CN114720899A - Retired battery echelon utilization and sorting method and system, electronic equipment and storage medium - Google Patents
Retired battery echelon utilization and sorting method and system, electronic equipment and storage medium Download PDFInfo
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- 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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- 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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
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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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- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
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Abstract
The invention discloses a retired battery echelon utilization sorting method and system, electronic equipment and a storage medium. The ex-service battery echelon utilization and sorting method comprises the following steps: testing a plurality of batteries to be tested in the charging process to obtain corresponding characteristic parameters of the batteries to be tested in the charging process, wherein the characteristic parameters comprise the peak height of a current capacity increment curve; obtaining current estimated capacities corresponding to a plurality of batteries to be detected according to the peak height of the current capacity increment curve and a preset capacity estimation model, wherein the capacity estimation model is generated based on historical charging data of a plurality of reference batteries with the same types as the batteries to be detected; and sorting the batteries to be tested according to the current estimated capacities. The invention can realize rapid evaluation and sorting of the health state of the battery, fully ensure the sorting efficiency and the consistency of the battery, reduce the sorting cost of the battery and ensure the safety quality when the battery is sorted.
Description
Technical Field
The invention relates to the field of energy storage batteries, in particular to a method and a system for utilizing and sorting retired batteries in a gradient manner, electronic equipment and a storage medium.
Background
In recent years, the market reserve of lithium ion power batteries is continuously increased, a large amount of power batteries are retired in the future along with the increase of service time, and the secondary utilization of the retired batteries can effectively relieve ecological pressure, reduce resource waste, effectively reduce the cost in the power battery industry chain, and contribute to relieving energy waste and social resource pressure caused by direct retirement and recovery of a large amount of power batteries.
The performance and the health state of the battery can quantitatively describe the degradation degree of the battery to be used as an important sorting index for the gradient utilization of the retired battery, the performance of the retired battery gradually declines along with the increase of the cycle number of the battery, but the health state evaluation and the battery sorting of the retired battery at present have the disadvantages that the time consumption of an analysis process is long, the rapid evaluation and the sorting cannot be carried out, the sorting error percentage is high, the consistency and the quality of the sorted battery are uneven, the product cost of the gradient utilization of the retired battery is overhigh, and the potential safety quality problem exists.
Disclosure of Invention
In view of this, the invention provides a method and a system for sorting retired batteries by gradient utilization, an electronic device and a storage medium, which can realize rapid evaluation and sorting of health states of batteries, fully ensure sorting efficiency and battery consistency, reduce battery sorting cost and ensure safety quality when sorting batteries.
In order to achieve the purpose, the invention provides a gradient utilization and sorting method for retired batteries, which comprises the following steps: testing a plurality of batteries to be tested in the charging process to obtain corresponding characteristic parameters of the batteries to be tested in the charging process, wherein the characteristic parameters comprise the peak height of a current capacity increment curve; obtaining current estimated capacities corresponding to a plurality of batteries to be detected according to the peak heights of the current capacity increment curves and a preset capacity estimation model, wherein the capacity estimation model is generated based on historical charging data of one or more reference batteries with the same type as the batteries to be detected; and sorting the batteries to be tested according to the current estimated capacities.
Further, the capacity estimation model is generated based on the following steps: acquiring historical charging data of a plurality of reference batteries with the same type as the battery to be tested; acquiring reference capacity and reference capacity increment curve peak heights corresponding to different cycle turns of the plurality of reference batteries according to the historical charging data; and generating the capacity estimation model according to the reference capacity and the peak height of the reference capacity increment curve.
Further, the capacity estimation model is generated based on the following steps: acquiring historical charging data of a plurality of reference batteries with the same type as the battery to be tested; acquiring charging voltage drop, reference capacity and reference capacity increment curve peak height corresponding to different cycle turns of the plurality of reference batteries according to the historical charging data; and generating the capacity estimation model according to the voltage drop, the reference capacity and the peak height of the reference capacity increment curve.
Further, the characteristic parameters further include direct current internal resistance, and correspondingly, the method for sorting the retired battery by utilizing the retired battery in the echelon mode further includes: testing a battery to be tested in the charging process to obtain the direct current internal resistance of the battery to be tested in the charging process; and screening abnormal batteries in the batteries to be tested according to the direct current internal resistance and a preset internal resistance distribution curve.
Further, the internal resistance distribution curve is generated by: performing direct current impedance test on the plurality of reference batteries to obtain reference internal resistances corresponding to the plurality of reference batteries; and acquiring the internal resistance distribution curve according to the plurality of reference internal resistances.
