Single cell, battery pack, battery screening system, method and battery pack

Abstract

This application provides a single-cell battery, a battery pack, a battery screening system, a method, and a battery module. It relates to the field of battery technology. Numerical differentiation is performed on OCV-SOC data to determine a first SOC range corresponding to the inflection point region of the OCV-SOC data; trend analysis is performed on DCR-SOC data to determine a second SOC range where the slope of the DCR-SOC data is greater than or equal to a slope threshold, and a third SOC range corresponding to the local DCR peak region is determined; the first, second, and third SOC ranges are correlated and superimposed to obtain multiple overlapping SOC ranges, and a preset range is determined based on these multiple overlapping SOC ranges. This approach acquires multiple electrochemical performance data, performs collaborative analysis and cross-validation of these data, comprehensively evaluates factors affecting battery storage performance, avoids the limitations of single electrochemical performance data, and thus obtains an accurate preset range for optimizing battery storage performance, improving the accuracy of storage performance optimization.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a single cell, a battery pack, a battery screening system, a method, and a battery module. Background Technology

[0002] Throughout a battery's entire lifecycle, including both usage and storage scenarios, the battery's performance in its usage scenarios and its storage performance in its storage scenarios jointly influence its reliability, economic viability, and safety risks.

[0003] In related technologies, the storage performance of a battery is optimized by analyzing its single electrochemical performance data and storing the battery based on the analysis results.

[0004] However, this method has a large margin of error, resulting in low accuracy in storage performance optimization. Summary of the Invention

[0005] This application provides embodiments of a single cell, a battery pack, a battery screening system, a method, and a battery module to improve the accuracy of storage performance optimization.

[0006] In a first aspect, embodiments of this application provide a single-cell battery for long-term storage. The target difference between the current state of charge (SOC) of the single-cell battery and a preset range is greater than a preset value. The target difference is the minimum difference between the current SOC and any endpoint of the preset range. The preset range is determined after evaluation based on sample batteries of the same batch or model as the single-cell battery. The method for evaluating the sample batteries includes: acquiring open-circuit voltage state of charge (OCV-SOC) data and DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample batteries; performing numerical differentiation on the OCV-SOC data to determine a first SOC range corresponding to the inflection point region of the OCV-SOC data; performing trend analysis on the DCR-SOC data to determine a second SOC range where the slope of the DCR-SOC data is greater than or equal to a slope threshold, and determining a third SOC range corresponding to a local DCR peak region; associating and superimposing the first SOC range, the second SOC range, and the third SOC range to obtain multiple overlapping SOC ranges, and determining the preset range based on the multiple overlapping SOC ranges.

[0007] Secondly, embodiments of this application provide a battery pack comprising at least two individual cells as described in the first aspect, wherein each individual cell is electrically connected to the other.

[0008] Thirdly, embodiments of this application provide a battery screening system, including a battery acquisition system and a controller; the battery acquisition system is used for acquiring OCV-SOC data and DCR-SOC data of sample batteries; the controller is used for: acquiring OCV-SOC data and DCR-SOC data corresponding to multiple preset SOCs of the sample batteries; performing numerical differentiation processing on the OCV-SOC data to determine a first SOC range corresponding to the inflection point region of the OCV-SOC data; performing trend analysis on the DCR-SOC data to determine a second SOC range where the slope of the DCR-SOC data is greater than or equal to a slope threshold, and determining a third SOC range corresponding to a local DCR peak region; associating and superimposing the first SOC range, the second SOC range, and the third SOC range to obtain multiple overlapping SOC ranges, and determining the preset range based on the multiple overlapping SOC ranges, wherein the preset range is used to screen single batteries suitable for long-term storage.

[0009] Fourthly, embodiments of this application provide a battery screening method, comprising: acquiring open-circuit voltage-state-of-charge (OCV-SOC) data and DC resistance-state-of-charge (DCR-SOC) data corresponding to multiple preset SOCs of a sample battery; performing numerical differentiation processing on the OCV-SOC data to determine a first SOC range corresponding to the inflection point region of the OCV-SOC data; performing trend analysis on the DCR-SOC data to determine a second SOC range in which the slope of the DCR-SOC data is greater than or equal to a slope threshold, and determining a third SOC range corresponding to a local DCR peak region; associating and superimposing the first SOC range, the second SOC range, and the third SOC range to obtain multiple overlapping SOC ranges, and determining a preset range based on the multiple overlapping SOC ranges, wherein the preset range is used to screen individual batteries suitable for long-term storage.

[0010] Fifthly, embodiments of this application provide a battery pack, including: a housing and at least two battery packs as described in the second aspect, each battery pack being disposed within the housing and electrically connected to each other.

[0011] Sixthly, embodiments of this application provide an electric vehicle that includes at least the battery pack described in the fifth aspect.

[0012] In a seventh aspect, embodiments of this application provide an electrical device that includes at least a single battery cell as described in the first aspect.

[0013] Eighthly, embodiments of this application provide a battery screening device, comprising: an acquisition module, configured to acquire open-circuit voltage state of charge (OCV-SOC) data and DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery; a calculation module, configured to perform numerical differentiation processing on the OCV-SOC data to determine a first SOC range corresponding to the inflection point region of the OCV-SOC data; an analysis module, configured to perform trend analysis on the DCR-SOC data to determine a second SOC range in which the slope of the DCR-SOC data is greater than or equal to a slope threshold, and to determine a third SOC range corresponding to a local DCR peak region; and an association module, configured to associate and superimpose the first SOC range, the second SOC range, and the third SOC range to obtain multiple overlapping SOC ranges, and to determine the preset range based on the multiple overlapping SOC ranges.

[0014] Ninthly, embodiments of this application provide an electronic device, including: a memory and a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, causing the processor to perform the embodiments described in the fourth aspect above.

[0015] In a tenth aspect, embodiments of this application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the embodiments described in the fourth aspect above.

[0016] Eleventhly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the implementation methods described in the fourth aspect above.

[0017] This application provides a single-cell battery, a battery pack, a battery screening system, a method, and a battery module. It acquires multiple electrochemical performance data, performs synergistic analysis and cross-validation on these data, comprehensively evaluates factors affecting battery storage performance, avoids the limitations of single electrochemical performance data, and thus obtains an accurate preset range for optimizing battery storage performance, improving the accuracy of storage performance optimization. Attached Figure Description

[0018] 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.

