CN115248393A - Battery consistency sorting method, device, equipment and storage medium - Google Patents
Battery consistency sorting method, device, equipment and storage medium Download PDFInfo
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- CN115248393A CN115248393A CN202210567787.4A CN202210567787A CN115248393A CN 115248393 A CN115248393 A CN 115248393A CN 202210567787 A CN202210567787 A CN 202210567787A CN 115248393 A CN115248393 A CN 115248393A
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
- B07C5/344—Sorting according to other particular properties according to electric or electromagnetic properties
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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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention belongs to the technical field of batteries, and discloses a battery consistency sorting method, a device, equipment and a storage medium. The method comprises the following steps: carrying out charge and discharge test, battery internal resistance measurement and open-circuit voltage measurement on a plurality of single batteries to obtain a plurality of sorting variables, voltage curves and energy curves corresponding to the single batteries; carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery and a second battery; and sorting the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result. Through the mode, the overall performance of the battery is considered, the sorting accuracy is improved, sorting is performed by utilizing a plurality of sorting variables, and dynamic sorting is performed by utilizing the voltage curve and the energy curve of the battery, so that the energy utilization rate of the battery is maximized, and the cycle service life of the lithium battery is prolonged to the maximum extent.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery consistency sorting method, device, equipment and storage medium.
Background
The battery consistency is an important factor influencing the safety, the service life, the capacity attenuation and the like of the electric automobile, the larger the difference between the initial parameters of the single batteries in the module and the parameters in the using process is, the larger the number of the batteries is, and the more serious the consistency problem is. The current battery consistency sorting method mostly sorts the single batteries by using parameters such as voltage, capacity and internal resistance, and then dynamically sorts the single batteries by using a voltage curve, but the method cannot reflect performance parameters such as current and capacity, does not consider the influence of the sorted single batteries on the consistency of the battery pack in a series-parallel connection mode, and has the defects of low battery energy utilization rate, short cycle service life of the lithium battery and the like.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a battery consistency sorting method, a device, equipment and a storage medium, and aims to solve the technical problems of low battery energy utilization rate, short cycle service life of a lithium battery and the like in the conventional battery consistency sorting method.
To achieve the above object, the present invention provides a method for consistently sorting batteries, the method comprising the steps of:
carrying out charge and discharge test, battery internal resistance measurement and open-circuit voltage measurement on a plurality of single batteries to obtain a plurality of sorting variables, voltage curves and energy curves corresponding to the single batteries;
performing multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery and a second battery;
and sorting the second battery according to the voltage curve and the energy curve to obtain a battery sorting result.
Optionally, the plurality of sorting variables includes at least a battery capacity, a charge voltage, a discharge voltage, a difference between a charge energy and a discharge energy, a charge internal resistance, and a discharge internal resistance.
Optionally, the multi-parameter sorting the plurality of single batteries according to the plurality of sorting variables to obtain a multi-parameter sorting result includes:
calculating a matrix of correlation coefficients between the plurality of sorting variables;
solving an eigenvalue according to the correlation coefficient matrix, and determining a common factor number and a common factor matched with the common factor number according to the eigenvalue;
determining principal component contribution rates corresponding to the common factors;
determining variable coefficients corresponding to the common factors according to the principal component contribution rate and the characteristic value;
determining corresponding factor variable scores according to the variable coefficients and the sorting variables;
and carrying out multi-parameter sorting on the plurality of single batteries according to the factor variable scores to obtain multi-parameter sorting results.
Optionally, the multi-parameter sorting the plurality of single batteries according to the factor variable scores to obtain a multi-parameter sorting result includes:
calculating the corresponding squared Euclidean distance according to the factor variable scores corresponding to any two single batteries;
and performing inter-group connection clustering on the plurality of single batteries based on the squared Euclidean distance to obtain a multi-parameter sorting result.
Optionally, the sorting the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result includes:
respectively carrying out normalization processing on data on a voltage curve and an energy curve corresponding to the second battery to obtain a target voltage curve and a target energy curve;
splicing the target voltage curve and the target energy curve to obtain a reference curve corresponding to each second battery;
calculating a distance average value corresponding to the target voltage curve and the target energy curve;
determining a frequency distribution histogram and a frequency distribution curve according to the distance mean value;
determining a corresponding cluster K value based on the frequency distribution histogram and the frequency distribution curve;
selecting K clustering center curves from the reference curves corresponding to the second batteries according to the clustering K values;
and clustering the residual reference curves based on the K clustering center curves to obtain a battery sorting result.
Optionally, the clustering a plurality of remaining reference curves based on the K clustering center curves to obtain a battery sorting result includes:
calculating the average distance between a target reference curve and the K clustering center curves respectively, wherein the target reference curve is any one of a plurality of residual reference curves;
selecting a clustering center curve with the minimum average distance from the target reference curve as a clustering center curve to which the target reference curve belongs;
calculating the average Euclidean deviation corresponding to the battery class to which each clustering center curve belongs;
and judging whether the average Euclidean deviation reaches a preset threshold value, if not, continuing iteration until the current iteration number reaches the maximum iteration number or the average Euclidean deviation corresponding to each battery reaches the preset threshold value, and obtaining a battery sorting result.
Optionally, before performing the charge and discharge test, the battery internal resistance measurement, and the open-circuit voltage measurement on the plurality of single batteries to obtain a plurality of sorting variables, voltage curves, and energy curves corresponding to the single batteries, the method further includes:
and carrying out standard static inspection on the plurality of batteries to be sorted, and screening out the batteries which do not meet the preset static requirements to obtain a plurality of single batteries.