Further, the method further comprises: and carrying out consistency check on the batteries to be tested and/or the reference batteries, and screening abnormal batteries in the batteries to be tested and/or the reference batteries.
Further, the sorting the plurality of batteries to be tested according to the plurality of current estimated capacities includes: generating a capacity distribution curve based on a plurality of the current estimated capacities; and sorting the batteries to be tested according to the capacity distribution curve and a preset confidence interval.
The invention also provides a graded utilization and sorting system for the retired battery, which comprises the following components: the characteristic parameter testing module is used for testing a plurality of batteries to be tested in the charging process to obtain corresponding characteristic parameters of the batteries to be tested in the charging process, and the characteristic parameters comprise the peak height of a current capacity increment curve; the estimated capacity module is used for generating a capacity estimated model based on historical charging data of a plurality of reference batteries with the same types as the batteries to be detected, and obtaining current estimated capacities corresponding to the batteries to be detected according to the peak height of the current capacity incremental curve and the capacity estimated model; and the sorting module is used for sorting the batteries to be tested according to the current estimated capacities.
The invention also provides an electronic device comprising a memory and a processor; the memory stores a computer program, and the processor is configured to execute the computer program in the memory to perform the operations of the retired battery gradient utilization sorting method.
The present invention also provides a storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor to perform the steps of the retired battery echelon utilization sorting method described above.
The technical effect of the invention is to provide a retired battery echelon utilization and sorting method, which establishes a capacity vs capacity increment curve peak height linear model based on historical charging data to realize rapid battery capacity calculation. And through direct current impedance test, fully guarantee separation efficiency and battery uniformity. Therefore, the invention provides a graded utilization and sorting method and system for retired batteries, electronic equipment and a storage medium, which can realize rapid evaluation and sorting of the health states of the batteries, fully guarantee the sorting efficiency and the consistency of the batteries, reduce the sorting cost of the batteries and ensure the safety quality of the sorted batteries.
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The technical solution and other advantages of the present invention will become apparent from the following detailed description of specific embodiments of the present invention, which is to be read in connection with the accompanying drawings.
Fig. 1 is a flowchart of a retired battery echelon utilization sorting method according to an embodiment of the present invention;
FIG. 2 is a graph illustrating incremental charge capacity of a battery cell in a cycling process according to an embodiment of the present invention;
fig. 3 is a graph of a relationship between the peak height of peak 2 and the peak height of peak 3 in fig. 2 and the capacity of a charge capacity increment curve of a battery cell in a cycle process under a low-temperature environment according to an embodiment of the present invention;
fig. 4 is a graph showing a relationship between the peak height of peak 2, the peak height of peak 3 and the capacity of a charging capacity increment curve in a cycle process in a high-temperature environment of a battery cell according to an embodiment of the present invention;
fig. 5 is a characteristic parameter diagram of a charging voltage drop of a battery cell according to an embodiment of the present invention;
fig. 6 is a diagram illustrating a relationship between a peak height of a capacity increment curve and a capacity when a battery module is nearly fully charged according to an embodiment of the present invention;
fig. 7 is a statistical histogram of current capacity values of battery cells according to an embodiment of the present invention;
fig. 8 is a statistical histogram of dc internal resistance of a battery cell according to an embodiment of the present invention;
fig. 9 is a block diagram of a retired battery echelon utilization sorting system according to an embodiment of the present invention.
Description of reference numerals:
100. a first acquisition module; 200. building a module; 300. a second acquisition module; 400. a pre-estimation module; 500. a sorting module.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Specifically, as shown in fig. 1, one embodiment of the present invention provides a method for sorting retired batteries by using a gradient. The retired battery can be a battery monomer, a battery module, a battery pack, a battery system and the like.
The graded utilization and sorting method for the retired batteries mainly comprises the following steps: a feature parameter testing step S1, an estimate capacity step S2, and a sorting step S3. Wherein:
characteristic parameter testing step S1: testing a plurality of batteries to be tested in the charging process to obtain corresponding characteristic parameters of the batteries to be tested in the charging process, wherein the characteristic parameters comprise the peak height of a current capacity increment curve;
predicted capacity step S2: obtaining current estimated capacities corresponding to a plurality of batteries to be detected according to the peak heights of the current capacity increment curves and a preset capacity estimation model, wherein the capacity estimation model is generated based on historical charging data of a plurality of reference batteries with the same types as the batteries to be detected;
sorting step S3: and sorting the batteries to be tested according to the current estimated capacities. The capacity increment curve is a curve obtained by deriving a capacity with respect to a voltage, and belongs to the capacity increment analysis (ICA).
