CN112798963A - Method, apparatus and medium for detecting battery charging characteristic abnormality based on time series - Google Patents
Method, apparatus and medium for detecting battery charging characteristic abnormality based on time series Download PDFInfo
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Abstract
The invention relates to the technical field of lithium battery charging processing, in particular to a method, a device and a medium for detecting battery charging characteristic abnormity based on time series. The method comprises the following steps: step S1, data acquisition; step S2, data processing; s3, detecting the abnormality based on the historical data of the single battery cell; and step S4, detecting the abnormality based on the current data of all the battery cells. The apparatus is for implementing the above method, and the medium is for storing the above method. By the method, whether the single battery cell is abnormal in the life cycle of the battery cell can be preferably detected, and whether any battery cell is abnormal relative to other battery cells can be preferably detected, so that the charging abnormity can be preferably judged.
Description
Technical Field
The invention relates to the technical field of lithium battery charging processing, in particular to a method, a device and a medium for detecting battery charging characteristic abnormity based on time series.
Background
The charging of the battery is an important part in the use of the battery, and the abnormal charging of the battery directly influences the endurance mileage and the like of the battery. The battery charging abnormity detection can be used for discovering the battery which is abnormally shown in the charging process in advance, so that the operation and maintenance of the whole battery can be better sensed and processed in advance, and the stability and the safety of the whole battery system can be guaranteed.
Chinese patent publication No. CN 103809124 a discloses a battery abnormality detection method and system, which collects voltage variation data at equal intervals during charging, verifies all collected voltage variation data based on normal distribution, and determines whether the battery is abnormal. The detection method is realized on the premise that the voltage change values of the batteries in fixed charging time are the same and accord with a normal distribution rule, and the assumption has the following two problems:
1. as the number of times of charging the battery is accumulated, the battery gradually loses, which directly causes a large deviation of voltage variation values of the same battery in different loss states within a fixed time;
2. because of the battery loss, the same normal distribution model cannot be used to make better determinations for batteries with different loss degrees.
Based on the above 2 points, it is found that the conventional method for detecting battery charging abnormality cannot take the battery loss into consideration, and thus the erroneous determination rate is high.
Disclosure of Invention
The invention provides a battery charging characteristic abnormity detection method based on time series, which can better solve the problem of high misjudgment rate caused by the fact that the loss of a battery cannot be considered in the conventional battery charging abnormity detection method.
The invention discloses a battery charging characteristic abnormity detection method based on time series, which comprises the following steps:
step S1, data acquisition
In the step, the current charging times k of the battery and the charging time of each battery cell i in a set voltage section are collected
And obtaining the cell charging time sequence data of each cell i
，
And acquires battery charging time-series data D of the battery,
；
step S2, data processing
In the step, the charging time of each battery cell i is calculated based on the STL algorithm
Processing is carried out to further acquire trend components
Periodic component of
And remainder
，
；
Step S3, abnormity detection based on single battery cell historical data
In the step, the battery cell charging time sequence data are calculated based on the GESD algorithm
Trend component of (1)
And remainder
If the trend component of a certain cell i is detected
Or the remainder
If the deviation degree of the cell i is abnormal, judging that the cell i is abnormal;
step S4, abnormity detection based on all electric core current data
In the step, trend components of all the battery cores i in the same charging process are acquired based on the battery charging time sequence data D
Array and remainder
Array of numbers, respectively obtaining trend components
Array and remainder
Mean and standard deviation of the series and based on 3
Trend component of criterion for all cells i
And remainder
Making a determination and determining a trend component
Or the remainder
Out of range [ X-3
，X+3
]The cell i is abnormal.
Through the steps S1 to S4, the time sequence of the charging time of the battery in the same set voltage section in the whole service cycle can be preferably collected; through the judgment based on the historical data of the single battery cell, whether the current charging of the single battery cell is abnormal or not can be judged better; by judging based on the current data of all the battery cells under the same charging times, the corresponding normal distribution model can be preferably constructed under the premise of the same loss state, and further whether each battery cell has charging abnormity is judged, so that the method has a lower misjudgment rate.
