CN117148175A - Method and system for detecting quality of battery cell - Google Patents
Method and system for detecting quality of battery cell Download PDFInfo
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- CN117148175A CN117148175A CN202311143466.2A CN202311143466A CN117148175A CN 117148175 A CN117148175 A CN 117148175A CN 202311143466 A CN202311143466 A CN 202311143466A CN 117148175 A CN117148175 A CN 117148175A
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000001514 detection method Methods 0.000 claims abstract description 49
- 238000010606 normalization Methods 0.000 claims abstract description 6
- 238000000605 extraction Methods 0.000 claims description 11
- 238000007781 pre-processing Methods 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 230000008030 elimination Effects 0.000 claims description 4
- 238000003379 elimination reaction Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 230000002950 deficient Effects 0.000 description 11
- 238000009826 distribution Methods 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 229910052744 lithium Inorganic materials 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 239000010405 anode material Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000010406 cathode material Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 229910021654 trace metal Inorganic materials 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The invention provides an electric quality detection method and system, wherein the method comprises the following steps: acquiring open-circuit voltage data of each cell, and acquiring open-circuit voltage acquisition time; performing missing value rejection operation on the open circuit voltage data to obtain preprocessed open circuit voltage data; extracting the k value and the open-circuit voltage of each battery cell from the preprocessed open-circuit voltage data, and performing standardization and normalization operation to obtain the characteristic data of the k value of the battery cell; and carrying out KNN detection on the characteristic data of the k value of the battery cell so as to obtain and output a battery cell quality detection result. The invention solves the technical problem that the quality of the battery cells in different batches cannot be detected.
Description
Technical Field
The invention relates to the technical field of lithium battery detection, in particular to a method and a system for detecting the quality of a battery cell.
Background
Before delivering the lithium battery, the quality of each battery core needs to be detected, and defective products are screened out. In the production process of the battery cell, particles or trace metal residues on the pole piece, micro defects on the diaphragm, dust introduced in the assembly process of the battery cell and the like can cause micro short circuits in the battery cell. Second, some cells have too high a voltage drop after long-term storage to meet customer requirements. The K value is an irreplaceable value as a project that must be tested before the battery cell leaves the factory. The conventional K value is calculated by dividing the difference obtained by subtracting OCV1 from OCV2 of each cell by the time difference, and the K value is detected using a threshold method. For example, the method of the prior patent application publication No. CN110988715A, namely a method for detecting the self-discharge current of a battery cell, comprises the following steps: 1) Charging and discharging the sample battery, simulating the self-discharging process of the sample battery to obtain a V-Q curve of the sample battery, and differentiating the V-Q curve to obtain a dV/dQ-V curve; 2) Testing the K value of the sample battery under each voltage in the long-term placement process; 3) And calculating the self-discharge current of the battery cell according to the following formula: f (V) =dv/dq=dv/d (t×i) =dv/d (T) ×i=k/I; i=k/F (V). And the prior patent application publication No. CN110444826A, namely the lithium battery formation and capacity division method, comprising the steps of pre-charging, normal temperature ageing, formation, high temperature ageing, K value test, capacity division and separation, wherein Open Circuit Voltage (OCV) detection is carried out in the steps of pre-charging, formation, K value test, capacity division and separation. The foregoing prior art has the following problems. The threshold is fixed in conventional methods based on K-value detection in conventional methods. However, the distribution of K values will also be different due to different anode and cathode materials, electrolyte types and storage conditions, so that the use of the fixed threshold method cannot effectively screen defective products of the cells in different batches.
In summary, the prior art has the technical problem that quality detection cannot be performed on different batches of battery cells.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: how to solve the technical problem that the quality detection of the battery cells in different batches can not be performed in the prior art.
