential route condition parameters;
AI evaluation of EV potential routes of travel from said EV designated origin location or EV present location to said EV designated destination location based on said EV attribute parameters, said EV potential route condition parameters and said expert defined propositional logic inference rules, and further wherein EV potential routes of travel include visiting battery charging stations as necessary to maintain proper EV battery charge levels to reach said EV designated destination location; and,
AI expert system optimization of selection of a particular route of travel based on said AI evaluation of said EV potential routes of travel comprising expert system analysis of one or more multidimensional combinations of said two or more interrelated multidimensional parameters of said EV attribute parameters and said EV potential route condition parameters.
2. The AI EV route optimization method of claim 1 , further comprising accessing said EV potential route condition parameters using internet telecommunications technology.
3. The AI EV route optimization method of claim 1 , further comprising accessing of said EV potential route condition parameters using cellular communication technology to receive or transmit information between said EV and said external information sources.
4. The AI EV route optimization method of claim 1 , further comprising exchanging selected of said EV attribute parameters of said EV directly with other motor vehicles or indirectly from other remote information source facilities.
5. The AI EV route optimization method of claim 1 , wherein said EV route optimization is further based upon battery charging station usage and actual or probable requests for route guidance from other EVs traveling within a defined distance from said EV present location, and wherein information that is accessed from said other EV's affects the expected waiting times or queues encountered at battery charging stations on possible routes of travel.
6. The AI EV route optimization method of claim 1 , wherein said EV present location information is derived from motor vehicle GPS (Global Positioning System) signal sensors or from determination of a distance of said EV from cellular telephone towers or other known fixed locations transmitting signals received by an artificial intelligence (AI) Electric Vehicle (EV) receivers.
7. The AI EV route optimization method of claim 1 , wherein said potential route condition parameters comprise dynamic roadway conditions including one or more of traffic congestion, weather conditions, police reported concerns, or other dynamic roadway condition information received from an external information source database or data processing units.
8. The AI EV route optimization method of claim 1 , wherein EV route selection decisions comprise consideration of potential dynamically changing charging requirements from other vehicles within a defined radius or distance from said EV present location.
9. The AI EV route optimization method of claim 1 , comprising communicating with said external information sources and operating an RFID (radio frequency identification) tag device used to identify the EV and communicate information with RFID tag readers located along highways tollways or roadways along which the EV is traveling.
10. The AI EV route optimization method of claim 7 , wherein said external information source database or data processing units are cloud based and are accessed through the internet or cellular telephone communication networks.
11. The AI EV route optimization method of claim 1 , further comprising Bluetooth wireless RF signals, Wi-Fi wireless RF signals, or other voice or data telecommunication capabilities for communicating with charging stations or other nearby vehicles present in ongoing traffic or waiting for use of charging stations.
12. The AI EV route optimization method of claim 1 , wherein EV potential route condition parameters from external information sources comprise pedestrian or crowd information.
13. The AI EV route optimization method of claim 1 , wherein said EV accesses information from communication network applications.
14. The AI EV route optimization method of claim 13 , wherein EV access of said communication network applications further comprises one or more of a Navigation System Application, Traffic Database Application, EV Account Application, Battery Charger/Replacement Station Application, Weather Data Application, Police Report Application, Special Event Application, or Road Condition Application.
15. The AI EV route optimization method of claim 14 , wherein said Traffic Database Application comprises EV vehicle traffic congestion or density data.
16. The AI EV route optimization method of claim 14 , wherein said Special Event Application comprises traffic or crowd congestion arising from special events along potential routes of travel.
17. The AI EV route optimization method of claim 1 , wherein EV attribute parameters and said EV potential route condition parameters from external information sources are stored in a remote database and wherein said remote database may be accessed and updated via vehicle-to-network connections.
18. The AI EV route optimization method of claim 1 , wherein EV is a driverless or autonomous driving vehicle.
19. An artificial intelligence (AI) Electric Vehicle (EV) route optimization system for an EV comprising:
an electronic, specifically programmed, communication computer AI system performing EV route optimization for travel of said EV from an EV designated origin location or EV present location to an EV designated destination location with intermediate stops at intervening battery charging stations to maintain battery charge levels;
a memory for storing one or more EV attribute parameters comprising EV operational status parameters, EV location parameters, or EV battery status parameters;
artificial intelligence (AI) Electric Vehicle (EV) route optimization system derivation of EV potential route condition parameters for said EV based on information exchanges with at least two of: (1) communication network connections with application servers, (2) communication network connections with other motor vehicles, (3) communication network connections with pedestrians, and (4) communication network connections with roadside monitoring and control units;
artificial intelligence (AI) Electric Vehicle (EV) route optimization system evaluating and assigning AI expert defined value ranges stored in memory to selected of said EV attribute parameters and selected of said EV potential route condition parameters and wherein said expert defined value ranges depend on individual parameter importance to EV route optimization;
a memory for storing expert defined propositional logic inference rules defining multiple range dependent conditional relationships between two or more interrelated multidimensional parameters comprising selected said EV attribute parameters and selected said EV potential route condition parameters;
AI evaluation of EV potential routes of travel from said EV designated origin location or EV present location to said EV designated destination location based on said EV attribute parameters, said EV potential route of travel parameters and said expert defined propositional logic inference rules, and further wherein said EV potential routes of travel include visiting battery charging stations as necessary to maintain proper EV battery charge levels to reach said EV designated destination location; and,
AI expert system optimization with said artificial intelligence (AI) Electric Vehicle (EV) route optimization system of selection of a particular route of travel based on said AI evaluation of said EV potential routes of travel comprising expert system analysis of one or more multidimensional combinations of said two or more interrelated multidimensional parameters of said EV attribute parameters and said EV potential route condition parameters.
20. The artificial intelligence (AI) Electric Vehicle (EV) route optimization system of claim 19 , further comprising accessing said EV potential route condition parameters using cellular or internet telecommunications technology.