A method for identifying a lithium battery capacity attenuation diving point

Abstract

The application provides a lithium battery capacity attenuation diving point identification method, which optimizes the data of the charge cycle number and the battery capacity retention rate used in the method through the operations of deleting abnormal noise points of initial data points, creating a first battery capacity attenuation curve, judging the attenuation trend category of the first battery capacity attenuation curve, and fitting local data of the first battery capacity attenuation curve. Based on the optimized data of the charge cycle number and the battery capacity retention rate, a target nonlinear regression model is established and fitted, and then the lithium battery capacity attenuation diving point is obtained, which improves the identification accuracy and efficiency of the lithium battery diving point, and provides an effective basis for battery health state management, battery capacity early warning and battery life prediction.

Description

Technical Field

[0001] The present application belongs to the field of new energy battery monitoring technology, and in particular relates to a method for identifying a capacity attenuation drop point of a lithium battery. Background Art

[0002] With the widespread adoption of new energy technologies, lithium-ion batteries, due to their large capacity, excellent performance, and easy production, are widely used in new energy vehicles, outdoor mobile charging devices, drones, and other equipment. During the use of lithium-ion batteries, changes such as diaphragm aging, loss of active materials, electrolyte decomposition, and increased internal resistance can cause lithium-ion battery capacity to decay. The lithium-ion battery decay curve is nonlinear, and when it decays to a certain level, it will experience a significant drop. The critical point in the lithium battery decay process where the decay rate changes from slow to fast is generally referred to as the battery capacity rapid decay critical point, and this critical point of battery capacity rapid decay is referred to as the dive point.

[0003] Existing electrochemical methods for determining the charge-diving point of lithium batteries are difficult to directly determine using a single electrochemical mechanism and parameters due to the complexity and uncertainty of electrochemical processes and the sensitivity of test conditions. To achieve this, a function curve is required to plot the relationship between the number of charge cycles and the battery capacity retention rate. The point where the curvature of the function curve reaches its maximum corresponds to the attenuation charge-diving point, so the second-order derivative of the charge capacity function curve must be calculated. However, in actual applications, the battery capacity retention rate corresponding to the number of charge cycles of a lithium battery is discrete and includes a large number of noise points. Therefore, directly using the second-order derivative to calculate the charge-diving point will amplify the noise points of the charge capacity function curve, resulting in a large deviation in the numerical value of the obtained charge-diving point of the lithium battery. Summary of the Invention

[0004] To solve the above problems, one or more embodiments of this specification describe a method for identifying a capacity decay diving point of a lithium battery.

[0005] According to a first aspect, a method for identifying a capacity attenuation drop point of a lithium battery is provided, the method comprising:

[0006] Obtaining the number of charging cycles of the lithium battery and the battery capacity retention rate corresponding to each charging cycle number, and establishing a first battery capacity decay scatter plot based on the charging cycle number and the battery capacity retention rate;

[0007] generating a first battery capacity decay curve based on the battery capacity decay scatter plot;

[0008] determining a curve attenuation type of the first battery capacity attenuation curve, and performing local data fitting on the first battery capacity attenuation curve whose curve attenuation type is a superlinear attenuation curve to obtain a second battery capacity attenuation curve;

[0009] constructing a target nonlinear regression model based on each first data point of the second battery capacity attenuation curve, performing global data fitting on the target nonlinear regression model to obtain a third battery capacity attenuation curve, and determining a capacity attenuation diving point of the lithium battery corresponding to the third battery capacity attenuation curve.

[0010] Further, the first battery capacity attenuation scatter diagram is established based on the charging cycle number and the battery capacity retention rate, and a first battery capacity attenuation curve is obtained, including:

[0011] An initial battery capacity attenuation scatter diagram is established based on initial data points of each first battery capacity attenuation scatter diagram, wherein the abscissa of the initial data points is used to represent the charging cycle number, and the ordinate of the initial data points is used to represent the battery capacity retention rate.

[0012] The position of each initial data point in the initial battery capacity attenuation scatter diagram is confirmed, and each initial data point whose battery capacity retention rate exceeds the target preset limit of the initial battery capacity attenuation scatter diagram is deleted.

[0013] Each initial data point in the first battery capacity attenuation scatter diagram that is not deleted is smoothed to obtain a first battery capacity attenuation curve.

