Electrical performance test method for rapidly judging performance of battery pack and implementation path

Abstract

The invention discloses an electrical performance test method for rapidly judging the performance of a battery pack and an implementation path, and relates to the field of electrical performance test of the battery pack. The invention discloses an electrical performance test method for rapidly judging the performance of a battery pack. The method comprises the following specific steps: S1, establishing a very simple step test method; s2, constructing a prediction algorithm regression equation for predicting related dynamic and static pressure difference results of the extremely simple step test as dynamic and static pressure differences of full charge and full discharge, and constructing a prediction model according to the algorithm regression equation; s3, establishing a long-step electrical measurement method: adding a step of emptying the battery pack between traditional full-step electrical measurement methods; according to the method, a battery pack electrical performance test iteration upgrading path from conventional full-charge full-discharge full-step electrical testing to full-charge shallow-discharge short-step electrical testing to initial SOC emptying and recharging to extremely-simple step electrical testing of the delivery SOC is provided, the purpose of rapidly judging the performance of the battery pack is achieved, the testing time is shortened, the quality is guaranteed, and meanwhile the cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery pack electrical performance testing, and in particular relates to an electrical performance testing method and implementation path for quickly determining battery pack performance. Background Art

[0002] The battery pack, as the power source of electric vehicles, its performance directly determines the quality of the electric vehicle's performance.

[0003] Therefore, in addition to strict control over the cell and pack production processes to prevent defects, it is also necessary to have means to detect and intercept abnormalities at the pack production end. Typically, after the battery pack is assembled, a full-step electrical performance test, including full charge and full discharge, is required to verify the performance of the battery pack.

[0004] However, the complete electrical performance test goes through the process of full charging and full discharging, and a large amount of heat accumulates, resulting in the risk of temperature exceeding the limit during the electrical performance test. As a result, it is necessary to reduce the charging rate, extend the static time, and even split the electrical performance test step into two electrical test steps. This undoubtedly greatly increases the test time, occupies test resources, and causes additional waste of manpower and material resources.

[0005] Of course, some battery manufacturers are very confident in the battery cells they produce and only charge the battery packs to the SOC required by the customer before shipping. However, it is undeniable that there is a certain proportion of complaints about the battery packs of leading companies in the market due to defective batteries. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems in the prior art and to propose an electrical performance testing method and implementation path for quickly determining the performance of a battery pack.

[0007] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0008] A method for quickly determining the performance of a battery pack is provided. The specific steps are as follows:

[0009] S1: Establish a minimalist process test method;

[0010] S2: Construct a prediction algorithm regression equation to predict the dynamic and static pressure difference results of the minimalist process step test into the dynamic and static pressure differences of full charge and full discharge, and build a prediction model based on the algorithm regression equation.

[0011] S3: Establish a long-step electrical measurement method: Add a battery pack discharge step between the traditional full-step electrical measurement method to obtain the dynamic and static pressure differences of the initial discharge;

[0012] Measure the dynamic and static pressure differences during full charge and full discharge to obtain actual data values, and set the standard range based on the sigma distribution level of the actual values;

[0013] Compare the predicted value of the battery pack predicted by the prediction model with the standard value to determine whether it is within the standard range. If it is within the standard range, it will be circulated normally. If it is not within the standard range, a secondary inspection will be carried out using a long-step electrical measurement method.

[0014] The predicted value of the battery pack predicted by the prediction model is compared with the actual value obtained through the long-step electrical performance test to determine whether it is within the qualified accuracy error range. If it is not within the error range, the parameters are modified according to the regression equation constructed above to achieve self-learning of the prediction model.

[0015] Preferably, the battery packs of the same batch are divided into two groups according to a certain ratio, one group adopts a minimalist process step test method, and the other group adopts a long process step test method, so as to avoid invalid testing of the same batch caused by model errors.

