Method, device and equipment for predicting state of health of battery, medium and program product

Abstract

The invention relates to a battery health state prediction method and device, equipment, a medium and a program product. The method comprises the steps of obtaining design data, process data and test and characterization data of a to-be-predicted target battery; inputting the design data, the process data and the test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of a health state curve of the target battery; determining a health state curve corresponding to the target battery according to a pre-constructed health state decline relational expression and the at least one key feature; wherein the health state decline relational expression is used for representing an association relationship between the health state and the use duration. The method can accurately predict the health state of the battery, and accelerates the research and development speed of the battery.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, equipment, medium and program product for predicting the health status of a battery. Background Art

[0002] With the development of new energy technologies, secondary batteries represented by lithium-ion batteries have gradually gained market favor due to their high energy density and low cost, and have been widely used in automotive power, energy storage and other fields.

[0003] At present, the battery SOH (State of Health) test cycle is long and the adjustment dimensions are wide, which seriously prolongs the R&D cycle. Therefore, a method that can accurately predict SOH is urgently needed to accelerate the speed of battery R&D. Summary of the invention

[0004] Based on the above problems, the present application provides a method, device, equipment, medium and program product for predicting the health status of a battery, which can accurately predict the health status of the battery and accelerate the research and development of the battery.

[0005] In a first aspect, the present application provides a method for predicting a battery health status, the method comprising: obtaining design data, process data, test and characterization data of a target battery to be predicted; inputting the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of a health status curve of the target battery; determining a health status curve corresponding to the target battery based on a pre-constructed health status decay relationship and at least one key feature; wherein the health status decay relationship is used to characterize the correlation between the health status and the usage time.

[0006] The technical solution provided in the embodiment of the present application is a feature prediction model that can predict the health status of the battery based on design data, process data, test and characterization data, taking full account of the influence of materials and processes. In addition, the feature prediction model focuses on the core degradation mechanism and outputs key features related to the trend of changes in the health status, which is conducive to improving the accuracy of health status prediction, thereby accelerating the research and development of batteries.

[0007] In some embodiments, the health state curve corresponding to the target battery is determined based on a pre-constructed health state decay relationship and at least one key feature, including: substituting at least one key feature as a parameter into the health state decay relationship; calculating the health state corresponding to different usage times according to the health state decay relationship after substituting the parameters, and obtaining the health state curve corresponding to the target battery. In the technical solution provided in the embodiment of the present application, the feature prediction model obtains the key features through the dynamic relationship between the data learning features, and the health state decay relationship provides prior knowledge of battery decay (such as the nonlinear law of degradation). The combination of the two can accurately predict the health state of the target battery and reduce the dependence on massive data, especially in scenarios with insufficient data or high noise.

[0008] In some embodiments, the method further includes: obtaining actual curves of the health status of multiple sample batteries, and determining actual curves belonging to the same design type based on the design data of each sample battery; performing curve fitting on multiple actual curves belonging to the same design type and the initial decay formula, and determining the target value of each parameter item in the initial decay formula based on the fitting situation; substituting the target value of each parameter item into the initial decay formula to obtain a health status decay relationship. The technical solution provided in the embodiment of the present application utilizes the actual curve of the health status of the sample battery to fit the decay formula, automatically captures the core laws of the battery decay process, and the extracted features are directly related to the SOH dynamics, ensuring the physical meaning and validity of the features. In addition, it provides a basis for using the health status decay relationship to predict SOH.

[0009] In some embodiments, the parameter items in the health state decay relationship include exponential items, and multiple actual curves belonging to the same design type are curve-fitted with the initial decay formula, and the target value of each parameter item in the initial decay formula is determined according to the fitting situation, including: obtaining a set of candidate values ​​for the exponential item; in each round of iteration, substituting a candidate value in the candidate value set into the initial decay formula to obtain an intermediate decay formula, and fitting multiple actual curves belonging to the same design type with the curve corresponding to the intermediate decay formula to obtain an average fitting error; determining the target value of the exponential item according to the average fitting error of multiple rounds of iterations. In the technical solution of the embodiment of the present application, overfitting can be avoided by iteratively determining the target value of the exponential item, ensuring that the target value of the exponential item is more stable and reliable.

[0010] In some embodiments, the health state decay relationship includes one of the Arrhenius exponential formula, the battery decay power function formula, and the inflection point fitting formula; the parameter items in the Arrhenius exponential formula include the battery decay rate and the constant item; the parameter items in the battery decay power function formula include the coefficient items of the root square, the linear shape, the second-order power, and the constant item; the parameter items in the inflection point fitting formula include the horizontal coordinate of the slope change point, the starting slope, the vertical coordinate of the starting time, and the slope difference before and after the inflection point. The health state decay relationship in the embodiment of the present application provides the key features of the health state curve (such as slope, inflection point, decay rate, etc.), can automatically capture the core laws of the battery decay process, and the extracted features are directly related to the SOH dynamics, ensuring the physical meaning and effectiveness of the features.

