A lithium battery lithium precipitation early warning method, device, equipment and storage medium

Abstract

The application provides a lithium battery lithium precipitation early warning method, device, equipment and storage medium, relating to the technical field of lithium battery. The lithium battery lithium precipitation early warning method comprises the following steps: after a to-be-tested lithium battery is subjected to a preset number of charge-discharge cycles, discharging the lithium battery to a rated voltage; disassembling the cycled lithium battery, and taking out a folded positive plate in the lithium battery; regionally dividing the folded positive plate along a first direction, and obtaining residual capacity distribution of each region along the first direction of the folded positive plate, wherein the first direction is the length direction of the folded positive plate; and determining the lithium precipitation risk level of each region of the lithium battery according to the residual capacity distribution. The residual capacity distribution of the folded positive plate is detected to indirectly reflect the local lithium precipitation condition of the negative electrode, early warning can be realized, the test cycle is shortened, and the residual capacity distribution of each region can be obtained by regionally dividing the folded positive plate along the first direction, so that comprehensive evaluation of each part of the flat surface is realized.

Description

Technical Field

[0001] This application relates to the technical field of lithium batteries, and in particular to a method, apparatus, device and storage medium for early warning of lithium plating in lithium batteries. Background Technology

[0002] Lithium plating is one of the key failure modes affecting the safety, lifespan, and performance stability of lithium-ion batteries during cycling. The root cause of lithium plating lies in the inability of the negative electrode to effectively and promptly insert lithium ions migrating from the positive electrode during charging, leading to lithium metal deposition on the negative electrode surface. In actual cell manufacturing and cycling processes, certain localized areas of the negative electrode (such as the electrode overlap area (OH region) or gaps formed by abnormal winding processes) are unable to undergo normal lithium insertion and extraction due to abnormal distances between the positive and negative electrodes. During charging, lithium ions from surrounding areas diffuse into these abnormal areas; during discharging, lithium in these areas diffuses back outwards, resulting in localized lithium content higher than normal. As cycling progresses, lithium accumulates locally, eventually inducing lithium plating on the negative electrode surface, while an uneven distribution of decreasing or increasing lithium content also appears in the corresponding positive electrode area.

[0003] Currently, the mainstream lithium plating detection technologies in the industry mainly fall into two categories: one is the interface observation method after the cell is fully charged, and the other is the three-electrode detection method. The interface observation method requires observation only after the lithium plating reaction has occurred, and usually requires a long cycling process to make the lithium plating identifiable. This method is time-consuming, cannot provide early warning, and is difficult to quantitatively assess the risk of lithium plating. The three-electrode detection method involves placing a reference electrode on the negative electrode surface to monitor the negative electrode potential during battery cycling or resting. However, this method is complex to operate and difficult to accurately locate potential changes in small local areas of the negative electrode. Furthermore, the reference electrode (such as copper wire, gold wire, lithium titanate electrode sheet, or its current collector with attached lithium metal) needs to be placed between the negative electrode and the separator during battery manufacturing. This introduction process deteriorates battery safety and cycle life, and is usually only suitable for small-scale product testing in the R&D stage, making it difficult to apply in large-scale engineering product development. Therefore, existing lithium plating detection technologies share the following shortcomings: they cannot provide early, non-destructive, and quantitative assessment of the risk of lithium plating in local areas of the negative electrode without introducing additional electrodes or damaging the original battery structure.

[0004] Therefore, there is an urgent need to develop a method, device, equipment, or storage medium for early warning of lithium plating in lithium batteries, so as to achieve early detection and comprehensive and accurate quantitative assessment of lithium plating risk. Summary of the Invention

[0005] This application provides a lithium battery lithium plating early warning method, device, equipment, and storage medium, aiming to solve the problems of limited detection range of the negative electrode, slow response, and inability to comprehensively assess the risk of lithium plating in different parts in the prior art.

[0006] In a first aspect, this application provides a lithium battery lithium plating early warning method, comprising: performing a preset number of charge-discharge cycles on the lithium battery under test, and then discharging it to a rated voltage; disassembling the cycled lithium battery and removing a positive electrode sheet from the lithium battery; dividing the positive electrode sheet into regions along a first direction and obtaining the residual capacity distribution of each region along the first direction of the positive electrode sheet, wherein the first direction is the length direction of the positive electrode sheet; and determining the lithium plating risk level of each region of the lithium battery based on the residual capacity distribution.

[0007] The above scheme indirectly reflects the local lithium deposition in the negative electrode by detecting the residual capacity distribution of the folded positive electrode sheet. Lithium migration in the OH region leads to a decrease in lithium content at the edge of the folded positive electrode sheet and an increase in lithium content in nearby areas. This spatial difference in residual capacity distribution can be accurately captured by this scheme. This scheme only requires a preset number of cycles (e.g., 30-100 cycles) to detect local anomalies through the residual capacity distribution of the positive electrode. It does not need to accumulate to a certain level, such as when the negative electrode potential drops below 0V, to provide an early warning. Therefore, it can achieve early warning and significantly shorten the testing cycle. Furthermore, this scheme divides the area along the first direction (length or width) of the folded positive electrode sheet to obtain the residual capacity distribution of each area, achieving a comprehensive evaluation of all parts of the flat surface.

[0008] In one possible design, obtaining the residual capacity distribution of each region along the first direction of a folded positive electrode sheet includes: selecting multiple sampling locations in each region to obtain multiple positive electrode samples; assembling a coin cell with each positive electrode sample as the positive electrode and a lithium metal sheet as the negative electrode, and performing charge-discharge tests, recording the initial charge capacity as the residual capacity corresponding to that sampling location; taking the coin cells corresponding to the multiple sampling locations in each region as parallel samples, calculating the average residual capacity of the multiple coin cells in the parallel samples, and using the average value as the residual capacity of the corresponding region.

