Lithium ion battery automatic discharge abnormity screening method and system

Abstract

The invention discloses a lithium ion battery automatic discharge abnormity screening method and system, and the method comprises the following steps: S1, charging a lithium ion battery cell at a multiplying power of 0.01-0.05 C, and obtaining a corresponding relation curve between a voltage V and an electric quantity Q; s2, performing differential processing on the V-Q relation curve to generate a (dV / dQ)-SOC curve, and identifying an SOC interval corresponding to a wave crest in the (dV / dQ)-SOC curve; s3, adjusting the battery cell to an SOC state corresponding to the wave crest, carrying out a shelving test at a selected temperature, and calculating a self-discharge K value; and S4, screening the battery cells with abnormal self-discharge according to the K value difference. According to the method, the sensitive SOC interval is analyzed and positioned through the dV / dQ curve, multi-temperature verification is combined, high precision, high efficiency and high universality of lithium ion battery self-discharge detection are achieved, the technical bottlenecks of a traditional method in the aspects of SOC sensitivity, temperature influence, system compatibility and the like are solved, and an innovative scheme is provided for battery quality control.

Description

Technical Field

[0001] The present invention relates to the field of new energy lithium-ion batteries, and in particular to a method and system for automatically screening abnormal discharge of lithium-ion batteries. Background Art

[0002] Lithium-ion batteries have high energy density per unit volume / mass, no memory effect, low internal resistance, and long cycle life, making them widely used in many industries. However, cells with high self-discharge have low capacity retention during storage, and their diaphragms may be punctured, posing a safety hazard. Currently, lithium-ion batteries generally measure their voltage difference before and after storage, but the specific SOC (state of charge) of cells in different systems, as well as whether the SOC varies under different storage temperatures, are not clearly defined. This is not accurate and rigorous, and cannot effectively screen out cells with poor self-discharge. Summary of the Invention

[0003] In order to solve the existing problems, the present invention provides a method and system for automatically screening abnormal discharge of lithium-ion batteries. The specific scheme is as follows:

[0004] A method for screening abnormal automatic discharge of a lithium-ion battery comprises the following steps:

[0005] S1, charging the lithium-ion battery cell at a rate of 0.01-0.05C to obtain a corresponding relationship curve between voltage V and charge Q;

[0006] S2, performing differentiation processing on the VQ relationship curve to generate a (dV / dQ)~SOC curve, and identifying the SOC interval corresponding to the peak in the (dV / dQ)~SOC curve;

[0007] S3, adjusting the battery cell to the SOC state corresponding to the peak, performing a shelf test at a selected temperature, and calculating the self-discharge K value;

[0008] S4, screening out abnormal self-discharge cells based on K value differences.

[0009] Preferably, the lithium-ion battery cells in step S1 include lithium iron phosphate battery cells and ternary lithium-ion battery cells.

[0010] Preferably, the shelving test in step S3 includes the following steps:

[0011] S31, measure the initial voltage OCV1 after the first storage for 12-24 hours;

[0012] S32, measure the voltage OCV2 after the second shelf for 120-168h;

[0013] S33, calculate the K value using the formula as follows:

[0014] Wherein, t is the difference between the second lay-up time and the first lay-up time, in days.

[0015] Preferably, the temperatures selected in step S3 are 25°C and 0°C.

[0016] Preferably, a comparative test is also included, that is, the same batch of battery cells are adjusted to the peak SOC and the flat area SOC respectively, and a shelf test is carried out simultaneously to compare the difference in K values.

[0017] Preferably, the lithium-ion battery self-discharge abnormality screening system based on any of the above methods includes:

[0018] The charge and discharge module is used to charge and discharge at a rate of 0.01-0.05C, so that the curve can reflect the phase change of the electrochemical reaction of each material;

[0019] The data processing module is used to generate the (dV / dQ)~SOC curve, identify the SOC interval corresponding to the peak, and calculate the K value in the shelf test;

[0020] Temperature control module, used to adjust the test environment temperature;

[0021] The determination module is used to determine whether the battery cell is abnormal based on the K value threshold.

[0022] The present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is run, any of the above methods is executed.

[0023] The present invention also discloses a computer system, including a processor and a storage medium, wherein a computer program is stored on the storage medium, and the processor reads and runs the computer program from the storage medium to execute any of the methods described above.