[0014] Further, the curve attenuation type of the first battery capacity attenuation curve is determined, including:

[0015] The first derivative and the second derivative of each first data point of the first battery capacity attenuation curve are calculated respectively.

[0016] The curve attenuation type of the first battery capacity attenuation curve is determined as a super-linear attenuation curve when the first derivative calculation result is negative and the second derivative calculation result is negative.

[0017] Further, before the local data fitting of the first battery capacity attenuation curve with the curve attenuation type being a super-linear attenuation curve, further including:

[0018] The battery capacity retention rate corresponding to each second data point of the first battery capacity attenuation curve is z-score standardized.

[0019] Further, the local data fitting of the first battery capacity attenuation curve with the curve attenuation type being a super-linear attenuation curve is performed to obtain a second battery capacity attenuation curve, including:

[0020] The second data point with the minimum first derivative and the battery capacity retention rate greater than a first preset battery capacity retention rate of the first battery capacity attenuation curve is taken as a battery life starting point.

[0021] a second data point with the smallest first derivative and less than a second preset battery capacity retention rate is taken as the battery life end point;

[0022] the first data points in a first preset length interval and the first data points in a second preset length interval are respectively fitted by a least square method to obtain a second battery capacity attenuation curve, a middle point of the first preset length interval being the battery life start point and a middle point of the second preset length interval being the battery life end point.

[0023] Further, the target nonlinear regression model is a Bacon-Watts model.

[0024] Further, the target nonlinear regression model is a Bacon-Watts model.

[0025] a confidence interval of the attenuation diving point corresponding to the target nonlinear regression model is set;

[0026] based on the confidence interval, the target nonlinear regression model is fitted by a least square method to obtain a third battery capacity attenuation curve, and the attenuation diving point of the lithium battery capacity corresponding to the third battery capacity attenuation curve is determined.

[0027] According to a second aspect, a computer readable storage medium having a computer program stored thereon is provided, and the computer readable storage medium has instructions stored therein, and when the instructions are run on a computer or a processor, the computer or the processor performs all steps of the lithium battery capacity attenuation diving point identification method according to the first aspect.

[0028] According to a third aspect, an electronic device is provided, including a processor and a memory.

[0029] The processor is connected with the memory.

[0030] The memory is used for storing executable program codes.

[0031] The processor runs a program corresponding to the executable program codes by reading the executable program codes stored in the memory, so as to execute all steps of the lithium battery capacity attenuation diving point identification method according to the first aspect.

[0032] Advantages:

[0033] This method optimizes the data on charging cycles and battery capacity retention used in this method by removing abnormal noise points from initial data points, creating a first battery capacity decay curve, determining the decay trend of the first battery capacity decay curve, and fitting local data of the first battery capacity decay curve. A target nonlinear regression model is then established and fitted based on the optimized charging cycle and battery capacity retention data to determine the lithium battery capacity decay drop point. This improves the accuracy and efficiency of identifying the drop point for lithium batteries, providing an effective basis for battery health management, battery capacity early warning, and battery life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A flow chart of a method for identifying a lithium battery capacity decay diving point provided in an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the capacity attenuation curves of each battery in a method for identifying a lithium battery capacity attenuation diving point provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0038] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application. Different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing one or more of all other possible combinations of A, B, C, and D, even though the embodiment may not be clearly described in the following text.

[0039] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the elements described without departing from the scope of the present application. Various examples may appropriately omit, replace, or add various processes or components. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted, or combined. In addition, features described in some examples may be combined in other examples.

[0040] like Figure 1 As shown, Figure 1 : This is a flow chart of a method for identifying a lithium battery capacity attenuation drop point provided by an embodiment of the present application. The method of this embodiment includes:

[0041] S1. Obtain the number of charging cycles of a lithium battery and the battery capacity retention rate corresponding to each charging cycle number, and establish a first battery capacity decay scatter plot based on the charging cycle number and the battery capacity retention rate.

[0042] This method is performed by an on-site controller of a battery testing device, which uses a battery testing control terminal to perform direct on-site testing of the battery cells and, using the method of this embodiment, measures the lithium battery capacity decay drop point in real time. The method can also be performed by a central controller of a remote monitoring platform, which controls the battery testing device to collect battery cell cycle charging data and transmits it to the remote monitoring platform. The central controller of the remote monitoring platform can then process the battery cell cycle charging data using the method of this embodiment to determine the lithium battery capacity decay drop point.

