Battery capacity prediction method and device, medium and product

Abstract

The invention discloses a battery capacity prediction method and device, a medium and a product, and the method comprises the steps: obtaining the first cycle time and the first negative electrode particle stress of a to-be-predicted battery when a battery capacity prediction instruction is received; sending the first cycle time and the first negative electrode particle stress to a first predetermined model, so that the first predetermined model outputs a first negative electrode solid phase volume fraction; sending the first negative electrode solid phase volume fraction to a second predetermined model, so that the second predetermined model outputs new first negative electrode particle stress and first battery capacity corresponding to the first cycle time based on the first negative electrode solid phase volume fraction; increasing the first cycle time according to a first predetermined step length to obtain new first cycle time; and sending the new first cycle time and the new first negative electrode particle stress to a first predetermined model to cyclically execute the above steps, thereby obtaining first battery capacities corresponding to different first cycle times. According to the invention, the battery capacity prediction precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery life prediction, in particular to a battery capacity prediction method, device, medium and product. BACKGROUND

[0002] With the wide application of lithium ion batteries, people are increasingly concerned about their service life. In some low temperature, high temperature, high rate charging and discharging and other working conditions, the service life of lithium batteries will be significantly reduced, which makes it difficult to meet the requirements of battery life under certain conditions, and more likely to cause serious accidents, affecting its commercial large-scale application. Therefore, it is of great significance to effectively predict the service life of lithium batteries.

[0003] Battery service life is generally determined by battery capacity. Currently, the algorithms for predicting the capacity of lithium batteries are mainly divided into empirical models, data models and mechanism models. Among them, the empirical model uses a specific empirical formula to fit the capacity decay curve of the battery, which can quickly and accurately calculate the battery capacity characteristics under specific working conditions. However, this algorithm has low universality and is not suitable for complex and variable working conditions. The data model can predict the capacity decay curve of the battery under different working conditions by learning a large amount of experimental data, but its accuracy often depends on the selection of experimental data, and a large amount of experimental data is needed to improve its prediction accuracy. The mechanism model has high accuracy and universality, and the existing mechanism model uses the electrochemical performance of the battery to predict the capacity decay curve of the battery, mainly considering the negative electrode solid phase volume fraction, but the decay curve still has a large error and cannot give a more accurate description. SUMMARY

[0004] The purpose of the present application is to provide a battery capacity prediction method, device, medium and product to solve the problems described in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following solutions: In a first aspect, the present application provides a battery capacity prediction method, comprising: When receiving a battery capacity prediction instruction, obtaining a first cycle time and a first negative electrode particle stress of a battery to be predicted; sending the first cycle time and the first negative electrode particle stress to a first predetermined model, so that the first predetermined model outputs a first negative electrode solid phase volume fraction; sending the first negative electrode solid phase volume fraction to a second predetermined model, so that the second predetermined model outputs a new first negative electrode particle stress and a first battery capacity corresponding to the first cycle time based on the first negative electrode solid phase volume fraction; increasing the first cycle time by a first predetermined step to obtain a new first cycle time; The new first cycle time and the new first negative electrode particle stress are sent to the first predetermined model to iteratively execute "obtain the first cycle time and first negative electrode particle stress of the battery to be predicted" to "obtain the new first cycle time", thereby obtaining the first battery capacity corresponding to different first cycle times.

[0006] Optionally, the first predetermined model is a chemical stress mechanism model, and the second predetermined model is a quasi-two-dimensional model. The chemical stress mechanism model is used to reflect the relationship between the negative electrode solid phase volume fraction, cycle time, and negative electrode particle stress of the battery.

[0007] Optionally, the chemical stress mechanism model is represented by the following formula: in: This represents the volume fraction of the negative electrode solid phase. For the cycle time, and Two types of stress are represented, namely the maximum and minimum hydrostatic stress of the negative electrode particles during the cycling process. The yield stress of the negative electrode material. The fatigue strength factor of the negative electrode material. These parameters are determined by the actual temperature and charge / discharge rate of the battery under test.