[0019] Figure 1 This is a schematic diagram illustrating an application scenario of a battery screening system provided in an embodiment of this application;

[0020] Figure 2A schematic flowchart of a battery screening method provided in an embodiment of this application;

[0021] Figure 3 A schematic flowchart illustrating another battery screening method provided in an embodiment of this application;

[0022] Figure 4 A schematic diagram of the OCV-SOC curve provided in the embodiments of this application;

[0023] Figure 5 A schematic diagram of the DCR-SOC curve provided in the embodiments of this application;

[0024] Figure 6 This is a schematic diagram of the structure of a battery screening device provided in an embodiment of this application;

[0025] Figure 7 This is a schematic diagram of another battery screening device provided in an embodiment of this application;

[0026] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0027] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. 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.

[0029] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0030] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the display interface provided in the embodiments of this application is merely an example, and the display interface may include more or less content.

[0031] It should be noted that the single cell, battery pack, battery screening system, method and battery pack of this application can be used in the field of battery technology, and can also be used in any field other than batteries. The application field of the single cell, battery pack, battery screening system, method and battery pack of this application is not limited.

[0032] Figure 1 This is a schematic diagram illustrating an application scenario of a battery screening system provided in an embodiment of this application. The scenario illustrated is as follows: identifying the storage strategy corresponding to a single battery cell, and storing the single battery cell according to the storage strategy to improve the storage performance of the single battery cell.

[0033] For example, the performance of a single battery cell includes usage performance and storage performance. Usage performance refers to the performance of a single battery cell during charging and discharging, such as rate capability, long-cycle performance, range, and cycle life. Storage performance refers to the performance of a single battery cell during long-term storage, that is, the ability of a single battery cell to maintain its capacity, power, and safety in a non-operating state, such as storage stability and calendar life.

[0034] Calendar lifetime indicates the rate at which the State of Health (SOH) of a single cell degrades under long-term storage conditions. The slower the degradation, the better the storage performance of the single cell.

[0035] To illustrate with a scenario example, taking an electric vehicle using a single battery cell as an example, if the battery is in a high-risk state of charge (SOC) range while the vehicle is parked, it may cause irreversible degradation of the single battery cell capacity, affecting the driving range.

[0036] Taking electrical equipment that uses individual batteries as an example, if the state of charge (SOC) of the individual batteries in the electrical equipment is not properly managed, it may lead to thermal runaway risk and threaten the safety of the equipment.

[0037] In related technologies, analysis of individual electrochemical performance data of single cells is used to pinpoint aging-sensitive regions that significantly impact storage performance, such as the state of charge (SOC) range. During long-term storage of single cells, strategies to avoid these aging-sensitive regions are employed to optimize battery storage performance.

[0038] However, relying solely on electrochemical performance data presents challenges such as identifying false features, making it difficult to accurately identify factors affecting storage performance and resulting in low accuracy in storage performance optimization.

[0039] The battery screening method provided in this application aims to solve the above-mentioned technical problems in related technologies.

[0040] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0041] Figure 2 This is a flowchart illustrating a battery screening method provided in an embodiment of this application. The method includes the following steps:

[0042] S201. Obtain the open-circuit voltage state of charge (OCV-SOC) data and the DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery.

[0043] The sample batteries are those from the same batch or model as the individual batteries for which the storage strategy is to be determined. The sample batteries are evaluated to determine a preset range, and the storage strategy for each individual battery is then determined based on this preset range.

[0044] For example, the open circuit voltage (OCV) is the potential difference between the positive and negative terminals of the sample battery when no current flows through it. The direct current resistance (DCR) is the equivalent DC resistance of the sample battery calculated using Ohm's law under a specific DC load condition.

[0045] For example, OCV-SOC data includes the OCV value corresponding to each preset SOC point. DCR-SOC data includes the DCR value corresponding to each preset SOC point.

[0046] OCV can only characterize the static equilibrium state of a sample cell and cannot reflect its dynamic response characteristics. For example, OCV can accurately locate the phase transition node of a sample cell, but it is limited by test polarization interference and cannot reflect the dynamic load changes during the storage process of the sample cell. It is also prone to misjudging the boundary due to small voltage fluctuations.

[0047] DCR can only characterize the dynamic impedance characteristics of a sample battery and cannot reflect the static reference stability. For example, DCR can reflect the power carrying capacity of a sample battery, but it is limited by temperature drift in the storage environment and test noise. When used as a sole criterion, its boundaries are blurred and it is easily interfered with. Therefore, the preset range determined by a single electrochemical performance data in related technologies has the problem of low accuracy.

[0048] S202. Perform numerical differentiation on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data.

[0049] For example, numerical differentiation is performed on the OCV-SOC data to determine the rate of change of the OCV-SOC data, i.e., the instantaneous rate at which OCV changes with SOC.

[0050] Taking a lithium-ion battery as an example, in the stable reaction region of a lithium-ion battery, that is, in a region where lithium-ion insertion and extraction are stable and the structure of the electrode material does not undergo significant abrupt changes, the storage performance of the lithium-ion battery is good. At this time, the rate of change is stable and the change is gradual.

[0051] In the phase transition reaction region of a lithium-ion battery, when the lithium-ion insertion / extraction ratio reaches a certain level, the crystal structure of the electrode material undergoes abrupt changes (e.g., from single-phase to two-phase), leading to instability and poor storage performance in the lithium-ion battery. At this point, the OCV (Optical Value Change) will fluctuate drastically with the SOC (State of Charge), meaning the rate of change will exhibit abrupt changes.

[0052] For example, the location where the rate of change abruptly occurs is the inflection point region, which is characterized by a significant change in the slope of the OCV-SOC curve. The SOC range corresponding to the inflection point region is the first SOC range. The sample battery exhibits poor storage performance within the first SOC range, and this range should be avoided when determining storage strategies.

[0053] S203. Perform trend analysis on the DCR-SOC data to determine the second SOC range where the slope of the DCR-SOC data is greater than or equal to the slope threshold, and determine the third SOC range corresponding to the local DCR peak area.

[0054] For example, for DCR-SOC data, the trend is decomposed based on the change of DCR relative to SOC to obtain two types of core dynamic features: large slope features and local DCR peak features.

[0055] A large slope characteristic is characterized by a slope of DCR relative to SOC that is greater than or equal to a slope threshold. The continuous SOC range corresponding to this large slope characteristic is the second SOC range. DCR values ​​within the second SOC range fluctuate significantly and are unstable, reflecting intensified electrode interface polarization and abnormally increased ion migration resistance.

[0056] The DCR peak characteristic is determined by excluding the first and last points of the SOC sequence and comparing the DCR values ​​of each SOC point with its left and right adjacent points. Local SOC points with significantly higher DCR values ​​than their adjacent points are identified, and the continuous SOC range corresponding to these points is the third SOC range. The third SOC range reflects that the internal side reactions (such as electrolyte decomposition and electrode pulverization) of the sample battery are in a severe stage.

