Method, control apparatus and powertrain system controller for real-time, self-learning control based on individual operating style

• US 20090306866A1
• Filed: 06/05/2009
• Published: 12/10/2009
• Est. Priority Date: 06/10/2008
• Status: Active Grant

• Current Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Original Assignee: Board of Regents of the University of Michigan (University of Michigan)
• Inventors: Malikopoulos, Andreas
• Granted Patent: US 8,612,107 B2
• Time in Patent Office: Days
• US Class Current: 701/59
• CPC Class Codes: B60W 30/1882 characterised by the working point of the engine, e.g. by using engine output chart

Abstract

Method, control apparatus and powertrain system controller are provided for real-time, self-learning control based on individual operating style. The method calibrates powertrain system performance in a passenger vehicle in real-time based on individual operating style. The method includes powering the vehicle with the system and generating a sequence of system operating point transitions based on an operating style of an operator of the vehicle during the step of powering. The method further includes learning a set of optimum values of controllable system variables in real-time during the steps of powering and generating based on the sequence of system operating point transitions and predetermined performance criteria for the system. The method still further includes generating control signals based on the set of optimum values to control operation of the system.

Claims

1. A method for calibrating powertrain system performance in a passenger vehicle in real-time based on individual operating style, the method comprising:

powering the vehicle with the system;

generating a sequence of system operating point transitions based on an operating style of an operator of the vehicle during the step of powering;

learning a set of optimum values of controllable system variables in real-time during the steps of powering and generating based on the sequence of system operating point transitions and predetermined performance criteria for the system; and

generating control signals based on the set of optimum values to control operation of the system.

2. The method as claimed in claim 1, wherein the step of learning includes the step of learning first and second controllable system variables of the set in parallel phases.

3. The method as claimed in claim 1, further comprising providing a base set of system operating points and corresponding values of the controllable system variables wherein the step of learning is also based on the base set.

4. The method as claimed in claim 1, wherein the step of learning is at least partially performed utilizing a decentralized learning control algorithm.

5. The method as claimed in claim 1, wherein the step of learning is at least partially performed utilizing a predictive optimal stochastic control algorithm.

6. The method as claimed in claim 1, wherein the system includes an internal combustion engine and wherein the vehicle is one of a land traversing vehicle, watercraft and aircraft.

7. The method as claimed in claim 1, wherein the step of learning is at least partially performed utilizing a lookahead algorithm.

8. A control apparatus for a powertrain system which powers a passenger vehicle wherein system performance is calibrated in real-time based on individual operating style, the apparatus comprising:

means for generating a sequence of system operating point transitions based on an operating style of an operator of the vehicle while the system is powering the vehicle;

a self-learning controller for learning a set of optimum values of controllable system variables in real-time while the system is powering the vehicle based on the sequence of system operating point transitions and predetermined performance criteria for the system; and

means for generating control signals based on the set of optimum values to control operation of the system.

9. The apparatus as claimed in claim 8, wherein the self-learning controller learns first and second controllable system variables of the set in parallel phases.

10. The apparatus as claimed in claim 8, further comprising a base set of system operating points and corresponding values of the controllable system variables wherein the self-learning controller learns the set of optimum values based on the base set.

11. The apparatus as claimed in claim 8, wherein the self-learning controller learns based on a decentralized learning control algorithm.

12. The apparatus as claimed in claim 8, wherein the self-learning controller learns based on a predictive optimal stochastic control algorithm.

13. The apparatus as claimed in claim 8, wherein the system includes an internal combustion engine and wherein the vehicle is one of a land traversing vehicle, watercraft and aircraft.

14. The apparatus as claimed in claim 8, wherein the self-learning controller learns based on a lookahead algorithm.

15. A powertrain system controller comprising:

a processor; and

a storage device having processor-readable code embodied thereon for programming the processor to perform a method comprising;

generating a sequence of system operating point transitions based on an operating style of an operator of the vehicle while the system is powering a passenger vehicle;

learning a set of optimum values of controllable system variables in real-time during the step of generating based on the sequence of system operating point transitions and predetermined performance criteria for the system; and

generating control signals based on the set of optimum values to control operation of the system.

16. The controller as claimed in claim 15, wherein the step of learning includes the step of learning first and second controllable system variables of the set in parallel phases.

17. The controller as claimed in claim 15, wherein the storage device contains a processor-readable base set of system operating points and corresponding values of the controllable system variables wherein the step of learning is also based on the base set.

18. The controller as claimed in claim 15, wherein the step of learning is at least partially performed utilizing a decentralized learning control algorithm.

19. The controller as claimed in claim 15, wherein the step of learning is at least partially performed utilizing a predictive optimal stochastic control algorithm.

20. The controller as claimed in claim 15, wherein the system includes an internal combustion engine and wherein the vehicle is one of a land traversing vehicle, watercraft and aircraft.

21. The controller as claimed in claim 15, wherein the step of learning is at least partially performed utilizing a lookahead algorithm.

22. A method for calibrating powertrain system performance in a vehicle in real-time based on individual operating style, the method comprising:

powering the vehicle with the system;

generating a sequence of system operating point transitions based on an operating style of an operator of the vehicle during the step of powering;

learning a set of optimum values of controllable system variables in real-time during the steps of powering and generating based on the sequence of system operating point transitions and predetermined performance criteria for the system; and

generating control signals based on the set of optimum values to control operation of the system.