[0011] In some embodiments, the method further includes: obtaining design data, process data, test and characterization data of multiple sample batteries, and target values ​​of parameter items obtained by fitting the actual curves of the health status of multiple sample batteries; using the design data, process data, test and characterization data as training samples, and using the target values ​​of the parameter items as annotations for model training to obtain a feature prediction model. In the technical solution of the embodiment of the present application, the target value of the parameter value is the key feature of the health status curve. Direct modeling of the key features can focus on the core decay mechanism and reduce the complexity of the model. At the same time, the interpretability of the features also provides a physical basis for subsequent analysis. In addition, using the key features as the direct output target of the machine learning model can reduce the impact of noise and the overfitting problem of machine learning, and improve the accuracy and stability of model training. Furthermore, the trained model only needs to predict a small number of key features (such as 2-3 parameters), rather than the entire SOH curve, reducing the computational burden of real-time prediction.

[0012] In some embodiments, the design data, process data, test and characterization data of the target battery to be predicted are obtained, including: obtaining the design information, process information and test and characterization information of the target battery; performing data preprocessing on the design information, process information and test and characterization information to obtain the design data, process data, test and characterization data of the target battery. In the technical solution of the embodiment of the present application, the influence of factors such as design, process, and test on the health status is fully considered, so that the prediction of SOH for the battery under development can be realized. In addition, physical and chemical parameters, process adjustments, etc. are taken into consideration in the prediction, which is conducive to improving the accuracy of the prediction of the health status.

[0013] In a second aspect, the present application also provides a device for predicting a battery health status, the device comprising:

[0014] A data acquisition module, used to acquire design data, process data, test and characterization data of the target battery to be predicted;

[0015] A feature prediction module, used to input the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of the health status curve of the target battery;

[0016] The health state determination module is used to determine the health state curve corresponding to the target battery based on a pre-constructed health state decay relationship and at least one key feature; wherein the health state decay relationship is used to characterize the correlation between the health state and the usage time.

[0017] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any one of the methods in the first aspect when executing the computer program.

[0018] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method of any one of the first aspects is implemented.

[0019] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements any one of the methods in the first aspect when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the optional embodiments below. The accompanying drawings are only used for the purpose of illustrating the optional embodiments and are not to be considered as limiting the present application. Moreover, the same reference numerals are used throughout the drawings to represent the same components. In the drawings:

[0021] Figure 1 is a schematic diagram of an application environment of a method for predicting a battery health state according to an embodiment of the present application;

[0022] Figure 2 is a flowchart of a method for predicting a battery health status according to an embodiment of the present application;

[0023] Figure 3 is a flowchart of the steps of determining a health status curve corresponding to a target battery according to an embodiment of the present application;

[0024] Figure 4 is a flowchart of steps for constructing a health state decay relationship in one embodiment of the present application;

[0025] Figure 5 is a flowchart of the step of determining the target value of each parameter item in one embodiment of the present application;

[0026] Figure 6It is a flowchart of a training process of a feature prediction model according to an embodiment of the present application;

[0027] Figure 7 It is a flowchart of the steps of obtaining design data, process data, test and characterization data of a target battery according to an embodiment of the present application;

[0028] Figure 8 This is one of the structural block diagrams of a device for predicting a battery health status according to an embodiment of the present application;

[0029] Fig. 9 This is the second structural block diagram of the device for predicting the battery health status according to one embodiment of the present application;

[0030] Fig.10 This is the third structural block diagram of the device for predicting the battery health status according to one embodiment of the present application;

[0031] Fig.11 It is a diagram of the internal structure of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following embodiments of the technical solution of the present application are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians 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" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0034] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0035] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0036] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0037] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0038] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0039] With the development of new energy technologies, secondary batteries represented by lithium-ion batteries have gradually gained market favor due to their high energy density and low cost, and have been widely used in the fields of vehicle power and energy storage. During the use and storage of batteries, the capacity fades, the internal resistance increases, and the performance declines due to cyclic charging and discharging, chemical reaction aging, and environmental factors (such as temperature, deep discharge, etc.). Therefore, it is necessary to evaluate the SOH of the battery and quantify the degree of battery aging or damage by comparing the deviation between its current performance and the initial design performance.

[0040] At present, due to the long test cycle and wide adjustment dimensions of battery SOH, the research and development cycle is seriously extended. Therefore, there is an urgent need for a method that can accurately predict SOH to accelerate the research and development of batteries. However, most of the existing SOH prediction methods are aimed at marketed batteries, and do not consider the impact of adjustments at the material and process levels on SOH. Therefore, the prediction effect for R&D batteries is not ideal. Moreover, the relationship between material and process adjustments and SOH is nonlinear, and some existing prediction models are also difficult to accurately predict SOH. Furthermore, the existing SOH prediction methods are difficult to associate with battery degradation mechanisms, and are therefore difficult to use to summarize battery degradation mechanisms.

[0041] In response to the above-mentioned problems, an embodiment of the present application provides a method for predicting the health status of a battery, which obtains the design data, process data, test and characterization data of the target battery to be predicted; inputs the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of the health status curve of the target battery; and determines the health status curve corresponding to the target battery based on a pre-constructed health status decay relationship and at least one key feature. In the technical solution provided by the embodiment of the present application, the feature prediction model can predict the health status of the battery based on the design data, process data, test and characterization data, taking into full account the influence of materials and processes; and the feature prediction model focuses on the core decay mechanism and outputs key features related to the health status change trend, which is conducive to improving the accuracy of health status prediction, thereby accelerating the research and development of batteries.

