Lithium battery capacity prediction method and system

Abstract

A lithium battery capacity prediction method and system. The method comprises: performing charging and discharging processing on a lithium battery under test, and then recharging same to an SOC required for self-discharge screening (S10); acquiring a discharge capacity corresponding to a formation discharge cutoff voltage, and a discharge temperature when discharge ends (S20); on the basis of a temperature compensation formula, using the discharge temperature to calibrate and compensate for the discharge capacity, so as to obtain a compensated discharge capacity (S30); and putting the compensated discharge capacity into a pre-fitted capacity prediction formula, so as to obtain a full-discharge capacity of said lithium battery (S40). By means of extracting some effective feature values under a formation process, i.e., a discharge capacity corresponding to a discharge cutoff voltage, and a corresponding discharge temperature when discharge ends, the actual graded capacity of a battery cell can be predicted according to a pre-established capacity prediction model.

Description

Lithium battery capacity prediction method and system Technical Field

[0001] The present invention relates to the technical field of lithium battery testing, and in particular to a lithium battery capacity prediction method and system. Background Art

[0002] Capacity estimation is a key indicator of lithium-ion batteries. Capacity loss in lithium-ion batteries indicates their lifespan, so estimating the capacity of lithium-ion batteries is crucial. To measure the capacity of a cell, cells must be graded. Currently, this process is typically based on a full charge and discharge cycle. This process is time-consuming, significantly impacting production capacity, and requires high equipment precision, resulting in high equipment investment costs. Furthermore, due to issues with accurately collecting temperature and voltage data during the process, actual cell capacity predictions are inaccurate.

[0003] In the related art, the patent application document with publication number CN112034367A proposes to improve the accuracy of battery capacity through capacity prediction and temperature compensation. This scheme performs temperature compensation on the predicted battery discharge capacity based on the charging temperature and capacity relationship curve. However, there is capacity loss between the charging capacity and discharge capacity of the same battery cell, and the charging temperature affects the charging capacity. The accuracy of the predicted discharge capacity is poor, and the temperature compensation curve is only based on a small amount of sample constant temperature test data, which has great limitations.

[0004] Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to accurately predict the actual capacity of the battery cell.

[0006] The present invention solves the above technical problems through the following technical means:

[0007] A lithium battery capacity prediction method is proposed, the method comprising:

[0008] After the lithium battery to be tested is charged and discharged, it is recharged to the SOC required for self-discharge screening;

[0009] Obtain the discharge capacity corresponding to the formation discharge cut-off voltage and the discharge temperature at the end of discharge;

[0010] Based on a temperature compensation formula, the discharge capacity is compensated by using a discharge temperature calibration to obtain a compensated discharge capacity;

[0011] The compensated discharge capacity is fed into the pre-fitted capacity prediction formula to obtain the full discharge capacity of the lithium battery to be tested. Furthermore, the temperature compensation formula is expressed as: C1'=C1-(T1-C)*D

[0012] Wherein, C and D are constants, * is a multiplication sign, T1 is the discharge temperature, C1 is the discharge capacity, and C1' is the discharge capacity after replenishment.

[0013] Furthermore, the capacity prediction formula is expressed as: C2'=A*C1'+B

[0014] Where A and B are constants, * is a multiplication sign, C1' is the discharge capacity after replenishment, and C2' is the predicted full discharge capacity. Furthermore, the temperature compensation formula is expressed as: C1' = C1-(T1-52.156)*160.93

[0015] Where T1 is the discharge temperature, C1 is the discharge capacity, C1' is the discharge capacity after replenishment, and * is the multiplication sign.

[0016] Furthermore, the capacity prediction formula is expressed as: C2'=0.214460575229372*C1'+49721.0074883561

[0017] Where C1' is the discharge capacity after replenishment, C2' is the predicted full discharge capacity, and * is the multiplication sign.

[0018] Furthermore, the process of establishing the temperature compensation formula includes:

[0019] Extract the discharge flux and discharge temperature of a set number of cells at the end of the discharge step, and calculate the mean discharge capacity and the mean discharge temperature;

[0020] Calculate the capacity difference of each cell based on the average discharge temperature = (discharge capacity of each cell - average discharge capacity) / (discharge temperature of each cell - average discharge temperature);

[0021] The temperature compensation formula is established based on the discharge flux, the discharge temperature and the capacity difference.