[0043] In the embodiments of this specification, the battery cell cycle charging data includes data such as the number of charging cycles, the current and voltage of the battery cell, the battery capacity retention rate, and the charge and discharge temperature of the battery cell. The data used in the method of this embodiment is the number of charging cycles and the battery capacity retention rate corresponding to each charging cycle number. In order to make the measured lithium battery capacity decay diving point more accurate, it is necessary to visualize the number of charging cycles and the battery capacity retention rate corresponding to each charging cycle number, that is, the number of charging cycles and the battery capacity retention rate in the data table are intuitively displayed using a two-dimensional image. Therefore, a first battery capacity decay scatter plot is established to accurately display the battery capacity retention rate corresponding to each charging cycle number. The horizontal axis of the first battery capacity decay scatter plot is the number of charging cycles, and the vertical axis is the battery capacity retention rate corresponding to each charging cycle number.

[0044] S2. Generate a first battery capacity decay curve based on the battery capacity decay scatter plot.

[0045] In the embodiments of the present application, due to the interference of external temperature and humidity and other factors during measurement, some noise points may appear in part of the initial data points in the first battery capacity attenuation scatter diagram, wherein the initial data points refer to the data scatter points corresponding to each charge cycle number value of the first battery capacity attenuation scatter diagram. If the noise points are fitted into the battery capacity attenuation curve, the deviation of the lithium battery capacity attenuation jump point will be large, and therefore, the initial data points in the first battery capacity attenuation scatter diagram need to be preprocessed to remove a large number of useless noise points to obtain the first battery capacity attenuation curve.

[0046] In an implementable manner, the first battery capacity attenuation curve is obtained based on the first battery capacity attenuation scatter diagram, including:

[0047] An initial battery capacity attenuation scatter diagram is established based on the initial data points of each first battery capacity attenuation scatter diagram, wherein the abscissa of the initial data points is used to represent the charge cycle number, and the ordinate of the initial data points is used to represent the battery capacity retention rate.

[0048] The positions of the initial data points in the initial battery capacity attenuation scatter diagram are confirmed, and each initial data point whose battery capacity retention rate exceeds the target preset limit of the initial battery capacity attenuation scatter diagram is deleted.

[0049] The initial data points in the first battery capacity attenuation scatter diagram that are not deleted are smoothed to obtain the first battery capacity attenuation curve.

[0050] In the embodiments of the present application, the target preset limit of the initial battery capacity attenuation scatter diagram is determined according to the actual model of the selected battery, that is, the upper limit of the battery capacity target preset limit of different models of batteries is different, and the upper limit of the target preset limit is generally slightly less than 1, and the lower limit is generally set as the battery capacity retention rate corresponding to the scrap standard of the lithium battery, for example, the upper limit of the target preset limit is set as the battery capacity retention rate corresponding to the theoretical initial production state of the lithium battery, and the lower limit of the target preset limit is set as the battery capacity retention rate corresponding to the theoretical minimum scrap standard of the lithium battery. After removing the initial data points exceeding the target preset limit, the initial data points that are not deleted are connected, and the connected results of the initial data points are processed in a smoothing manner to obtain a smooth battery capacity attenuation curve. The smoothing manner adopted in the present example is a non-model fitting manner such as moving average or filter filtering, which is to preserve the original data of the charge cycle number and the battery capacity retention rate corresponding to each charge cycle number as much as possible, and to maintain the accuracy of the subsequent local fitting of the first battery capacity attenuation curve, so that the lithium battery capacity attenuation jump point obtained by the method of the present embodiment is more accurate.

[0051] S3. Determine a curve attenuation type of the first battery capacity attenuation curve, and perform local data fitting on the first battery capacity attenuation curve whose curve attenuation type is a superlinear attenuation curve to obtain a second battery capacity attenuation curve.

[0052] In the embodiments of this specification, the attenuation curve of the lithium battery capacity has three different transformation trends, namely linear, sublinear and superlinear. Sublinear means that the attenuation curve of the lithium battery capacity is concave and has a small deviation from the corresponding data points of the linear attenuation graph, that is, the sublinear curve is the lithium battery capacity attenuation curve whose attenuation rate gradually changes from the maximum to approaching 0 with the number of charging cycles. When the attenuation curve of the lithium battery capacity is sublinear and linear, it proves that the capacity loss of the battery is low, close to the theoretical capacity and theoretical life. At this time, it is difficult to detect the diving point of the lithium battery. In order to measure the attenuation diving point of the lithium battery capacity to estimate the battery life, it is necessary to determine whether the curve attenuation type of the first battery capacity attenuation curve is a superlinear attenuation curve. Local data fitting is performed on the first battery capacity attenuation curve, that is, nonlinear fitting is performed on a part of the second data points of the first battery capacity attenuation curve, so that the curve trend of the first battery capacity attenuation curve at these second data points changes smoothly, which is convenient for subsequent calculation and processing.