[0008] Optionally, in the chemical stress mechanism model and This can be achieved beforehand using the following method: Obtain the capacity decay curves of the second battery under different operating conditions to obtain multiple capacity decay curves of the second battery. The different operating conditions are at least one different among temperature and charge / discharge rate. For each operating condition, based on the second battery capacity decay curve under that condition, determine and first , obtained multiple and multiple firsts ; For multiple first Calculate the average of the values ​​to obtain the second... m Values; and based on multiple temperatures, multiple charge / discharge rates, and multiple Fitting temperature, charge / discharge rate, The first function between; For any given operating condition, obtain the corresponding temperature and charge / discharge rate, and calculate the value based on the first function. .

[0009] Optionally, for each operating condition, the second battery capacity decay curve under that operating condition is used to determine... and the first , obtaining a plurality of and the first , comprising: For the second battery capacity, according to a second predetermined step size, determining a second cycle time and a second negative electrode solid phase volume fraction corresponding to different second battery capacities; Sending the second negative electrode solid phase volume fraction to a third predetermined model, so that the third predetermined model outputs a second negative electrode particle stress; Sending the second cycle time, the second negative electrode solid phase volume fraction and the second negative electrode particle stress to a fourth predetermined model, so that the fourth predetermined model outputs a second function expression related to and the first ; For each second battery capacity corresponding to a second predetermined step size, performing the above "for the second battery capacity, according to the second predetermined step size" to "so that the fourth predetermined model outputs a second function expression related to and the first "; Obtaining a plurality of second function expressions; Calculating the and the first by the plurality of second function expressions.

[0010] Optionally, for each second battery capacity, assuming that the derivative of the second negative electrode solid phase volume fraction with respect to time is equal to the derivative of the second battery capacity with respect to the cycle number, the second negative electrode solid phase volume fraction corresponding to the second battery capacity is determined by the following method: Wherein: is the negative electrode solid phase volume fraction, is the cycle time, Q is the battery capacity, and N is the cycle number.

[0011] Optionally, the calculating the and the first by the plurality of second function expressions comprises: Fitting the plurality of second function expressions using a simulated annealing algorithm to obtain initial values of and the first ; Optimizing the initial values using a Levenberg-Marquardt algorithm to obtain optimized values of and the first .

[0012] In a second aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of the first aspect.

[0013] In a third aspect, the present application provides a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the steps of the method according to any one of the first aspect.

[0014] In a fourth aspect, the present application provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of the first aspect.

[0015] According to the embodiments provided in the present application, the following technical effects are disclosed: The present application provides a battery capacity prediction method, device, medium and product. When predicting the battery capacity, the method considers not only the direct influence of the change of the negative electrode solid phase volume fraction on the battery capacity, but also the influence of the negative electrode particle stress on the change of the negative electrode solid phase volume fraction, that is, indirectly considers the influence of the negative electrode particle stress on the battery capacity, or considers the loss of active material caused by the crushing of the negative electrode particles, thereby affecting the battery capacity. The prior art only considers the influence of the negative electrode solid phase volume fraction on the battery capacity, resulting in inaccurate battery capacity prediction of the prior art. Therefore, compared with the prior art, the present application not only considers the influence of the negative electrode solid phase volume fraction on the battery capacity, but also considers the influence of the negative electrode particle stress on the battery capacity, enriches the factors affecting the battery capacity prediction, and improves the accuracy of the battery capacity prediction. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 An application environment diagram of a battery capacity prediction method according to an embodiment of the present application; Figure 2 A flowchart of a battery capacity prediction method according to an embodiment of the present application; Figure 3 A flowchart of a method for determining β and the second m method according to an embodiment of the present application; Figure 4 A method for predicting battery capacity is provided in another embodiment of the present application Figure 3 A detailed flowchart of step 302 in the method for predicting battery capacity is provided in another embodiment of the present application Figure 5 A method for predicting battery capacity is provided in another embodiment of the present application Figure 4 A detailed flowchart of step 3025 in the method for predicting battery capacity is provided in another embodiment of the present application Figure 6 A structural diagram of a computer device is provided in another embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0019] The above purposes, features and advantages of the present application will be more apparent and understandable. The present application will be described in further detail below with reference to the drawings and specific embodiments.

[0020] The battery capacity prediction method provided in the embodiments of the present application can be applied in an application environment as shown in Figure 1 The application environment includes a terminal and a server. The terminal communicates with the server through a network. A data storage system can store data required to be processed by the server. The data storage system can be separately arranged, integrated on the server, placed on a cloud or other servers.

