Lithium battery self-discharge screening method, device, equipment and storage medium

Abstract

The application discloses a lithium battery self-discharge screening method and device, equipment and a storage medium, and relates to the technical field of lithium batteries. The application obtains a standard formation depth through a formation depth experiment. The application screens a cell self-discharge based on the standard formation depth, and obtains a self-discharge K value of the cell. The application screens a cell self-discharge anomaly according to the self-discharge K value and a preset self-discharge K value, solves the problem of a large error in lithium battery self-discharge anomaly screening, and improves the accuracy of lithium battery self-discharge anomaly screening.

Description

Technical Field

[0001] This application relates to the technical field of lithium batteries, and in particular to a method, apparatus, device and storage medium for screening the self-discharge of lithium batteries. Background Technology

[0002] With the rapid development of the global new energy industry, lithium iron phosphate (LFP) batteries, as important energy storage components, have received increasing attention from the industry regarding performance optimization and quality control. Among these, self-discharge rate is one of the key indicators for measuring the quality of LFP batteries, directly affecting the overall lifespan, safety, and user experience of the battery pack. Currently, in the manufacturing process of lithium batteries, self-discharge rate control is mainly achieved through optimizing production processes, adjusting charging and discharging methods, and resting processes. However, these methods still face many challenges in practical applications, such as poor self-discharge consistency and difficulty in standardizing screening criteria. These issues lead to significant changes in the SOC (State of Charge) of some cells during storage, thus affecting the overall performance and safety of the battery pack.

[0003] Therefore, how to improve the accuracy of screening for lithium battery self-discharge anomalies and achieve comprehensive identification and effective control of cell self-discharge anomalies has become a pressing technical challenge in the current new energy field. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, device, and storage medium for screening lithium battery self-discharge, aiming to solve the technical problem of how to improve the accuracy of screening for lithium battery self-discharge anomalies.

[0005] To achieve the above objectives, this application proposes a method for screening the self-discharge of lithium batteries, the method comprising:

[0006] Obtain the normalized formation depth obtained through the formation depth experiment;

[0007] Based on the normalized depth, the cell self-discharge is screened to obtain the cell self-discharge K value;

[0008] Cell self-discharge anomalies are screened based on the self-discharge K value and the preset self-discharge K value.

[0009] In one embodiment, before obtaining the normalized formation depth obtained through the formation depth experiment, the method further includes:

[0010] Obtain the first charging rate, the second charging rate, the first charging time, and the target depth;

[0011] Formation is performed based on the first charging rate, the second charging rate, the first charging time, and the target formation depth;

[0012] Obtain the cell capacity, voltage, cell thickness, and self-discharge rate at different target formation depths;

[0013] The normalized depth is obtained based on the cell capacity, the voltage, the cell thickness, and the self-discharge rate.

[0014] In one embodiment, the step of obtaining the normalized depth based on the cell capacity, the voltage, the cell thickness, and the self-discharge rate includes:

[0015] A comprehensive score is obtained by weighted scoring of the cell capacity, voltage, cell thickness, and self-discharge rate.

[0016] The comprehensive score that meets the preset scoring criteria is converted into a depth as the standardization depth.

[0017] In one embodiment, the step of performing cell self-discharge screening based on the normalized depth to obtain the cell's self-discharge K value includes:

[0018] At a preset temperature, the battery cell, which is standardized to a deep state, is self-discharged according to the target charge / discharge rate.

[0019] Obtain the self-discharge start time, self-discharge end time, first open-circuit voltage, and second open-circuit voltage;

[0020] The self-discharge K value of the battery cell is obtained based on the self-discharge start time, the self-discharge end time, the first open-circuit voltage, and the second open-circuit voltage.

[0021] In one embodiment, before performing cell self-discharge anomaly screening based on the self-discharge K value and a preset self-discharge K value, the method further includes:

[0022] Obtain vibration frequency, acceleration, and duration;

[0023] The cell is subjected to vibration testing based on the vibration frequency, the acceleration, and the duration.

[0024] Abnormal battery cells are screened based on the vibration test.