[0053] In one embodiment, determining the curve attenuation type of the first battery capacity attenuation curve includes:

[0054] respectively calculating a first-order derivative and a second-order derivative of each second data point of the first battery capacity decay curve;

[0055] The attenuation type of the first battery capacity decay curve, whose first-order derivative calculation results are all negative and second-order derivative calculation results are all negative, is determined to be a superlinear decay curve.

[0056] In the embodiments of this specification, the second data point refers to the data point corresponding to the value of each charging cycle of the first battery capacity decay curve, and the first data point refers to the data point corresponding to the value of each charging cycle of the second battery capacity decay curve. To determine the type of curve decay of the first battery capacity decay curve, it is necessary to use the characteristics of a convex function, that is, to determine it according to the changing trend of the first-order derivative and the second-order derivative of each second data point of the first battery capacity decay curve. When the calculation results of the first-order derivative of the second data point are all negative, it indicates that the data may have a trend of superlinear decay. At this time, it is necessary to combine the second-order derivative of each second data point to make a judgment. When the calculation results of the first-order derivative and the second-order derivative of the second data point are both negative, it means that the first battery capacity decay curve is a convex downward curve, which is a superlinear decay curve. In addition, when the first-order derivative of each second data point of the first battery capacity decay curve is negative and the second-order derivative is positive, it means that the curve decay type of the first battery capacity decay curve is sublinear.

[0057] In one embodiment, before performing local data fitting on the first battery capacity decay curve whose curve decay type is a superlinear decay curve, the method further includes:

[0058] The battery capacity retention rate corresponding to each second data point of the first battery capacity decay curve is z-score normalized.

[0059] In the embodiment of this specification, a 0-1 normalization method such as z-score is used to scale the battery capacity retention rate in this embodiment, so as to analyze and calculate various parameters of the first battery capacity decay curve, such as the formula: ,in x is the battery capacity retention rate at the initial data point, μ is the sample mean of the battery capacity retention rate of the initial data point, σ is the sample variance of the battery capacity retention rate of the initial data point, xnew The z-score of the initial data point is the battery capacity retention rate after normalization. After the initial data point is normalized, it can be used as the second data point to draw and analyze the first battery capacity decay curve.

[0060] In one embodiment, performing local data fitting on the first battery capacity decay curve whose curve decay type is a superlinear decay curve to obtain a second battery capacity decay curve includes:

[0061] The second data point of the first battery capacity decay curve at which the battery capacity retention rate is greater than the first preset battery capacity retention rate and the first-order derivative is the smallest is used as the starting point of the battery life;

[0062] The second data point of the first battery capacity decay curve at which the battery capacity retention rate is less than the second preset battery capacity retention rate and the first-order derivative is the smallest is used as the battery life end point;

[0063] Least squares fitting is performed on each second data point within the first preset length interval of the first battery capacity decay curve and each second data point within the second preset length interval to obtain a second battery capacity decay curve. The midpoint of the first preset length interval is the starting point of the battery life, and the midpoint of the second preset length interval is the end point of the battery life.

[0064] In the embodiment of this specification, the first preset battery capacity retention rate and the second preset battery capacity retention rate are set according to actual needs, and the fitting method for the second data point can be the least squares fitting method. Figure 2 As shown, for example, in this embodiment, a ternary lithium battery at 45°C is used, and the first preset battery capacity retention rate is set to 0.95, and the second preset battery capacity retention rate is set to 0.8. The lengths of the first preset length interval and the second preset length interval are set to 10 charging cycles, as shown in FIG. Figure 2 As shown, since the first battery capacity decay curve of the lithium battery will have a serious jump phenomenon at the starting point and the end point of the battery life, it is necessary to set a first preset length interval and a second preset interval to perform nonlinear smoothing on each second data point within the interval. After the first battery capacity decay curve is locally fitted, the second battery capacity decay curve is obtained by combining the unfitted curve part. The second battery capacity decay curve is shown in FIG. Figure 2 As shown in the solid curve, the horizontal axis represents the number of charging cycles and the vertical axis represents the battery capacity retention rate.