Preferably, in step S1, data acquisition is performed based on the following steps,
step S11, recording the current voltage and the time stamp of each battery cell i at a set sampling interval T in the battery charging process;
step S12, for any battery cell i, acquiring the starting time stamp of the battery cell i at the starting value of the set voltage segment
And its end time stamp at the set voltage segment end value
Then, then
。
By the above, the charging time can be preferably realized
And (4) obtaining. In terms of difficulty in implementation, it is difficult for the current battery management system or voltage sampling method to capture a specific voltage value, but the current battery management system can realize the function of periodic sampling, so that the charging time is realized by the periodic sampling method
The calculation and acquisition method can be better suitable for the current main flow battery management system, and has the advantage of low implementation difficulty.
Preferably, in step S12, if a time stamp at the start value or the end value of the set voltage segment is not acquired, any time stamp adjacent to the start value or the end value of the set voltage segment is recorded as the start time stamp
Or expiration time stamp
. Therefore, the problem that relevant data are not collected can be solved better.
Preferably, in step S12, when the time stamp at the start value or the end value of the set voltage segment is not acquired, the value of the time stamp is used as the dependent variable and the voltage value of the battery cell is used as the independent variable, and linear fitting is performed on 2 measurement points adjacent to the start value or the end value of the set voltage segment, so as to acquire the start time stamp
Or expiration time stamp
. Therefore, the related data can be acquired more accurately, and the accuracy of the data can be better ensured.
Preferably, in step S3, the charging time-series data is used
The median of the corresponding data is used as the mean value of the GESD algorithm, and the charging time sequence data is adopted
The absolute median difference of the corresponding data is calculated as GESDStandard deviation of the method. So that the robustness thereof can be preferably improved.
Based on any battery charging characteristic abnormality detection method in the invention, the invention also provides a battery charging characteristic abnormality detection device based on time series, which comprises a data acquisition unit, a data processing unit and a data judgment unit, wherein the data acquisition unit is used for realizing the step S1, the data processing unit is used for realizing the step S2, and the data judgment unit is used for realizing the steps S3 and S4. Thus, the battery charging characteristic abnormality detection method of the present invention can be preferably implemented.
The present invention also provides a medium having stored thereon any one of the above-described battery charging characteristic abnormality detection methods based on a time series.
Drawings
Fig. 1 is a schematic flowchart of a battery charging characteristic abnormality detection method in embodiment 1;
fig. 2 is a block diagram schematically showing a battery charging characteristic abnormality detection device in embodiment 1.
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.
Example 1
Referring to fig. 1, the present embodiment provides a method for detecting battery charging characteristic abnormality based on time series, which includes the following steps:
step S1, data acquisition
In the step, the current charging times k of the battery and the charging time of each battery cell i in a set voltage section are collected
And obtaining the cell charging time sequence data of each cell i
，
And acquires battery charging time-series data D of the battery,
；
step S2, data processing
In the step, the charging time of each battery cell i is calculated based on the STL algorithm
Processing is carried out to further acquire trend components
Periodic component of
And remainder
，
；
Step S3, abnormity detection based on single battery cell historical data
In the step, the battery cell charging time sequence data are calculated based on the GESD algorithm
Trend component of (1)
And remainder
If the trend component of a certain cell i is detected
Or the remainder
If the deviation degree of the cell i is abnormal, judging that the cell i is abnormal;
step S4, abnormity detection based on all electric core current data
In the step, trend components of all the battery cores i in the same charging process are acquired based on the battery charging time sequence data D
Array and remainder
Array of numbers, respectively obtaining trend components
Array and remainder
Mean and standard deviation of the series and based on 3
Trend component of criterion for all cells i
And remainder
Making a determination and determining a trend component
Or the remainder
Out of range [ X-3
，X+3
]The cell i is abnormal.
In this embodiment, through steps S1 to S4, the time sequence of the charging time of the battery in the same set voltage segment in the whole service cycle can be preferably collected; through the judgment based on the historical data of the single battery cell, whether the current charging of the single battery cell is abnormal or not can be judged better; by judging based on the current data of all the battery cells under the same charging times, the corresponding normal distribution model can be preferably constructed under the premise of the same loss state, and further whether each battery cell has charging abnormity is judged, so that the method has a lower misjudgment rate.