[0042] The battery health status prediction method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The application environment includes a terminal 102 and a server 104, wherein the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers. The data storage system can store various data in the battery research and development and production process and various data related to the battery health status. When the SOH of the target battery needs to be predicted, the terminal 102 can indicate the target battery to be predicted to the server 104. The server 104 can pre-train the feature prediction model and construct the health state decay relationship, and then obtain the design data, process data, test and characterization data of the target battery from the data storage system, and use the design data, process data, test and characterization data, feature prediction model and health state decay relationship of the target battery to predict the health state of the target battery. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0043] According to some embodiments of the present application, referring to Figure 2 , provides a battery health status prediction method, which can be applied to Figure 1 Taking the server in as an example, the following steps may be included:

[0044] Step 201, obtaining design data, process data, test and characterization data of a target battery to be predicted.

[0045] Among them, the test and characterization data includes the SOH sequence data of the battery.

[0046] The terminal obtains the design data, process data, test and characterization data of the target battery to be predicted, and sends the design data, process data, test and characterization data to the server. The server can obtain the design data, process data, test and characterization data of the target battery.

[0047] In some embodiments, the terminal obtains battery information of the target battery to be predicted, such as battery identification, battery type, etc., and then sends the battery information to the server. The server receives the battery information and obtains design data, process data, test and characterization data of the target battery from the data storage system according to the battery information.

[0048] In some embodiments, the server may also trigger a prediction task at a preset time to automatically obtain design data, process data, test and characterization data of the target battery from a data storage system.

[0049] It should be noted that the method for obtaining design data, process data, test and characterization data is not limited to the above examples and can be set according to actual conditions.

[0050] Step 202 , input the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of the health status curve of the target battery.

[0051] Among them, the key features are used to characterize the representative features of the health status curve during the change process. For example, the key features can be the inflection point position of the health status curve, the slope of the curve before and after the inflection point, the decay rate of the health status, etc. It should be noted that the key features are not limited to the above examples and can be set according to actual conditions.

[0052] The server pre-trains a feature prediction model whose input is design data, process data, test and characterization data, and whose output is the key features of the health status curve.

[0053] In actual applications, after obtaining the design data, process data, test and characterization data of the target battery, the server inputs the design data, process data, test and characterization data into the feature prediction model. The feature prediction model analyzes the internal reaction rate, physical and chemical changes, and health status decay of the target battery based on the design data, process data, test and characterization data, and then outputs the key features of the health status curve of the target battery.

[0054] Step 203: Determine a health state curve corresponding to the target battery according to a pre-constructed health state degradation relationship and at least one key feature.

[0055] The health state decay equation is used to characterize the correlation between the health state and the usage time. For example, the health state decay equation can be expressed as SOH=f(t), where SOH is the health state and t is the usage time. The usage time can be date, hours, number of cycles, etc.

[0056] The health status curve includes health status corresponding to different usage durations. For example, the health status curve includes the health status corresponding to January 1, the health status corresponding to February 1, and the health status corresponding to December 1; the health status curve may also include the health status corresponding to t1 hours of use, the health status corresponding to t2 hours of use, and the health status corresponding to tn hours of use; the health status curve may also include the health status corresponding to 10 cycles, the health status corresponding to 50 cycles, and the health status corresponding to 100 cycles.

[0057] The server can know the decay of the health status based on the key characteristics and determine the important parameters in the health status decay relationship; then, the server sets multiple sampling points of usage time, substitutes each sampling point into the health status decay relationship, obtains the health status corresponding to each sampling point, and the health status curve of the target battery is composed of the health status corresponding to multiple sampling points.

[0058] In the above embodiment, the design data, process data, test and characterization data of the target battery to be predicted are obtained; the design data, process data, test and characterization data of the target battery are input into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of the health status curve of the target battery; based on the pre-constructed health status decay relationship and at least one key feature, the health status curve corresponding to the target battery is determined. In the technical solution provided by the embodiment of the present application, the feature prediction model can predict the health status of the battery based on the design data, process data, test and characterization data, taking into full account the influence of materials and processes; and the feature prediction model focuses on the core decay mechanism and outputs key features related to the health status change trend, which is conducive to improving the accuracy of health status prediction, thereby accelerating the research and development speed of the battery.

[0059] According to some embodiments of the present application, the health state decay relationship includes one of the Arrhenius exponential formula, the battery decay power function formula and the inflection point fitting formula.

[0060] The Arrhenius exponential formula refers to formula (1), and the parameter terms in the Arrhenius exponential formula include the battery degradation rate and the constant term.

[0061] --------------------------(1)

[0062] Among them, x is the usage time, y is the health state SOH, b is the constant term, k is the battery degradation rate, and z is the exponential term. This formula is derived based on the Arrhenius formula. The parameter k is greatly affected by temperature, and the parameter z is related to the battery degradation reaction. The Arrhenius formula describes the relationship between the chemical reaction rate constant and temperature.

[0063] The formula of the battery degradation power function refers to formula (2). The parameter terms in the formula of the battery degradation power function include square root, linear, second-order power coefficient terms and constant terms.

[0064] ------------------------(2)

[0065] Among them, x is the usage time, y is the health status SOH, a is a constant term, b, c, d are the coefficient terms of root square, linear, and second-order power respectively. This formula is easy to fit and has good generalization.

[0066] The inflection point fitting formula refers to formula (3). The parameters in the inflection point fitting formula include the horizontal coordinate of the slope change point, the starting slope, the vertical coordinate of the starting time, and the slope difference before and after the inflection point.

