Method for identifying capacity fading diving point of lithium battery

Abstract

The invention provides a method for identifying a capacity fading diving point of a lithium battery. According to the method, data of the number of charging cycles and the battery capacity retention ratio used in the method are optimized through operations such as deletion of abnormal noise points of initial data points, creation of a first battery capacity attenuation curve, judgment of attenuation trend types of the first battery capacity attenuation curve, local data fitting of the first battery capacity attenuation curve and the like. And a target nonlinear regression model is established and fitted based on the optimized data of the number of charging cycles and the battery capacity retention ratio, so that the attenuation diving point of the lithium battery capacity is obtained, and the recognition accuracy and efficiency of the diving point of the lithium battery are improved. And an effective basis is provided for battery health state management, battery capacity early warning and battery life prediction.

Description

Technical Field

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

[0002] With the widespread application of new energy technologies, lithium-ion batteries are widely used in new energy vehicles, outdoor mobile charging devices, drones and other equipment due to their large capacity, good performance and convenient production. During the use of lithium batteries, changes such as diaphragm aging, loss of active substances, electrolyte decomposition and increase in internal resistance will cause the capacity of lithium-ion batteries to decay. The attenuation curve of lithium-ion batteries is nonlinear, and when it decays to a certain extent, it will plunge sharply. The critical point where the attenuation rate changes from slow to fast during the attenuation of lithium batteries is generally called the critical point of rapid attenuation of battery capacity. This type of critical point of rapid attenuation of battery capacity is referred to as the diving point.

[0003] The existing electrochemical method for determining the diving point of lithium batteries is difficult to directly determine the cycle diving point through a single electrochemical mechanism and parameter due to the complexity and uncertainty of the electrochemical process and the sensitivity of the test conditions. To this end, it is necessary to obtain a function curve diagram of the relationship between the number of cycle charging and the battery capacity retention rate, and the point where the curvature of the function curve diagram is the maximum corresponds to the attenuation diving point, so it is necessary to solve the second-order derivative of the charging capacity function curve. However, in actual applications, the battery capacity retention rate corresponding to the number of cycle charging of lithium batteries is discrete and includes a large number of noise points. Therefore, the mathematical method of directly using the second-order derivative to calculate the diving point will amplify the noise points of the charging capacity function curve, resulting in a large numerical deviation of the diving point of the obtained lithium battery. Summary of the invention

[0004] In order 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 decay diving point of a lithium battery is provided, the method comprising: 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 attenuation scatter plot based on the charging cycle number and the battery capacity retention rate; Generate 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; A target nonlinear regression model is constructed based on each first data point of the second battery capacity decay curve, and global data fitting is performed on the target nonlinear regression model 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.

[0006] Further, the first battery capacity attenuation scatter plot is established based on the number of charging cycles and the battery capacity retention rate to obtain a first battery capacity attenuation curve, including: 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.

[0007] Further, 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 first data point of the first battery capacity decay curve; The curve attenuation type of the first battery capacity attenuation curve whose first-order derivative calculation results are all negative values ​​and whose second-order derivative calculation results are all negative values ​​is determined to be a superlinear attenuation curve.

[0008] Furthermore, 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 standardized.

[0009] Further, 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: The second point of the first battery capacity decay curve where the battery capacity retention rate is greater than the first preset battery capacity retention rate and the first-order derivative is the smallest is taken as the starting point of the battery life; Taking the second data point of the first battery capacity decay curve where the battery capacity retention rate is less than the second preset battery capacity retention rate and the first-order derivative is the smallest as the battery life end point; The first data points within the first preset length interval and the first data points within the second preset length interval of the first battery capacity decay curve are respectively fitted by least squares method to obtain the 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.

[0010] Furthermore, the target nonlinear regression model is a Bacon-Watts model.

[0011] Furthermore, performing global data fitting on the target nonlinear regression model to obtain a third battery capacity decay curve, and determining a decay diving point of the lithium battery capacity 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 by using the least square 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.

[0012] According to a second aspect, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer or a processor, the computer or the processor executes all steps of a method for identifying a lithium battery capacity decay diving point as provided in the first aspect.

[0013] According to a third aspect, there is provided 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 all steps of the method for identifying the capacity decay diving point of a lithium battery as provided in the first aspect. Beneficial effects: The present invention optimizes the data of the number of charging cycles and the battery capacity retention rate used in the method by deleting the abnormal noise points of the initial data points, creating the first battery capacity decay curve, judging the decay trend type of the first battery capacity decay curve, and fitting the local data of the first battery capacity decay curve. Based on the optimized data of the number of charging cycles and the battery capacity retention rate, a target nonlinear regression model is established and fitted to obtain the attenuation diving point of the lithium battery capacity, thereby improving the recognition accuracy and efficiency of the diving point of the lithium battery, and providing an effective basis for battery health status management, battery capacity early warning and battery life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0015] Figure 1 A schematic diagram of a flow chart of a method for identifying a lithium battery capacity decay diving point provided in an embodiment of the present application; Figure 2 A schematic diagram of capacity decay curves of various decaying batteries in a method for identifying a lithium battery capacity decay diving point provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0017] 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, and 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 recorded. 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, although the embodiment may not be clearly recorded in the following text.