In this embodiment, compared to the prior art in which the voltage variation value within the set time is used as the determination target, the charging time required to cross the set voltage segment is used as the determination target; this is because, regarding the characteristics of the lithium battery, even if the lithium battery is charged in a constant current manner, the charging characteristic curve of the lithium battery does not show linear change in a primary charging process, so that by using the charging time required to cross a set voltage segment as a determination target, not only the source of the determination target can be more scientific, but also the robustness of the method in the embodiment can be better improved.
The set voltage segment in this embodiment can be set to [3.4V,4.0V ], because the voltage range of the current lithium battery cell is generally 3.0-4.2V, and the interval of [3.4V,4.0V ] is the strongest voltage segment of the lithium battery cell, and by using the interval as the set voltage segment, errors in data acquisition caused by, for example, battery loss can be preferably avoided. This is because the charging time required to be consumed by the lithium battery cell in the strongest voltage range is the longest and stable, and the strongest voltage range is hardly affected by the battery loss, and therefore, the cell can be preferably used as the set voltage range in the present embodiment.
Therefore, the data collected and recorded in step S1 in this embodiment is the time that it takes for each battery cell to reach 4.0V from 3.4V in each charging process.
In this embodiment, each acquisition object (i.e., the time required to be spent) can be better decomposed by using an STL algorithm (secure-Trend decomposition procedure on stress), so as to obtain a Trend component thereof
Periodic component of
And remainder
Then by only aligning trend components
And remainder
Analysis is performed to preferably exclude the periodic component
The influence on the detection result, so that the detection result is more scientific and the detection accuracy can be better improved.
The STL algorithm is used as a relatively mature algorithm, and the scheme in this embodiment is only applied to the STL algorithm, and does not involve the improvement of the STL algorithm, so the specific principle and process of the STL algorithm in this embodiment are not described in detail.
In this embodiment, when determining the charging abnormality of a single battery cell, the determination may be performed according to the historical data and the current data of the single battery cell, specifically, the trend component of the single battery cell may be determined
Time series and remainder of
Are respectively judged based on GESD algorithmWhether the deviation degree is abnormal or not is judged, and if any numerical value is abnormal, the charging abnormality of the corresponding battery cell is judged; based on this, the change rule of the same battery cell in different losses can be preferably used as a judgment basis, so that the charging abnormity detection of a single battery cell can be preferably realized, and a better accuracy rate can be achieved.
In this embodiment, the GESD algorithm is used as a relatively mature algorithm, and the scheme in this embodiment is only applied to the algorithm, and does not involve the improvement of the algorithm, so the specific principle and process of the STL algorithm in this embodiment are not described in detail. It will be appreciated that the GESD algorithm is only one preferred detection method for the solution in the present embodiment, and it can also be detected using the existing S-H-ESD algorithm.
In this embodiment, charging anomaly detection can be performed according to the current data of all the battery cells, and detection errors caused by battery loss can be better avoided by using the data under the same charging times as a data source. It can be understood that the trend components of all the cells are equal to each other under the condition of the same loss
And remainder
Can maintain a preferred normal distribution characteristic, so pass 3
The criterion can preferably determine whether an abnormal value occurs, so that the determination of the charging abnormality can be preferably realized.
By the method in the embodiment, whether the single battery cell is abnormal in the life cycle of the single battery cell can be preferably detected, namely, the trend component of the single battery cell is detected
The time series of the battery cell can be judged, and whether the battery cell has the charge time increase or not in the current charging process can be preferably judgedAbnormal change such as too fast, by the remainder of a single cell
The time series of the battery cell is judged, so that whether the battery cell is abnormal or not in the accumulation process of the use times (namely loss) can be preferably judged; and whether any electric core is abnormal relative to other electric cores can be better detected, so that the abnormal charging judgment can be better realized.
Therefore, the method in the embodiment can better comprehensively consider the loss of the battery, so that whether the battery has abnormal charging can be more accurately judged.
In step S1 of the present embodiment, data collection is performed based on the following steps,
step S11, recording the current voltage and the time stamp of each battery cell i at a set sampling interval T in the battery charging process;
step S12, for any battery cell i, acquiring the starting time stamp of the battery cell i at the starting value of the set voltage segment
And its end time stamp at the set voltage segment end value
Then, then
。
By the above, the charging time can be preferably realized
And (4) obtaining. In terms of difficulty in implementation, it is difficult for the current battery management system or voltage sampling method to capture a specific voltage value, but the current battery management system can implement a periodic sampling function, in this embodiment, the charging time is implemented by a periodic sampling mode
The calculation and acquisition method can be better suitable for the current main flow battery management system, and has the advantage of low implementation difficulty.