[0021] The data required to be processed includes electrochemical performance parameters of a battery to be predicted in a current state and related data of a non-predicted battery. In the present application, the non-predicted battery can be understood as a battery that does not need to be predicted for battery capacity. The related data of the non-predicted battery includes battery capacity attenuation curves of the battery provided by a battery manufacturer under different working conditions, electrochemical performance parameters of the battery, etc. Further, the electrochemical performance parameters of the battery include hundreds of types, such as negative electrode solid phase volume fraction.

[0022] Further, the battery to be predicted can be a new battery or a used old battery, as long as the electrochemical performance parameters of the battery to be predicted in a current state, cycle time and other parameters are stored, the battery capacity prediction method described below can be used to predict the battery capacity.

[0023] The user can send a battery capacity prediction instruction to the server through the terminal. When the server receives the battery capacity prediction instruction, it executes the following battery capacity prediction method to obtain the battery capacity attenuation curve. The server can feed back the obtained battery capacity attenuation curve to the terminal. In addition, in some embodiments, the battery capacity prediction method can also be implemented by the server or the terminal alone, such as directly performing battery capacity prediction on the battery to be predicted by the terminal, or obtaining the relevant information of the battery to be predicted from the data storage system by the server and performing battery capacity prediction on the battery to be predicted.

[0024] The terminal can be, but is not limited to, various desktop computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0025] In an exemplary embodiment, as shown in Figure 2 , a battery capacity prediction method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or can be executed by a terminal and a server together. In the embodiments of the present application, the server in Figure 1 is taken as an example for illustration, which includes the following steps 201 to 205: Step 201, when receiving a battery capacity prediction instruction, obtaining a first cycle time and a first negative electrode particle stress of a battery to be predicted.

[0026] The battery capacity prediction instruction can be sent by the user to the server through the user interface displayed by the terminal. Further, the prediction instruction can carry the battery identifier of the battery to be predicted, so that the server can obtain the relevant data of the battery to be predicted from the data storage system according to the battery identifier.

[0027] The above-mentioned first cycle time and first negative electrode particle stress are the cycle time and negative electrode particle stress under the current state of the battery to be predicted. When the battery to be predicted is a new battery, the first cycle time and the first negative electrode particle stress can be obtained from the information provided by the manufacturer; when the battery to be predicted is an old battery, the first cycle time and the first negative electrode particle stress can be obtained from the related equipment using the battery to be predicted and stored in the data storage system in Figure 1 .

[0028] The cycle time is the product of the cycle number and the single cycle time, and one charge and discharge is counted as one cycle. In the present application, it is assumed that the single cycle time is a fixed time, for example, 10 hours.

[0029] wherein the negative electrode particle stress can be calculated using a pseudo-two-dimensions (P2D) model. When the negative electrode particle stress is calculated using the P2D model, the input of the P2D model is electrochemical performance parameters. The electrochemical performance parameters include more than one hundred, mainly negative electrode solid phase volume parameters.

[0030] Further, the negative electrode particle stress output by the P2D model includes multiple types, such as the maximum hydrostatic stress and the minimum hydrostatic stress of the negative electrode particle during the cycle process, respectively denoted as and .

[0031] Further, the electrochemical performance parameters can be obtained from the factory manual provided by the manufacturer.

[0032] Step 202, sending the first cycle time and the first negative electrode particle stress to a first predetermined model, so that the first predetermined model outputs a first negative electrode solid phase volume fraction.

[0033] wherein the first predetermined model is a model capable of determining the first negative electrode solid phase volume fraction from the first cycle time and the first negative electrode particle stress, such as a chemical stress mechanism model.

[0034] Step 203, sending the first negative electrode solid phase volume fraction to a second predetermined model, so that the second predetermined model outputs a new first negative electrode particle stress corresponding to the first cycle time and a first battery capacity corresponding to the first cycle time based on the first negative electrode solid phase volume fraction.

[0035] In this application, the first battery capacity is the battery capacity of the battery to be predicted, and the second battery capacity is the battery capacity of the non-predicted battery. The first battery capacity decay curve is the battery capacity decay curve of the battery to be predicted, and the second battery capacity decay curve is the battery capacity decay curve of the non-predicted battery.

