Method and device for calculating full cycle equivalent value of lithium batteries in same batch and storage medium

Abstract

The invention discloses a method and equipment for calculating a full-cycle equivalent value of lithium batteries in the same batch and a storage medium, belongs to the technical field of lithium ion battery testing, and solves the problem of how to calculate the full-cycle equivalent value of a future period according to the current full-cycle equivalent value of the lithium battery. The method comprises the following steps of: establishing a linear model, calculating a full cycle equivalent value in a future set period, further calculating cycle equivalent values of other lithium ion batteries in the same batch in the future set period based on the characteristic that the lithium batteries in the same batch have relevance, and performing grouping test on the sampled batteries in the same batch under multiple temperature conditions to obtain a complete cycle equivalent value of the lithium ion batteries in the future set period. A universal linear model is established, various temperature working conditions are covered, cyclic test parameters do not need to be changed, and meanwhile, the performance difference of the lithium ion battery in the multi-temperature environment can be reflected.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium-ion battery testing, and relates to a method, device and storage medium for calculating the full-cycle equivalent value of lithium batteries of the same batch. Background Art

[0002] Lithium-ion batteries have achieved great success in the field of chemical energy storage due to their excellent energy density, cycle life and power performance. In recent years, with the rapid development of new energy vehicles, lithium-ion batteries have been widely used in the field of power batteries. Among them, the cycle life of lithium-ion batteries, as a major performance index of lithium-ion batteries, has increasingly become a key technology in the design and R & D of electric vehicles.

[0003] The performance of lithium-ion batteries will decline to varying degrees with use, which is a slow and irreversible change process. A complete charging cycle includes the process from full charge to complete discharge and then recharging, and this process will affect the life of the battery. Specifically, the cycle life of the battery refers to the number of charge-discharge cycles that the battery can complete under specific conditions until its capacity drops to a certain threshold of the initial capacity (usually 80% capacity retention rate). The prior art such as the application publication number CN107688154A discloses a method for predicting the cycle life of long-life lithium-ion batteries. The battery to be evaluated is placed under specified conditions for short-term testing, and part of the capacity data is selected, and linear fitting analysis is performed using Minitab software to accurately predict the cycle life of long-life lithium-ion batteries in the short term, reducing the waste of resources and energy brought by conventional testing, and at the same time ensuring the timeliness of battery cycle performance evaluation.

[0004] However, the cycle period of lithium batteries is only a digital cumulative record, and the actual operating conditions are very different from the standard charge-discharge cycle test. In actual operation, the service life of lithium-ion batteries cannot be directly measured by the number of cycles. Therefore, battery manufacturers pay more and more attention to the full-cycle equivalent value of the battery. The full-cycle equivalent value is obtained through the combined operation of the discharge capacity values of each cycle during the cycle process and has future relevance. During the cycle process, specific tests need to be carried out at specific full-cycle equivalent values. However, most of the prior art is to manually record the discharge capacity of each cycle throughout the process, resulting in high manual monitoring costs and low test efficiency. Therefore, it is particularly important to calculate the full-cycle equivalent value of the battery under a future set cycle based on the current cycle period and full-cycle equivalent value of the lithium battery, and then calculate the full-cycle equivalent value of lithium batteries of the same batch. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to calculate the full-cycle equivalent value of a future cycle based on the current full-cycle equivalent value of a lithium battery.

[0006] The present invention solves the above technical problem through the following technical solutions:

[0007] Method for calculating the full cycle equivalent value of lithium batteries in the same batch, comprising the following steps:

[0008] S1. Randomly select multiple lithium ion batteries in the same batch as sampling batteries, and divide the sampling batteries into multiple groups;

[0009] S2. Perform cyclic charge and discharge tests on each group of sampling batteries under different preset temperature conditions;

[0010] S3. After each group of sampling batteries complete the cyclic test and reach the preset cycle number X, count the discharge capacity of each circle of each group of sampling batteries within the preset cycle number X, and calculate the corresponding full cycle equivalent value Y;

[0011] S4. Based on the corresponding relationship between the cycle number X and the full cycle equivalent value Y of each group of sampling batteries, establish a linear model;

[0012] S5. According to the linear model, calculate the full cycle equivalent value Y corresponding to the future cycle number X of the lithium ion batteries in the same batch under the same temperature conditions n n .