[0067] ---(3)

[0068] Among them, x is the usage time; y is the health status SOH; a is the horizontal coordinate of the slope change point; b is the starting slope; c is the vertical coordinate of the starting time, that is, the vertical coordinate when x=0; d is the slope difference before and after the inflection point.

[0069] It should be noted that formula (3) has a good fitting ability for SOH curves with significant inflection points, and the formula parameters are related to the geometric information of the SOH curve, but it is not suitable for fitting SOH curves without inflection points.

[0070] The health state decay relationship in the embodiment of the present application provides key features of the health state curve (such as slope, inflection point, decay rate, etc.), which can automatically capture the core laws of the battery decay process. The extracted features are directly related to the SOH dynamics, ensuring the physical meaning and validity of the features.

[0071] According to some embodiments of the present application, referring to Figure 3 In the above embodiment, “determining a health state curve corresponding to the target battery according to a pre-constructed health state decay relationship and at least one key feature” may include the following steps:

[0072] Step 301: Substitute at least one key feature as a parameter into a health state decay equation.

[0073] Taking the health state decay relation as Arrhenius exponential formula as an example, the key features include battery decay rate and constant term. That is, the design data, process data, test and characterization data of the target battery are input into the feature prediction model, and the feature prediction model outputs the target values ​​of battery decay rate and constant term. When calculating the SOH of the target battery, the battery decay rate and constant term are substituted into the health state decay relation.

[0074] Taking the health state degradation relationship as an example, which is a battery degradation power function formula, the key features include square roots, linearity, coefficients of second-order powers, and constant terms. That is, the design data, process data, test and characterization data of the target battery are input into the feature prediction model, and the feature prediction model outputs square roots, linearity, coefficients of second-order powers, and constant terms. When calculating the SOH of the target battery, the square roots, linearity, coefficients of second-order powers, and constant terms are substituted into the health state degradation relationship.

[0075] Taking the health state decay relationship as an inflection point fitting formula as an example, the key features include the horizontal coordinate of the slope change point, the starting slope, the vertical coordinate of the starting time, and the slope difference before and after the inflection point. That is, the design data, process data, test and characterization data of the target battery are input into the feature prediction model, and the feature prediction model outputs the horizontal coordinate of the slope change point, the starting slope, the vertical coordinate of the starting time, and the slope difference before and after the inflection point. When calculating the SOH of the target battery, the horizontal coordinate of the slope change point, the starting slope, the vertical coordinate of the starting time, and the slope difference before and after the inflection point are substituted into the health state decay relationship.

[0076] Step 302, calculating the health status corresponding to different usage time according to the health status decay relationship after substituting the parameters, and obtaining the health status curve corresponding to the target battery.

[0077] After the parameters are substituted into the health state decay equation, multiple pre-selected sampling points (usage time x) are substituted into the health state decay equation in turn, and multiple health states y are calculated to obtain a health state curve corresponding to the target battery.

[0078] For example, multiple sampling points are x1=50 hours, x2=100 hours, x3=150 hours, etc. Substitute x1 into the health state decay relationship to calculate the health state y1; substitute x2 into the health state decay relationship to calculate the health state y2; substitute x3 into the health state decay relationship to calculate the health state y3. And so on, multiple health states y1, y2, y3, etc. are obtained, and the health state curve corresponding to the target battery is composed of multiple health states.

[0079] In some embodiments, a boundary condition may be set and the health state curve of the target battery may be determined according to the boundary condition. For example, if the boundary condition is SOH ≥ 80%, when forming the health state curve, the portion with a health state less than 80% is discarded and the portion with a health state greater than or equal to 80% is retained.

[0080] In the above embodiment, at least one key feature is substituted as a parameter into the health state decay relationship; the health state corresponding to different usage time is calculated according to the health state decay relationship after the parameter is substituted, and the health state curve corresponding to the target battery is obtained. In the technical solution provided in the embodiment of the present application, the feature prediction model obtains the key features through the dynamic relationship between the data learning features, and the health state decay relationship provides the prior knowledge of battery decay (such as the nonlinear law of degradation). The combination of the two can accurately predict the health state of the target battery and reduce the dependence on massive data, especially in scenarios with insufficient data or high noise.

[0081] According to some embodiments of the present application, referring to Figure 4 , the process of constructing the health state decay relationship may include the following steps:

[0082] Step 401 : obtaining actual curves of health status of a plurality of sample batteries, and determining actual curves belonging to the same design type according to design data of each sample battery.

[0083] The multiple sample batteries may be batteries tested using a traditional testing method, or may be batteries that are in use or have been used.

[0084] For each sample battery, the health status of the sample battery is recorded multiple times during the life cycle of the sample battery, and an actual curve of the health status of the sample battery is constructed based on the recorded data.

[0085] In some embodiments, the sample battery is set in a new energy vehicle, and the new energy vehicle uploads the health status of the sample battery to the server at intervals of a preset time; or, the new energy vehicle uploads the health status of the sample battery to the server at intervals of a preset number of cycles. The server can establish an actual curve of the health status of the sample battery based on the data uploaded by the new energy vehicle, with time or number of cycles as the horizontal axis and health status as the vertical axis.

[0086] In some embodiments, the test device tests the sample battery and uploads the health status of the sample battery to the server at each preset time interval; or uploads the health status of the sample battery to the server at each preset number of cycles. The server can establish an actual curve of the health status of the sample battery based on the data uploaded by the test device, with time or number of cycles as the horizontal axis and health status as the vertical axis.