In addition, in order to achieve the above object, the present invention further provides a battery consistency sorting apparatus, including:
the testing module is used for carrying out charge and discharge testing, battery internal resistance measuring and open-circuit voltage measuring on a plurality of single batteries to obtain a plurality of sorting variables, voltage curves and energy curves corresponding to the single batteries;
the multi-parameter sorting module is used for carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery type and a second battery type;
and the dynamic sorting module is used for sorting the second battery according to the voltage curve and the energy curve to obtain a battery sorting result.
Further, to achieve the above object, the present invention also provides a battery consistency sorting apparatus including: a memory, a processor, and a battery consistent sorting program stored on the memory and executable on the processor, the battery consistent sorting program configured to implement a battery consistent sorting method as described above.
Furthermore, to achieve the above object, the present invention also proposes a storage medium having a battery consistency sorting program stored thereon, which when executed by a processor implements the battery consistency sorting method as described above.
The method comprises the steps of carrying out charge-discharge test, battery internal resistance measurement and open-circuit voltage measurement on a plurality of single batteries to obtain a plurality of sorting variables, voltage curves and energy curves corresponding to the single batteries; carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into first batteries and second batteries; and sorting the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result. Through the mode, the overall performance of the battery is considered, the sorting accuracy is improved, sorting is performed by utilizing a plurality of sorting variables, and dynamic sorting is performed by utilizing the voltage curve and the energy curve of the battery, so that the energy utilization rate of the battery is maximized, and the cycle service life of the lithium battery is prolonged to the maximum extent.
Drawings
FIG. 1 is a schematic diagram of a battery consistency sorting facility in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a consistent sorting method for batteries according to the present invention;
FIG. 3 is a schematic diagram of a curve of any single cell in the method for consistently sorting cells according to the present invention;
FIG. 4 is a schematic flow chart of a battery uniformity sorting method according to a second embodiment of the present invention;
FIG. 5 is a schematic flow chart of a battery uniformity sorting method according to a third embodiment of the present invention;
FIG. 6 is a frequency distribution histogram and a frequency distribution curve of an embodiment of the method for consistent sorting of batteries according to the present invention;
fig. 7 is a block diagram showing the structure of the first embodiment of the battery consistency sorting apparatus according to the invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a battery consistency sorting apparatus according to a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the battery uniformity sorting apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of a battery uniformity sorting apparatus, and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a battery-consistency sorting program.
In the battery consistency sorting apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the battery consistency sorting apparatus according to the present invention may be provided in the battery consistency sorting apparatus, and the battery consistency sorting apparatus calls the battery consistency sorting program stored in the memory 1005 through the processor 1001 and executes the battery consistency sorting method provided by the embodiment of the present invention.
An embodiment of the present invention provides a method for sorting battery consistency, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the method for sorting battery consistency according to the present invention.
In this embodiment, the battery consistency sorting method includes the following steps:
step S10: and carrying out charge and discharge test, battery internal resistance measurement and open-circuit voltage measurement on the single batteries to obtain a plurality of sorting variables, voltage curves and energy curves corresponding to the single batteries.
It should be understood that the execution subject of this embodiment is a battery consistency sorting device, and the battery consistency sorting device may be a computer, a terminal, or the like that loads software required by the test, and may also be other devices that have functions of test control and data processing, which is not limited in this embodiment.
It should be noted that, in this embodiment, a battery comprehensive test system, a programmable constant temperature and humidity test chamber, an upper computer loaded with software required for the test, and an experimental platform built by the single lithium batteries are provided to perform charge and discharge tests, an internal resistance tester performs measurement of internal resistance and open-circuit voltage of the batteries, records sorting variables corresponding to the single batteries, and records voltages and energies corresponding to different time points to generate a voltage curve and an energy curve. Referring to fig. 3, fig. 3 is a schematic diagram of a curve of any single battery in the battery consistency sorting method of the present invention, a voltage curve during constant voltage charging cannot show the variation trend of the battery energy and capacity, and an energy curve during resting cannot show the battery voltage variation, so that the sorting effect is not good when the energy curve is considered alone or the voltage curve is considered alone.
Specifically, in order to avoid the influence of temperature on parameters in the battery charging and discharging process, in the charging and discharging test in this embodiment, in a constant temperature state, constant-current charging, constant-voltage charging, and constant-current discharging are sequentially performed on a plurality of single batteries, and battery internal resistance measurement and open-circuit voltage measurement are performed on the plurality of single batteries, so as to obtain battery capacity, charging voltage, discharging voltage, a difference between charging energy and discharging energy, charging internal resistance, discharging internal resistance, a voltage curve, and an energy curve corresponding to each single battery.
It should be understood that the unit cell is sequentially subjected to constant current charging, constant voltage charging and constant current discharging at T deg.c, where T is a fixed temperature set in advance, specifically, an optimum temperature determined by experiments may be used, and a room temperature of 25 deg.c may be used. The open-circuit voltage measurement refers to the voltage value after the battery is fully charged and the standing time d is more than or equal to 30 min.
Specifically, the plurality of sorting variables includes at least a battery capacity, a charging voltage, a discharging voltage, a difference between a charging energy and a discharging energy, a charging internal resistance, and a discharging internal resistance.