[0057] Based on the above implementation methods, by identifying the second SOC range and the third SOC range respectively, the SOC range that affects the storage performance of the sample battery can be comprehensively determined from different dimensions, thereby accurately optimizing the storage performance of a single battery.

[0058] S204. The first SOC range, the second SOC range, and the third SOC range are correlated and superimposed to obtain multiple overlapping SOC ranges, and a preset range is determined based on the multiple overlapping SOC ranges.

[0059] For example, using SOC values ​​as a unified benchmark, the first SOC range (OCV inflection point region), the second SOC range (DCR steep rise region), and the third SOC range (DCR peak region) are spatially correlated and superimposed. During the superposition process, continuous SOC intervals that simultaneously cover two or more SOC ranges are identified; these intervals are the overlapping SOC ranges.

[0060] By combining the interval boundaries and coverage of all overlapping SOC ranges, a preset range was finally determined to characterize the high degradation risk of sample battery storage.

[0061] With the illustration of scenario examples, the SOC range that simultaneously satisfies two or more of the features in the first SOC range, the second SOC range, and the third SOC range has multi-dimensional evidence to prove the risk of storage performance degradation. This can reduce the identification of false features and its reliability is far higher than that of the range corresponding to a single feature.

[0062] For example, a preset range is determined based on multiple overlapping SOC ranges, which controls the SOC range that individual cells avoid. This improves the storage performance of individual cells.

[0063] For example, the difference between the current SOC and each endpoint of the preset range is calculated, and the minimum difference is determined as the target difference. The target difference represents the degree of closeness between the current SOC and the preset range. When the target difference between the current SOC of a single cell and the preset range is greater than a preset value, it can be ensured that the current SOC is far from the unstable state corresponding to the preset range. This prevents the single cell from degrading its storage performance due to being in an unstable state.

[0064] The battery screening method provided in this application acquires open-circuit voltage-state-of-charge (OCV-SOC) data and DC resistance-state-of-charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery; performs numerical differentiation on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data; performs trend analysis on the DCR-SOC data to determine the second SOC range where the slope of the DCR-SOC data is greater than or equal to the slope threshold, and determines the third SOC range corresponding to the local DCR peak region; correlates and superimposes the first, second, and third SOC ranges to obtain multiple overlapping SOC ranges, and determines the preset range based on the multiple overlapping SOC ranges. This approach acquires multiple electrochemical performance data, performs collaborative analysis and cross-validation of these data, comprehensively evaluates the factors affecting battery storage performance, avoids the limitations of single electrochemical performance data, and thus obtains an accurate preset range for optimizing battery storage performance, improving the accuracy of storage performance optimization.

[0065] Based on any of the above embodiments, the following, in conjunction with Figure 3 The detailed process of battery screening is explained.

[0066] Figure 3 This is a schematic flowchart illustrating another battery screening method provided in an embodiment of this application. Figure 3 As shown, the method includes:

[0067] S301. Obtain the open-circuit voltage state of charge (OCV-SOC) data and the DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery.

[0068] Optionally, the test preparation process for the sample battery can be as follows: discharge the sample battery at a constant current at a fixed rate (e.g., 0.33C) to the cutoff voltage (e.g., 2.5V) in a constant temperature environment (e.g., 25°C±2°C), let it stand for 30 minutes, charge it at a fixed current at a fixed rate to the cutoff voltage (e.g., 3.65V), and then charge it at a constant voltage until the cutoff current reaches 0.05C, and let it stand for a preset time (e.g., 2 hours).

[0069] Optionally, the OCV data acquisition process can be as follows: The sample battery is intermittently discharged at a fixed current rate using a constant current discharge method. After each preset SOC (e.g., 5% SOC) is reached, the discharge is stopped, and the battery is allowed to stand for a preset time to allow internal polarization relaxation and voltage stabilization. The open-circuit voltage value at this point is recorded. This process is repeated until the sample battery reaches a completely discharged state. This yields a set of discrete OCV-SOC data from 100% to 0% SOC.

[0070] Optionally, the DCR data acquisition process can be as follows: A standard DC pulse test is performed at each preset SOC to obtain the DCR value. Specifically, after resting for 1 hour at each target SOC, a 3C constant current discharge / charge pulse lasting 30 seconds is applied, and the resting voltage V1 before the pulse starts and the voltage V2 at the end of the pulse are recorded. According to the formula... Calculate the DC internal resistance at the SOC point, where I represents the pulse current.

[0071] One feasible implementation method for determining OCV-SOC data and DCR-SOC data includes: acquiring multiple initial OCV-SOC data and multiple initial DCR-SOC data; performing interpolation processing on the multiple initial OCV-SOC data and multiple initial DCR-SOC data based on multiple preset SOCs to obtain multiple preprocessed OCV-SOC data and multiple preprocessed DCR-SOC data; and performing smoothing and noise reduction processing on the multiple preprocessed OCV-SOC data and multiple preprocessed DCR-SOC data to obtain OCV-SOC data and DCR-SOC data.

[0072] For example, using multiple preset SOCs as reference interpolation nodes, interpolation calculations are performed on the initial OCV-SOC and initial DCR-SOC data respectively. For gaps not covered by SOC sampling points or uneven distribution of sampling points in the initial data, data gaps are filled by interpolation to obtain preprocessed OCV-SOC data and preprocessed DCR-SOC data that perfectly match the preset SOC points, are continuous in sequence, and have no gaps.

[0073] For example, the preprocessed OCV-SOC and preprocessed DCR-SOC data are smoothed and denoised. By using a preset denoising algorithm (such as moving average or Gaussian filtering), random noise, test interference, and local abnormal fluctuations in the data are filtered out, while the core trends reflecting the true electrochemical evolution of the sample battery are retained. Finally, smooth, continuous, and noise-free OCV-SOC and DCR-SOC data are obtained.

[0074] In this feasible implementation, interpolation and smoothing noise reduction effectively reduce errors caused by SOC sampling point mismatch, data gaps, and test noise interference in the initial data, thereby improving the accuracy of storage performance optimization.

[0075] S302. Perform numerical differentiation on the OCV-SOC data to obtain the first-order derivative values ​​of multiple OCVs with respect to SOC corresponding to multiple preset SOCs.

[0076] For example, based on the acquired matching OCV-SOC data, the mathematical method of numerical differentiation is used to calculate the derivative of the open circuit voltage with respect to the state of charge at each preset SOC point, i.e., dOCV / dSOC, forming a set of first-order derivative values ​​that correspond one-to-one with the preset SOC.