In the characteristic parameter testing step S1, the battery is mainly charged and discharged, and DCR testing is performed on the battery, the purpose of the dc impedance (DCR) testing is to test the DCR value after the battery is assembled into a module or PACK, the tested parameter value mainly evaluates the impedance value of the welding or connection terminal, and the DCR value is applied to evaluate the capability of the discharging power or energy. Collecting characteristic parameters of the battery in the testing process, wherein the characteristic parameters are parameters with the significance of battery performance representation, such as charging voltage drop, reference capacity corresponding to different cycle numbers, high peak of a reference capacity increment curve and the like, and the characteristic parameters can be used as a reference for judging whether the sorted battery meets retired recycling or not; in this embodiment, the peak heights of the capacity increment curves of the multiple batteries to be tested in the charging process are obtained as characteristic parameters for subsequent battery sorting.
In the step S2 of estimating capacity, a plurality of batteries to be measured are estimated and analyzed according to the peak of the capacity increment curve, which is the obtained characteristic parameter, to obtain corresponding current estimated capacity, specifically, based on a capacity estimation model trained in advance, the capacity increment curve is used as an input parameter of the model, and the output value is used as the current estimated capacity of the battery. The capacity estimation model is a model obtained by analyzing a plurality of retired batteries with the same types as the batteries to be tested, the model is in the form of a characteristic parameter curve which can objectively reflect the current condition of the batteries and is obtained by analyzing a large amount of data, and the purpose is to accurately predict whether the currently sorted batteries meet the requirement of retired recycling or not based on objective rules in the characteristic parameter curves.
Wherein the sorting of the plurality of batteries to be tested according to the plurality of current estimated capacities in the sorting step S3 includes: generating a capacity distribution curve based on a plurality of the current estimated capacities; and sorting the batteries to be tested according to the capacity distribution curve and a preset confidence interval. And the batteries in the preset confidence interval range are considered as qualified retired batteries which can be recycled, and can be used in a gradient manner. The preset confidence interval range is usually within the 95% confidence interval range of the estimated capacity Weibull distribution of the batteries to be measured in the same batch.
The capacity distribution curve is formed by using the normal distribution rule of the historical charging data based on the historical charging data and taking the current estimated capacity as a measurement standard. The pre-established peak-to-height linear model of the capacity vs capacity incremental curve can obtain the current estimated capacities corresponding to the multiple batteries to be tested in step S2, and then generate corresponding capacity distribution curves based on the normal distribution of the multiple current estimated capacities, determine the confidence intervals of the normal distribution, and quickly sort the multiple batteries to be tested depending on the capacity distribution curves, specifically, for example, the range of the preset confidence intervals is the 95% confidence interval range of the capacity distribution curve, and determine whether the current estimated capacities corresponding to the batteries to be tested are located in the capacity distribution curve, so as to quickly sort the multiple batteries to be tested, thereby fully ensuring the sorting efficiency and the consistency of the batteries.
Further, the current capacity values of the plurality of retired batteries are counted to obtain a current capacity distribution curve of the retired batteries. Specifically, the current capacity values of 95 lithium iron phosphate series-connected battery cells with the rated capacity of 52Ah are counted, and the statistical result is shown in fig. 7. Further, the skewness s, which is a parameter characterizing the statistical data distribution skewness direction and degree, can be adopted. The calculation formula is
The distribution of the current capacity of the retired battery shown in fig. 7 is in a right-biased state, the skewness is-0.955, the capacity of most batteries is concentrated in the interval of 47-52Ah, the capacity fading speed of a small number of batteries is high, the capacity decays to 44Ah, and the statistical distribution of the current capacity of the retired battery is more in line with Weibull (Weibull) distribution characteristics. The probability density function of the Weibull distribution can be expressed as
Wherein beta is a shape parameter and eta is a proportion parameter.
Specifically, the capacity estimation model is generated based on the following steps:
acquiring historical charging data of a plurality of reference batteries with the same types as the batteries to be tested;
acquiring reference capacity and reference capacity increment curve peak heights corresponding to different cycle turns of the plurality of reference batteries according to the historical charging data;
and generating the capacity estimation model according to the reference capacity and the peak height of the reference capacity increment curve.