The invention adopts the following technical scheme to solve the technical problems: the battery cell quality detection method comprises the following steps:
s1, acquiring open-circuit voltage data of each cell, and acquiring open-circuit voltage acquisition time;
s2, performing missing value elimination operation on the open-circuit voltage data to obtain preprocessed open-circuit voltage data;
s3, extracting the k value and the open-circuit voltage of each battery cell from the preprocessed open-circuit voltage data, and performing standardization and normalization operation to obtain the characteristic data of the k value of the battery cell;
s4, KNN detection is carried out on the characteristic data of the k value of the battery cell, so that a battery cell quality detection result is obtained and output.
According to the invention, abnormal point detection is carried out on the K value-OCV 3 of each batch of cells, so that defective products of the cells in different batches are effectively screened out. The invention discards the fixed threshold method in the prior art, and the defective product detection is carried out by using the dynamic K value, so that the defective product screening can be effectively carried out on the battery cells in different batches.
In a more specific technical solution, step S2 includes:
s21, acquiring a missing value in open-circuit voltage data in the current batch of battery cells, and outputting alarm information according to the missing value;
s22, eliminating the open circuit voltage data and the open circuit voltage acquisition time of the batch according to the alarm information.
In a more specific technical solution, step S3 includes:
s31, extracting a k value of each cell and a third open circuit voltage OCV3;
s32, carrying out standardization operation and normalization processing on the k value and the third open-circuit voltage OCV3 to obtain the characteristic data of the k value of the battery cell.
In a more specific embodiment, in step S31, the k value is extracted using the following logic:
in a more specific technical scheme, in step S4, KNN detection is performed on the cell k value characteristic data by using the following logic:
K＝[k 1 ,k 2 ,...,k m ],O＝[OCV3 1 ,OCV3 2 ,...,OCV3 m ]
wherein m represents m cells in total, K represents K value sets of m cells, and O represents OCV3 set of m cells.
According to the invention, the K value and the OCV3 characteristic of the battery core are extracted, and the KNN detection algorithm is used, so that batch battery core quality detection with different numerical distributions can be effectively performed.
In a more specific technical solution, step S4 further includes:
s41, calculating the characteristic distance between the battery cells;
s42, finding out at least 2 adjacent cells with the current cell according to the characteristic distance;
s43, calculating the average characteristic distance of the adjacent battery cells;
s44, selecting the abnormal battery cells according to the average characteristic distance by utilizing a preset threshold value.
In a more specific technical scheme, in step S41, the following logic is used to calculate the characteristic distance between the cells;
d i ＝[d i1 ,d i2 ,...,d im ]
wherein d i Represents the characteristic distance set of the ith battery cell and other battery cells, d ij And the characteristic distance between the ith battery cell and the jth battery cell is represented.
In a more specific technical scheme, in step S42, the following logic is used to find out the neighboring cells;
wherein d i，k Representing the characteristic distance from the nearest cell to the ith cell.
In a more specific technical scheme, in step S43, the average feature distance of the neighboring cells is calculated using the following logic;
where D represents the average characteristic distance of each cell from the nearest 50 cells.
In a more specific aspect, a system for detecting cell quality includes:
the data acquisition module is used for acquiring open-circuit voltage data of each cell and acquiring open-circuit voltage acquisition time;
the data preprocessing module is used for carrying out missing value elimination operation on the open-circuit voltage data so as to obtain preprocessed open-circuit voltage data, and is connected with the data acquisition module;
the characteristic extraction module is used for extracting the k value and the open-circuit voltage of each battery cell from the preprocessed open-circuit voltage data, and performing standardized and normalized operation to obtain the characteristic data of the k value of the battery cell, and is connected with the data preprocessing module;
and the KNN detection and output module is used for carrying out KNN detection on the k value characteristic data of the battery cell so as to obtain and output a battery cell quality detection result, and is connected with the characteristic extraction module.
Compared with the prior art, the invention has the following advantages: according to the invention, abnormal point detection is carried out on the K value-OCV 3 of each batch of cells, so that defective products of the cells in different batches are effectively screened out. The invention discards the fixed threshold method in the prior art, and the defective product detection is carried out by using the dynamic K value, so that the defective product screening can be effectively carried out on the battery cells in different batches.