It should be noted that the capacity of the battery pack is lower than the minimum capacity of the single batteries, and therefore, the capacity of the battery pack of the present embodiment is determined by the current dischargeable minimum electric quantity and the chargeable minimum electric quantity of all the single batteries. The internal resistance of the battery directly influences the external parameter difference of the battery, the internal resistance of each monomer can reflect the consistency among the monomers in the cyclic charge-discharge process, the more concentrated the internal resistance distribution of all the monomers is, the better the consistency is, and the more dispersed the internal resistance distribution of all the monomers is, the worse the consistency is. Considering that the internal resistance of the battery and the internal resistance of the connecting piece consume part of electric energy in the charging and discharging processes of the battery, and the electrochemical polarization and concentration polarization of Li + insertion and extraction also cause part of energy loss, therefore, the difference of the battery is described by adopting the difference between charging energy and discharging energy in the embodiment, the chemical polarization and concentration polarization in the general testing process are replaced, and the classification accuracy of the battery is improved. In order to prevent the battery from being overcharged and overdischarged, the whole battery pack stops charging when the highest single battery voltage reaches the charging cut-off voltage in the charging process; and in the discharging process, stopping discharging when the lowest single battery voltage reaches the discharging cut-off voltage.
Further, before the step S10, the method further includes: and carrying out standard static inspection on the plurality of batteries to be sorted, and screening out the batteries which do not meet the preset static requirements to obtain a plurality of single batteries.
It should be noted that the preset static requirements at least include: the appearance has no deformation and crack, the surface is smooth and dry, no external injury and no pollutant and the like, and the mark is clear and correct; the positive and negative electrode marks are clear; no leakage. In specific implementation, static inspection can be performed in an image acquisition and target identification mode, and defect identification is performed on the acquired image through a neural network model, so that whether each battery to be sorted meets preset static requirements or not is determined.
Step S20: and carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into first batteries and second batteries.
It should be understood that, in the present embodiment, a plurality of single batteries are tested, and according to the expression of the battery inconsistency, 6 parameters of the battery capacity, the charging voltage, the discharging voltage, the difference between the charging energy and the discharging energy, the internal charging resistance and the internal discharging resistance are determined as the sorting indexes of the multiple parameters. In specific implementation, a charge-discharge test is performed on 100 segments of 2Ah lithium batteries, the battery parameters are shown in table 1, and the specific test process is as follows: discharging to 3V at 2A constant current; (2) standing for 1h; (3) charging to 4.2V at a constant current and a constant voltage of 2A; (4) standing for 1h; (5) Repeating the step 1 time, monitoring and recording the energy, voltage, capacity and other data of each battery, and obtaining a test data table shown in table 2.
Table 1:
| parameter(s) | Numerical value |
| Nominal capacity | 2Ah |
| Nominal voltage | 3.7V |
| Cut-off voltage of charging | 4.2V |
| Discharge cut-off voltage | 3V |
| Cutoff current of discharge | 0.1A |
| Temperature of use | -10℃～45℃ |
In table 2, the battery capacity is represented by Q; the charging and discharging voltage is the voltage value of the battery when the battery is fully charged and discharged under the standard charging and discharging condition, and the charging voltage is U 1 U for indicating and discharging voltage 2 Represents; the energy difference is the difference between the charging energy and the discharging energy of the battery under the standard charging and discharging conditions at 25 ℃, and is represented by E; the internal charging and discharging resistance is the internal resistance of the battery when the battery is fully charged and discharged under the standard charging and discharging conditions, and R is used for the internal charging resistance 1 R for indicating and discharging internal resistance 2 And (4) showing.
Table 2:
| numbering | Q | U 1 | U 2 | E | R 1 | R 2 |
| 1 | 1.6629 | 4.15556 | 3.48674 | 1.1281 | 49.00 | 45.75 |
| 2 | 1.7287 | 4.15728 | 3.46043 | 1.2328 | 42.71 | 38.77 |
| 3 | 1.7825 | 4.15539 | 3.43394 | 1.2493 | 37.99 | 34.00 |
| 4 | 1.6408 | 4.15797 | 3.46039 | 1.2955 | 45.07 | 42.20 |
| 5 | 1.6985 | 4.15793 | 3.45068 | 1.2375 | 48.80 | 42.45 |
| … | … | … | … | … | … | … |
| 96 | 1.6767 | 4.15949 | 3.47249 | 1.2763 | 46.44 | 42.55 |
| 97 | 1.6173 | 4.15736 | 3.47696 | 1.1711 | 48.90 | 45.05 |
| 98 | 1.6681 | 4.16010 | 3.50326 | 1.2248 | 47.87 | 44.41 |
| 99 | 1.5552 | 4.15755 | 3.52076 | 1.3722 | 45.63 | 42.16 |
| 100 | 1.6036 | 4.14769 | 3.46908 | 1.1796 | 40.61 | 41.78 |
It should be noted that, in this embodiment, 6 parameters of the battery capacity, the charging voltage, the discharging voltage, the difference between the charging energy and the discharging energy, the internal charging resistance, and the internal discharging resistance are used as multi-parameter sorting indexes, sorting variables are simplified in a factor analysis manner, clustering is performed in an inter-group connection clustering manner, sorting is performed by using a squared euclidean distance as a measurement standard, multi-parameter sorting is implemented, and a plurality of single batteries are sorted into a first type battery and a second type battery.
Step S30: and sorting the second battery according to the voltage curve and the energy curve to obtain a battery sorting result.