[0013] The present invention provides a method for calculating the full cycle equivalent value with high efficiency, low cost and high precision. According to the cycle number and full cycle equivalent value of the current lithium battery, the full cycle equivalent value under the future cycle can be predicted based on the linear model, and then the full cycle equivalent value of the lithium batteries in the same batch can be calculated, reducing manual intervention and lowering the manual monitoring cost.

[0014] Further, the number of groups of the sampling batteries in S1 is the same as the number of temperature conditions.

[0015] Further, the preset different temperature conditions in S2 include low temperature, normal temperature and high temperature conditions.

[0016] Based on the grouped tests of the sampling batteries in the same batch under multiple temperature conditions, the present invention establishes a universal linear model, covering a variety of temperature conditions, without changing the cyclic test parameters, and can reflect the performance differences of lithium ion batteries in a multi-temperature environment. Under different test temperature conditions, the deviation between the calculated full cycle equivalent value and the actually tested value is very small, with high accuracy.

[0017] Further, the full cycle equivalent value Y in S3 is the ratio of the cumulative value of the discharge capacity of each circle during the cycle process to the rated capacity of the battery.

[0018] Further, the linear model in S4 is Y = k*X + b, where k is the slope and b is the intercept.

[0019] Further, the linear model is obtained by fitting through linear regression analysis.​

[0020] Further, the future cycle period X described in S5 n The corresponding full-cycle equivalent value Y n is Y n = k * X n + b.

[0021] Further, the future cycle period X n is greater than the cycle period X.

[0022] Based on the characteristic that lithium batteries in the same batch are correlated, the present invention further calculates the cycle equivalent values of other lithium-ion batteries in the same batch in a future set period, overcomes the defect of large resource consumption in the traditional one-by-one testing of batteries in the same batch, and greatly improves the testing efficiency.

[0023] An electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the above-mentioned method for calculating the full-cycle equivalent value of lithium batteries in the same batch, and the processor is configured to execute the program stored in the memory.

[0024] A storage medium stores a computer program. When the computer program is run by a processor, it executes the steps of the above-mentioned method for calculating the full-cycle equivalent value of lithium batteries in the same batch.

[0025] The advantages of the present invention are as follows:

[0026] (1) According to the current cycle period and full-cycle equivalent value of a certain lithium-ion battery in the same batch, the present invention establishes a linear model to calculate its full-cycle equivalent value in a future set period, reduces manual intervention, and lowers the manual monitoring cost; at the same time, based on the characteristic that lithium batteries in the same batch are correlated, the present invention further calculates the cycle equivalent values of other lithium-ion batteries in the same batch in a future set period, overcomes the defect of large resource consumption in the traditional one-by-one testing of batteries in the same batch, and greatly improves the testing efficiency, having good application prospects.

[0027] (2) At the same time, based on the grouped testing of the sampled batteries in the same batch under multiple temperature conditions, the present invention establishes a universal linear model, covering a variety of temperature conditions, without changing the cycle test parameters, and can reflect the performance differences of lithium-ion batteries in a multi-temperature environment. Under different test temperature conditions, the deviation between the calculated full-cycle equivalent value and the actually tested value is very small, with high accuracy. Description of the Drawings

[0028] Figure 1 is a flowchart of the method for calculating the full-cycle equivalent value of lithium batteries in the same batch in Embodiment 1 of the present invention;

[0029] Figure 2It is a linear relationship diagram of the cycle period X and the full cycle equivalent value Y of the 3# and 4# sampling batteries in the first embodiment of the present invention;

[0030] Figure 3 It is a linear relationship comparison diagram between the measured X-Y trend and the predicted X n -Y n trend of the 3# sampling battery in the first embodiment of the present invention. Detailed implementation manners

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings of the specification and specific embodiments:

[0033] Embodiment 1

[0034] As Figure 1 shown, specifically, a method for calculating the full cycle equivalent value of lithium-ion batteries of the same batch is disclosed, including the following steps:

[0035] S1. Randomly select 6 batteries from a certain batch of lithium-ion batteries as sampling batteries, and randomly divide them into 3 groups, with 2 batteries in each group. The sampling batteries are all lithium-ion batteries of the same batch.