[0009] The above scheme obtains multiple positive electrode samples by selecting multiple sampling locations within each region. Each sample is then used to assemble coin cells for charge-discharge testing, and the average value is calculated using multiple coin cells as parallel samples. Multi-point sampling increases spatial coverage, capturing local microscopic differences and improving sample representativeness; parallel sample testing cancels out random errors, and the average value approaches the true value; coin cell testing methods are mature, equipment is widely available, and easy to implement; the initial charge capacity directly reflects the residual lithium intercalation capacity of the positive electrode in that region, with clear physical significance. Ultimately, this eliminates the random errors of single-point testing, improving data accuracy and repeatability.

[0010] In one possible design, the lithium plating risk level is determined based on the residual capacity distribution, including: calculating the theoretical residual capacity Cm of each region along the first direction; obtaining the state of charge difference ΔSOCn based on the residual capacity Cn of each region along the first direction and its corresponding theoretical residual capacity Cm; and determining the lithium plating risk level of each region based on the state of charge difference ΔSOCn.

[0011] The above scheme calculates the theoretical residual capacity Cm for each region along the first direction, and obtains the state-of-charge difference ΔSOCn based on the residual capacity Cn of each region and its corresponding theoretical residual capacity Cm. Then, the lithium plating risk level of each region is determined based on ΔSOCn. Converting absolute capacity into relative deviation ΔSOCn eliminates the influence of differences in battery model, fluctuations in testing conditions, and differences in basic capacity between batches, achieving horizontal comparability between batteries of different specifications and vertical tracking of the same battery. A unified quantitative evaluation scale is established, which can accurately locate the degree of anomaly in each specific region. Furthermore, this numerical output is suitable for algorithmic judgment, laying the foundation for automated and objective evaluation.

[0012] In one possible design, the theoretical residual capacity Cm is the average value of the residual capacity of all regions along the first direction, or the median value of the residual capacity distribution along the first direction, or a preset standard value of the residual capacity of the normal region. Using the above scheme, the theoretical residual capacity Cm can be the average, median, or a preset standard value of the residual capacity of all regions along the first direction. When the data distribution is symmetrical, the average is used to fully utilize all data information, resulting in the highest statistical efficiency. When edge effects or outliers exist, the median is used as a robust statistic unaffected by extreme values, ensuring the benchmark is not skewed. For batch testing or quality control, the preset standard value is used to facilitate direct comparison of test results from different batches and at different times, establishing a unified quality standard. These three value methods flexibly adapt to different data distribution characteristics and application scenarios, ensuring the accuracy and reliability of the benchmark.

[0013] In one possible design, the formula for calculating the state of charge difference ΔSOCn is: ΔSOCn=(Cn-Cm) / Cm, where Cn is the residual capacity of the nth region and Cm is the theoretical residual capacity of the region; when ΔSOCn is greater than a preset threshold, it is determined that there is a risk of lithium plating in the corresponding region.

[0014] Using the above scheme, the formula for calculating the state-of-charge difference ΔSOCn is ΔSOCn=(Cn-Cm) / Cm. When ΔSOCn is greater than a preset threshold, the corresponding region is considered to have a risk of lithium plating. Normalization eliminates differences in battery capacity specifications, enabling universal evaluation for different battery models; the preset threshold establishes a clear pass / fail judgment standard, transforming "potentially problematic" into a clear numerical boundary, reducing subjective human judgment; the formula structure is simple, suitable for programming implementation, and provides a foundation for fully automated detection; it has high sensitivity, capable of detecting minute capacity deviations and achieving early warning.

[0015] In one possible design, the lithium plating risk level is determined based on the residual capacity distribution, including: determining a potentially abnormal region Cmax and a normal region Cnormal based on the residual capacity distribution along a first direction, wherein the potentially abnormal region Cmax is the region with the largest residual capacity; calculating the state of charge difference ΔSOCmax between the potentially abnormal region Cmax and the normal region Cnormal; and determining the lithium plating risk level of the potentially abnormal region Cmax based on the ΔSOCmax value.

[0016] The above scheme identifies the potentially abnormal region Cmax (the region with the largest residual capacity) and the normal region Cnormal based on the residual capacity distribution along the first direction. The difference in state of charge between Cmax and Cnormal, ΔSOCmax, is calculated, and the lithium plating risk level of Cmax is determined based on the ΔSOCmax value. This approach focuses on the most severely risky region, allowing identification even if only one region exhibits severe lithium plating. It closely aligns with the failure mechanism of lithium plating developing from a localized point, with extreme value regions often being the failure origin. The scheme requires only comparison of two key values, simplifying calculations and making it suitable for rapid screening and online monitoring.

[0017] In one possible design, the normal region Cnormal is the average or median value of the residual capacity of the remaining regions after removing the potentially abnormal region Cmax from the residual capacity distribution along the first direction.

[0018] Using the above scheme, the normal region Cnormal is the average or median value of the residual capacity in the remaining regions after removing the potentially abnormal region Cmax from the residual capacity distribution along the first direction. By excluding known abnormal regions, the calculation benchmark ensures that Cnormal truly reflects the normal state and is not contaminated by the abnormal points to be evaluated; it avoids the "peak clipping" effect, making ΔSOCmax more reflective of the true risk level and preventing missed detections; even if lithium plating has occurred in some regions, the remaining sample is still sufficient (n-1≥2), and the average value remains stable, ensuring the sensitivity and reliability of the assessment.