[0025] In one embodiment, the step of screening abnormal battery cells based on the vibration test includes:

[0026] Obtain the voltage change, current change, and internal resistance change values ​​during the cell vibration test;

[0027] When the voltage change value is greater than a preset voltage change value, the current change value is greater than a preset current change value, or the internal resistance change value is greater than a preset internal resistance change value, the cell is determined to be abnormal.

[0028] In one embodiment, the step of screening for cell self-discharge anomalies based on the self-discharge K value and a preset self-discharge K value includes:

[0029] The deviation value is calculated based on the self-discharge K value and the preset self-discharge K value;

[0030] When the deviation value is greater than the absolute value of the preset deviation value, the cell self-discharge is determined to be abnormal.

[0031] Furthermore, to achieve the above objectives, this application also proposes a lithium battery self-discharge screening device, the device comprising:

[0032] The standard acquisition module is used to acquire the normalized formation depth obtained through the formation depth experiment;

[0033] The data processing module is used to perform cell self-discharge screening based on the normalization depth to obtain the cell self-discharge K value;

[0034] The cell screening module is used to screen cells for abnormal self-discharge based on the self-discharge K value and a preset self-discharge K value.

[0035] In addition, to achieve the above objectives, this application also proposes a lithium battery self-discharge screening device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the lithium battery self-discharge screening method described above.

[0036] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the lithium battery self-discharge screening method described above.

[0037] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the lithium battery self-discharge screening method described above.

[0038] This application provides a method for screening self-discharge in lithium batteries. The method includes: obtaining a standardized formation depth obtained through a formation depth experiment; performing cell self-discharge screening based on the standardized formation depth to obtain a self-discharge K-value for the cell; and performing cell self-discharge anomaly screening based on the self-discharge K-value and a preset self-discharge K-value. In summary, this application, by optimizing the formation depth standard, reduces the impact of different battery-related factors on self-discharge anomaly screening, solves the problem of large errors in lithium battery self-discharge anomaly screening, and improves the accuracy of lithium battery self-discharge anomaly screening. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating the first embodiment of the lithium battery self-discharge screening method of this application.

[0042] Figure 2 A flowchart illustrating the second embodiment of the lithium battery self-discharge screening method of this application;

[0043] Figure 3 A flowchart illustrating the third embodiment of the lithium battery self-discharge screening method of this application;

[0044] Figure 4 This is a schematic diagram of the module structure of the lithium battery self-discharge screening device according to an embodiment of this application;

[0045] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the lithium battery self-discharge screening method in the embodiments of this application.

[0046] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0047] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0048] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solution of this application embodiment is: to obtain the standardized formation depth obtained through the formation depth experiment; to perform cell self-discharge screening based on the standardized formation depth to obtain the cell self-discharge K value; and to perform cell self-discharge anomaly screening based on the self-discharge K value and a preset self-discharge K value.

[0050] With the rapid development of the global new energy industry, lithium iron phosphate (LFP) batteries, as important energy storage components, have received increasing attention from the industry regarding performance optimization and quality control. Self-discharge rate is one of the key indicators for measuring the quality of LFP batteries, directly affecting the overall lifespan, safety, and user experience of the battery pack. Currently, in the manufacturing process of lithium batteries, self-discharge rate control is mainly achieved through optimizing production processes, adjusting charging and discharging methods, and static storage processes. However, these methods still face many challenges in practical applications, such as poor self-discharge consistency and difficulty in unifying screening standards. This leads to significant changes in the state of charge (SOC) of some cells during storage, thus affecting the overall performance and safety of the battery pack. Therefore, improving the accuracy of screening for abnormal self-discharge in lithium batteries and achieving comprehensive identification and effective control of cell self-discharge anomalies has become a pressing technical challenge in the current new energy field.

[0051] This application reduces the impact of different battery-related factors on self-discharge anomaly screening by optimizing the formation depth standard, thus solving the problem of large errors in lithium battery self-discharge anomaly screening and improving the accuracy of lithium battery self-discharge anomaly screening.

[0052] It should be noted that the executing entity in this embodiment can be a lithium battery self-discharge screening system, a computing service device with data processing, network communication, and program execution functions, or an electronic device capable of implementing the aforementioned lithium battery self-discharge screening function, etc. This embodiment does not specifically limit it in this way. The following uses a lithium battery self-discharge screening system as an example to describe this embodiment and the following embodiments.

