A battery cell state detection method, device, system and storage medium

Abstract

The application provides a battery cell state detection method, device, system and storage medium. The method comprises: obtaining a target polarization value of a battery cell to be detected; in a formation stage, if it is detected that the polarization value of the battery cell to be detected reaches the target polarization value, collecting battery cell charging data of the battery cell to be detected to obtain a first charging data set, and determining a state type of the battery cell to be detected based on the first charging data set. Thus, the collection of battery cell charging data is performed at the time when the polarization value of the battery cell to be detected reaches the target polarization value in the formation stage, the state type analysis of the abnormal battery cell is performed based on the collected charging data set, the abnormal battery cell caused by the formation abnormality can be detected in time, the existing abnormal battery cell can be screened out in advance, the collected battery cell charging data corresponds to the target polarization value position, the abnormal signal is less affected by accidental interference, the qualified battery cell can be avoided from being misjudged as abnormal, and the detection accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of battery testing technology, and in particular to a method, apparatus, system and storage medium for detecting the state of a battery cell. Background Technology

[0002] Batteries, such as lithium-ion batteries, are core components in new energy application terminals, and their quality is directly related to the user experience and functional stability of the terminal products. Therefore, the industry is becoming increasingly strict in its quality requirements.

[0003] However, issues such as abnormal film formation and uneven lithium intercalation can occur during battery cell production. Relevant detection methods are lagging behind, relying heavily on post-formation capacity testing, which makes it difficult to identify these latent defects in advance. This leads to defective cells entering the market, resulting in performance degradation and insufficient stability during use. Therefore, there is an urgent need for technology that can accurately detect these anomalies to achieve quality interception. Summary of the Invention

[0004] This application provides a method, apparatus, system, and storage medium for detecting the state of battery cells. By detecting the state of battery cells during the formation stage, abnormal battery cells can be identified in a timely manner, and the accuracy of the detection can be guaranteed.

[0005] To achieve the above objectives, the first aspect of this application provides a cell state detection method, the method comprising: acquiring a target polarization value of a cell under test; during the formation stage, if the polarization value of the cell under test is detected to reach the target polarization value, then collecting cell charging data of the cell under test to obtain a first charging dataset; the cell charging data includes a cell current value; and determining the state type of the cell under test based on the first charging dataset; wherein the state type includes normal or abnormal.

[0006] In the solution provided in this application, by collecting cell charging data during the formation stage when the polarization value of the cell under test reaches the target polarization value, and performing state type analysis on the collected charging data set, abnormal cells caused by formation abnormalities can be detected, and abnormal cells with hidden problems such as uneven lithium intercalation and abnormal film formation can be screened out in advance. Moreover, the collected cell charging data corresponds to the target polarization value position, and the abnormal signal is less affected by accidental interference, which can avoid misjudging qualified cells as abnormal and improve the accuracy of detection.

[0007] In some optional embodiments, obtaining the target polarization value of the battery cell under test includes: obtaining the target charging state parameter value of the battery cell under test; wherein the target charging state parameter value and the target polarization value have a corresponding relationship; the step of collecting the battery cell charging data of the battery cell under test to obtain a first charging dataset during the formation stage if the polarization value of the battery cell under test is detected to reach the target polarization value includes: collecting the battery cell charging data of the battery cell under test to obtain a first charging dataset during the formation stage if the charging state parameter value of the battery cell under test is detected to reach the target charging state parameter value.

[0008] The solution provided in this application can obtain the target charging state parameter value corresponding to the target polarization value of the battery cell under test. By detecting the charging state parameter value related to the polarization value, the detection node can be determined, which can more efficiently monitor and locate the detection node and improve the timeliness of data acquisition.

[0009] In some optional embodiments, obtaining the target charging state parameter value of the cell under test includes: charging the sample cell to a first preset charging cutoff voltage at a constant current according to a first preset charging rate, and obtaining the cell charging data of the sample cell to obtain a second charging dataset; and determining the target charging state parameter value of the cell under test based on the second charging dataset.

[0010] In the solution provided in this application, the correspondence between the cell polarization value and the state of charge parameter value is determined by using sample cells for testing, which helps to improve the accuracy of determining the target polarization value and the target state of charge parameter value.

[0011] In some optional embodiments, the second charging dataset includes a first cell voltage value and the cell's cumulative charging capacity. Determining the target charging state parameter value of the cell under test based on the second charging dataset includes: determining a differential voltage curve based on the cell voltage value and the cell's cumulative charging capacity; obtaining the peak value within a preset voltage range in the differential voltage curve; wherein the peak value corresponds to the polarization value of the cell under test; and determining the first cell voltage value corresponding to the peak value that is greater than a first preset threshold as the target charging state parameter value of the cell under test.

[0012] In the solution provided in this application, a differential voltage curve is plotted based on the cell voltage and the cumulative charging capacity of the cell in the charging data set of the sample cell. The differential voltage curve can more efficiently and accurately locate the target polarization value of the cell and analyze the correspondence between the polarization value and the voltage value.

[0013] In some optional embodiments, obtaining the peak value within a preset voltage range in the differential voltage curve includes: filtering the differential voltage curve to obtain a target differential voltage curve; and obtaining the peak value within the preset voltage range in the target differential voltage curve.

[0014] In the solution provided in this application, the differential voltage curve can be filtered to avoid noise interference in the originally calculated differential curve, which would affect the accuracy of the analysis. Peak value analysis and acquisition based on the filtered target differential voltage curve can further improve the accuracy of polarization value determination.

[0015] In some optional embodiments, obtaining the target charging state parameter value of the cell under test includes: charging multiple sample cells with different initial charges to a second preset charging cutoff voltage using a constant current according to a second preset charging rate, and obtaining the cell charging data of the sample cells to obtain a third charging dataset; and determining the target charging state parameter value of the cell under test based on the third charging dataset.

[0016] In the solution provided in this application, multiple sample cells can be selected for testing to obtain the correspondence between the state of charge (SOC) parameter values ​​and the polarization values. Using sample cells for testing to determine the correspondence between cell polarization values ​​and SOC parameter values ​​helps improve the accuracy of determining the target polarization value and the target SOC parameter value.

[0017] In some optional embodiments, the third charging dataset includes the cell depolarization current and the second cell voltage value. Determining the target charging state parameter value of the cell under test based on the third charging dataset includes: determining the decay rate value of the cell depolarization current based on the cell depolarization current; and determining the second cell voltage value corresponding to the decay rate value that reaches a preset speed threshold as the target charging state parameter value of the cell under test.

[0018] In the solution provided in this application, by analyzing the changing trend of depolarization current of sample cells with different initial charge during the charging process, the maximum polarization value of the cell can be located more efficiently and accurately, and the correspondence between polarization value and voltage value can be analyzed.

[0019] In some optional embodiments, the first charging dataset further includes charging time, and determining the state type of the cell under test based on the first charging dataset includes: inputting the cell current value and the charging time into a preset electrochemical diffusion model to obtain a current-time variation curve; and determining the state type of the cell under test based on the current-time variation curve.

[0020] In the solution provided in this application, the original collected data is transformed by using a preset electrochemical diffusion model to obtain a current-time change curve that can reflect the cell formation effect, focusing on the internal state of the cell, thereby more accurately distinguishing between true and false anomalies, avoiding misjudgment, and effectively improving the accuracy of the detection results.

[0021] In some optional embodiments, the parameters of the electrochemical diffusion model include: charging current, cell structure coefficient, coefficient of variation of interfacial liquid phase concentration difference, coefficient of variation of current, and overall diffusion coefficient.

[0022] In the scheme provided in this application, the electrochemical diffusion model can quantify and analyze the mass transfer process inside the cell by combining the current value with the inherent properties of the battery (such as coefficients related to the cell structure and the comprehensive diffusion coefficient) and process state quantities (such as the change in liquid phase concentration difference at the interface and coefficients related to the change in current), which helps to understand the intrinsic reasons for current decay.

[0023] In some optional embodiments, determining the state type of the cell under test based on the current-time variation curve includes: determining the slope value of the target linear region in the current-time variation curve; and determining the state type of the cell under test based on the curve slope value.

[0024] In the solution provided in this application, the state type of the battery cell is evaluated by the slope of the linear region in the current-time change curve after conversion by the electrochemical diffusion model. Since the slope of this curve is affected by the characteristic parameters of the mass transfer process of the battery cell in the model, the slope of this curve can characterize the performance of the battery cell, thereby improving the accuracy of the battery cell state type detection results.

