Battery capacity data attenuation trajectory construction method and device, equipment and medium

Abstract

The invention relates to a battery capacity data attenuation trajectory construction method and device, equipment and a medium. The method comprises the following steps: acquiring a plurality of capacity sampling data of a to-be-predicted battery; each piece of capacity sampling data comprises battery capacity data under each charge-discharge cycle index; determining a plurality of sampling nodes according to the number of charging cycles in each piece of capacity sampling data, and dividing the plurality of pieces of capacity sampling data into a plurality of sampling intervals according to the plurality of sampling nodes; according to a preset function continuity condition and a boundary condition, performing polynomial function fitting on the capacity sampling data in each sampling interval to obtain a piecewise polynomial function of each sampling interval; according to the piecewise polynomial function of each sampling interval, constructing an overall spline fitting curve corresponding to the plurality of capacity sampling data; the overall spline fitting curve represents a battery capacity data attenuation trajectory of the to-be-predicted battery. By adopting the method, the accuracy of the determined attenuation trajectory of the battery capacity data can be improved.

Description

Technical Field

[0001] This application relates to the field of new energy technologies, and particularly to a method and device, equipment, and medium for constructing a battery capacity data attenuation trajectory. Background Art

[0002] In the context of the widespread application of electric vehicles, energy storage systems, and various electronic devices, the performance of batteries is becoming the focus of the industry and scientific research. With the increase in the usage frequency of batteries, it is very important to determine the battery capacity data attenuation trajectory and evaluate the battery performance through the battery capacity data attenuation trajectory.

[0003] In related technologies, the battery capacity data attenuation trajectory is often obtained by analyzing the capacity data of the battery to be predicted collected at each charge-discharge cycle number. However, there is a problem of low accuracy in the determined battery capacity data attenuation trajectory in related technologies. Summary of the Invention

[0004] Based on this, this application provides a method and device, equipment, and medium for constructing a battery capacity data attenuation trajectory, which can improve the accuracy of the determined battery capacity data attenuation trajectory.

[0005] In a first aspect, this application provides a method for constructing a battery capacity data attenuation trajectory, the method comprising:

[0006] Obtain a plurality of capacity sampling data of the battery to be predicted; each capacity sampling data includes the battery capacity data at each charge-discharge cycle number;

[0007] Determine a plurality of sampling nodes according to the charge cycle numbers in each capacity sampling data, and divide the plurality of capacity sampling data into a plurality of sampling intervals according to the plurality of sampling nodes;

[0008] Perform polynomial function fitting on the capacity sampling data in each sampling interval according to the preset function continuity condition and boundary condition to obtain a piecewise polynomial function for each sampling interval;

[0009] Construct an overall spline fitting curve corresponding to the plurality of capacity sampling data according to the piecewise polynomial functions of each sampling interval; the overall spline fitting curve represents the battery capacity data attenuation trajectory of the battery to be predicted.

[0010] In some embodiments, obtaining a plurality of capacity sampling data of the battery to be predicted includes:

[0011] Obtain a plurality of original sampling data of the battery to be predicted; each original sampling data is the battery capacity data of the battery to be predicted collected at each charge-discharge cycle number;

[0012] Preprocess the plurality of original sampling data to obtain a plurality of battery capacity data to be cleaned;

[0013] Data cleaning is performed on multiple battery capacity data to be cleaned to obtain multiple capacity sampling data.

[0014] In some embodiments, multiple sampling nodes are determined according to the number of charge cycles in each capacity sampling data, and multiple capacity sampling data are divided into multiple sampling intervals according to the multiple sampling nodes, including:

[0015] Obtain multiple capacity sampling data to be analyzed with the current sampling node as the starting sampling node, and determine the coefficient of variation of the multiple capacity sampling data to be analyzed; the first current sampling node is the first capacity sampling data;

[0016] According to the coefficient of variation, determine the next sampling node from the multiple capacity sampling data to be analyzed;

[0017] Divide the capacity sampling data between the current sampling node and the next sampling node into one sampling interval;

[0018] Use the next sampling node as the new current sampling node, and repeat the above steps to divide multiple capacity sampling data into multiple sampling intervals.

[0019] In some embodiments, according to the coefficient of variation, determining the next sampling node from the multiple capacity sampling data to be analyzed includes:

[0020] When the coefficient of variation is less than or equal to the preset coefficient, determine the last capacity sampling data among the multiple capacity sampling data to be analyzed as the next sampling node;

[0021] When the coefficient of variation is greater than the preset coefficient, determine the next sampling node from the multiple capacity sampling data to be analyzed according to the ratio between the coefficient of variation and the preset coefficient.

[0022] In some embodiments, the method further includes:

[0023] Determine that the first derivative value of the first sampling node in the first piecewise polynomial is the first target value and the second derivative value is the second target value, and the first derivative value of the last sampling node in the last piecewise polynomial is the third target value and the second derivative value is the fourth target value as boundary conditions;

[0024] Determine that the first derivative values of the common sampling nodes of two adjacent sampling intervals are the same and the second derivative values are the same in two adjacent sampling intervals as the function continuity condition.

[0025] In some embodiments, the method further includes:

[0026] Determine the first derivative value of the common sampling node based on the common sampling node and the previous capacity sampling data of the common sampling node;

[0027] Determine the second derivative value of the common sampling node based on the common sampling node, the previous capacity sampling data of the common sampling node, and the subsequent capacity sampling data of the common sampling node.

[0028] In some embodiments, according to the piecewise polynomial functions of each sampling interval, construct an overall spline fitting curve corresponding to multiple capacity sampling data, including:

[0029] Obtain the extended intervals of multiple sampling intervals;

[0030] Determine the piecewise polynomial function of the extended interval according to the piecewise polynomial function of the last at least one sampling interval;

[0031] Merge the curves corresponding to the piecewise polynomial functions of each sampling interval and the curves corresponding to the piecewise polynomial functions of the extended intervals to obtain an overall spline fitting curve corresponding to multiple capacity sampling data.

[0032] In a second aspect, the present application provides a device for constructing a battery capacity data attenuation trajectory, the device includes:

[0033] An acquisition module, configured to acquire multiple capacity sampling data of a battery to be predicted; each capacity sampling data includes battery capacity data at each charge-discharge cycle number;

[0034] A partitioning module, configured to determine multiple sampling nodes according to the charge cycle numbers in each capacity sampling data, and partition the multiple capacity sampling data into multiple sampling intervals according to the multiple sampling nodes;

[0035] A fitting module, configured to perform polynomial function fitting on the capacity sampling data in each sampling interval according to preset function continuity conditions and boundary conditions to obtain piecewise polynomial functions of each sampling interval;

[0036] A construction module, configured to construct an overall spline fitting curve corresponding to multiple capacity sampling data according to the piecewise polynomial functions of each sampling interval; the overall spline fitting curve represents the battery capacity data attenuation trajectory of the battery to be predicted.

[0037] In a third aspect, the present application provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the method according to any one of the first aspect are implemented.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method according to any one of the first aspect are implemented.

[0039] In the technical solution provided by the embodiment of the present application, a plurality of capacity sampling data are divided into a plurality of sampling intervals, and the capacity sampling data in each sampling interval are segmented and fitted with a polynomial function to obtain a piecewise polynomial function for each sampling interval. Then, according to the piecewise polynomial functions of each sampling interval, an overall spline fitting curve corresponding to a plurality of capacity sampling data is constructed. Thus, the piecewise polynomial function of each sampling interval can accurately represent the true attenuation law of the battery in each sampling interval. Furthermore, the overall spline fitting curve constructed by the piecewise polynomial functions of each sampling interval can accurately express the overall attenuation law of the battery. Therefore, the accuracy of the determined battery capacity data attenuation trajectory can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can be obtained based on these drawings.

