Method for simplified real-time diagnoses using adaptive modeling

• US 20050278146A1
• Filed: 05/28/2004
• Published: 12/15/2005
• Est. Priority Date: 05/28/2004
• Status: Active Grant

• Current Assignee: OL Security LLC (Intellectual Ventures LLC)
• Original Assignee: Daimler Chrysler Company LLC (Stellantis NV)
• Inventors: Weiss, Wolfgang, Schroedl, Stefan, Nitsche, Christof
• Granted Patent: US 7,136,779 B2
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 3/0053 relating to fuel cells
• CPC Class Codes: B60L 58/30 for monitoring or controlling fuel cells
• CPC Class Codes: B60W 50/0205 Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: G05B 23/0229 knowledge based, e.g. expert systems; genetic algorithms
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: Y02T 90/40 Application of hydrogen technology to transportation, e.g. using fuel cells

Abstract

A method for on-board real-time diagnostics of a mobile technical system using an adaptive technique to approximate stationary characteristic curves resulting from a workshop test. This adaptive technique uses observed non-stationary normal driving data to eliminate confounding variables.

Claims

1. A method for on-board real-time diagnostics of a system, said method comprising the steps:

providing a reference model containing predefined operating conditions and predefined confounding variables of said system and outputting a reference characteristic;

measuring real-world operating conditions and real-world confounding variables of said system and outputting a plurality of system output variables;

providing an adaptive model input with said real-world operating conditions and said real-world confounding variables in a first phase and inputting said predefined operating conditions and said predefined confounding variables in a second phase;

providing a first comparator for comparing said plurality of system output variables with an output of said adaptive model;

feedback means for feeding the output of said first comparator to an input of said adaptive model during said first phase;

providing a second comparator to compare the output of said adaptive model during the second phase with said reference characteristic output of said reference model;

providing a diagnostics module receiving the output of said second comparator during said second phase in order to output a diagnosis of said system.

2. The method according to claim 1, further including the step of switching between said first phase and said second phase wherein said first phase is a training phase and said second phase in a diagnostics phase.

3. The method according to claim 1, wherein said reference characteristic is a series of measured response functions generated by a stationary test of said system.

4. The method according to claim 3, wherein said measured response function provides a polarization curve generated by a stationary test of a fuel cell powertrain.

5. The method according to claim 3, wherein said measured response function provides a speed/torque curve generated by a stationary test of an internal combustion engine.

6. The method according to claim 1, wherein said system is a fuel cell powertrain.

7. The method according to claim 1, wherein said real-world operating conditions and said real-world confounding variables are generated when a vehicle containing said system is being driven during normal operation.

8. The method according to claim 1, wherein said system is a mobile technical system of a vehicle.

9. An arrangement for real time diagnostics of a system, comprising:

a reference model receiving predefined operating conditions and predefined confounding variables of said system and outputting a reference characteristic;

means for inputting to said system real-world operating conditions and real-world confounding variables of said system wherein the output of said system provides system output variables;

an adaptive model receiving, in a first phase, said real-world operating conditions and said real-world confounding variables and, in a second phase said predefined operating conditions and said predefined confounding variables to provide a first output during said first phase and a second output during said second phase;

first comparator means for comparing said system output variables with said first output of said adaptive model;

feedback means receiving an output of said first comparator means and feeding said output to said adaptive model during said first phase;

second comparator means for comparing an output of said reference model with the second output of said adaptive model during said second phase;

a diagnostics module receiving an output of said second comparator during said second phase;

switching means for switching between said first and second phase.

10. The arrangement according to claim 9, wherein said first phase is a training phase and said second phase is a diagnostics phase.

11. The arrangement according to claim 9, wherein said reference characteristics are a series of measured response functions generated by a stationary test of said system.

12. The arrangement according to claim 11, wherein said measured response functions provide a polarization curve generated by a stationary test of a fuel cell powertrain.

13. The arrangement according to claim 11, wherein said measured response functions provide speed/torque curve generated by a stationary test of an internal combustion engine.

14. The arrangement according to claim 9, wherein said system is a fuel cell powertrain.

15. The arrangement according to claim 9, wherein said real-world operation conditions and said real-world confounding variables are generated from a measuring means during the normal driving operation of a vehicle containing said system.

16. A method for on board real-time measurement of a system, comprising:

providing a reference model to output a reference characteristic relationship as a function of predefined values of said system;

providing an adaptive model responsive to on-board measurements of real-world values of said system and providing a real world characteristic relationship;

comparing said reference characteristic relationship with said real-world characteristic relationship to provide an output which indicates an operating condition of said system.

17. The method according to claim 16, wherein said predefined values are operating conditions and confounding values resulting from a stationary bench test of said system.

18. The method according to claim 16, wherein said real-world values are operating conditions and confounding values resulting from operation of a vehicle containing said system under normal driving condition.

19. The method according to claim 16, further including the step of operating said adaptive model in a training mode to obtain a training result prior to operating in a diagnostics mode to provide said output which indicates an operating condition of said system.

20. The method according to claim 16, wherein said system is a fuel cell powertrain.

21. The method according to claim 19, further including the step of controllably switching between said training mode and said diagnostics mode and wherein said training mode employs a feedback arrangement.

22. The method according to claim 16, wherein said system is a mobile technical system of a vehicle.