[0025] In some optional embodiments, determining the state type of the cell under test based on the curve slope value includes: obtaining a preset slope threshold range; if the curve slope value is within the preset slope threshold range, then the state type of the cell under test is determined to be normal; if the curve slope value exceeds the preset slope threshold range, then the state type of the cell under test is determined to be abnormal.

[0026] In the solution provided in this application, by comparing the calculated curve slope threshold with the preset slope threshold, cells with abnormal formation can be quickly screened out, effectively improving the detection efficiency.

[0027] In some optional embodiments, the target charging state parameter value is the target cell voltage value, and the cell formation stage includes: charging the cell under test to a first preset power threshold according to a first preset charging strategy; charging the cell under test to the target cell voltage value according to a third preset charging rate; performing constant voltage charging on the cell under test to a preset charging cutoff condition according to the target cell voltage value; and charging the cell under test to a second preset power threshold according to a second preset charging strategy; wherein the second preset power threshold is greater than the first preset power threshold.

[0028] In the solution provided in this application, by adopting different strategies at different formation stages, the process parameters are precisely matched to the objectives of each formation stage, which is beneficial to improving the formation effect.

[0029] In some optional embodiments, charging the cell under test to a first preset power threshold according to a first preset charging strategy includes: applying a negative pressure to the cell under test to a preset negative pressure range; charging the cell under test to each preset power level according to a fourth preset charging rate and a preset charging time corresponding to each preset power level; wherein the preset power level includes the first preset power threshold.

[0030] In the solution provided in this application, the first preset charging strategy is to use an appropriate charging rate and sufficient charging time at different charge stages. This achieves the goals of SEI film formation and charge accumulation while avoiding safety risks such as gas generation, thus improving the cell formation effect. Negative pressure treatment before charging avoids gas accumulation that could lead to diaphragm deformation or internal short circuits, and allows the electrolyte to better wet the electrode materials, improving the formation effect.

[0031] In some optional embodiments, the cell status detection method further includes: acquiring a first waiting time corresponding to a negative pressure application event and a second waiting time corresponding to each preset charge level; if the internal pressure value of the cell under test is within the preset negative pressure range, then waiting for the first waiting time; if the charge level of the cell under test reaches the preset charge level, then waiting for the second waiting time.

[0032] In the solution provided in this application, a preset settling time is performed when each formation stage reaches its corresponding charging target, which allows for the complete removal of gases. By releasing excess gases in a timely manner, problems such as interface black spots caused by gas residue can be avoided to the greatest extent, ensuring the quality of film formation.

[0033] In some optional embodiments, the cell status detection method further includes: if the internal pressure value of the cell under test is within the preset negative pressure range, then the cell under test is charged according to the fifth preset charging rate and the third preset charging cutoff voltage value; the fifth preset charging rate is less than any of the fourth preset charging rates.

[0034] In the solution provided in this application, the cell under test can be pre-charged at a low rate between the completion of negative pressure regulation and the start of constant current charging to a preset power threshold. By using low current and low voltage charging parameters for pre-charging before formal formation, the cell immersion effect can be detected, potential safety hazards can be avoided in advance, and subsequent risks can be reduced.

[0035] A second aspect of this application provides a battery cell state detection device, comprising: an acquisition module for acquiring a target polarization value of a battery cell under test; a collection module for, during the formation stage, if the polarization value of the battery cell under test is detected to reach the target polarization value, collecting battery cell charging data of the battery cell under test to obtain a first charging data set; the battery cell charging data includes a battery cell current value; and a detection module for determining the state type of the battery cell under test based on the first charging data set; wherein the state type includes normal or abnormal.

[0036] A third aspect of this application provides a battery cell status detection system, including a memory and a processor, wherein the processor is used to execute a computer program stored in the memory, and when the processor executes the computer program, it implements the battery cell status detection method provided in the first aspect of this application.

[0037] The fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the cell state detection method provided in the first aspect of this application.

[0038] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A schematic diagram illustrating a scenario for a cell status detection method provided in some embodiments of this application;

[0041] Figure 2A schematic flowchart illustrating a cell status detection method provided in some embodiments of this application;

[0042] Figure 3 A flowchart illustrating another cell status detection method provided in some embodiments of this application;

[0043] Figure 4 A flowchart illustrating another cell status detection method provided in some embodiments of this application;

[0044] Figure 5 A schematic diagram of a differential voltage curve provided for some embodiments of this application;

[0045] Figure 6 A flowchart illustrating another cell status detection method provided in some embodiments of this application;

[0046] Figure 7 A schematic diagram of a first type of current-time variation curve provided for some embodiments of this application;

[0047] Figure 8 A flowchart illustrating another cell status detection method provided in some embodiments of this application;

[0048] Figure 9 A schematic diagram of a second type of current-time variation curve provided in some embodiments of this application;

[0049] Figure 10 A schematic diagram of a third type of current-time variation curve provided in some embodiments of this application;

[0050] Figure 11 A flowchart illustrating another cell status detection method provided in some embodiments of this application;

[0051] Figure 12 A schematic diagram of the slope distribution of a mass-produced battery cell provided for some embodiments of this application;

[0052] Figure 13 A flowchart illustrating another cell status detection method provided in some embodiments of this application;

[0053] Figure 14 A detailed flowchart illustrating the cell state detection method provided in some embodiments of this application;

[0054] Figure 15 A schematic diagram of a cell status detection device provided in some embodiments of this application;

[0055] Figure 16 This is a schematic diagram of the structure of a cell condition detection system provided in some embodiments of this application;

[0056] Figure 17 This is a schematic diagram of the structure of a computer-readable storage medium for storing or carrying program code that implements the cell state detection method provided in the embodiments of this application, as provided in some embodiments of this application. Detailed Implementation

[0057] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. 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.

[0058] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0059] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0060] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0061] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0062] Furthermore, in the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0063] In the battery cell manufacturing process, formation is a key step in cell activation, directly determining the formation quality of the SEI film (solid electrolyte interface film) and its subsequent performance. The integrity and density of the SEI film not only affect the irreversible capacity loss of the battery cell, but also relate to its stability during long-term cycling.

[0064] Detection of formation anomalies often relies on threshold judgments of macroscopic parameters such as voltage and time, or on screening defective products through capacity testing after formation. These methods suffer from significant time lag and insufficient accuracy. For example, if capacity testing is performed after formation, a large amount of production resources have already been consumed by the defective cells, and the root cause of the problem cannot be traced. Some latent defects (such as unstable SEI film and uneven lithium intercalation) are difficult to detect in the initial capacity testing and will gradually emerge with cycling. Furthermore, judging solely by voltage thresholds is easily affected by ambient temperature and equipment fluctuations, and it is difficult to distinguish between different types of anomalies such as poor equipment contact and internal micro-short circuits.

[0065] To address the aforementioned issues, the battery cell state detection method, apparatus, system, and storage medium provided in this application acquire the target polarization value of the battery cell under test. During the formation stage, if the polarization value of the battery cell under test reaches the target polarization value, the battery cell charging data of the battery cell under test is collected to obtain a first charging dataset. Based on the first charging dataset, the state type of the battery cell under test is determined, including normal or abnormal. Therefore, by collecting battery cell charging data during the period when the polarization value of the battery cell under test reaches the target polarization value during the formation stage, and performing state type analysis of abnormal batteries based on the collected charging dataset, abnormal batteries caused by formation anomalies can be detected in a timely manner. Abnormal batteries with hidden problems such as uneven lithium intercalation or abnormal film formation can be screened out in advance. Furthermore, the collected battery cell charging data corresponds to the target polarization value position, and the abnormal signal is less susceptible to accidental interference, which can avoid misjudging qualified batteries as abnormal and improve the accuracy of detection.

[0066] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments.

[0067] Please see Figure 1 , Figure 1 This is a schematic diagram of a scenario for a cell status detection method provided in some embodiments of this application. The scenario mainly includes a cell under test 11 and a cell status detection system 12. The cell under test 11 is the detection object, and the cell status detection system 12 is used to detect the cell under test 11.

[0068] The cells to be tested 11 (i.e., cells to be formed) can be in batches or single cells, and can be placed in batches at the testing station using a cell tray as the unit (e.g., 20 cells on a single tray). Each cell can be connected to the cell status detection system 12 via a tab clamp. The cells can be cylindrical, pouch, prismatic, or other types.

[0069] The cell status detection system 12 can perform data acquisition, processing, and signal interaction functions, and may include a formation cabinet. Using the formation cabinet as a power source, it supplies power to the cells to be formed according to a preset formation process (such as constant current or constant voltage charging), and simultaneously provides a data acquisition interface (connected to the positive and negative terminals of the cell to collect charging data such as current and voltage). The acquired data can be used to identify abnormal cells. If the formation cabinet has a built-in high-performance processor that meets the processing requirements, data processing can be completed directly within the cabinet; if the formation cabinet has simple functions, an external independent detection module (equipped with a main control chip such as an MCU) can be connected, which processes the data and outputs the judgment results.