[0053] Based on this, the embodiments of this application provide a method for screening the self-discharge of lithium batteries, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the lithium battery self-discharge screening method of this application.

[0054] In this embodiment, the lithium battery self-discharge screening method includes steps S10 to S30:

[0055] Step S10: Obtain the normalized formation depth obtained through the formation depth experiment.

[0056] It should be noted that the formation depth refers to the percentage of the battery's rated capacity reached during the first charge, while the standard formation depth is the optimal formation depth, at which the cell exhibits the best self-discharge consistency and the fewest side reactions.

[0057] Understandably, a deeper formation depth leads to a higher degree of activation of the electrode materials inside the battery, resulting in a more stable SEI (Solid Electrolyte Interface) film and helping to reduce the battery's self-discharge rate. Conversely, insufficient formation depth may lead to inadequate activation of the electrode materials, unstable SEI film formation, and thus increased battery self-discharge rate. It should also be noted that the SEI film is a protective film formed on the surface of the battery's negative electrode.

[0058] Step S20: Based on the normalized depth, perform cell self-discharge screening to obtain the cell self-discharge K value.

[0059] It should be noted that self-discharge refers to the phenomenon of charge separation and accumulation occurring within a battery in the absence of an external power source or field, leading to a gradual loss of battery capacity. When the self-discharge rate exceeds the normal range, it is considered an abnormal self-discharge. The self-discharge K-value of a battery cell is an indicator of its self-discharge performance, reflecting the rate of self-discharge per unit time under specific conditions (temperature, charging rate).

[0060] In one feasible implementation, step S20 specifically includes:

[0061] Step S201: At a preset temperature, perform self-discharge on the battery cell in the normalized deep state according to the target charge / discharge rate.

[0062] Understandably, the purpose of this step is to simulate the self-discharge process of the battery cell in a real-world usage environment in order to evaluate its self-discharge performance.

[0063] It should be noted that in this step, the system places the battery cell in a preset high-temperature environment (e.g., 45°C) to accelerate the internal chemical reaction and make the self-discharge phenomenon more pronounced. Subsequently, according to a pre-set target charge / discharge rate (e.g., the maximum allowable charge / discharge rate of the battery cell), the system performs charge and discharge operations to ensure the battery cell is in a standardized formation depth state (e.g., the optimal formation depth determined through the aforementioned experiments, such as 60% SOC). For example, assuming the optimal formation depth was determined to be 60% SOC through previous experiments, in this step, the system will charge the battery cell to 60% SOC and then place it in a 45°C constant temperature chamber for a period of time (e.g., 24 hours) to stabilize the battery cell's state. Then, a short charge / discharge cycle is performed according to the maximum allowable charge / discharge rate of the battery cell (e.g., 1C) to simulate the charge / discharge process in actual use and ensure the battery cell is in a stable self-discharge test preparation state.

[0064] Additionally, it should be noted that the preset temperature is a pre-set high-temperature environment designed to accelerate the chemical reactions within the battery cell, making the self-discharge phenomenon more pronounced and facilitating measurement and evaluation. The target charge / discharge rate refers to testing at the maximum allowable charge / discharge rate of the battery cell, which can maximize the exposure of the cell's self-discharge performance under extreme conditions, ensuring the comprehensiveness and accuracy of the test.

[0065] Step S202: Obtain the self-discharge start time, self-discharge end time, first open-circuit voltage, and second open-circuit voltage.

[0066] It should be noted that, specifically, when the cell begins to self-discharge, the self-discharge start time (T1) is recorded. After the self-discharge process ends (which can be determined by setting a time threshold or monitoring voltage changes), the self-discharge end time (T2) is recorded. Simultaneously, before and after the self-discharge begins, the cell's open-circuit voltage is measured and recorded, namely the first open-circuit voltage (V1) and the second open-circuit voltage (V2). For example, when the cell begins to self-discharge, the system immediately records time T1. When the cell's self-discharge ends, time T2 is recorded as the self-discharge end time. At the same time, the cell's open-circuit voltage V1 is measured and recorded at time T1, and the open-circuit voltage V2 is measured and recorded at time T2.

[0067] Additionally, it should be noted that open-circuit voltage is the voltage value that the battery cell stabilizes after a certain period of time (such as a few minutes) after the external circuit is disconnected. It reflects the current potential state of the battery cell.