[0018] 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 functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. 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.

[0019] like Figure 1 As shown, Figure 1 : is a flow chart of a method for identifying a lithium battery capacity attenuation diving point provided in an embodiment of the present application. The method in this embodiment includes: 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 attenuation scatter plot based on the charging cycle number and the battery capacity retention rate.

[0020] The execution subject of this method is the field controller of the battery detection equipment, which directly detects the battery cells on site through the battery detection control terminal, and uses the method of this embodiment to measure the lithium battery capacity decay diving point in real time. The execution subject can also be the central controller of the remote monitoring platform, which controls the battery detection equipment to collect the battery cell cycle charging data and transmits it to the remote monitoring platform. Then, the central controller of the remote monitoring platform can use the method of this embodiment to process the battery cell cycle charging data and obtain the lithium battery capacity decay diving point.

[0021] 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 charging and discharging temperature of the battery cell. The data used in the method of this embodiment are 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.

[0022] S2. Generate a first battery capacity decay curve based on the battery capacity decay scatter plot.

[0023] In the embodiment of this specification, due to the interference of external factors such as temperature and humidity during measurement, some noise points will appear in some initial data points in the first battery capacity decay scatter plot, where the initial data points refer to the data scatter points corresponding to the values ​​of each charging cycle number of the first battery capacity decay scatter plot. If the noise points are fitted into the battery capacity decay curve, the deviation of the lithium battery capacity decay diving point will be large, so it is necessary to pre-process the data of each initial data point in the first battery capacity decay scatter plot to remove a large number of useless noise points to obtain the first battery capacity decay curve.

[0024] In one possible implementation, obtaining a first battery capacity decay curve based on the first battery capacity decay scatter plot 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.

[0025] In the embodiment of the present specification, the setting of the target preset limit of the initial battery capacity decay scatter plot 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 to 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 to 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 to 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 have not been deleted are connected, and the connection results of the initial data points are processed by smoothing to obtain a smooth battery capacity decay curve. The smoothing method used in this example is a non-model fitting method such as moving average or filter filtering, so as to save the original data of the number of charging cycles and the battery capacity retention rate corresponding to each number of charging cycles as much as possible, and then maintain the accuracy of the subsequent local fitting of the first battery capacity decay curve, so that the attenuation diving point of the lithium battery capacity obtained by the method of this embodiment is more accurate.

[0026] 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.

[0027] In the embodiments of the present specification, the attenuation curve of the lithium battery capacity has three different transformation trends, namely, linear trend, sublinear and superlinear trend. Sublinear means that the attenuation curve of the lithium battery capacity is concave and has a small deviation from each corresponding data point of the linear attenuation graph, that is, the sublinear curve is the lithium battery capacity attenuation curve, and the attenuation rate gradually changes from the maximum to approaching 0 as the number of charging cycles increases. 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 for the lithium battery to detect the diving point. In order to measure the attenuation diving point of the lithium battery capacity to estimate the battery life, it is necessary to determine that the curve attenuation type of the first battery capacity attenuation curve is a superlinear attenuation curve. The first battery capacity attenuation curve is locally fitted, that is, a part of the second data points of the first battery capacity attenuation curve are nonlinearly fitted, 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.

[0028] In one possible implementation, determining 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; The curve attenuation type of the first battery capacity attenuation curve whose first-order derivative calculation results are all negative values ​​and whose second-order derivative calculation results are all negative values ​​is determined to be a superlinear attenuation curve.

[0029] 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 convex function characteristics to determine, that is, to determine according to the change 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 values, 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 values, it means that the first battery capacity decay curve is a convex downward curve, which belongs to a superlinear decay curve. In addition, when the first-order derivatives of each second data point of the first battery capacity decay curve are all negative numbers and the second-order derivative is positive, it means that the curve decay type of the first battery capacity decay curve is sublinear.

[0030] In one possible implementation manner, 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 standardized.

[0031] In the embodiment of this specification, the z-score 0-1 standardization method 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 is the battery capacity retention rate of the normalized initial data point. After the initial data point is normalized by z-score, it can be used as the second data point to draw and analyze the first battery capacity decay curve.

[0032] In one possible implementation, 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: Taking the second data point of the first battery capacity decay curve where the battery capacity retention rate is greater than the first preset battery capacity retention rate and the first-order derivative is the smallest as the battery life starting point; Taking the second data point of the first battery capacity decay curve where the battery capacity retention rate is less than the second preset battery capacity retention rate and the first-order derivative is the smallest as the battery life end point; The least squares method is performed on each second data point in the first preset length interval and each second data point in the second preset length interval of the first battery capacity decay curve to obtain a second battery capacity decay curve, wherein 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.

