Method for predicting cycle life of battery

Abstract

The invention discloses a method for predicting the cycle life of a battery, and the method comprises the steps: calibrating the initial capacity of a to-be-tested sample, carrying out the initial DVA test, and drawing an initial DVA curve; the battery is subjected to charging and discharging circulation according to constant current / power, capacity calibration and circulation DVA testing are carried out after each circulation is cut off, and a circulation DVA curve under the capacity retention ratio corresponding to the number of circulation turns is obtained; analyzing and recording the discharge capacity of the lithium ion loss stage through a DVA curve; and on the basis of the relational expression, according to the capacity retention ratio of the cycle number of the to-be-predicted battery, calculating the cycle number of the to-be-predicted battery estimated under the corresponding capacity retention ratio according to the capacity retention ratio of the cycle number of the to-be-predicted battery. The method is simple and easy to implement, lossy disassembly testing does not need to be carried out on the battery, the related parameter obtaining method is simple, and the original testing time is effectively saved.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular to a method for quickly predicting battery cycle life. Background Art

[0002] For large-capacity, long-life batteries, it takes a long time to measure the battery life through long-term cycles during the R&D stage. Customers require a shorter R&D cycle, and R&D requires the simultaneous development of multiple solutions and multiple systems. Therefore, the industry urgently needs a method to quickly evaluate the cycle life of long-life lithium-ion batteries.

[0003] Evaluating battery life typically requires long-term charge-discharge cycle testing, which is time-consuming. While existing technologies can shorten lifespan evaluation cycles, most require destructive battery disassembly analysis or involve complex calculations.

[0004] For example, the method for predicting the cycle life of lithium iron phosphate batteries disclosed in Chinese patent CN119199613A calculates the relationship between the number of cycles and the capacity retention rate after a certain number of cycles. The battery is then disassembled to measure the expansion rate of the negative electrode plate, and a corresponding relationship is established to predict the cycle life. This method requires battery disassembly.

[0005] For example, the method of predicting battery cycle life using an electrochemical model disclosed in Chinese patent CN115047364A also requires testing relevant parameters after disassembling the battery and combining the electrochemical model and related mathematical algorithms to complete the prediction of battery life.

[0006] For example, the method for predicting battery cycle life disclosed in Chinese patent CN115236528A calculates the loss of positive electrode active material and positive electrode offset and completes life prediction through related calculations. It mainly considers the impact of the positive electrode on life attenuation, and the calculation process is slightly complicated. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings and defects of the existing technology and provide a method for quickly predicting the battery cycle life. The DVA calculation method is used to evaluate the battery cycle life. The DVA test calculation method is simple and easy to operate, and the error in predicting the cycle life is small, which can achieve a rapid prediction of the battery life; it can predict the number of cycles corresponding to each life state between normal cycle decay and 70% SOH, and can be used for battery cycle prediction in the range of 25℃-45℃.

[0008] The present invention is achieved in that:

[0009] A method for predicting battery cycle life comprises the following steps:

[0010] Perform initial capacity calibration and initial DVA test on the sample to be tested, record the charge and discharge capacity and voltage, and draw the initial DVA curve; subject the battery to constant current / power charge and discharge cycles, perform capacity calibration and cyclic DVA test after each cycle, and obtain the cyclic DVA curve at the capacity retention rate corresponding to the number of cycles; record the discharge capacity during the lithium ion loss stage through DVA curve analysis;

[0011] The fitting relationship between capacity retention rate, battery cycle number and discharge capacity is obtained by data fitting:

[0012] Based on the fitting relationship, according to the capacity retention rate of the battery cycle number to be predicted, the discharge capacity in the lithium ion loss stage corresponding to the battery cycle number to be predicted is calculated, and the battery cycle number to be predicted is calculated based on the discharge capacity in the lithium ion loss stage corresponding to the battery cycle number to be predicted.

[0013] Preferably, the fitting relationship between the capacity retention rate and the discharge capacity is as follows:

[0014] (Q LLI )n i =a*(Xn i %SOH)+b;wherein, a and b are constants, Xn i %SOH represents the number of battery cycles n corresponding to one cycle cutoff i The capacity retention rate, (Q LLI )n i Indicates the discharge capacity during the lithium ion loss stage.