[0068] Step S203: Obtain the self-discharge K value of the battery cell based on the self-discharge start time, the self-discharge end time, the first open-circuit voltage, and the second open-circuit voltage.

[0069] It should be noted that in this step, the system will perform calculations based on the data (T1, T2, V1, V2) obtained in step S202. Specifically, the calculation formula is: K = (V1 - V2) / (T2 - T1), where K is the self-discharge K value, V1 and V2 are the open-circuit voltages before and after self-discharge, and T2 and T1 are the end and start times of self-discharge, respectively.

[0070] Step S30: Screen for abnormal self-discharge of the battery cell based on the self-discharge K value and the preset self-discharge K value.

[0071] It should be noted that the preset self-discharge K-value is a value pre-set within the system based on factors such as the test environment and battery type to ensure the rationality and effectiveness of the screening criteria. Simultaneously, the stability and consistency of test conditions should be ensured during the screening process to improve the reliability of the screening results. Specifically, the system compares the self-discharge K-value of each cell with the preset self-discharge K-value. If the deviation between the cell's self-discharge K-value and the preset self-discharge K-value is too large, the cell is determined to have an abnormal self-discharge.

[0072] In one feasible implementation, step S30 specifically includes:

[0073] Step S301: Calculate the deviation value based on the self-discharge K value and the preset self-discharge K value.

[0074] It should be noted that the deviation value is a threshold used to determine whether the self-discharge K-value of a battery cell exceeds the normal deviation range. In this step, the system extracts the self-discharge K-value for each battery cell from the experimental data; then, it calculates the difference between this value and the preset self-discharge K-value to obtain the deviation value. The absolute value of the deviation value directly reflects the degree to which the battery cell's self-discharge performance deviates from the standard value.

[0075] Step S302: When the deviation value is greater than the absolute value of the preset deviation value, the cell self-discharge is determined to be abnormal.

[0076] It should be noted that, specifically, when the absolute value of the deviation calculated by the system is greater than the preset deviation value, the cell is considered to have an abnormal self-discharge. Understandably, this step, by calculating the self-discharge K-value and comparing it with the deviation value, effectively filters out abnormal self-discharge in the cell, providing a strong guarantee for improving the overall performance and safety of the battery.

[0077] This embodiment provides a method for screening self-discharge in lithium batteries. The method involves obtaining a standardized formation depth through a formation depth experiment; screening cells for self-discharge based on the standardized formation depth to obtain the self-discharge K-value of the cell; and screening for cell self-discharge anomalies based on the self-discharge K-value and a preset self-discharge K-value. In summary, this embodiment optimizes the formation depth standard, reducing the impact of different battery-related factors on self-discharge anomaly screening, solving the problem of large errors in lithium battery self-discharge anomaly screening, and improving the accuracy of lithium battery self-discharge anomaly screening.

[0078] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the lithium battery self-discharge screening method of this application. Before step S10, the method further includes:

[0079] Step S01: Obtain the first charging rate, the second charging rate, the first charging time, and the target transformation depth.

[0080] It should be noted that in this step, the charging parameters required for the formation experiment need to be determined first, including the charging rate in two stages. The first charging rate is set to 0.02C to generate a dense solid electrolyte interphase (SEI) film, ensuring the stability of the battery's subsequent performance. The second charging rate is set to 0.2C for further charging after the SEI film formation, to adjust the final formation depth. The first charging time is a fixed charging time required to generate a dense SEI film; the specific duration is determined based on the battery characteristics to ensure that all cells can generate a uniform SEI film during the first stage of charging. The target formation depth is based on the state of charge (SOC) reached by the experimentally designed battery after the formation process, and multiple gradients (such as 40% SOC, 60% SOC, 80% SOC, and 100% SOC) are specifically set to explore the optimal formation depth range.

[0081] Step S02: Perform formation based on the first charging rate, the second charging rate, the first charging time, and the target formation depth.