[0041] Figure 1 It is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided in the first embodiment;

[0042] Figure 2 It is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided in the second embodiment;

[0043] Figure 3 It is a schematic flowchart of a method for dividing capacity sampling data provided in some embodiments;

[0044] Figure 4 It is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided in the third embodiment;

[0045] Figure 5 It is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided in the fourth embodiment;

[0046] Figure 6 It is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided in the fifth embodiment;

[0047] Figure 7 It is a schematic structural diagram of a device for constructing a battery capacity data attenuation trajectory provided in some embodiments;

[0048] Figure 8 It is a schematic structural diagram of a computer device provided in some embodiments. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] The embodiments of the technical solution of the present application will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, so they are only examples and cannot be used to limit the protection scope of the present application.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the description of the specification, claims and above-mentioned drawings of this application are intended to cover non-exclusive inclusion.

[0051] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order or primary-secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "a plurality" is more than two unless otherwise specifically defined. In the description of the embodiments of the present application, "each" means each or every one of a plurality unless otherwise specifically defined.

[0052] Referring to "embodiments" herein means that the specific features, structures or characteristics described in connection with the embodiments may be included in at least one embodiment of the present application. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0053] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0054] The battery capacity data decay trajectory is very important for the evaluation of battery performance. By analyzing the capacity decay trajectory, batteries that do not meet the quality standards can be screened out during the product quality control stage to ensure the reliable quality of the battery products put on the market.

[0055] In some embodiments, capacity data of a battery to be predicted at each charge-discharge cycle number is collected, and by analyzing these capacity data, a battery capacity data attenuation trajectory is obtained. For example, the least squares method is used to fit these capacity data, and the fitting result is used as the battery capacity data attenuation trajectory. However, the capacity data of the battery has different attenuation laws in different stages. Therefore, if a function model used to describe one attenuation law is used to fit these capacity data, the obtained battery capacity data attenuation trajectory cannot accurately represent the true attenuation law of the battery, resulting in the problem of low accuracy of the determined battery capacity data attenuation trajectory.

[0056] Based on this, the method, device, equipment, and medium for constructing a battery capacity data attenuation trajectory provided in the embodiments of the present application divide a plurality of capacity sampling data into a plurality of sampling intervals, and perform piecewise polynomial function fitting on the capacity sampling data in each sampling interval to obtain a piecewise polynomial function for each sampling interval. Then, according to the piecewise polynomial functions of each sampling interval, an overall spline fitting curve corresponding to the plurality of capacity sampling data is constructed. Thus, the piecewise polynomial function of each sampling interval can accurately represent the true attenuation law of the battery in each sampling interval. Furthermore, the overall spline fitting curve constructed by the piecewise polynomial functions of each sampling interval can accurately express the overall attenuation law of the battery. Therefore, the accuracy of the determined battery capacity data attenuation trajectory can be improved.

[0057] The battery in the embodiments of the present application can be a lithium-ion battery or other types of batteries, and the embodiments of the present application do not limit this.

[0058] In the embodiments of the present application, the capacity data can be the current capacity value of the battery when it is fully charged (for example, the state of charge of the battery is 100%), or the capacity data can be the ratio of the current capacity value of the battery when it is fully charged (for example, the state of charge of the battery is 100%) to the rated capacity of the battery (also known as the capacity retention rate).

[0059] The computer device in the embodiments of the present application can be a combination of one or at least two of the following: battery management system, server, mobile phone, tablet computer (Pad), computer with transceiver function, palmtop computer, desktop computer, personal digital assistant, portable media player, smart speaker, navigation device, smart watch, smart glasses, wearable devices such as smart necklace, pedometer, digital TV, virtual reality (VR) device, augmented reality (AR) device, device in industrial control, device in self-driving, device in remote medical surgery, device in smart grid, device in transportation safety, device in smart city, device in smart home, vehicle, in-vehicle device, in-vehicle module, and so on.

[0060] Figure 1 The flowchart of a method for constructing a battery capacity data attenuation trajectory provided for the first embodiment. This method is applied to a computer device and includes:

[0061] S101. Obtain multiple capacity sampling data of the battery to be predicted; each capacity sampling data includes the battery capacity data at each charge-discharge cycle number.

[0062] The capacity sampling data can be the sampled capacity data. Exemplarily, the capacity sampling data can be the original sampling data of the battery collected. Additionally, the capacity sampling data can be the battery capacity data obtained after preprocessing and / or data cleaning of the original sampling data of the battery collected.

[0063] In some embodiments, the battery to be predicted can be a battery of a certain specification to be predicted, or the battery to be predicted can be a battery of a certain specification generated by a certain production line to be predicted, or the battery to be predicted can be a battery that has been developed. Among them, the battery to be predicted can include one battery, or the battery to be predicted can include at least two batteries.

[0064] A charge-discharge cycle refers to a process where a charging cycle includes the battery starting from a fully charged state, gradually discharging until the power is exhausted, and then being charged again to full. Exemplarily, if the battery discharges from a state of charge of 100% to 0% and then is charged back to a state of charge of 100%, this is a complete charge-discharge cycle. The number of charge-discharge cycles refers to the number of times the battery undergoes a complete charge-discharge process from being completely depleted of power to being completely charged during normal use. Among them, charging the battery to full can mean that the state of charge of the battery is 100%.

[0065] Among them, each capacity sampling data includes a battery capacity data at each charge-discharge cycle number. The charge-discharge cycle numbers can be continuous or discontinuous.

[0066] Multiple capacity sampling data can correspond to a preset range of charge-discharge cycle numbers. Exemplarily, the range of charge-discharge cycle numbers can be from 0 to a preset number, and the preset number can be determined according to the attributes of the battery. For example, when the battery design is completed, it is necessary to determine the maximum number of charge-discharge cycles in the attributes of the battery. In this case, the method in the embodiments of the present application can also be used to collect multiple capacity sampling data of the battery to be predicted in real time. According to the piecewise polynomial functions of each sampling interval, when it is determined that the coefficients of the piecewise polynomial functions of the last consecutive N (N is an integer greater than or equal to 2) sampling intervals are the same or the change amount of the coefficients is within a preset range, it is determined that the change of the battery capacity data tends to be stable, and the range corresponding to the collected charge-discharge cycle numbers is determined as the preset range of charge-discharge cycle numbers. Another example is that the preset number can be the maximum number of charge-discharge cycles of the battery. Another example is that the preset number can be the product of the maximum number of charge-discharge cycles of the battery and a preset multiple, and the preset multiple can be a real number greater than 0 and less than 1.

[0067] S102. Determine multiple sampling nodes according to the charge-discharge cycle numbers in each capacity sampling data, and divide the multiple capacity sampling data into multiple sampling intervals according to the multiple sampling nodes.

[0068] Among them, the first sampling node among the multiple sampling nodes can be the first charge-discharge cycle number, and the last sampling node is the last charge-discharge cycle number.