[0036] It should be noted here that although the negative electrode solid phase volume fraction is an important parameter affecting the negative electrode particle stress and the battery capacity, in addition to the negative electrode solid phase volume fraction, other electrochemical performance parameters will also affect the negative electrode particle stress and the battery capacity to a greater or lesser extent. Therefore, in addition to the negative electrode solid phase volume fraction, the parameters input into the second predetermined model also include more than one hundred other electrochemical performance parameters.

[0037] wherein the second predetermined model can be a P2D model.

[0038] Step 204, increasing the first cycle time by a first predetermined step to obtain a new first cycle time.

[0039] The first predetermined step length can be determined according to experience, such as 1000 hours.

[0040] In step 205, the new first cycle time and the new first negative electrode particle stress are sent to the first predetermined model to cyclically execute steps 201-204, so as to obtain the first battery capacity corresponding to different first cycle times.

[0041] When the first predetermined model receives the new first cycle time and the new first negative electrode particle stress, the new first negative electrode solid phase volume fraction is calculated, and the new first negative electrode volume fraction is sent to the second predetermined model, so that the second predetermined model outputs the new first negative electrode particle stress and the battery capacity. This cycle continues until the second battery capacity decays to a predetermined capacity. The predetermined capacity is small, generally less than the battery capacity required to support normal operation of the device, such as 3%.

[0042] Further, different first cycle times correspond to different first battery capacities, thereby establishing a correspondence between the first cycle time and the first battery capacity, and obtaining a battery capacity decay curve.

[0043] Further, the first cycle time can be converted into the first cycle number, thereby establishing a correspondence between the first cycle number and the first battery capacity, and obtaining another battery capacity decay curve.

[0044] The application provides a battery capacity prediction method through the above embodiments. When predicting the battery capacity, in addition to considering the direct impact of the change of the negative electrode solid phase volume fraction on the battery capacity, the impact of the negative electrode particle stress on the change of the negative electrode solid phase volume fraction is also considered, that is, the impact of the negative electrode particle stress on the battery capacity is indirectly considered, or the loss of active material caused by the crushing of the negative electrode particles is considered, thereby affecting the battery capacity. For the prior art, only the impact of the negative electrode solid phase volume fraction on the battery capacity is considered, resulting in inaccurate battery capacity predicted by the prior art. Therefore, compared with the prior art, the application not only considers the impact of the negative electrode solid phase volume fraction on the battery capacity, but also considers the impact of the negative electrode particle stress on the battery capacity, thereby enriching the factors affecting the battery capacity prediction and improving the accuracy of the battery capacity prediction.

[0045] In addition, in the later stage of battery use, the crushing of the negative electrode particles is more serious, which will cause serious loss of active material of the negative electrode particles, thereby causing rapid decay of the battery capacity. Therefore, by introducing the factor of the negative electrode particle stress, the loss of active material caused by the crushing of the negative electrode particles is considered, so as to improve the prediction accuracy of the feature of rapid decay of the battery capacity in the later stage of battery cycle.

[0046] Optionally, in another exemplary embodiment of the present application, the first predetermined model is a chemical stress mechanism model, and the second predetermined model is a quasi-two-dimensional model, and the chemical stress mechanism model is used to reflect the relationship among the solid phase volume fraction of the negative electrode, the cycle time, and the negative electrode particle stress of the battery.

[0047] Further, the chemical stress mechanism model is represented by the following formula (1): (1) Wherein: is the solid phase volume fraction of the negative electrode, is the cycle time, and are two negative electrode particle stresses, which are the maximum hydrostatic stress and the minimum hydrostatic stress of the negative electrode particles during the cycle process, is the yield stress of the negative electrode material, is the fatigue strength factor of the negative electrode material, is a parameter determined by the actual temperature and the charge-discharge rate of the battery to be tested.

[0048] In the present application, it is assumed that the working condition of the battery to be tested is always unchanged.

[0049] Wherein: and can be calculated by the quasi-two-dimensional model.

[0050] Wherein: is the inherent property of the negative electrode material, which is related to the negative electrode material, and can be obtained by consulting relevant data, such as relevant data provided by the manufacturer.