[0065] S4. Construct a target nonlinear regression model based on each first data point of the second battery capacity decay curve, perform global data fitting on the target nonlinear regression model to obtain a third battery capacity decay curve, and determine a decay diving point of the lithium battery capacity corresponding to the third battery capacity decay curve.

[0066] In the embodiment of the present specification, since the second battery capacity decay curve is obtained after local fitting of the first battery capacity decay curve, the second battery capacity decay curve still has a large number of first data points with low smoothness at some positions of the curve. Therefore, it is necessary to construct a target nonlinear regression model based on each first data point and perform global data fitting on the target nonlinear regression model. After the global data fitting, the third battery capacity decay curve can be obtained. In the process of fitting the second battery capacity decay curve, the various data points of the third battery capacity decay curve can also be calculated, including the attenuation diving point of the lithium battery capacity.

[0067] In one embodiment, the target nonlinear regression model is a Bacon-Watts model.

[0068] In the embodiments of this specification, the Bacon-Watts model is a statistical model used to describe and predict time series data. The model can accurately capture the nonlinear trends and local changes of time series data, and is therefore suitable for sudden changes on a smooth change curve such as the lithium battery capacity diving point. In addition, the Bacon-Watts model structure is simpler than existing nonlinear regression models such as spline regression models, generalized additive models, and machine learning models, and can also meet the requirements of lithium battery capacity diving point identification. In this embodiment, the Bacon-Watts model expression is:

[0069]

[0070] in, β 0 , β 1 and β 2 represents the model parameters, α represents the smoothness control parameter , tanh represents the hyperbolic tangent function, γ represents the lithium battery capacity drop point, and x represents the abscissa of the corresponding data point, where the abscissa is the number of charging cycles.

[0071] In one embodiment, performing global data fitting on the target nonlinear regression model to obtain a third battery capacity decay curve, and determining a lithium battery capacity decay diving point corresponding to the third battery capacity decay curve, includes:

[0072] Set the confidence interval of the attenuation dive point corresponding to the target nonlinear regression model;

[0073] Based on the confidence interval, the target nonlinear regression model is fitted using the least squares method to obtain a third battery capacity decay curve, and a decay diving point of the lithium battery capacity corresponding to the third battery capacity decay curve is determined.

[0074] In the examples of this specification, the confidence interval for the attenuation dive point corresponding to the target nonlinear regression model is defined as 95%. The definition of the confidence interval is determined based on actual needs. However, this example uses the Bootstrap percentile method to define the execution interval, using the α / 2×100% and (1−α / 2)×100% quantiles as the upper and lower limits of the confidence interval, where α = 1-95%.

[0075] In this embodiment, the target nonlinear regression model is the Bacon-Watts model. After defining the confidence interval, the Bacon-Watts model needs to be fitted to obtain the attenuation drop point of the lithium battery capacity. In this method, the least squares method is used to perform a global fit of the Bacon-Watts model, as shown in the formula:

[0076]

[0077] in, β 0 , β 1 and β 2 represents the model parameters, α represents the smoothness control parameter , tanh represents the hyperbolic tangent function, γ Indicates the lithium battery capacity diving point, Represents the number of charging cycles of the i-th data point, Represents the battery capacity retention rate of the i-th data point. Figure 2 As shown, the third battery capacity decay curve after global fitting of the Bacon-Watts model is shown as a dotted curve, where the horizontal axis represents the number of charging cycles and the vertical axis represents the battery capacity retention rate. Figure 2 The diving point noted in the remarks is the attenuation diving point of the lithium battery capacity fitted by the method of this embodiment.

[0078] This implementation method removes initial data points with excessively large deviations in battery capacity retention rates from the first battery capacity decay scatter plot as noise points, then performs local data fitting on the remaining initial data points. Finally, a global data fitting is performed on the target nonlinear regression model to accurately identify the lithium battery capacity decay drop point. This embodiment improves the accuracy of identifying the lithium battery capacity decay drop point by sequentially using local and global data fitting.

[0079] The present application also provides an electronic device, comprising a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute any step in the method of this embodiment.

