Autonomous electric vehicle charging

• US 10,828,999 B1
• Filed: 07/24/2018
• Issued: 11/10/2020
• Est. Priority Date: 01/22/2016
• Status: Active Grant

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group), Kia Corp.
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Konrardy, Blake, Christensen, Scott T., Hayward, Gregory, Farris, Scott
• Primary Examiner(s): Nolan, Peter D
• Assistant Examiner(s): Huynh, Luat T
• Application Number:
  US16/043,783
• Time in Patent Office: 840 Days
• Field of Search: 701 22
• CPC Class Codes: B60L 53/36 by positioning the vehicle
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60P 3/12 for salvaging damaged vehicles
• CPC Class Codes: B60R 16/0234 related to maintenance or repairing of vehicles
• CPC Class Codes: B60R 2021/0027 Post collision measures, e.g. notifying emergency services
• CPC Class Codes: B60R 2021/01013 Means for detecting collision, impending collision or roll-over
• CPC Class Codes: B60R 2025/1013 Alarm systems characterised by the type of warning signal, e.g. visual, audible
• CPC Class Codes: B60R 21/0136 responsive to actual contact with an obstacle , e.g. to vehicle deformation, bumper displacement or bumper velocity relative to the vehicle monitoring crash strength in combination with passenger seat detection B60R21/01558
• CPC Class Codes: B60R 21/34 Protecting non-occupants of a vehicle, e.g. pedestrians B60R19/02 takes precedence
• CPC Class Codes: B60R 25/04 operating on the propulsion system, e.g. engine or drive motor
• CPC Class Codes: B60R 25/10 actuating a signalling device
• CPC Class Codes: B60R 25/1001 Alarm systems associated with another car fitting or mechanism, e.g. door lock or knob, pedals features related to the general power supply B60R25/1018
• CPC Class Codes: B60R 25/102 a signal being sent to a remote location, e.g. a radio signal being transmitted to a police station, a security company or the owner
• CPC Class Codes: B60R 25/104 characterised by the type of theft warning signal, e.g. visual or audible signals with special characteristics
• CPC Class Codes: B60R 25/252 Fingerprint recognition
• CPC Class Codes: B60R 25/255 Eye recognition
• CPC Class Codes: B60R 25/305 using a camera
• CPC Class Codes: B60R 25/31 of human presence inside or outside the vehicle
• CPC Class Codes: B60W 10/04 including control of propulsion units
• CPC Class Codes: B60W 10/18 including control of braking systems
• CPC Class Codes: B60W 10/20 : including control of steering systems
• CPC Class Codes: B60W 2420/403 : Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 : Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2530/209 : Fuel quantity remaining in tank
• CPC Class Codes: B60W 2540/229 : Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2552/05 : Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2552/35 : Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2554/4026 : Cycles
• CPC Class Codes: B60W 2554/4029 : Pedestrians
• CPC Class Codes: B60W 2554/4041 : Position
• CPC Class Codes: B60W 2554/406 : Traffic density
• CPC Class Codes: B60W 2556/10 : Historical data
• CPC Class Codes: B60W 30/0956 : the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/12 : Lane keeping
• CPC Class Codes: B60W 30/16 : Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 30/18163 : Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 40/04 : Traffic conditions
• CPC Class Codes: B60W 60/0023 : in response to energy consumption
• CPC Class Codes: B60W 60/0053 : from vehicle to occupant
• CPC Class Codes: B60W 60/0059 : Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: G01B 21/00 : Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
• CPC Class Codes: G01C 21/34 : Route searching; Route guidance
• CPC Class Codes: G01C 21/3415 : Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• 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 21/3438 : Rendez-vous, i.e. searching a destination where several users can meet, and the routes to this destination for these users; Ride sharing, i.e. searching a route such that at least two users can share a vehicle for at least part of the route
• CPC Class Codes: G01C 21/3453 : Special cost functions, i.e. other than distance or default speed limit of road segments
• CPC Class Codes: G01C 21/3461 : Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3469 : Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3617 : using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01C 21/362 : received from an external device or application, e.g. PDA, mobile phone or calendar application
• CPC Class Codes: G01C 21/3697 : Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G01S 19/13 : Receivers
• CPC Class Codes: G01S 19/42 : Determining position
• CPC Class Codes: G05B 15/02 : electric
• CPC Class Codes: G05B 23/0245 : based on a qualitative model, e.g. rule based; if-then decisions
• CPC Class Codes: G05D 1/0011 : associated with a remote control arrangement
• CPC Class Codes: G05D 1/0055 : with safety arrangements
• CPC Class Codes: G05D 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0231 : using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0255 : using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• CPC Class Codes: G05D 1/0285 : using signals transmitted via a public communication network, e.g. GSM network