[0070] Please see Figure 2 , Figure 2 This is a flowchart illustrating a cell state detection method provided in some embodiments of this application. In specific embodiments, this cell state detection method can be applied to, for example... Figure 1 The specific process of the battery cell status detection method shown in the illustration is as follows:

[0071] S201, obtain the target polarization value of the cell under test.

[0072] Cell polarization refers to the deviation between the actual electrode potential and the equilibrium potential during the charging and discharging process of a battery cell. Essentially, it's the energy loss caused by the electrochemical process rate failing to keep up with changes in external current. This manifests as a deviation of the cell's terminal voltage from its theoretical value, a phenomenon observed during cell operation. For example, when the external current is large during charging and discharging, lithium ions may not have enough time to insert into or extract from the electrodes or migrate in the electrolyte, leading to charge accumulation on the electrode surface. This causes the actual potential to deviate from the equilibrium value; this deviation is called polarization.

[0073] During the cell formation stage, issues such as poor film formation and uneven lithium intercalation can lead to cell polarization. Polarization points can expose cells with potential process defects. Different polarization values ​​may expose different types of anomalies with varying sensitivities. For example, the abnormal signal intensity at the polarization maximum point is higher, deviates significantly from the threshold, and is easily identified, making it more likely to expose cells with potential process defects. Therefore, obtaining the target polarization value of the cell under test before testing can be the maximum polarization value. This maximum polarization value can be obtained through historical experience or through testing.

[0074] S202, during the formation stage, if the polarization value of the cell under test is detected to reach the target polarization value, the cell charging data of the cell under test is collected to obtain the first charging dataset.

[0075] Among them, formation is the core process of transforming a battery cell from a physical component into a rechargeable battery cell. The core is to form a stable and dense SEI film (solid electrolyte interface film) on the surface of the negative electrode of the battery cell through a specific charging and discharging process, while activating the electrochemical activity of the electrode material, laying the foundation for the subsequent charge and discharge cycles and performance stability of the battery cell.

[0076] In this embodiment, during the formation stage of the cell under test, it is detected whether the polarization value of the cell under test has reached the target polarization value. If the polarization value of the cell under test reaches the target polarization value, charging data of the cell under test is collected. Since there may be multiple polarization values ​​that meet the conditions during the formation stage of the cell under test, multiple cell charging data can be collected. The type of cell charging data can be cell charging voltage, cell current, etc. In a single collection, one or more types of cell charging data can be collected. The collection frequency can be selected according to indicators such as detection accuracy.

[0077] S203, determine the state type of the battery cell under test based on the first charging dataset.

[0078] In this embodiment, the state type (normal or abnormal) of the battery cell under test can be determined by collecting cell charging data (such as voltage curves, current curves, gas generation data, temperature data, etc.) during the formation stage. The determination method can be to compare the actual data characteristics of the battery cell under test with the characteristics of standard charging data; if the difference exceeds a threshold, it is judged as abnormal, thereby achieving the purpose of cell quality screening in mass production. Furthermore, since the collected cell charging data is acquired at the point where the polarization value of the battery cell under test reaches the target polarization value, the polarization point is a moment when the cell's ion transport and interfacial reaction resistance are significant. At this time, latent problems such as uneven lithium intercalation and abnormal film formation during the formation process will be amplified, effectively filtering out accidental interference from equipment or the environment, avoiding misjudging qualified cells as abnormal, and improving the accuracy of detection.

[0079] Based on the technical solution of the above embodiments of this application, by performing charging tests on sample cells and obtaining the cell voltage value corresponding to the target polarization value, during the formation stage, if the voltage value of the cell under test is detected to reach the target cell voltage value, the cell charging data of the cell under test is collected to obtain a charging dataset. The charging dataset is input into a preset electrochemical diffusion model to obtain a target current-time curve. By analyzing the slope value of the linear part of the curve, the state type of the cell under test, such as normal or abnormal, can be determined. Therefore, by collecting cell charging data during the period when the polarization value of the cell under test reaches the target polarization value in the formation stage, and performing state type analysis of abnormal cells based on the collected charging dataset, abnormal cells caused by formation abnormalities can be detected in a timely manner, and abnormal cells with hidden problems such as uneven lithium intercalation and abnormal film formation can be screened out in advance. Moreover, the collected cell charging data corresponds to the target polarization value position, and the abnormal signal is less affected by accidental interference, which can avoid misjudging qualified cells as abnormal and improve the accuracy of detection.

[0080] In the embodiments of this application, there are various strategies for determining the timing of collecting cell charging data of the cell under test based on the polarization value of the cell under test during the formation stage.

[0081] Please see Figure 3 , Figure 3 This is a schematic flowchart of another cell status detection method provided in an embodiment of this application. Figure 3 S2011 and S2021 are respectively Figure 2 A specific implementation method of S201 and S202.

[0082] S2011, Obtain the target charging state parameter value of the battery cell under test.

[0083] In this embodiment, there is a correspondence between the target charging state parameter value and the target polarization value. Changes in the charging state parameter will cause fluctuations in the polarization value. Therefore, the polarization value of the cell under test can be associated with various charging state parameters, such as the charging voltage and the charging capacity of the cell. Thus, the polarization value of the cell under test can also be represented by obtaining the charging state parameter value associated with the polarization value, for example, obtaining the target charging state parameter value corresponding to the target polarization value of the cell under test.

[0084] S2021, during the formation stage, if the charging state parameter value of the cell under test is detected to reach the target charging state parameter value, the cell charging data of the cell under test is collected to obtain the first charging dataset.

[0085] In this embodiment, during the formation stage of the cell under test, it is detected whether the polarization value of the cell under test has reached the target polarization value. If the polarization value of the cell under test reaches the target polarization value, charging data is collected. Besides directly detecting the cell polarization value, the detection node can also be determined by detecting charging state parameter values ​​related to the polarization value. For example, when the charging state parameter value of the cell under test reaches the target charging state parameter value corresponding to the target polarization value, charging data detection can be initiated. Directly judging the charging state parameter value to locate the target polarization value allows for more efficient monitoring and location of detection nodes, improving the timeliness of data collection.

[0086] In the embodiments of this application, various strategies can be used to determine the target state-of-charge parameter values ​​of the battery cell under test. Please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic flowchart of another cell status detection method provided in an embodiment of this application. Figure 4 S2011A to S2011B are Figure 3 A specific implementation method of S2011.

[0087] S2011A, according to the first preset charging rate, the sample cell is charged with constant current to the first preset charging cutoff voltage, and the cell charging data of the sample cell is obtained to obtain the second charging dataset;

[0088] S2011B, Based on the second charging dataset, determine the target charging state parameter value of the cell under test.

[0089] In this embodiment, the correspondence between the charging state parameter value and the polarization value can be obtained by testing the sample battery cell. The sample battery cell and the battery cell under test maintain consistency in key dimensions such as production batch, model, and formation process parameters. When testing the sample battery cell, a fixed current intensity can be used for continuous charging. The current remains constant throughout the charging process until the battery cell voltage reaches a preset charging cutoff voltage, such as the maximum limiting voltage Vmax. The current intensity can be determined by the charging rate, which is the ratio of the charging current to the rated capacity of the battery cell (current = rate × capacity). For example, for a 1000mAh battery cell, the current is 500mA at a charging rate of 0.5C. When testing the sample battery cell, a low charging rate (e.g., ≤0.04C) can be used, as low-rate charging is gentler and causes less damage to the battery cell.

[0090] During charging, charging data from sample cells can be collected in real time or at preset frequencies to obtain a charging dataset. Based on this dataset, the charging state parameter value corresponding to the polarization value can be determined, leading to the target charging state parameter value corresponding to the target polarization value of the cell under test. For example, by calculating the polarization value from real-time charging data and recording the corresponding charging state parameter value, a correlation table or curve between the polarization value and the charging state parameter can be established. Testing with sample cells to determine the correspondence between cell polarization values ​​and charging state parameter values ​​improves the accuracy of determining the target polarization value and the target charging state parameter value.

[0091] In some embodiments, the second charging dataset includes the first cell voltage value and the cell's accumulated charging capacity. Based on the second charging dataset, determining the target charging state parameter value of the cell under test includes: determining a differential voltage curve based on the cell voltage value and the cell's accumulated charging capacity; obtaining the peak value within a preset voltage range in the differential voltage curve; wherein the peak value corresponds to the polarization value of the cell under test; and determining the first cell voltage value corresponding to the peak value greater than a first preset threshold as the target charging state parameter value of the cell under test.

