Engine self-tuning methods and systems

• US 20070233326A1
• Filed: 03/31/2006
• Published: 10/04/2007
• Est. Priority Date: 03/31/2006
• Status: Abandoned Application

• Current Assignee: Caterpillar Incorporated
• Original Assignee: Caterpillar Incorporated
• Inventors: Jayachandran, Amit
• Application Number:
  US11/393,956
• Publication Number:
  US 20070233326A1
• Time in Patent Office: Days
• US Class Current: 701/1
• CPC Class Codes: F02D 41/1405 Neural network control
• CPC Class Codes: G05B 13/027 using neural networks only

Abstract

A method is provided for controlling an engine. The method may include generating a first neural network model indicative of interrelationships between a plurality of sensing parameters and a plurality of engine operational parameters. The method may also include generating a second neural network model indicative of interrelationships between the plurality of engine operational parameters and at least a desired emission level. The method may also include providing, by the first neural network model, a first set of values of the plurality of engine operational parameters to the second neural network model and to the engine. Further, the method may include determining, by the second neural network model, values of adjusting parameters of the first neural network model based on the values of the plurality of engine operational parameters, the desired emission level, and an actual emission level of the engine.

Claims

1. A method for controlling an engine, comprising:

generating a first neural network model indicative of interrelationships between a plurality of sensing parameters and a plurality of engine operational parameters;

generating a second neural network model indicative of interrelationships between the plurality of engine operational parameters and at least a desired emission level;

providing, by the first neural network model, a first set of values of the plurality of engine operational parameters to the second neural network model and to the engine;

determining, by the second neural network model, values of adjusting parameters of the first neural network model based on the values of the plurality of engine operational parameters, the desired emission level, and an actual emission level of the engine; and

providing a second set of values of the plurality of engine operational parameters, by the first neural network model, based on the values of adjusting parameters to the engine.

2. The method according to claim 1, wherein providing the second set of values includes:

providing, by the second neural network model, the values of the adjusting parameters to the first neural network model; and

re-training the first neural network model based on the values of the adjusting parameters.

3. The method according to claim 2, further including:

determined the second set of values of the plurality of engine operational parameters based on the re-trained first neural network model; and

providing the second set of values of the plurality of engine operational parameters to the engine.

4. The method according to claim 1, wherein the desired emission level is a desired NOx emission level and the actual emission level is an actual NOx emission level.

5. The method according to claim 4, wherein the actual NOx emission level is provided by a NOx sensor.

6. The method according to claim 5, the method further including:

calculating a difference between the desired NOx emission level, and the actual NOx emission level;

determining whether the difference is within a predetermined range; and

determining a failure of the NOx sensor if the difference is out of the predetermined range.

7. The method according to claim 2, wherein the plurality of engine operational parameters include injection timing and injection pressure of the engine.

8. The method according to claim 2, wherein the first neural network model is an inverse neural network model.

9. The method according to claim 8, wherein the adjusting parameters includes a back-propagation error of the first neural network model and the re-training further includes:

adjusting weights of the first neural network model based on the back-propagation error to minimize the back-propagation error.

10. The method according to claim 1, wherein the providing further includes:

obtaining the values of the plurality of sensing parameters through various physical sensors;

determining the values of the plurality of engine operational parameters based on the first neural network model and the values of the plurality of sensing parameters; and

providing the determined values of the plurality of engine operational parameters to the second neural network model and to the engine.

11. An engine control system for controlling an engine, comprising:

plural physical sensors configured to provide a plurality of sensing parameters; and

a processor configured to;

generate a first neural network model indicative of interrelationships between the plurality of sensing parameters and a plurality of engine operational parameters;

generate a second neural network model indicative of interrelationships between the plurality of engine operational parameters and at least a desired emission level;

provide, via the first neural network model, a first set of values of the plurality of engine operational parameters to the second neural network model and to the engine; and

determine, via the second neural network model, values of adjusting parameters of the first neural network model based on the values of the plurality of engine operational parameters, the desired emission level, and an actual emission level of the engine.

12. The engine control system according to claim 11, wherein the adjusting parameters include a back-propagation error, and the processor is further configured to:

provide, via the second neural network model, the back-propagation error to the first neural network model; and

re-train the first neural network model based on the back-propagation error.

13. The engine control system according to claim 12, wherein the processor is further configured to:

determine a second set of values of the plurality of engine operational parameters based on the re-trained first neural network model; and

provide the second set of values of the plurality of engine operational parameters to the engine.

14. The engine control system according to claim 12, wherein, to re-train the first neural network, the processor is further configured to:

adjust weights of the first neural network model based on the back-propagation error to minimize the back-propagation error.

15. A vehicle, comprising:

an engine which provides power to the vehicle and produces NOx emission at an actual NOx emission level; and

a control system configured to control the engine, the control system including a processor configured to;

generate a first neural network model indicative of interrelationships between a plurality of sensing parameters and a plurality of engine operational parameters;

generate a second neural network model indicative of interrelationships between the plurality of engine operational parameters and at least a desired NOx emission level;

provide, via the first neural network model, a first set of values of the plurality of engine operational parameters to the second neural network model and to the engine; and

determine, via the second neural network model, values of adjusting parameters of the first neural network model based on the values of the plurality of engine operational parameters, the desired NOx emission level, and the actual NOx emission level of the engine.

16. The vehicle according to claim 15, wherein the adjusting parameters include a back-propagation error, and the processor is further configured to:

provide, via the second neural network model, the back-propagation error to the first neural network model; and

re-train the first neural network model based on the back-propagation error.

17. The vehicle according to claim 16, wherein the processor is further configured to:

determine a second set of values of the plurality of engine operational parameters based on the re-trained first neural network model; and

provide the second set of values of the plurality of engine operational parameters to the engine.

18. The vehicle according to claim 16, wherein, to re-train the first neural network, the processor is further configured to:

adjust weights of the first neural network model based on the back-propagation error to minimize the back-propagation error.

19. The vehicle according to claim 16, wherein the processor is further configured to:

calculate a difference between the desired NOx emission level, and the actual NOx emission level;

determine whether the difference is within a predetermined range; and

determine a failure of the NOx sensor if the difference is out of the predetermined range.

20. The vehicle according to claim 16, wherein, to provide the first set of values of the plurality of engine operational parameters, the processor is further configured to:

obtain the values of the plurality of sensing parameters through various physical sensors;

determine the values of the plurality of engine operational parameters based on the first neural network model and the values of the plurality of sensing parameters; and

provide the determined values of the plurality of engine operational parameters to the second neural network model and to the engine.