• CPC Class Codes: G05D 1/0287 : involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G05D 1/0289 : with means for avoiding collisions between vehicles vehicle fittings for automatically controlling speed including means for detecting potential obstacles B60K31/0008; avoiding obstacles by action on the steering system B62D; radar, sonar, lidar systems designed for anti-collision purposes G01S13/93, G01S15/93, G01S17/93
• CPC Class Codes: G05D 1/0293 : Convoy travelling
• CPC Class Codes: G05D 1/0295 : by at least one leading vehicle of the fleet
• CPC Class Codes: G05D 1/223 : Command input arrangements on the remote controller, e.g. joysticks or touch screens
• CPC Class Codes: G05D 1/227 : Handing over between remote control and on-board control; Handing over between remote control arrangements
• 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 1/249 : from positioning sensors located off-board the vehicle, e.g. from cameras
• CPC Class Codes: G05D 1/617 : Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards arrangements for controlling the position or course of two or more vehicles for avoiding collisions therebetween G05D1/693; arrangements for reacting to or preventing system or operator failure G05D1/80
• CPC Class Codes: G05D 1/646 : Following a predefined trajectory, e.g. a line marked on the floor or a flight path
• CPC Class Codes: G05D 1/69 : Coordinated control of the position or course of two or more vehicles
• CPC Class Codes: G05D 1/692 : involving a plurality of disparate vehicles
• CPC Class Codes: G05D 1/693 : for avoiding collisions between vehicles
• CPC Class Codes: G05D 1/695 : for maintaining a fixed relative position of the vehicles, e.g. for convoy travelling or formation flight
• CPC Class Codes: G05D 1/697 : for rendezvous of two or more vehicles, e.g. for in-flight refuelling docking at a base station G05D1/661
• CPC Class Codes: G06F 11/3688 : for test execution, e.g. scheduling of test suites
• CPC Class Codes: G06F 11/3692 : for test results analysis
• CPC Class Codes: G06F 16/2455 : Query execution
• CPC Class Codes: G06F 16/90335 : Query processing
• CPC Class Codes: G06F 17/00 : Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06F 21/32 : using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 21/55 : Detecting local intrusion or implementing counter-measures
• CPC Class Codes: G06F 2221/034 : Test or assess a computer or a system
• CPC Class Codes: G06F 30/15 : Vehicle, aircraft or watercraft design
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06Q 10/1095 : Meeting or appointment
• CPC Class Codes: G06Q 10/20 : Administration of product repair or maintenance
• CPC Class Codes: G06Q 30/0284 : Time or distance, e.g. usage of parking meters or taximeters
• CPC Class Codes: G06Q 30/0645 : Rental transactions; Leasing transactions
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06Q 50/163 : Real estate management
• CPC Class Codes: G06Q 50/265 : Personal security, identity or safety
• CPC Class Codes: G06Q 50/40 : Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07C 5/006 : Indicating maintenance
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 : Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/0841 : Registering performance data recording measured values G01D; information storage G11B
• CPC Class Codes: G07C 9/00563 : using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
• CPC Class Codes: G08B 21/00 : Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
• CPC Class Codes: G08B 21/02 : Alarms for ensuring the safety of persons
• CPC Class Codes: G08B 21/18 : Status alarms G08B21/02 takes precedence
• CPC Class Codes: G08B 25/00 : Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
• CPC Class Codes: G08B 25/014 : Alarm signalling to a central station with two-way communication, e.g. with signalling back
• CPC Class Codes: G08G 1/017 : identifying vehicles G08G1/015, G08G1/054 take precedence
• CPC Class Codes: G08G 1/0965 : responding to signals from another vehicle, e.g. emergency vehicle
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096783 : where the origin of the information is a roadside individual element
• CPC Class Codes: G08G 1/143 : inside the vehicles
• CPC Class Codes: G08G 1/146 : where the parking area is a limited parking space, e.g. parking garage, restricted space
• CPC Class Codes: G08G 1/148 : Management of a network of parking areas
• CPC Class Codes: G08G 1/161 : Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: G08G 1/20 : Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: G08G 1/205 : Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• CPC Class Codes: G16Y 10/80 : Homes; Buildings
• CPC Class Codes: G16Y 30/00 : IoT infrastructure
• CPC Class Codes: H04L 12/2803 : Home automation networks
• CPC Class Codes: H04L 12/2816 : Controlling appliance services of a home automation network by calling their functionalities arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station; in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom H04Q9/00
• CPC Class Codes: H04L 12/2825 : Reporting to a device located outside the home and the home network access arrangements H04L12/2856; for remote control or remote monitoring of applications H04L67/025; telephonic communication systems adapted for combination with telemetering systems H04M11/002
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: Y02A 10/40 : Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
• 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

