Battery diagnosing apparatus, battery system and battery diagnosing method

1 . A battery diagnosing apparatus, comprising:

a voltage measuring circuit configured to generate a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

a database configured to store a plurality of abnormal patterns, a plurality of reference time series, and a predetermined corresponding relationship between the plurality of abnormal patterns and the plurality of reference time series; and

a control unit configured to generate a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal,

wherein the control unit is configured to extract an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other,

wherein the control unit is configured to identify an abnormal pattern of the abnormal input time series among the plurality of abnormal patterns by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the control unit is configured to:

calculate a first similarity value representing a signal distance between the abnormal input time series and a respective reference time series by using dynamic time warping;

convert the abnormal input time series and the respective reference time series into a first arranged time series and a second arranged time series having a same time length, respectively, by using the dynamic time warping;

calculate a second similarity value representing a Pearson correlation coefficient between the first arranged time series and the second arranged time series; and

determine a matching index between the abnormal input time series and the respective reference time series to be identical to a value obtained by dividing the first similarity value by the second similarity value, and

wherein the control unit is configured to identify the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.

2 . The battery diagnosing apparatus according to claim 1 ,

wherein the control unit is configured to identify the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with a minimum matching index among the plurality of matching indexes.

3 . The battery diagnosing apparatus according to claim 1 ,

wherein the control unit is configured to add the abnormal input time series to the database as a new reference time series corresponding to the identified abnormal pattern.

4 . A battery system, comprising the battery diagnosing apparatus according to claim 1 .

5 . A battery diagnosing apparatus, comprising:

a voltage measuring circuit configured to generate a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

a database configured to store a plurality of abnormal patterns, a plurality of reference time series, and a predetermined corresponding relationship between the plurality of abnormal patterns and the plurality of reference time series; and

a control unit configured to generate a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal,

wherein the control unit is configured to extract an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other,

wherein the control unit is configured to identify an abnormal pattern of the abnormal input time series among the plurality of abnormal patterns by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the control unit is configured to:

convert the abnormal input time series and a respective reference time series into a first normalized time series and a second normalized time series, respectively, by using maximum-minimum normalization;

calculate a first similarity value representing a signal distance between the first normalized time series and the second normalized time series by using dynamic time warping;

convert the first normalized time series and the second normalized time series into a first arranged time series and a second arranged time series having a same time length, respectively, by using the dynamic time warping;

calculate a second similarity value representing a Pearson correlation coefficient between the first arranged time series and the second arranged time series; and

determine a matching index between the abnormal input time series and the respective reference time series to be identical to a value obtained by dividing the first similarity value by the second similarity value, and

wherein the control unit is configured to identify the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.

6 . The battery diagnosing apparatus according to claim 5 ,

wherein the control unit is configured to identify the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with a minimum matching index among the plurality of matching indexes.

7 . A battery diagnosing apparatus, comprising:

a voltage measuring circuit configured to generate a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

a database configured to store a plurality of abnormal patterns, a plurality of reference time series, and a predetermined corresponding relationship between the plurality of abnormal patterns and the plurality of reference time series; and

a control unit configured to generate a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal,

wherein the control unit is configured to extract an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other,

wherein the control unit is configured to identify an abnormal pattern of the abnormal input time series among the plurality of abnormal patterns by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the control unit is configured to:

calculate a first similarity value representing a signal distance between the abnormal input time series and a respective reference time series by using dynamic time warping;

convert the abnormal input time series and the respective reference time series into a first arranged time series and a second arranged time series having a same time length, respectively, by using the dynamic time warping;

calculate a second similarity value representing a Pearson correlation coefficient between the first arranged time series and the second arranged time series;

convert the first arranged time series and the second arranged time series into a first normalized time series and a second normalized time series, respectively, by using maximum-minimum normalization;

calculate a third similarity value representing a signal distance between the first normalized time series and the second normalized time series by using dynamic time warping;

calculate a fourth similarity value representing a Pearson correlation coefficient between the first normalized time series and the second normalized time series; and

determine a matching index between the abnormal input time series and the respective reference time series by dividing any one of the first similarity value, the third similarity value or a product of the first and third similarity values by any one of the second similarity value, the fourth similarity value or a product of the second and fourth similarity values, and

wherein the control unit is configured to identify the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.

8 . The battery diagnosing apparatus according to claim 7 ,

wherein the control unit is configured to identify the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with a minimum matching index among the plurality of matching indexes.