[0022] Furthermore, the process of establishing the capacity prediction formula includes:

[0023] After charging and discharging the sample cells, recharge them to the SOC required for self-discharge screening;

[0024] Measure the discharge capacity of each sample cell at different discharge cut-off voltages, analyze the correlation between multiple discharge capacities and full discharge capacity corresponding to each sample cell, and determine the discharge capacity with the strongest correlation with full discharge capacity as the target discharge capacity;

[0025] The target discharge capacity and full discharge capacity corresponding to all sample cells are linearly fitted to obtain the capacity prediction formula.

[0026] Furthermore, analyzing the correlation between the multiple discharge capacities corresponding to each sample battery cell and the full discharge capacity, and determining the discharge capacity with the strongest correlation with the full discharge capacity as the target discharge capacity, includes:

[0027] The CORREL function is used to calculate the correlation between each discharge capacity and the full discharge capacity, and the discharge cut-off voltage with a strong correlation with the full discharge capacity is determined;

[0028] The discharge capacity corresponding to the discharge cut-off voltage that has a strong correlation with the full discharge capacity is used as the target discharge capacity.

[0029] Furthermore, the charging and discharging process of the lithium battery to be tested and then charging the battery to the SOC required for self-discharge screening includes:

[0030] The lithium battery to be tested is charged to 50%-70% SOC, then discharged to 3.2-3.3V, and then recharged to 30-60% SOC required for self-discharge screening.

[0031] In addition, the present invention also proposes a lithium battery capacity prediction system, the system comprising:

[0032] The formation treatment module is used to charge and discharge the lithium battery to be tested and then recharge it to the SOC required for self-discharge screening;

[0033] A discharge information acquisition module is used to obtain the discharge capacity corresponding to the formation discharge cut-off voltage and the discharge temperature at the end of discharge;

[0034] a temperature compensation module, configured to calibrate and compensate the discharge capacity using the discharge temperature based on a temperature compensation formula to obtain a compensated discharge capacity;

[0035] The capacity prediction module is used to input the compensated discharge capacity into the pre-fitted capacity prediction formula to obtain the full discharge capacity of the lithium battery to be tested.

[0036] The advantages of the present invention are:

[0037] (1) The present invention extracts some effective characteristic values ​​under the new formation process, namely the discharge capacity corresponding to the discharge cut-off voltage and the discharge temperature corresponding to the end of discharge, and uses the discharge temperature to compensate the discharge capacity more accurately according to the pre-established capacity prediction model, so as to accurately predict the actual capacity of the battery cell. Moreover, when applied in the formation process, the formation is high-temperature formation, the temperature consistency is good, and the discharge temperature depends on the temperature in the process. The temperature compensation curve is established on big data, including many situations in the process, which is conducive to ensuring the accuracy of capacity prediction, and the average prediction accuracy can reach within 3‰.

[0038] (2) The present invention is applied to the formation process, and the data of the formation process can be directly used to predict the full discharge capacity. The full discharge capacity is basically generated in the capacity division process, and the investment in the capacity division equipment or the capacity division process can be directly cancelled. Therefore, the solution proposed by the present invention can save 2 / 3 of the equipment purchase cost of the capacity division cabinet, and only a few capacity division cabinets are needed for random inspection. In the later stage, continuous optimization of the prediction solution can cancel the capacity division process and simplify the battery cell production process. This will greatly shorten the battery cell production cycle, improve production capacity, save energy consumption and hardware investment in the lithium battery capacity division process.

[0039] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] FIG1 is a schematic flow chart of a lithium battery capacity prediction method according to an embodiment of the present invention;

[0041] FIG2 is a schematic diagram of a capacity prediction model construction route according to an embodiment of the present invention;

[0042] FIG3 is a schematic diagram of an application route of a capacity prediction model system according to an embodiment of the present invention;

[0043] FIG4 is a comparison diagram of sample predicted capacity and actual capacity results in one embodiment of the present invention;

[0044] FIG5 is a diagram showing the capacity difference between the sample predicted capacity and the actual capacity in one embodiment of the present invention;

[0045] 6 is a schematic diagram of a formation step process data file according to an embodiment of the present invention;

[0046] FIG7 is a schematic structural diagram of a lithium battery capacity prediction system proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] As shown in FIG1 and FIG3 , the first embodiment of the present invention discloses a method for predicting the capacity of a lithium battery, the method comprising the following steps:

[0049] S10, charging and discharging the lithium battery to be tested and then recharging it to the SOC required for self-discharge screening;

[0050] Specifically, in this embodiment, the lithium battery to be tested can be placed on a formation cabinet to perform formation charge and discharge processing.