[0015] Preferably, the fitting relationship between the battery cycle number and discharge capacity is as follows:

[0016] (Q LLI )n i =c*(n i )+d; where c and d are constants, n i Indicates the number of battery cycles.

[0017] Preferably, the battery is charged and discharged in a constant current / power cycle, and the cycle termination condition includes the cycle termination when the battery reaches the corresponding battery capacity retention rate in each cycle, or the cycle termination when a preset number of battery cycles n1 is completed.

[0018] Preferably, the magnification of the initial DVA test is 0.05C-0.1C.

[0019] Preferably, the initial capacity calibration and initial DVA test of the sample to be tested are performed, the charge and discharge capacity and voltage are recorded, and when the initial DVA curve is drawn, the voltage of the nth data point is subtracted from the voltage of the n+1th data point to obtain the voltage differential; the capacity of each n+1th data point is subtracted from the capacity of the nth data point to obtain the capacity differential, and then the initial DVA curve is drawn with the voltage differential and the capacity differential.

[0020] Preferably, the charging and discharging cycle of the battery at a constant current / power is performed in a constant temperature box environment.

[0021] Preferably, when the battery is charged and discharged in a constant current / power cycle, after each cycle is terminated, the capacity is calibrated at a constant current / power, and then a cyclic DVA test is performed; wherein, if the battery is cycled at a constant power, the capacity is calibrated at a constant power, and if the battery is cycled at a constant current, the capacity is calibrated at a constant current.

[0022] Preferably, after each cycle is terminated, the format of the cyclic DVA test is the same as the format of the initial DVA test, and the data processing method for drawing the cyclic DVA curve is the same as the data processing method for drawing the initial DVA curve.

[0023] Preferably, when the battery is charged and discharged in a constant current / power cycle, the battery cycle temperature is 25° C.-45° C.

[0024] The present invention performs DVA tests (low-rate constant-current charge and discharge tests) on batteries that have been cycled to different life states to obtain capacity and voltage data. After differentiating the data, a DVA curve (voltage-capacity differential curve) is obtained. The different phase change stages of the battery discharge process are recorded through DVA curve analysis, and the number of battery cycles is predicted through a fitting relationship established through regression calculation. The method is simple and easy to use, does not require destructive disassembly testing of the battery, and the method for obtaining relevant parameters is simple, which can effectively save the original testing time. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the method for predicting battery cycle life of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] In an exemplary embodiment of the present application, the method for predicting the battery cycle life is based on the DVA calculation method to estimate the battery life, analyze the changes in the balance of each phase of the battery during the discharge process through the DVA curve, divide the characteristic peak position, and record the discharge capacity (QLLI )n i , respectively fitted and calculated to obtain (Q LLI )n i The linear relationship between capacity retention rate and cycle number can be used to predict the cycle number by substituting the capacity retention rate, thereby realizing a rapid evaluation of the battery cycle life.

[0028] See also Figure 1 As shown, in an exemplary embodiment of the present application, the method for predicting battery cycle life comprises the following steps:

[0029] Perform initial capacity calibration and initial DVA test on the sample to be tested, record the charge and discharge capacity and voltage, and draw the initial DVA curve; subject the battery to constant current / power charge and discharge cycles, perform capacity calibration and cyclic DVA test after each cycle, and obtain the cyclic DVA curve at the capacity retention rate corresponding to the number of cycles; record the discharge capacity during the lithium ion loss stage through DVA curve analysis;

[0030] The fitting relationship between capacity retention rate, battery cycle number and discharge capacity is obtained by data fitting:

[0031] Based on the fitting relationship, according to the capacity retention rate of the battery cycle number to be predicted, the discharge capacity in the lithium ion loss stage corresponding to the battery cycle number to be predicted is calculated, and the battery cycle number to be predicted is calculated based on the discharge capacity in the lithium ion loss stage corresponding to the battery cycle number to be predicted.