The reference battery can be a part of the batteries in the batch of batteries to be tested, or can be another batch of batteries serving as training samples, and the acquired historical charging data can be ensured to objectively feed back the capacity condition of the batteries to be tested as long as the batteries are the same in type as the batch of batteries to be tested. Specifically, historical data of a reference battery in different states can be acquired, and the model of the reference battery is determined to be consistent with the model of a to-be-tested retired battery, wherein the different states refer to different charging and discharging cycle times of the reference battery, and the historical data comprises historical capacity and historical capacity increment curve peak height which are respectively used as the reference capacity and the reference capacity increment curve peak height. And generating the capacity estimation model based on the linear regression relation between the reference capacity and the peak height of the reference capacity incremental curve. The linear regression relation of the peak height of the reference capacity increment curve is substantially the objective capacity change curve of a large number of batteries, the current estimated capacity of the battery at a certain position can be correspondingly found out based on the linear regression relation of the peak height of the reference capacity increment curve, the estimated capacity is supported by a large number of objective data, the accuracy is high, and therefore the current estimated capacity can correspond to the reference capacity of the corresponding battery.
Furthermore, the characteristic parameters when the capacity estimation model is generated can also comprise charging voltage drop, and the identification accuracy of the capacity estimation model can be effectively improved by adding the charging voltage drop as one of the characteristic parameters of the model.
Specifically, the capacity estimation model is generated based on the following steps:
acquiring historical charging data of a plurality of reference batteries with the same type as the battery to be tested;
acquiring charging voltage drops corresponding to a plurality of reference batteries, reference capacities corresponding to different cycle numbers and reference capacity increment curve peak heights according to the historical charging data;
and generating the capacity estimation model according to the voltage drop, the reference capacity and the peak height of the reference capacity increment curve.
When the capacity estimation model is generated, the capacity estimation model can be generated together according to the corresponding relationship between the charging voltage drop corresponding to the plurality of reference batteries and the peak height of the reference capacity and the reference capacity increment curve besides the linear regression relationship between the reference capacity and the peak height of the reference capacity increment curve. The voltage drop is used as an additional parameter, a capacity estimation model is trained and generated, and the identification accuracy of the capacity estimation model can be improved.
In order to improve the sorting efficiency and ensure the safety of the process of sorting the retired batteries, abnormal batteries can be directly removed according to the direct current internal resistance data of the batteries, and the capacity of the batteries after removal is not estimated, so that the batteries which are not removed are all batteries with qualified internal resistance, and the sorting speed can be accelerated. Correspondingly, the ex-service battery echelon utilization sorting method further comprises the following steps: testing a battery to be tested in the charging process to obtain the direct current internal resistance of the battery to be tested in the charging process; and screening abnormal batteries in the batteries to be tested according to the direct current internal resistance and a preset internal resistance distribution curve. Specifically, based on the obtained direct current internal resistance, internal resistance distribution statistics is carried out, batteries with internal resistance values within 95% of normal distribution confidence interval and internal resistance values not greater than 1.5Ro are screened, and next sorting is carried out, wherein Ro is the conventional internal resistance value of the battery when the battery leaves a factory, and 1.5 is a numerical multiple determined based on conventional cognition.
In some embodiments, the internal resistance distribution curve is generated by: performing direct current impedance test on the plurality of reference batteries to obtain reference internal resistances corresponding to the plurality of reference batteries; and acquiring a corresponding internal resistance distribution curve according to the normal distribution condition of the reference internal resistances. The reference battery may be a part of the batteries in the batch of batteries to be tested, or another batch of batteries used as a training sample, specifically, the reference internal resistance of the reference battery is subjected to normal distribution processing to generate a corresponding normal distribution curve and an internal resistance distribution curve, and the batteries to be tested are screened according to a confidence interval corresponding to the internal resistance distribution curve.
Therefore, the sorting operation of the present application is performed based on the characteristic parameters (such as the peak height of the current capacity increment curve, the direct current internal resistance, and the like) of the multiple batteries to be tested and/or the multiple reference batteries of the same model, and in order to ensure that the multiple batteries to be tested and/or the multiple reference batteries have the same model or the same specification, the retired battery echelon sorting method further includes, before the characteristic parameter testing step: and checking consistency. The consistency checking step is mainly used for checking whether the batteries have the same model or specification, eliminating abnormal batteries and preventing data interference of the abnormal batteries. Specifically, the consistency check step includes: carrying out consistency check on the batteries to be tested and/or the reference batteries, and screening abnormal batteries in the batteries to be tested and/or the reference batteries; the consistency check comprises basic checks such as battery appearance check, model, batch, service life, rated capacity, rated power, upper limit voltage value, lower limit voltage value, working voltage and the like, so as to ensure reasonable grouping of the retired batteries.