[0077] With the aid of scenario examples, the form of OCV-SOC data directly reflects the electrochemical laws of ion insertion / extraction within the sample battery, and its first derivative is a quantitative representation of the instantaneous rate of change. That is, when the slope of the OCV-SOC data changes abruptly, i.e., at an inflection point, the value of the first derivative will show significant fluctuations or abrupt changes.

[0078] S303. Based on the preset derivative value threshold and the first derivative values ​​of multiple OCVs with respect to SOC, determine multiple candidate inflection points of the OCV-SOC data. The first derivative value of the OCV with respect to SOC corresponding to each candidate inflection point is greater than or equal to the derivative value threshold.

[0079] For example, based on the electrochemical characteristics of the sample battery's material system (e.g., lithium iron phosphate, ternary lithium), a derivative value threshold is pre-calibrated. Each first derivative value is iterated through, and SOC points that satisfy the condition that the first derivative value is greater than or equal to the derivative value threshold are selected and defined as candidate inflection points.

[0080] For example, the derivative threshold is a quantitative definition of the degree of slope change. Only when the first derivative reaches the threshold does it indicate that the OCV change rate at the SOC point is large enough, corresponding to a significant phase change reaction inside the sample battery, which is a potential SOC point with storage performance risk.

[0081] Based on the above implementation methods, the derivative threshold is determined according to the material system of the sample battery to avoid errors introduced by subjective judgment, accurately determine the inflection point, and thus improve the accuracy of storage performance optimization.

[0082] S304. Determine the range of the first SOC based on multiple candidate inflection points.

[0083] One feasible implementation method is to determine the first SOC range by: identifying multiple isolated inflection points from multiple candidate inflection points, wherein the number of consecutive candidate inflection points in the neighborhood of each isolated inflection point is less than or equal to a preset number threshold; removing multiple isolated inflection points from the multiple candidate inflection points to obtain multiple target inflection points; determining an inflection point region based on the multiple target inflection points; and determining the SOC range corresponding to the inflection point region as the first SOC range.

[0084] For example, plotting OCV-SOC data on a coordinate axis yields an OCV-SOC curve, where the inflection point is the point where the curve slope changes significantly.

[0085] Below, in conjunction with Figure 4 Explanation of the OCV-SOC curve.

[0086] Figure 4 This is a schematic diagram of the OCV-SOC curve provided in an embodiment of this application. Figure 4 As shown, OCV-SOC curves were generated for multiple sample batteries at the same temperature. In the OCV-SOC curves, the overall OCV increases with increasing SOC. The growth rate of OCV is a variable value; at the inflection point, the OCV growth rate decreases. At the inflection point, the sample battery is in an unstable state, exhibiting poor storage performance.

[0087] For example, all the selected candidate inflection points are traversed, and the number of consecutive candidate inflection points in the neighborhood of each candidate inflection point is checked. If the number of consecutive candidate inflection points in the neighborhood of a candidate inflection point is less than or equal to a preset threshold, then the candidate inflection point is defined as an isolated inflection point.

[0088] As illustrated by the scenario example, the electrochemical phase transition inside the sample battery is a continuous process. Therefore, the actual inflection points are multiple adjacent points, rather than isolated single points. Isolated inflection points are usually caused by accidental factors such as test noise, data interpolation errors, and environmental interference, and do not represent the true phase transition characteristics of the sample battery material.

[0089] For example, isolated inflection points are eliminated from multiple candidate inflection points, and only the target inflection point with real electrochemical phase transition significance is retained.

[0090] For example, the inflection point region is the interval covered by a series of inflection points. The inflection point region represents the continuous time period of the electrochemical phase transition, thus covering the dynamic process of the electrochemical phase transition. The SOC range corresponding to the inflection point region is determined as the first SOC range.

[0091] In this feasible implementation, by identifying and proposing isolated inflection points, interference from non-real electrochemical features is filtered out, ensuring that only the target inflection points corresponding to real phase transitions are retained, thereby reducing error interference and improving the accuracy of storage performance optimization.

[0092] One feasible implementation method is to determine the inflection point region by the following steps: for each target inflection point, determine the maximum and minimum inflection point values ​​based on a preset extension value and the target inflection point; determine the target inflection point interval corresponding to the target inflection point based on the maximum and minimum inflection point values; traverse all target inflection points and determine each target inflection point interval corresponding to all target inflection points as an inflection point region; wherein, different sample batteries correspond to different preset extension values.

[0093] For example, based on the material system (e.g., lithium iron phosphate, ternary lithium) or electrochemical characteristics of the sample battery, the SOC range is pre-defined as a reference scale for subsequent extension of the inflection point region.

[0094] With the help of scenario examples, the electrochemical phase transition range of sample batteries of different systems has a relatively fixed typical range (for example, the phase transition range of lithium iron phosphate is concentrated in a certain SOC segment, while the phase transition range of ternary lithium is more gradually distributed). The preset SOC range is a quantification and solidification of this objective electrochemical law.

[0095] For example, iterate through all the selected target inflection points, and using the SOC point corresponding to each target inflection point as the center, extend the SOC range to the left (low SOC direction) and right (high SOC direction) by a preset SOC interval length to obtain multiple consecutive or adjacent target inflection point intervals corresponding to the target inflection point. Integrate each target inflection point interval corresponding to each target inflection point to obtain the inflection point region.

[0096] For example, by extending the operation, the entire range of electrochemical phase transitions inside the sample battery is fully covered, ensuring that the final inflection point region can truly characterize the region of accelerated degradation during storage.

[0097] In this feasible implementation, a standardized and unified reference scale is provided for the extension of the inflection point area by pre-setting the SOC range, which reduces subjective errors and thus improves the accuracy of storage performance optimization.

[0098] S305. Perform trend analysis on the DCR-SOC data to determine the second SOC range where the slope of the DCR-SOC data is greater than or equal to the slope threshold, and determine the third SOC range corresponding to the local DCR peak area.

[0099] One feasible implementation method for determining the second SOC range includes: dividing the DCR-SOC data into multiple consecutive candidate sub-intervals; calculating the average slope of the DCR for each sub-interval to obtain multiple interval slopes corresponding to the multiple candidate sub-intervals; filtering the multiple candidate sub-intervals based on a preset slope threshold, multiple interval slopes, and a preset DCR increase threshold to obtain multiple target sub-intervals, wherein the DCR interval slope corresponding to each target sub-interval is greater than or equal to the preset slope threshold, and the DCR increase corresponding to each target sub-interval is greater than or equal to the preset DCR increase threshold; and merging adjacent intervals of the multiple target sub-intervals to obtain the second SOC range.

[0100] Below, in conjunction with Figure 5 Explanation of the DCR-SOC curve.

