Self-learning satellite navigation assisted hybrid vehicle controls system
A self-learning assisted hybrid vehicle system that includes a main power source for providing power to the vehicle, a supplemental power source for providing supplemental power for providing power to the vehicle and an electric motor or other mechanical system for driving the vehicle. The system also includes a self-learning controls unit that receives and stores information from a plurality of inputs associated with the vehicle. The self-learning controls unit uses the information to make predictions about future driving conditions of the vehicle to efficiently utilize the power sources of the hybrid vehicle.
1. A self-learning assisted hybrid vehicle system for a vehicle, said system comprising:
a GPS receiver for determining the location of the vehicle;
a main power source for providing main power for the vehicle;
a supplemental power source for providing supplemental power for the vehicle;
an electric motor receiving power from the supplemental power source and/or the main power source and driving the vehicle; and
a self-learning controls unit programmed to be responsive to information from the GPS receiver and information from a plurality of inputs associated with the vehicle, wherein the plurality of inputs include vehicle load information from one or more vehicle load monitoring devices including infant/child seat information from infant/child seat monitoring devices, said self-learning controls unit using the information to make predictions about future driving conditions of the vehicle to efficiently utilize the main and supplemental power sources, wherein the self-learning controls unit optimizes the power distribution between the main power source and the supplemental power source to reduce fuel consumption, reduce emissions and/or improve performance of the vehicle.
2. The system according to claim 1 , wherein the plurality of inputs include speed and distance information from a speed/odometer input.
3. The system according to claim 1 , wherein the plurality of inputs include information from a map database, commute data from a commute information database, and driver information from a driver identification system/database.
4. The system according to claim 1 , wherein the plurality of inputs include time of day, day of week and calendar day information.
5. The system according to claim 1 , wherein the plurality of inputs include wheel slip information from one or more wheel slip monitoring devices and wind/weather conditions information from one or more wind/weather conditions monitoring devices.
6. The system according to claim 1 , wherein the plurality of inputs include plug-in information from one or more plug-in monitoring devices and/or a plug-in database to predict when the vehicle will be plugged into a wall charger or similar external charging device.
7. The system according to claim 1 , wherein the self-learning controls unit predicts future driving conditions to enable the vehicle to maximize the usable energy available from regenerative braking.
8. The system according to claim 1 , further comprising an external hard-drive that is capable of sending and receiving information from the self-learning controls unit using the GPS receiver so as to enable a large amount of data from the vehicle to be collected and analyzed while minimizing the amount of nonvolatile random access memory required on the vehicle.
9. The system according to claim 1 , wherein the supplemental power source is a DC battery, a flywheel or an ultracapacitor.
10. The system according to claim 1 , wherein the main power source is a fuel cell system or an internal combustion engine.
11. A self-learning assisted hybrid vehicle system for a vehicle, said system comprising:
a GPS receiver for determining the location of the vehicle;
a main power source for providing main power for the vehicle, said main power source being an internal combustion engine or a fuel cell system;
a battery for providing supplemental power for the vehicle;
an electric motor or other mechanical system receiving power from the battery and driving the vehicle;
power electronics for conditioning power from the battery and regenerative braking energy that charges the battery; and
a self-learning controls unit that is programmed to receive information from a plurality of inputs associated with the vehicle, wherein the plurality of inputs include vehicle load information from one or more vehicle load monitoring devices including infant/child seat information from infant/child seat monitoring devices, said self-learning controls unit using the information to make predictions about future driving conditions of the vehicle to efficiently utilize the main and supplemental power sources so as to reduce fuel consumption, reduce emissions and/or improve performance of the vehicle.
12. The system according to claim 11 , further comprising an external hard-drive that is capable of sending and receiving information from the self-learning controls unit using the GPS receiver so as to enable a large amount of data from the vehicle to be collected and analyzed while minimizing the amount of nonvolatile random access memory required on the vehicle.
13. The system according to claim 11 , wherein the self-learning controls unit predicts future driving conditions to enable the vehicle to maximize the usable energy from regenerative braking.
14. The system according to claim 11 , wherein the plurality of inputs include plug-in information from one or more plug-in monitoring devices and/or a plug-in database to predict when the vehicle will be plugged into a wall charger or similar external charging device.
15. The system according to claim 11 , wherein the plurality of inputs include speed and distance information from a speed/odometer input.
16. The system according to claim 11 , wherein the plurality of inputs include information from a map database, commute data from a commute information database, and driver information from a driver identification system/database.
17. The system according to claim 11 , the plurality of inputs include time of day, day of week and calendar day information.
18. The system according to claim 11 , wherein the plurality of inputs include wheel slip information from one or more wheel slip monitoring devices, and wind/weather conditions information from one or more wind/weather conditions monitoring devices.
19. A method for a self-learning assisted operating method for a hybrid vehicle, said method comprising:
providing a GPS receiver for determining the location of the vehicle;
providing a main power source for powering the vehicle;
providing a supplemental power source for providing supplemental power for the vehicle;
providing an electric motor or other mechanical system for driving the vehicle;
powering the electric motor and the main power source; and
providing a self-learning controls unit that is programmed to receive information from the GPS receiver and information from a plurality of inputs associated with the vehicle, wherein the plurality of inputs include vehicle load information from one or more vehicle load monitoring devices including infant/child seat information from infant/child seat monitoring devices, said self-learning controls unit using the information to make predictions about future driving conditions of the vehicle to efficiently utilize the power sources of the hybrid vehicle so as to reduce the fuel consumption, reduce emissions and/or improve performance of the hybrid vehicle.