A method for lithium-ion battery life qualitative prediction using EIS test

Abstract

The application discloses a method for lithium ion battery life qualitative prediction by EIS test. Firstly, the scheme of lithium ion battery accelerated aging is determined: under the temperature condition of 45-60 DEG C, the cycle mode of 0.3-2C charging and 0.3-2C discharging is adopted to carry out charge-discharge cycle on the battery cell. After the charge-discharge cycle, the battery cell is placed into a constant temperature box for constant temperature treatment for 2-10h, then EIS test is carried out on the battery cell, EIS data is collected, Zview software is used to fit the EIS data, the change curve of Rct growth rate with cycle number is drawn, and the lithium ion battery life is qualitatively predicted according to the change curve. The method for lithium ion battery life qualitative prediction by EIS test in the application is nondestructive test, does not affect the subsequent test results, has short prediction time, high adaptability, does not need to deeply study the electrochemical reaction mechanism of the battery cell, and has high accuracy, and the accuracy can reach more than 90%.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery testing, specifically relating to a method for qualitative prediction of lithium-ion battery life using EIS testing. Background Technology

[0002] Lithium-ion batteries possess advantages such as high energy density, high rated voltage, low density, strong adaptability to high and low temperatures, and environmental friendliness. With the continuous development of new energy sources, lithium-ion batteries are widely used in household appliances, mobile phones, computers, electric vehicles, and other fields. Currently, the performance of lithium-ion batteries is constantly improving, and their application range is expanding accordingly. This has led to increasingly higher requirements for the lifespan of lithium-ion batteries in both production and consumption. Therefore, employing reasonable, accurate, and efficient methods to evaluate the lifespan of lithium-ion batteries has become a research hotspot.

[0003] Lithium-ion batteries are rechargeable batteries that primarily function by the migration of lithium ions between the positive and negative electrodes. The battery's chemical driving force comes from the difference in chemical potential between the two electrodes. Ideally, lithium-ion migration is reversible, allowing for an unlimited number of charge-discharge cycles. However, in practical use, some irreversible processes occur, leading to increased internal impedance and reduced battery capacity. As these changes accumulate, the battery capacity gradually decreases, affecting its cycle life and ultimately causing battery failure. Reasonably, accurately, and efficiently predicting the lifespan of lithium-ion batteries is beneficial for effectively assessing battery performance and preventing unnecessary losses due to battery failure.

[0004] Currently, most assessments of lithium-ion battery cycle life are based on long cycles (≥1000 clcs), resulting in long evaluation periods, high resource consumption, and an intangible increase in cell manufacturing costs. For example, the Chinese invention patent application number 201310317219.X, entitled "A Method for Estimating Lithium-ion Battery Capacity and Predicting Remaining Cycle Life," collects data on the number of charge-discharge cycles, the discharge voltage and battery capacity of each cycle, and the remaining capacity after each charge-discharge cycle. It then uses piecewise cubic Hermite interpolation to expand the data and employs a GPR model for extrapolation to predict the remaining capacity of the lithium battery after multiple cycles. This method uses quantitative data and modeling analysis to predict the lifespan of lithium batteries, exhibiting high accuracy. However, the prediction method requires more than 3000 cycles of the lithium battery, resulting in a long evaluation period and complex computational methods. Chinese invention patent application number 201710640200.7, entitled "A Method and System for Evaluating the Lifespan of Lithium Batteries," plots a curve showing the change in internal resistance of lithium batteries during accelerated aging. It then calculates an acceleration factor using the Arrhenius model and plots a trend chart of internal resistance changes at room temperature based on this acceleration factor. This analysis of the aging process at room temperature allows for the assessment of the lithium battery's lifespan. While this method can effectively detect potentially defective batteries, it involves a large amount of testing data and is relatively complex.

[0005] Electrochemical impedance spectroscopy (EIS) is one of the most practical and powerful tools for studying the electrochemical processes occurring at the electrode / electrolyte interface in lithium-ion batteries. It is widely used to study the insertion and extraction of lithium ions in the active materials of lithium-ion battery intercalation electrodes. EIS spectroscopy can be used to analyze the patterns or parameters of charge transfer resistance, electronic resistance of active materials, and resistance of lithium ions through the solid electrolyte interphase (SEI) film during lithium-ion battery operation. Currently, methods for predicting the lifespan of lithium-ion batteries using EIS testing are rarely reported. Chinese patent application number 201710507702.2, entitled "A Method for Testing the Electronic Lifespan of Perovskite Solar Cells Based on EIS Analysis," calculates the electronic lifetime of perovskite solar cells through EIS analysis and the establishment of an equivalent circuit model. This method has advantages such as minimal damage to the solar cell, fast calculation speed, and high accuracy. However, as a quantitative method for calculating battery lifespan, it objectively suffers from the problem of a relatively complex calculation principle and process.