[0019] In one possible design, the formula for calculating ΔSOCmax is: ΔSOCmax=(Cmax-Cnormal) / Cnormal; the standard for determining the lithium plating risk level based on the ΔSOCmax value is: ΔSOCmax>4% is high risk, 1%≤ΔSOCmax≤4% is medium risk, and ΔSOCmax<1% is low risk.

[0020] The above scheme establishes a clear three-tiered risk quantification range, with each range directly corresponding to a specific response measure. For example, if the result is high risk, a scrap warning can be issued, and the manufactured lithium batteries can be reworked. If the result is medium risk, the batch of lithium batteries can be monitored or used at a reduced rate. If the result is low risk, the batch of lithium batteries can be circulated normally without further discussion. The thresholds in this scheme have been experimentally verified to be correlated with the actual failure probability of the batteries, supporting quality traceability and database establishment. The thresholds can be optimized based on historical data to improve prediction accuracy.

[0021] In one possible design, the preset number of charge-discharge cycles is 30 to 100; discharging to the rated voltage is done at a rated current of 0.5C to 2C to the rated voltage.

[0022] The above scheme allows for a preset number of charge-discharge cycles of 30 to 100, with discharge to the rated voltage achieved at a rated current of 0.5C to 2C. The 30-100 cycle range balances the conflict between "signal accumulation" and "test time": signals are sufficiently significant above 30 cycles, while the first 100 cycles are still considered early stages, representing less than 10% of the battery's total lifespan (typically >1000 cycles), thus achieving true early warning and significantly shortening the test cycle. The 0.5C-2C discharge rate is moderate, ensuring sufficient discharge while avoiding high-current polarization or low-current lithium diffusion re-intercalation, ensuring consistent lithium distribution during testing and at the end of the cycle, thus preserving data accuracy. The clearly defined parameter range ensures comparability of results from different laboratories and batches, facilitating standardized promotion.

[0023] Secondly, this application provides a lithium battery lithium plating early warning device, comprising: a cycle charge-discharge module for discharging the lithium battery under test to a rated voltage after performing a preset number of charge-discharge cycles; a disassembly and sampling module for disassembling the cycled lithium battery and removing one fold of the positive electrode sheet from the lithium battery; a regional residual capacity distribution acquisition module for dividing the positive electrode sheet into regions along a first direction and acquiring the residual capacity distribution of each region along the first direction of the positive electrode sheet, wherein the first direction is the length direction of the positive electrode sheet; and a data processing and evaluation module for determining the lithium plating risk level of each region of the lithium battery based on the residual capacity distribution.

[0024] The advantages of the lithium battery lithium plating warning device provided in the second aspect and the various possible designs of the second aspect can be found in the first aspect and the various possible implementations of the first aspect, and will not be repeated here.

[0025] Thirdly, this application provides a lithium battery lithium plating early warning device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the lithium battery lithium plating early warning method of any one of the first aspects.

[0026] The beneficial effects of the lithium battery lithium plating early warning device provided in the third aspect and the various possible designs of the third aspect can be referred to the beneficial effects brought about by the first aspect and the various possible implementations of the first aspect, and will not be repeated here.

[0027] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the lithium battery lithium plating early warning method of any one of the first aspects.

[0028] The advantages of the computer-readable storage medium provided in the fourth aspect and the various possible designs of the fourth aspect can be seen in the advantages of the first aspect and the various possible implementations of the first aspect, and will not be repeated here.

[0029] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 This is a flowchart of a lithium battery lithium plating early warning method provided in an embodiment of this application.

[0032] Figure 2 This is a schematic diagram of a folded positive electrode and sampling position provided in an embodiment of this application.

[0033] Figure 3 This is a schematic diagram of a lithium battery lithium plating early warning device provided in an embodiment of this application.

[0034] Explanation of reference numerals in the attached figures: 200. Lithium-ion battery lithium plating early warning device; 201. Cyclic charge and discharge module; 202. Disassembly and sampling module; 203. Regional residual capacity distribution acquisition module; 204. Data processing and evaluation module. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims and drawings of this application are intended to cover non-exclusive inclusion.

[0037] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of the phrase "embodiment" in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0038] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists, A and B exist simultaneously, or B exists. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0039] Furthermore, the terms "first," "second," etc., in the specification and claims of this application or in the aforementioned drawings are used to distinguish different objects rather than to describe a specific order, and may explicitly or implicitly include one or more of the features.

[0040] In the description of this application, unless otherwise stated, "multiple" means two or more (including two), and similarly, "multiple groups" means two or more (including two groups).

[0041] As can be seen from the background technology, current lithium plating detection technologies have the problem of being unable to conduct early, non-destructive, and quantitative assessments of lithium plating risks in localized areas of the negative electrode without introducing additional electrodes or damaging the original battery structure.

[0042] Analysis revealed that the existing three-electrode detection method for lithium plating detection has the following technical drawbacks: First, the detection range is limited. This type of method can only detect the risk of lithium plating caused by the limitation of lithium intercalation kinetics in the negative electrode (such as uniform lithium plating on the negative electrode surface caused by high-rate charging, low-temperature charging, etc.), but it cannot detect the risk of local lithium plating at the edge of the negative electrode caused by lithium migration in the OH region, nor can it detect lithium plating caused by uneven lithium diffusion in various parts of the flat surface.

[0043] Second, there is a response lag. The three-electrode method requires lithium ions to accumulate to a certain level at the negative electrode before the negative electrode potential drops below 0V. However, the accumulation process requires a long cycle, making early warning impossible.

[0044] Third, the accuracy of the test is difficult to guarantee. During the preparation of the three electrodes, many factors, such as the state of the copper wire itself, the lithium plating process, and the placement of the reference electrode, can affect the accuracy of the test results. Moreover, the operation is complex and the process is difficult to control.