[0032] In this application, based on the DVA curve analysis, data fitting is performed, and the fitting relationship between the capacity retention rate and the discharge capacity is obtained as follows:

[0033] (Q LLI )n i =a*(Xn i %SOH)+b;wherein, a and b are constants, Xn i %SOH represents the number of cycles n i The corresponding capacity retention rate, (Q LLI )n i Indicates the discharge capacity during the lithium ion loss stage.

[0034] In this application, based on the DVA curve analysis, data fitting is performed, and the fitting relationship between the number of battery cycles and discharge capacity is obtained as follows:

[0035] (Q LLI )n i =c*(n i )+d; where c and d are constants, n i Indicates the number of cycles of the battery.

[0036] After obtaining the fitting relationship between the capacity retention rate, the number of battery cycles and the discharge capacity, based on the fitting relationship between the capacity retention rate, the number of battery cycles and the discharge capacity, after inputting the capacity retention rate of the corresponding battery, the number of cycles of the corresponding battery can be estimated and calculated.

[0037] Specifically, the corresponding discharge capacity is first calculated based on the input battery capacity retention rate through the fitting relationship between the capacity retention rate and the discharge capacity, and then the number of battery cycles or times corresponding to the input battery capacity retention rate is calculated based on the calculated discharge capacity and the fitting relationship between the battery cycle number and the discharge capacity.

[0038] It should be noted that, in the embodiment of the present application, the number of times the battery is charged and discharged at a constant current / power cycle can be set according to the situation. After each cycle, a capacity calibration and a cycle DVA test are performed to obtain a cyclic DVA curve under the capacity retention rate corresponding to the current number of cycles, and the discharge capacity of the current lithium ion loss stage is recorded through DVA curve analysis. In this way, after multiple charge and discharge cycles, a capacity calibration and a cycle DVA test are performed after each cycle, and the DVA curve of each cycle is analyzed and compared with the initial DVA curve. The changes in the phase balance of the battery during the discharge process are analyzed, and the characteristic peak positions are divided to obtain the discharge capacity of the lithium ion loss stage corresponding to the capacity retention rate of each cycle.

[0039] Among them, in the embodiment of the present application, the battery is charged and discharged in a constant current / power cycle, and the cycle termination condition can include the cycle termination when the battery reaches the corresponding battery capacity retention rate in each cycle, or the cycle termination when the number of cycles of the battery is completed. The specific selection can be made during implementation.

[0040] For example, in the embodiment of the present application, the magnification of the initial DVA test is selected between 0.05C and 0.1C, which can be set as needed, or other magnifications can be used. For example, in the embodiment of the present application, after each cycle is terminated, the format of the cyclic DVA test is the same as the format of the initial DVA test.

[0041] For example, in the embodiments of the present application, the sample to be tested is subjected to initial capacity calibration and initial DVA testing, and the charge and discharge capacity and voltage are recorded. When plotting the initial DVA curve, the voltage of the nth data point is subtracted from the voltage of the n+1th data point to obtain the voltage differential; the capacity of the nth data point is subtracted from the capacity of the n+1th data point to obtain the capacity differential, and then the initial DVA curve is plotted using the voltage differential and the capacity differential. For example, the data processing method for plotting the cyclic DVA curve is the same as the data processing method for plotting the initial DVA curve.

[0042] For example, in the embodiments of the present application, the battery is subjected to constant current / power charge-discharge cycling in a constant temperature environment, specifically, in a constant temperature box. Preferably, the battery is subjected to constant current / power charge-discharge cycling at a battery cycle temperature of 25°C to 45°C.

[0043] For example, in the embodiment of the present application, when the battery is charged and discharged at a constant current / power cycle, after each cycle is terminated, the capacity is calibrated at a constant current / power, and then a cyclic DVA test is performed; wherein, if the battery is cycled at a constant power, the capacity is calibrated at a constant power, and if the battery is cycled at a constant current, the capacity is calibrated at a constant current.

[0044] Numerous experiments have shown that the prediction method of the present invention can predict the number of cycles corresponding to each life state of the battery from normal cycle decay to 70% SOH, and in particular, can be used for battery cycle prediction in the range of 25°C-45°C.