[0101] Figure 5 This is a schematic diagram of the DCR-SOC curve provided in an embodiment of this application. Figure 5 As shown, DCR-SOC curves were generated for the DCR-SOC data of multiple sample batteries. In the DCR-SOC curves, DCR changes with SOC. Within a relatively stable DCR range, such as the 20%-40% SOC range, the sample battery's DCR is relatively stable, indicating high storage performance. Conversely, within a SOC range where DCR fluctuates significantly, the polarization at the sample battery's electrode interface intensifies, corresponding to lower storage performance.

[0102] For example, the DCR-SOC data is divided into several continuous, non-overlapping, and contiguous candidate sub-intervals. Each sub-interval contains a continuous SOC sampling point and the corresponding DCR value.

[0103] For example, for each candidate sub-interval, the SOC value and corresponding DCR value of its left and right endpoints are extracted, and the average slope of the DCR as a function of SOC within the candidate sub-interval is calculated by linear fitting, thereby obtaining multiple interval slopes.

[0104] As illustrated by the scenario example, the larger the absolute value of the slope, the more drastic the change in DCR. Compared to single-point fluctuations, the average slope can more objectively and stably reflect the internal resistance trend within a SOC range, eliminating the interference of random noise.

[0105] For example, a preset slope threshold quantifies the drastic change in internal resistance, and a preset DCR increase threshold quantifies the actual increase in internal resistance. All candidate sub-intervals are traversed, and sub-intervals that simultaneously satisfy the following two conditions are selected and defined as target sub-intervals.

[0106] Specifically, if the slope of the target sub-interval is greater than or equal to a preset slope threshold, it satisfies a steep upward trend. If the actual increase in DCR within the target sub-interval is greater than or equal to a preset DCR increase threshold, it satisfies the increase requirement.

[0107] With scenario examples, filtering solely based on a preset slope threshold may miss invalid intervals where the slope meets the standard but the increase is minimal, such as a gradual increase in DCR without a significant increase in actual internal resistance. Filtering solely based on a preset DCR increase threshold may misidentify pseudo-steep rise intervals where the slope does not meet the standard but there is a sudden increase at a single point. By using a dual-threshold linkage filtering method, both the dramatic trend and the significant magnitude can be covered simultaneously, accurately identifying the true abnormal evolution of internal resistance in the sample battery.

[0108] Optionally, the slope threshold and DCR increase threshold can be predetermined based on the material system (e.g., lithium iron phosphate, ternary lithium) or electrochemical characteristics of the sample battery.

[0109] For example, multiple target sub-intervals are identified as adjacent target sub-intervals with continuous SOC and no gaps between them. These adjacent target sub-intervals are then merged to form a complete continuous SOC interval, i.e., the second SOC range. This integration of discrete intervals into a continuous interval fully characterizes the abnormal range of the sample battery's internal resistance.

[0110] In this feasible implementation, the DCR change trend is transformed into a quantifiable numerical indicator by calculating the average slope, which accurately reflects the rate of change of DCR, avoids the interference of single-point noise on the overall trend, and thus improves the accuracy of storage performance optimization.

[0111] One feasible implementation method is to determine the third SOC range by: comparing adjacent values ​​of DCR corresponding to each SOC in the DCR-SOC data according to a preset difference threshold to obtain multiple candidate DCR peak points; removing DCR peak points whose peak values ​​are less than or equal to a preset peak threshold from the multiple candidate DCR peak points to obtain multiple target DCR peak points; and extending the range according to the preset SOC interval with each target DCR peak point as the center to obtain the third SOC range.

[0112] For example, taking all SOC points in the DCR-SOC data except for the first and last points of the SOC sequence, and centering on each point, compare its DCR value with the DCR values ​​of the adjacent SOC points on the left and right. If the DCR value of a certain SOC point is higher than the DCR values ​​of both the left and right adjacent points, and the DCR difference between the SOC point and its adjacent points reaches or exceeds a preset difference threshold, then the SOC point is defined as a candidate DCR peak point.

[0113] With the help of scenario examples, the local peak of DCR corresponds to the core points where ion migration is hindered and electrode polarization is intensified inside the sample battery.

[0114] For example, the DCR value of each candidate peak point is compared with the preset peak threshold. Candidate peak points with DCR values ​​less than or equal to the peak threshold are eliminated, and the remaining candidate peak points are defined as target DCR peak points.

[0115] As illustrated by the scenario example, the amplitude of the DCR peak directly reflects the severity of the internal resistance anomaly. Only DCR peaks reaching a certain amplitude correspond to the region of intensified actual side reactions (such as electrolyte decomposition and electrode pulverization) inside the sample battery. Low-amplitude candidate peaks may be caused by test noise or minor environmental fluctuations and need to be filtered out.

[0116] For example, taking the SOC point corresponding to each target peak point as the center, extend the SOC range to the left and right by a preset SOC interval length. Integrate the continuous or adjacent SOC ranges formed after extending all target peak points to obtain the third SOC range.

[0117] Based on the scenario example, the electrochemical side reactions inside the sample battery exist in a continuous transition region centered on the peak point. In this region, the ion migration resistance remains high, and the storage decay rate is significantly accelerated. Identifying the third SOC range is consistent with the actual law of the evolution of internal resistance inside the sample battery.

[0118] Optionally, the difference threshold and peak threshold can be predetermined based on the material system (e.g., lithium iron phosphate, ternary lithium) or electrochemical characteristics of the sample battery.

[0119] In this feasible implementation, by comparing adjacent values ​​on both sides and determining the preset difference threshold, candidate peak points with real local convexity features are objectively selected, reducing the error introduced by manual judgment, thereby improving the accuracy of storage performance optimization.

[0120] S306. The first SOC range, the second SOC range, and the third SOC range are correlated and superimposed to obtain multiple overlapping SOC ranges.

[0121] For example, the fluctuations of OCV and DCR are combined to perform feature extraction and correlation analysis on sample batteries.

[0122] Below, we will use Table 1 as an example to illustrate feature analysis based on data:

[0123] Table 1

[0124]

[0125] Based on the analysis in Table 1, the 55%-65% SOC represents the inflection point of the OCV curve. The 50%-70% SOC represents the region where DCR (Distributed Rate of Change) rises sharply. SOC > 90% represents a second region where DCR rises sharply. Within the 50%-65% range, the inflection point of the OCV-SOC curve overlaps with the region where DCR-SOC rises sharply. The predicted SOC ranges for poor storage performance are 50%-65% and >90%.

[0126] Below, we will provide an example of experimental verification based on Table 2:

[0127] Table 2

[0128]

[0129] Referring to Table 2, the storage performance degradation at 65% SOC demonstrates that the storage performance is poor within the overlapping area of ​​the OCV-SOC curve inflection point and the DCR-SOC curve steep rise. 15% SOC is not within the predicted range of poor storage performance, and the test results reflect only slight capacity degradation. 100% SOC falls within the predicted range of SOC > 90%, and the test results confirm a significant degradation in storage performance at high SOC.