Furthermore, before the retired battery to be tested is charged and the peak height of the current capacity increment curve of the retired battery to be tested is obtained, the retired battery to be tested can be checked to screen out abnormal batteries. The inspection comprises but is not limited to inspecting the appearance, model, batch, service life, rated capacity, rated power, upper limit voltage value, lower limit voltage value, working voltage and the like of the retired battery to be inspected, and on the basis, the model of the retired battery to be inspected can be ensured to be consistent with the model of the reference battery; on the other hand, whether the retired battery to be tested is an abnormal battery can be checked. Wherein the basic inspections such as battery sample appearance, performance, safety after disassembling include appearance inspections such as battery sample label integrality, battery case weeping, mar, pencil and port integrality, battery sample insulating internal resistance inspection, withstand voltage test etc..
The technical scheme of the whole application can show that: the retired battery echelon utilization sorting method provided by the embodiment of the invention is based on historical charging data, and a linear model of the peak height of the capacity vs capacity increment curve is established, so that the rapid calculation of the battery capacity is realized. And through direct current impedance test and consistency check, fully ensure separation efficiency and battery uniformity, guarantee the safety quality when selecting separately the battery.
The reference battery may be a retired battery, and certainly the reference battery is not limited to a retired battery, and may also be a non-retired battery. For example, the model of the retired battery is 3914895, and the model of the reference battery is 3914895.
Further, the state is the charge and discharge cycle number of the reference battery, that is, the historical data of the reference battery under different charge and discharge cycle numbers can be used as the reference data of the retired battery to be tested. For example, if the number of charge/discharge cycles of the reference battery is 100, the historical data of each of the 100 charge/discharge cycles of the reference battery may be obtained. Therefore, when the historical capacity and the peak height of the increment curve of the historical capacity at each time in the 100 times of charge and discharge cycles of the reference battery are obtained, the historical capacity can be the discharge capacity at each charge and discharge cycle; the historical capacity increase curve peak height may be a peak height of a charge segment capacity increase curve in each charge-discharge cycle.
Further, the historical data may be data in the use process of the reference battery, or may be data obtained through an experimental method. The historical capacity of the battery monomer under the low-temperature condition can be obtained by, for example, placing the battery monomer in an incubator for a certain time (for example, 2 hours) until the battery reaches a preset temperature (for example, -10 ℃); setting the temperature in the incubator to a predetermined temperature, for example, -10 ℃; then, the battery is charged to full charge in a standard charging mode specified by a battery manufacturer, wherein the standard charging mode can be that the battery is charged to cut-off voltage at a multiplying power within the range of 0.1C to 1C, and then constant voltage charging is carried out until the current is reduced to 0.05C; then, the mixture is left for a period of time, such as 2 hours, and then discharged to a discharge cut-off voltage in a standard discharge manner specified by a battery manufacturer, such as a discharge manner of 0.1C to 0.5C. Followed by a period of rest, such as 2 hours. And subsequently repeating the steps of charging, standing and discharging, acquiring n-circle cycle data, and taking the discharge capacity of the nth circle as the capacity of the nth circle.
In this embodiment, a linear regression model based on the historical capacity and the peak height of the incremental curve of the historical capacity may be constructed according to the linear regression relationship between the reference capacity and the peak height of the incremental curve of the reference capacity. Specifically, the formula of the linear regression model is Y ═ β0+Xβ1Wherein, in the step (A),
y is a one-dimensional matrix formed by discharge capacities measured under different charge-discharge cycle turns, YiFor reference of the discharge capacity of the battery at the i-th charge-discharge cycle number, n is a natural number not less than 2, for example, n is 100, and Y is a one-dimensional matrix formed by the discharge capacity measured at the 100-th charge-discharge cycle number
Further, a linear regression model is constructed below by taking the reference battery as an example of the battery module, and specifically, fig. 6 is data of the peak height of the obtained charging process historical capacity increment curve of the battery module. The linear model of the battery module was analyzed and constructed according to the measured data of fig. 6: y is 2.63X +33.42, where Y is a one-dimensional matrix composed of discharge capacities measured at different numbers of charge-discharge cycles, X is a one-dimensional matrix composed of charge segments at different numbers of charge-discharge cycles and peak heights of a capacity increment curve, the root mean square error value of the obtained model is 1.09, and the average error is 0.85%, and the error values are explained as follows: the smaller the root mean square error value, the higher the prediction accuracy of the model; the average error is less than 10%, which indicates that the accuracy of the model is high.
Further, the ex-service battery echelon utilization sorting method according to the embodiment of the present invention further includes: the historical charging voltage drop Δ V of the reference battery in different states is obtained, for example, fig. 5 shows the charging voltage drop Δ V within 10 minutes of the fully charged battery cell. Further, the charging voltage drop Δ V is a voltage drop that lasts for a certain duration, for example, 5min to 1h, from the charging current drop to 0A.