[0045] The specific implementation process of the embodiment of this application is as follows:

[0046] 1. Select the sample to be tested and conduct initial capacity calibration and initial DVA testing. Set the DVA test rate, such as 0.05C, and record the charge and discharge parameters to obtain capacity and voltage data. Subtract the voltage of the nth data point from the voltage of the n+1th data point to obtain the voltage differential. Subtract the capacity of the nth data point from the capacity of the n+1th data point to obtain the capacity differential. Draw the initial DVA curve.

[0047] 2. Set the constant temperature of the thermostat to cycle the battery at constant current / constant power; set the cycle cutoff condition to cycle when the battery capacity retention rate reaches Xn1% SOH, where Xn1% SOH is the battery capacity retention rate after the first cycle is cut off; after this battery cycle is cut off, perform a capacity calibration and cycle DVA test. The standard initial DVA test of the cycle DVA test obtains the DVA curve under n1% SOH; analyze the changes in the balance of each phase of the battery during the discharge process through the DVA curve, divide the characteristic peak position, and record the discharge capacity (Q) corresponding to the lithium ion loss stage at the end of this battery cycle. LLI )n1; Continue to set the battery cycle cut-off condition, so that the battery capacity retention rate reaches Xn2% SOH when the cycle is cut off, and record the discharge capacity corresponding to the lithium ion loss stage after the second cycle cut-off (Q LLI )n2; Continue to set the battery cycle cut-off condition, so that the battery capacity retention rate reaches Xn3% SOH when the cycle is cut off, and record the discharge capacity corresponding to the lithium ion loss stage after the third cycle cut-off (Q LLI )n3; proceed as above and record the nth iThe discharge capacity corresponding to the lithium ion loss stage after the end of the second cycle (Q LLI )n i ,n i is the corresponding number of battery cycles;

[0048] 3. Through data fitting, the capacity retention rate Xn of the battery cycle number is obtained i %SOH and corresponding (Q LLI )n i The relationship between: (Q LLI )n i =a*(Xn i % SOH) + b, and the number of battery cycles n i The corresponding (Q LLI )n i The relationship between LLI )n i =c*(n i )+d;

[0049] 4. The capacity retention rate of the battery cycle to be predicted is Xn i Substitute %SOH into the formula (Q LLI )n i =a*(Xn i %SOH)+b, calculated to get (Q LLI )n i ’ , (Q LLI )n i ’ Substitute into the formula (Q LLI )n i =c*(n i )+d, we get (Q LLI )n i ’ =c*(n i )+d; through (Q LLI )n i ’ =c*(n i )+d can be used to get the estimated number of cycles n under the corresponding capacity retention rate i .

[0050] The method in this embodiment uses the DVA calculation method to evaluate battery cycle life, predicting the number of cycles corresponding to each life state between normal cycle decay and 70% SOH. This method can be used to predict battery cycle life in the 25°C-45°C range. The following examples illustrate battery cycle life predictions at two temperatures, 25°C and 45°C.

[0051] Example 1:

[0052] The lithium iron phosphate system LP71173207-314Ah battery was used as the test object, and the battery cycle temperature was set to 25°C to describe the embodiment:

[0053] 1. Perform the initial DVA test on the 314Ah battery after the first capacity calibration. Set the charging current to 31.4A, constant current charging to 3.65V, then switch to constant voltage charging with a cut-off current of 15.7A; set the discharge current to 31.4A, constant current discharge to 2.5V.

[0054] 2. Set the constant temperature box to 25°C, and perform a standard cycle of the battery at a constant power of 502.4W in the constant temperature box; set the battery to the discharge capacity cutoff, such as when the capacity retention rate is 98% SOH, stop the cycle; perform the standard capacity at a constant power of 502.4W in the constant temperature box, and then carry out the DVA test after the standard capacity. The charge and discharge rate requirements are the same as the initial DVA test, and obtain the cycle DVA curve of the battery after the 98% SOH discharge capacity cutoff; divide the peak position of each characteristic peak position of the DVA curve, and record the discharge capacity after the n1th cycle (Q LLI )n1;

[0055] Continue the above method in this step to cycle: when the battery capacity retention rate is set to different capacity retention rates such as 98% SOH, 95% SOH, 92% SOH, 90% SOH, etc., stop the cycle and obtain the corresponding battery cycle DVA curve; divide the peak position according to the characteristic peak position of the DVA curve, and record the n2th cycle, n3th cycle, and nth cycle. i The discharge capacity after the first cycle (Q LLI )n1,(Q LLI )n2,(Q LLI )n3……(Q LLI )n i ;

[0056] 3. With the number of cycles as the horizontal axis, (Q LLI )n i The data is fitted as the vertical axis, and the linear correlation formula A between the two is obtained based on the data fitting obtained above; the capacity retention rate is taken as the horizontal axis, (Q LLI )n i Perform a linear fit on the vertical axis, and based on the data fitting obtained above, obtain the linear correlation formula B between the two.