It should be understood that, in the existing dynamic sorting method, a voltage curve is generally used for sorting, and performance parameters such as current and capacitance cannot be reflected, referring to fig. 3, the voltage curve cannot show the variation trend of battery energy and capacity during constant voltage charging, and the energy curve cannot show the variation condition of battery voltage during resting, so that the sorting effect is not good when the energy curve is considered alone or the voltage curve is considered alone. In the embodiment, the voltage curve and the energy curve are considered at the same time for sorting, the influence of the battery series-parallel connection mode on the consistency of the battery pack is analyzed, specifically, clustering is performed based on the voltage curve and the energy curve corresponding to each single battery based on a clustering algorithm, a plurality of battery types are determined, and a battery sorting result is obtained.
Further, after the step S30, the method further includes: when voltage requirements and/or energy requirements are/is acquired, selecting a plurality of proper single batteries and corresponding connection modes according to the battery sorting result; and forming a target battery pack according to the plurality of single batteries and the corresponding connection modes.
It should be noted that, according to the requirement of the user for the voltage and/or energy of the battery pack, a plurality of suitable single batteries and a series-parallel connection mode are selected, and the plurality of single batteries are connected together according to the corresponding series-parallel connection mode to form the target battery pack. The battery sorting scene related in the embodiment further comprises a retired battery sorting scene, and particularly, after the retired battery is subjected to quality detection and state evaluation, a battery pack is formed according to user requirements and is put into use, secondary utilization of the retired battery is achieved, the value of the battery is brought into play to the greatest extent, and the cost of energy storage products is reduced.
In the embodiment, a plurality of separation variables, voltage curves and energy curves corresponding to the single batteries are obtained by performing charge and discharge tests, battery internal resistance measurements and open-circuit voltage measurements on the single batteries; carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery and a second battery; and sorting the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result. Through the mode, the overall performance of the battery is considered, the sorting accuracy is improved, sorting is performed by utilizing a plurality of sorting variables, and dynamic sorting is performed by utilizing the voltage curve and the energy curve of the battery, so that the energy utilization rate of the battery is maximized, and the cycle service life of the lithium battery is prolonged to the maximum extent.
Referring to fig. 4, fig. 4 is a schematic flow chart of a battery uniformity sorting method according to a second embodiment of the present invention.
Based on the first embodiment, the step S20 of the method for consistently sorting batteries according to this embodiment includes:
step S201: a matrix of correlation coefficients between the plurality of sorting variables is calculated.
In a specific implementation, since the basic attributes of the sorting parameters are different, the present embodiment performs a standardized calculation on each sorting variable according to formula (1):
wherein f is 1 ,f 2 ,…,f l (l ≦ m) is a common factor for each component of X, each common factor f i The mean value is 0, the variance is 1, and the mean value and the variance are independent of each other; epsilon i Is x i Specific factor of (2), only for x i And (4) acting. Each epsilon i Mean value of 0 and each ε i Are independent of each other. Common factor is independent of specific factor, X mean is 0, covariance matrix sigma (= (sigma) ij ) m×m Matrix a = (a) ij ) Referred to as a factor load matrix. Element a in A ij Is referred to as x i Variance of (f) is i And (3) load on the workpiece.
It should be understood that assume there are p raw battery samples, x 1 ,x 2 ,…,x m For original sorting variables, z 1 ,z 2 ,…,z l Is a new sort variable (principal component), at which time m>l, calculating a correlation coefficient matrix between the original sorting variables according to the formulas (2) and (3):
wherein the content of the first and second substances,
and
the average values, x, of the raw data of the ith and jth sort variables, respectively ki 、x kj Raw data for the ith and jth sort variables, respectively, for the kth sample.
It should be noted that whether the correlation coefficient in the correlation coefficient matrix is greater than 0.3 and whether the number of coefficients greater than 0.3 exceeds a certain threshold is judged, and Bartlett and KMO tests show that each sorting variable meets factor analysis conditions.
Step S202: and solving an eigenvalue according to the correlation coefficient matrix, and determining the common factor number and a common factor matched with the common factor number according to the eigenvalue.
It should be understood that let λ be assumed 1 ,λ 2 ,…,λ m Is a characteristic value, eta, of a matrix of correlation coefficients 1 ,η 2 ,…,η m Determining the number of common factors for corresponding orthonormal eigenvectors according to the eigenvalue greater than 1, solving the orthonormal eigenvectors corresponding to the common factors, and then expressing the factor load matrix of the principal component analysis of the sample correlation matrix by a formula (4) as follows:
wherein eta is j Is in the sense of trace f j The variance contribution to X is determined in particular by equation (5):
wherein, g j The sum of squares of the elements in the jth column of a is determined by equation (6):
in a specific implementation, the factor load matrix is orthogonally rotated to obtain a matrix C 1 = CD variance maximum, where D is the orthogonal matrix.
Step S203: and determining the principal component contribution rate corresponding to each common factor.
Step S204: and determining variable coefficients corresponding to the common factors according to the principal component contribution rate and the characteristic value.
Step S205: and determining corresponding factor variable scores according to the variable coefficients and the sorting variables.
It should be noted that, factor analysis is performed by principal component analysis, and the principal component contribution rate corresponding to each common factor is determined. Determining the coefficient of each common factor represented by the original variable based on the principal component contribution rate and the characteristic value to obtain a variable coefficient, and calculating the weighted sum based on the variable coefficient and the sorting variables to obtain the factor variable score corresponding to each single battery.
Step S206: and carrying out multi-parameter sorting on the single batteries according to the factor variable scores to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery and a second battery.
It should be understood that the plurality of single batteries are clustered according to the factor variable scores, and the plurality of single batteries are uniformly classified into a first type battery and a second type battery.