[0006] In the process of evaluating the lifespan of lithium-ion batteries, we are more concerned with the differences between the positive and negative electrode materials and the control group being evaluated. Therefore, qualitative evaluation of the cycle life of the cell can achieve the desired effect. However, the accelerated aging method commonly used in qualitative lifespan prediction has certain limitations in the selection of evaluation parameters. For example, using the cycle capacity retention rate of accelerated aging in a short period of time for evaluation will result in a large error. Therefore, finding a method that can complete the qualitative prediction of cell lifespan in a short time is the key to achieving reasonable, accurate and efficient prediction results. Summary of the Invention

[0007] To address the shortcomings of the existing technology, this invention provides a method for qualitative prediction of lithium-ion battery life using EIS testing. First, accelerated aging schemes for lithium-ion batteries were studied. After screening, the optimal accelerated aging scheme was determined to be: charging and discharging the battery cells at 0.3-2C and 0.3-2C rates under a temperature of 45-60℃, with cycle numbers of 0, 5, 10, 20, 30, 50, 70, 100, or higher. After charge-discharge cycles, the battery cells were placed in a 35-42℃ constant temperature chamber for 2-10 hours. Then, each battery cell was individually placed on a test fixture for EIS testing to obtain the correspondence between the battery cell's EIS characteristics and the number of cycle numbers. To improve testing accuracy, the battery cells were not removed from the constant temperature chamber during the test, and cells requiring simultaneous evaluation were tested within 2-4 hours. During the testing process, EIS data is collected, and then Zview software is used to fit the EIS data to extract Rct data (cell charge transfer impedance data). A curve showing the Rct growth rate versus cycle number is plotted. Based on this curve, the lifespan of the lithium-ion battery is qualitatively predicted. The curve shows that a faster Rct growth rate corresponds to poorer cycle performance. This invention utilizes EIS testing for qualitative prediction of lithium-ion battery lifespan, with a prediction time of 18-25 days and high prediction efficiency. The EIS test used is non-destructive, accurately collecting impedance spectrum characteristics without affecting subsequent test results. This method is highly adaptable, requiring no in-depth study of the cell's electrochemical reaction mechanism. Furthermore, this method boasts high accuracy, exceeding 90%.

[0008] The technical effects to be achieved by this invention are accomplished through the following solutions:

[0009] This invention discloses a method for qualitative prediction of lithium-ion battery life using EIS testing, comprising the following steps:

[0010] S01, the battery cell to be tested is kept at a constant temperature in a constant temperature chamber, and then the battery cell to be tested is placed one by one on the test fixture for EIS testing. The battery cell to be tested is not removed from the constant temperature chamber during the test.

[0011] S02, the battery cells that have completed the EIS test in S01 are cycled at a temperature of 45-60℃;

[0012] S03, after the cells in S02 have completed the cycle, place them in a constant temperature chamber for constant temperature treatment, and then place the cells one by one on the test fixture for EIS testing. The cells are not removed from the constant temperature chamber during the test.

[0013] S04, use data processing software to fit the EIS test data, fit the curve of the cell charge transfer impedance growth rate with the number of cycles, and make a qualitative prediction of the lithium-ion battery life based on the curve.

[0014] This invention discloses a method for qualitative prediction of lithium-ion battery life using EIS testing. First, accelerated aging schemes for lithium-ion batteries were studied. After screening, the optimal accelerated aging scheme was determined to be: charging and discharging the battery cells at 0.3-2C and 0.3-2C cycles at 45-60℃, with cycle numbers of 0, 5, 10, 20, 30, 50, 70, 100, or higher. This invention uses short cycles at 45-60℃ for battery aging, which is time-efficient, highly effective, and the aging process is simple and easy to implement, requiring fewer resources and saving on battery cell manufacturing costs. After charge-discharge cycling, the battery cells are placed in a 35-42℃ constant temperature chamber for 2-10 hours. Then, each cell is individually placed on a test fixture for EIS testing to obtain the correspondence between the cell's EIS characteristics and the number of cycles. To improve testing accuracy, the cells were not removed from the constant temperature chamber during the test. Cells to be evaluated together were tested within 2-4 hours. EIS data was collected during the test, and then Zview software was used to fit the EIS data, extract Rct data, and plot the Rct growth rate as a function of cycle number. Based on this curve, a qualitative prediction of the lithium-ion battery life was made. The curve showed that a faster Rct growth rate corresponded to poorer cycle performance of the lithium-ion battery.