[0045] Fourth, it can only detect fixed locations. The reference electrode can only detect the potential at its specific location, and cannot comprehensively assess the risk of lithium plating in different parts of the battery.

[0046] In view of this, embodiments of this application provide a lithium battery lithium plating early warning method, apparatus, device, and storage medium. The lithium early warning method includes: performing a preset number of charge-discharge cycles on the lithium battery under test, and then discharging it to a rated voltage; disassembling the cycled lithium battery and removing a folded positive electrode sheet from the lithium battery; dividing the folded positive electrode sheet into regions along a first direction and obtaining the residual capacity distribution of each region along the first direction of the folded positive electrode sheet, wherein the first direction is the length direction of the folded positive electrode sheet; and determining the lithium plating risk level of each region of the lithium battery based on the residual capacity distribution. The residual capacity distribution of the folded positive electrode sheet indirectly reflects the local lithium plating situation at the negative electrode. Early warning can be achieved without accumulating to a certain level, such as when the negative electrode potential drops below 0V, significantly shortening the testing cycle. Furthermore, in this solution, dividing the folded positive electrode sheet into regions along the first direction (length direction or width direction) allows for the acquisition of the residual capacity distribution of each region, achieving a comprehensive evaluation of all parts of the flat surface.

[0047] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0048] Example 1 Figure 1 This is a flowchart of the lithium battery lithium plating early warning method provided in this embodiment. Please refer to it. Figure 1 This application provides a lithium battery lithium plating early warning method, comprising: Step 101: After performing a preset number of charge-discharge cycles on the lithium battery under test, discharge it to the rated voltage.

[0049] It is understood that the lithium battery to be tested can be prepared in this embodiment through the following steps: Positive and negative electrode homogenization: The positive electrode active material, conductive agent, binder and solvent are mixed evenly to prepare the positive electrode slurry; the negative electrode active material, conductive agent, binder and solvent are mixed evenly to prepare the negative electrode slurry.

[0050] Coating: The positive electrode slurry is coated on the surface of the aluminum foil, and the negative electrode slurry is coated on the surface of the copper foil. After drying, the positive electrode coating and the negative electrode coating are formed.

[0051] Rolling: The coated electrode sheet is rolled to densify the coating and achieve the target compaction density.

[0052] Cutting: Cut the rolled electrode sheets into positive and negative electrode sheets of the required size.

[0053] Winding or stacking: The positive electrode, separator, and negative electrode are stacked or wound in a predetermined manner to form a battery cell.

[0054] Hot pressing: The battery cell is hot-pressed to shape it, so that the layers are tightly bonded together.

[0055] Electrode welding: Weld the positive electrode tab and the negative electrode tab to the positive electrode plate and the negative electrode plate respectively.

[0056] Packaging: The battery cell is placed inside an aluminum-plastic film casing and then top-sealed and side-sealed to form a soft-pack battery.

[0057] Electrolyte injection: Injecting electrolyte into the packaged battery. The electrolyte uses lithium hexafluorophosphate as the lithium salt, carbonate as the solvent, and ethylene as the additive.

[0058] Formation: The battery is formed after electrolyte injection to form a stable solid electrolyte interphase (SEI) membrane.

[0059] Aging: The formed battery is left to age at a set temperature to stabilize its performance.

[0060] All of the above processes are standard procedures in lithium battery manufacturing, and this embodiment does not impose any special limitations.

[0061] In this embodiment, the positive electrode active material can be a conventional positive electrode material for lithium batteries, such as lithium cobalt oxide, nickel-cobalt-manganese ternary material, lithium iron phosphate, or lithium manganese oxide; the negative electrode active material can be a graphite negative electrode material, silicon-carbon negative electrode material, hard carbon material, or soft carbon material.

[0062] The battery's cyclic charge and discharge current is determined through methods such as cyclic capacity retention testing, cyclic DC internal resistance (DCR) testing, cyclic disassembly observation of lithium plating, conventional three-electrode testing of negative electrode potential, and simulation, to ensure that lithium plating does not occur on a large scale throughout the entire cycle of the battery.

[0063] In this embodiment, the preset number of charge-discharge cycles is 30 to 100; discharging to the rated voltage is done by discharging to the rated voltage with a rated current of 0.5C to 2C.

[0064] Understandably, the preset charge-discharge cycles are 30 to 100, and discharging to the rated voltage is achieved by discharging to the rated voltage at a rated current of 0.5C to 2C. The 30-100 cycle range balances the conflict between "signal accumulation" and "test time": signals are sufficiently significant above 30 cycles, while the first 100 cycles are still considered the early stage, representing less than 10% of the battery's total lifespan (typically >1000 cycles), thus achieving true early warning and significantly shortening the test cycle; the 0.5C-2C discharge rate is moderate, ensuring sufficient discharge while avoiding high-current polarization or low-current lithium diffusion and re-intercalation, ensuring that the lithium distribution during testing is consistent with that at the end of the cycle, thus preserving data accuracy; the parameter range is clearly defined, ensuring comparability of results from different laboratories and different batches, facilitating standardized promotion.

[0065] For example, specifically, the cyclic charge / discharge current can be set to 1C / 1C, and the number of cycles can be set to 50.

[0066] In this embodiment, the lithium battery under test is charged and discharged 50 times at the rated current, and then discharged to the rated voltage at the rated current. For example, the rated voltage of the lithium battery in this embodiment is 2.5V.

[0067] Step 102: Disassemble the cycled lithium battery and remove one fold of the positive electrode from the lithium battery.

[0068] After the battery cell is fully loaded, it is disassembled in the glove box. A folded positive electrode sheet is taken out. It can be understood that there is a bent area on the positive electrode sheet in the battery cell, such as the arc-shaped transition area on the side of the wound battery cell, which is called the R area. This folded positive electrode sheet is the flat surface left after the R area is removed from the positive electrode sheet after the battery cell is disassembled.