[0069] In the embodiments of the present application, adjacent sampling intervals overlap at a sampling node, and each sampling interval includes at least two capacity sampling data. For example, adjacent sampling intervals are [a1, a2] and [a2, a3] respectively, where a2 is a sampling node. In this way, the sampling node a2 is not only divided into the sampling interval [a1, a2], but also divided into the sampling interval [a2, a3].

[0070] S103. According to the preset function continuity conditions and boundary conditions, perform polynomial function fitting on the capacity sampling data in each sampling interval to obtain the piecewise polynomial functions of each sampling interval.

[0071] Among them, the function continuity condition means that the fitting curves of every two adjacent sampling intervals should meet the continuity requirements, that is, there is no break and a smooth transition between the fitting curves of every two adjacent sampling intervals. Among them, the boundary condition means that the first capacity sampling data in the first sampling interval and the last capacity sampling data in the last sampling interval meet the deterministic requirements.

[0072] In some embodiments, polynomial function fitting is a commonly used mathematical and machine learning method that uses a polynomial function to fit a set of data points. The embodiments of the present application do not limit the degree of the polynomial function. For example, the polynomial function can be a cubic function, a quartic function, a quintic function, etc.

[0073] In some embodiments, the polynomial function can be a cubic function. In other embodiments, a function with a higher degree can be used in the first target number of sampling intervals, and a function with a lower degree can be used in the sampling intervals after the first target number of sampling intervals. Among them, the first target number of sampling intervals can be an interval where the change range of the capacity data is large, and this interval can be input by the user to the computer device through the computer device, or can be determined by the computer device according to multiple capacity sampling data.

[0074] Exemplarily, methods such as least square polynomial fitting, Lagrange interpolation polynomial fitting, or Newton interpolation polynomial fitting can be used to perform polynomial function fitting on the capacity sampling data in each sampling interval. Additionally, for the capacity sampling data in each sampling interval, at least two fitting methods can be used respectively for fitting. For example, the least square polynomial fitting, Lagrange interpolation polynomial fitting, and Newton interpolation polynomial fitting methods can be used respectively for fitting to obtain three fitting results corresponding to these three fitting methods, and three fitting evaluation index values (such as mean square error, root mean square error, mean absolute error, coefficient of determination, or significance test value, etc.) corresponding to each of them can be determined according to these three fitting results and the capacity sampling data in each sampling interval. The evaluation index value with the best fitting effect is selected from the three fitting evaluation index values, and the polynomial function corresponding to the evaluation index value with the best fitting effect is determined as the piecewise polynomial function of this sampling interval.

[0075] S104. According to the piecewise polynomial functions of each sampling interval, construct an overall spline fitting curve corresponding to multiple capacity sampling data; the overall spline fitting curve represents the battery capacity data attenuation trajectory of the battery to be predicted.

[0076] Spline fitting is a commonly used technique in the fields of mathematics and engineering for generating a smooth curve through a set of discrete data points. Spline fitting not only requires the curve to pass through or be close to these data points, but also ensures a smooth transition at the joints.

[0077] In some embodiments, the curves corresponding to the piecewise polynomial functions of each sampling interval can be combined to obtain an overall spline fitting curve. In other embodiments, extended intervals outside the multiple sampling intervals can be obtained, and the extended intervals are adjacent to the last sampling interval or the first sampling interval in the multiple sampling intervals. The piecewise polynomial function of the extended interval is determined, and the curves corresponding to the piecewise polynomial functions of each sampling interval and the curve corresponding to the piecewise polynomial function of the extended interval are combined to obtain an overall spline fitting curve.

[0078] In some embodiments, based on the overall spline fitting curve, the capacity data at the target charge-discharge cycle number can be determined. Among them, the target charge-discharge cycle number is within the range of the charge-discharge cycle numbers corresponding to the overall spline fitting curve. The target charge-discharge cycle number can be one or at least two charge-discharge cycle numbers.

[0079] In some embodiments, based on the capacity data at the target charge-discharge cycle number, the detection result of the battery to be predicted can be determined. For example, when the capacity data at the target charge-discharge cycle number is within a preset range, it is determined that the detection result of the battery to be predicted is passed, indicating that the performance of the battery to be predicted meets the requirements. On the contrary, when the capacity data at the target charge-discharge cycle number is not within the preset range, it is determined that the detection result of the battery to be predicted is not passed, indicating that the performance of the battery to be predicted does not meet the requirements.

[0080] In the technical solution provided by the embodiments of the present application, multiple capacity sampling data are divided into multiple sampling intervals, and the capacity sampling data in each sampling interval are segmented for polynomial function fitting to obtain the piecewise polynomial functions of each sampling interval. Then, based on the piecewise polynomial functions of each sampling interval, an overall spline fitting curve corresponding to the multiple capacity sampling data is constructed. Thus, the piecewise polynomial functions of each sampling interval can accurately represent the true attenuation law of the battery in each sampling interval. Furthermore, through the overall spline fitting curve constructed by the piecewise polynomial functions of each sampling interval, the overall attenuation law of the battery can be accurately expressed. Therefore, the accuracy of the determined battery capacity data attenuation trajectory can be improved.

[0081] Figure 2 It is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided for the second embodiment. This method is applied to a computer device. Figure 2 The difference between the embodiments Figure 1 is that S101 includes the following S1011 to S1013:

[0082] S1011. Obtain multiple original sampling data of the battery to be predicted; each original sampling data is the battery capacity data of the battery to be predicted collected at each charge-discharge cycle number.

[0083] Exemplarily, a long-term charge-discharge test can be performed on the battery to be predicted, and the capacity data of the battery at different charge-discharge cycle numbers is recorded at a certain time interval or charge-discharge cycle number as a period. These recorded data are the original sampling data. For example, every time a charge-discharge cycle is completed, the capacity data of the battery is recorded once, so as to obtain multiple original sampling data.

[0084] In some embodiments, in response to the start instruction of each charge-discharge cycle, the battery is charged at the maximum safe charge rate allowed in each state of charge interval among multiple states of charge intervals until the state of charge of the battery reaches the maximum value; the battery is discharged at the maximum safe discharge rate allowed in each state of charge interval among multiple states of charge intervals until the state of charge of the battery reaches the minimum value; and each original sampling data is determined according to the maximum safe discharge rate allowed in each state of charge interval and the discharge duration of each state of charge interval.

[0085] S1012. Preprocess the multiple original sampling data to obtain multiple battery capacity data to be cleaned.

[0086] In some embodiments, in one original sampling data, the number of battery capacity data at each charge-discharge cycle number can be one or at least two. For example, in order to improve the accuracy of the battery capacity data decay trajectory, the battery capacity data of each battery among multiple batteries at each charge-discharge cycle number can be obtained. Exemplarily, the multiple batteries can be in the same environment. Also exemplarily, the multiple batteries can be in different environments. For example, at least one of the following is different in the environments where different batteries are located: temperature, humidity, charge rate, discharge rate. For example, there are two batteries, and the charge rates and discharge rates of these two batteries are both different.

[0087] In some embodiments, preprocessing the multiple original sampling data to obtain multiple battery capacity data to be cleaned may include: removing or interpolating the sampling data with empty capacity data among the multiple original capacity data at multiple charge-discharge cycle numbers to obtain multiple capacity data to be processed; determining the target capacity data at each charge-discharge cycle number according to at least one capacity data at each charge-discharge cycle number among the multiple capacity data to be processed; and performing format conversion on the target capacity data at each charge-discharge cycle number to obtain multiple capacity data to be cleaned.