[0016] Preferably, the minimalist electrical measurement method comprises the following steps:

[0017] A101: The battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and differential pressure data at the end of discharge are collected;

[0018] A102: Let it stand for 20 to 30 minutes. Collect the voltage and differential pressure data at the end of the standstill period as the discharge static voltage and differential pressure. To ensure depolarization, it is generally necessary to let it stand for 20 to 30 minutes.

[0019] A103: Charges at a constant rate from 0% SOC to the SOC required for shipment, collecting dynamic voltage and voltage difference data at the charging terminal;

[0020] A104: After standing still, collect the voltage and voltage difference data at the end as the charging static voltage and voltage difference.

[0021] Preferably, the long-step electrical testing method includes a full-charge, shallow-discharge, short-step electrical testing method; the steps of the full-charge, shallow-discharge, short-step electrical testing method are as follows:

[0022] The short-step electrical measurement includes the following steps:

[0023] A201: The battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and differential pressure data at the end of discharge are collected;

[0024] A202: Charge from 0% SOC to full SOC at a constant rate, collecting dynamic voltage and voltage difference data at the end of charge;

[0025] A203: Standstill, collect the voltage and voltage difference data at the end of the standstill as the charging static voltage and voltage difference;

[0026] A204: After the rest period, the battery pack is discharged from the fully charged SOC to the SOC required for shipment, and the dynamic voltage and differential pressure data at the end of discharge are collected.

[0027] Preferably, the specific steps of the long-step electrical measurement method are as follows:

[0028] B1: The battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and differential pressure data at the end of discharge are collected;

[0029] B2: Charge from 0% SOC to full SOC at a constant rate, collecting dynamic voltage and voltage difference data at the end of charge;

[0030] B3: Stand still, collect the voltage and pressure difference data at the end of the standstill as the charging static voltage and pressure difference;

[0031] B4: After the rest period, the battery pack is discharged from full SOC to 0% SOC. The dynamic voltage and differential pressure data at the end of discharge, as well as the final differential pressure data after charging to the shipping SOC, are collected.

[0032] A method for implementing an electrical performance test to quickly determine battery pack performance includes constructing a prediction algorithm regression equation. The steps are as follows:

[0033] C1: Select a certain number of battery packs to go online for long-step electrical testing;

[0034] C2: During the electrical measurement process, data such as the dynamic and static pressure difference of the initial SOC when the battery is empty, the pressure difference when the battery is charged to the shipping SOC, the dynamic and static pressure difference when the battery is fully charged, and the dynamic and static pressure difference when the battery is empty are collected.

[0035] C3: Based on the collected data, a first-order linear regression equation y=wx+b is fitted, where the factor (x) is the static pressure difference of the initial SOC discharge, and the response (y) is the static pressure difference of the full discharge; a second-order regression equation Y=X is fitted, where the factor (X) includes the dynamic pressure difference of the initial SOC discharge, and the response (y) is the dynamic pressure difference of the full discharge. T W1X+W2X+C;

[0036] C4: Fit the factor (x) as the static pressure difference from charging to shipping SOC, and the response (y) as the first-order linear regression equation y = wx + b for the static pressure difference at full charge; Fit the factor (X) including the dynamic pressure difference from charging to shipping SOC, charging start voltage, etc., and the response (y) as the second-order regression equation Y = X for the dynamic pressure difference at full charge T W1X+W2X+C;

[0037] C5: Construct a prediction model based on the above regression equation and embed it into the electrical performance test system. This allows batch battery packs to undergo minimalist electrical performance testing in the electrical performance test system embedded with the prediction model, thus achieving the goal of automatically identifying and intercepting abnormal battery packs.