[0051] In addition, it should be noted that the chemical stress mechanism model is derived from the S-N (stress-life) curve of the material, combined with the linear fatigue cumulative damage theory, and then the equivalent replacement is made to obtain the relationship between the solid phase volume fraction of the battery and the negative electrode particle stress. Further, according to the actual test data of the battery, compared with the positive electrode material, the proportion of the negative active material mainly affects the cycle life of the battery, so only the related parameters of the performance of the negative electrode material of the battery are considered in the chemical stress mechanism model.

[0052] Optionally, referring to Figure 3 , the and in the chemical stress mechanism model are obtained by the following steps 301-304 in advance: Step 301, obtaining the second battery capacity attenuation curves under different working conditions to obtain a plurality of second battery capacity attenuation curves, wherein the different working conditions are at least one of the temperature and the charge-discharge rate.

[0053] The second battery capacity decay curve can be a curve showing the relationship between the number of second cycles and the capacity of the second battery.

[0054] The second battery capacity degradation curve here is provided by the manufacturer or obtained through other means, and is a known battery capacity degradation curve.

[0055] In addition, after obtaining the capacity decay curves of the second battery under different operating conditions, the data can be cleaned to remove irrelevant data and retain the correspondence between the second battery capacity and the second cycle time or the number of second cycles.

[0056] Step 302: For each operating condition, based on the second battery capacity degradation curve under that operating condition, determine... β and first m , obtained multiple β and multiple firsts m .

[0057] For example, multiple and multiple firsts m As shown in Table 1 below: Table 1. Under different working conditions and first

[0058] First m It is mainly determined by battery performance; different materials or batches of batteries will have different performance characteristics. m They may be different.

[0059] Step 303, for multiple first m Calculate the average of the values ​​to obtain the second... m Values; and based on multiple temperatures, multiple charge / discharge rates, and multiple β Fitting temperature, charge / discharge rate, β The first function between them.

[0060] Among them, the second m This refers to the chemical stress mechanism model mentioned above. m .

[0061] For example, The relationship between temperature and charge / discharge rate is linear, and the fitted relationship is shown in the following formula (2): (2) Where T represents temperature, C represents charge / discharge rate, and a, b, and d are constant coefficients.

[0062] Step 304: For any operating condition, obtain the corresponding temperature and charge / discharge rate, and calculate the value based on the first function.β .

[0063] Exemplarily, taking formula (2) as an example, first, the temperature and the charge-discharge rate corresponding to the working condition are obtained, and then the obtained temperature and the charge-discharge rate are substituted into formula (2), so that .

[0064] In addition, the application predicts the battery capacity based on the chemical stress mechanism model, which not only has high prediction accuracy, but also has good universality, and does not depend on a large amount of experimental data. Only the fitting results under several characteristic working conditions are needed to predict the capacity attenuation trend of the battery under most working conditions, that is, the working condition extrapolation in the charge-discharge rate and the temperature can be realized.

[0065] Optionally, referring to Figure 4 For each working condition, step 302 is implemented by the following steps 3021-3025: Step 3021, for the second battery capacity, the second cycle time and the second negative electrode solid phase volume fraction corresponding to different second battery capacities are determined according to the second predetermined step length.

[0066] Wherein, for the second cycle time, the following method is used: for each second battery capacity, the corresponding second cycle number is obtained from the second battery capacity attenuation curve, and then the product of the second cycle number and the time required for a single cycle is calculated to obtain the second cycle time.

[0067] Further, for each second battery capacity, the derivative of the negative electrode solid phase volume fraction with respect to time is assumed to be equal to the derivative of the battery capacity with respect to the cycle number, and the second negative electrode solid phase volume fraction corresponding to the second battery capacity is determined by the following method: Wherein: is the negative electrode solid phase volume fraction, is the cycle time, and Q is the battery capacity, and N is the cycle number.

[0068] Step 3022, the second negative electrode solid phase volume fraction is sent to a third predetermined model, so that the third predetermined model outputs a second negative electrode particle stress.

[0069] It should be noted here that when calculating the second negative electrode particle stress based on the second negative electrode solid phase volume fraction, the third predetermined model needs the second negative electrode solid phase volume fraction and other electrochemical performance parameters.