According to the invention, the K value and the OCV3 characteristic of the battery core are extracted, and the KNN detection algorithm is used, so that batch battery core quality detection with different numerical distributions can be effectively performed.
The invention solves the technical problem that the quality detection of the battery cells in different batches cannot be carried out in the prior art.
Drawings
Fig. 1 is a schematic connection diagram of basic modules of a battery cell quality detection system according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram showing basic steps of a method for detecting quality of a battery cell according to embodiment 2 of the present invention;
fig. 3 is a schematic diagram of specific steps of the KNN detection algorithm in embodiment 3 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the system for detecting the quality of the battery cell provided by the invention comprises the following basic modules:
the device comprises a data acquisition module 1, a data preprocessing module 2, a characteristic extraction module 3, a KNN detection module 4 and a prediction output module 5.
In this embodiment, the data acquisition module 1 is connected with the data preprocessing module 2, the data preprocessing module 2 is connected with the feature extraction module 3, the feature extraction module 3 is connected with the KNN detection module 4, and the KNN detection module 4 is connected with the prediction output module 5.
Example 2
As shown in fig. 2, the method for detecting the quality of the battery cell provided by the invention comprises the following basic steps:
s1, data acquisition;
s2, eliminating missing values;
s3, extracting features;
s4, KNN detection;
s5, outputting a detection result.
In this embodiment, the data acquisition module 1 is utilized to acquire the first open circuit voltage OCV1, the second open circuit voltage OCV2, the third open circuit voltage OCV3 and the acquisition time T1, T2, T3 of each batch of battery cells.
In this embodiment, each cell is placed at a time T1 and a first open circuit voltage OCV1 is acquired, each cell is placed at a time T2 and a second open circuit voltage OCV2 is acquired, and each cell is placed at a time T3 and a third open circuit voltage OCV3 is acquired.
In this embodiment, the data preprocessing module 2 is utilized to preprocess the missing value in the data collected by the data collecting module 1, so as to ensure the quality of the battery cells from the factory, the missing value data does not adopt a data filling method, but outputs early warning information, and the early warning information is removed from the batch of data.
In this embodiment, the k value and OCV3 value of each cell are extracted by the feature extraction module 3, and standard normalization processing is performed. The k value extraction method is shown as the following formula:
in the present embodiment, the KNN detection module 4 is used: the features extracted by the detection feature extraction module 3:
K＝[k 1 ,k 2 ,...,k m ],O＝[OCV3 1 ,OCV3 2 ,...,OCV3 m ]
wherein m represents m cells in total, K represents K value sets of m cells, and O represents OCV3 sets of m cells.
As shown in fig. 3, in the present embodiment, the KNN detection algorithm in the foregoing step S4 includes the following specific steps:
s41, calculating the characteristic distance between each battery cell and other battery cells;
d i ＝[d i1 ,d i2 ,...,d im ]
wherein d i Represents the characteristic distance set of the ith battery cell and other battery cells, d ij And the characteristic distance calculation formula of the ith battery cell and the jth battery cell is shown.
S42, finding out 50 nearest electric cores of each electric core;
wherein d i，k Representing the characteristic distance from the nearest cell to the ith cell.
S43, calculating the average characteristic distance of the nearest 50 cells of each cell;
where D represents the average characteristic distance of each cell from the nearest 50 cells.
S44, selecting the abnormal battery cells in the D by setting a threshold value or a proportion.
In the embodiment, a threshold is set, and all data subscripts larger than the threshold in the D are screened out; setting a proportion, sorting the data in the D from small to large, and screening out the subscript of the larger data with the set proportion.
In this embodiment, in the prediction output module 5, the subscript detected by the KNN detection module 4 is restored to the corresponding cell code output.
In conclusion, abnormal point detection is carried out on the K value-OCV 3 of each batch of battery cells, and defective products of battery cells in different batches are effectively screened out. The invention discards the fixed threshold method in the prior art, and the defective product detection is carried out by using the dynamic K value, so that the defective product screening can be effectively carried out on the battery cells in different batches.