[0024] The beneficial effects of the present invention are:

[0025] This invention locates the sensitive SOC range through dV / dQ curve analysis and combines it with multi-temperature verification to achieve high-precision, high-efficiency and strong universality of lithium-ion battery self-discharge detection. It solves the technical bottlenecks of traditional methods in SOC sensitivity, temperature influence, system compatibility, etc., and provides an innovative solution for battery quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 The voltage-capacity differential curve of lithium iron phosphate battery cells at different SOC states at 25℃ and 0℃;

[0028] Figure 2 The voltage-capacity differential curve of the ternary lithium battery cell at different SOC states at 25°C. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0030] The present invention provides a method for screening lithium-ion battery cells with poor self-discharge based on their K values ​​after they come off the production line. The prepared cells are charged at 0.01-0.05C, at a low rate, to more clearly show the peaks of each voltage change in the cells. The voltage-capacity curve is then processed to generate a dV / dQ curve. A dV / dQ vs. SOC curve is then generated, and the location with the largest peak is selected. This method is simple and easy to operate and can be applied to lithium batteries of different systems and models.

[0031] Table 1

[0032]

[0033] Example 1: Object: A certain lithium iron phosphate battery cell such as Figure 1 As shown in the figure, under different SOC states, the voltage-electricity differential curve is obtained by Figure 1 As can be seen, at both 25°C and 0°C, this cell exhibits a dV / dQ peak at 14%-17% SOC. The dV / dQ value is lower between 30%-50% SOC, indicating that the voltage changes less with charge at this point. Furthermore, the dV / dQ values ​​within these two SOC ranges at 25°C and 0°C closely match each other.

[0034] At 25°C, adjust the SOC of the six cells off the capacity line to 16% and 35% of the charge state. After the cells are set aside for 12-24 hours, measure their voltage and record it as OCV1. Then, after 120-168 hours, record the voltage OCV2. The self-discharge K value is based on the formula: It is concluded that; where t is the difference between the second shelving time and the first shelving time, in days. From Table 1, it can be seen that the K value of the battery cell at 35% SOC is between 0.922-0.936mV / day, and there is no obvious abnormality in the k values ​​of the 6 battery cells, which are all small, indicating that the K values ​​of the 6 battery cells in this batch are qualified. At 16% SOC, four of the 6 battery cells are 1.353-1.375mV / day, and the K values ​​of two are 1.510 and 1.587mV / day. The voltage values ​​of this batch are all larger than the K value at 35% SOC. At the same time, there are two battery cells with large outliers of K value, indicating that at the peak position of the large dV / dQ value, the battery voltage drops significantly with the power during the shelving period, and it can accurately screen out the battery cells with poor K value, and accurately intercept the battery cells with poor K value.

[0035] At the same time, at 0°C, the SOC of each of the 6 battery cells off the capacity line was adjusted to 16% and 35% power states. The voltage of the battery cells was measured after being shelved for 12-24 hours and recorded as OCV1. Then, after being shelved for 120-168 hours, the voltage OCV2 was recorded. It can be seen from Table 1 that the K value of the battery cells at 35% SOC state is between 0.811-0.853mV / day. There is no obvious abnormality in the k values ​​of the 6 battery cells, and they are all small, indicating that the K values ​​of the 6 battery cells in this batch are qualified. At 16% SOC, the K values ​​of three of the six cells were 1.105-1.113 mV / day, and the K values ​​of the other three were 1.295, 1.275, and 1.251 mV / day. The voltage values ​​of this batch were all larger than the K values ​​at 35% SOC. At the same time, three cells with large and outlier K values ​​appeared, indicating that even at 0°C where the dV / dQ value was at a large peak, the battery voltage decreased significantly with the charge during storage, and the method was able to accurately screen out cells with poor K values ​​and accurately intercept cells with poor K values. This method is also applicable at different temperatures.

[0036] Example 2: Object: A ternary lithium-ion battery cell with a positive electrode nickel-cobalt-manganese molar ratio of 6:2:2.

[0037] like Figure 2 As shown in FIG, at 25° C., it is a voltage-capacity differential curve of the above-mentioned ternary lithium-ion battery cell and the SOC curve of the battery cell.

[0038] Depend on Figure 2 It can be seen that the dV / dQ value of the battery is low and has no peak value at 20-40% SOC, and there is a dV / dQ peak between 50% and 53% SOC, indicating that the voltage at this point changes greatly with the charge.

[0039] Six cells with a relatively small dV / dQ value, which were taken offline at 30% SOC, were selected. The (OCV1-OCV2) / day values ​​before and after storage were distributed between 1.125-1.163mV, with no obvious outliers and no cells with abnormal K values. Six cells with an SOC of 52% were selected and stored for 7 days. Four of them had K values ​​of 1.263-1.356mV / day, showing no abnormalities. Two of them had relatively large K values ​​of 1.592 and 1.672mV, respectively, and were eliminated as abnormalities. The above cases show that lithium-ion batteries have different K values ​​when stored for the same number of days at different SOC states. Moreover, only when the dV / dQ value is in the SOC peak region does the voltage value fluctuate greatly with the charge value, allowing accurate selection of cells with large self-discharge.