[0069] Step 103: Divide the positive electrode sheet along the first direction into regions and obtain the residual capacity distribution of each region along the first direction of the positive electrode sheet, wherein the first direction is the length direction of the positive electrode sheet.

[0070] like Figure 2 As shown, the positive electrode sheet is divided into regions along a first direction. In this embodiment, the first direction is the length direction from the tab edge to the bottom edge. The positive electrode sheet is divided into n regions along its length direction. For example, in this embodiment, n=4, which are region 1 (near the tab edge), region 2, region 3, and region 4 (near the bottom edge).

[0071] Within each region, a coin cell slicer is used to sequentially remove small circular pieces from multiple sampling locations. For example, in this embodiment, four small circular pieces are removed from four sampling locations. The area of ​​each small circular piece is approximately 1.13 cm², and the diameter is approximately 1.2 cm. Therefore, a total of four positive electrode samples are obtained from each region.

[0072] Understandably, the area of ​​the small disc can be any area suitable for making button cells and facilitating sampling within the defined area.

[0073] The residual capacity distribution of a single-fold positive electrode sheet is used to indirectly reflect the local lithium deposition in the negative electrode. Lithium migration in the OH region leads to a decrease in lithium content at the edge of the single-fold positive electrode sheet and an increase in lithium content in the surrounding area. This spatial difference in residual capacity distribution can be accurately captured by this method.

[0074] In this embodiment, obtaining the residual capacity distribution of each region along the first direction of the folded positive electrode sheet includes: selecting multiple sampling locations in each region to obtain multiple positive electrode samples; assembling a coin cell with each positive electrode sample as the positive electrode and a lithium metal sheet as the negative electrode, and performing charge-discharge tests, recording the first charge capacity as the residual capacity corresponding to the sampling location; taking the coin cells corresponding to the multiple sampling locations in each region as parallel samples, calculating the average residual capacity of the multiple coin cells in the parallel samples, and using the average value as the residual capacity of the corresponding region.

[0075] Multiple cathode samples were obtained by selecting multiple sampling locations within each region. Each sample was used to assemble coin cells for charge-discharge testing, and the average value was calculated using multiple coin cells as parallel samples. Multi-point sampling increases spatial coverage, captures local microscopic differences, and improves sample representativeness; parallel sample testing allows random errors to cancel each other out, and the average value approaches the true value; coin cell testing methods are mature, equipment is widely available, and easy to implement; the initial charge capacity directly reflects the residual lithium intercalation capacity of the cathode in that region, with clear physical significance. Ultimately, the random errors of single-point testing are eliminated, improving data accuracy and repeatability.

[0076] In this embodiment, a coin cell is assembled using each positive electrode sample as the positive electrode and a lithium metal sheet as the negative electrode. The assembly sequence is as follows: positive electrode shell, one-fold positive electrode sheet, separator, electrolyte, lithium metal, nickel foam, and negative electrode shell.

[0077] Place the assembled coin cell on the battery testing system, input the mass of the positive electrode active material and the nominal capacity of the positive electrode material, and set the test parameters: within the rated cutoff voltage range (2.0V-4.3V), perform two charge-discharge cycles at the rated low current (0.1C). Record the initial charge capacity as the residual capacity corresponding to that sampling location.

[0078] Step 104: Determine the lithium plating risk level of each region of the lithium battery based on the residual capacity distribution.

[0079] This scheme divides the flat surface of the positive electrode into regions, thereby obtaining the residual capacity distribution of each region and achieving a comprehensive assessment of the lithium plating risk at each part of the flat surface.

[0080] In one embodiment, determining the lithium plating risk level based on the residual capacity distribution includes: calculating the theoretical residual capacity Cm of each region along a first direction; obtaining the state of charge difference ΔSOCn based on the residual capacity Cn of each region along the first direction and its corresponding theoretical residual capacity Cm; and determining the lithium plating risk level of each region based on the state of charge difference ΔSOCn.

[0081] By calculating the theoretical residual capacity Cm of each region along the first direction, and obtaining the state-of-charge difference ΔSOCn based on the residual capacity Cn of each region and its corresponding theoretical residual capacity Cm, the lithium plating risk level of each region is determined. Converting absolute capacity into relative deviation ΔSOCn eliminates the influence of differences in battery model, fluctuations in testing conditions, and differences in basic capacity between batches, achieving horizontal comparability between batteries of different specifications and vertical tracking of the same battery. A unified quantitative evaluation scale is established, which can accurately locate the degree of anomaly in each specific region. Furthermore, this numerical output in this case is suitable for algorithmic judgment, laying the foundation for automated and objective evaluation.

[0082] In one embodiment, the theoretical residual capacity Cm is the average value of the residual capacity of all regions along the first direction, or the median value of the residual capacity distribution along the first direction, or a preset standard value of the residual capacity of normal regions. Using the above scheme, the theoretical residual capacity Cm can be the average, median, or a preset standard value of the residual capacity of all regions along the first direction. When the data distribution is symmetrical, the average is used to fully utilize all data information, resulting in the highest statistical efficiency. When edge effects or outliers exist, the median is used as a robust statistic unaffected by extreme values, ensuring the benchmark is not skewed. For batch testing or quality control, the preset standard value is used to facilitate direct comparison of test results from different batches and at different times, establishing a unified quality standard. These three value methods flexibly adapt to different data distribution characteristics and application scenarios, ensuring the accuracy and reliability of the benchmark.