[0080] Those skilled in the art will clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. "Processor" and "memory" in this specification refer to software and / or hardware that can independently perform or cooperate with other components to perform specific functions, where the hardware can be, for example, a field-programmable gate array (FPGA) or an integrated circuit (IC).

[0081] The present application also provides a computer-readable storage medium on which a computer program is stored. The computer-readable storage medium stores instructions. When the instructions are executed on a computer or a processor, the computer or processor executes all the steps of the method of this embodiment.

[0082] Among them, computer-readable storage media may include, but are not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0083] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0084] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0085] In addition, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0086] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0087] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for identifying a lithium battery capacity attenuation diving point, characterized in that: The method comprises: Obtaining the number of charging cycles of the lithium battery and the battery capacity retention rate corresponding to each charging cycle number, and establishing a first battery capacity decay scatter plot based on the charging cycle number and the battery capacity retention rate; generating a first battery capacity decay curve based on the battery capacity decay scatter plot; determining a curve attenuation type of the first battery capacity attenuation curve, and performing local data fitting on the first battery capacity attenuation curve whose curve attenuation type is a superlinear attenuation curve to obtain a second battery capacity attenuation curve; Building a target nonlinear regression model based on each first data point of the second battery capacity decay curve, performing global data fitting on the target nonlinear regression model to obtain a third battery capacity decay curve, and determining a lithium battery capacity decay drop point corresponding to the third battery capacity decay curve; The determining of the curve attenuation type of the first battery capacity attenuation curve includes: respectively calculating a first-order derivative and a second-order derivative of each second data point of the first battery capacity decay curve; Determining the attenuation type of the first battery capacity attenuation curve, in which the calculation results of the first-order derivatives are all negative and the calculation results of the second-order derivatives are all negative, as a superlinear attenuation curve; The method further comprises: performing local data fitting on the first battery capacity decay curve whose curve decay type is a superlinear decay curve to obtain a second battery capacity decay curve. The second data point of the first battery capacity decay curve at which the battery capacity retention rate is greater than the first preset battery capacity retention rate and the first-order derivative is the smallest is used as the starting point of the battery life; The second data point of the first battery capacity decay curve at which the battery capacity retention rate is less than the second preset battery capacity retention rate and the first-order derivative is the smallest is used as the battery life end point; Least squares fitting is performed on each first data point within the first preset length interval of the first battery capacity decay curve and each first data point within the second preset length interval to obtain a second battery capacity decay curve, where the midpoint of the first preset length interval is the starting point of the battery life, and the midpoint of the second preset length interval is the end point of the battery life.

2. The method for identifying a lithium battery capacity attenuation diving point according to claim 1, wherein: The step of establishing a first battery capacity decay scatter plot based on the number of charging cycles and the battery capacity retention rate to obtain a first battery capacity decay curve includes: Establishing an initial battery capacity decay scatter plot based on the initial data points of each of the first battery capacity decay scatter plots, wherein the abscissa of the initial data point is used to represent the number of charging cycles, and the ordinate of the initial data point is used to represent the battery capacity retention rate; confirming the position of each of the initial data points in the initial battery capacity decay scatter plot, and deleting each of the initial data points whose battery capacity retention rate exceeds a target preset limit of the initial battery capacity decay scatter plot; Smoothing is performed on each of the initial data points that have not been deleted in the first battery capacity decay scatter plot to obtain a first battery capacity decay curve.

3. The method for identifying a lithium battery capacity attenuation diving point according to claim 1, wherein: Before performing local data fitting on the first battery capacity decay curve whose curve decay type is a superlinear decay curve, the method further includes: The battery capacity retention rate corresponding to each second data point of the first battery capacity decay curve is z-score normalized.

4. The method for identifying a lithium battery capacity attenuation diving point according to claim 1, wherein: The target nonlinear regression model is the Bacon-Watts model.

5. The method for identifying a lithium battery capacity attenuation diving point according to claim 1, wherein: The performing global data fitting on the target nonlinear regression model to obtain a third battery capacity decay curve, and determining a lithium battery capacity decay drop point corresponding to the third battery capacity decay curve, includes: Set the confidence interval of the attenuation dive point corresponding to the target nonlinear regression model; Based on the confidence interval, the target nonlinear regression model is fitted using the least squares method to obtain a third battery capacity decay curve, and a decay diving point of the lithium battery capacity corresponding to the third battery capacity decay curve is determined.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 5.

7. An electronic device comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method according to any one of claims 1 to 5.

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