Wherein the content of the first and second substances,
the Δ Vci is the historical charging voltage drop of the reference battery at the ith charging and discharging cycle, the charging voltage drop is used as a parameter for constructing a linear regression model, and the linear regression model is trained by increasing the charging voltage drop, so that the precision of the linear regression model can be improved. Further, a linear regression model is constructed below by taking the reference battery as an example of the battery cell of the ternary lithium battery with the same model. Specifically, fig. 2 is a graph of historical charge capacity increment of a battery cell, wherein the charge process in the historical charge-discharge cycle of the battery cell counts 4 characteristic peak positions, and the peak heights of the 4 characteristic peak positions are respectively represented as P1, P2, P3 and P4. Fig. 3 is a graph showing a relationship between P2 and P3 and capacity in a charging capacity increment curve of a battery cell in a cycle process under a high-temperature environment, fig. 4 is a graph showing a relationship between P2 and P3 and capacity in a charging capacity increment curve of a battery cell in a cycle process under a low-temperature environment, and fig. 5 is a voltage drop of the battery cell within 10min of full charge.
Further, the linear model of the battery cell is obtained by analyzing according to different characteristic parameters shown in fig. 3, 4 and 5: y is-48.699 × Δ Vc-0.016 × P2+0.626 × P3+21.852, where Y is a one-dimensional matrix composed of discharge capacities measured at different numbers of charge and discharge cycles, X is a charge segment at different numbers of charge and discharge cycles, and X is a one-dimensional or multi-dimensional matrix composed of peak heights of a capacity increment curve, and the root mean square error value of the obtained model is 0.531, and the average error value is 0.43%.
In the embodiment, the retired battery to be tested is charged, and the peak height of the current capacity increment curve of the retired battery to be tested is obtained at the same time. Specifically, for example, the retired battery to be tested may be charged at a charging rate of 0.5C-1C or a charging rate specified by a battery manufacturer, and a current capacity increment curve of the retired battery to be tested may be obtained, and a peak height of the current capacity increment curve may be selected on the current capacity increment curve of the retired battery to be tested. Furthermore, the end-of-charge voltage of the retired battery may not reach the upper limit voltage of the retired battery, and the peak-to-peak voltage of the current capacity increment curve is included between the start-of-charge voltage and the end-of-charge voltage. If the battery is fully charged, namely the voltage reaches the upper limit voltage, Δ Vc can be additionally obtained. In the embodiment of the invention, the delta Vc is a characteristic parameter which can be selected according to actual requirements, and can be used as an input parameter of the model in the actual requirements, so that the model precision is further improved.
In this embodiment, the pre-estimated value of the current capacity of the retired battery to be tested can be obtained according to the linear regression model and the peak height of the current capacity increment curve. The method comprises the steps that the ex-service battery to be tested is sorted according to a distribution curve of the current capacity of the ex-service battery and a pre-estimated value of the current capacity of the ex-service battery to be tested, and specifically, when the pre-estimated value of the current capacity of the ex-service battery to be tested is located outside a 95% confidence interval of the distribution curve of the current capacity, the ex-service battery to be tested is an abnormal battery. That is, the batteries with the current capacity distribution in the 95% confidence interval of the Weibull distribution are sorted into the same group. For example, the linear regression model for the reference cell is: y ═ 0.016 xp 1+0.626 xp 2+21.852, two peak heights of the current capacity incremental curve of the retired battery to be tested (i.e., P1 and P2) may be substituted into the linear regression model: and when Y is-0.016 xP 1+0.626 xP 2+21.852, the estimated value of the current capacity of the retired battery to be tested can be obtained. In this way, the current capacity values of the plurality of retired batteries, which are the current capacities of the retired batteries, can be obtained. The current capacity value may also be the capacity values of a plurality of retired batteries, for example, the current capacity values of 95 single lithium iron phosphate series batteries with the rated capacity of 52Ah are obtained.
In one embodiment, the current dc internal resistance of the retired battery to be tested may be obtained by performing a dc impedance test on the retired battery to be tested. Specifically, the dc impedance test may be performed during the process of charging the retired battery to be tested and acquiring the peak height of the current capacity increment curve of the retired battery to be tested, so as to synchronously acquire the current dc internal resistance of the retired battery to be tested. Further, the dc impedance test of the charging process should be performed within 30% SOC (state of charge) to 80% SOC, and the dc impedance test should be performed with a voltage not equal to the characteristic peak voltage of the capacity increment curve.