[0051] S20, obtaining the discharge capacity corresponding to the formation discharge cut-off voltage and the discharge temperature at the end of discharge;

[0052] S30, based on a temperature compensation formula, using discharge temperature calibration to compensate for the discharge capacity to obtain a compensated discharge capacity;

[0053] S40, inputting the compensated discharge capacity into a capacity prediction formula obtained by pre-fitting to obtain the full discharge capacity of the lithium battery to be tested.

[0054] This embodiment extracts some effective characteristic values ​​under the new formation process, namely the discharge capacity corresponding to the discharge cut-off voltage and the discharge temperature corresponding to the end of discharge. According to the pre-established capacity prediction model, the actual capacity of the battery cell can be accurately predicted. Moreover, when applied in the formation process, the formation is high-temperature formation, and the temperature consistency is good, which is conducive to ensuring the accuracy of the capacity prediction. The average prediction accuracy can reach within 3‰.

[0055] In one embodiment, the temperature compensation formula is expressed as: C1'=C1-(T1-C)*D

[0056] Wherein, C and D are constants, * is the multiplication sign, T1 is the discharge temperature, C1 is the discharge capacity, and C1' is the discharge capacity after replenishment. Specifically, C represents the average discharge temperature of all battery cells in the discharge step, and D represents the capacity difference of all battery cells compared to the capacity at the average discharge temperature.

[0057] In one embodiment, the capacity prediction formula is expressed as: C2'=A*C1'+B

[0058] Wherein, A and B are constants, * is a multiplication sign, C1' is the discharge capacity after replenishment, and C2' is the predicted full discharge capacity. Specifically, A represents the capacity prediction coefficient, and B represents the capacity difference.

[0059] It should be noted that due to the significant impact of temperature on capacity, under the combined effects of process ambient temperature, heat generated by equipment operation, and heat generated during the charging and discharging process of the battery cells, the capacity value presented by the battery cell after charging / discharging in the test cabinet deviates from the actual capacity to a certain extent. The capacity value at the standard temperature is obtained through big data analysis. For deviations from this temperature, a temperature compensation formula is established to calibrate the discharge capacity of battery cells with a discharge temperature outside 25°C to the average temperature. Then, a fitting curve is drawn between the calibrated C1' and the known full-discharge capacity C2. The predicted full-discharge capacity C2' can be obtained through the known C1' and the capacity prediction formula. The C2' obtained through temperature compensation correction is more in line with the actual capacity of the battery cell itself, and the prediction accuracy is higher.

[0060] In one embodiment, the temperature compensation formula is expressed as: C1'=C1-(T1-52.156)*160.93

[0061] Where T1 is the discharge temperature, C1 is the discharge capacity, C1' is the discharge capacity after replenishment, and * is the multiplication sign.

[0062] In one embodiment, the capacity prediction formula is expressed as: C2'=0.214460575229372*C1'+49721.0074883561

[0063] Where C1' is the discharge capacity after replenishment, C2' is the predicted full discharge capacity, and * is the multiplication sign.

[0064] In one embodiment, the step S10 of performing charge and discharge processing on the lithium battery to be tested and then recharging the battery to the SOC required for self-discharge screening specifically includes:

[0065] On the ternary platform, the lithium battery to be tested is placed on the formation cabinet and charged with constant current and constant voltage to a certain SOC of 50% to 70%. Then, it is discharged with constant current to 3.2-3.3V, and then recharged to the 30-60% SOC required for screening self-discharge.

[0066] Specifically, the starting SOC of the iron-lithium platform is 20-30% SOC, and the starting SOC of the ternary platform is 50-70% SOC; then the iron-lithium platform is discharged to 0-5% SOC, and the ternary platform is discharged to 30% SOC; then the iron-lithium platform is replenished to 17% SOC, and the ternary platform is replenished to 60% SOC.

