Neural net optimization of continuously variable powertrain
A method for optimizing a vehicle system involves using a neural network to optimize at least one operating parameter of a transmission portion of a continuously variable powertrain based at least in part on a classified current state of a vehicle, wherein the current state is associated with a predicted future vehicle state.
1. A method for optimizing a vehicle system, the method comprising:
classifying a current state as a classified current state of a vehicle based on at least one operating parameter of a transmission of a continuously variable powertrain of the vehicle, wherein the classified current state includes a transmission ratio;
predicting a future vehicle state as a predicted future vehicle state based on the classified current state, wherein the predicted future vehicle state includes at least one of:
operating in a presence of ice, or changing the transmission ratio on approaching a curve; and
using a neural network to optimize the at least one operating parameter of the transmission of the continuously variable powertrain based at least in part on the classified current state and the predicted future vehicle state, wherein the at least one operating parameter of the transmission includes the transmission ratio.
2. The method of claim 1 , wherein the at least one operating parameter of the transmission further includes a safe-driving mode.
3. The method of claim 1 , wherein the neural network is trained to optimize the at least one operating parameter of the transmission based on a data set of outcomes.
4. The method of claim 1 , wherein the neural network is one of at least one neural network, wherein classifying the current state includes using a first neural network portion of the at least one neural network, and wherein predicting the future vehicle state includes using a second neural network portion of the at least one neural network.
5. The method of claim 1 , wherein the neural network is configured to optimize the at least one operating parameter of the transmission to achieve a favorable rider mood.
6. The method of claim 1 , wherein the neural network is further configured to optimize the at least one operating parameter of the transmission to affect a proximity to other vehicles along a route.
7. The method of claim 1 , wherein the neural network is further configured to optimize the at least one operating parameter of the transmission to affect an acceleration of the vehicle.
8. The method of claim 1 , wherein the neural network is further configured to optimize the at least one operating parameter of the transmission to affect a deceleration of the vehicle.
9. The method of claim 1 , wherein the neural network is further configured to optimize the at least one operating parameter of the transmission to affect a speed of the vehicle.
10. The method of claim 1 , wherein the neural network is further configured to optimize the at least one operating parameter of the transmission to affect a route of the vehicle.
11. The method of claim 1 , wherein the neural network is further configured to optimize the at least one operating parameter of the transmission based on analysis of rider interactions with an electronic commerce system interface.
12. The method of claim 1 , wherein the neural network is further configured to optimize the at least one operating parameter of the transmission based on a classified rider state through recognition of aspects of captured rider interactions.
13. A vehicle, comprising:
a continuously variable powertrain with a transmission portion having at least one operating parameter, wherein the at least one operating parameter includes a transmission ratio; and
a processor programmed to:
classify a current state as a classified current state of the vehicle based, at least in part, on the at least one operating parameter of the transmission portion, wherein the classified current state includes the transmission ratio;
predict a future vehicle state as a predicted future vehicle state based on the classified current state, wherein the predicted future vehicle state includes at least one of: operating in a presence of ice, or changing the transmission ratio on approaching a curve; and
execute a neural network configured to optimize the at least one operating parameter based, at least in part, on the classified current state of the vehicle and the predicted future vehicle state, wherein the optimized at least one operating parameter includes the transmission ratio.
14. The vehicle of claim 13 , wherein the neural network is a hybrid neural network for optimizing an operating state of the continuously variable powertrain of the vehicle.
15. The vehicle of claim 13 , wherein the optimized at least one operating parameter includes a safe-driving mode.
16. The vehicle of claim 13 , wherein the neural network is trained to optimize the at least one operating parameter based on a data set of outcomes.
17. The vehicle of claim 13 , wherein the processor is further programmed to optimize the at least one operating parameter to achieve a favorable rider mood.
18. The vehicle of claim 13 , wherein the processor is further programmed to optimize the at least one operating parameter to affect a proximity to other vehicles along a route.
19. The vehicle of claim 13 , wherein the processor is further programmed to optimize the at least one operating parameter to affect at least one of: an acceleration of the vehicle, a deceleration of the vehicle, or a speed of the vehicle.
20. The vehicle of claim 13 , wherein the processor is further programmed to optimize the at least one operating parameter to affect a route of the vehicle.
21. The vehicle of claim 13 , wherein the processor is further programmed to optimize the at least one operating parameter based on analysis of rider interactions with an electronic commerce system interface.
22. The vehicle of claim 13 , wherein the processor is further programmed to optimize the at least one operating parameter based on a classified rider state through recognition of aspects of captured rider interactions.