[0092] In this embodiment, taking the cell voltage as the charging state parameter as an example, the process of determining the target charging state parameter value corresponding to the target polarization value based on the collected charging data of the sample cell is explained. When the charging state parameter is the cell voltage, the data type of the collected sample cell charging data can be cell voltage or the cell's accumulated charging capacity (i.e., the total amount of charge that has been added to the cell from the start of charging to the current moment). The resulting charging dataset contains both types of charging data. The differential voltage value (dV / dQ), i.e., the rate of change of voltage with respect to capacity, can be calculated based on the collected cell voltage (V) and the cell's accumulated charging capacity (Q). Then, a differential voltage curve is plotted based on this differential voltage value (dV / dQ) and the cell voltage (V), for example... Figure 5 As shown, Figure 5 The horizontal axis represents the cell voltage (V), and the vertical axis represents the differential voltage value (dV / dQ, also known as Value). This curve reveals the electrochemical characteristics inside the cell. For example, the change in polarization value can be analyzed based on the peak value of the curve, thus allowing the cell voltage value corresponding to the target polarization value to be determined. When the target polarization value is the maximum polarization value, it can be determined that the target polarization value corresponds to the maximum peak value, that is, the target peak value greater than a preset threshold or the target peak value of the obtained peak values. At this time, the cell voltage value corresponding to the target polarization value can be determined, for example... Figure 5The curve shown represents the cell voltage value of 3.36V. It should also be noted that the collected charging dataset includes charging data for the entire charging process. However, in specific voltage ranges, such as the early stages of charging, crystal structure changes may occur (e.g., from hexagonal to monoclinic phase). During this time, the voltage change is small, and the corresponding differential voltage curve will show a characteristic peak unrelated to polarization. Therefore, this interfering peak can be excluded, and peak value acquisition and polarization analysis can be performed only within specific voltage ranges. Thus, based on the cell voltage (V) and cumulative charge (Q) in the sample cell charging dataset, a differential voltage curve is plotted. This curve allows for more efficient and accurate location of the target polarization value of the cell, as well as analysis of the correspondence between polarization and voltage values.

[0093] In some embodiments, obtaining the peak value within a preset voltage range in the differential voltage curve includes: filtering the differential voltage curve to obtain a target differential voltage curve; and obtaining the peak value within the preset voltage range in the target differential voltage curve.

[0094] In this embodiment, to more accurately capture the characteristic peaks of the differential voltage curve, the differential voltage curve plotted based on the cell voltage (V) and cumulative charging capacity (Q) in the sample cell charging dataset can be filtered to avoid noise interference in the original calculated differential curve, which could affect the accuracy of the analysis. Filtering methods such as Savitzky-Golay filtering and Gaussian filtering can be used to filter the original differential voltage curve. SG filtering is a sliding window filtering method based on polynomial fitting. It fits the curve using a polynomial within the neighborhood of each data point (e.g., five points before and after), and then replaces the original value with the value of the fitted curve. This filtering method can effectively reduce noise while preserving the peak values ​​and inflection points of the curve (avoiding over-smoothing that leads to feature loss). Gaussian filtering is a weighted average filtering method based on the Gaussian function. For each data point, it replaces the original value with the weighted average of surrounding points (the closer the distance, the greater the weight). This filtering method provides smoother noise reduction and is suitable for scenarios with high noise levels. The appropriate filtering method can be selected to filter the original differential voltage curve according to the actual filtering requirements. Peak value analysis and acquisition based on the filtered target differential voltage curve can further improve the accuracy of polarization value determination.

[0095] In the embodiments of this application, various strategies can be used to determine the target state-of-charge parameter values ​​of the battery cell under test. Please refer to [link / reference]. Figure 6 , Figure 6 This is a schematic flowchart of another cell status detection method provided in an embodiment of this application. Figure 6 S2011C to S2011D are Figure 3 Another specific implementation method of S2011.

[0096] S2011C, according to the second preset charging rate, perform constant current charging on multiple sample cells with different initial charges to the second preset charging cutoff voltage, and obtain the cell charging data of the sample cells to obtain the third charging dataset;

[0097] S2011D, based on the third charging dataset, determines the target charging state parameter value of the cell under test.

[0098] In this embodiment, the correspondence between the state of charge parameter value and the polarization value can be obtained by testing a sample cell. This sample cell maintains consistency with the cell under test in key dimensions such as production batch, model, and formation process parameters. One or more sample cells can be selected. When multiple sample cells are selected, they can have different initial capacities. For example, the initial capacities of the multiple sample cells can be 10% SOC, 25% SOC, 60% SOC, and 100% SOC, respectively. 10% SOC refers to the cell's current state of charge (SOC) being 10%, meaning the cell's current remaining capacity accounts for 10% of its rated capacity, which can be used to measure the amount of charge remaining in the cell.

[0099] When testing multiple sample cells with different initial capacities, a fixed current intensity can be used for charging. The current remains constant throughout the charging process until the cell voltage reaches a preset charging cutoff voltage, such as the maximum limiting voltage. The current intensity can be determined by the charging rate. When testing sample cells, a low charging rate (e.g., ≤0.04C) can be used. During charging, charging data for each sample cell can be acquired in real-time or at preset frequencies to obtain a charging dataset. Based on this dataset, the charging state parameter value corresponding to the polarization value can be determined, leading to the target charging state parameter value corresponding to the target polarization value of the cell under test. For example, key charging data can be extracted from the acquired charging data to calculate the polarization value, and the corresponding charging state parameter value can be recorded. A correlation table or curve between the polarization value and the charging state parameter can be established. By using sample cells for testing and determining the correspondence between cell polarization values ​​and charging state parameter values, the accuracy of determining the target polarization value and the target charging state parameter value can be improved.

[0100] In some embodiments, the third charging dataset includes the cell depolarization current and the second cell voltage value. Based on the third charging dataset, the target charging state parameter value of the cell under test is determined, including: determining the decay rate value of the cell depolarization current based on the cell depolarization current; and determining the second cell voltage value corresponding to the decay rate value that reaches a preset speed threshold as the target charging state parameter value of the cell under test.

[0101] In this embodiment, taking the cell voltage as the charging state parameter as an example, the process of determining the target charging state parameter value corresponding to the target polarization value based on the charging data of sample cells with different initial capacities is explained. When the charging state parameter is the cell voltage and multiple sample cells are tested, the data type of the collected sample cell charging data can be cell voltage and cell depolarization current (the current component used to counteract the cell polarization effect). The charging state and polarization characteristics of the cell can be determined based on the changing trend of the cell depolarization current of different sample cells. For example, current-time variation curves of sample cells with different initial capacities (such as lithium-ion cells) can be plotted. Figure 7 As shown, Figure 7 The horizontal axis represents charging time (Time), and the vertical axis represents depolarization current (Current, in A). In the initial charging phase (0% to 60% SOC), the positive electrode active material is unsaturated, and lithium-ion insertion channels are abundant. As SOC increases, the resistance to lithium-ion movement increases, leading to enhanced polarization and an increasing depolarization current. In the later stages of charging (60% to 100% SOC), the positive electrode active material approaches saturation, and polarization reaches a high level. Due to limited reaction rates, the demand for depolarization current decreases, resulting in a decreasing depolarization current. Therefore, the polarization value can be analyzed by observing the trend of depolarization current changes.

[0102] When the target polarization value is the maximum polarization value, it can be determined that the target polarization value corresponds to the slowest current decay rate, that is, the position where the current decay rate reaches the maximum preset threshold or the position where the minimum value among all decay rates is obtained. By obtaining the cell voltage value at the corresponding time point at that position, the target cell voltage value corresponding to the target polarization value can be obtained. Figure 7 In the current-time curve shown, the cell voltage corresponding to the minimum depolarization current decay rate is 3.36V. By analyzing the changing trend of depolarization current during the charging process of sample cells with different initial charges, we can more efficiently and accurately locate the maximum polarization value of the cell and analyze the correspondence between polarization value and voltage value.

[0103] In this embodiment of the application, there can be multiple strategies for determining the state type of the battery cell under test based on the first charging dataset. Please refer to [link / reference]. Figure 8 , Figure 8 This is a schematic flowchart of another cell status detection method provided in an embodiment of this application. Figure 8 S2031 to S2032 are Figure 2 In one specific implementation of S203, the first charging dataset includes the cell current value and the charging time.

[0104] S2031, input the cell current value and charging time of the first charging data into the preset electrochemical diffusion model to obtain the current-time change curve;

[0105] S2032 determines the state type of the battery cell under test based on the current-time variation curve.