In step S12 of this embodiment, when the time stamp at the start value or the end value of the set voltage segment is not acquired, any time stamp adjacent to the start value or the end value of the set voltage segment can be recorded as the start time stamp
Or expiration time stamp
. Therefore, the problem that relevant data are not collected can be solved better. It is worth mentioning that this processing method has the advantages of easy implementation and low cost, but it is difficult to ensure the accuracy of the data.
As a preferred embodiment, in step S12, when the time stamp at the start value or the end value of the set voltage segment is not acquired, the value of the time stamp is used as a dependent variable and the voltage value of the battery cell is used as an independent variable, and linear fitting is performed on 2 measurement points adjacent to the start value or the end value of the set voltage segment, so as to acquire a start time stamp
Or expiration time stamp
. Therefore, the related data can be acquired more accurately, and the accuracy of the data can be better ensured.
The value of the time stamp can be converted into the same unit, such as time, minute or second, so that the data fitting can be preferably facilitated. In this embodiment, the adopted linear fitting model can be y = wx + b, and the values of w and b can be preferably obtained by substituting 2 measurement points adjacent to the start value or the end value of the set voltage segment, and then the corresponding timestamp information can be preferably obtained by substituting the start value or the end value of the set voltage segment.
In step S3 of the present embodiment, the charging time-series data are used
The median of the corresponding data is used as the mean value of the GESD algorithm, and the charging time sequence data is adopted
The absolute median difference of the corresponding data in (b) is taken as the standard deviation of the GESD algorithm. So that the robustness thereof can be preferably improved.
In order to further describe the method in this embodiment, this embodiment takes the data of the 10 th charging of a 60V lithium battery with 12 cells as an example to describe the scheme of the present application. I =1, 2, 3.· 12 in the present case; k = 10. That is, in this embodiment, 12 cells are cell 1 to cell 12 in sequence. The data on the 12 battery cells collected at intervals of 30S (i.e., T =30 in step S11) are as follows.
The upper table is the data obtained in step S11, and the timestamp in the upper table is the current time of the system.
Here, the time stamps of the cells 5 and 10 at the start value (3.4V) are not acquired, and the time stamps of the cells 5 and 12 at the end value (4.0V) are not acquired. Taking the time stamp at the initial value (3.4V) of the electric core 5 as an example, in this case, the linear fitting model is established. The method specifically comprises the following steps:
1. unifying units of time stamps before and after the initial value (3.4V) of the battery cell 5, taking the unified unit as one second(s) as an example, the time stamp recorded before the initial value is 34037s, and the time stamp recorded after the initial value is 34067 s;
2. known quantities (34037 s, 3.37V) and (34037 s, 3.42V) are substituted into the linear fitting model y = wx + b, i.e. w =600, b = 32015;
3. substituting x =3.4v to obtain y =34055 s; that is, the time stamp of the battery cell 5 at the initial value (3.4V) is 34055s, that is, 09: 27: 35.
the collected values can be obtained in turn according to the above.
Then, according to step S12, the charging time of the battery cells 1-12 can be obtained
。
Therefore, in step S1, the battery charging time series data D is the last page
Cell charging time sequence data
I.e. being
，...，
}。
Then, in step S2, the battery charging time series data D and the cell charging time series data are preferably obtained by the STL algorithm
Is decomposed.
Then, in step S3, the cell charging time series data of 12 cells may be individually obtained through the GESD algorithm
Trend component of (1)
And remainder
Separate analyses were performed. The mean value in the GESD algorithm adopts the median of the corresponding number sequence, the standard deviation in the GESD algorithm adopts the absolute median of the corresponding number sequence, and the rest super-parameter settings of the GESD algorithm can be adjusted according to specific expected standards.
Thereafter, based on 3
The criterion can be used to better determine the trend component in the battery charging time series data D
And remainder
Separate analyses were performed.