[0057] 4. The capacity retention rate to be predicted is Xn i Substituting %SOH into the linear correlation formula B, we can get (Q LLI )n i Value, then (Q LLI )n iSubstituting this into the linear correlation formula A, the predicted number of cycles can be calculated.

[0058] The calculated and actual number of cycles are shown in Table 1 below, and the prediction errors are both less than 4%.

[0059] Note: Deviation = |(actual number of cycles - expected number of cycles) / actual number of cycles * 100%|.

[0060] Table 1 Predicted battery cycle number and actual battery cycle number and error (Example 1)

[0061]

[0062]

[0063] Example 2:

[0064] The lithium iron phosphate system LP71173207-314Ah battery was used as the test object, and the battery cycle temperature was set to 45°C to describe the embodiment:

[0065] 1. Perform the initial DVA test on the 314Ah battery after the first capacity calibration. Set the charging current to 31.4A, constant current charging to 3.65V, then switch to constant voltage charging with a cut-off current of 15.7A; set the discharge current to 31.4A, constant current discharge to 2.5V.

[0066] 2. Set the constant temperature box to 45°C, and perform a standard cycle of the battery at a constant power of 502.4W in the constant temperature box; set the battery to the discharge capacity cutoff, such as when the capacity retention rate is 98% SOH, stop the cycle; perform the standard capacity at a constant power of 502.4W in the constant temperature box, and then carry out the DVA test. The rate charge and discharge requires the initial DVA test to obtain the cycle DVA curve of the battery after the 98% SOH discharge capacity cutoff; divide the peak position of each characteristic peak position of the DVA curve, and record the discharge capacity after the n1th cycle (Q LLI )n1;

[0067] Continue the above method in this step to cycle: wherein, when setting the battery capacity retention rate to different capacity retention rates such as 95% SOH, 92% SOH, 90% SOH, etc., stop the cycle after multiple cycles to obtain the corresponding battery cycle DVA curve; divide the peak positions of each characteristic peak position of the DVA curve, and record the n2th cycle, n3th cycle, and nth cycle. i The discharge capacity after the first cycle (Q LLI )n1,(Q LLI )n2,(Q LLI )n3……(Q LLI )n i ;

[0068] 3. With the number of cycles as the horizontal axis, (Q LLI )n i The data is fitted as the vertical axis, and the linear correlation formula A between the two is obtained based on the data fitting obtained above; the capacity retention rate is taken as the horizontal axis, (Q LLI )n i A linear fitting is performed on the vertical axis, and a linear correlation formula B between the two is obtained based on the data obtained above.

[0069] 4. The capacity retention rate to be predicted n i Substituting %SOH into the linear correlation formula B, we can get (Q LLI )n i Value, then (Q LLI )n i Substituting this into the linear correlation formula A, the predicted number of cycles can be calculated.

[0070] The calculated values ​​and the actual number of cycles are shown in Table 2 below, and the prediction errors are all less than 4%.

[0071] Note: Deviation = |(actual number of cycles - expected number of cycles) / actual number of cycles * 100%|.