[0106] In this embodiment, the cell charging data collected during the formation stage of the cell under test can be the cell current value. The collection time can also be recorded during data collection, and can be represented by the cumulative charging time corresponding to the current collection time. The state type of the cell under test can be analyzed by observing the trend of this current data. Furthermore, the current change trend can be observed from the current-time change curve of the cell current data and the corresponding time data, for example... Figure 9 The current-time variation curve shown is from Figure 9 As can be seen, with the increase of constant voltage charging time, the corresponding cell current slowly decreases, and the decreasing trend gradually stabilizes. However, it is difficult to evaluate the depolarization effect directly from this current-time change curve. The depolarization effect reflects the formation quality of the SEI film and the uniformity of the internal reaction of the cell during the formation stage. These two indicators can be used as evaluation criteria for the formation effect. Therefore, the raw data can be transformed, for example, by using a preset electrochemical diffusion model to transform the data format to obtain a current-time change curve that reflects the cell formation effect, which facilitates the analysis of the state type of the cell under test. By transforming the raw charging data and analyzing the cell formation effect, focusing on the internal state of the cell, it is possible to more accurately distinguish between true anomalies (problems with the cell itself) and false anomalies (external interference), avoid misjudgments, and improve the accuracy of the test results.

[0107] In some embodiments, the parameters of the electrochemical diffusion model include: charging current, cell structure coefficient, coefficient of variation of interfacial liquid phase concentration difference, coefficient of variation of current, and overall diffusion coefficient.

[0108] In this embodiment, the electrochemical diffusion model can be achieved by combining the current value I collected at the node where the cell polarization value meets the target polarization value with inherent battery properties (such as the coefficient α related to the cell structure and the comprehensive diffusion coefficient D) and process state quantities (such as the change in liquid phase concentration difference at the interface). The coefficient β, which is related to current changes, is used to quantify the mass transfer process (solid-phase or liquid-phase diffusion) within the battery cell, helping to understand the intrinsic reasons for current decay. The cell structure coefficient α can be determined by the cell's inherent properties (such as electrode thickness, porosity, and cell geometry), and the cell structure correction coefficients are generally consistent within the same batch of cells. The coefficient of change in liquid phase concentration difference at the interface (the surface where the cell electrode material contacts the electrolyte) is also relevant. (e.g., change in liquid phase concentration difference), represents the change in the concentration difference of lithium ions in the electrolyte (liquid phase) inside the cell during the data acquisition phase (e.g., constant voltage charging phase); the comprehensive diffusion coefficient D integrates solid-phase diffusion (the rate of lithium ion insertion or extraction from the electrode material) and liquid-phase diffusion (the rate of lithium ion migration in the electrolyte), reflecting the mass transfer efficiency of lithium ions inside the cell; the current change coefficient β describes the current change rate characteristics during the data acquisition phase. This electrochemical diffusion model can be expressed as: Where t is the charging time. Through this model transformation, the nonlinear current-time variation curve It can be converted into a target current-time variation I⁻¹ / ⁻¹ with a linear component. Curves, for example Figure 10 The current-time curve shown; where the horizontal axis represents the time 1 / 2 after conversion according to the electrochemical model. The vertical axis represents the charging current I.

[0109] In the embodiments of this application, there can be multiple strategies for determining the state type of the battery cell under test based on the current-time variation curve. Please refer to [link / reference]. Figure 11 , Figure 11 This is a schematic flowchart of another cell status detection method provided in an embodiment of this application. Figure 11 S2032A to S2032B are Figure 8 A specific implementation method of S2032.

[0110] S2032A, determine the slope value of the target linear region in the current-time variation curve;

[0111] S2032B determines the state type of the battery cell under test based on the slope value of the curve.

[0112] In this embodiment, after transforming the original current-time variation curve, the resulting target current-time variation curve exhibits a linear region, for example... Figure 10 The current-time variation curve is shown. Figure 10 In the figure, the horizontal axis represents the time 1 / 2 after conversion according to the electrochemical model. The vertical axis represents the charging current I. From... Figure 10 As can be seen, in the early stage of constant voltage charging (e.g., from 0 to 300 seconds, i.e.) Figure 10 The current decrease in the right-hand portion (0.06 value, 0.1s) is mainly due to liquid-phase depolarization. Figure 10 The graph shows a non-sloping form due to changes in the horizontal axis; and after 300s, the current slowly decreases, which is affected by the formation effect of the battery cell (such as lithium-ion cells) (e.g., film formation effect, uneven lithium intercalation, etc.). Figure 10 The curve appears as a sloping line. According to the working principle of the model, the slope of the linear portion of the current-time change curve after conversion is affected by the battery structure coefficient. Changes in liquid phase concentration difference Overall diffusion coefficient The slope of this curve can be used as a quantitative indicator of the cell formation effect, considering the influence of the current change coefficient β during the data acquisition phase. Therefore, by calculating the slope value of the curve in this linear region, the state type of the cell under test can be determined.

[0113] The state type of the battery cell can be assessed by the slope of the linear region in the current-time change curve after conversion by the electrochemical diffusion model. Since the slope of this curve is affected by the characteristic parameters of the mass transfer process of the battery cell in the model, the slope of this curve can characterize the performance of the battery cell, thereby improving the accuracy of the battery cell state type detection results.

[0114] In some embodiments, determining the state type of the battery cell under test based on the curve slope value includes: obtaining a preset slope threshold range; if the curve slope value is within the preset slope threshold range, then determining the state type of the battery cell under test as normal; if the curve slope value exceeds the preset slope threshold range, then determining the state type of the battery cell under test as abnormal.

[0115] In this embodiment, the specific implementation of determining the state type of the cell under test based on the slope value of the oblique part of the target current time change curve obtained by conversion can be as follows: the state of the cell under test can be quickly determined by judging whether the obtained curve slope is within a preset reasonable range (slope threshold range). This slope threshold range can be obtained by performing formation tests on a large number of known normal cells, recording the slope of the converted curve, and taking the average value of these slopes ± a reasonable deviation as the threshold range to avoid misjudgment.

[0116] Since the first charging dataset is collected at the target polarization value, if the tested cell is normal (e.g., normal film formation, uniform graphite lithium intercalation concentration, etc.), the slope value of the constant voltage charging stage curve calculated by the test should conform to the normal distribution range, and the slope value of the curve for cells in the same batch should be highly consistent. If the following abnormalities occur during the cell formation process: such as abnormal film formation (e.g., excessive lithium consumption due to abnormal water content, abnormal HF (hydrofluoric acid) content, etc., resulting in the graphite lithium intercalation at the target polarization value not reaching the expected level, leading to abnormal film formation), or uneven lithium intercalation (e.g., insufficient wetting of some graphite, making lithium intercalation difficult, but having already been charged with the preset amount of electricity, resulting in higher-than-expected lithium intercalation of other fully wetting graphite, leading to uneven lithium intercalation), the calculated constant voltage charging stage current slope value will be significantly lower. For example Figure 12 The curve slope distribution graph shown below. Figure 12 The horizontal axis represents cell data, and the vertical axis represents the curve slope. Figure 12The curve slope values ​​for approximately 400 battery cells were provided. It can be seen that the current slope values ​​of many tested cells are within the preset range, but two cells exhibit significant abnormalities (their slope values ​​exceed the preset range). Disassembly revealed that these two cells had obvious localized black spots (caused by poor local wetting), and the black spot areas failed to form a sufficient film. Continued use in such cases carries risks of gas generation deterioration, interface lithium plating, and lifespan degradation. By comparing the calculated curve slope threshold with the preset slope threshold, during batch testing of mass-produced battery cells, cells with formation abnormalities can be quickly screened out by comparing the consistency of their curve slopes, effectively improving testing efficiency.

[0117] In this application embodiment, various charging strategies can be adopted during the formation stage of the battery cell under test. Please refer to [link / reference]. Figure 13 , Figure 13 This is a flowchart illustrating another cell state detection method provided in an embodiment of this application. In this embodiment, the target charging state parameter value is the target cell voltage value, and the charging strategy includes the following process:

[0118] S204, according to the first preset charging strategy, charge the battery cell under test to the first preset power threshold;

[0119] S205, charge the battery cell under test to the target battery cell voltage value according to the third preset charging rate;

[0120] S206, according to the target cell voltage value, perform constant voltage charging on the cell under test until the preset charging cutoff condition;

[0121] S207, according to the second preset charging strategy, charge the battery cell under test to the second preset power threshold.