[0087] The actual curves of the health status of multiple sample batteries form a curve sequence Batt={batt1, batt2, batt3, ... battn}, where battn is the actual curve corresponding to the nth sample battery.

[0088] It should be noted that the traditional method may use batteries of different design types for fitting, which may easily lead to inaccurate fitting. However, the present application first obtains the actual curves of the health status of multiple sample batteries, and then groups the actual curves according to the design data, thereby selecting the actual curves belonging to the same design type, so as to fit the batteries of a specific design, which can avoid cross-design interference and improve the accuracy of the fitting results.

[0089] Step 402 , curve fitting is performed between a plurality of actual curves belonging to the same design type and the initial decay formula, and target values ​​of various parameter items in the initial decay formula are determined according to the fitting results.

[0090] Among them, the initial decay formula is a healthy state decay relationship with unknown parameters. For example, the initial decay formula is an Arrhenius exponential formula with unknown battery decay rate and constant terms; or, the initial decay formula is a battery decay power function formula with unknown coefficient terms of root square, linear, second-order power and constant terms; or, the initial decay formula is an inflection point fitting formula with unknown horizontal coordinates of the slope change point, the starting slope, the vertical coordinates of the starting time and the slope difference before and after the inflection point.

[0091] Multiple actual curves of the same design type are curve-fitted with the initial decay formula, for example, actual curve batt1 is curve-fitted with the exponential formula of Arrhenius with unknown parameters, actual curve batt2 is curve-fitted with the exponential formula of Arrhenius with unknown parameters...actual curve battn is curve-fitted with the exponential formula of Arrhenius with unknown parameters. According to the fitting situation, the target values ​​of the battery decay rate and the constant term in the exponential formula of Arrhenius can be determined.

[0092] The process of fitting multiple actual curves of the same design type with the battery degradation power function formula and the inflection point fitting formula to obtain the target value of each parameter item is described in detail in the above example, and the embodiments of the present application will not be repeated here.

[0093] Step 403, substituting the target value of each parameter item into the initial decay formula to obtain the health state decay relationship.

[0094] After obtaining the target value of each parameter item, the target value is substituted into the initial degradation formula to obtain the health state degradation relationship. For example, the battery degradation rate and constant term are substituted into the Arrhenius exponential formula with unknown parameters to obtain the Arrhenius exponential formula with known parameters, that is, the health state degradation relationship is obtained.

[0095] In the above embodiment, the actual curves of the health status of multiple sample batteries are obtained, and the actual curves belonging to the same design type are determined based on the design data of each sample battery; the multiple actual curves belonging to the same design type are curve-fitted with the initial decay formula, and the target value of each parameter item in the initial decay formula is determined based on the fitting situation; the target value of each parameter item is substituted into the initial decay formula to obtain the health status decay relationship. The technical solution provided in the embodiment of the present application utilizes the actual curve of the health status of the sample battery to fit the decay formula, automatically captures the core laws of the battery decay process, and the extracted features are directly related to the SOH dynamics, ensuring the physical meaning and effectiveness of the features. In addition, it provides a basis for SOH prediction using the health status decay relationship.

[0096] According to some embodiments of the present application, the parameter term in the health state decay relationship includes an exponential term, referring to Figure 5 In the above embodiment, “curve fitting multiple actual curves belonging to the same design type with the initial decay formula, and determining the target value of each parameter item in the initial decay formula according to the fitting situation” may include the following steps:

[0097] Step 501: Obtain a candidate value set for an index item.

[0098] Taking the Arrhenius exponential formula as an example, in the fitting process, the candidate value set Z={z1, z2, z3…zm} of the exponential term z is first obtained, where zm is the candidate value used in the mth iteration and m is a positive integer.

[0099] Step 502, in each round of iteration, a candidate value in the candidate value set is substituted into the initial decay formula to obtain an intermediate decay formula, and multiple actual curves belonging to the same design type are fitted with the curve corresponding to the intermediate decay formula to obtain an average fitting error.

[0100] Taking the Arrhenius exponential formula with unknown parameters as an example, in the first round of iteration, the first candidate value z1 in the candidate value set is substituted into the initial decay formula to obtain the intermediate decay formula. After that, multiple actual curves belonging to the same design type are fitted with the curve corresponding to the intermediate decay formula in turn to obtain the constant term b and the battery decay rate k in the Arrhenius exponential formula. After each actual curve is fitted with the curve corresponding to the intermediate decay formula, a fitting error can be obtained. The fitting errors corresponding to multiple actual curves of the same design type are averaged to obtain the average fitting error E1 of the first round of iteration.

[0101] According to the above iterative process, the second round of iteration, the third round of iteration, and finally the mth round of iteration can be calculated to obtain multiple average fitting errors E1, E2, ..., Em.

[0102] Step 503: determine the target value of the exponential term according to the average fitting error of multiple rounds of iterations.

[0103] The minimum average fitting error Emin in multiple rounds of iterations is determined, and the candidate value zmin corresponding to the minimum average fitting error Emin is determined as the target value of the exponential term.