[0082] It should be noted that, specifically, after determining the charging parameters and target formation depth, the system will perform formation on the battery cell according to a preset charging scheme. First, a first-stage charging is performed using a 0.02C rate for the first charging time to generate a dense SEI film. Subsequently, the charging rate is adjusted to 0.2C, and the charging time for the second stage is controlled according to the target formation depth to ensure the battery cell ultimately reaches the predetermined formation depth. For example, with a target formation depth of 40% SOC, the battery cell is charged at a 0.02C rate for 2 hours in the first stage, then enters the second stage, continuing to charge at a 0.2C rate until 40% SOC is reached. For other target formation depths (such as 60%, 80%, and 100% SOC), the charging time for the second stage needs to be adjusted to match the corresponding formation depth.

[0083] Step S03: Obtain the cell capacity, voltage, cell thickness, and self-discharge rate at different target formation depths.

[0084] It should be noted that, specifically, after the cell formation is completed, performance testing is required to record and acquire key data such as cell capacity, voltage, cell thickness, and self-discharge rate at different target formation depths. Understandably, this data will be used for subsequent data analysis and optimization of the formation depth selection.

[0085] Step S04: Obtain the normalized depth based on the cell capacity, the voltage, the cell thickness, and the self-discharge rate.

[0086] It should be noted that in this step, the system will comprehensively analyze the collected cell performance parameters (capacity, voltage, thickness, self-discharge rate), and by comparing the various performance indicators under different target formation depths, the formation depth with the best performance will be taken as the standard formation depth.

[0087] In one feasible implementation, step S04 specifically includes:

[0088] Step A10: The cell capacity, voltage, cell thickness, and self-discharge rate are processed by weighted scoring to obtain a comprehensive score.

[0089] It should be noted that in this step, different weights can be assigned based on the relative importance of each parameter. Since the cell's capacity, voltage, thickness, and self-discharge rate all significantly impact its performance and stability, but the importance of each indicator may vary depending on the specific application scenario, the weight of each parameter needs to be determined through pre-assessment or analysis of experimental data. For example, assuming that cell capacity and self-discharge rate have relatively high weights, they can be set to 0.4 and 0.3 respectively, while voltage and cell thickness have relatively low weights, set to 0.2 and 0.1 respectively. Then, the score corresponding to the measured data of each cell (e.g., 0-100) is multiplied by its corresponding weight, and these weighted values ​​are summed to obtain the comprehensive score for each cell.

[0090] Step A20: The comprehensive score that meets the preset scoring criteria is converted into a depth as the standardization depth.

[0091] It should be noted that the preset scoring criteria are pre-defined based on historical experimental data and are used to determine which formation depths yield optimal cell performance. Specifically, the preset scoring criteria may include a threshold or range for a comprehensive score; only formation depths with a comprehensive score higher than this threshold or within this range are considered to meet the requirements for standardized formation depth. Furthermore, it should be noted that the preset scoring criteria must be based on sufficient experimental data and practical application needs to ensure that the selected standardized formation depths meet the performance and stability requirements of the cells. In addition, as experimental data accumulates and different types of cells are introduced, the preset scoring criteria also need to be updated and changed regularly.

[0092] In this embodiment, by optimizing the experimental process for screening the self-discharge of lithium iron phosphate batteries, precisely controlling the charging rate during the formation process, collecting performance data and conducting in-depth analysis, the optimal formation depth was finally determined, ensuring the consistency and accuracy of screening for lithium battery self-discharge anomalies.

[0093] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the lithium battery self-discharge screening method of this application. Before step S30, the method further includes:

[0094] Step B10: Obtain the vibration frequency, acceleration, and duration.

[0095] It should be noted that in this step, the system acquires the vibration frequency, acceleration, and duration based on different cell specifications. Understandably, the vibration frequency and acceleration determine the intensity of the vibration test, ensuring that the test effectively identifies potential problems without causing excessive damage to the cell.

[0096] Step B20: Perform a vibration test on the battery cell based on the vibration frequency, the acceleration, and the duration.

[0097] It should be noted that in this step, the system controls the battery cell to undergo vibration testing based on the vibration frequency, acceleration, and duration data obtained in step B10. During the test, the system records the changes in the battery cell's state and collects relevant data.

[0098] Step B30: Screen abnormal battery cells based on the vibration test.

[0099] It should be noted that, specifically, after the vibration test is completed, the system will screen the battery cells based on the test data and identify battery cells with abnormal self-discharge caused by potential reasons.