[0040] The above two embodiments can be summarized as a method for screening abnormal automatic discharge of lithium-ion batteries of the present invention, which includes the following steps:

[0041] S1, charging the lithium-ion battery cell at a rate of 0.01-0.05C to obtain a corresponding relationship curve between voltage V and charge Q. The lithium-ion battery cell includes a lithium iron phosphate battery cell and a ternary lithium-ion battery cell.

[0042] S2: Differentiate the VQ relationship curve to generate a (dV / dQ)~SOC curve, and identify the SOC interval corresponding to the peak in the (dV / dQ)~SOC curve.

[0043] S3, adjust the battery cell to the SOC state corresponding to the peak, perform a shelf test at a selected temperature, and calculate the self-discharge K value. The selected temperatures are 25°C and 0°C. Specifically, the shelf test includes the following steps:

[0044] S31, measure the initial voltage OCV1 after the first storage for 12-24 hours;

[0045] S32, measure the voltage OCV2 after the second shelf for 120-168h;

[0046] S33, calculate the K value using the formula as follows:

[0047] Wherein, t is the difference between the second lay-up time and the first lay-up time, in days.

[0048] S4, screening out abnormal self-discharge cells based on K value differences.

[0049] The present invention also includes a comparative test, whereby cells from the same batch are adjusted to both peak SOC and flat SOC, and then simultaneously subjected to a shelf test to compare the differences in K values. This leads to the conclusion that the shelf test at peak SOC can accurately screen out cells with poor K values, effectively intercepting them.

[0050] The lithium-ion battery self-discharge abnormality screening system based on any of the above methods includes the following modules:

[0051] The charge and discharge module is used to charge and discharge at a rate of 0.01-0.05C, so that the curve can reflect the phase change of the electrochemical reaction of each material;

[0052] The data processing module is used to generate the (dV / dQ)~SOC curve, identify the SOC interval corresponding to the peak, and calculate the K value in the shelf test;

[0053] Temperature control module, used to adjust the test environment temperature;

[0054] The determination module is used to determine whether the battery cell is abnormal based on the K value threshold.

[0055] The present invention has the following advantages:

[0056] 1. Significantly improve self-discharge detection accuracy

[0057] Traditional pain points: Conventional methods have difficulty distinguishing between normal and abnormal cells at low SOC or in the flat area of ​​the voltage platform (such as 30%-50% SOC of lithium iron phosphate), resulting in missed detection.

[0058] Advantages of this solution:

[0059] By analyzing the dV / dQ curve and selecting the SOC range corresponding to the peak (such as 14%-17% SOC for lithium iron phosphate and 50%-53% SOC for ternary cells), the sensitivity of voltage to charge is amplified, making the K value difference of cells with abnormal self-discharge more significant.

[0060] Example verification:

[0061] At 16% SOC (peak region), the K value of lithium iron phosphate cells reaches as high as 1.587mV / day, while at 35% SOC (flat region), it is only 0.936mV / day, allowing abnormal cells to be accurately identified.

[0062] At 52% SOC (peak region), the abnormal K value of the ternary battery cell reached 1.672mV / day, while there was no abnormality at 30% SOC (flat region).

[0063] 2. Enhance temperature adaptability

[0064] Traditional pain point: Existing methods do not clearly define the impact of temperature on self-discharge behavior under SOC state, resulting in fluctuations in detection results.

[0065] Advantages of this solution:

[0066] Multi-temperature verification: Abnormal cells can be accurately screened at both 25°C and 0°C.

[0067] Example verification:

[0068] At 0°C and 16% SOC, the K value for lithium iron phosphate cells still reached 1.295mV / day, consistent with the results at 25°C, demonstrating the method's low-temperature applicability. The screening results for ternary cells at 25°C were stable, requiring no additional temperature compensation.

[0069] 3. Strong universality and compatible with different battery systems

[0070] Traditional pain points: Different material systems (such as lithium iron phosphate and ternary) require customized testing solutions, which increases costs.

[0071] Advantages of this solution:

[0072] Unified method adaptation: Based on the peak positioning of the dV / dQ curve, it is applicable to various lithium-ion batteries (such as NCM 622, LFP, etc.).