[0038] Preferably, a prediction model constructed based on the prediction algorithm regression equation is implanted into the electrical performance test system;

[0039] The battery packs divided into two groups according to a certain ratio were tested using the simple process step test method and the long process step test method respectively;

[0040] Obtain data standard values ​​by measuring the dynamic and static voltage differences of the battery pack during full charge and full discharge;

[0041] Preferably, the battery pack is tested by the minimalist process step method: the battery pack is tested by the minimalist process step electrical performance test to obtain relevant pressure difference data, as well as the full charge and discharge voltage difference data predicted by the regression equation, and the differences between the predicted values ​​of the full charge and discharge dynamic and static pressure differences and the standard values ​​are compared to determine whether they are within the set standard range; if they are within the standard range, the battery pack is circulated normally; if not, the battery pack is mixed into a randomly selected second battery pack for a long process step electrical performance test.

[0042] Preferably, for a battery pack subjected to a long-step electrical measurement method: first, the predicted data is obtained through the model, and then the battery pack is subjected to a long-step electrical performance test to obtain the actual data; the predicted value and the actual value are compared to determine whether they are within the qualified accuracy error range. If they are not within the error range, the parameters are modified according to the regression equation constructed above to achieve self-learning of the prediction model.

[0043] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: the present invention;

[0044] This electrical performance test method for quickly determining battery pack performance proposes an iterative upgrade path for battery pack electrical performance testing, from conventional full-charge and discharge full-step electrical testing to full-charge and shallow-discharge short-step testing, and then to the initial SOC discharge and then charge to the shipping SOC. This achieves the purpose of quickly determining battery pack performance, reduces test time, ensures quality and reduces costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In the attached figure:

[0046] Figure 1 This is a schematic diagram of a simplified electrical testing method for quickly determining battery pack performance, as proposed by the present invention;

[0047] Figure 2 A schematic diagram of a traditional full-step electrical testing method for quickly determining battery pack performance, as proposed by the present invention;

[0048] Figure 3 A schematic diagram of a long-step electrical testing method for quickly determining battery pack performance, proposed by the present invention;

[0049] Figure 4 A schematic diagram of a full-charge, shallow-discharge, short-step electrical testing method for an electrical performance testing method for quickly determining battery pack performance proposed by the present invention;

[0050] Figure 5 A schematic diagram of a simplified process flow for implementing an electrical performance test method for quickly determining battery pack performance, as proposed by the present invention;

[0051] Figure 6 A flowchart of a battery pack recharging method for a rapid determination of battery pack performance without the ability to determine cell performance, according to the present invention;

[0052] Figure 7 This is a schematic diagram of the simplified process steps of the electrical performance testing method for quickly determining the performance of a battery pack proposed by the present invention, including electrical measurement to determine the performance of the battery pack and self-learning process. DETAILED DESCRIPTION

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments so that those skilled in the art can implement the invention with reference to the description.

[0054] It should be understood that terms such as “having”, “including” and “comprising” used herein do not preclude the existence or addition of one or more other elements or combinations thereof.

[0055] In the description of the present invention, the terms "horizontal", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be understood as limiting the present invention.

[0056] Reference Figure 1-Figure 7 A method for quickly determining the performance of a battery pack is disclosed, comprising a long-step electrical testing method, a simplified electrical testing method, and a full-charge, shallow-discharge, short-step electrical testing method. The long-step electrical testing method includes all steps of the simplified electrical testing method and the full-charge, shallow-discharge, short-step electrical testing method.

[0057] Construct a prediction algorithm regression equation to predict the dynamic and static pressure differences of the battery pack during full charge and full discharge, as tested using a minimalist process test method. A prediction model is constructed based on the algorithm regression equation. The dynamic pressure difference refers to the pressure difference at the end of the full charge stage and the end of the full discharge stage, and the static pressure difference refers to the pressure difference measured after standing for 20 minutes at the end of the full charge stage and the end of the full discharge stage, respectively.