[0130] S307. Based on the electrochemical characteristics of the sample battery, determine the extreme SOC range of the electrode potential where side reactions are frequent.

[0131] For example, by combining the material system (such as lithium iron phosphate, ternary lithium), positive and negative electrode material characteristics and electrolyte system of the sample battery, through theoretical analysis and / or experimental verification, the SOC range in which the electrode potential of the sample battery is in an extreme state (too high or too low) and internal side reactions (such as electrolyte decomposition, electrode corrosion, lithium dendrite growth) are significantly high is determined. This range is the extreme SOC range of the electrode potential.

[0132] With scenario examples, it is clear that the electrode potential of the sample battery is directly related to its state of charge (SOC). In the low SOC range (e.g., 0%~10%), the negative electrode potential is too low, easily leading to electrolyte reduction and decomposition, and lithium dendrite precipitation. In the high SOC range (e.g., 90%~100%), the positive electrode potential is too high, easily leading to electrolyte oxidation and decomposition, and electrode material structural collapse. The extreme SOC ranges, regardless of whether they overlap with other SOC ranges, represent the areas with the fastest degradation rate and highest risk during sample battery storage; these are inherently high-degradation zones and should therefore be identified separately.

[0133] S308. Merge multiple overlapping SOC ranges with the electrode potential extreme SOC ranges to obtain a preset range.

[0134] For example, the positional relationship between multiple overlapping SOC ranges and the electrode potential extreme SOC range is compared. If a certain overlapping SOC range is adjacent to the electrode potential extreme SOC range, the two ranges are merged into a single continuous SOC range. The merged continuous SOC range is then used as a preset range.

[0135] With the help of scenario examples, merging adjacent ranges can restore the continuity of high attenuation risk ranges and avoid the omission of risks due to range dispersion.

[0136] In this feasible implementation, by identifying the extreme SOC range of the electrode potential, the blind zone of the high-attenuation-risk SOC range is accurately supplemented, improving the comprehensiveness of the preset range coverage, thereby improving the accuracy of storage performance optimization.

[0137] One feasible implementation method, after determining the preset range, includes the following assessment method: determining the overlap type of the preset range, where the overlap type is a pairwise overlapping SOC range or a three-way overlapping SOC range; classifying the preset range into risk levels according to the overlap type to obtain the risk level classification result; and determining the corresponding preset value according to the risk level classification result.

[0138] For example, the final determined preset range is broken down, and compared with the previously obtained first SOC range, second SOC range, and third SOC range, the source of overlap for each consecutive SOC within the preset range is identified, and the overlap type is divided into two categories:

[0139] The overlapping SOC ranges are those that, within the preset range, simultaneously cover only any two of the three SOC ranges in a continuous sequence.

[0140] The overlapping SOC range of the three is a continuous SOC range that simultaneously covers the first SOC range, the second SOC range, and the third SOC range within the preset range.

[0141] With the help of scenario examples, the essence of overlapping types is the degree of superposition of high decay risk characteristics. The more overlapping SOC ranges there are, the more comprehensive the high decay characteristics (such as static phase transition, dynamic steep rise, and dynamic peak) that the corresponding SOC range possesses at the same time. The more intense the internal side reactions of the sample battery are, the higher the storage decay risk is.

[0142] For example, the risk level corresponding to the SOC range can be determined based on the overlap type. For instance, a medium risk level can be set for SOC ranges that overlap in pairs, and a high risk level can be set for SOC ranges that overlap in three pairs. The risk differences between different SOCs are clearly defined.

[0143] For example, the higher the risk level, the more stringent the storage strategy for the optimized battery, and the larger the preset value.

[0144] To illustrate with a scenario example, consider a preset range including 30%-40% SOC, where the risk level is considered high. During battery storage, the current SOC of individual cells should be strictly controlled to stay away from this range. This can be achieved by setting a larger preset value. For example, a preset value of 4% would place the current SOC of individual cells outside the 26%-44% SOC range, improving their storage performance. For a medium-risk SOC range, a smaller preset value, such as 2%, can be set.

[0145] In this feasible implementation, by identifying overlapping types and classifying risk levels, high and medium risk ranges within a preset range can be accurately distinguished, making storage optimization strategies more targeted and thus improving the accuracy of storage performance optimization.

[0146] This application provides a single-cell battery for long-term storage. The target difference between the current state of charge (SOC) of the single-cell battery and a preset range is greater than a preset value. The target difference is the minimum difference between the current SOC and any endpoint of the preset range. The preset range is determined after evaluation based on sample batteries of the same batch or model as the single-cell battery.

[0147] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0148] This application provides a battery pack comprising at least two of the above-described individual cells, each of which is electrically connected to the other.

[0149] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0150] This application provides a battery screening system, including a battery acquisition system and a controller;

[0151] The battery acquisition system is used to acquire OCV-SOC data and DCR-SOC data of sample batteries;

[0152] The controller is used for:

[0153] Obtain OCV-SOC data and DCR-SOC data corresponding to multiple preset SOCs of the sample battery;

[0154] Numerical differentiation is performed on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data;

[0155] Trend analysis is performed on the DCR-SOC data to determine the second SOC range where the slope of the DCR-SOC data is greater than or equal to the slope threshold, and to determine the third SOC range corresponding to the local DCR peak area.

[0156] The first SOC range, the second SOC range, and the third SOC range are correlated and superimposed to obtain multiple overlapping SOC ranges. A preset range is determined based on the multiple overlapping SOC ranges. The preset range is used to select individual batteries suitable for long-term storage.

[0157] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0158] This application provides a battery pack, including a housing and at least two battery packs as described above, each battery pack being disposed within the housing and electrically connected to each other.

[0159] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0160] This application provides an electric vehicle that includes at least the aforementioned battery pack.

[0161] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0162] This application provides an electrical device that includes at least the aforementioned single battery cell.

[0163] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0164] Figure 6 This is a schematic diagram of a battery screening device provided in an embodiment of this application. Figure 6 As shown, the battery screening device 60 may include: an acquisition module 61, a calculation module 62, an analysis module 63, and an association module 64.

[0165] The acquisition module 61 is used to acquire open-circuit voltage state of charge (OCV-SOC) data and DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery.

[0166] The calculation module 62 is used to perform numerical differentiation processing on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data.

[0167] Analysis module 63 is used to perform trend analysis on DCR-SOC data to determine the second SOC range where the slope of the DCR-SOC data is greater than or equal to the slope threshold, and to determine the third SOC range corresponding to the local DCR peak area.