[0088] Exemplarily, for the case of missing data, interpolation can be used for filling. For example, if the capacity data at the nth charge-discharge cycle is missing, the capacity data at the nth cycle can be estimated by using the linear interpolation method (such as taking the average value) based on the capacity data at the (n - 1)th and (n + 1)th cycles.

[0089] Exemplarily, according to at least one capacity data at each charge-discharge cycle number in the capacity data to be processed, determining the target capacity data at each charge-discharge cycle number may include: determining the average value, weighted sum value or weighted average value of at least one capacity data at each charge-discharge cycle number in the capacity data to be processed as the target capacity data at each charge-discharge cycle number. For example, the weight value corresponding to the battery in the standard environment may be greater than or equal to the weight value corresponding to the battery in other environments for weighted summation or weighted averaging. The standard environment may be the optimal working temperature of the battery, or the standard environmental temperature may be the average value of the actual working temperatures of the battery.

[0090] Exemplarily, performing format conversion on the target capacity data at each charge-discharge cycle number to obtain a plurality of capacity data to be cleaned may include: converting each charge-discharge cycle number into data in an integer format, and converting the target capacity data into data in a floating-point format to obtain a plurality of battery capacity data to be cleaned.

[0091] S1013. Performing data cleaning on a plurality of battery capacity data to be cleaned to obtain a plurality of capacity sampling data.

[0092] Exemplarily, after obtaining the battery capacity data to be cleaned, further data cleaning is required. By setting a reasonable threshold range, outliers that significantly deviate from the normal range can be identified and removed. For example, if it is found that the battery capacity data at a certain charge-discharge cycle number differs too much from the capacity data at adjacent cycle numbers and exceeds the normal fluctuation range, this data is regarded as an outlier and excluded. After data cleaning, a plurality of capacity sampling data are finally obtained.

[0093] In some embodiments, performing data cleaning on a plurality of battery capacity data to be cleaned to obtain a plurality of capacity sampling data can be achieved by the following method: selecting the fitting model with the highest goodness of fit from a plurality of fitting models according to the plurality of capacity data to be cleaned, and performing abnormal data cleaning on the plurality of capacity data to be cleaned according to the fitting result corresponding to the fitting model with the highest goodness of fit to obtain candidate capacity data; re-selecting the fitting model with the highest goodness of fit from a plurality of fitting models according to the candidate capacity data, and performing abnormal data cleaning on the candidate capacity data according to the fitting result corresponding to the fitting model with the highest goodness of fit to obtain new candidate capacity data, and iteratively executing this process until a preset iteration termination condition is met to obtain a plurality of capacity data after the battery cleaning is completed.

[0094] In some other embodiments, data cleaning is performed on multiple battery capacity data to be cleaned to obtain multiple capacity sampling data, which can be achieved in the following manner: select the fitting model with the highest goodness of fit from multiple fitting models according to the multiple battery capacity data to be cleaned, and perform abnormal data cleaning on the multiple battery capacity data to be cleaned according to the fitting result corresponding to the fitting model with the highest goodness of fit, so as to obtain multiple capacity data of the batteries after cleaning is completed.

[0095] In the technical solution provided by the embodiments of the present application, by preprocessing multiple original sampling data to obtain multiple battery capacity data to be cleaned, and performing data cleaning on the multiple battery capacity data to be cleaned to obtain multiple capacity sampling data, the influence of abnormal data on the constructed overall spline fitting curve is reduced, thereby improving the accuracy of the determined overall spline fitting curve.

[0096] In some other embodiments, data cleaning of the capacity data to be cleaned (i.e., the above-mentioned multiple battery capacity data to be cleaned) may include the following steps: select the fitting model with the highest goodness of fit from multiple fitting models according to the capacity data to be cleaned, and perform abnormal data cleaning on the capacity data to be cleaned according to the fitting result corresponding to the fitting model with the highest goodness of fit to obtain candidate capacity data; re-select the fitting model with the highest goodness of fit from the multiple fitting models according to the candidate capacity data, and perform abnormal data cleaning on the candidate capacity data according to the fitting result corresponding to the fitting model with the highest goodness of fit to obtain new candidate capacity data, and iteratively execute this process until a preset iteration termination condition is met to obtain the capacity data of the batteries after cleaning is completed.

[0097] Exemplarily, the step of selecting the fitting model with the highest goodness of fit from multiple fitting models according to the capacity data to be cleaned includes: respectively fitting the capacity data to be cleaned by using the multiple fitting models to obtain multiple fitting results; determining the goodness of fit of each fitting result according to each fitting result and the capacity data to be cleaned; and determining the fitting model corresponding to the maximum goodness of fit as the fitting model with the highest goodness of fit.

[0098] Exemplarily, determining the goodness of fit of each of the fitting results according to each of the fitting results and the data of the capacity to be cleaned includes: obtaining the constant value range in each fitting model among the multiple fitting models, and determining the sum of squared residuals of each of the fitting results according to each of the fitting results and the data of the capacity to be cleaned; in the case where the sum of squared residuals of any one of the fitting results is less than or equal to a preset threshold and the constant in any one of the fitting results is within the corresponding constant value range, determining the goodness of fit of any one of the fitting results according to any one of the fitting results and the data of the capacity to be cleaned; in the case where the sum of squared residuals of any one of the fitting results is greater than the preset threshold and / or the constant in any one of the fitting results is outside the corresponding constant value range, determining the goodness of fit of any one of the fitting results as a target value.

[0099] Exemplarily, cleaning the abnormal data of the data of the capacity to be cleaned according to the fitting result corresponding to the fitting model with the highest goodness of fit to obtain candidate capacity data includes: determining the predicted capacity data at each charge-discharge cycle according to the fitting result corresponding to the fitting model with the highest goodness of fit; determining each deviation amount between each capacity data in the data of the capacity to be cleaned and each of the predicted capacity data; removing the abnormal capacity data and the corresponding charge-discharge cycle numbers from the data of the capacity to be cleaned according to each of the deviation amounts to obtain the candidate capacity data.

[0100] Exemplarily, removing the abnormal capacity data and the corresponding charge-discharge cycle numbers from the data of the capacity to be cleaned according to each of the deviation amounts to obtain the candidate capacity data includes: obtaining a maximum residual threshold and a minimum residual threshold, determining a target residual threshold according to the maximum residual threshold, the minimum residual threshold, and each of the deviation amounts, and determining at least two target deviation amounts greater than the target residual threshold from each of the deviation amounts; in the case where the number of the target deviation amounts is greater than a preset number, determining the capacity data corresponding to the maximum preset number of the target deviation amounts among the at least two target deviation amounts as the abnormal capacity data, and in the case where the number of the target deviation amounts is less than or equal to the preset number, determining the capacity data corresponding to the at least two target deviation amounts as the abnormal capacity data; removing the abnormal capacity data and the corresponding charge-discharge cycle numbers from the data of the capacity to be cleaned to obtain the candidate capacity data.

[0101] Exemplarily, determining the target residual threshold according to the highest residual threshold, the lowest residual threshold, and each of the deviation amounts includes: obtaining a residual threshold adjustment parameter, and determining a standard deviation of the deviation amounts according to each of the deviation amounts; determining a dynamic threshold according to the residual threshold adjustment parameter and the standard deviation of the deviation amounts; determining, as the target threshold, a larger value between the dynamic threshold and the lowest residual threshold; and determining, as the target residual threshold, a smaller value between the highest residual threshold and the target threshold.