And further, sorting the retired battery to be tested according to the distribution curve of the current direct-current internal resistance of the retired battery and the current direct-current internal resistance of the retired battery to be tested. Therefore, the ex-service battery to be tested with abnormal direct current internal resistance can be selected, the sorting efficiency and the battery consistency are fully guaranteed, and the safety quality during battery sorting is guaranteed.
Specifically, before sorting the retired battery to be tested, the current direct-current internal resistance of the plurality of retired batteries may be obtained, and the retired battery to be tested is sorted with reference to the direct-current internal resistance. The current direct current internal resistance of a plurality of retired batteries, such as 10, 20, 30, etc., can be obtained, for example, the current direct current internal resistance of 95 lithium iron phosphate series-connected battery cells with the rated capacity of 52Ah is obtained.
And counting the current direct current internal resistances of the plurality of retired batteries to obtain a distribution curve of the current direct current internal resistances of the retired batteries. Specifically, the current direct current internal resistances of 95 lithium iron phosphate series-connected battery cells with the rated capacity of 52Ah are counted, and the statistical result is shown in fig. 8. Furthermore, the deviation direction and degree of the statistical data distribution of the parameter characterization parameter of the deviation degree s can be acquired, and the calculation formula is
The distribution of the current direct-current internal resistance of the retired battery shown in fig. 8 is in a left partial state, the skewness is 1.088, the internal resistance of most batteries is concentrated in the range of 2.9-3.4m Ω, but the internal resistance of a small part of batteries reaches 3.7m Ω. The statistical distribution of the direct current internal resistance of the battery is more in accordance with the normal distribution characteristic. Probability density function of normal distribution sumCan be expressed as
Where mu and sigma2Expressed as the mean and variance of the sample, respectively.
Further, sorting the retired battery to be tested according to the distribution curve of the current direct-current internal resistance of the retired battery and the current direct-current internal resistance of the retired battery to be tested, and specifically comprises the following steps: and when the current direct current internal resistance of the retired battery to be tested is positioned outside a confidence interval of 95% of the distribution curve of the current direct current internal resistance, the retired battery to be tested is an abnormal battery. Therefore, according to the statistical analysis results, when the batteries are screened, the batteries with direct current internal resistance outside the 95% confidence interval of normal distribution and the batteries with capacity unexpectedly distributed in the 95% confidence interval of Weibull distribution are rejected.
As shown in fig. 9, the embodiment of the present invention further provides a decommissioned battery echelon utilization sorting system 10, which includes a characteristic parameter testing module 1, an estimated capacity module 2, and a sorting module 3. The characteristic parameter testing module 1 is used for testing a plurality of batteries to be tested in the charging process to obtain corresponding characteristic parameters of the plurality of batteries to be tested in the charging process, wherein the characteristic parameters comprise peak heights of current capacity increment curves; the estimated capacity module 2 is used for generating a capacity estimated model based on historical charging data of a plurality of reference batteries with the same types as the batteries to be detected, and obtaining current estimated capacities corresponding to the batteries to be detected according to the peak height of the current capacity incremental curve and the capacity estimated model; the sorting module 3 is configured to sort the multiple batteries to be tested according to the multiple current estimated capacities. The retired battery echelon utilization sorting system provided by the embodiment of the invention establishes a linear model of the peak height of the capacity vs capacity increment curve based on historical charging data, and realizes rapid calculation of the battery capacity. And through combining direct current impedance test and consistency check, fully guarantee to select separately efficiency and battery uniformity, the safety quality when guaranteeing to select separately the battery.
The invention also provides an electronic device comprising a memory and a processor; the memory stores a computer program, and the processor is configured to execute the computer program in the memory to perform the operations of the retired battery gradient utilization sorting method.
The present invention also provides a storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor to perform the steps of the retired battery echelon utilization sorting method described above.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The retired battery echelon utilization sorting method and system, the electronic device and the storage medium provided by the embodiment of the invention are introduced in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the technical scheme and the core idea of the invention; those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. The ex-service battery echelon utilization and sorting method is characterized by comprising the following steps:
testing a plurality of batteries to be tested in the charging process to obtain corresponding characteristic parameters of the batteries to be tested in the charging process, wherein the characteristic parameters comprise the peak height of a current capacity increment curve;
obtaining current estimated capacities corresponding to a plurality of batteries to be detected according to the peak heights of the current capacity increment curves and a preset capacity estimation model, wherein the capacity estimation model is generated based on historical charging data of a plurality of reference batteries with the same types as the batteries to be detected;
and sorting the batteries to be tested according to the current estimated capacities.