[0122] In order to carry out cell formation, the preliminary work needs to complete cell assembly, such as assembling raw materials such as electrodes, separators, and electrolytes into a sealed cell that can be injected with electrolyte. Under vacuum conditions, electrolyte is injected into the cell through the injection port to ensure that the electrolyte fully wets the electrodes and separators. High temperature (≥45℃) wetting method can be used, and the cell is left to stand for a preset time (≥15h) to ensure full wetting and remove air bubbles, so as to provide a medium for electrochemical reaction.

[0123] During the formation stage, the cell under test after liquid injection is connected to the formation cabinet and charged according to the first preset charging strategy until its charge reaches the first preset charge threshold. This first charge threshold can be the charge point corresponding to the stable formation film, such as 15% SOC, reaching the gas generation end. Next, the lithium intercalation degree is further increased by continuing to charge the cell under test using a preset charging rate until it reaches the target charging state parameter value corresponding to the determined target polarization value, such as the target cell voltage value or the target charge value. After the cell voltage or charge value reaches the corresponding target value, a certain time can be waited, such as 2 minutes, and then the cell under test can be charged again using constant voltage charging. This constant voltage value can be the target cell voltage value corresponding to the target polarization value, such as 3.36V. During the constant voltage charging process, the target cell voltage value is maintained constant, allowing the current to decay naturally until the current drops to the cutoff value (e.g., 0.001C), or the constant voltage charging mode can be directly used for a preset charging time (e.g., 1 hour). Since the cell voltage value at this stage corresponds to the target polarization value, cell charging data can be collected to obtain a charging dataset containing charging data from multiple cells. After the constant voltage charging reaches the charging cutoff condition, a preset time (e.g., 2 minutes) can be waited before the cell under test is charged with constant current according to the second preset charging strategy until it reaches the second preset charge threshold, which is greater than the first preset charge threshold. The second preset charge threshold can serve as the starting charge point for subsequent high-temperature aging procedures; this second preset charge threshold can be 70% SOC. The second preset charging strategy can be to use a fourth preset charging rate, such as 0.33C, to charge the cell under test with constant current for 18 minutes. By employing different strategies at different formation stages, and precisely matching process parameters to the targets of each formation stage, the formation effect can be improved.

[0124] In some embodiments, charging the battery cell under test to a first preset power threshold according to a first preset charging strategy includes: applying a negative pressure to the battery cell under test to a preset negative pressure range; and charging the battery cell under test to each preset power level according to a fourth preset charging rate and a preset charging time corresponding to each preset power level; wherein the preset power level includes the first preset power threshold.

[0125] During the charging of the battery cell under test using the first preset charging strategy to bring its charge to the first preset charge threshold, gradient current charging is mainly performed, that is, gradually increasing the charging current to allow more lithium ions to embed into the negative electrode, fully activate the active material, and further thicken and stabilize the SEI film. The charging stage can be divided into different charge thresholds, and charging rate and charging cutoff adjustment, such as charging duration, can be set for each charge threshold. The range of the power threshold can be set according to actual conditions. For example, the range can be set to within 5% SOC. First, the cell under test is charged to 1.5% SOC according to the preset charging rate and charging time to initially form the SEI film. Then, one or both of the charging rate and charging time are adjusted to charge to the next power threshold, such as 3% SOC. Then, one or both of the charging rate and charging time are adjusted to charge to the next power threshold, such as 5% SOC. Then, one or both of the charging rate and charging time are adjusted to charge to the next power threshold, such as 8% SOC. Then, one or both of the charging rate and charging time are adjusted to charge to the next power threshold, such as 10% SOC. Finally, one or both of the charging rate and charging time are adjusted to charge to the preset first power threshold, such as 15% SOC. In this embodiment, the first preset charging strategy is set as follows: constant current charging is used throughout the charging process, and a low rate (such as ≤0.05C) is used for charging in the early charging stage (such as 0 to 10% SOC).

[0126] For example, the charging rate and charging time corresponding to 1.25% SOC can be 0.05C and 15min, respectively; the charging rate and charging time corresponding to 3% SOC can be 0.05C and 21min, respectively; the charging rate and charging time corresponding to 5% SOC can be 0.05C and 24min, respectively; the charging rate and charging time corresponding to 8% SOC can be 0.05C and 36min, respectively; the charging rate and charging time corresponding to 10% SOC can be 0.05C and 24min, respectively; and the charging rate and charging time corresponding to 15% SOC can be 0.1C and 30min, respectively. Therefore, by using appropriate charging rates and sufficient durations at different SOC stages, the goals of SEI film formation and charge accumulation can be achieved, while avoiding safety risks such as gas generation, which is beneficial to improving the cell formation effect.

[0127] In addition, before charging the battery cell under test according to the first preset charging strategy, it is necessary to apply negative pressure to the battery, that is, to evacuate the battery cell to make its interior reach a preset negative pressure range, such as -90 to -80 kPa. Putting the battery in a negative pressure state helps to expel the gas inside the battery, avoid gas accumulation that could lead to problems such as membrane deformation or internal short circuits, and also helps the electrolyte to better wet the electrode materials, thus improving the formation effect.

[0128] In some embodiments, the cell state detection method further includes: acquiring a first waiting time corresponding to a negative pressure application event and a second waiting time corresponding to each preset charge level; if the internal pressure value of the cell under test is within a preset negative pressure range, then waiting for the first waiting time; if the charge level of the cell under test reaches a preset charge level, then waiting for the second waiting time.

[0129] In this embodiment, a settling period can be implemented at each node where the gas production reaches a preset value, or at nodes where gas production may lead to abnormal film formation. This allows for thorough gas removal under negative pressure. For example, a settling and venting period can be implemented for a preset duration at each charging stage, and the settling time can be adjusted according to the gas production. For instance, after adjusting the negative pressure, a settling period of 10 to 30 seconds can be implemented; 10 minutes can be implemented when charging to 1.25% SOC according to the preset charging rate; 10 minutes can be implemented when charging to 3% SOC according to the preset charging rate; 10 minutes can be implemented when charging to 5% SOC according to the preset charging rate; 10 minutes can be implemented when charging to 8% SOC according to the preset charging rate; 10 minutes can be implemented when charging to 10% SOC according to the preset charging rate; and 5 minutes can be implemented when charging to 15% SOC according to the preset charging rate. By releasing excess gas in a timely manner, problems such as interface black spots caused by gas residue can be avoided to the greatest extent, ensuring film quality.

[0130] In some embodiments, the cell status detection method further includes: if the internal pressure value of the cell under test is within a preset negative pressure range, then the cell under test is charged according to a fifth preset charging rate and a third preset charging cutoff voltage value; the fifth preset charging rate is less than any fourth preset charging rate.

[0131] In this embodiment, the cell under test can also be pre-charged at a low rate between the completion of negative voltage regulation and the start of constant current charging to a preset charge threshold. For example, the cell under test can be pre-charged at a charging rate lower than any charging rate in the first preset charging strategy (e.g., ≤0.02C) until the charging cutoff voltage, such as 1.5V, is reached. By using low current and low voltage charging parameters for pre-charging before formal formation, the cell immersion effect can be detected, potential safety hazards can be avoided in advance, and subsequent risks can be reduced.

[0132] Based on the technical solution of the above embodiments of this application, the target polarization value of the battery cell under test is obtained. During the formation stage, if the polarization value of the battery cell under test is detected to reach the target polarization value, the battery cell charging data of the battery cell under test is collected to obtain a first charging dataset. Based on the first charging dataset, the state type of the battery cell under test is determined, including normal or abnormal. Thus, by collecting battery cell charging data during the period when the polarization value of the battery cell under test reaches the target polarization value during the formation stage, and performing state type analysis of abnormal battery cells based on the collected charging dataset, abnormal battery cells caused by formation abnormalities can be detected in a timely manner. Abnormal battery cells with hidden problems such as uneven lithium intercalation and abnormal film formation can be screened out in advance. Moreover, the collected battery cell charging data corresponds to the target polarization value position, and the abnormal signal is less affected by accidental interference, which can avoid misjudging qualified battery cells as abnormal and improve the accuracy of detection.

[0133] To better illustrate the cell state detection scheme in the foregoing embodiments, this application also provides a refined cell state detection method. Please refer to [link to relevant documentation]. Figure 14 , Figure 14 This is a detailed flowchart illustrating a cell state detection method provided in some embodiments of this application, which includes the following steps:

[0134] S301, according to the first preset charging rate, the sample cell is charged with constant current to the preset charging cutoff voltage, and the cell charging data of the sample cell is obtained to obtain the first charging dataset;

[0135] S302, Based on the cell voltage value and the cumulative charging capacity of the cell in the first charging data set, determine the differential voltage curve and obtain the peak value within the preset voltage range;

[0136] S303, the cell voltage value corresponding to the peak value that is greater than the preset threshold is determined as the target cell voltage value corresponding to the target polarization value of the cell under test;

[0137] S304, according to the preset charging strategy, charge the battery cell under test to the preset power threshold;

[0138] S305, charge the battery cell under test to the target battery cell voltage value according to the second preset charging rate;

[0139] S306, During the formation stage, if the voltage value of the cell under test is detected to reach the target cell voltage value, the cell charging data of the cell under test is collected to obtain the second charging dataset.