9 . A battery diagnosing method, comprising:

collecting a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

generating a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal;

extracting an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other; and

identifying an abnormal pattern of the abnormal input time series among a plurality of abnormal patterns having a predetermined corresponding relationship with a plurality of reference time series by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the battery diagnosing method further comprises:

calculating a first similarity value representing a signal distance between the abnormal input time series and a respective reference time series by using dynamic time warping;

converting the abnormal input time series and the respective reference time series into a first arranged time series and a second arranged time series having a same time length, respectively, by using the dynamic time warping;

calculating a second similarity value representing a Pearson correlation coefficient between the first arranged time series and the second arranged time series; and

determining a matching index between the abnormal input time series and the respective reference time series to be identical to a value obtained by dividing the first similarity value by the second similarity value, and

wherein the battery diagnosing method further comprises identifying the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.

10 . A battery diagnosing apparatus, comprising:

a voltage measuring circuit configured to generate a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

a database configured to store a plurality of abnormal patterns, a plurality of reference time series, and a predetermined corresponding relationship between the plurality of abnormal patterns and the plurality of reference time series; and

a control unit configured to generate a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal,

wherein the control unit is configured to extract an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other,

wherein the control unit is configured to identify an abnormal pattern of the abnormal input time series among the plurality of abnormal patterns by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the control unit is configured to:

calculate a first similarity value representing a signal distance between the abnormal input time series and a respective reference time series by using dynamic time warping; and

determine a matching index between the abnormal input time series and the respective reference time series to be identical to a value obtained based on at least the first similarity value, and

wherein the control unit is configured to identify the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.

11 . A battery diagnosing method, comprising:

collecting a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

generating a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal;

extracting an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other; and

identifying an abnormal pattern of the abnormal input time series among a plurality of abnormal patterns having a predetermined corresponding relationship with a plurality of reference time series by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the battery diagnosing method further comprises:

converting the abnormal input time series and a respective reference time series into a first normalized time series and a second normalized time series, respectively, by using maximum-minimum normalization;

calculating a first similarity value representing a signal distance between the first normalized time series and the second normalized time series by using dynamic time warping;

converting the first normalized time series and the second normalized time series into a first arranged time series and a second arranged time series having a same time length, respectively, by using the dynamic time warping;

calculating a second similarity value representing a Pearson correlation coefficient between the first arranged time series and the second arranged time series; and

determining a matching index between the abnormal input time series and the respective reference time series to be identical to a value obtained by dividing the first similarity value by the second similarity value, and

wherein the battery diagnosing method further comprises identifying the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.

12 . A battery diagnosing method, comprising:

collecting a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

generating a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal;

extracting an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other; and

identifying an abnormal pattern of the abnormal input time series among a plurality of abnormal patterns having a predetermined corresponding relationship with a plurality of reference time series by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the battery diagnosing method further comprises:

calculating a first similarity value representing a signal distance between the abnormal input time series and a respective reference time series by using dynamic time warping;

converting the abnormal input time series and the respective reference time series into a first arranged time series and a second arranged time series having a same time length, respectively, by using the dynamic time warping;

calculating a second similarity value representing a Pearson correlation coefficient between the first arranged time series and the second arranged time series;

converting the first arranged time series and the second arranged time series into a first normalized time series and a second normalized time series, respectively, by using maximum-minimum normalization;

calculating a third similarity value representing a signal distance between the first normalized time series and the second normalized time series by using dynamic time warping;

calculating a fourth similarity value representing a Pearson correlation coefficient between the first normalized time series and the second normalized time series; and

determining a matching index between the abnormal input time series and the respective reference time series by dividing any one of the first similarity value, the third similarity value or a product of the first and third similarity values by any one of the second similarity value, the fourth similarity value or a product of the second and fourth similarity values, and

wherein the battery diagnosing method further comprises identifying the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.

13 . A battery diagnosing method, comprising:

collecting a voltage signal representing a battery voltage that is a voltage across both ends of each of a plurality of batteries;

generating a plurality of input time series associated with the plurality of batteries, wherein each of the plurality of input time series represents a change history of the battery voltage of a corresponding one of the plurality of batteries, based on a corresponding voltage signal;

extracting an abnormal input time series representing an abnormal voltage behavior among the plurality of input time series by comparing the plurality of input time series with each other; and

identifying an abnormal pattern of the abnormal input time series among a plurality of abnormal patterns having a predetermined corresponding relationship with a plurality of reference time series by comparing the abnormal input time series with the plurality of reference time series one by one,

wherein for each of the plurality of reference time series, the battery diagnosing method further comprises:

calculating a first similarity value representing a signal distance between the abnormal input time series and a respective reference time series by using dynamic time warping; and

determining a matching index between the abnormal input time series and the respective reference time series to be identical to a value obtained based on at least the first similarity value, and

wherein the battery diagnosing method further comprises identifying the abnormal pattern of the abnormal input time series to be identical to an abnormal pattern corresponding to a reference time series associated with one of the plurality of matching indexes determined for the plurality of reference time series.