Specifically, the step S206 includes: calculating a corresponding squared Euclidean distance according to the factor variable scores corresponding to any two single batteries; and performing inter-group connection clustering on the plurality of single batteries based on the squared Euclidean distance to obtain a multi-parameter sorting result.
It should be noted that, by using an inter-group connection clustering manner, a plurality of single batteries are classified by using a squared euclidean distance as a metric, and specifically, the squared euclidean distance between factor variable scores corresponding to any two single batteries is calculated by formula (7):
wherein x is i And y i And respectively representing the factor variable scores of two single battery samples, wherein i and k represent the number of variables.
In specific implementation, data of 6 sorting variables of 100 lithium batteries are used as input, a correlation coefficient matrix is calculated and is shown in table 3, and the 6 variables have large correlation.
Table 3:
| sorting variables | Q | U 1 | U 2 | E | R 1 | R 2 |
| Q | 1.000 | 0.109 | 0.625 | -0.274 | -0.542 | -0.557 |
| U 1 | 0.109 | 1.000 | -0.171 | 0.117 | -0.111 | -0.135 |
| U 2 | -0.625 | -0.171 | 1.000 | -0.075 | 0.773 | 0.747 |
| E | -0.274 | 0.117 | -0.75 | 1.000 | -0.149 | -0.175 |
| R 1 | -0.542 | -0.111 | 0.773 | -0.149 | 1.000 | 0.941 |
| R 2 | -0.557 | -0.135 | 0.747 | -0.175 | 0.941 | 1.000 |
In a specific implementation, as shown in table 4, before performing the factor analysis, the correlation coefficient matrix is fully detected, which is to mainly check the correlation between the variables in the correlation coefficient matrix, that is, check whether each variable is independent, in this embodiment, KMO =0.711>, 0.500, sig =0.000 < 0.050, and satisfy the condition of the factor analysis.
Table 4:
factor analysis was performed by principal component analysis, and referring to Table 5, table 5 shows the principal component contribution ratio of this example, wherein F 1 、F 2 Is greater than 1, and therefore F is selected 1 、F 2 These two common factors serve as new variables representing the original sort variables.
Table 5:
| factor(s) | Characteristic value | Contribution rate/%) | Cumulative contribution rate/%) |
| F1 | 3.149 | 52.483 | 52.483 |
| F2 | 1.223 | 20.375 | 72.858 |
| F3 | 0.945 | 15.756 | 88.614 |
| F4 | 0.366 | 6.095 | 94.709 |
| F5 | 0.262 | 4.360 | 99.069 |
| F6 | 0.056 | 0.931 | 100.000 |
Referring to table 6, each column of data in table 6 identifies the coefficients whose common factor is represented by the original variable.
Table 6:
in this embodiment, after the factor analysis, 6 sorting variables are converted into 2 sorting variables, and these two common factors can express most of the information of the original sorting variables, where:
F1＝-0.295Q-0.020U1+0.282U2-0.097E+0.280R1+0.276R2；
F2＝-0.341Q+0.301U1-0.037U2+0.748E-0.126R1-0.144R2。
referring to table 7, table 7 is a sorting result example table of this example.
Table 7:
in the concrete implementation, the discrete conditions of the voltages of various batteries after calculation and sorting are calculated by the formulas (8) and (9):
wherein σ represents a standard deviation of the voltage, n represents the number of voltage data, and x i The ith battery voltage value is represented, and the average value of the voltages in each category is represented. The standard deviation of the voltage of 100 lithium batteries before sorting is 0.0431, the standard deviation of the voltage of the first battery after sorting is 0.2037, and the standard deviation of the voltage of the second battery is 0.0111. It can be seen that the first type of cells had poor voltage uniformity, indicating that the degradation in battery performance was caused by very little degradation in cell performance.
In the embodiment, a plurality of separation variables, voltage curves and energy curves corresponding to the single batteries are obtained by performing charge and discharge tests, battery internal resistance measurements and open-circuit voltage measurements on the single batteries; calculating a correlation coefficient matrix among a plurality of sorting variables; solving the eigenvalue according to the correlation coefficient matrix, and determining the common factor number and the common factor matched with the common factor number according to the eigenvalue; determining the principal component contribution rate corresponding to each common factor; determining variable coefficients corresponding to the common factors according to the principal component contribution rate and the characteristic value; determining corresponding factor variable scores according to the variable coefficients and the sorting variables; carrying out multi-parameter sorting on the single batteries according to the factor variable scores to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into first batteries and second batteries; and sorting the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result. Through the mode, the sorting variables are simplified in a factor analysis mode, clustering is carried out in an inter-group connection clustering mode, multi-parameter sorting is achieved, dynamic sorting is carried out by utilizing the voltage curve and the energy curve of the battery, the energy utilization rate of the battery is maximized, the cycle service life of the lithium battery is prolonged to the maximum extent, the voltage curve and the energy curve are spliced, the overall performance of the battery can be reflected comprehensively by the spliced reference curve, and the sorting accuracy is improved.
Referring to fig. 5, fig. 5 is a schematic flow chart of a battery uniformity sorting method according to a third embodiment of the present invention.
Based on the first embodiment, the step S30 of the method for consistently sorting batteries according to this embodiment includes:
step S301: and respectively carrying out normalization processing on the data on the voltage curve and the energy curve corresponding to the second battery to obtain a target voltage curve and a target energy curve.