Abstract

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

Claims

1. A computer-implemented method for automatically recharging an autonomous electric vehicle, comprising:

detecting, by one or more sensors disposed within the autonomous electric vehicle, charge information associated with a charge level of a battery of the autonomous electric vehicle;

generating, by the one or more processors, a predicted use profile for the autonomous electric vehicle based upon prior vehicle use data;

determining, by the one or more processors, a time and a location at which to charge the battery based upon the charge level and the predicted use profile;

controlling, by the one or more processors, the autonomous electric vehicle to travel fully autonomously to the determined location at the determined time;

causing, by the one or more processors, the battery of the autonomous electric vehicle to charge at the location;

determining, by the one or more processors, a return location for the vehicle; and

controlling, by the one or more processors, the autonomous electric vehicle to travel fully autonomously to the return location.

2. The computer-implemented method of claim 1, further comprising:

determining, by one or more processors, the charge level of the battery based upon the charge information when the autonomous electric vehicle is not in use.

3. The computer-implemented method of claim 1, further comprising:

determining, by the one or more processors, that the charge level is below a maximum recharging threshold,

wherein the time and location are determined when the charge level is below the maximum recharging threshold.

4. The computer-implemented method of claim 3, wherein:

the predicted use profile indicates a next predicted use of the autonomous electric vehicle; and

the time and location are determined when sufficient time exists to recharge the battery before the next predicted use.

5. The computer-implemented method of claim 1, further comprising:

determining, by one or more processors, the charge level of the battery based upon the charge information when the autonomous electric vehicle is in use, wherein

the predicted use profile includes one or more predicted breaks in vehicle operation, each predicted break being associated with a break time and a break location; and

the time and location are determined based upon the one or more predicted breaks.

6. The computer-implemented method of claim 1, wherein the location at which to charge the battery is associated with a charging station selected from a plurality of charging stations based at least in part upon availability of the selected charging station.

7. The computer-implemented method of claim 1, wherein the return location is determined based upon the predicted use profile, and is distinct from a prior location from which the autonomous electric vehicle travels to the location at which to charge the battery.

8. The computer-implemented method of claim 1, further comprising:

identifying, using one or more geolocation components within the autonomous electric vehicle, a current location of the autonomous electric vehicle; and

identifying, by the one or more processors, one or more charging stations in an area surrounding the current location from a database including location data for a plurality of charging stations,

wherein the location at which to charge the battery is selected from the location data associated with the one or more charging stations based at least in part upon distance from the current location.