[0015] Furthermore, the battery cells to be tested, as described in S01, are first divided into two groups for 3-6 weeks before constant temperature treatment to ensure that the battery cells are fully charged and free from overcharging. During the selection of battery cells to be tested, the cells after production are divided into two groups, and cells with high consistency in voltage, internal resistance, capacity, and median voltage are selected for testing. Regarding voltage, cells with no self-discharge issues and high voltage consistency are selected; regarding internal resistance, the internal resistance of the selected battery cells is as close as possible to the average internal resistance of cells in the same batch; regarding capacity, cells that meet the nominal capacity of cells in the same system are selected. Cells that simultaneously meet the above three conditions of voltage, internal resistance, and capacity are selected as the battery cells to be tested. At least three cells from each batch are selected for testing. After the selection of battery cells to be tested is completed, the battery cells to be tested are divided into two groups 3-6 times at room temperature to ensure that the battery cells are fully charged and free from overcharging. After the above selection of battery cells to be tested is completed, the next step of testing is performed.

[0016] Furthermore, the temperature of the constant temperature chamber in S01 is set to 35-42℃, and the constant temperature treatment time in S01 is 2-10 hours. The temperature of the constant temperature chamber in S03 is also set to 35-42℃, and the constant temperature treatment time in S03 is 2-10 hours. This ensures that the temperature of the battery cell body matches the temperature set in the constant temperature chamber before EIS testing is performed.

[0017] Furthermore, the frequency range of the EIS test described in S01 is 0.05-10. 5 The EIS test described in S01 is completed within 2-4 hours. The frequency range of the EIS test described in S03 is 0.05-10 Hz. 5 Hz; The EIS test described in S03 shall be completed within 2-4 hours. To improve the accuracy of the test, the cells shall not be removed from the constant temperature chamber during the test, and cells that need to be evaluated together shall be tested within 2-4 hours.

[0018] Furthermore, the cycle described in S02 is a charge-discharge cycle of 0.3-2C charging and 0.3-2C discharging. Choosing the 0.3-2C charging and 0.3-2C discharging cycle system is beneficial for completing the aging process of the battery cell within a shorter number of cycles.

[0019] Furthermore, the data processing software mentioned in S04 is Zview software. Zview software is used to fit the EIS data, extract the Rct data, and plot the Rct growth rate as a function of cycle number. Based on this curve, a qualitative prediction of the lithium-ion battery life is made. The curve shows that a faster Rct growth rate corresponds to poorer cycle performance of the lithium-ion battery.

[0020] Furthermore, the method for qualitative prediction of lithium-ion battery life using EIS testing is employed to perform qualitative prediction of lithium-ion battery life, with a prediction time of 18-25 days. From the selection of the cell to be tested to obtaining the final prediction report, the required time is 18-25 days.

[0021] Furthermore, the method for qualitative prediction of lithium-ion battery life using EIS testing is employed to qualitatively predict the lifespan of lithium-ion batteries, achieving an accuracy greater than 90%. The prediction method in this invention does not require in-depth research into the electrochemical reaction mechanism of the battery cell, while maintaining high accuracy (greater than 90%).

[0022] The present invention has the following advantages:

[0023] 1. The EIS test used in this invention is a non-destructive test that can accurately collect impedance spectrum characteristics without affecting subsequent test results.

[0024] 2. The qualitative prediction method for lithium-ion battery life in this invention is highly adaptable and does not require in-depth research on the electrochemical reaction mechanism of the battery cell.

[0025] 3. The qualitative prediction method for lithium-ion battery life in this invention has high accuracy, reaching over 90%.

[0026] 4. The qualitative prediction method for lithium-ion battery life of the present invention uses short-cycle experiments on the battery under high temperature conditions, which requires less time and is highly efficient. Attached Figure Description

[0027] Figure 1 , Figure 2 The image shows the electrochemical impedance spectroscopy curves of the battery cell under test in this invention at different cycle numbers under 60°C.

[0028] Figure 3 , Figure 4 , Figure 5 , Figure 6 The curve showing the change of Rct growth rate with the number of cycles is obtained by fitting the EIS data of the battery cell under test in this invention using Zview software.