[0083] In one embodiment, the formula for calculating the state of charge difference ΔSOCn is: ΔSOCn=(Cn-Cm) / Cm, where Cn is the residual capacity of the nth region and Cm is the theoretical residual capacity of the region; when ΔSOCn is greater than a preset threshold, it is determined that there is a risk of lithium plating in the corresponding region.

[0084] Using the above scheme, the formula for calculating the state-of-charge difference ΔSOCn is ΔSOCn=(Cn-Cm) / Cm. When ΔSOCn is greater than a preset threshold, the corresponding region is considered to have a risk of lithium plating. Normalization eliminates differences in battery capacity specifications, enabling universal evaluation for different battery models; the preset threshold establishes a clear pass / fail judgment standard, transforming "potentially problematic" into a clear numerical boundary, reducing subjective human judgment; the formula structure is simple, suitable for programming implementation, and provides a foundation for fully automated detection; it has high sensitivity, capable of detecting minute capacity deviations and achieving early warning.

[0085] In one embodiment, determining the lithium plating risk level based on the residual capacity distribution includes: determining a potentially abnormal region Cmax and a normal region Cnormal based on the residual capacity distribution along a first direction, wherein the potentially abnormal region Cmax is the region with the largest residual capacity; calculating the state of charge difference ΔSOCmax between the potentially abnormal region Cmax and the normal region Cnormal; and determining the lithium plating risk level of the potentially abnormal region Cmax based on the ΔSOCmax value.

[0086] The above scheme identifies the potentially abnormal region Cmax (the region with the largest residual capacity) and the normal region Cnormal based on the residual capacity distribution along the first direction. The difference in state of charge between Cmax and Cnormal, ΔSOCmax, is calculated, and the lithium plating risk level of Cmax is determined based on the ΔSOCmax value. This approach focuses on the most severely risky region, allowing identification even if only one region exhibits severe lithium plating. It closely aligns with the failure mechanism of lithium plating developing from a localized point, with extreme value regions often being the failure origin. The scheme requires only comparison of two key values, simplifying calculations and making it suitable for rapid screening and online monitoring.

[0087] For example, the residual capacity distribution is calculated as follows: For each region, the coin cells corresponding to the four sampling locations within that region are used as parallel samples. The average value of the initial charge capacity of the four coin cells is calculated as the residual capacity of that region. In this embodiment, the standard deviation δ of the four parallel samples is ≤1mAh / g, indicating that the test data have good consistency.

[0088] The residual capacity distribution of a single-fold positive electrode sheet from the tab edge to the bottom edge was obtained through the above calculations, and the results are shown in Table 1.

[0089] Table 1:

[0090] As shown in Table 1, the residual capacity of region 4 (near the bottom edge) is 148.9 mAh / g, which is significantly higher than that of other regions, indicating that there may be an anomaly in the positive electrode region corresponding to this area. In other words, the Cmax corresponding to region 4 is 148.9 mAh / g.

[0091] In one embodiment, the normal region Cnormal is the average or median value of the residual capacity of the remaining regions after removing the potentially abnormal region Cmax from the residual capacity distribution along the first direction.

[0092] Using the above scheme, the normal region Cnormal is the average or median value of the residual capacity in the remaining regions after removing the potentially abnormal region Cmax from the residual capacity distribution along the first direction. By excluding known abnormal regions, the calculation benchmark ensures that Cnormal truly reflects the normal state and is not contaminated by the abnormal points to be evaluated; it avoids the "peak clipping" effect, making ΔSOCmax more reflective of the true risk level and preventing missed detections; even if lithium plating has occurred in some regions, the remaining sample is still sufficient (n-1≥2), and the average value remains stable, ensuring the sensitivity and reliability of the assessment.

[0093] In one embodiment, the formula for calculating ΔSOCmax is: ΔSOCmax=(Cmax-Cnormal) / Cnormal; the standard for determining the lithium plating risk level based on the ΔSOCmax value is: ΔSOCmax>4% is high risk, 1≤%ΔSOCmax≤4% is medium risk, and ΔSOCmax<1% is low risk.

[0094] The above scheme establishes a clear three-tiered risk quantification range, with each range directly corresponding to a specific response measure. For example, if the result is high risk, a scrap warning can be issued, and the manufactured lithium batteries can be reworked. If the result is medium risk, the batch of lithium batteries can be monitored or used at a reduced rate. If the result is low risk, the batch of lithium batteries can be circulated normally without further discussion. The thresholds in this scheme have been experimentally verified to be correlated with the actual failure probability of the batteries, supporting quality traceability and database establishment. The thresholds can be optimized based on historical data to improve prediction accuracy.

[0095] For example, (1) determine the possible abnormal region Cmax and the normal region Cnormal. Cmax is the region with the largest residual capacity, i.e., region 4 (148.9 mAh / g); Cnormal is the average residual capacity of the remaining regions after removing Cmax, i.e. (143.2+143.5+143.8) / 3=143.5 mAh / g.

[0096] (2) Calculate the difference in state of charge ΔSOCmax: ΔSOCmax=(Cmax-Cnormal) / Cnormal=(148.9-143.5) / 143.5=0.0376≈3.76%.

[0097] (3) Determine the lithium plating risk level based on the ΔSOCmax value.

[0098] In this embodiment, the risk level classification standard is as follows: ΔSOCmax > 4% is high risk, 1% ≤ ΔSOCmax ≤ 4% is medium risk, and ΔSOCmax < 1% is low risk. Since ΔSOCmax = 3.76%, which falls within the medium risk range (1%-4%), the battery is determined to have a medium risk of lithium plating.