[0072] Table 2 Predicted battery cycle number and actual battery cycle number and error (Example 2)

[0073] Capacity retention rate Estimated number of cycles Actual number of cycles deviation 92.00％ 631 620 1.77％ 90.00％ 832 862 3.48％ 88.00％ 1106 1111 0.45％ 86.00％ 1349 1353 0.30％ 84.00％ 1591 1607 1.00％ 82.00％ 1834 1853 1.03％ 80.00％ 2076 2099 1.10％ 78.00％ 2319 2345 1.11％ 76.00％ 2561 2590 1.12％ 74.00％ 2803 2836 1.16％ 72.00％ 3046 3082 1.17％ 70.00％ 3287 3327 1.20％

[0074] From the above, it can be seen that the present invention estimates the battery cycle life through the DVA calculation method, and can predict the number of cycles when the battery reaches the end of life through the first few hundred cycles, effectively saving up to 60%-70% of the test time. In addition, the technical method is simple to operate and does not require disassembly and other operations to obtain the relevant parameters of the battery. The prediction can be achieved only through DVA curve analysis after each cycle is terminated. It is non-destructive to the sample and solves the problem of long-term cycle evaluation of battery life.

[0075] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention.

[0076] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for predicting battery cycle life, characterized in that: The following steps are involved: Perform initial capacity calibration and initial DVA test on the sample to be tested, record the charge and discharge capacity and voltage, and draw the initial DVA curve; subject the battery to constant current / power charge and discharge cycles, perform capacity calibration and cyclic DVA test after each cycle, and obtain the cyclic DVA curve at the capacity retention rate corresponding to the number of battery cycles; record the discharge capacity during the lithium ion loss stage through DVA curve analysis; The fitting relationship between capacity retention rate, battery cycle number and discharge capacity is obtained by data fitting: Based on the fitting relationship, according to the capacity retention rate of the battery cycle number to be predicted, the discharge capacity in the lithium ion loss stage corresponding to the battery cycle number to be predicted is calculated, and the battery cycle number to be predicted is calculated based on the discharge capacity in the lithium ion loss stage corresponding to the battery cycle number to be predicted.

2. The method for predicting battery cycle life according to claim 1, characterized in that: The fitting relationship between the capacity retention rate and the discharge capacity is as follows: (Q LLI )n i =a*(Xn i %SOH)+b;wherein, a and b are constants, Xn i %SOH represents the number of battery cycles n corresponding to one cycle cutoff i The capacity retention rate, (Q LLI )n i Indicates the discharge capacity during the lithium ion loss stage.

3. The method for predicting battery cycle life according to claim 1, characterized in that: The fitting relationship between the battery cycle number and discharge capacity is as follows: (Q LLI )n i =c*(n i )+d; where c and d are constants, n i Indicates the number of battery cycles.

4. The method for predicting battery cycle life according to claim 1, characterized in that: The battery is charged and discharged in a constant current / power cycle, and the cycle termination conditions include the cycle termination when the battery reaches the corresponding battery capacity retention rate in each cycle, or the cycle termination when a preset number of battery cycles are completed.

5. The method for predicting battery cycle life according to claim 1, characterized in that: The initial DVA test was performed at a magnification of 0.05C-0.1C.

6. The method for predicting battery cycle life according to claim 1, characterized in that: The initial capacity calibration and initial DVA test of the sample to be tested are performed, and the charge and discharge capacity and voltage are recorded. When drawing the initial DVA curve, the voltage of the nth data point is subtracted from the voltage of the n+1th data point to obtain the voltage differential; the capacity of the nth data point is subtracted from the capacity of the n+1th data point to obtain the capacity differential, and then the initial DVA curve is drawn using the voltage differential and the capacity differential.

7. The method for predicting battery cycle life according to claim 1, characterized in that: The battery is charged and discharged in a constant current / power cycle in a constant temperature box environment.

8. The method for predicting battery cycle life according to claim 1, characterized in that: When the battery is charged and discharged at a constant current / power cycle, after each cycle is terminated, the capacity is calibrated at a constant current / power, and then a cyclic DVA test is performed; wherein, if the battery is cycled at a constant power, the capacity is calibrated at a constant power; if the battery is cycled at a constant current, the capacity is calibrated at a constant current.

9. The method for predicting battery cycle life according to claim 1, characterized in that: After each cycle is terminated, the format of the cyclic DVA test is the same as that of the initial DVA test, and the data processing method for drawing the cyclic DVA curve is the same as that for drawing the initial DVA curve.

10. The method for predicting battery cycle life according to claim 1, characterized in that: When the battery is charged and discharged in a constant current / power cycle, the battery cycle temperature is 25° C.-45° C.

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