Method for identifying the condition of an energy accumulator

• US 6,668,233 B1
• Filed: 08/06/2001
• Issued: 12/23/2003
• Est. Priority Date: 12/08/1999
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Schoch, Eberhard, Schoettle, Richard, Sarfert, Christel
• Primary Examiner(s): Barlow, John
• Assistant Examiner(s): Pretlow, Demetrius R.
• Application Number:
  US09/890,849
• Time in Patent Office: 869 Days
• Field of Search: 702/63, 702/189, 702/190, 702/65
• US Class Current: 702/63
• CPC Class Codes: G01R 31/006 on road vehicles, e.g. automobiles or trucks testing of ignition installations peculiar to internal combustion engines F02P17/00
• CPC Class Codes: G01R 31/3648 comprising digital calculation means, e.g. for performing an algorithm
• CPC Class Codes: G01R 31/367 Software therefor, e.g. for battery testing using modelling or look-up tables

Abstract

A method for detecting the status of an energy storage means (1) provides that its actual values (2, 3, 4) can be supplied to an estimate routine (5). The actual values (2, 3, 4) can be supplied in a decoupled manner to a model-based parameter estimator (5) and to a filter (7), which are subordinate to a monitoring level (12). Parameterization values (8, 11) obtained are supplied to a predictor (17) that extrapolates the behavior of the energy storage means (1).

Claims

1. Method for detecting the status of an energy storage means (1), wherein actual values (2, 3, 4) are determined, wherein said actual values (2, 3, 4) are supplied to an estimation routine, characterized in that the estimation routine includes a model-based parameter estimator and a model-based filter (7), wherein the actual values (2, 3, 4) in a decoupled fashion are supplied to the model-based parameter estimator (5) and the model-based filter (7), wherein the model-based parameter estimator (5) and the model-based filter (7) are subordinate to a monitoring level (12), wherein values (8, 11) obtained in the model-based filter (7) impacts a predictor (17) that extrapolates the behavior of the energy storage means (1).

2. The method according to claim 1, characterized in that the model-based parameter estimator (5) includes adaptive model parameters (6), and by means of changes in the actual values (2, 3, 4) of the energy storage means (1), adaptations of the adaptive model parameters (6) of the model-based parameter estimator continuously take place.

3. The method according to claim 1, characterized in that the actual values (2, 3, 4) can be supplied to the model-based parameter estimator (5) as input values (9, 10) in longer time intervals t₁.

4. The method according to claim 3, characterized in that the actual values (2, 3, 4) can be supplied to the model-based filter (7) at shorter time intervals t₂, where t₂<

t₁.

5. The method according to claim 1, characterized in that the values (8, 11) estimated by means of the model-based parameter estimator (5) and the model-based filter (7) are subjected to a plausibility testing in a monitoring level (12) and with non-plausibility, predeterminable conditions are met.

6. The method according to claim 5, characterized in that the monitoring level (12) initializes a resetting (14, 16) of the estimated values (8, 11) and a re-estimation of the values (8, 11 ) or initiates a resetting of the estimated values (8, 11) to the plausible estimated values that were determined last.

7. The method according to claim 1, characterized in that the plausibility testing of the estimated values (8, 11) takes place on the basis of a predeterminable value range.

8. The method according to claim 1, characterized in that the model-based parameter estimator (5) can be activated at any time by the monitoring level (12).

9. The method according to claim 1, characterized in that the predictor (17) simulates possible loads from a load spectrum based on estimated values (8, 11) from the model-based parameter estimator (5) and the model-based filter (7).

10. The method according to claim 1, characterized in that by means of use-specific stimuli (20), safety critical simulation routines (19.1, 19.2, 19.3) are executed in the predictor (17) in order to predict the behavior of the energy storage means (1).

11. The method according to claim 1, characterized in that input values (9, 10) of the model-based parameter estimator (5) in the model-based filter (7) are accompanied by changes in the model parameters of the model-based parameter estimator (5) by means of parameter transfer (8).

12. Method for detecting the status of an energy storage means (1), wherein actual values (2, 3, 4) are determined, wherein said actual values (2, 3, 4) are supplied to an estimation routine, characterized in that the estimation routine includes a model-based parameter estimator and a model-based filter (7), wherein the actual values (2, 3, 4) in a decoupled fashion are supplied to the model-based parameter estimator (5) and the model-based filter (7), wherein the model-based parameter estimator (5) and the model-based filter (7) are subordinate to a monitoring level (12), wherein values (8, 11) obtained in the model-based filter (7) impacts a predictor (17) that extrapolates the behavior of the energy storage means (1), and wherein the model-based filter (7) is a Kalman filter which estimates the inner status of the energy storage means on the basis of a mathematical model.