[0029] Figure 7 , Figure 8 , Figure 9 , Figure 10 This is a curve showing the change in the capacity retention rate of the battery cell under test at room temperature as a function of the number of cycles. Detailed Implementation

[0030] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] Example 1

[0032] Two batches of 18650 cells were selected as the cells to be tested, numbered batch 1 and batch 2. The difference between batch 1 and batch 2 lies in the negative electrode material used. The cells to be tested were first divided into five cycles to ensure that the cells were fully charged and without overcharging.

[0033] S01, the battery cell to be tested is kept at a constant temperature of 40℃ for 5 hours, and then the battery cell to be tested is placed one by one on the test fixture for EIS testing. The battery cell to be tested is not removed from the constant temperature chamber during the test; the frequency range of the EIS test is 0.05-10. 5 Hz, EIS test completed within 2 hours.

[0034] S02, the battery cells that have completed the EIS test in S01 are cycled at a temperature of 60°C. The cycle mode is a charge-discharge cycle of 0.5C charging and 0.5C discharging, and the number of cycles is 0, 10, 30 and 50.

[0035] S03: After completing the cycles in S02, the battery cells are placed in a 40°C constant temperature chamber for 5 hours. Then, the cells are individually placed onto the test fixture for EIS testing. The cells are not removed from the constant temperature chamber during the test. The frequency range of the EIS test is 0.05-10 Hz. 5 Hz, EIS test completed within 2 hours.

[0036] S04. Using Zview software, the above EIS data is fitted to generate a curve showing the change of Rct growth rate with the number of cycles. Based on the curve, the lifespan of the lithium-ion battery is qualitatively predicted.

[0037] Appendix Figure 1 Appendix Figure 2 The EIS curves for batches 1 and 2 of the tested cells in this embodiment are respectively the curves after 0, 10, 30 and 50 cycles at 60°C.

[0038] Appendix Figure 3 The curves showing the change of Rct growth rate of the tested cells in batches 1 and 2 as a function of the number of cycles are shown in this embodiment.

[0039] Appendix Figure 7 The curves show the change in cell capacity retention rate with the number of cycles for batches 1 and 2 of the tested cells in this embodiment under normal temperature conditions and charge / discharge at a rate of 0.5C.

[0040] From the appendix Figure 3 It can be seen that the Rct growth rate of the tested cells in batch #1 is faster than that in batch #2, indicating that the cycle performance of the tested cells in batch #1 is poor. (See attached...) Figure 7It can be seen that, under normal temperature conditions, the capacity retention rate of the tested cells in batch #1 is lower than that of the tested cells in batch #2.

[0041] The qualitative prediction results of the battery cell lifespan for both batches were consistent with the actual cycle results.

[0042] Example 2

[0043] Two batches of 18650 cells were selected as the cells to be tested, numbered batch 3 and batch 4. The difference between batch 3 and batch 4 lies in the positive electrode material used. The experimental method is the same as in Example 1.

[0044] Appendix Figure 4 The curves showing the Rct growth rate of the tested cells in batches 3 and 4 of this embodiment vary with the number of cycles.

[0045] Appendix Figure 8 The curves show the change in cell capacity retention rate with the number of cycles for batches 3 and 4 of the tested cells in this embodiment under normal temperature conditions and charge / discharge at a rate of 0.5C.

[0046] From the appendix Figure 4 It can be seen that the Rct growth rate of the tested cells in batch #4 is faster than that in batch #3, indicating that the cycle performance of the tested cells in batch #4 is poor. (See attached...) Figure 8 It can be seen that, under normal temperature conditions, the capacity retention rate of the tested cells in batch #4 is lower than that of the tested cells in batch #3.

[0047] The qualitative prediction results of the battery cell lifespan for both batches were consistent with the actual cycle results.

[0048] Example 3

[0049] Two batches of 18650 cells were selected as the cells to be tested, numbered batch 5# and batch 6#. The difference between batch 5# and batch 6# lies in the negative electrode material used. The experimental method is the same as in Example 1.

[0050] Appendix Figure 5 The curves showing the Rct growth rate of the tested cells in batches 5 and 6 in this embodiment vary with the number of cycles.

[0051] Appendix Figure 9 The curves show the change in cell capacity retention rate with the number of cycles for batches 5 and 6 of the tested cells in this embodiment under normal temperature conditions and charge / discharge at a rate of 0.5C.