[0168] The association module 64 is used to associate and superimpose the first SOC range, the second SOC range and the third SOC range to obtain multiple overlapping SOC ranges, and to determine a preset range based on the multiple overlapping SOC ranges.

[0169] Optionally, module 61 can be executed. Figure 2 S201 in the embodiment.

[0170] Optionally, the calculation module 62 can perform... Figure 2 S202 in the embodiment.

[0171] Optionally, analysis module 63 can perform... Figure 2 S203 in the embodiment.

[0172] Optionally, associated module 64 can execute Figure 2 S204 in the embodiment.

[0173] It should be noted that the battery screening device shown in the embodiments of this application can perform the technical solution shown in the above method embodiments, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0174] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0175] In one possible implementation, the computing module 62 is specifically used for:

[0176] Numerical differentiation of OCV-SOC data yields the first-order derivative values ​​of multiple OCVs with respect to SOCs corresponding to multiple preset SOCs.

[0177] Based on the preset derivative value threshold and the first derivative values ​​of multiple OCVs with respect to SOC, multiple candidate inflection points of OCV-SOC data are determined. The first derivative value of OCV with respect to SOC corresponding to each candidate inflection point is greater than or equal to the derivative value threshold.

[0178] The first SOC range is determined based on multiple candidate inflection points.

[0179] In one possible implementation, the computing module 62 is specifically used for:

[0180] Multiple isolated inflection points are determined from multiple candidate inflection points, and the number of consecutive candidate inflection points in the neighborhood of each isolated inflection point is less than or equal to a preset number threshold.

[0181] Multiple isolated inflection points are eliminated from multiple candidate inflection points to obtain multiple target inflection points;

[0182] Determine the inflection point zone based on multiple target inflection points;

[0183] The SOC range corresponding to the inflection point region is determined as the first SOC range.

[0184] In one possible implementation, the computing module 62 is specifically used for:

[0185] For each target inflection point, the maximum and minimum values ​​of the inflection point are determined based on the preset extension value and the target inflection point.

[0186] The target inflection point interval corresponding to the target inflection point is determined based on the maximum and minimum inflection point values.

[0187] Traverse all target inflection points and determine each target inflection point interval corresponding to all target inflection points as an inflection point region;

[0188] Different sample batteries correspond to different preset extension values.

[0189] In one possible implementation, the analysis module 63 is specifically used for:

[0190] The DCR-SOC data is divided into multiple consecutive candidate sub-intervals;

[0191] Calculate the average slope of DCR for each sub-interval to obtain the slope of multiple intervals corresponding to multiple candidate sub-intervals;

[0192] Based on the preset slope threshold, multiple interval slopes, and the preset DCR increase threshold, multiple candidate sub-intervals are filtered to obtain multiple target sub-intervals. The DCR interval slope corresponding to each target sub-interval is greater than or equal to the preset slope threshold, and the DCR increase corresponding to each target sub-interval is greater than or equal to the preset DCR increase threshold.

[0193] The second SOC range is obtained by merging adjacent sub-intervals of multiple target sub-intervals.

[0194] In one possible implementation, the analysis module 63 is specifically used for:

[0195] Based on the preset difference threshold, the adjacent values ​​of the DCR corresponding to each SOC in the DCR-SOC data are compared to obtain multiple candidate DCR peak points.

[0196] From multiple candidate DCR peak points, remove DCR peak points whose peak values ​​are less than or equal to a preset peak threshold to obtain multiple target DCR peak points;

[0197] Taking each target DCR peak point as the center, the third SOC range is obtained by extending it according to the preset SOC interval.

[0198] In one possible implementation, the acquisition module 61 is specifically used for:

[0199] Acquire multiple initial OCV-SOC data and multiple initial DCR-SOC data;

[0200] Based on multiple preset SOCs, interpolation processing is performed on multiple initial OCV-SOC data and multiple initial DCR-SOC data to obtain multiple preprocessed OCV-SOC data and multiple preprocessed DCR-SOC data.

[0201] Smoothing and noise reduction processing is performed on multiple preprocessed OCV-SOC data and multiple preprocessed DCR-SOC data to obtain OCV-SOC data and DCR-SOC data.

[0202] Figure 7 This is a schematic diagram of another battery screening device provided in an embodiment of this application. Figure 6 Based on the illustrated embodiments, as Figure 8 As shown, the battery sorting device 60 also includes an execution module 65 and a grading module 66.

[0203] Execution module 65 is used for:

[0204] Based on the electrochemical characteristics of the sample batteries, determine the extreme SOC range of the electrode potential where side reactions are frequent;

[0205] Multiple overlapping SOC ranges are merged with the extreme SOC ranges of the electrode potential to obtain a preset range.

[0206] Hierarchical module 66 is used for:

[0207] Determine the overlap type of the preset range, which is either a pairwise overlapping SOC range or a three-way overlapping SOC range;

[0208] The risk level is classified according to the overlap type of the preset range to obtain the risk level classification result.

[0209] Based on the risk level classification results, the corresponding preset values ​​are determined.

[0210] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 8 As shown, the electronic device includes:

[0211] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods of the above embodiments.

[0212] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0213] The memory 292, as a non-volatile computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above-described method embodiments.

[0214] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0215] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0216] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in the foregoing embodiments.

[0217] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0218] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in the foregoing embodiments.

[0219] Based on the above implementation methods, multiple electrochemical performance data are obtained, and the multiple electrochemical performance data are synergistically analyzed and cross-validated to comprehensively evaluate the factors affecting battery storage performance, avoid the limitations of single electrochemical performance data, and thus obtain an accurate preset range for optimizing battery storage performance, thereby improving the accuracy of storage performance optimization.

[0220] 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 all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0221] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages, which do not necessarily complete at the same time but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential but can be alternated or carried out in turn with other steps or at least some of the sub-steps or stages of other steps.

[0222] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0223] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0224] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. The processor can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC. The storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0225] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, 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 memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0226] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0227] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0228] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A single-cell battery, characterized in that, The single cell is used for long-term storage. The target difference between the current state of charge (SOC) of the single cell and a preset range is greater than a preset value. The target difference is the minimum value of the difference between the current SOC and any endpoint of the preset range. The preset range is determined after evaluation based on sample batteries of the same batch or model corresponding to the single battery; A method for evaluating the sample battery includes: Obtain open-circuit voltage state of charge (OCV-SOC) data and DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery; Numerical differentiation is performed on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data; Trend analysis is performed on the DCR-SOC data to determine the second SOC range in which the slope of the DCR-SOC data is greater than or equal to the slope threshold, and to determine the third SOC range corresponding to the local DCR peak area. The first SOC range, the second SOC range, and the third SOC range are correlated and superimposed to obtain multiple overlapping SOC ranges, and the preset range is determined based on the multiple overlapping SOC ranges.