[0140] S307, input the cell current value and charging time of the second charging data into the preset electrochemical diffusion model to obtain the current-time change curve;

[0141] S308, determine the slope value of the target linear region in the current-time variation curve;

[0142] S309, determine the state type of the cell under test based on the slope value of the curve.

[0143] Based on the technical solution of the above embodiments of this application, by performing charging tests on sample cells and obtaining the cell voltage value corresponding to the target polarization value, during the formation stage, if the voltage value of the cell under test is detected to reach the target cell voltage value, the cell charging data of the cell under test is collected to obtain a charging dataset. The charging dataset is input into a preset electrochemical diffusion model to obtain a target current-time curve. By analyzing the slope value of the linear part of the curve, the state type of the cell under test, such as normal or abnormal, can be determined. Therefore, by collecting cell charging data during the period when the polarization value of the cell under test reaches the target polarization value in the formation stage, and performing state type analysis of abnormal cells based on the collected charging dataset, abnormal cells caused by formation abnormalities can be detected in a timely manner, and abnormal cells with hidden problems such as uneven lithium intercalation and abnormal film formation can be screened out in advance. Moreover, the collected cell charging data corresponds to the target polarization value position, and the abnormal signal is less affected by accidental interference, which can avoid misjudging qualified cells as abnormal and improve the accuracy of detection.

[0144] It should be understood that the sequence number of each step in this embodiment does not imply the order in which the steps are executed. The execution order of each step should be determined by its function and internal logic, and should not constitute a unique limitation on the implementation process of this application embodiment.

[0145] Based on the same inventive concept, embodiments of this application also provide related products for implementing the above methods. It should be understood that the implementation solutions proposed by the related products for solving the problems are similar to the above methods.

[0146] This application also provides a battery cell status detection device. Please refer to... Figure 15 , Figure 15 This is a schematic diagram of the structure of a battery cell status detection device provided in some embodiments of this application. The battery cell status detection device 400 may include an acquisition module 401, a data collection module 402, and a detection module 403, as detailed below:

[0147] The acquisition module 401 is used to acquire the target polarization value of the battery cell under test;

[0148] The acquisition module 402 is used to acquire the cell charging data of the cell under test if the polarization value of the cell under test is detected to reach the target polarization value, and obtain the first charging data set; the cell charging data includes the cell current value;

[0149] The detection module 403 is used to determine the state type of the battery cell under test based on the first charging dataset; wherein the state type includes normal or abnormal.

[0150] In some embodiments, the acquisition module 401 can be used to: acquire the target charging state parameter value of the battery cell under test; wherein the target charging state parameter value has a corresponding relationship with the target polarization value.

[0151] In some embodiments, the acquisition module 402 can be used to: during the formation stage, if the charging state parameter value of the cell under test is detected to reach the target charging state parameter value, then acquire the cell charging data of the cell under test to obtain a first charging dataset.

[0152] In some embodiments, the acquisition module 401 may also be used to: charge the sample cell to a first preset charging cutoff voltage at a constant current according to a first preset charging rate, and acquire the cell charging data of the sample cell to obtain a second charging dataset; and determine the target charging state parameter value of the cell to be tested according to the second charging dataset.

[0153] In some embodiments, the second charging dataset includes the first cell voltage value and the cell's cumulative charging capacity. The acquisition module 401 can also be used to: determine a differential voltage curve based on the cell voltage value and the cell's cumulative charging capacity; acquire the peak value within a preset voltage range in the differential voltage curve; wherein the peak value corresponds to the polarization value of the cell under test; and determine the first cell voltage value corresponding to the peak value that is greater than a first preset threshold as the target charging state parameter value of the cell under test.

[0154] In some embodiments, the acquisition module 401 can also be used to: filter the differential voltage curve to obtain a target differential voltage curve; and acquire the peak value within a preset voltage range in the target differential voltage curve.

[0155] In some embodiments, the acquisition module 401 can also be used to: perform constant current charging on multiple sample cells with different initial charges to a second preset charging cutoff voltage according to a second preset charging rate, and acquire the cell charging data of the sample cells to obtain a third charging dataset; and determine the target charging state parameter value of the cell to be tested based on the third charging dataset.

[0156] In some embodiments, the third charging dataset includes the cell depolarization current and the second cell voltage value. The acquisition module 401 can also be used to: determine the decay rate value of the cell depolarization current based on the cell depolarization current; and determine the second cell voltage value corresponding to the decay rate value that reaches the preset speed threshold as the target charging state parameter value of the cell under test.

[0157] In some embodiments, the first charging dataset also includes charging time, and the detection module 403 can also be used to: input the cell current value and charging time into a preset electrochemical diffusion model to obtain a current-time change curve; and determine the state type of the cell under test based on the current-time change curve; the parameters of the electrochemical diffusion model include: charging current, cell structure coefficient, interface liquid phase concentration difference change coefficient, current change coefficient, and comprehensive diffusion coefficient.

[0158] In some embodiments, the detection module 403 can also be used to: determine the slope value of the target linear region in the current-time change curve; and determine the state type of the cell under test based on the slope value.

[0159] In some embodiments, the detection module 403 can also be used to: obtain a preset slope threshold range; if the curve slope value is within the preset slope threshold range, determine that the state type of the cell under test is normal; if the curve slope value exceeds the preset slope threshold range, determine that the state type of the cell under test is abnormal.

[0160] In some embodiments, the cell status detection device may further include a charging control module, which may be used to: charge the cell under test to a first preset power threshold according to a first preset charging strategy; charge the cell under test to a target cell voltage value according to a third preset charging rate; perform constant voltage charging on the cell under test to a preset charging cutoff condition according to the target cell voltage value; and charge the cell under test to a second preset power threshold according to a second preset charging strategy; wherein the second preset power threshold is greater than the first preset power threshold.

[0161] In some embodiments, the charging control module can also be used to: apply a negative pressure to the battery cell under test to a preset negative pressure range; charge the battery cell under test to each preset charge level according to the fourth preset charging rate and the preset charging time corresponding to each preset charge level; wherein the preset charge level includes a first preset charge level threshold.

[0162] In some embodiments, the charging control module can also be used to: acquire a first waiting time corresponding to a negative pressure application event and a second waiting time corresponding to each preset charge level; if the internal pressure value of the cell under test is within a preset negative pressure range, wait for the first waiting time; if the charge level of the cell under test reaches a preset charge level, wait for the second waiting time.

[0163] In some embodiments, the charging control module can also be used to: if the internal pressure value of the battery cell under test is within a preset negative pressure range, charge the battery cell under test according to the fifth preset charging rate and the third preset charging cut-off voltage value; the fifth preset charging rate is less than any fourth preset charging rate.

[0164] It should be noted that the cell status detection methods in the foregoing embodiments can all be implemented based on the cell status detection device provided in this embodiment. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the cell status detection device described in this embodiment can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0165] Based on the technical solution of the above embodiments of this application, the target polarization value of the battery cell under test is obtained. During the formation stage, if the polarization value of the battery cell under test is detected to reach the target polarization value, the battery cell charging data of the battery cell under test is collected to obtain a first charging dataset. Based on the first charging dataset, the state type of the battery cell under test is determined, including normal or abnormal. Thus, by collecting battery cell charging data during the period when the polarization value of the battery cell under test reaches the target polarization value during the formation stage, and performing state type analysis of abnormal battery cells based on the collected charging dataset, abnormal battery cells caused by formation abnormalities can be detected in a timely manner. Abnormal battery cells with hidden problems such as uneven lithium intercalation or abnormal film formation can be screened out in advance. Moreover, the collected battery cell charging data corresponds to the target polarization value position, and the abnormal signal is less affected by accidental interference, which can avoid misjudging qualified battery cells as abnormal and improve the accuracy of detection.

[0166] In addition, this application also provides a cell status detection system, please refer to [link to relevant documentation]. Figure 16 , Figure 16 This is a schematic diagram of the structure of a battery cell status detection system provided in some embodiments of this application. The battery cell status detection system 500 can be used to implement the battery cell status detection method in the aforementioned embodiments, and mainly includes a processor 501 and a memory 502. The processor 501 and the memory 502 are electrically connected.

