MONITORING CHARGE STORED IN A BATTERY

• US 20160139210A1
• Filed: 06/18/2014
• Published: 05/19/2016
• Est. Priority Date: 06/18/2013
• Status: Active Grant

• Current Assignee: Vlaamse Instelling Voor Technologisch Onderzoek Nv (Vito Nv)
• Original Assignee: Vlaamse Instelling Voor Technologisch Onderzoek Nv (Vito Nv)
• Inventors: COENEN, Peter
• Granted Patent: US 10,295,600 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60L 2240/545 Temperature
• CPC Class Codes: B60L 2240/547 Voltage
• CPC Class Codes: B60L 2240/549 Current
• CPC Class Codes: B60L 2240/80 Time limits
• CPC Class Codes: B60L 2260/44 by parameter estimation
• CPC Class Codes: B60L 3/0046 relating to electric energy storage systems, e.g. batteries or capacitors
• CPC Class Codes: B60L 58/13 Maintaining the SoC within a determined range
• CPC Class Codes: B60L 58/16 responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• 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
• CPC Class Codes: G01R 31/3842 combining voltage and current measurements
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries

Abstract

A method and corresponding device, for monitoring a charge of a battery. The method obtains a battery current measurement value and a battery voltage measurement value, and applies a current integration method to update a primary charge estimate value representative of the charge stored in the battery by taking into account the battery current measurement value. The method further determines an ancillary charge estimate value representative of the charge stored in the battery using a battery model taking into account the battery voltage measurement value, and determines an error value for the ancillary charge estimate value, in which the error value expresses the reliability of the battery model. The method also applies a correction to the primary charge estimate value as function of the ancillary charge estimate value and the error value. Interpretation of the correction applied in this manner allows determination of current sensor offset and battery capacity change.

Claims

1-15. -15. (canceled)

16. A method for monitoring a charge of a battery, the method comprising:

obtaining a battery current measurement value (k) and a battery voltage measurement value (VL),

applying a current integration method to update a primary charge estimate value representative of the charge stored in the battery by taking into account the battery current measurement value (k),

determining an ancillary charge estimate value representative of the charge stored in the battery using a battery model taking into account the battery voltage measurement value (VL),

determining an error value for said ancillary charge estimate value, said error value expressing the reliability of said battery model taking the battery voltage measurement value (VL) into account,

applying a correction to the primary charge estimate value as function of the ancillary charge estimate value and the error value, and

determining a systematic error of the battery current measurement value

(k) taking into account said correction to the primary charge estimate value.

17. The method according to claim 16, further comprising the step of determining a total charge capacity of the battery taking into account said correction to the primary charge estimate value.

18. The method according to claim 16, in which applying the current integration method comprises performing a prediction step of a linear quadratic estimation filter method for recursively updating said primary charge estimate value which reflects an underlying system state corresponding to the charge stored in the battery, taking into account a sequence of noise input data whereby the filter method can include a control variable commensurate with said battery current measurement value.

19. The method according to claim 18, in which applying the correction comprises applying an update step of said linear quadratic estimation filter method further comprising an observation variable commensurate with the ancillary charge estimate value having an associated variance commensurate with said error value.

20. The method according to claim 18, in which said linear quadratic estimation filter method is selected form a one-dimensional linear quadratic estimation filter method, a Kalman filter, a Sequential Kalman filter, an Information filter, a Cholesky matrix square root algorithm, a Potter'"'"'s square root measurement-update algorithm, the Household algorithm, the Gram-Schmidt algorithm, the U-D measurement update, the U-D time update, or the particle filter.

21. The method according to claim 16, in which applying the current integration method comprises performing a prediction step of a Kalman filter to update said primary charge estimate value, wherein said primary charge estimate value is a Kalman prediction value of a hidden state variable corresponding to the charge stored in the battery, and said Kalman filter further comprises a control variable commensurate with said battery current measurement value (IL).

22. The method according to claim 21, in which applying the correction comprises applying an update step of said Kalman filter, in which said Kalman filter further comprises an observation variable commensurate with the ancillary charge estimate value having an associated variance commensurate with said error value.

23. The method according to claim 21, in which said Kalman filter is a one-dimensional Kalman filter.

24. The method according to claim 16, in which said ancillary charge estimate value comprises a state-of-charge value expressed as fraction of a total charge capacity of the battery.

25. The method according to claim 16, in which said determining the ancillary charge estimate value comprises determining an electromotive force (EMF) of the battery.

26. The method according to claim 16, in which said determining the ancillary charge estimate value takes the battery current measurement value (IL) and/or a battery temperature measurement into account.

27. The method according to claim 16, further comprising monitoring charge stored in a battery.

28. The method according to claim 27, the battery being selected from a battery for powering an electric or hybrid vehicle, and a battery powering an electric power back-up unit.

29. A device for monitoring the charge of a battery comprising a current sensor for providing a battery current measurement value (IL), a voltage sensor for providing a battery voltage measurement value (VL), and a processing unit:

means for applying a current integration method to update a primary charge estimate value representative of the charge stored in the battery by taking into account the battery current measurement value (IL),

means for determining an ancillary charge estimate value representative of the charge stored in the battery using a battery model taking into account the battery voltage measurement value (VL),

means for determining an error value for said ancillary charge estimate value, said error value expressing the reliability of said battery model taking the battery voltage measurement value (VL) into account,

means for applying a correction to the primary charge estimate value as function of the ancillary charge estimate value and the error value, and

means for determining a systematic error of the battery current measurement value (IL) taking into account said correction to the primary charge estimate value.

30. The device according to claim 29, further comprise means for determining a total charge capacity of the battery taking into account said correction to the primary charge estimate value.

31. The device according to claim 29, wherein said processing unit has means for performing a prediction step of a linear quadratic estimation filter method for recursively updating an estimated quantity which reflects an underlying system state such as the charge stored in the battery, taking into account a sequence of noise input data whereby the filter method including a control variable commensurate with said battery current measurement value.

32. The device according to claim 31, wherein a Kalman filter is adapted to update said primary charge estimate value, wherein said primary charge estimate value is a Kalman prediction value of a hidden state variable corresponding to the charge stored in the battery, and said Kalman filter further comprises a control variable commensurate with said battery current measurement value.

33. The device according to claim 32, wherein said means for applying the correction comprise means for applying an updating of said linear quadratic estimation filter method, in which said linear quadratic estimation method further comprises an observation variable commensurate with the ancillary charge estimate value having an associated variance commensurate with said error value.

34. The device of claim 31, the filter method is selected from a Kalman filter, a Sequential Kalman filter, an Information filter, a Cholesky matrix square root algorithm, a Potter'"'"'s square root measurement-update algorithm, the Household algorithm, the Gram-Schmidt algorithm, the U-D measurement update, the U-D time update, or the particle filter.

35. The device of claim 29, wherein the ancillary charge estimate value is a state-of-charge value expressed as fraction of a total charge capacity of the battery.

36. The device of claim 29, wherein said means for determining the ancillary charge estimate value is selected from means for determining an electromotive force (EMF) of the battery and means taking into account the battery current measurement value and/or a battery temperature measurement.