[0099] To verify the accuracy of the method in this embodiment, the same batch of batteries were cycled again until obvious lithium plating appeared. The results showed that the battery exhibited edge lithium plating after 1000 cycles, consistent with the medium-risk assessment. In contrast, among the control batteries, the battery with ΔSOCmax = 4.5% showed edge lithium plating after only 150 cycles (high risk), while the battery with ΔSOCmax = 0.25% showed no edge lithium plating after 3000 cycles (low risk). The verification results demonstrate that the method in this embodiment can effectively predict lithium plating risk, and the risk level highly matches the actual lithium plating situation.

[0100] Example 2 The main difference between this embodiment and Embodiment 1 lies in the lithium plating risk assessment method. This embodiment uses a "region-by-region assessment method" to determine the lithium plating risk level of each region. The specific steps are as follows: In this embodiment, the theoretical residual capacity Cm is taken as the average value of the residual capacity of all regions along the first direction, that is: Cm=(143.2+143.5+143.8+148.9) / 4=144.85mAh / g.

[0101] Calculate the difference in state of charge (ΔSOCn) for each region, where ΔSOCn = (Cn - Cm) / Cm. The calculation results are shown in Table 2.

[0102] Table 2. Calculation results of ΔSOCn for each region:

[0103] Determine the lithium plating risk level of each region based on ΔSOCn. In this embodiment, the preset threshold is set to 1%. When ΔSOCn > 1%, the corresponding region is determined to have a risk of lithium plating. As shown in Table 2, the ΔSOCn of region 4 is 2.80% > 1%, so region 4 is determined to have a risk of lithium plating; the ΔSOCn of other regions is less than 1%, so there is no risk. The risk level classification standard is the same as in Embodiment 1: ΔSOCn > 4% is high risk, 1% ≤ ΔSOCn ≤ 4% is medium risk, and ΔSOCn < 1% is low risk or no risk.

[0104] The region-by-region evaluation method in this embodiment can accurately locate each region with a risk of lithium plating, and is suitable for scenarios that require a comprehensive evaluation of the battery interior and accurate location of abnormal regions.

[0105] Example 3 The difference between this embodiment and Embodiment 2 lies in the way the theoretical residual capacity Cm is determined. In this embodiment, considering the possibility of edge effects or outlier interference, the median value is used as the theoretical residual capacity Cm.

[0106] The residual capacity of each region was sorted by size as follows: 143.2, 143.5, 143.8, 148.9, with a median value of (143.5+143.8) / 2=143.65mAh / g.

[0107] The ΔSOCn for each region was calculated, and the results are shown in Table 3.

[0108] Table 3 shows the calculation results of ΔSOCn using the median value as Cm:

[0109] When the median is used as the benchmark, ΔSOCmax = 3.66%, still classified as medium risk, consistent with the conclusion of Example 2, but with a slight numerical difference. Using the median can prevent the benchmark from being skewed by outliers when the data distribution is asymmetrical or outliers are present, making it a robust statistical method.

[0110] Example 4 The difference between this embodiment and Embodiment 2 lies in the way the theoretical residual capacity Cm is determined. In this embodiment, used for batch quality control scenarios, a preset standard value for the residual capacity of the normal area is used as Cm.

[0111] Based on extensive preliminary experiments, a standard value of 144.0 mAh / g for the residual capacity in the normal region of this battery model was established. Using this standard value as Cm, ΔSOCn for each region was calculated, and the results are shown in Table 4.

[0112] Table 4 uses the preset standard value as the calculation result of ΔSOCn for Cm:

[0113] Using preset standard values ​​as a benchmark facilitates direct comparison of test results from different batches and at different times, which is conducive to establishing unified quality standards and realizing automated judgment of batch testing.

[0114] Performance verification To verify the effectiveness of the technical solution of this application, three lithium battery samples with different designs were tested, and the test results are shown in Table 5.

[0115] Table 5. Lithium plating risk test results for different lithium battery designs:

[0116] As can be seen from Table 5: High-risk samples (ΔSOC=4.5%) showed edge lithium plating after 150 cycles, verifying the accuracy of the high-risk determination. The medium-risk sample (ΔSOC=2.1%) showed edge lithium plating after 1000 cycles, indicating that the method can achieve early warning. No lithium plating was observed in the low-risk sample (ΔSOC=0.25%) after 3000 cycles, verifying the reliability of the low-risk determination.

[0117] Test results show that the lithium plating early warning method, device, equipment and storage medium provided in this application can accurately and early identify the lithium plating risk of lithium batteries. The risk level classification is highly consistent with the actual battery failure situation and has good engineering application value.

[0118] like Figure 3 As shown, based on the above embodiments, this application also provides a lithium battery lithium plating early warning device 200, used to implement the lithium plating early warning method of any one of embodiments 1 to 4. The device includes the following modules: The cyclic charge-discharge module 201 is used to discharge the lithium battery under test to its rated voltage after performing a preset number of charge-discharge cycles. This module includes a battery test channel, a temperature control unit, and a data acquisition unit, and can perform cyclic charge-discharge tests on the battery according to set current, voltage, and temperature parameters.

[0119] The disassembly and sampling module 202 is used to disassemble a cycled lithium battery and remove one fold of the positive electrode from the battery. This module includes a glove box, cutting tools, and sampling molds, and can disassemble and sample the battery under inert gas protection.

[0120] The residual capacity distribution acquisition module 203 is used to divide the positive electrode sheet into regions along a first direction and acquire the residual capacity distribution of each region along the first direction of the positive electrode sheet. This module includes a coin cell assembly unit and an electrochemical testing unit, which can perform coin cell assembly and charge-discharge tests on the positive electrode sample and record residual capacity data.

[0121] The data processing and evaluation module 204 is used to determine the lithium plating risk level of each region of the lithium battery based on the residual capacity distribution. This module includes a data storage unit and a calculation and analysis unit, and can perform data processing functions such as residual capacity distribution calculation, ΔSOC calculation, and risk level determination.