[0036] S2. The three groups of sampling batteries are respectively subjected to cyclic charge and discharge tests at 0 °C, 25 °C, and 45 °C to obtain cyclic performance data under different temperature environments.

[0037] In this embodiment, 6 square lithium-ion batteries with a nominal capacity of C (Ah) of the same batch are randomly selected as sampling batteries, and randomly divided into 3 groups, with 2 batteries in each group. The low temperature, normal temperature, and high temperature conditions are respectively set as 0 °C, 25 °C, and 45 °C. The specific grouping is as follows:

[0038] The 1# and 2# sampling batteries are cycled and tested under the condition of 0 °C;

[0039] The 3# and 4# sampling batteries are cycled and tested under the condition of 25 °C;

[0040] The 5# and 6# sampling batteries are cycled and tested under the condition of 45 °C.

[0041] S3. After each group of sampled batteries undergoes cyclic testing and reaches the preset cyclic period X, the discharge capacity of each group of sampled batteries in each cycle within the preset cyclic period X is statistically analyzed, and the full-cycle equivalent value Y corresponding to each cycle from the 1st cycle to the Xth cycle is calculated. Among them, the full-cycle equivalent value Y is the ratio of the cumulative value of the discharge capacity in each cycle during the cycling process to the rated capacity of the battery.

[0042] Further, the preset cyclic period X is a set of multiple consecutive cycle numbers, and at least one discharge capacity statistic is included in each cyclic period.

[0043] In this embodiment, 6 batteries undergo cyclic testing at different temperatures and reach the set cyclic period X. The discharge capacity of each battery in each cycle is statistically analyzed respectively, and the full-cycle equivalent value Y corresponding to each cycle from the 1st cycle to the Xth cycle is calculated. The specific cyclic testing conditions are as follows:

[0044] S31. Under the condition of 0°C, the 1# and 2# sampled batteries first perform a constant current discharge at 0.5C to the lower limit of the cut-off voltage, and stand for 30 min; then perform a constant current and constant voltage charge at 0.2C to the upper limit of the cut-off voltage, with a cut-off current of 0.05C, and stand for 30 min, and cycle 100 times.

[0045] S32. The 3# and 4# sampled batteries are under the condition of 25°C, and the 5# and 6# sampled batteries are under the condition of 45°C. They respectively perform a constant current discharge at 1C to the lower limit of the cut-off voltage, and stand for 30 min; a constant current and constant voltage charge at 1C to the upper limit of the cut-off voltage, with a cut-off current of 0.05C, and stand for 30 min, and cycle 500 times.

[0046] S33. Statistically analyze the discharge capacity corresponding to each week from the 1st to the 100th week of the 1# and 2# sampled batteries; statistically analyze the discharge capacity corresponding to each week from the 1st to the 500th week of the 3# and 4# sampled batteries; statistically analyze the discharge capacity corresponding to each week from the 1st to the 500th week of the 5# and 6# sampled batteries.

[0047] S34. Calculate the full-cycle equivalent value corresponding to each cyclic period of the 1# to 6# sampled batteries respectively.

[0048] S4. Respectively plot the X-Y curve graph of the cyclic period X and the full-cycle equivalent value Y of each battery at different test temperatures. Based on the corresponding relationship between the cyclic period X and the full-cycle equivalent value Y of each group of sampled batteries, a linear regression model is constructed to fit the corresponding relationship between the cyclic period X and the full-cycle equivalent value Y. This model is in the form of a linear function: Y = k*X + b, and the values of the slope k and the intercept b are determined. Among them, both the slope k and the intercept b are constants.