2. The method for gradient utilization sorting of retired batteries according to claim 1, wherein the capacity estimation model is generated based on the following steps:
acquiring historical charging data of a plurality of reference batteries with the same type as the battery to be tested;
obtaining reference capacity and reference capacity increment curve peak height corresponding to different cycle turns of a plurality of reference batteries according to the historical charging data;
and generating the capacity estimation model according to the reference capacity and the peak height of the reference capacity increment curve.
3. The method for echelon utilization sorting of ex-service batteries according to claim 1, wherein the capacity prediction model is generated based on the following steps:
acquiring historical charging data of a plurality of reference batteries with the same types as the batteries to be tested;
acquiring charging voltage drops corresponding to a plurality of reference batteries, reference capacities corresponding to different cycle numbers and reference capacity increment curve peak heights according to the historical charging data;
and generating the capacity estimation model according to the voltage drop, the reference capacity and the peak height of the reference capacity increment curve.
4. The method for sorting out-of-service battery echelon utilization according to claim 1, wherein the method for sorting out-of-service battery echelon utilization further comprises:
testing a plurality of batteries to be tested in the charging process to obtain the direct current internal resistance of the plurality of batteries to be tested in the charging process;
and screening abnormal batteries in the batteries to be tested according to the direct current internal resistance and a preset internal resistance distribution curve.
5. The method for echelon utilization sorting of ex-service batteries according to claim 4, wherein the internal resistance distribution curve is generated by:
performing direct current impedance test on the plurality of reference batteries to obtain reference internal resistances corresponding to the plurality of reference batteries;
and carrying out normal distribution on the reference internal resistances to generate the internal resistance distribution curve.
6. The method for echelon utilization sorting of ex-service batteries according to claim 1, further comprising:
and carrying out consistency check on the batteries to be tested and/or the reference batteries, and screening abnormal batteries in the batteries to be tested and/or the reference batteries.
7. The method for echelon utilization sorting of ex-service batteries according to claim 1, wherein the sorting of the plurality of batteries to be tested according to the plurality of current estimated capacities comprises:
generating a capacity distribution curve based on a plurality of the current estimated capacities;
and sorting the batteries to be tested according to the capacity distribution curve and a preset confidence interval.
8. The utility model provides a retired battery echelon utilization sorting system which characterized in that includes:
the characteristic parameter testing module is used for testing a plurality of batteries to be tested in the charging process to obtain corresponding characteristic parameters of the batteries to be tested in the charging process, and the characteristic parameters comprise the peak height of a current capacity increment curve;
the estimated capacity module is used for generating a capacity estimated model based on historical charging data of a plurality of reference batteries with the same types as the batteries to be detected, and obtaining current estimated capacities corresponding to the batteries to be detected according to the peak height of the current capacity increment curve and the capacity estimated model;
and the sorting module is used for sorting the batteries to be tested according to the current estimated capacities.
9. An electronic device comprising a memory and a processor; the memory stores a computer program, and the processor is configured to execute the computer program in the memory to perform the operation of the retired battery gradient utilization sorting method according to any one of claims 1 to 7.
10. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the steps of the retired battery gradient utilization sorting method of any of claims 1-7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115421046A (en) * | 2022-09-16 | 2022-12-02 | 广东邦普循环科技有限公司 | Power battery gradient utilization screening method, device, equipment and storage medium |
| CN116298991A (en) * | 2023-05-25 | 2023-06-23 | 湖南锂汇通新能源科技有限责任公司 | Method and system for rapidly detecting and evaluating capacity of retired battery |
| CN117199569A (en) * | 2023-09-27 | 2023-12-08 | 吉奥环朋科技(扬州)有限公司 | Method for gradient utilization of retired battery |
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2021
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115421046A (en) * | 2022-09-16 | 2022-12-02 | 广东邦普循环科技有限公司 | Power battery gradient utilization screening method, device, equipment and storage medium |
| CN115421046B (en) * | 2022-09-16 | 2023-11-03 | 广东邦普循环科技有限公司 | Gradient utilization screening method, device and equipment for power battery and storage medium |
| CN116298991A (en) * | 2023-05-25 | 2023-06-23 | 湖南锂汇通新能源科技有限责任公司 | Method and system for rapidly detecting and evaluating capacity of retired battery |
| CN116298991B (en) * | 2023-05-25 | 2023-09-12 | 湖南锂汇通新能源科技有限责任公司 | Method and system for rapidly detecting and evaluating capacity of retired battery |
| CN117199569A (en) * | 2023-09-27 | 2023-12-08 | 吉奥环朋科技(扬州)有限公司 | Method for gradient utilization of retired battery |
| CN117199569B (en) * | 2023-09-27 | 2024-03-22 | 吉奥环朋科技(扬州)有限公司 | Method for gradient utilization of retired battery |
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