It should be understood that, in this embodiment, the data collected on the voltage curve and the energy curve of the second-half cycle of the second type battery are normalized according to the following equations (10) and (11), respectively, and normalized to the interval [0,1 ]:
wherein X c,norm Is a value after voltage normalization, X v,i Is the raw data of the ith cell voltage (Data on the voltage curve),
is the minimum value of the ith cell voltage raw data,
the maximum value of the original voltage data of the ith single battery is obtained; x e,norm Is the energy normalized value, X e,i As raw data of the ith cell energy (data on the energy curve),
is the minimum value of the ith cell energy raw data,
the maximum value of the ith single battery energy raw data.
Step S302: and splicing the target voltage curve and the target energy curve to obtain a reference curve corresponding to each second-type battery.
It should be noted that, in this embodiment, the target voltage curve and the target energy curve are spliced together, that is, two curves normalized to the [0,1] interval are spliced together to form a reference curve on the [0.2] interval, so that the overall performance of the battery can be more fully reflected.
Step S303: and calculating the distance mean value corresponding to the target voltage curve and the target energy curve.
In a specific implementation, the distance mean value corresponding to the target voltage curve and the target energy curve is calculated according to the formula (12):
wherein m represents the number of points on the target voltage curve, n represents the number of points on the target energy curve, ρ (v, y) represents the ordinate value of the points on the target voltage curve, and ρ (e, y) represents the ordinate value of the points on the target energy curve.
Step S304: and determining a frequency distribution histogram and a frequency distribution curve according to the distance mean value.
Step S305: and determining a corresponding cluster K value based on the frequency distribution histogram and the frequency distribution curve.
Step S306: and selecting K clustering center curves from the reference curves corresponding to the second batteries according to the clustering K values.
It should be understood that, in the present embodiment, the frequency distribution histogram and the frequency distribution curve are calculated by using the average distance between the target voltage curve and the target energy curve, and the clustering K value is determined according to the frequency distribution histogram and the frequency distribution curve, so as to solve the problem of difficult classification caused by artificially determining the K value. Referring to fig. 6, fig. 6 is a frequency distribution histogram and a frequency distribution curve according to an embodiment of the battery consistency sorting method of the present invention, the frequency distribution histogram and the frequency distribution curve are analyzed to determine to classify the second type of battery into three types, and K =3 is set.
Step S307: and clustering the residual reference curves based on the K clustering center curves to obtain a battery sorting result.
In the specific implementation, the maximum iteration frequency is set, a plurality of residual reference curves are sequentially added to clusters corresponding to K clustering center curves according to the nearest neighbor principle in the iteration process, the clustering center curve position is readjusted according to an averaging method, then iteration adjustment is continued until the current iteration frequency reaches the maximum iteration frequency or each cluster meets certain requirements, and at the moment, a battery sorting result is obtained.
Specifically, the step S307 includes: calculating the average distance between a target reference curve and the K clustering center curves respectively, wherein the target reference curve is any one of a plurality of residual reference curves; selecting a clustering center curve with the minimum average distance from the target reference curve as a clustering center curve to which the target reference curve belongs; calculating the average Euclidean deviation corresponding to the battery class to which each clustering center curve belongs; and judging whether the average Euclidean deviation reaches a preset threshold value, if not, continuing iteration until the current iteration number reaches the maximum iteration number or the average Euclidean deviation corresponding to each battery reaches the preset threshold value, and obtaining a battery sorting result.
It should be understood that, in the present embodiment, the average distance from the remaining reference curve to the cluster center curve is calculated by formula (13):
where ρ is y Ordinate values, p, representing points on the remaining reference curve ycenter And the ordinate value of the point on the clustering center curve is represented.
In this embodiment, a cluster corresponding to the cluster center is represented as a battery class, and for any remaining reference curve, in an iterative process, the cluster is classified into a cluster where the cluster center curve with the smallest average distance is located, and an average euclidean deviation corresponding to each cluster is calculated according to a formula (14):
wherein p represents the total number of all the unit cells.
It should be noted that the preset threshold is a preset fixed value, for example, 0, and may also be other smaller values. And when the average Euclidean deviation of each cluster does not reach the minimum, adjusting the position of a cluster center curve, optionally, calculating the average center curve of a plurality of curves in each cluster, taking the calculated average center curve as a new cluster center curve, and continuing iteration until the current iteration number reaches the maximum iteration number or the average Euclidean deviation of each cluster reaches the minimum. At this time, the algorithm is finished, and a battery sorting result is obtained. The results obtained by sorting 96 second type batteries according to this example are shown in table 8.
Table 8:
in the embodiment, a plurality of separation variables, voltage curves and energy curves corresponding to the single batteries are obtained by performing charge and discharge tests, battery internal resistance measurements and open-circuit voltage measurements on the single batteries; carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery and a second battery; respectively carrying out normalization processing on data on a voltage curve and an energy curve corresponding to the second battery to obtain a target voltage curve and a target energy curve; splicing the target voltage curve and the target energy curve to obtain a reference curve corresponding to each second-class battery; calculating a distance average value corresponding to the target voltage curve and the target energy curve; determining a frequency distribution histogram and a frequency distribution curve according to the distance mean value; determining a corresponding clustering K value based on the frequency distribution histogram and the frequency distribution curve; selecting K clustering center curves from the reference curves corresponding to the second batteries according to the clustering K values; and clustering the residual reference curves based on the K clustering center curves to obtain a battery sorting result. Through the mode, a plurality of sorting variables are utilized for sorting, the voltage curve and the energy curve of the battery are utilized for dynamic sorting at the same time, so that the energy utilization rate of the battery reaches the maximum, the cycle service life of the lithium battery is prolonged to the maximum extent, the voltage curve and the energy curve are spliced, the overall performance of the battery can be reflected more comprehensively by the spliced reference curve, the sorting accuracy is improved, the frequency distribution histogram and the frequency distribution curve are adopted for determining the K value, and the problem that the classification is difficult due to the fact that the K value is determined manually is solved.