[0102] Figure 3 A flowchart of a method for partitioning capacity sampling data provided for some embodiments is shown as Figure 3 shown. This method is a further description of S102 and includes:

[0103] S1021. Taking the first capacity sampling data as the first current sampling node.

[0104] S1022. Obtaining a plurality of capacity sampling data to be analyzed with the current sampling node as the starting sampling node, and determining a coefficient of variation of the plurality of capacity sampling data to be analyzed.

[0105] The coefficient of variation refers to an index value that measures the degree of difference between the various values of a variable observed randomly and is used to measure the size of risk. Exemplarily, the coefficient of variation can be one of the following: range coefficient, mean difference coefficient, standard deviation coefficient, variance coefficient. Among them, the larger the value of the coefficient of variation, the larger the fluctuation range of the values in the data set, indicating a higher degree of dispersion of the data; conversely, the smaller the coefficient of variation, the smaller the fluctuation between the data, indicating a lower degree of dispersion of the data.

[0106] In some embodiments, when the number of capacity sampling data in the starting sampling node and all subsequent capacity sampling data is greater than or equal to a set number, and when the number of capacity sampling data in all capacity sampling data after the set number of capacity sampling data is greater than or equal to the set number, the capacity sampling data between the starting sampling node and the set number of capacity sampling data is determined as the plurality of capacity sampling data to be analyzed.

[0107] In some embodiments, when the number of capacity sampling data in the starting sampling node and all subsequent capacity sampling data is greater than or equal to a set number, and when the number of capacity sampling data in all capacity sampling data after the set number of capacity sampling data is less than the set number, the starting sampling node and all subsequent capacity sampling data are determined as the plurality of capacity sampling data to be analyzed.

[0108] In some embodiments, when the number of capacity sampling data among the starting sampling node and all subsequent capacity sampling data is less than a set number, the starting sampling node and all subsequent capacity sampling data are determined as multiple capacity sampling data to be analyzed.

[0109] S1023. Determine the next sampling node from the multiple capacity sampling data to be analyzed according to the coefficient of variation.

[0110] In some embodiments, when the coefficient of variation is less than or equal to a preset coefficient, the last capacity sampling data among the multiple capacity sampling data to be analyzed is determined as the next sampling node.

[0111] In some embodiments, when the coefficient of variation is greater than the preset coefficient, a capacity sampling data other than the first and the last capacity sampling data among the multiple capacity sampling data to be analyzed is determined as the next sampling node.

[0112] In some embodiments, determining the next sampling node from the multiple capacity sampling data to be analyzed according to the coefficient of variation includes: when the coefficient of variation is less than or equal to the preset coefficient, determining the last capacity sampling data among the multiple capacity sampling data to be analyzed as the next sampling node; when the coefficient of variation is greater than the preset coefficient, determining the next sampling node from the multiple capacity sampling data to be analyzed according to the ratio between the coefficient of variation and the preset coefficient.

[0113] Exemplarily, determining the next sampling node from the multiple capacity sampling data to be analyzed according to the ratio between the coefficient of variation and the preset coefficient can be implemented by the following method: multiplying the ratio by the number of the capacity sampling data to be analyzed corresponding to the multiple capacity sampling data to be analyzed to obtain a preset value, rounding the preset value to obtain a specified value, and determining the specified value-th capacity sampling data among the multiple capacity sampling data to be analyzed as the next sampling node.

[0114] S1024. Divide the capacity sampling data between the current sampling node and the next sampling node into one sampling interval.

[0115] In some embodiments, after S1024, it can be determined whether the next sampling node is the last sampling node. When the next sampling node is the last sampling node, multiple sampling intervals are obtained. When the next sampling node is not the last sampling node, S1025 is executed.

[0116] S1025. Use the next sampling node as the new current sampling node.

[0117] After S1025, it can be turned to S1022.

[0118] In the embodiments of the present application, according to the coefficient of variation, the next sampling node is determined from multiple capacity sampling data to be analyzed, so that the division of each sampling interval is flexibly divided according to the distribution of the capacity sampling data, thereby improving the accuracy of the determined overall spline fitting curve.

[0119] Figure 4 It is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided for the third embodiment. This method is applied to a computer device. Figure 4 The difference between the embodiments Figure 1 is that before S103, steps S105 to S106 may further be included:

[0120] S105. Determine the first derivative value of the first sampling node in the first piecewise polynomial as the first target value and the second derivative value as the second target value, and the first derivative value of the last sampling node in the last piecewise polynomial as the third target value and the second derivative value as the fourth target value as boundary conditions.

[0121] In some embodiments, the first target value to the fourth target value are all 0.

[0122] In some other embodiments, the first target value can be determined according to the first battery capacity data and the second battery capacity data. For example, obtain the first change amount of the second battery capacity data and the first battery capacity data, obtain the second change amount of the second charge and discharge cycle number and the first charge and discharge cycle number, and determine the ratio of the first change amount to the second change amount as the first target value. The second target value can be determined according to the first battery capacity data to the third battery capacity data. For example, obtain the third change amount of the third battery capacity data and the second battery capacity data, obtain the fourth change amount of the third charge and discharge cycle number and the second charge and discharge cycle number, determine the ratio of the third change amount to the fourth change amount as the first predetermined value, and determine the difference between the first predetermined value and the first target value as the second target value.

[0123] In some other embodiments, the third target value may be determined according to the penultimate battery capacity data and the last battery capacity data. For example, obtain the fifth change amount between the last battery capacity data and the penultimate battery capacity data, obtain the sixth change amount between the last charge and discharge cycle number and the penultimate charge and discharge cycle number, and determine the ratio of the fifth change amount to the sixth change amount as the third target value. The fourth target value may be determined according to the third-to-last battery capacity data to the last battery capacity data. For example, obtain the seventh change amount between the penultimate battery capacity data and the third-to-last battery capacity data, obtain the eighth change amount between the penultimate charge and discharge cycle number and the third-to-last charge and discharge cycle number, determine the ratio of the seventh change amount to the eighth change amount as the second predetermined value, and determine the difference between the third target value and the second predetermined value as the fourth target value.

[0124] S106. Determine that the first derivative values and the second derivative values of the common sampling nodes in two adjacent sampling intervals are the same as the function continuity condition.

[0125] In some embodiments, determining that the first derivative values and the second derivative values of the common sampling nodes in two adjacent sampling intervals are the same as the function continuity condition can be achieved in the following manner: Determine the first derivative value of the common sampling node according to the common sampling node and the previous capacity sampling data of the common sampling node; Determine the second derivative value of the common sampling node according to the common sampling node, the previous capacity sampling data of the common sampling node, and the subsequent capacity sampling data of the common sampling node.

[0126] Exemplarily, the first derivative value of the common sampling node may be determined in the following manner: Obtain the ninth change amount between the battery capacity data of the common sampling node and the battery capacity data of the previous capacity sampling data, obtain the tenth change amount between the charge and discharge cycle number of the common sampling node and the previous charge and discharge cycle number, and determine the ratio of the ninth change amount to the tenth change amount as the first derivative value of the common sampling node.

[0127] Exemplarily, the second derivative value of the common sampling node may be determined in the following manner: Obtain the eleventh change amount between the battery capacity data of the subsequent capacity sampling data of the common sampling node and the battery capacity data of the common sampling node, obtain the twelfth change amount between the charge and discharge cycle number of the subsequent capacity sampling data and the charge and discharge cycle number of the common sampling node, determine the ratio of the eleventh change amount to the twelfth change amount as the third predetermined value, and determine the difference between the third predetermined value and the first derivative value of the common sampling node as the second derivative value of the common sampling node.