[0070] Wherein, the third predetermined model is a P2D model, that is, the third predetermined model is the same as the second predetermined model.

[0071] Step 3023, send the second cycle time, the second negative electrode solid phase volume fraction and the second negative electrode particle stress to the fourth predetermined model, so that the fourth predetermined model outputs the second function of the first function and the second function. and the first function.

[0072] Wherein, the fourth predetermined model is a chemical stress mechanism model, that is, the fourth predetermined model is the same as the first predetermined model.

[0073] Further, the second function can be simplified as Wherein, Q can be calculated by E is calculated by .

[0074] Step 3024, for each second battery capacity corresponding to a second predetermined step, execute the above steps 3021-3023 to obtain a plurality of second functions.

[0075] Exemplarily, the plurality of second functions includes three, as follows: Step 3025, calculate the second function of the first function and the second function by the plurality of second functions. β and the first m .

[0076] It should be noted here that when obtaining the second battery capacity, it is obtained according to the second predetermined step, which can improve the balance of the obtained second battery capacity and improve the accuracy of the second function. Of course, it can also be obtained without the second predetermined step, as long as it can obtain enough second battery capacity to calculate a plurality of second functions.

[0077] In this application, the quasi-two-dimensional model is mainly used to calculate the negative electrode particle stress and the battery capacity. The related content of the quasi-two-dimensional model can be referred to in the related prior art, which will not be described in detail herein.

[0078] Optionally, referring to Figure 5 In another exemplary embodiment of the present application, step 3025 is implemented by the following steps 3025a and 3025b: Step 3025a, use the simulated annealing algorithm to fit the plurality of second functions to obtain the initial value of the first function and the second function. and the first function.

[0079] Further, the process of step 3025a is as follows: Simulate the internal energy E as the objective function value ftemperature T evolution into control parameters t , from initial solution i and control parameter initial value t , the iteration of "generating new solution -> calculating objective function difference -> accepting or discarding" is repeated for the current solution, and the t value is gradually attenuated , and the current solution at the termination of the algorithm is the obtained approximate optimal solution.

[0080] Further, the specific steps of the simulated annealing algorithm are as follows: Step 1: Select an initial solution , let the current solution ; the current iteration step number ; the current temperature ; Step 2: If the inner loop stop condition is reached at this temperature, go to Step 3; otherwise, randomly generate a new solution , calculate , if , then ; otherwise, if , then , repeat Step 2; Step 3: , , if the termination condition is met, go to Step 4, otherwise go back to Step 2; Step 4: Output the calculation result and stop.

[0081] Step 3025b, using the Levenberg-Marquardt algorithm to optimize the initial value, obtaining and the optimized value of the first .

[0082] Further, the process of Step 3025b is as follows: Step 1: Given the initial value , and the initial optimization radius ; Step 2: For the kth iteration, calculate the Jacobian matrix , solve the matrix equation , and obtain the step size ; Step 3: Calculate ; Step 4: If , this iteration is valid, take ; Step 5: If , this iteration is invalid, take , n is the current number of consecutive invalid iterations; Step 6: judging whether the result meets the error requirement, if yes, outputting the fitting result and stopping iteration, otherwise, returning to step 2.

[0083] The present application calculates and the first , combines the simulated annealing algorithm and the Levenberg-Marquardt algorithm, and has the characteristics of low dependence on initial value selection of the simulated annealing algorithm and the advantages of fast solution speed and high precision of the Levenberg-Marquardt algorithm, so that and the first are quickly and accurately obtained.

[0084] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in Figure 6 . The computer device includes a processor, a memory, an input / output interface (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 operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store battery capacity prediction related data. 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 through network connection. The computer program is executed by the processor to implement a battery capacity prediction method.

[0085] Those skilled in the art can understand Figure 6 that the structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0086] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0087] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0088] In an example embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0089] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0090] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present 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 storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric 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 but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0091] The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0092] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, it should be understood that the application encompasses all possible combinations of the technical features described above.

[0093] The principles and implementation manners of the present application are described herein by using specific examples, and the above embodiments are only used to help understand the method of the present application and its core idea; meanwhile, according to the idea of the present application, the specific implementation manners and application scopes will be changed by those skilled in the art. In conclusion, the content of the present specification should not be understood as a limitation of the present application.