[0049] As Figure 2The XY curves of the cycle period X and the full cycle equivalent value Y of the 3# and 4# sample batteries under the cycle test condition of 25°C from the 1st to the 500th week show a linear relationship, and the XY curves of the 3# and 4# sample batteries are almost identical. The linear relationship presented by the lithium batteries of the same batch is correlated.

[0050] In this embodiment, it is required that the performance of this batch of batteries is stable during the cycle under different temperature conditions, the occasional jump points caused by external factors are acceptable, the cycle performance decays normally, and there is no capacity decay too fast. The sampled batteries are non-exploratory performance test batteries.

[0051] S5. Based on the linear model, first calculate the future cycle period X of each sampled battery under the same temperature conditions. n The corresponding full cycle equivalent value Y n , and then further calculate the future cycle X of the same batch of batteries under the same temperature conditions n (X n >X) corresponds to the full cycle equivalent value Y n .

[0052] In this embodiment, the deviation between the estimated full cycle equivalent value obtained by the linear model and the measured full cycle equivalent value of each group of sampled batteries is compared. The measured full cycle equivalent value is a manual test record, which is generated and updated once at each discharge capacity. The specific situation is as follows:

[0053] The deviations between the estimated full cycle equivalent values ​​and the measured full cycle equivalent values ​​for S51, 1#, and 2# sampled batteries every 20 weeks are shown in Table 1 below:

[0054] Table 1 Calculated and measured full cycle equivalent values ​​of 1# and 2# sample batteries

[0055]

[0056] The deviations between the estimated full cycle equivalent values ​​and the measured full cycle equivalent values ​​for S52, 3#, and 4# sampled batteries every 50 weeks are shown in Table 2 below:

[0057] Table 2 Estimated and measured full cycle equivalent values ​​of 3# and 4# sampled batteries

[0058]

[0059]

[0060] The deviations between the estimated full cycle equivalent values ​​and the measured full cycle equivalent values ​​for S53, 5#, and 6# sampled batteries every 50 weeks are shown in Table 3 below:

[0061] Table 3 Estimated full-cycle equivalent values and measured full-cycle equivalent values of the 5# and 6# sampled batteries

[0062]

[0063] As can be seen from Tables 1 to 3, under the same temperature conditions, the deviation between the estimated full-cycle equivalent value and the measured full-cycle equivalent value of the 1# and 2# sampled batteries is small at 0°C, and the deviation value is less than 0.5%;

[0064] The deviation between the estimated full-cycle equivalent value and the measured full-cycle equivalent value of the 3# and 4# sampled batteries is small at 25°C, and the deviation value is less than 0.5%;

[0065] The deviation between the estimated full-cycle equivalent value and the measured full-cycle equivalent value of the 5# and 6# sampled batteries is small at 45°C, and the deviation value is about 0.5%.

[0066] As can be seen from the above, the present invention can control the deviation of the estimated linear model within 0.5% under multi-temperature working conditions, greatly improving the accuracy of model estimation. Since the linear relationships presented by lithium-ion batteries of the same batch are related, it is considered that they have the same slope k and intercept b as the sampled batteries. Therefore, for the future cycle period X n of lithium-ion batteries of the same batch and the corresponding full-cycle equivalent value Y n the linear model can be expressed as Y n = k*X n + b, as Figure 3 shown.

[0067] In fact, as an important parameter for evaluating the cycle life of lithium-ion batteries, the full-cycle equivalent value has been studied less at present. The full-cycle equivalent value is related to the capacity of each full discharge (discharging from the upper voltage limit to the lower voltage limit after charging) during the battery cycle test. Since the discharge capacity of each cycle of the battery can only be recorded after actual discharge, the discharge capacity of each cycle of the battery is future-oriented and belongs to an unknown parameter, resulting in the full-cycle equivalent value corresponding to the discharge capacity of each cycle also being unknown. During the actual cycle test process, it is often necessary to change the cycle test parameters when a certain full-cycle equivalent value is reached, and it is necessary to wait for human intervention.