[0128] In the technical solution provided by the embodiments of the present application, by defining boundary conditions and function continuity conditions, the overall spline fitting curve meets smoothness and physical rationality.

[0129] Figure 5 FIG. 4 is a schematic flowchart of a method for constructing a battery capacity data attenuation trajectory provided for the fourth embodiment. This method is applied to a computer device. Figure 5 The embodiments are compared with Figure 1 The difference from the embodiment is that S104 may include steps S1041 to S1043:

[0130] S1041. Obtain extended intervals of multiple sampling intervals.

[0131] In some embodiments, the left endpoint of the extended interval may be the number of charge and discharge cycles of the last sampling node among multiple sampling nodes.

[0132] In some embodiments, the right endpoint of the extended interval may be determined according to the number of charge and discharge cycles of the last sampling node. For example, the value of the right endpoint of the extended interval may be the number of charge and discharge cycles of the last sampling node multiplied by (1 + A). Wherein, the value of A may be a real number greater than 0 and less than or equal to 1. For example, the value of A may be in the range of 0.1 to 0.5. For example, the value of A may be 0.2 or 0.3, etc.

[0133] In other embodiments, the right endpoint of the extended interval may be the maximum number of charge and discharge cycles of the battery or the rated number of charge and discharge cycles of the battery.

[0134] In still other embodiments, the right endpoint of the extended interval may be the number of charge and discharge cycles at a predetermined battery capacity retention rate determined using the piecewise polynomial function of the last sampling interval. Exemplarily, the predetermined battery capacity retention rate may be any real number from 0 to 90%. For example, the predetermined battery capacity retention rate may be 0, 70%, 75%, 80% or 85%, etc. The embodiments of the present application are not limited thereto.

[0135] S1042. Determine the piecewise polynomial function of the extended interval according to the piecewise polynomial function of the last at least one sampling interval.

[0136] In some embodiments, the piecewise polynomial function of the last sampling interval may be determined as the piecewise polynomial function of the extended interval.

[0137] In some other embodiments, it may be determined whether the constant difference corresponding to the constant term in the piecewise polynomial functions of the last at least two sampling intervals is less than or equal to a preset threshold. If it is less than or equal, the piecewise polynomial function of the last sampling interval is determined as the piecewise polynomial function of the extended interval. If it is greater, the piecewise polynomial function of the extended interval is determined according to the piecewise polynomial functions of the last at least two sampling intervals. For example, the constant in the piecewise polynomial function of the extended interval is determined according to the constants of the piecewise polynomial functions of the last at least two sampling intervals, thereby obtaining the piecewise polynomial function of the extended interval.

[0138] In some embodiments, the piecewise polynomial function of the last sampling interval and the piecewise polynomial function of the extended interval also need to satisfy a preset function continuity condition.

[0139] S1043. Merge the curves corresponding to the piecewise polynomial functions of each sampling interval and the curves corresponding to the piecewise polynomial function of the extended interval to obtain the overall spline fitting curve corresponding to multiple capacity sampling data.

[0140] In the embodiments of the present application, by merging the curves corresponding to the piecewise polynomial functions of each sampling interval and the curves corresponding to the piecewise polynomial function of the extended interval, the overall spline fitting curve corresponding to multiple capacity sampling data is obtained. Thus, the overall spline fitting curve can not only reflect the variation law of the collected battery capacity data, but also reflect the variation law of the battery capacity data in the extended interval.

[0141] The embodiments of the present application relate to a battery management system and its data analysis method. Specifically, it relates to a method for accurately fitting the capacity decay trajectory of a lithium-ion battery using spline fitting technology and predicting the future capacity retention rate. This technology has wide applications in portable electronic devices, electric vehicles, and large-scale energy storage systems. With the increasing usage frequency of lithium-ion batteries, the stability of battery performance and life prediction have become key issues in R & D and practical applications. Traditional capacity decay tests usually require long-term charge and discharge cycles to observe the changes in battery performance, which is not only time-consuming and costly, but also difficult to quickly evaluate the optimization effect of battery design in the initial stage of R & D. Therefore, there is an urgent need for a method that can accurately predict the long-term performance of batteries through an efficient data fitting method in the early stage of batteries, so as to shorten the test cycle, reduce R & D costs, and improve R & D efficiency.

[0142] In some embodiments, by introducing spline fitting technology, the flexibility and continuity of piecewise polynomial functions are utilized to efficiently and accurately fit the capacity decay trajectory of lithium-ion batteries.

[0143] The innovations of the present invention are as follows: (1) Multi-segment piecewise fitting: Using univariate cubic spline fitting, by selecting multiple nodes on the independent variable axis, the data is divided into multiple intervals, and an independent cubic polynomial function is used for fitting within each interval to improve the adaptability and accuracy of fitting. (2) Continuity and smoothness constraints: Enforcing zero-order, first-order, and second-order continuity at each node to ensure the smoothness and physical rationality of the fitting curve. (3) Dynamic characteristic analysis: Calculating the first derivative (slope) and second derivative (rate of change of slope) of the fitting curve to deeply analyze the rate of battery capacity decay and its changing trend.

[0144] In some embodiments, a systematic method is provided to achieve accurate fitting of the capacity decay trajectory of lithium-ion batteries and prediction of future capacity retention rate through spline fitting technology. The specific implementation steps are as follows:

[0145] (1) Data input and preprocessing. Among them, data input and preprocessing include the following:

[0146] Data collection: Obtain the capacity data of lithium-ion batteries at different charge-discharge cycle numbers to form data pairs of independent variable x (cycle number) and dependent variable y (capacity retention rate).

[0147] Data format conversion: Convert the collected data into a standard numerical format (such as a floating-point number array) to ensure the consistency and processability of the data.

[0148] Removal of abnormal points: Remove the data abnormal points caused by measurement errors during the collection process.

[0149] Generate an extended independent variable range: Based on the maximum cycle number of the actual data Generate an extended independent variable range for predicting future capacity.

[0150] (2) Definition of the spline fitting model.

[0151] Select several nodes on the independent variable x, and these nodes divide the data into multiple intervals. The selection of nodes is based on the distribution density of data points. Usually, the number of nodes is increased in the regions where the capacity decay changes drastically to improve the fitting accuracy. Then, within each interval , define a cubic polynomial function : . Among them, are undetermined coefficients. represents the cubic polynomial function of the th interval, is greater than or equal to 1 and less than or equal to .

[0152] (3)Application of continuity and boundary conditions.

[0153] The continuity conditions include ; among which, represents any ; First derivative continuity: ; Second derivative continuity: . Among which, the first derivative is: . Among which, the second derivative is: .

[0154] Boundary conditions: Assume that the second derivatives of the spline at the leftmost end and the rightmost end are zero.

[0155] (4)Construct a system of linear equations and solve for the coefficients.

[0156] Exemplarily, the above continuity conditions and boundary conditions can be transformed into a system of linear equations to form a matrix equation: Among which, is the coefficient matrix, is the vector of polynomial coefficients to be solved, is the constant vector. Exemplarily, solve the system of equations through Gaussian elimination or other efficient linear algebra methods to obtain the coefficients of all piecewise polynomial functions.

[0157] (5)Construction and prediction of the fitting curve.

[0158] Construct the overall spline fitting curve: Combine the cubic polynomial functions in each interval to form the overall spline fitting curve .