[0122] The lithium battery lithium plating early warning device 200 in this embodiment can realize automated and standardized lithium plating risk detection, and is suitable for batch screening and quality control in the product development stage.

[0123] Based on the above embodiments, this application also provides a lithium battery lithium plating early warning device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the lithium plating early warning method of any one of embodiments 1 to 4.

[0124] The memory can be a non-volatile storage medium such as read-only memory (ROM), random access memory (RAM), or flash memory. The processor can be a central processing unit (CPU), microcontroller (MCU), digital signal processor (DSP), or other chip or circuit with data processing capabilities.

[0125] Based on the above embodiments, this application also provides a computer-readable storage medium storing a computer program thereon. When the program is executed by a processor, it implements the lithium plating warning method in any of the above embodiments.

[0126] Computer-readable storage media can be any medium capable of storing computer programs, such as USB flash drives, portable hard drives, optical discs, magnetic disks, and SD cards.

[0127] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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 this application.

Claims

1. A method for early warning of lithium plating in lithium batteries, characterized in that, Including the following steps: After performing a preset number of charge-discharge cycles on the lithium battery under test, discharge it to the rated voltage. Disassemble the cycled lithium battery and remove one fold of the positive electrode from the lithium battery; The single-fold positive electrode sheet is divided into regions along a first direction, and the residual capacity distribution of each region along the first direction of the single-fold positive electrode sheet is obtained, wherein the first direction is the length direction of the single-fold positive electrode sheet; The lithium plating risk level of each region of the lithium battery is determined based on the residual capacity distribution.

2. The lithium battery lithium plating early warning method according to claim 1, characterized in that, The step of obtaining the residual capacity distribution of each region along the first direction of the folded positive electrode includes the following steps: Multiple sampling locations were selected within each region to obtain multiple positive electrode samples; Using each of the positive electrode samples as the positive electrode and the lithium metal sheet as the negative electrode, assemble a coin cell battery and perform charge and discharge tests. Record the capacity of the first charge as the residual capacity corresponding to the sampling location. The corresponding coin cells at multiple sampling locations in each region are taken as parallel samples. The average value of the residual capacity of the multiple coin cells in the parallel samples is calculated, and the average value is taken as the residual capacity of the corresponding region.

3. The lithium battery lithium plating early warning method according to claim 2, characterized in that, The process of determining the lithium plating risk level based on the residual capacity distribution includes the following steps: Calculate the theoretical residual capacity Cm for each region along the first direction; The state of charge difference ΔSOCn is obtained by comparing the residual capacity Cn of each region along the first direction with its corresponding theoretical residual capacity Cm. The lithium plating risk level of each region is determined based on the difference in state of charge ΔSOCn.

4. The lithium battery lithium plating early warning method according to claim 3, characterized in that, The theoretical residual capacity Cm is the average value of the residual capacity of all regions along the first direction, or the median value of the residual capacity distribution along the first direction, or a preset standard value of the residual capacity of normal regions.

5. The lithium battery lithium plating early warning method according to claim 3, characterized in that, The formula for calculating the state of charge difference ΔSOCn is: ΔSOCn=(Cn-Cm) / Cm, where Cn is the residual capacity of the nth region and Cm is the theoretical residual capacity of the region; when ΔSOCn is greater than a preset threshold, it is determined that there is a risk of lithium plating in the corresponding region.

6. The lithium battery lithium plating early warning method according to claim 2, characterized in that, The process of determining the lithium plating risk level based on the residual capacity distribution includes the following steps: Based on the residual capacity distribution along the first direction, a potentially abnormal region Cmax and a normal region Cnormal are determined, wherein the potentially abnormal region Cmax is the region with the largest residual capacity. Calculate the difference in state of charge ΔSOCmax between the potentially abnormal region Cmax and the normal region Cnormal; The lithium plating risk level of the potentially abnormal region Cmax is determined based on the ΔSOCmax value.

7. The lithium battery lithium plating early warning method according to claim 6, characterized in that, The normal region Cnormal is the average or median value of the residual capacity in the remaining regions after removing the possible abnormal region Cmax from the residual capacity distribution along the first direction.

8. The lithium battery lithium plating early warning method according to claim 6, characterized in that, The formula for calculating ΔSOCmax is: ΔSOCmax = (Cmax - Cnormal) / Cnormal; The standard for determining the lithium plating risk level based on the ΔSOCmax value is as follows: ΔSOCmax > 4% is high risk, 1% ≤ ΔSOCmax ≤ 4% is medium risk, and ΔSOCmax < 1% is low risk.

9. The lithium battery lithium plating early warning method according to claim 1, characterized in that, The preset number of charge-discharge cycles is 30 to 100 cycles; the discharge to rated voltage is to discharge to the rated voltage with a rated current of 0.5C to 2C.

10. A lithium battery lithium plating early warning device, characterized in that, include: The cyclic charge-discharge module is used to discharge the lithium battery under test to the rated voltage after performing a preset number of charge-discharge cycles. The disassembly sampling module is used to disassemble the cycled lithium battery and remove one fold of the positive electrode sheet from the lithium battery. The residual capacity distribution acquisition module is used to divide the one-fold positive electrode sheet into regions along a first direction and acquire the residual capacity distribution of each region along the first direction of the one-fold positive electrode sheet, wherein the first direction is the length direction or the width direction of the one-fold positive electrode sheet. The data processing and evaluation module is used to determine the lithium plating risk level of each region of the lithium battery based on the residual capacity distribution.

11. A lithium battery lithium plating early warning device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the program, implements the lithium battery lithium plating early warning method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the lithium battery lithium plating early warning method according to any one of claims 1 to 9.

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