Claims

1. A method for predicting battery capacity, characterized in that, include: When a battery capacity prediction command is received, the first cycle time and the first negative electrode particle stress of the battery to be predicted are obtained. The first cycle time and the first negative electrode particle stress are sent to the first predetermined model so that the first predetermined model outputs the first negative electrode solid phase volume fraction. The first negative electrode solid phase volume fraction is sent to the second predetermined model so that the second predetermined model outputs a new first negative electrode particle stress and a first battery capacity corresponding to the first cycle time based on the first negative electrode solid phase volume fraction. The first cycle time is increased by the first predetermined step size to obtain a new first cycle time; The new first cycle time and the new first negative electrode particle stress are sent to the first predetermined model to iteratively execute "obtain the first cycle time and first negative electrode particle stress of the battery to be predicted" to "obtain the new first cycle time", thereby obtaining the first battery capacity corresponding to different first cycle times.

2. The battery capacity prediction method according to claim 1, characterized in that, The first predetermined model is a chemical stress mechanism model, and the second predetermined model is a quasi-two-dimensional model. The chemical stress mechanism model is used to reflect the relationship between the negative electrode solid phase volume fraction, cycle time, and negative electrode particle stress of the battery.

3. The battery capacity prediction method according to claim 2, characterized in that, The chemical stress mechanism model is represented by the following formula: in: This represents the volume fraction of the negative electrode solid phase. For the cycle time, and Two types of stress are represented, namely the maximum and minimum hydrostatic stress of the negative electrode particles during the cycling process. The yield stress of the negative electrode material. The fatigue strength factor of the negative electrode material. These parameters are determined by the actual temperature and charge / discharge rate of the battery under test.

4. The battery capacity prediction method according to claim 3, characterized in that, In the chemical stress mechanism model and This can be achieved beforehand using the following method: Obtain the capacity decay curves of the second battery under different operating conditions to obtain multiple capacity decay curves of the second battery. The different operating conditions are at least one different among temperature and charge / discharge rate. For each operating condition, based on the second battery capacity decay curve under that condition, determine and first , obtained multiple and multiple firsts ; For multiple first Calculate the average of the values ​​to obtain the second... m Values; and based on multiple temperatures, multiple charge / discharge rates, and multiple Fitting temperature, charge / discharge rate, The first function between; For any given operating condition, obtain the corresponding temperature and charge / discharge rate, and calculate the value based on the first function. .

5. The battery capacity prediction method according to claim 4, characterized in that, For each operating condition, the second battery capacity decay curve under that operating condition is determined... and first , obtained multiple and multiple firsts ,include: For the second battery capacity, the second cycle time and the second negative electrode solid phase volume fraction are determined according to the second predetermined step size; The volume fraction of the second negative electrode solid phase is sent to the third predetermined model so that the third predetermined model outputs the stress of the second negative electrode particles. The second cycle time, the second negative electrode solid volume fraction, and the second negative electrode particle stress are sent to the fourth predetermined model so that the fourth predetermined model outputs relevant... and first The second function expression; For each second predetermined step size corresponding to the second battery capacity, execute "For the second battery capacity, according to the second predetermined step size" to "so that the fourth predetermined model outputs relevant information". and first The second function expression is obtained by deriving multiple second function expressions; The calculation is performed using the multiple second functional expressions. and first .

6. The battery capacity prediction method according to claim 5, characterized in that, For each second battery capacity, assuming that the derivative of the second negative electrode solid phase volume fraction with respect to time is equal to the derivative of the second battery capacity with respect to the number of cycles, the second negative electrode solid phase volume fraction corresponding to the second battery capacity is determined by the following method: in: This represents the volume fraction of the negative electrode solid phase. Where is the cycle time, is the battery capacity, and is the number of cycles.

7. The battery capacity prediction method according to claim 5, characterized in that, The calculation is performed using the plurality of second functional expressions. and first ,include: The simulated annealing algorithm was used to fit the multiple second functions to obtain... and first The initial value; The initial values ​​were optimized using the Levenberg-Marquardt algorithm to obtain... and first The optimized value.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the battery capacity prediction method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the battery capacity prediction method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the battery capacity prediction method according to any one of claims 1-7.

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