2. The single-cell battery according to claim 1, characterized in that, Numerical differentiation is performed on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data, including: The OCV-SOC data is numerically differentiated to obtain the first-order derivative values ​​of multiple OCVs with respect to the SOCs corresponding to the multiple preset SOCs; Based on a preset derivative value threshold and the first derivative values ​​of the multiple OCVs with respect to SOC, multiple candidate inflection points of the OCV-SOC data are determined, and the first derivative value of the OCV with respect to SOC corresponding to each candidate inflection point is greater than or equal to the derivative value threshold. The first SOC range is determined based on the multiple candidate inflection points.

3. The single-cell battery according to claim 2, characterized in that, Based on the multiple candidate inflection points, the range of the first SOC is determined, including: Multiple isolated inflection points are determined from the plurality of candidate inflection points, wherein the number of consecutive candidate inflection points in the neighborhood of each isolated inflection point is less than or equal to a preset number threshold. Multiple isolated inflection points are removed from the multiple candidate inflection points to obtain multiple target inflection points; Based on the multiple target inflection points, determine the inflection point region; The SOC range corresponding to the inflection point region is determined as the first SOC range.

4. The single-cell battery according to claim 3, characterized in that, Based on the multiple target inflection points, the inflection point region is determined, including: For each target inflection point, the maximum and minimum inflection point values ​​are determined based on the preset extension value and the target inflection point. The target inflection point interval corresponding to the target inflection point is determined based on the maximum value and the minimum value of the inflection point. Traverse all target inflection points and determine each target inflection point interval corresponding to all target inflection points as the inflection point region; Different sample batteries correspond to different preset extension values.

5. The single-cell battery according to claim 1, characterized in that, Perform trend analysis on the DCR-SOC data to determine a second SOC range where the slope of the DCR-SOC data is greater than or equal to a slope threshold, including: The DCR-SOC data is divided into multiple consecutive candidate sub-intervals; Calculate the average slope of DCR for each sub-interval to obtain the slope of multiple intervals corresponding to the multiple candidate sub-intervals; Based on a preset slope threshold, the slopes of the multiple intervals, and a preset DCR increase threshold, the multiple candidate sub-intervals are filtered to obtain multiple target sub-intervals. The slope of the DCR interval corresponding to each target sub-interval is greater than or equal to the preset slope threshold, and the increase of the DCR corresponding to each target sub-interval is greater than or equal to the preset DCR increase threshold. The adjacent intervals of the multiple target sub-intervals are merged to obtain the second SOC range.

6. The single-cell battery according to claim 1, characterized in that, Determine the third SOC range corresponding to the local DCR peak region, including: Based on a preset difference threshold, the adjacent values ​​of the DCR corresponding to each SOC in the DCR-SOC data are compared to obtain multiple candidate DCR peak points. From the multiple candidate DCR peak points, DCR peak points with peak values ​​less than or equal to a preset peak threshold are removed to obtain multiple target DCR peak points; Taking each target DCR peak point as the center, the third SOC range is obtained by extending it according to the preset SOC interval.

7. The single-cell battery according to claim 1, characterized in that, Acquire open-circuit voltage state of charge (OCV-SOC) data and DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery, including: Acquire multiple initial OCV-SOC data and multiple initial DCR-SOC data; Based on the multiple preset SOCs, interpolation processing is performed on the multiple initial OCV-SOC data and the multiple initial DCR-SOC data to obtain multiple preprocessed OCV-SOC data and multiple preprocessed DCR-SOC data. Smoothing and noise reduction processing is performed on the multiple preprocessed OCV-SOC data and the multiple preprocessed DCR-SOC data to obtain the OCV-SOC data and the DCR-SOC data.

8. The single-cell battery according to any one of claims 1-7, characterized in that, Determining the preset range based on the plurality of overlapping SOC ranges includes: Based on the electrochemical characteristics of the sample batteries, the extreme SOC range of the electrode potential where side reactions are frequent was determined; The multiple overlapping SOC ranges are merged with the electrode potential extreme SOC range to obtain the preset range.

9. The single-cell battery according to claim 8, characterized in that, After determining the preset range, the method further includes: Determine the overlap type of the preset range, wherein the overlap type is a pairwise overlapping SOC range or a three-way overlapping SOC range; The risk level of the preset range is divided according to the overlap type to obtain the risk level division result; Based on the risk level classification results, the corresponding preset values ​​are determined.

10. A battery pack, characterized in that, include: It includes at least two individual cells as described in any one of claims 1-9, and each of the individual cells is electrically connected to the other.

11. A battery sorting system, characterized in that, This includes the battery acquisition system and controller; The battery acquisition system is used for acquiring OCV-SOC data and DCR-SOC data of the sample batteries; The controller is used for: Obtain OCV-SOC data and DCR-SOC data corresponding to multiple preset SOCs of the sample battery; Numerical differentiation is performed on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data; Trend analysis is performed on the DCR-SOC data to determine the second SOC range in which the slope of the DCR-SOC data is greater than or equal to the slope threshold, and to determine the third SOC range corresponding to the local DCR peak area. The first SOC range, the second SOC range, and the third SOC range are correlated and superimposed to obtain multiple overlapping SOC ranges. The preset range is determined based on the multiple overlapping SOC ranges. The preset range is used to screen single cells suitable for long-term storage.

12. A battery screening method, characterized in that, include: Acquire open-circuit voltage state of charge (OCV-SOC) data and DC resistance state of charge (DCR-SOC) data corresponding to multiple preset SOCs of the sample battery; Numerical differentiation is performed on the OCV-SOC data to determine the first SOC range corresponding to the inflection point region of the OCV-SOC data; Trend analysis is performed on the DCR-SOC data to determine the second SOC range in which the slope of the DCR-SOC data is greater than or equal to the slope threshold, and to determine the third SOC range corresponding to the local DCR peak area. The first SOC range, the second SOC range, and the third SOC range are correlated and superimposed to obtain multiple overlapping SOC ranges. A preset range is determined based on the multiple overlapping SOC ranges. The preset range is used to screen individual battery cells suitable for long-term storage.

13. A battery pack, characterized in that, It includes a housing and at least two battery packs as described in claim 10, each of the battery packs being disposed within the housing and electrically connected to each other.

14. An electric vehicle, characterized in that, It includes at least the battery pack as described in claim 13.

15. An electrical appliance, characterized in that, It includes at least the single cell battery as described in any one of claims 1-9.

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