Furthermore, an embodiment of the present invention further provides a storage medium, where the storage medium stores a battery consistency sorting program, and the battery consistency sorting program implements the battery consistency sorting method as described above when executed by a processor.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
Referring to fig. 7, fig. 7 is a block diagram illustrating a first embodiment of the battery uniformity sorting apparatus according to the present invention.
As shown in fig. 7, the battery consistency sorting apparatus according to an embodiment of the present invention includes:
the testing module 10 is configured to perform charge and discharge testing, battery internal resistance measuring, and open-circuit voltage measuring on a plurality of single batteries to obtain a plurality of sorting variables, voltage curves, and energy curves corresponding to the single batteries.
The multi-parameter sorting module 20 is configured to perform multi-parameter sorting on the multiple single batteries according to the multiple sorting variables to obtain a multi-parameter sorting result, where the multiple single batteries in the multi-parameter sorting result are sorted into a first type battery and a second type battery.
And the dynamic sorting module 30 is configured to sort the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited in this respect.
In the embodiment, a plurality of separation variables, voltage curves and energy curves corresponding to the single batteries are obtained by performing charge and discharge tests, battery internal resistance measurements and open-circuit voltage measurements on the single batteries; carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery and a second battery; and sorting the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result. By the mode, the overall performance of the battery is considered, the sorting accuracy is improved, the sorting is carried out by utilizing a plurality of sorting variables, and the voltage curve and the energy curve of the battery are utilized to carry out dynamic sorting so that the energy utilization rate of the battery can be maximized and the cycle service life of the lithium battery can be prolonged to the maximum extent.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may be referred to the battery consistency sorting method provided in any embodiment of the present invention, and are not described herein again.
In one embodiment, the plurality of sorting variables includes at least a battery capacity, a charge voltage, a discharge voltage, a difference between a charge energy and a discharge energy, a charge internal resistance, and a discharge internal resistance.
In one embodiment, the multi-parameter sorting module 20 is further configured to calculate a correlation coefficient matrix between the plurality of sorting variables; solving an eigenvalue according to the correlation coefficient matrix, and determining a common factor number and a common factor matched with the common factor number according to the eigenvalue; determining principal component contribution rates corresponding to the common factors; determining variable coefficients corresponding to the common factors according to the principal component contribution rate and the characteristic value; determining corresponding factor variable scores according to the variable coefficients and the sorting variables; and carrying out multi-parameter sorting on the plurality of single batteries according to the factor variable scores to obtain multi-parameter sorting results.
In an embodiment, the multi-parameter sorting module 20 is further configured to calculate a corresponding squared euclidean distance according to the factor variable scores corresponding to any two single batteries; and performing inter-group connection clustering on the plurality of single batteries based on the squared Euclidean distance to obtain a multi-parameter sorting result.
In an embodiment, the dynamic sorting module 30 is further configured to perform normalization processing on data on a voltage curve and data on an energy curve corresponding to the second type of battery, respectively, to obtain a target voltage curve and a target energy curve; splicing the target voltage curve and the target energy curve to obtain a reference curve corresponding to each second battery; calculating a distance average value corresponding to the target voltage curve and the target energy curve; determining a frequency distribution histogram and a frequency distribution curve according to the distance mean value; determining a corresponding cluster K value based on the frequency distribution histogram and the frequency distribution curve; selecting K clustering center curves from the reference curves corresponding to the second batteries according to the clustering K values; and clustering the residual reference curves based on the K clustering center curves to obtain a battery sorting result.
In an embodiment, the dynamic sorting module 30 is further configured to calculate an average distance between a target reference curve and each of the K cluster center curves, where the target reference curve is any one of a plurality of remaining reference curves; selecting a clustering center curve with the minimum average distance from the target reference curve as a clustering center curve to which the target reference curve belongs; calculating the average Euclidean deviation corresponding to the battery class to which each clustering center curve belongs; and judging whether the average Euclidean deviation reaches a preset threshold value, if not, continuing iteration until the current iteration number reaches the maximum iteration number or the average Euclidean deviation corresponding to each battery reaches the preset threshold value, and obtaining a battery sorting result.
In one embodiment, the battery consistency sorting apparatus further comprises a static check module;
and the static inspection module is used for performing standard static inspection on a plurality of batteries to be sorted, screening out batteries which do not meet the preset static requirements, and obtaining a plurality of single batteries.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. a Read Only Memory (ROM)/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.
Claims (10)
1. A battery uniformity sorting method, characterized in that the battery uniformity sorting method comprises:
carrying out charge and discharge test, battery internal resistance measurement and open-circuit voltage measurement on a plurality of single batteries to obtain a plurality of sorting variables, voltage curves and energy curves corresponding to the single batteries;
performing multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery and a second battery;
and sorting the second battery according to the voltage curve and the energy curve to obtain a battery sorting result.
2. The battery uniformity sorting method of claim 1, wherein the plurality of sorting variables includes at least a battery capacity, a charge voltage, a discharge voltage, a difference between a charge energy and a discharge energy, an internal charge resistance, and an internal discharge resistance.