[0100] In one feasible implementation, step B30 specifically includes:

[0101] Step B301: Obtain the voltage change value, current change value, and internal resistance change value during the cell vibration test.

[0102] It should be noted that the voltage change value refers to the change in the cell's terminal voltage relative to its initial value during the vibration test. The current change value refers to the change in the cell's charging and discharging current during the vibration test. The internal resistance change value refers to the change in the cell's internal resistance during the vibration test.

[0103] Step B302: When the voltage change value is greater than the preset voltage change value, the current change value is greater than the preset current change value, or the internal resistance change value is greater than the preset internal resistance change value, the cell is determined to be abnormal.

[0104] It should be noted that in this step, the system compares the voltage, current, and internal resistance changes collected in step B301 with preset thresholds. If any of these changes exceeds the corresponding threshold, the cell is determined to be an abnormal cell. Understandably, the system will then record the abnormal cell's number, test data, and judgment result for subsequent data analysis and processing. For example, after vibration testing, the system might analyze the data and find that the voltage change of a certain cell exceeds a preset threshold (e.g., 5%), while the current and internal resistance changes are within the normal range. Based on the judgment criteria, the cell is determined to be an abnormal cell.

[0105] Additionally, it should be noted that the abnormality of the battery cell may be caused by dust leading to internal short circuits or increased internal resistance, poor contact or uneven electrolyte distribution within the battery cell, as well as abnormal aging, damage, or reduced electrolyte within the battery cell. Further analysis of the cause and corresponding measures should be taken to address these issues. This embodiment does not impose any limitations on these aspects.

[0106] In this embodiment, by eliminating potential quality problems (such as dust) that may be encountered during the actual production process of the battery cells, the performance stability of the battery cells was further verified. The changes in the electrical performance of the battery cells under vibration conditions were observed and analyzed, thereby initially screening out battery cells that may have self-discharge abnormalities, ensuring the accuracy and reliability of the final battery cell self-discharge screening.

[0107] This application also provides a lithium battery self-discharge screening device, please refer to... Figure 4 The lithium battery self-discharge screening device includes:

[0108] Standard acquisition module 10 is used to acquire the standardized formation depth obtained through the formation depth experiment.

[0109] The data processing module 20 is used to perform cell self-discharge screening based on the normalization depth to obtain the cell self-discharge K value.

[0110] The cell screening module 30 is used to screen cells for abnormal self-discharge based on the self-discharge K value and a preset self-discharge K value.

[0111] The lithium battery self-discharge screening device provided in this application, employing the lithium battery self-discharge screening method in the above embodiments, can solve the technical problem of how to improve the accuracy of lithium battery self-discharge anomaly screening. Compared with the prior art, the beneficial effects of the lithium battery self-discharge screening device provided in this application are the same as those of the lithium battery self-discharge screening method provided in the above embodiments, and other technical features in the lithium battery self-discharge screening device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0112] This application provides a lithium battery self-discharge screening device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the lithium battery self-discharge screening method in the above embodiment 1.

[0113] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the lithium battery self-discharge screening device in the embodiments of this application. The lithium battery self-discharge screening device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The lithium battery self-discharge screening device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0114] like Figure 5As shown, the lithium battery self-discharge screening device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the lithium battery self-discharge screening device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the lithium battery self-discharge screening device to communicate wirelessly or wiredly with other devices to exchange data. Although lithium battery self-discharge screening devices with various systems are shown in the figures, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0115] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0116] The lithium battery self-discharge screening device provided in this application, employing the lithium battery self-discharge screening method described in the above embodiments, can solve the technical problem of how to improve the accuracy of lithium battery self-discharge anomaly screening. Compared with the prior art, the beneficial effects of the lithium battery self-discharge screening device provided in this application are the same as those of the lithium battery self-discharge screening method provided in the above embodiments, and other technical features in this lithium battery self-discharge screening device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0117] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0118] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0119] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the lithium battery self-discharge screening method in the above embodiments.

[0120] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0121] The aforementioned computer-readable storage medium may be included in the lithium battery self-discharge screening device; or it may exist independently and not assembled into the lithium battery self-discharge screening device.