[0104] It is understandable that if the target value z of the exponential term is selected based only on a single battery, it may be interfered by experimental noise; however, the present application calculates the average fitting error for sample batteries of the same design type, and can smooth individual differences through group data to ensure that the target value z of the selected exponential term is more stable and reliable. In addition, if a single battery is fitted directly, its individual characteristics may be overfitted; however, the embodiment of the present application adopts the minimum value of the average fitting error, which can improve the universal applicability of the parameter item to batteries of the same type as the sample battery and avoid overfitting.

[0105] In the above embodiment, a candidate value set of the exponential term is obtained; in each round of iteration, a candidate value in the candidate value set is substituted into the initial decay formula to obtain an intermediate decay formula, and multiple actual curves belonging to the same design type are respectively fitted with the curve corresponding to the intermediate decay formula to obtain an average fitting error; the target value of the exponential term is determined according to the average fitting error of multiple rounds of iterations. In the technical solution of the embodiment of the present application, overfitting can be avoided by iteratively determining the target value of the exponential term, ensuring that the target value of the exponential term is more stable and reliable.

[0106] According to some embodiments of the present application, referring to Figure 6 , the training process of the feature prediction model can include the following steps:

[0107] Step 601, obtaining design data, process data, test and characterization data of a plurality of sample batteries, and target values ​​of parameter items obtained by fitting actual curves of health status of the plurality of sample batteries.

[0108] The server can obtain design data, process data, test and characterization data of multiple sample batteries from the data storage system; according to the method of the above embodiment, fitting is performed based on the actual curves of the health status of multiple sample batteries to obtain the target value of each parameter item in the health status degradation relationship.

[0109] Step 602, using the design data, process data, test and characterization data of the sample battery as training samples, and using the target values ​​of the parameter items as annotations to perform model training to obtain a feature prediction model.

[0110] The overall process of training the feature prediction model includes steps such as model construction, model training, hyperparameter optimization, and statistical model error.

[0111] Model construction can first build multiple applicable machine learning models, and adjust the model structure to make each machine learning model suitable for design data, process data, test and characterization data as training samples, and the target value of the parameter item as an annotation.

[0112] Model training should select an appropriate training method based on the machine learning model.

[0113] Hyperparameter optimization adjusts the main hyperparameters of the machine learning model. You can use automatic hyperparameter optimization methods such as Bayesian optimization to make the model achieve the best results on the current training samples and annotations. Among them, hyperparameters are parameters that need to be manually set before training the machine learning model. Hyperparameters are not adjusted through the learning of the optimization algorithm during the training process, but are determined by developers or researchers based on experience, experiments, or automatic tuning methods. Bayesian hyperparameter optimization is a method used in the field of machine learning to find the optimal hyperparameter combination.

[0114] The statistical model error should obtain the model output and calculate the average difference between the corresponding annotation as the model estimation error. Finally, the model with the smallest estimation error is selected as the feature prediction model.

[0115] In the above embodiment, the design data, process data, test and characterization data of multiple sample batteries, and the target values ​​of the parameter items obtained by fitting the actual curves of the health status of the multiple sample batteries are obtained; the design data, process data, test and characterization data are used as training samples, and the target values ​​of the parameter items are used as annotations for model training to obtain a feature prediction model. In the technical scheme of the embodiment of the present application, the target value of the parameter value is the key feature of the health status curve. Direct modeling of the key features can focus on the core decay mechanism and reduce the complexity of the model. At the same time, the interpretability of the features also provides a physical basis for subsequent analysis. In addition, using the key features as the direct output target of the machine learning model can reduce the impact of noise and the overfitting problem of machine learning, and improve the accuracy and stability of model training. Furthermore, the trained model only needs to predict a small number of key features (such as 2-3 parameters), rather than the entire SOH curve, which reduces the computational burden of real-time prediction.

[0116] According to some embodiments of the present application, referring to Figure 7 In the above embodiment, “obtaining design data, process data, test and characterization data of the target battery to be predicted” may include the following steps:

[0117] Step 701, obtaining design information, process information, and testing and characterization information of a target battery.

[0118] Among them, design information includes component size, positive electrode material and ratio, negative electrode material and ratio, electrolyte material and ratio, separator selection, binder material and ratio, etc.; process information includes first effect, compaction density, substrate thickness, coating weight, cold pressing density, cold pressing thickness, active film area size, film resistance, pole piece adhesion, OH, hot pressing pressure / time, etc.; test and characterization information includes voltage, current, capacity, temperature, electrochemical impedance spectroscopy data, X-ray diffraction data, scanning electron microscope data, etc. of charge and discharge tests. Test and characterization information should include the SOH sequence data of the battery.

[0119] The server may obtain design information, process information, and test and characterization information of the target battery from the terminal or from a data storage system.

[0120] Step 702, preprocessing the design information, process information, and test and characterization information to obtain the design data, process data, and test and characterization data of the target battery.

[0121] Among them, data preprocessing can include one or more of data cleaning, data conversion, feature engineering, categorical variable conversion, and data segmentation. Data cleaning can remove abnormal data and perform data filling, etc. Data conversion can convert the format and normalize the data; feature engineering can calculate known information to obtain new information; categorical variable conversion can convert the dimension and physical unit of data, and data segmentation can group data according to a preset time window.

[0122] It should be noted that the data preprocessing method is not limited to the above examples and can be set according to actual conditions.

[0123] The server performs data preprocessing on the acquired design information, process information, and test and characterization information to obtain the design data, process data, test and characterization data of the target battery.