[0067] It should be noted that the charging capacity, discharge capacity and self-discharge screening capacity of iron-lithium and ternary are different. This embodiment combines the production status of the production line, obtains the discharge process data without affecting the production efficiency and the amount of electricity required for self-discharge screening, analyzes the data output and demonstrates the prediction plan route of iron-lithium / ternary.

[0068] Furthermore, this embodiment sets the required charge and discharge steps according to the formation process, and adjusts the charge and discharge rates to control the predicted duration of the formation step process to within 6 hours, greatly shortening the process cycle to increase production capacity and reduce energy consumption.

[0069] It should be noted that the temperature compensation formula and capacity prediction formula used in the lithium battery capacity prediction process of this embodiment are pre-established. The roadmap is shown in FIG2 , where:

[0070] (1) The pre-construction process of the temperature compensation formula includes:

[0071] Extract the discharge flux and discharge temperature of a set number of cells at the end of the discharge step, and calculate the mean discharge capacity and the mean discharge temperature;

[0072] Calculate the capacity difference of each cell based on the average discharge temperature = (discharge capacity of each cell - average discharge capacity) / (discharge temperature of each cell - average discharge temperature);

[0073] The temperature compensation formula is established based on the discharge flux, the discharge temperature and the capacity difference.

[0074] (2) The process of establishing the capacity prediction formula includes:

[0075] After charging and discharging the sample cells, recharge them to the SOC required for self-discharge screening;

[0076] Measure the discharge capacity of each sample cell at different discharge cut-off voltages, analyze the correlation between multiple discharge capacities and full discharge capacity corresponding to each sample cell, and determine the discharge capacity with the strongest correlation with full discharge capacity as the target discharge capacity;

[0077] The target discharge capacity and full discharge capacity corresponding to all sample cells are linearly fitted to obtain the capacity prediction formula.

[0078] This embodiment obtains the capacity C1 corresponding to different discharge cut-off voltages of multiple battery cells, uses the CORREL function to calculate the correlation between each discharge capacity C1 and the full discharge capacity C2, determines the discharge cut-off voltage U1 with a strong correlation with the full discharge capacity, obtains the discharge capacity C1 and the full discharge capacity C2 corresponding to the discharge cut-off voltage U1 of multiple battery cells, performs linear fitting, and obtains the fitting formula. Specifically,

[0079] 1) After the battery is charged to a certain SOC, the iron-lithium cell is discharged to 2.0V and the ternary cell is discharged to 2.8V. 2) Both cells are then fully charged and discharged, and the full-discharge capacity is obtained as a comparison value. 3) In the process data of the iron-lithium discharge to 2.0V / ternary discharge to 2.8V, starting from the starting voltage, integer voltage values ​​and corresponding capacity values ​​are extracted at intervals of 20mV. The capacity value is correlated with the full-discharge capacity. If the correlation coefficient is >0.6, a strongly correlated fit curve is presented. The discharge capacity C1 and the full-discharge capacity C2 that meet the strong correlation are linearly fitted to obtain the capacity prediction formula.

[0080] Specifically, in this embodiment, 200 batteries are batch-tested. According to the formation steps, they are first formed into constant current and constant voltage charging to 65% SOC, then discharged to 3260mV, and then recharged to 60% SOC; the battery is then fully charged and discharged to obtain the full discharge capacity C2 of the battery. By extracting the discharge temperature T1 and the discharge capacity C1, the temperature compensation discharge capacity C1' is obtained by establishing the relationship between the discharge temperature T1 and the discharge capacity C1, and then fitting the linear equation of the discharge capacity C1' and the full discharge capacity C2 to obtain the linear equation set: C2'=A*C1'+B C1'=C1-(T1-C)*D

[0081] Another 200 batteries were collected and the formation steps were repeated for model verification. The full discharge capacity C2' of the battery was predicted based on the established set of linear equations. The deviation was then compared with the actual capacity C2 of the battery. The predicted capacity deviation could be controlled within 3‰, and the proportion of prediction errors ≤10‰ was ≥100%. This embodiment uses 55Ah platform battery data as an example for prediction. The verification results are shown in Figures 4 and 5. The predicted capacity is highly consistent with the actual capacity distribution scatter plot of the capacity, and the average prediction accuracy of the predicted capacity and actual capacity is about 1.48‰, which is far lower than the set prediction target of 3‰, thereby verifying the accuracy and stability of the capacity prediction method of the present invention.