[0033] 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, such as Figure 2As shown in FIG. 1 , since the first battery capacity decay curve of the lithium battery will have a serious jump phenomenon at the starting point of the battery life 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 processing on each second data point in 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.

[0034] 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.

[0035] 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 more first data points with lower 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.

[0036] In one possible implementation, the target nonlinear regression model is a Bacon-Watts model.

[0037] 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 mutation points 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 for identifying lithium battery capacity diving points. The Bacon-Watts model expression in this embodiment is: in, β 0 , β 1 and β 2 represents the model parameters, α Represents the smoothness control parameter , tanhrepresents the hyperbolic tangent function, γ represents the lithium battery capacity diving point, and x represents the abscissa of the corresponding data point, and the abscissa is the number of charging cycles.

[0038] In one possible implementation, performing global data fitting on the target nonlinear regression model to obtain a third battery capacity decay curve, and determining a decay diving point of the lithium battery capacity 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 by using the least square 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.

[0039] In the embodiment of this specification, the confidence interval of the attenuation dive point corresponding to the target nonlinear regression model is defined as 95%, and the definition of the confidence interval is defined according to actual needs. In this embodiment, the Bootstrap percentile method is used to define the execution interval, that is, the α / 2×100% and (1−α / 2)×100% quantiles are taken as the upper and lower limits of the confidence interval, where α=1-95%.

[0040] 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 diving point of the lithium battery capacity. In this method, the least squares method is used to globally fit the Bacon-Watts model, as shown in the formula: 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 at 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, wherein the abscissa represents the number of charging cycles, and the ordinate 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.

[0041] This implementation method removes some initial data points with excessive deviations in the battery capacity retention rate in the first battery capacity decay scatter plot as noise points, then performs local data fitting on the remaining initial data points, and finally performs global data fitting on the target nonlinear regression model, thereby obtaining an accurate lithium battery capacity decay diving point. This embodiment improves the accuracy of the identified lithium battery capacity decay diving point by using local data fitting and global data fitting in sequence.

[0042] 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.

[0043] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "processor" and "memory" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0044] 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 the present embodiment.

[0045] 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.

[0046] 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 the present application is not limited by the described order of actions, because according to the present 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 the present application.

[0047] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0048] 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 prior art, 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, including a number of instructions to enable 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, disk or optical disk and other media that can store program codes.

[0049] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware. The program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0050] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. 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 here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, 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 technical field not recorded in the present disclosure. The description and examples are 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 attenuation scatter plot based on the charging cycle number and the battery capacity retention rate; Generate 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; A target nonlinear regression model is constructed based on each first data point of the second battery capacity decay curve, and global data fitting is performed on the target nonlinear regression model 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.

2. A method for identifying a lithium battery capacity attenuation diving point as claimed in claim 1, characterized in that: The step of establishing a first battery capacity attenuation scatter plot based on the number of charging cycles and the battery capacity retention rate to obtain a first battery capacity attenuation 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. A method for identifying a lithium battery capacity attenuation diving point as claimed in claim 1, characterized in that: 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; The curve attenuation type of the first battery capacity attenuation curve whose first-order derivative calculation results are all negative values ​​and whose second-order derivative calculation results are all negative values ​​is determined to be a superlinear attenuation curve.

4. A method for identifying a lithium battery capacity attenuation diving point as claimed in claim 1, characterized in that: Before performing local data fitting on the first battery capacity decay curve whose curve decay type is a super linear 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 standardized.

5. A method for identifying a lithium battery capacity attenuation diving point as claimed in claim 1, characterized in that: 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, comprising: Taking the second data point of the first battery capacity decay curve where the battery capacity retention rate is greater than the first preset battery capacity retention rate and the first-order derivative is the smallest as the battery life starting point; Taking the second data point of the first battery capacity decay curve where the battery capacity retention rate is less than the second preset battery capacity retention rate and the first-order derivative is the smallest as the battery life end point; The first data points within the first preset length interval and the first data points within the second preset length interval of the first battery capacity decay curve are respectively fitted by least squares method to obtain the 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.

6. A method for identifying a lithium battery capacity attenuation diving point as claimed in claim 1, characterized in that: The target nonlinear regression model is the Bacon-Watts model.

7. A method for identifying a lithium battery capacity attenuation diving point as claimed in claim 1, characterized in that: The method further comprises: performing global data fitting on the target nonlinear regression model to obtain a third battery capacity attenuation curve, and determining a lithium battery capacity attenuation drop point corresponding to the third battery capacity attenuation curve. 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 by using the least square 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.

8. 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 7.

9. 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 7.

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