[0124] In the above embodiment, the design information, process information, and test and characterization information of the target battery are obtained; the design information, process information, and test and characterization information are preprocessed to obtain the design data, process data, test and characterization data of the target battery. In the technical solution of the embodiment of the present application, the influence of factors such as design, process, and test on the health status is fully considered, so that the prediction of SOH for the battery under development can be realized. In addition, physical and chemical parameters, process adjustments, etc. are taken into account in the prediction, which is conducive to improving the accuracy of the prediction of the health status.

[0125] According to some embodiments of the present application, a method for predicting the health status of a battery is provided. Figure 1 Taking the computer device in the example as an example, the following steps may be included:

[0126] Step 1, obtaining actual curves of the health status of multiple sample batteries; wherein the multiple sample batteries belong to the same design type.

[0127] Step 2: Obtain a set of candidate values ​​for the index item.

[0128] Step 3: In each round of iteration, a candidate value in the candidate value set is substituted into the initial decay formula to obtain an intermediate decay formula, and multiple actual curves belonging to the same design type are fitted with the curve corresponding to the intermediate decay formula to obtain an average fitting error.

[0129] Step 4: Determine the target value of the exponential term based on the average fitting error of multiple rounds of iterations.

[0130] Step 5: Substitute the target value of each parameter item into the initial decay formula to obtain the health state decay relationship.

[0131] Step 6, obtaining design data, process data, test and characterization data of multiple sample batteries, and target values ​​of parameter items obtained by fitting actual curves of the health status of the multiple sample batteries.

[0132] Step 7: Use the design data, process data, test and characterization data as training samples, and use the target values ​​of the parameter items as annotations to perform model training to obtain a feature prediction model.

[0133] Step 8, obtaining design information, process information, and testing and characterization information of the target battery.

[0134] Step 9, preprocessing the design information, process information, and test and characterization information to obtain the design data, process data, test and characterization data of the target battery.

[0135] Step 10, inputting the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of the health status curve of the target battery.

[0136] Step 11, substitute at least one key feature as a parameter into a health state decay relationship; calculate the health state corresponding to different usage time according to the health state decay relationship after substituting the parameters, and obtain the health state curve corresponding to the target battery.

[0137] The technical solution provided in the embodiment of the present application is a feature prediction model that can predict the health status of the battery based on design data, process data, test and characterization data, taking full account of the influence of materials and processes. In addition, the feature prediction model focuses on the core degradation mechanism and outputs key features related to the trend of changes in the health status, which is conducive to improving the accuracy of health status prediction, thereby accelerating the research and development of batteries.

[0138] It should be understood that, although the various steps in the above flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above flowchart may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0139] Based on the same inventive concept, the embodiment of the present application also provides a battery health state prediction device for implementing the battery health state prediction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more battery health state prediction device embodiments provided below can refer to the limitations of the battery health state prediction method above, and will not be repeated here.

[0140] According to some embodiments of the present application, referring to Figure 8 , provides a battery health status prediction device, the device comprising:

[0141] The data acquisition module 801 is used to acquire the design data, process data, test and characterization data of the target battery to be predicted;

[0142] A feature prediction module 802 is used to input the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of the health state curve of the target battery;

[0143] The health state determination module 803 is used to determine the health state curve corresponding to the target battery based on a pre-constructed health state decay relationship and at least one key feature; wherein the health state decay relationship is used to characterize the correlation between the health state and the usage time.

[0144] In some embodiments, the health status determination module 803 is specifically used to substitute at least one key feature as a parameter into a health status decay relationship; calculate the health status corresponding to different usage times according to the health status decay relationship after substituting the parameters, and obtain the health status curve corresponding to the target battery.

[0145] In some embodiments, reference Fig. 9 , the device further comprises:

[0146] An actual curve acquisition module 804 is used to acquire actual curves of health status of multiple sample batteries, and determine actual curves belonging to the same design type according to design data of each sample battery;

[0147] The curve fitting module 805 is used to perform curve fitting on multiple actual curves of the same design type and the initial decay formula, and determine the target value of each parameter item in the initial decay formula according to the fitting situation;

[0148] The relationship determination module 806 is used to substitute the target value of each parameter item into the initial decay formula to obtain the health state decay relationship.

[0149] In some embodiments, the parameter terms in the health status decay relationship include exponential terms, and the curve fitting module 805 is specifically used to obtain a set of candidate values ​​for the exponential terms; in each round of iteration, a candidate value in the candidate value set is substituted into the initial decay formula to obtain an intermediate decay formula, and multiple actual curves belonging to the same design type are respectively fitted with the curve corresponding to the intermediate decay formula to obtain an average fitting error; the target value of the exponential term is determined based on the average fitting error of multiple rounds of iterations.

[0150] In some embodiments, the health state decay relationship includes one of an Arrhenius exponential formula, a battery decay power function formula, and an inflection point fitting formula;

[0151] The parameter terms in Arrhenius' exponential formula include the battery degradation rate and the constant term;

[0152] The parameter terms in the battery degradation power function formula include root square, linear, second-order power coefficient terms and constant terms;

[0153] The parameters in the inflection point fitting formula include the horizontal coordinate of the slope change point, the starting slope, the vertical coordinate of the starting time, and the slope difference before and after the inflection point.