[0159] For example, the values of the fitting curve within the original data range can be calculated: . For example, the predicted values of the fitting curve within the extended range can be calculated: .

[0160] Figure 6 FIG. Figure 6 is a schematic flow chart of a method for constructing a battery capacity data attenuation trajectory provided by the fifth embodiment. This method is applied to a computer device,

[0161] S601. Obtain each capacity sampling data, including the battery capacity data at each charge and discharge cycle number.

[0162] S602. Determine multiple sampling nodes according to the charge cycle numbers in each capacity sampling data, and divide the multiple capacity sampling data into multiple sampling intervals according to the multiple sampling nodes.

[0163] S603. Construct a piecewise polynomial function model for each sampling interval.

[0164] S604. Apply the preset function continuity conditions and boundary conditions.

[0165] S605. Form a system of linear equations and solve the system of linear equations to obtain the coefficients of the piecewise polynomial function model for each sampling interval, so as to determine the piecewise polynomial function for each sampling interval.

[0166] S606. According to the piecewise polynomial functions for each sampling interval, construct an overall spline fitting curve corresponding to multiple capacity sampling data.

[0167] S607. Output and visualize the overall spline fitting curve.

[0168] In some embodiments, not only the overall spline fitting curve can be output, but also the battery capacity data at each charge and discharge cycle number can be output.

[0169] In the embodiments of the present application, spline fitting uses a piecewise cubic polynomial function, which can more flexibly and accurately capture the non-linear characteristics of battery capacity decay and significantly reduce the prediction error. In the embodiments of the present application, by calculating the first derivative (slope) and the second derivative (rate of change of slope) of the fitting curve, the rate of battery capacity decay and its change trend are deeply analyzed, providing a scientific basis for battery performance optimization. In the embodiments of the present application, the long-term performance of the battery can be accurately predicted at the early stage of the battery, significantly shortening the time of battery performance testing and the R & D cycle, and reducing the R & D cost. In the embodiments of the present application, the flexibility of node selection and piecewise polynomial in spline fitting enables it to adapt to different types and complexities of capacity decay data, providing a wider range of application applicability. In the embodiments of the present application, a comprehensive fitting curve graph and the best fitting model graph are provided, facilitating R & D personnel to intuitively understand and analyze battery performance data and assisting in decision-making and optimization.

[0170] Based on the same inventive concept, the embodiments of the present application also provide a device for constructing a battery capacity data decay trajectory for implementing the method for constructing a battery capacity data decay trajectory involved above. The solution provided by the device for solving the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for constructing a battery capacity data decay trajectory provided below can refer to the limitations on the method for constructing a battery capacity data decay trajectory in the above text and will not be repeated here.

[0171] In an exemplary embodiment, Figure 7 is a schematic structural diagram of a device for constructing a battery capacity data decay trajectory provided for some embodiments, as Figure 7As shown, the construction device 700 for the battery capacity data attenuation trajectory includes:

[0172] An acquisition module 701, configured to acquire multiple capacity sampling data of the battery to be predicted; each capacity sampling data includes the battery capacity data at each charge-discharge cycle number.

[0173] A division module 702, configured to determine multiple sampling nodes according to the charge cycle numbers in each capacity sampling data, and divide the multiple capacity sampling data into multiple sampling intervals according to the multiple sampling nodes.

[0174] A fitting module 703, configured to perform polynomial function fitting on the capacity sampling data in each sampling interval according to the preset function continuity condition and boundary condition, to obtain the piecewise polynomial function of each sampling interval.

[0175] A construction module 704, configured to construct an overall spline fitting curve corresponding to the multiple capacity sampling data according to the piecewise polynomial function of each sampling interval; the overall spline fitting curve represents the battery capacity data attenuation trajectory of the battery to be predicted.

[0176] In some embodiments, the acquisition module 701 includes a data acquisition unit, a preprocessing unit, and a data cleaning unit. The data acquisition unit is configured to acquire multiple original sampling data of the battery to be predicted; each original sampling data is the battery capacity data of the battery to be predicted collected at each charge-discharge cycle number. The preprocessing unit is configured to perform preprocessing on the multiple original sampling data to obtain multiple battery capacity data to be cleaned. The data cleaning unit is configured to perform data cleaning on the multiple battery capacity data to be cleaned to obtain multiple capacity sampling data.

[0177] In some embodiments, the division module 702 is configured to obtain multiple capacity sampling data to be analyzed with the current sampling node as the starting sampling node, and determine the discrete coefficient of the multiple capacity sampling data to be analyzed; the first current sampling node is the first capacity sampling data; according to the discrete coefficient, determine the next sampling node from the multiple capacity sampling data to be analyzed; divide the capacity sampling data between the current sampling node and the next sampling node into a sampling interval; use the next sampling node as the new current sampling node, and repeat the above steps to divide the multiple capacity sampling data into multiple sampling intervals.

[0178] In some embodiments, the division module 702 is further configured to, when the discrete coefficient is less than or equal to the preset coefficient, determine the last capacity sampling data in the multiple capacity sampling data to be analyzed as the next sampling node; when the discrete coefficient is greater than the preset coefficient, determine the next sampling node from the multiple capacity sampling data to be analyzed according to the ratio between the discrete coefficient and the preset coefficient.

[0179] In some embodiments, the device 700 for constructing the battery capacity data attenuation trajectory further includes a condition determination module, which is configured to determine the first derivative value of the first sampling node in the first piecewise polynomial as the first target value and the second derivative value as the second target value, and the first derivative value of the last sampling node in the last piecewise polynomial as the third target value and the second derivative value as the fourth target value as boundary conditions; and determine that the first derivative values and the second derivative values of the common sampling nodes in two adjacent sampling intervals are the same as the function continuity conditions.

[0180] In some embodiments, the condition determination module is further configured to determine the first derivative value of the common sampling node according to the common sampling node and the previous capacity sampling data of the common sampling node; and determine the second derivative value of the common sampling node according to the common sampling node, the previous capacity sampling data of the common sampling node, and the next capacity sampling data of the common sampling node.

[0181] In some embodiments, the construction module 704 includes an interval acquisition unit, a piecewise polynomial function acquisition unit, and a curve merging unit. The interval acquisition unit is configured to acquire the extended intervals of multiple sampling intervals; the piecewise polynomial function acquisition unit is configured to determine the piecewise polynomial function of the extended interval according to the piecewise polynomial function of the last at least one sampling interval; and the curve merging unit is configured to merge the curves corresponding to the piecewise polynomial functions of each sampling interval and the curves corresponding to the piecewise polynomial functions of the extended intervals to obtain the overall spline fitting curve corresponding to multiple capacity sampling data.

[0182] The description of the above device embodiments is similar to the description of the above method embodiments and has similar beneficial effects to those of the method embodiments. For the technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0183] Each module in the above device for constructing the battery capacity data attenuation trajectory can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or be stored in the memory in the computer device in software form so that the processor can call and execute the operations corresponding to the above respective modules.