[0058] The prediction algorithm regression equation predicts the dynamic and static pressure difference results of the minimalist step test as the dynamic and static pressure difference of full charge and full discharge, and then compares and determines based on the predicted data and the standard;

[0059] Obtain data standard values ​​by measuring the dynamic and static pressure differences of fully charged and fully discharged qualified battery packs, and set the standard range based on the standard values;

[0060] The predicted value is compared with the standard value to determine whether it is within the standard range. If it is within the standard range, it will flow normally. If it is not within the standard range, a long-step electrical measurement method is used for secondary detection. First, the predicted dynamic and static pressure difference data of full charge and discharge are obtained through the model, and then the battery pack is subjected to a long-step electrical performance test to obtain the actual data; the predicted value and the actual value are compared to determine whether they are within the qualified accuracy error range. If not, the parameters are modified according to the regression equation constructed above to achieve self-learning of the prediction model. At the same time, the actual value is compared to see if it is within the standard range. If it is within the standard range, it will flow normally. If it is not within the standard range, it will be treated as NG. The dynamic pressure difference refers to the pressure difference at the end of the full charge stage and the pressure difference at the end of the full discharge stage, as well as the static pressure difference measured after each of them has been left to stand for twenty minutes;

[0061] Specifically:

[0062] 1. The simplified electrical measurement method includes the following steps: Figure 1 As shown, A101: the battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and pressure difference data at the end of discharge are collected; A102: it is allowed to stand, and the voltage and pressure difference data at the end of standing are collected as the static voltage and pressure difference of discharge. To ensure depolarization, it is generally necessary to stand for 20 to 30 minutes; A103: it is charged from 0% SOC to the SOC required for shipment at a constant rate, and the dynamic voltage and pressure difference data at the end of charging are collected; A104: after standing, the voltage and pressure difference data at the end of standing are collected as the static voltage and pressure difference of charging.

[0063] 2. Full charge and shallow discharge short step electrical test includes the following steps: Figure 4As shown, A201: the battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and pressure difference data at the end of discharge are collected; A202: the battery pack is charged from 0% SOC to the fully charged SOC at a constant rate, and the dynamic voltage and pressure difference data at the end of charging are collected; A203: the battery pack is left to stand, and the voltage and pressure difference data at the end of the standstill are collected as the static voltage and pressure difference of discharge; A204: after the standstill is completed, the battery pack is discharged from the fully charged SOC to the SOC required for shipment, and the dynamic voltage and pressure difference data at the end of charging are collected.

[0064] 3. The specific steps of the long-step electrical measurement method are as follows: Figure 3 As shown, B1: the battery pack is discharged from the starting SOC and is stopped at 0% SOC, and the dynamic voltage and pressure difference data at the end of discharge are collected; B2: the battery pack is charged from 0% SOC to the fully charged SOC at a constant rate, and the dynamic voltage and pressure difference data at the end of charging are collected; B3: the battery pack is left to stand, and the voltage and pressure difference data at the end of the standstill are collected as the static voltage and pressure difference of discharge; B4: after the standstill is completed, the battery pack is discharged from the fully charged SOC and is stopped at 0% SOC, and the dynamic voltage and pressure difference data at the end of discharge are collected, as well as the shipping pressure difference data when the battery pack is finally charged to the shipping SOC;

[0065] from Figure 3 It can be concluded that if the charging process is divided into stage 1-stage 2: charging from 0% SOC to delivery SOC and stage 2-stage 3: charging from delivery SOC to 100% SOC, it is obvious that the short section from the starting stage to the end of the charging process 1-stage 2 is completely consistent with the test process of the minimalist process step electrical measurement we introduced, and the process from stage 2 to the end is completely consistent with the traditional full-step electrical measurement method, such as Figure 2 As shown;

[0066] Therefore, in order to construct a correlation equation between the minimalist step test results and the full-step electrical test results, this embodiment selects a certain number of battery packs for long-step electrical performance test verification including initial SOC discharge. The discharge starting temperature of the long-step electrical test is kept consistent with the discharge starting temperature of the traditional full-step electrical test. Therefore, the dynamic and static pressure difference results of the discharge end of the long-step electrical test can be equated with the discharge voltage difference results of the traditional full-step electrical test.