[0073] Example verification:

[0074] Lithium iron phosphate and ternary cells are screened through the peak ranges of 14%-17% SOC and 50%-53% SOC respectively, without adjusting the core process.

[0075] 4. Shorten the testing cycle and reduce production costs

[0076] Traditional pain points: Long-term shelving (such as several weeks) is time-consuming and affects production efficiency.

[0077] Advantages of this solution:

[0078] Targeted time reduction: Through sensitive SOC interval detection, K value determination can be completed in just 7 days (conventional methods require more than 14 days).

[0079] Example verification:

[0080] In all the above cases, the holding time was controlled within 168 hours (7 days), abnormal battery cells were quickly eliminated, and the production line efficiency was significantly improved.

[0081] 5. Reduce the risk of misjudgment and missed detection

[0082] Traditional pain point: The K value difference in the flat SOC range is small, which can easily lead to misjudgment (qualified batteries are rejected) or missed detection (abnormal batteries are not discovered).

[0083] Advantages of this solution:

[0084] Differentiation amplification effect: The K value in the peak range is significantly different (for example, the K value of abnormal lithium iron phosphate cells exceeds 1.5mV / day, while that of normal cells is less than 1.0mV / day), and the judgment threshold is clear.

[0085] Example verification:

[0086] At 16% SOC, the K values ​​of lithium iron phosphate cells within the same batch were discrete (1.105-1.587mV / day), and abnormal cells were clearly identified. At 35% SOC, the K values ​​were concentrated (0.811-0.936mV / day), and no misjudgments were made.

[0087] In summary, this invention locates the sensitive SOC range through dV / dQ curve analysis and combines it with multi-temperature verification to achieve high-precision, high-efficiency, and strong universality of lithium-ion battery self-discharge detection, solving the technical bottlenecks of traditional methods in SOC sensitivity, temperature influence, system compatibility, etc., and providing an innovative solution for battery quality control.

[0088] The present invention also discloses a computer-readable storage medium and a computer system, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, it performs any of the methods described above. A computer system includes a processor and a storage medium, wherein the storage medium stores the computer program, and the processor reads and executes the computer program from the storage medium to perform any of the methods described above.

[0089] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. A skilled person may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0090] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and storage medium may reside in a user terminal as discrete components.

[0091] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0092] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for screening abnormal automatic discharge of lithium-ion batteries, characterized in that: The following steps are involved: S1, charging the lithium-ion battery cell at a rate of 0.01-0.05C to obtain a corresponding relationship curve between voltage V and charge Q; S2, performing differentiation processing on the VQ relationship curve to generate a (dV / dQ)~SOC curve, and identifying the SOC interval corresponding to the peak in the (dV / dQ)~SOC curve; S3, adjusting the battery cell to the SOC state corresponding to the peak, performing a shelf test at a selected temperature, and calculating the self-discharge K value; S4, screening out abnormal self-discharge cells based on K value differences.

2. The method according to claim 1, wherein: The lithium-ion battery cells in step S1 include lithium iron phosphate battery cells and ternary lithium-ion battery cells.

3. The method according to claim 1, characterized in that The shelving test in step S3 includes the following steps: S31, measure the initial voltage OCV1 after the first storage for 12-24 hours; S32, measure the voltage OCV2 after the second shelf for 120-168h; S33, calculate the K value using the formula as follows: Wherein, t is the difference between the second lay-up time and the first lay-up time, in days.

4. The method according to claim 1, wherein: In step S3, the selected temperatures are 25°C and 0°C.

5. The method according to claim 1, wherein: It also includes comparative testing, which involves adjusting the same batch of battery cells to the peak SOC and flat zone SOC respectively, and conducting shelf tests simultaneously to compare the differences in K values.

6. A lithium-ion battery self-discharge abnormality screening system based on the method according to any one of claims 1 to 3, characterized in that: include: The charge and discharge module is used to charge and discharge at a rate of 0.01-0.05C, so that the curve can reflect the phase change of the electrochemical reaction of each material; The data processing module is used to generate the (dV / dQ)~SOC curve, identify the SOC interval corresponding to the peak, and calculate the K value in the shelf test; Temperature control module, used to adjust the test environment temperature; The determination module is used to determine whether the battery cell is abnormal based on the K value threshold.

7. A computer-readable storage medium, characterized in that: The medium stores a computer program, and after the computer program is run, the method according to any one of claims 1 to 5 is executed.

8. A computer system, characterized in that: The method comprises a processor and a storage medium, wherein the storage medium stores a computer program, and the processor reads and runs the computer program from the storage medium to execute the method according to any one of claims 1 to 5.

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