[0082] A sample of a chemical process data file is randomly selected to demonstrate the data feature values ​​extracted in the actual prediction, as shown in Figure 6.

[0083] In addition, as shown in FIG7 , a second embodiment of the present invention discloses a lithium battery capacity prediction system, the system comprising:

[0084] The formation treatment module 10 is used to perform charge and discharge treatment on the lithium battery to be tested and then recharge it to the SOC required for self-discharge screening;

[0085] The discharge information acquisition module 20 is used to obtain the discharge capacity corresponding to the formation discharge cut-off voltage and the discharge temperature at the end of discharge;

[0086] A temperature compensation module 30 is configured to calibrate and compensate the discharge capacity using the discharge temperature based on a temperature compensation formula to obtain a compensated discharge capacity;

[0087] The capacity prediction module 40 is used to input the compensated discharge capacity into a pre-fitted capacity prediction formula to obtain the full discharge capacity of the lithium battery to be tested.

[0088] In one embodiment, the temperature compensation formula is expressed as: C1'=C1-(T1-C)*D

[0089] Wherein, C and D are constants, * is a multiplication sign, T1 is the discharge temperature, C1 is the discharge capacity, and C1' is the discharge capacity after replenishment.

[0090] In one embodiment, the capacity prediction formula is expressed as: C2'=A*C1'+B

[0091] Where A and B are constants, * is a multiplication sign, C1' is the discharge capacity after replenishment, and C2' is the predicted full discharge capacity. In one embodiment, the temperature compensation formula is expressed as: C1' = C1-(T1-52.156)*160.93

[0092] Where T1 is the discharge temperature, C1 is the discharge capacity, C1' is the discharge capacity after replenishment, and * is the multiplication sign.

[0093] In one embodiment, the capacity prediction formula is expressed as: C2'=0.214460575229372*C1'+49721.0074883561

[0094] Where C1' is the discharge capacity after replenishment, C2' is the predicted full discharge capacity, and * is the multiplication sign.

[0095] In one embodiment, the system further includes a temperature compensation formula establishment module, specifically configured to:

[0096] Extract the discharge flux and discharge temperature of a set number of cells at the end of the discharge step, and calculate the mean discharge capacity and the mean discharge temperature;

[0097] Calculate the capacity difference of each cell based on the average discharge temperature = (discharge capacity of each cell - average discharge capacity) / (discharge temperature of each cell - average discharge temperature);

[0098] The temperature compensation formula is established based on the discharge flux, the discharge temperature and the capacity difference.

[0099] In one embodiment, the system further includes a capacity prediction formula establishment module, specifically configured to:

[0100] After charging and discharging the sample cells, recharge them to the SOC required for self-discharge screening;

[0101] Measure the discharge capacity of each sample cell at different discharge cut-off voltages, analyze the correlation between multiple discharge capacities and full discharge capacity corresponding to each sample cell, and determine the discharge capacity with the strongest correlation with full discharge capacity as the target discharge capacity;

[0102] The target discharge capacity and full discharge capacity corresponding to all sample cells are linearly fitted to obtain the capacity prediction formula.

[0103] In one embodiment, this embodiment specifically uses the CORREL function to calculate the correlation between each discharge capacity and the full discharge capacity, determines the discharge cut-off voltage with a strong correlation with the full discharge capacity, and uses the discharge capacity corresponding to the discharge cut-off voltage with a strong correlation with the full discharge capacity as the target discharge capacity.

[0104] In one embodiment, the chemical formation treatment module 10 is specifically used to:

[0105] The lithium battery to be tested is charged to 50%-70% SOC, then discharged to 3.2-3.3V, and then recharged to 30-60% SOC required for self-discharge screening.

[0106] It should be noted that other embodiments or implementation methods of the lithium battery capacity prediction system of the present invention can refer to the above-mentioned method embodiments, which will not be repeated here.