[0067] Record the voltage and differential pressure data of all these battery packs in each test stage, such as the dynamic and static differential pressure of discharge at the initial SOC when the battery is discharged; the charging differential pressure from stage 1 to stage 2 to the shipping SOC; the dynamic and static differential pressure of charging from stage 2 to stage 3 to 100% SOC; the differential pressure at the beginning of discharge after the quiescence of stage 3 to stage 4; the differential pressure when the battery is discharged to the shipping SOC from stage 4 to stage 5; the dynamic and static differential pressure of discharge at stage 5 to stage 6 when the battery is discharged, and the shipping differential pressure when the battery is finally charged to the shipping SOC. Because the energy accumulation during full charge and full discharge causes the temperature to rise rapidly, it is necessary to increase the quiescent time at the full charge, empty charge, and shipping SOC positions to ensure that the temperature range meets the standard requirements. If the full discharge process is not performed in the subsequent discharge stage and the battery is directly placed in the shipping SOC, that is, the battery is quiescent and the test step is ended at stage 5, then this shortened test step is called a full charge and shallow discharge short step. Figure 4 As shown, the simplified electrical performance test does not directly adopt Figure 6 The battery pack recharging process shown in the figure is a little longer than the recharging electrical test because the electrical performance test of this minimalist process takes a little longer than the time from the initial SOC discharge to the initial SOC charge, but because the dynamic and static voltage difference data of the initial empty electricity can be collected, it provides a basis for accurately predicting the full discharge voltage difference of the whole pack. In the actual test process, the batch battery packs are tested and judged for their electrical performance according to the minimalist electrical test process, and a part of the battery packs can be randomly tested for electrical performance control according to the long process. The basic performance of the same batch of batteries can be obtained with the data of the sampled packs, and the purpose of model correction and learning can be achieved. The specific process is as follows Figure 7 shown.

[0068] 4. The method of constructing the prediction algorithm regression equation is as follows:

[0069] C1: Select a certain number of battery packs to go online for long-step electrical testing;

[0070] C2: During the electrical measurement process, data such as the dynamic and static pressure difference of the initial SOC when the battery is empty, the pressure difference when the battery is charged to the shipping SOC, the dynamic and static pressure difference when the battery is fully charged, and the dynamic and static pressure difference when the battery is empty are collected.

[0071] C3: Based on the collected data, a first-order linear regression equation y=wx+b is fitted, where the factor (x) is the static pressure difference at the initial SOC discharge level and the response (y) is the static pressure difference at the full discharge level. A second-order regression equation Y=XTW1X+W2X+C is fitted, where the factor (X) includes the dynamic pressure difference at the initial SOC discharge level and the response (y) is the dynamic pressure difference at the full discharge level.

[0072] C4: Fit the first-order linear regression equation y = wx + b, where the factor (x) is the static pressure difference from charging to shipping SOC, and the response (y) is the static pressure difference at full charge. Fit the second-order linear regression equation Y = XTW1X + W2X + C, where the factor (X) includes the dynamic pressure difference from charging to shipping SOC, the charge start voltage, etc., and the response (y) is the dynamic pressure difference at full charge.

[0073] C5: Build a prediction model based on the above regression equation and embed it into the electrical performance test system. This allows batches of battery packs to undergo simplified electrical performance testing using the system embedded with the prediction model, automatically identifying and intercepting abnormal battery packs.

[0074] Working Principle: The prediction model is embedded in the electrical performance test system. A batch of battery packs is put online. A certain proportion of battery packs are randomly selected from the batch, and the batch is then divided into two parts: the first batch of battery packs and the randomly selected second batch of battery packs.