[0167] The processor 501 is the control center of the cell status detection system 500. It connects various parts of the entire cell status detection system through various interfaces and lines. By running or calling computer programs stored in memory 502 and calling data stored in memory 502, it executes various functions of the cell status detection system and processes data, thereby performing overall monitoring of the cell status detection system.

[0168] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the computer programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, computer programs required for at least one function, etc.; the data storage area may store data created based on the use of the cell status detection system, etc.

[0169] Furthermore, memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory 502 may also include a memory controller to provide processor 501 with access to memory 502.

[0170] In this embodiment, the processor 501 in the cell status detection system 500 loads the instructions corresponding to the processes of one or more computer programs into the memory 502 according to the following steps, and the processor 501 runs the computer programs stored in the memory 502 to realize various functions. For example: obtaining the target polarization value of the cell under test; during the formation stage, if the polarization value of the cell under test is detected to reach the target polarization value, the cell charging data of the cell under test is collected to obtain a first charging dataset; based on the first charging dataset, the status type of the cell under test is determined.

[0171] Please refer to Figure 17 This illustration shows a schematic diagram of the structure of a computer-readable storage medium provided in an embodiment of this application. The computer-readable storage medium 600 stores program code 601, which can be called by a processor to execute the methods described in the above method embodiments.

[0172] The computer-readable storage medium 600 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 600 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 600 has storage space for program code 601 that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code 601 may, for example, be compressed in a suitable form.

[0173] Since the instructions stored in the storage medium can execute the steps in any of the cell state detection methods provided in the embodiments of this application, the beneficial effects that any of the cell state detection methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0175] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0176] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0177] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0178] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0179] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0180] The above is a description of the cell status detection method, device, system and storage medium provided in this application. For those skilled in the art, based on the ideas of the embodiments of this application, there will be changes in the specific implementation and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for detecting the state of a battery cell, characterized in that, include: Obtain the target polarization value of the battery cell under test; The target polarization value includes the maximum polarization value; During the formation stage, if the polarization value of the cell under test is detected to reach the target polarization value, the cell charging data of the cell under test is collected to obtain the first charging data set; the cell charging data includes the cell current value and the charging time. Based on the first charging dataset, the state type of the battery cell under test is determined; wherein, the state type includes normal or abnormal. The step of determining the state type of the cell under test based on the first charging dataset includes: inputting the cell current value and the charging time into a preset electrochemical diffusion model to obtain a current-time variation curve containing a linear region; determining the state type of the cell under test based on the current-time variation curve; the step of determining the state type of the cell under test based on the current-time variation curve includes: determining the slope value of the target linear region in the current-time variation curve; determining the state type of the cell under test based on the slope value.

2. The cell condition detection method according to claim 1, characterized in that, The process of obtaining the target polarization value of the battery cell under test includes: Obtain the target state of charge parameter value of the battery cell under test; wherein, the target state of charge parameter value has a corresponding relationship with the target polarization value; During the formation stage, if the polarization value of the cell under test is detected to reach the target polarization value, the cell charging data of the cell under test is collected to obtain a first charging dataset, including: During the formation stage, if the charging state parameter value of the cell under test is detected to reach the target charging state parameter value, the cell charging data of the cell under test is collected to obtain the first charging dataset.

3. The cell condition detection method according to claim 2, characterized in that, The step of obtaining the target charging state parameter value of the battery cell under test includes: According to the first preset charging rate, the sample cell is charged with constant current to the first preset charging cutoff voltage, and the cell charging data of the sample cell is obtained to obtain the second charging dataset. Based on the second charging dataset, the target charging state parameter value of the battery cell under test is determined.

4. The cell condition detection method according to claim 3, characterized in that, The second charging dataset includes the first cell voltage value and the cell's accumulated charging capacity. Determining the target charging state parameter value of the cell under test based on the second charging dataset includes: The differential voltage curve is determined based on the cell voltage value and the cumulative charging capacity of the cell; Obtain the peak value within a preset voltage range in the differential voltage curve; wherein the peak value corresponds to the polarization value of the cell under test; The first cell voltage value corresponding to the peak value that is greater than the first preset threshold is determined as the target charging state parameter value of the cell under test.

5. The cell condition detection method according to claim 4, characterized in that, The step of obtaining the peak value within a preset voltage range in the differential voltage curve includes: The differential voltage curve is filtered to obtain the target differential voltage curve; Obtain the peak value within a preset voltage range in the target differential voltage curve.

6. The cell condition detection method according to claim 2, characterized in that, The step of obtaining the target charging state parameter value of the battery cell under test includes: According to the second preset charging rate, a number of sample cells with different initial charges are charged at constant current to the second preset charging cutoff voltage, and the cell charging data of the sample cells are obtained to obtain a third charging dataset. Based on the third charging dataset, the target charging state parameter value of the battery cell under test is determined.

7. The cell condition detection method according to claim 6, characterized in that, The third charging dataset includes the cell depolarization current and the second cell voltage value. Determining the target charging state parameter value of the cell under test based on the third charging dataset includes: The decay rate of the cell depolarization current is determined based on the cell depolarization current. The second cell voltage value corresponding to the attenuation rate value that reaches the preset speed threshold is determined as the target charging state parameter value of the cell under test.

8. The cell condition detection method according to claim 1, characterized in that, The parameters of the electrochemical diffusion model include: charging current, cell structure coefficient, coefficient of variation of interfacial liquid phase concentration difference, coefficient of variation of current, and overall diffusion coefficient.

9. The cell condition detection method according to claim 1, characterized in that, The step of determining the state type of the battery cell under test based on the slope value of the curve includes: Obtain the preset slope threshold range; If the slope value of the curve is within the preset slope threshold range, then the state type of the battery cell under test is determined to be normal. If the slope value of the curve exceeds the preset slope threshold range, the state type of the cell under test is determined to be abnormal.

10. The cell condition detection method according to claim 2, characterized in that, The target charging state parameter value is the target cell voltage value, and the cell formation stage under test includes: According to the first preset charging strategy, the battery cell under test is charged to the first preset power threshold. The battery cell under test is charged to the target battery cell voltage value according to the third preset charging rate. According to the target cell voltage value, the cell under test is charged at a constant voltage until the preset charging cutoff condition is met; According to the second preset charging strategy, the battery cell under test is charged to the second preset power threshold; wherein the second preset power threshold is greater than the first preset power threshold.

11. The cell condition detection method according to claim 10, characterized in that, The step of charging the battery cell under test to a first preset power threshold according to a first preset charging strategy includes: Apply a negative voltage to the battery cell under test to a preset negative voltage range; The battery cell under test is charged to the preset charge level according to the fourth preset charging rate and preset charging time corresponding to each preset charge level; wherein, the preset charge level includes the first preset charge level threshold.

12. The cell condition detection method according to claim 11, characterized in that, Also includes: The first waiting time corresponding to the negative pressure application event and the second waiting time corresponding to each preset power level are obtained respectively; If the internal pressure value of the battery cell under test is within the preset negative pressure range, then wait for the first waiting time. If the charge of the battery cell under test reaches the preset charge, then wait for the second waiting time.

13. The cell condition detection method according to claim 11, characterized in that, Also includes: If the internal pressure value of the battery cell under test is within the preset negative pressure range, then the battery cell under test is charged according to the fifth preset charging rate and the third preset charging cutoff voltage value. The fifth preset charging rate is less than any of the fourth preset charging rates.

14. A cell condition detection device, characterized in that, include: The acquisition module is used to acquire the target polarization value of the battery cell under test; The target polarization value includes the maximum polarization value; The acquisition module is used to acquire the cell charging data of the cell under test during the formation stage if the polarization value of the cell under test is detected to reach the target polarization value, and obtain a first charging data set; the cell charging data includes the cell current value and the charging time. The detection module is used to determine the state type of the battery cell under test based on the first charging dataset; wherein the state type includes normal or abnormal; determining the state type of the battery cell under test based on the first charging dataset includes: inputting the battery cell current value and the charging time into a preset electrochemical diffusion model to obtain a current-time variation curve containing a linear region; determining the state type of the battery cell under test based on the current-time variation curve; determining the state type of the battery cell under test based on the current-time variation curve includes: determining the slope value of the target linear region in the current-time variation curve; determining the state type of the battery cell under test based on the curve slope value.

15. A cell condition detection system, characterized in that, Includes memory and processor, of which: The processor is used to execute computer programs stored in the memory; When the processor executes the computer program, it implements the steps in the cell state detection method as described in any one of claims 1 to 13.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the cell state detection method as described in any one of claims 1 to 13.

Images