[0052] From the appendix Figure 5 It can be seen that the Rct growth rate of the tested cells in batch #6 is faster than that in batch #5, indicating that the cycle performance of the tested cells in batch #6 is poor. (See attached...) Figure 9It can be seen that, under normal temperature conditions, the capacity retention rate of the tested cells in batch #6 is lower than that of the tested cells in batch #5.

[0053] The qualitative prediction results of the battery cell lifespan for both batches were consistent with the actual cycle results.

[0054] Example 4

[0055] Three batches of 18650 cells were selected as the cells to be tested, numbered 7#, 8#, and 9#. The difference between batches 7#, 8#, and 9# lies in the positive electrode binder material used. The experimental method is the same as in Example 1.

[0056] Appendix Figure 6 The curves showing the Rct growth rate of the tested cells in batches 7, 8, and 9 in this embodiment vary with the number of cycles.

[0057] Appendix Figure 10 The curves show the change in cell capacity retention rate with the number of cycles for batches 7, 8, and 9 of the tested cells in this embodiment under normal temperature conditions and charge / discharge at a rate of 0.5C.

[0058] From the appendix Figure 6 It can be seen that the growth rate of Rct in the tested cells is the smallest for batch 7, and slightly larger for batch 9 than for batch 8. This indicates that among the three batches of tested cells, the cycle performance of batches 8 and 9 is similar, but both are worse than that of batch 7.

[0059] From the appendix Figure 10 It can be seen that, under normal temperature conditions, the capacity retention rates of batches 8 and 9 are similar, while the capacity retention rate of batch 7 is the highest.

[0060] The qualitative prediction results of the battery cell lifespan for the three batches were consistent with the actual cycle results.

[0061] The 18650 cells mentioned in Examples 1-4 are all selected from the mass-produced 18650 cells of our company.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and not to limit them. Although the embodiments of the present invention have been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the embodiments of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for lithium-ion battery life qualitative prediction using EIS test, characterized in that, The method comprises the following steps: S01, the to-be-tested battery is treated in a thermostat, and then the to-be-tested battery is placed on a test fixture one by one for EIS testing, and the to-be-tested battery is not taken out of the thermostat during the testing; S02, the battery after the EIS testing in S01 is cycled at a temperature of 45-60℃; S03, the battery after the cycling in S02 is treated in a thermostat, and then the battery is placed on a test fixture one by one for EIS testing, and the battery is not taken out of the thermostat during the testing; S04, the EIS testing data are fitted by using a data processing software, a curve of the charge transfer impedance growth rate of the battery versus the cycle number is fitted, and the life of the lithium ion battery is qualitatively predicted according to the curve; The temperature of the thermostat in S01 is set to 35-42℃; and the time of the thermostat treatment in S01 is 2-10h; The cycle in S02 is a charge-discharge cycle of 0.3-2C charging and 0.3-2C discharging; The temperature of the thermostat in S03 is set to 35-42℃; and the time of the thermostat treatment in S03 is 2-10h.

2. The method for qualitative life prediction of lithium-ion batteries using EIS test of claim 1, wherein: The to-be-tested battery in S01 is first divided into groups for 3-6 weeks before the thermostat treatment, so as to ensure that the to-be-tested battery is in a full charge state and has no overcharging phenomenon.

3. The method for qualitative life prediction of a lithium-ion battery using EIS test of claim 1, wherein: The frequency range of the EIS testing in S01 is 0.05-105Hz; and the EIS testing in S01 is completed within 2-4h.

4. The method for qualitative life prediction of Li-ion batteries using EIS test of claim 1, wherein: The frequency range of the EIS testing in S03 is 0.05-105Hz; and the EIS testing in S03 is completed within 2-4h.

5. The method for qualitative life prediction of Li-ion batteries using EIS test of claim 1, wherein: The data processing software in S04 is Zview software.

6. The method for qualitative prediction of the lifetime of a lithium-ion battery using EIS tests according to any one of claims 1 to 5, characterized in that: The method for qualitatively predicting the life of the lithium ion battery by using the EIS testing is used to qualitatively predict the life of the lithium ion battery, and the prediction time is 18-25 days.

7. The method for qualitative prediction of the lifetime of a lithium-ion battery using EIS tests according to any one of claims 1 to 5, characterized in that: The method for qualitatively predicting the life of the lithium ion battery by using the EIS testing is used to qualitatively predict the life of the lithium ion battery, and the prediction accuracy is greater than 90%.

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