9. The computer-implemented method of claim 8, further comprising:

accessing, by the one or more processors, map data containing map information regarding a plurality of road segments, the map information including location data associated with each road segment and an indication of suitability for autonomous operation feature use associated with each road segment; and

identifying, by the one or more processors, a route consisting of one or more road segments from the plurality of road segments between the current location and the location at which to charge the battery,

wherein controlling the autonomous electric vehicle to travel fully autonomously to the determined location includes controlling the autonomous electric vehicle along the identified route.

10. The computer-implemented method of claim 1, wherein the predicted use profile indicates a plurality of use periods and use locations over at least one day.

11. A computer system for automatically recharging an autonomous electric vehicle, comprising:

one or more processors disposed within the autonomous electric vehicle;

one or more sensors disposed within the autonomous electric vehicle and communicatively connected to the one or more processors; and

a program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to;

detect charge information associated with a charge level of a battery of the autonomous electric vehicle using the one or more sensors;

generate a predicted use profile for the autonomous electric vehicle based upon prior vehicle use data;

determine a time and a location at which to charge the battery based upon the charge level and the predicted use profile;

control the autonomous electric vehicle to travel fully autonomously to the determined location at the determined time;

cause the battery of the autonomous electric vehicle to charge at the location;

determine a return location for the vehicle; and

control the autonomous electric vehicle to travel fully autonomously to the return location.

12. The computer system of claim 11, wherein:

the predicted use profile indicates a next predicted use of the autonomous electric vehicle; and

the time and location are determined when sufficient time exists to recharge the battery before the next predicted use.

13. The computer system of claim 11, wherein the location at which to charge the battery is associated with a charging station selected from a plurality of charging stations based at least in part upon availability of the selected charging station.

14. The computer system of claim 11, wherein the return location is determined based upon the predicted use profile and is distinct from a prior location from which the autonomous electric vehicle travels to the location at which to charge the battery.

15. The computer system of claim 11, wherein:

the executable instructions further cause the computer system to;

identify a current location of the autonomous electric vehicle using one or more geolocation components within the autonomous electric vehicle; and

identify one or more charging stations in an area surrounding the current location from a database including location data for a plurality of charging stations; and

the location at which to charge the battery is selected from the location data associated with the one or more charging stations based at least in part upon distance from the current location.

16. The computer system of claim 11, wherein the predicted use profile indicates a plurality of use periods and use locations over at least one day.

17. A tangible, non-transitory computer-readable medium storing executable instructions for automatically recharging an autonomous electric vehicle that, when executed by at least one processor of a computer system, cause the computer system to:

detect charge information associated with a charge level of a battery of the autonomous electric vehicle using one or more sensors disposed within the autonomous electric vehicle;

generate a predicted use profile for the autonomous electric vehicle based upon prior vehicle use data;

determine a time and a location at which to charge the battery based upon the charge level and the predicted use profile;

control the autonomous electric vehicle to travel fully autonomously to the determined location at the determined time;

cause the battery of the autonomous electric vehicle to charge at the location;

determine a return location for the vehicle; and

control the autonomous electric vehicle to travel fully autonomously to the return location.

18. The tangible, non-transitory computer-readable medium of claim 17, wherein:

the predicted use profile indicates a next predicted use of the autonomous electric vehicle; and

the time and location are determined when sufficient time exists to recharge the battery before the next predicted use.

19. The tangible, non-transitory computer-readable medium of claim 17, wherein the location at which to charge the battery is associated with a charging station selected from a plurality of charging stations based at least in part upon availability of the selected charging station.

20. The tangible, non-transitory computer-readable medium of claim 17, further storing instructions that cause the computer system to:

identify a current location of the autonomous electric vehicle using one or more geolocation components within the autonomous electric vehicle; and

identify one or more charging stations in an area surrounding the current location from a database including location data for a plurality of charging stations,

wherein the location at which to charge the battery is selected from the location data associated with the one or more charging stations based at least in part upon distance from the current location.