[0075] Obtain data standard values ​​by measuring the dynamic and static voltage differences of the battery pack during full charge and full discharge;

[0076] The first battery pack is tested using a minimalist process. The battery pack undergoes electrical performance testing using a minimalist process to obtain relevant differential pressure data, as well as full-charge and discharge differential pressure data predicted using a regression equation. The predicted dynamic and static differential pressures for full charge and discharge are compared with the standard values ​​to determine whether they are within the set standard range. If they are within the standard range, the battery pack is recycled normally. If not, the battery pack is mixed with a randomly selected second battery pack for a longer process electrical performance test.

[0077] When testing the second battery pack, the model is first used to obtain the predicted dynamic and static pressure difference data for full charge and discharge, and then the battery pack is subjected to a long-step electrical performance test to obtain the actual data; the predicted and actual values ​​are compared to determine whether they are within the qualified accuracy error range. If not, the parameters are modified according to the regression equation constructed above to achieve self-learning of the prediction model. At the same time, the actual value is compared to see if it is within the standard range. If it is within the standard range, it will flow normally. If not, it will be treated as NG.

[0078] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patented invention. It should be noted that those skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention. These variations and improvements are equivalent modifications and improvements to the above embodiments based on the essential technology of the present invention and fall within the scope of protection of the present invention.

Claims

1. A method for quickly determining the performance of a battery pack, characterized in that: The specific steps are as follows: S1: Establish a minimalist process test method; S2: Construct a prediction algorithm regression equation to predict the dynamic and static pressure difference results of the minimalist process step test into the dynamic and static pressure differences of full charge and full discharge, and build a prediction model based on the algorithm regression equation. S3: Establish a long-step electrical measurement method: Add a battery pack discharge step between the traditional full-step electrical measurement method to obtain the dynamic and static pressure differences of the initial discharge; Measure the dynamic and static pressure differences during full charge and full discharge to obtain actual data values, and set the standard range based on the sigma distribution level of the actual values; Compare the predicted value of the battery pack predicted by the prediction model with the standard value to determine whether it is within the standard range. If it is within the standard range, it will be circulated normally. If it is not within the standard range, a secondary inspection will be carried out using a long-step electrical measurement method. The predicted value of the battery pack predicted by the prediction model is compared with the actual value obtained through the long-step electrical performance test to determine whether it is within the qualified accuracy error range. If it is not within the error range, the parameters are modified according to the regression equation constructed above to achieve self-learning of the prediction model.

2. The electrical performance testing method for quickly determining battery pack performance according to claim 1, characterized in that: The battery packs from the same batch are divided into two groups according to a certain ratio. One group adopts the minimalist process step test method, and the other group adopts the long process step test method to avoid invalid testing of the same batch due to model errors.

3. The electrical performance testing method for quickly determining battery pack performance according to claim 1, characterized in that: The extremely simple step electrical measurement method comprises the following steps: A101: The battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and differential pressure data at the end of discharge are collected; A102: Let it stand for 20 to 30 minutes. Collect the voltage and differential pressure data at the end of the standstill period as the discharge static voltage and differential pressure. To ensure depolarization, it is generally necessary to let it stand for 20 to 30 minutes. A103: Charges at a constant rate from 0% SOC to the SOC required for shipment, collecting dynamic voltage and voltage difference data at the charging terminal; A104: After standing still, collect the voltage and voltage difference data at the end as the charging static voltage and voltage difference.

4. The electrical performance testing method for quickly determining battery pack performance according to claim 3, characterized in that: The long-step electrical testing method includes a full-charge, shallow-discharge, and short-step electrical testing method. The steps of the full-charge, shallow-discharge, and short-step electrical testing method are as follows: The short-step electrical measurement includes the following steps: A201: The battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and differential pressure data at the end of discharge are collected; A202: Charge from 0% SOC to full SOC at a constant rate, collecting dynamic voltage and voltage difference data at the end of charge; A203: Standstill, collect the voltage and voltage difference data at the end of the standstill as the charging static voltage and voltage difference; A204: After the rest period, the battery pack is discharged from the fully charged SOC to the SOC required for shipment, and the dynamic voltage and differential pressure data at the end of discharge are collected.