[0068] The present invention establishes a linear model according to the current cycle period and full cycle equivalent value of a lithium ion battery in the same batch, and estimates the full cycle equivalent value in a future set cycle, thereby reducing manual intervention and reducing manual monitoring costs; at the same time, based on the characteristic that lithium batteries in the same batch have correlation, the cycle equivalent values ​​of other lithium ion batteries in the same batch in the future set cycle are further estimated, thereby overcoming the defects of traditional testing of batteries in the same batch one by one and large resource consumption, and greatly improving the test efficiency; and controlling the estimated linear model deviation within 0.5%, thereby greatly improving the accuracy of model estimation, and having good application prospects.

[0069] At the same time, the present invention is based on group testing of sampled batteries from the same batch under multiple temperature conditions to establish a universal linear model, covering a variety of temperature conditions, without changing the cycle test parameters, and can reflect the performance differences of lithium-ion batteries under multiple temperature environments. Under different test temperature conditions, the deflection between the extrapolated full-cycle equivalent value and the actual test value is very small, and the accuracy is high.

[0070] Embodiment 2

[0071] A device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the method for estimating the full-cycle equivalent value of lithium batteries in the same batch in Example 1, and the processor is configured to execute the program stored in the memory.

[0072] Embodiment 3

[0073] A storage medium stores a computer program, which, when executed by a processor, executes the steps of the method for estimating the full-cycle equivalent value of lithium batteries of the same batch in embodiment 1.

[0074] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Method for calculating the equivalent value of full cycles of lithium batteries in the same batch, characterized in that, It includes the following steps: S1. Randomly select multiple lithium-ion batteries in the same batch as sampling batteries, and divide the sampling batteries into multiple groups; S2. Perform cyclic charge and discharge tests on each group of sampling batteries under different preset temperature conditions; S3. After each group of sampling batteries reaches the preset cycle number X after cyclic testing, count the discharge capacity of each circle of each group of sampling batteries within the preset cycle number X, and calculate the corresponding full-cycle equivalent value Y; S4. Based on the corresponding relationship between the cycle number X and the full-cycle equivalent value Y of each group of sampling batteries, establish a linear model; S5. According to the linear model, estimate the future cycle period X of lithium-ion batteries of the same batch under the same temperature conditions n and the corresponding full-cycle equivalent value Y n .

2. The method for calculating the full-cycle equivalent value of lithium batteries in the same batch according to claim 1, wherein The number of groups of the sampling batteries described in S1 is the same as the number of temperature conditions.

3. The method for calculating the full-cycle equivalent value of lithium batteries in the same batch according to claim 1, characterized in that, The different preset temperature conditions described in S2 include low temperature, normal temperature and high temperature conditions.

4. The method for calculating the full-cycle equivalent value of lithium batteries in the same batch according to claim 1, characterized in that The full-cycle equivalent value Y described in S3 is the ratio of the cumulative value of the discharge capacity of each circle during the cycle to the rated capacity of the battery.

5. The method for calculating the full-cycle equivalent value of lithium batteries in the same batch according to claim 1, wherein The linear model described in S4 is Y = k*X + b, where k is the slope and b is the intercept.

6. The method for calculating the full-cycle equivalent value of lithium batteries in the same batch according to claim 5, wherein The linear model is obtained by fitting through linear regression analysis.

7. The method for calculating the full-cycle equivalent value of lithium batteries in the same batch according to claim 5, wherein The future cycle period X described in S5 n The corresponding full-cycle equivalent value Y n is Y n = k * X n + b 8. The method for calculating the full-cycle equivalent value of lithium batteries in the same batch according to claim 7, characterized in that, The future cycle period X n is greater than the cycle period X.

9. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program for supporting the processor to execute the method for calculating the full-cycle equivalent value of the same batch of lithium batteries according to any one of claims 1 to 8, and the processor is configured to execute the program stored in the memory.

10. A storage medium stores a computer program thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the method for calculating the full-cycle equivalent value of the same batch of lithium batteries according to any one of claims 1 to 8.

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