[0122] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the lithium battery self-discharge screening device, the lithium battery self-discharge screening device causes the following: to obtain the standardized formation depth obtained through the formation depth experiment; to perform cell self-discharge screening based on the standardized formation depth to obtain the self-discharge K value of the cell; and to perform cell self-discharge anomaly screening based on the self-discharge K value and a preset self-discharge K value.

[0123] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0125] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0126] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described lithium battery self-discharge screening method, thereby solving the technical problem of how to improve the accuracy of lithium battery self-discharge anomaly screening. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the lithium battery self-discharge screening method provided in the above embodiments, and will not be repeated here.

[0127] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the lithium battery self-discharge screening method described above.

[0128] The computer program product provided in this application can solve the technical problem of how to improve the accuracy of lithium battery self-discharge anomaly screening. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the lithium battery self-discharge screening method provided in the above embodiments, and will not be repeated here.

[0129] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method of self-discharge screening of lithium batteries, characterized in that, The method comprises: obtaining a first charging rate, a second charging rate, a first charging time and a target formation depth; forming according to the first charging rate, the second charging rate, the first charging time and the target formation depth; obtaining the capacity, voltage, thickness and self-discharge rate of the battery cell under different target formation depths; obtaining a standard formation depth according to the capacity, voltage, thickness and self-discharge rate of the battery cell; carrying out battery cell self-discharge screening based on the standard formation depth to obtain a self-discharge K value of the battery cell; carrying out battery cell self-discharge abnormality screening according to the self-discharge K value and a preset self-discharge K value.

2. The method of claim 1, wherein, The step of obtaining a standard formation depth according to the capacity, voltage, thickness and self-discharge rate of the battery cell comprises: processing the capacity, voltage, thickness and self-discharge rate of the battery cell by weighted scoring to obtain a comprehensive score; taking a target formation depth that meets a preset score standard as the standard formation depth.

3. The method of claim 1, wherein, The step of carrying out battery cell self-discharge screening based on the standard formation depth to obtain a self-discharge K value of the battery cell comprises: carrying out self-discharge on the battery cell in a standard formation depth state according to a target charge-discharge rate at a preset temperature; obtaining a self-discharge start time, a self-discharge end time, a first open circuit voltage and a second open circuit voltage; obtaining the self-discharge K value of the battery cell according to the self-discharge start time, the self-discharge end time, the first open circuit voltage and the second open circuit voltage.

4. The method of claim 1, wherein, The method further comprises, before the step of carrying out battery cell self-discharge abnormality screening according to the self-discharge K value and a preset self-discharge K value: obtaining a vibration frequency, an acceleration and a duration; carrying out vibration testing on the battery cell according to the vibration frequency, the acceleration and the duration; screening abnormal battery cells according to the vibration testing.

5. The method of claim 4, wherein, The step of screening abnormal battery cells according to the vibration testing comprises: obtaining a voltage change value, a current change value and an internal resistance change value during the vibration testing of the battery cell; determining that the battery cell is abnormal when the voltage change value is greater than a preset voltage change value, or the current change value is greater than a preset current change value, or the internal resistance change value is greater than a preset internal resistance change value.

6. The method of any one of claims 1 to 5, wherein, The step of carrying out battery cell self-discharge abnormality screening according to the self-discharge K value and a preset self-discharge K value comprises: calculating a deviation value according to the self-discharge K value and the preset self-discharge K value; determining that the battery cell self-discharge is abnormal when the deviation value is greater than an absolute value of a preset deviation value.

7. A lithium battery self-discharge screening device, characterized in that, The device comprises: a standard obtaining module, configured to obtain a first charging rate, a second charging rate, a first charging time and a target formation depth; form according to the first charging rate, the second charging rate, the first charging time and the target formation depth; obtain the capacity, voltage, thickness and self-discharge rate of the battery cell under different target formation depths; and obtain a standard formation depth according to the capacity, voltage, thickness and self-discharge rate of the battery cell; a data processing module, configured to carry out battery cell self-discharge screening based on the standard formation depth to obtain a self-discharge K value of the battery cell. The battery cell screening module is configured to perform battery cell self-discharge abnormality screening according to the self-discharge K value and a preset self-discharge K value.

8. A lithium battery self-discharge screening apparatus, characterized by, The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the lithium battery self-discharge screening method according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the lithium battery self-discharge screening method according to any one of claims 1 to 6.

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