Through the above, the loss factor of the battery can be combined better, and the abnormal detection of the battery charging can be realized in multiple directions, so that the accuracy is higher.
Compared with the existing method for detecting battery charging abnormity by simply adopting a normal distribution model, the method has the advantages that the normal distribution model has extremely high demand on samples, the method cannot be well applied to abnormity detection in the battery charging process in practice, and the detection accuracy rate is only about 30% -60%; in the embodiment, the accuracy can reach 80-95% due to the means of changing the sample object, improving the detection method and the like.
With reference to fig. 2, the present embodiment further provides a time-series-based battery charging characteristic abnormality detection apparatus, which includes a data acquisition unit, a data processing unit, and a data determination unit, wherein the data acquisition unit is configured to implement step S1, the data processing unit is configured to implement step S2, and the data determination unit is configured to implement steps S3 and S4. So that the method in the present embodiment can be preferably implemented.
The battery charging characteristic abnormality detection apparatus of the present embodiment may further include a storage unit for storing the relevant data.
Based on the method in the present embodiment, the present embodiment also provides a medium on which the time-series-based battery charging characteristic abnormality detection method in the present embodiment is stored.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (7)
1. The battery charging characteristic abnormality detection method based on time series comprises the following steps:
step S1, data acquisition
In the step, the current charging times k of the battery and the charging time of each battery cell i in a set voltage section are collected
And obtaining the cell charging time sequence data of each cell i
，
And acquires battery charging time-series data D of the battery,
；
step S2, data processing
In the step, the charging time of each battery cell i is calculated based on the STL algorithm
Processing is carried out to further acquire trend components
Periodic component of
And remainder
，
；
Step S3, abnormity detection based on single battery cell historical data
In the step, the battery cell charging time sequence data are calculated based on the GESD algorithm
Trend component of (1)
And remainder
If the trend component of a certain cell i is detected
Or the remainder
If the deviation degree of the cell i is abnormal, judging that the cell i is abnormal;
step S4, abnormity detection based on all electric core current data
In the step, trend components of all the battery cores i in the same charging process are acquired based on the battery charging time sequence data D
Array and remainder
Array of numbers, respectively obtaining trend components
Array and remainder
Mean and standard deviation of the series and based on 3
Trend component of criterion for all cells i
And remainder
Making a determination and determining a trend component
Or the remainder
Out of range [ X-3
，X+3
]The cell i is abnormal.
2. The time-series-based battery charging characteristic abnormality detection method according to claim 1, characterized in that: in step S1, data collection is performed based on the following steps,
step S11, recording the current voltage and the time stamp of each battery cell i at a set sampling interval T in the battery charging process;
step S12, for any battery cell i, acquiring the starting time stamp of the battery cell i at the starting value of the set voltage segment
And its end time stamp at the set voltage segment end value
Then, then
。
3. The time-series-based battery charging characteristic abnormality detection method according to claim 2, characterized in that: in step S12, if the time stamp at the start value or the end value of the set voltage segment is not acquired, any time stamp record adjacent to the start value or the end value of the set voltage segment is used as the start time stamp
Or expiration time stamp
。
4. The time-series-based battery charging characteristic abnormality detection method according to claim 2, characterized in that: in step S12, when the time stamp at the start value or the end value of the set voltage segment is not acquired, linear fitting is performed on 2 measurement points adjacent to the start value or the end value of the set voltage segment by using the value of the time stamp as a dependent variable and the voltage value of the battery cell as an independent variable, so as to obtain the start time stamp
Or expiration time stamp
。
5. The time-series-based battery charging characteristic abnormality detection method according to claim 1, characterized in that: in step S3, the charging time-series data are used
The median of the corresponding data is used as the mean value of the GESD algorithm, and the charging time sequence data is adopted
The absolute median difference of the corresponding data in (b) is taken as the standard deviation of the GESD algorithm.
6. A battery charging characteristic abnormality detection device based on time series, characterized in that: for implementing the time-series based battery charging characteristic abnormality detection method according to any one of claims 1 to 5, comprising a data acquisition unit for implementing step S1, a data processing unit for implementing step S2, and a data determination unit for implementing steps S3 and S4.
7. A medium characterized by: a time-series based battery charging characteristic abnormality detection method according to any one of claims 1 to 5, stored thereon.
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