According to the invention, the K value and the OCV3 characteristic of the battery core are extracted, and the KNN detection algorithm is used, so that batch battery core quality detection with different numerical distributions can be effectively performed.
The invention solves the technical problem that the quality detection of the battery cells in different batches cannot be carried out in the prior art.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The battery cell quality detection method is characterized by comprising the following steps:
s1, acquiring open-circuit voltage data of each cell, and acquiring open-circuit voltage acquisition time;
s2, performing missing value elimination operation on the open-circuit voltage data to obtain preprocessed open-circuit voltage data;
s3, extracting the k value and the open-circuit voltage of each battery cell from the preprocessed open-circuit voltage data, and performing standardization and normalization operation to obtain battery cell k value characteristic data;
s4, KNN detection is carried out on the characteristic data of the k value of the battery cell, so that a battery cell quality detection result is obtained and output.
2. The method according to claim 1, wherein the step S2 includes:
s21, acquiring a missing value in the open-circuit voltage data in the current batch of the battery cells, and outputting alarm information according to the missing value;
s22, eliminating the open circuit voltage data and the open circuit voltage acquisition time of the batch according to the alarm information.
3. The method according to claim 1, wherein the step S3 includes:
s31, extracting the k value and a third open circuit voltage OCV3 of each battery cell;
and S32, carrying out standardization operation and normalization processing on the k value and the third open-circuit voltage OCV3 to obtain the characteristic data of the k value of the battery cell.
4. The method according to claim 4, wherein in the step S31, the k value is extracted by using the following logic:
5. the method according to claim 1, wherein in the step S4, KNN detection is performed on the characteristic data of the k value of the cell by using the following logic:
K＝[k 1 ,k 2 ,...,k m ],O＝[OCV3 1 ,OCV3 2 ,...,OCV3 m ]
wherein m represents m cells in total, K represents K value sets of m cells, and O represents OCV3 set of m cells.
6. The method according to claim 1, wherein the step S4 further comprises:
s41, calculating the characteristic distance between the battery cells;
s42, finding out at least 2 adjacent cells with the current cell according to the characteristic distance;
s43, calculating the average characteristic distance of the adjacent battery cells;
s44, selecting the abnormal battery cells according to the average characteristic distance by utilizing a preset threshold value.
7. The method according to claim 6, wherein in the step S41, the characteristic distance between the cells is calculated by using the following logic;
d i ＝[d i1 ,d i2 ,...,d im ]
wherein d i Represents the characteristic distance set of the ith battery cell and other battery cells, d ij And the characteristic distance between the ith battery cell and the jth battery cell is represented.
8. The method according to claim 6, wherein in the step S42, the adjacent cells are found by using the following logic;
wherein d i，k Representing the characteristic distance from the nearest cell to the ith cell.
9. The method according to claim 6, wherein in the step S43, the average characteristic distance of the neighboring cells is calculated by using the following logic;
where D represents the average characteristic distance of each cell from the nearest 50 cells.
10. Cell quality detection system, characterized in that it comprises:
the data acquisition module is used for acquiring open-circuit voltage data of each cell and acquiring open-circuit voltage acquisition time;
the data preprocessing module is used for carrying out missing value elimination operation on the open-circuit voltage data so as to obtain preprocessed open-circuit voltage data, and the data preprocessing module is connected with the data acquisition module;
the characteristic extraction module is used for extracting the k value and the open-circuit voltage of each battery cell from the preprocessed open-circuit voltage data, and performing standardized and normalized operation to obtain battery cell k value characteristic data, and the characteristic extraction module is connected with the data preprocessing module;
and the KNN detection and output module is used for carrying out KNN detection on the k value characteristic data of the battery cell so as to obtain and output a battery cell quality detection result, and the KNN detection and output module is connected with the characteristic extraction module.
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