[0154] In some embodiments, reference Fig.10 , the device further comprises:

[0155] The sample acquisition module 807 is used to acquire design data, process data, test and characterization data of multiple sample batteries, and target values ​​of parameter items obtained by fitting actual curves of health status of multiple sample batteries;

[0156] The model training module 808 is used to use the design data, process data, test and characterization data as training samples and the target values ​​of the parameter items as labels to perform model training and obtain a feature prediction model.

[0157] In some embodiments, the data acquisition module 801 is specifically used to obtain design information, process information, and test and characterization information of the target battery; perform data preprocessing on the design information, process information, and test and characterization information to obtain the design data, process data, test and characterization data of the target battery.

[0158] Each module in the above-mentioned battery health status prediction device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0159] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store predicted data of the battery health state. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the battery health state is implemented.

[0160] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present 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 a different arrangement of components.

[0161] According to some embodiments of the present application, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory including instructions, and the above instructions can be executed by a processor of an electronic device to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0162] According to some embodiments of the present application, a computer program product is also provided, and when the computer program is executed by a processor, the above method can be implemented. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, part or all of the above method can be implemented in whole or in part according to the process or function of the embodiment of the present application.

[0163] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (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, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0164] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0165] The above embodiments only express several implementation methods of the present application, which is convenient for understanding the technical solution of the present application in detail, but it cannot be understood as limiting the scope of protection of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. It should be understood that the technical solutions obtained by those skilled in the art through logical analysis, reasoning or limited experiments on the basis of the technical solutions provided in the present application are all within the protection scope of the claims attached to the present application. Therefore, the protection scope of the patent of this application shall be based on the content of the attached claims, and the description and drawings can be used to explain the content of the claims.

Claims

1. A method for predicting battery health status, characterized in that: The method comprises: Obtain design data, process data, test and characterization data of the target battery to be predicted; Inputting the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing to obtain at least one key feature of the health state curve of the target battery; wherein the key feature is used to characterize the representative features of the health state curve during the change process; The health state curve corresponding to the target battery is determined according to a pre-constructed health state decay relationship and at least one of the key features; wherein the health state decay relationship is used to characterize the correlation between the health state and the usage time.

2. The method according to claim 1, characterized in that The determining, according to the pre-constructed health state decay relation and at least one of the key features, a health state curve corresponding to the target battery includes: Substituting at least one of the key features as a parameter into the health state decay relationship; The health states corresponding to different usage times are calculated according to the health state decay relationship after substituting the parameters, and the health state curve corresponding to the target battery is obtained.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Acquire actual curves of health status of multiple sample batteries, and determine actual curves belonging to the same design type according to design data of each of the sample batteries; Performing curve fitting on the multiple actual curves belonging to the same design type and the initial decay formula, and determining the target value of each parameter item in the initial decay formula according to the fitting situation; Substituting the target value of each parameter item into the initial decay formula, the health state decay relational expression is obtained.

4. The method according to claim 3, characterized in that The parameter items in the health state decay relationship include exponential items, and the curve fitting of the multiple actual curves belonging to the same design type with the initial decay formula, and determining the target value of each parameter item in the initial decay formula according to the fitting situation, includes: Obtaining a set of candidate values ​​for the index item; In each round of iteration, a candidate value in the candidate value set is substituted into the initial decay formula to obtain an intermediate decay formula, and a plurality of actual curves belonging to the same design type are respectively fitted with the curve corresponding to the intermediate decay formula to obtain an average fitting error; The target value of the exponential term is determined according to the average fitting error of multiple rounds of iterations.

5. The method according to claim 4, characterized in that The health state decay relationship includes one of the Arrhenius exponential formula, the battery decay power function formula and the inflection point fitting formula; The parameter terms in the Arrhenius exponential formula include the battery degradation rate and the constant term; The parameter terms in the battery degradation power function formula include square root, linear, second-order power coefficient terms and constant terms; The parameters in the inflection point fitting formula include the abscissa of the slope change point, the starting slope, the ordinate of the starting time, and the slope difference before and after the inflection point.

6. The method according to claim 3, characterized in that The method further comprises: Acquire design data, process data, test and characterization data of the plurality of sample batteries, and target values ​​of parameter items obtained by fitting actual curves of health status of the plurality of sample batteries; The design data, process data, test and characterization data of the sample battery are used as training samples, and the target values ​​of the parameter items are used as labels for model training to obtain the feature prediction model.

7. The method according to claim 1, characterized in that The obtaining of design data, process data, test and characterization data of the target battery to be predicted includes: Obtaining design information, process information, and testing and characterization information of the target battery; The design information, the process information and the test and characterization information are preprocessed to obtain the design data, process data, test and characterization data of the target battery.

8. A battery health status prediction device, characterized in that: The device comprises: A data acquisition module, used to acquire design data, process data, test and characterization data of the target battery to be predicted; A feature prediction module, used to input the design data, process data, test and characterization data of the target battery into a pre-trained feature prediction model for prediction processing, and obtain at least one key feature of the health state curve of the target battery; wherein the key feature is used to characterize the representative features of the health state curve during the change process; A health status determination module is used to determine the health status curve corresponding to the target battery based on a pre-constructed health status decay relationship and at least one of the key features; wherein the health status decay relationship is used to characterize the correlation between the health status and the usage time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When a processor executes the computer program, the method of any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program implements the method of any one of claims 1 to 7 when executed by a processor.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method of any one of claims 1 to 7 is implemented.

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