[0107] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0108] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0109] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A lithium battery capacity prediction method, characterized in that: The method comprises: After charging and discharging the lithium battery to be tested, recharge it to the SOC required for self-discharge screening; Obtain the discharge capacity corresponding to the formation discharge cut-off voltage and the discharge temperature at the end of discharge; Based on a temperature compensation formula, the discharge capacity is compensated by using a discharge temperature calibration to obtain a compensated discharge capacity; The compensated discharge capacity is fed into the pre-fitted capacity prediction formula to obtain the full discharge capacity of the lithium battery to be tested.

2. The lithium battery capacity prediction method according to claim 1, characterized in that: The temperature compensation formula is expressed as: C1' = C1-(T1-C)*D Wherein, C and D are constants, * is a multiplication sign, T1 is the discharge temperature, C1 is the discharge capacity, and C1' is the discharge capacity after replenishment.

3. The lithium battery capacity prediction method according to claim 1, characterized in that: The capacity prediction formula is expressed as: C2'=A*C1'+B Wherein, A and B are constants, * is a multiplication sign, C1' is the discharge capacity after replenishment, and C2' is the predicted full discharge capacity.

4. The lithium battery capacity prediction method according to claim 2, characterized in that: The temperature compensation formula is expressed as: C1' = C1-(T1-52.156)*160.93 Wherein, T1 is the discharge temperature, C1 is the discharge capacity, C1' is the discharge capacity after replenishment, and * is the multiplication sign.

5. The lithium battery capacity prediction method according to claim 1, characterized in that: The capacity prediction formula is expressed as: C2'=0.214460575229372*C1'+49721.0074883561 Wherein, C1' is the discharge capacity after replenishment, C2' is the predicted full discharge capacity, and * is the multiplication sign.

6. The lithium battery capacity prediction method according to claim 1, characterized in that: The process of establishing the temperature compensation formula includes: Extract the discharge flux and discharge temperature of a set number of cells at the end of the discharge step, and calculate the mean value of the discharge capacity and the mean value of the discharge temperature; Calculate the capacity difference of each battery cell based on the average discharge temperature = (discharge capacity of each battery cell - average discharge capacity) / (discharge temperature of each battery cell - average discharge temperature); The temperature compensation formula is established based on the discharge flux, the discharge temperature and the capacity difference.

7. The lithium battery capacity prediction method according to claim 1, characterized in that: The process of establishing the capacity prediction formula includes: After charging and discharging the sample battery cells, recharge them to the SOC required for self-discharge screening; Measure the discharge capacity corresponding to different discharge cut-off voltages of each sample battery cell, analyze the correlation between multiple discharge capacities corresponding to each sample battery cell and the full discharge capacity, and determine the discharge capacity with a strong correlation with the full discharge capacity as the target discharge capacity; The target discharge capacity and full discharge capacity corresponding to all sample cells are linearly fitted to obtain the capacity prediction formula.

8. The lithium battery capacity prediction method according to claim 7, characterized in that: The analyzing the correlation between the multiple discharge capacities corresponding to each sample battery cell and the full discharge capacity, and determining the discharge capacity with a strong correlation with the full discharge capacity as the target discharge capacity, includes: The CORREL function is used to calculate the correlation between each discharge capacity and the full discharge capacity, and the discharge cut-off voltage with a strong correlation with the full discharge capacity is determined; The discharge capacity corresponding to the discharge cut-off voltage that has a strong correlation with the full discharge capacity is used as the target discharge capacity.

9. The lithium battery capacity prediction method according to claim 1, characterized in that: The method of charging and discharging the lithium battery to be tested and then replenishing the battery to the SOC required for self-discharge screening includes: The lithium battery to be tested is charged to 50%-70% SOC, then discharged to 3.2-3.3V, and then recharged to 30-60% SOC required for self-discharge screening.

10. A lithium battery capacity prediction system, characterized in that: The system comprises: The formation treatment module is used to charge and discharge the lithium battery to be tested and then recharge it to the SOC required for self-discharge screening; A discharge information acquisition module is used to obtain the discharge capacity corresponding to the formation discharge cut-off voltage and the discharge temperature at the end of discharge; A temperature compensation module, used to calibrate and compensate the discharge capacity using the discharge temperature based on a temperature compensation formula to obtain a compensated discharge capacity; The capacity prediction module is used to input the compensated discharge capacity into the pre-fitted capacity prediction formula to obtain the full discharge capacity of the lithium battery to be tested.

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