5. The electrical performance testing method for quickly determining battery pack performance according to claim 4, characterized in that: The specific steps of the long-step electrical measurement method are as follows: B1: The battery pack is discharged from the starting SOC to 0% SOC, and the dynamic voltage and differential pressure data at the end of discharge are collected; B2: Charge from 0% SOC to full SOC at a constant rate, collecting dynamic voltage and voltage difference data at the end of charge; B3: Stand still, collect the voltage and pressure difference data at the end of the standstill as the charging static voltage and pressure difference; B4: After the rest period, the battery pack is discharged from full SOC to 0% SOC. The dynamic voltage and differential pressure data at the end of discharge, as well as the final differential pressure data after charging to the shipping SOC, are collected.

6. An implementation path for an electrical performance test for quickly determining battery pack performance, applied to the electrical performance test method for quickly determining battery pack performance according to claim 5, characterized in that: Including building a prediction algorithm regression equation; the steps are as follows: C1: Select a certain number of battery packs to go online for long-step electrical testing; C2: During the electrical measurement process, data such as the dynamic and static pressure difference of the initial SOC when the battery is empty, the pressure difference when the battery is charged to the shipping SOC, the dynamic and static pressure difference when the battery is fully charged, and the dynamic and static pressure difference when the battery is empty are collected. C3: Based on the collected data, a first-order linear regression equation y=wx+b is fitted, where the factor (x) is the static pressure difference of the initial SOC discharge, and the response (y) is the static pressure difference of the full discharge; a second-order regression equation Y=X is fitted, where the factor (X) includes the dynamic pressure difference of the initial SOC discharge, and the response (y) is the dynamic pressure difference of the full discharge. T W1X+W2X+C; C4: Fit the factor (x) as the static pressure difference from charging to shipping SOC, and the response (y) as the first-order linear regression equation y = wx + b for the static pressure difference at full charge; Fit the factor (X) including the dynamic pressure difference from charging to shipping SOC, charging start voltage, etc., and the response (y) as the second-order regression equation Y = X for the dynamic pressure difference at full charge T W1X+W2X+C; C5: Construct a prediction model based on the above regression equation and embed it into the electrical performance test system. This allows batch battery packs to undergo minimalist electrical performance testing in the electrical performance test system embedded with the prediction model, thus achieving the goal of automatically identifying and intercepting abnormal battery packs.

7. The implementation path of the electrical performance test for quickly determining the performance of a battery pack according to claim 6, characterized in that: The prediction model constructed based on the prediction algorithm regression equation is implanted into the electrical performance test system; The battery packs divided into two groups according to a certain ratio were tested using the simple process step test method and the long process step test method respectively; The data standard value is obtained by measuring the dynamic and static voltage differences of the battery pack during full charge and full discharge.

8. The implementation path of the electrical performance test for quickly determining the performance of a battery pack according to claim 7, characterized in that: Battery packs tested using the simplified process step test method: The battery packs undergo a simplified electrical performance test to obtain relevant pressure difference data, as well as full charge and discharge pressure difference data predicted by a regression equation. The predicted dynamic and static pressure differences for full charge and discharge are compared with the standard values ​​to determine whether they are within the set standard range. If they are within the standard range, the battery pack is circulated normally. If not, the battery pack is mixed with a second randomly selected battery pack for a longer process step electrical performance test.

9. The implementation path of the electrical performance test for quickly determining the performance of a battery pack according to claim 8, characterized in that: For battery packs that have undergone long-step electrical testing: First, the model is used to obtain predicted data, and then the battery pack is subjected to a long-step electrical performance test to obtain actual data. The predicted and actual values ​​are compared to determine whether they are within the qualified accuracy error range. If not, the parameters are modified according to the regression equation constructed above to achieve self-learning of the prediction model.

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