3. The battery consistency sorting method of claim 1, wherein the multi-parameter sorting of the plurality of single batteries according to the plurality of sorting variables to obtain a multi-parameter sorting result comprises:
calculating a matrix of correlation coefficients between the plurality of sorting variables;
solving an eigenvalue according to the correlation coefficient matrix, and determining a common factor number and a common factor matched with the common factor number according to the eigenvalue;
determining the principal component contribution rate corresponding to each common factor;
determining variable coefficients corresponding to the common factors according to the principal component contribution rate and the characteristic value;
determining corresponding factor variable scores according to the variable coefficients and the sorting variables;
and carrying out multi-parameter sorting on the plurality of single batteries according to the factor variable scores to obtain multi-parameter sorting results.
4. The battery consistency sorting method according to claim 3, wherein the multi-parameter sorting of the plurality of single batteries according to the factor variable scores to obtain a multi-parameter sorting result comprises:
calculating the corresponding squared Euclidean distance according to the factor variable scores corresponding to any two single batteries;
and performing inter-group connection clustering on the plurality of single batteries based on the squared Euclidean distance to obtain a multi-parameter sorting result.
5. The method for consistently sorting batteries according to claim 1, wherein the sorting the second type of battery according to the voltage curve and the energy curve to obtain a battery sorting result includes:
respectively carrying out normalization processing on data on a voltage curve and an energy curve corresponding to the second battery to obtain a target voltage curve and a target energy curve;
splicing the target voltage curve and the target energy curve to obtain a reference curve corresponding to each second battery;
calculating a distance average value corresponding to the target voltage curve and the target energy curve;
determining a frequency distribution histogram and a frequency distribution curve according to the distance mean value;
determining a corresponding cluster K value based on the frequency distribution histogram and the frequency distribution curve;
selecting K clustering center curves from the reference curves corresponding to the second batteries according to the clustering K values;
and clustering the residual reference curves based on the K clustering center curves to obtain a battery sorting result.
6. The battery uniformity sorting method of claim 5, wherein said clustering a plurality of remaining reference curves based on said K cluster center curves to obtain battery sorting results comprises:
calculating the average distance between a target reference curve and the K clustering center curves respectively, wherein the target reference curve is any one of a plurality of residual reference curves;
selecting a clustering center curve with the minimum average distance from the target reference curve as a clustering center curve to which the target reference curve belongs;
calculating the average Euclidean deviation corresponding to the battery class to which each clustering center curve belongs;
and judging whether the average Euclidean deviation reaches a preset threshold value, if not, continuing iteration until the current iteration number reaches the maximum iteration number or the average Euclidean deviation corresponding to each battery reaches the preset threshold value, and obtaining a battery sorting result.
7. The battery consistency sorting method according to any one of claims 1 to 6, wherein before performing the charge and discharge test, the battery internal resistance measurement, and the open-circuit voltage measurement on the plurality of single batteries to obtain a plurality of sorting variables, voltage curves, and energy curves corresponding to the respective single batteries, the method further comprises:
and carrying out standard static inspection on the plurality of batteries to be sorted, and screening out the batteries which do not meet the preset static requirements to obtain a plurality of single batteries.
8. A battery uniformity sorting apparatus, comprising:
the testing module is used for carrying out charge and discharge testing, battery internal resistance measuring and open-circuit voltage measuring on a plurality of single batteries to obtain a plurality of sorting variables, voltage curves and energy curves corresponding to the single batteries;
the multi-parameter sorting module is used for carrying out multi-parameter sorting on the single batteries according to the sorting variables to obtain a multi-parameter sorting result, wherein the single batteries in the multi-parameter sorting result are sorted into a first battery type and a second battery type;
and the dynamic sorting module is used for sorting the second type of batteries according to the voltage curve and the energy curve to obtain a battery sorting result.
9. A battery uniformity sorting apparatus, the apparatus comprising: a memory, a processor, and a battery uniformity sorting program stored on the memory and executable on the processor, the battery uniformity sorting program configured to implement the battery uniformity sorting method of any of claims 1-7.
10. A storage medium having stored thereon a battery consistency sorting program which, when executed by a processor, implements the battery consistency sorting method of any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116577687A (en) * | 2023-07-14 | 2023-08-11 | 南昌航空大学 | Cell screening method and system for quick-charging battery pack, storage medium and computer |
| CN116908720A (en) * | 2023-09-07 | 2023-10-20 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency state diagnosis method, device and storage medium |
| CN117250522A (en) * | 2023-11-17 | 2023-12-19 | 深圳蓝锂科技有限公司 | Data modeling method and system applied to retired battery management |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116577687A (en) * | 2023-07-14 | 2023-08-11 | 南昌航空大学 | Cell screening method and system for quick-charging battery pack, storage medium and computer |
| CN116577687B (en) * | 2023-07-14 | 2024-04-19 | 南昌航空大学 | Cell screening method and system for quick-charging battery pack, storage medium and computer |
| CN116908720A (en) * | 2023-09-07 | 2023-10-20 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency state diagnosis method, device and storage medium |
| CN116908720B (en) * | 2023-09-07 | 2023-12-26 | 中国华能集团清洁能源技术研究院有限公司 | Battery pack consistency state diagnosis method, device and storage medium |
| CN117250522A (en) * | 2023-11-17 | 2023-12-19 | 深圳蓝锂科技有限公司 | Data modeling method and system applied to retired battery management |
| CN117250522B (en) * | 2023-11-17 | 2024-02-23 | 深圳蓝锂科技有限公司 | Data modeling method and system applied to retired battery management |
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