[0184] In an exemplary embodiment, Figure 8Schematic structural diagram of a computer device provided for some embodiments. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through Wireless Fidelity (WIFI), a mobile cellular network, Near Field Communication (NFC), or other technologies. The computer program, when executed by the processor, implements a method for constructing a battery capacity data attenuation trajectory. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0185] Those skilled in the art can understand that Figure 8 the structure shown in

[0186] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0187] For example, in an exemplary embodiment, when the processor executes a computer program, it realizes: obtaining a plurality of capacity sampling data of the battery to be predicted; each capacity sampling data includes battery capacity data at each charge and discharge cycle number; determining a plurality of sampling nodes according to the charge cycle numbers in each capacity sampling data, and dividing the plurality of capacity sampling data into a plurality of sampling intervals according to the plurality of sampling nodes; performing polynomial function fitting on the capacity sampling data in each sampling interval according to the preset function continuity condition and boundary condition to obtain a piecewise polynomial function for each sampling interval; constructing an overall spline fitting curve corresponding to the plurality of capacity sampling data according to the piecewise polynomial functions of each sampling interval; the overall spline fitting curve represents the decay trajectory of the battery capacity data of the battery to be predicted.

[0188] In one embodiment, a computer-readable storage medium is provided. When a computer program is executed by a processor, it realizes the steps of the method provided in any of the above embodiments.

[0189] For example, in an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are realized: obtaining a plurality of capacity sampling data of the battery to be predicted; each capacity sampling data includes battery capacity data at each charge and discharge cycle number; determining a plurality of sampling nodes according to the charge cycle numbers in each capacity sampling data, and dividing the plurality of capacity sampling data into a plurality of sampling intervals according to the plurality of sampling nodes; performing polynomial function fitting on the capacity sampling data in each sampling interval according to the preset function continuity condition and boundary condition to obtain a piecewise polynomial function for each sampling interval; constructing an overall spline fitting curve corresponding to the plurality of capacity sampling data according to the piecewise polynomial functions of each sampling interval; the overall spline fitting curve represents the decay trajectory of the battery capacity data of the battery to be predicted.

[0190] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0191] The processor, each functional module, or each functional unit in any embodiment of the present application may include any one or more of the following integrations: general-purpose processor, application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), central processing unit (CPU), graphics processing unit (GPU), embedded neural network processor (NPU), controller, microcontroller, microprocessor, programmable logic device, discrete gate or transistor logic device, discrete hardware component, quantum computing-based data processing logic unit, artificial intelligence (AI) processor, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0192] The memory or computer-readable storage medium in any embodiment of the present application may include at least one of non-volatile memory and volatile memory. The non-volatile memory includes the integration of one or more of the following: Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Ferromagnetic Random Access Memory (FRAM), Flash Memory, magnetic surface memory, optical disc, Compact Disc Read-Only Memory (CD-ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, Resistive Random Access Memory (ReRAM), Magnetoresistive Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene memory, volatile memory, etc. The volatile memory includes the integration of one or more of the following: Random Access Memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc.

[0193] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present application.

[0194] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for constructing a battery capacity data attenuation trajectory, characterized in that, The method includes: Obtaining a plurality of capacity sampling data of the battery to be predicted; each of the capacity sampling data includes battery capacity data at each charge-discharge cycle number; Determining a plurality of sampling nodes according to the charge cycle numbers in each of the capacity sampling data, and dividing the plurality of capacity sampling data into a plurality of sampling intervals according to the plurality of sampling nodes; Performing polynomial function fitting on the capacity sampling data in each of the sampling intervals according to preset function continuity conditions and boundary conditions to obtain piecewise polynomial functions of each of the sampling intervals; Constructing an overall spline fitting curve corresponding to the plurality of capacity sampling data according to the piecewise polynomial functions of each of the sampling intervals; the overall spline fitting curve represents the battery capacity data attenuation trajectory of the battery to be predicted.

2. The method according to claim 1, characterized in that The obtaining a plurality of capacity sampling data of the battery to be predicted includes: Obtaining a plurality of original sampling data of the battery to be predicted; each of the original sampling data is the battery capacity data of the battery to be predicted collected at each charge-discharge cycle number; Performing preprocessing on the plurality of original sampling data to obtain a plurality of battery capacity data to be cleaned; Performing data cleaning on the plurality of battery capacity data to be cleaned to obtain the plurality of capacity sampling data.

3. The method according to claim 1, characterized in that The determining a plurality of sampling nodes according to the charge cycle numbers in each of the capacity sampling data, and dividing the plurality of capacity sampling data into a plurality of sampling intervals according to the plurality of sampling nodes includes: Obtaining a plurality of capacity sampling data to be analyzed with the current sampling node as the starting sampling node, and determining the discrete coefficient of the plurality of capacity sampling data to be analyzed; the first current sampling node is the first capacity sampling data; Determining the next sampling node from the plurality of capacity sampling data to be analyzed according to the discrete coefficient; Dividing the capacity sampling data between the current sampling node and the next sampling node into one sampling interval; Taking the next sampling node as the new current sampling node, and repeating the above steps to divide the plurality of capacity sampling data into the plurality of sampling intervals.

4. The method according to claim 3, wherein The determining the next sampling node from the plurality of capacity sampling data to be analyzed according to the discrete coefficient includes: In the case where the discrete coefficient is less than or equal to a preset coefficient, determining the last capacity sampling data in the plurality of capacity sampling data to be analyzed as the next sampling node; In the case where the discrete coefficient is greater than the preset coefficient, determining the next sampling node from the plurality of capacity sampling data to be analyzed according to the ratio between the discrete coefficient and the preset coefficient.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Determining the first derivative value of the first sampling node in the first piecewise polynomial as the first target value and the second derivative value as the second target value, and the first derivative value of the last sampling node in the last piecewise polynomial as the third target value and the second derivative value as the fourth target value as the boundary conditions; Determining that the first derivative values and the second derivative values of the common sampling nodes in two adjacent sampling intervals are the same in the two adjacent sampling intervals as the function continuity conditions.

6. The method according to claim 5, wherein The method further includes: Determine the first derivative value of the common sampling node according to the common sampling node and the previous capacity sampling data of the common sampling node; Determine the second derivative value of the common sampling node according to the common sampling node, the previous capacity sampling data of the common sampling node, and the subsequent capacity sampling data of the common sampling node.

7. The method according to any one of claims 1 to 4, characterized in that The constructing the overall spline fitting curve corresponding to the plurality of capacity sampling data according to the piecewise polynomial functions of the respective sampling intervals includes: Obtain the extended intervals of the plurality of sampling intervals; Determine the piecewise polynomial functions of the extended intervals according to the piecewise polynomial functions of the last at least one of the sampling intervals; Merge the curves corresponding to the piecewise polynomial functions of the respective sampling intervals and the curves corresponding to the piecewise polynomial functions of the extended intervals to obtain the overall spline fitting curve corresponding to the plurality of capacity sampling data.

8. A device for constructing a battery capacity data attenuation trajectory, characterized in that, The device includes: An acquisition module, configured to acquire a plurality of capacity sampling data of a battery to be predicted; each of the capacity sampling data includes battery capacity data at each charge and discharge cycle number; A division module, configured to determine a plurality of sampling nodes according to the charge cycle numbers in each of the capacity sampling data, and divide the plurality of capacity sampling data into a plurality of sampling intervals according to the plurality of sampling nodes; A fitting module, configured to perform polynomial function fitting on the capacity sampling data in each of the sampling intervals according to preset function continuity conditions and boundary conditions, to obtain piecewise polynomial functions of the respective sampling intervals; A construction module, configured to construct an overall spline fitting curve corresponding to the plurality of capacity sampling data according to the piecewise polynomial functions of the respective sampling intervals; the overall spline fitting curve represents the battery capacity data attenuation trajectory of the battery to be predicted.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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