CN116705210B

CN116705210B - Construction method of battery cell aging model and battery cell full life cycle performance prediction method

Info

Publication number: CN116705210B
Application number: CN202310969587.6A
Authority: CN
Inventors: 王新雷; 苏泽良; 苏仰涛; 周鑫; 黄贤坤; 吴兴远; 魏奕民
Original assignee: Contemporary Amperex Technology Co Ltd
Current assignee: Contemporary Amperex Technology Co Ltd
Priority date: 2023-08-03
Filing date: 2023-08-03
Publication date: 2024-06-21
Anticipated expiration: 2043-08-03
Also published as: CN116705210A

Abstract

The application relates to a method for constructing an aging model of a battery cell and a method for predicting the full life cycle performance of the battery cell. The method for constructing the battery cell aging model comprises the following steps: acquiring material identification data of a calibrated electrochemical model, wherein the calibrated electrochemical model is a sub-model in a pre-built cell aging model, the pre-built cell aging model comprises a calibrated electrochemical model and a pre-built attenuation mechanism model which are coupled with each other, matching a cell material attenuation parameter value corresponding to the material identification data of the electrochemical model from a pre-set cell material attenuation parameter library, and performing assignment processing on a cell material attenuation parameter of the pre-built attenuation mechanism model according to the cell material attenuation parameter value to obtain the cell aging model. The method for constructing the battery cell aging model can support accurate evaluation of the full life cycle performance of the battery cell. By adopting the method for constructing the battery cell aging model, the full life cycle performance of the battery cell can be accurately estimated.

Description

Construction method of battery cell aging model and battery cell full life cycle performance prediction method

Technical Field

The present application relates to the field of battery core technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for constructing a battery core aging model.

Background

In the development process of the power battery cell, the test and evaluation of the aging performance of the whole life cycle of the battery cell are an indispensable part in determining the design scheme of the battery cell. At present, the evaluation of the full life cycle performance of the battery cell is generally performed by calibrating a battery cell aging model based on a large amount of test data and then evaluating the full life cycle performance of the battery cell based on the calibrated battery cell aging model.

Regarding the calibration of the cell aging model, a traditional electrochemical simulation commercial software is generally adopted to establish the cell aging model, and then the calibrated cell aging model is adopted to evaluate the performance of the full life cycle of the cell after the cell aging model is debugged and calibrated through a large amount of test data.

However, in the above manner, the model simulation accuracy deviation is still large, and accurate evaluation of the full life cycle performance of the battery cell is difficult to realize.

Disclosure of Invention

Based on the foregoing, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for constructing a battery cell aging model capable of supporting accurate evaluation of the full life cycle performance of a battery cell, and a method, an apparatus, a computer device, a computer readable storage medium and a computer program product for predicting the full life cycle performance of a battery cell capable of realizing accurate evaluation of the full life cycle performance of a battery cell.

In a first aspect, the application provides a method for constructing a battery cell aging model. The method comprises the following steps:

Acquiring material identification data of a calibrated electrochemical model;

Matching a core material attenuation parameter value corresponding to the material identification data of the electrochemical model from a preset core material attenuation parameter library;

performing assignment treatment on the core material attenuation parameters of the pre-constructed attenuation mechanism model according to the core material attenuation parameter values to obtain a core aging model;

The calibrated electrochemical model is a sub-model in a pre-built cell aging model, and the pre-built cell aging model comprises a calibrated electrochemical model and a pre-built attenuation mechanism model which are coupled with each other.

In the technical scheme of the embodiment of the application, different from the traditional mode of manually modeling by relying on a professional simulation engineer, the battery material attenuation parameter library is built in advance, the battery cell aging model is built by taking the electrochemical model as a basis and coupling the attenuation mechanism model in advance, in the subsequent model building process, corresponding battery material attenuation parameter values are matched in the preset battery material attenuation parameter library only according to the material identification data of the electrochemical model, and then the battery cell material attenuation parameters of the attenuation mechanism model are assigned, so that the battery cell aging model can be obtained. The whole process is high in automation degree without complex data processing, operators can complete rapid modeling without professional simulation knowledge, and an attenuation mechanism model is coupled on the basis of an electrochemical model, so that performance evaluation of the whole life cycle of the battery cell can be realized. In summary, the adoption of the scheme can support the accurate evaluation of the full life cycle performance of the battery cell.

In some embodiments, prior to obtaining the material identification data for the calibrated electrochemical model, further comprising:

Receiving a model pre-construction instruction, wherein the model pre-construction instruction carries electrochemical parameters and cyclic aging test data of the battery cell to be tested;

according to electrochemical parameters and cyclic aging test data, parameter calibration is carried out on the pre-constructed electrochemical model, and a calibrated electrochemical model is obtained;

And coupling the calibrated electrochemical model with a pre-constructed attenuation mechanism model to obtain a pre-constructed cell aging model.

According to the technical scheme provided by the embodiment of the application, an operator can realize the calibration of the electrochemical model and the pre-construction of the cell aging model only by inputting relevant cell data, and the operator does not need to have professional model simulation knowledge, so that the construction process of the cell aging model is simplified, and the degree of automation is high.

In some embodiments, the method further comprises:

extracting target aging test data from the cyclic aging test data;

And writing the target aging test data into a preset file.

According to the technical scheme, the target aging test data in the cyclic aging test data are extracted, and the target aging test data are written into the preset file, so that the data can be conveniently taken out, and convenience is brought to the follow-up correction of the core aging model.

In some embodiments, after obtaining the cell aging model, further comprising:

Receiving a model correction instruction;

and correcting the cell aging model based on the target aging test data.

According to the technical scheme provided by the embodiment of the application, the battery cell aging model is corrected based on the target aging test data, so that the prediction precision of the battery cell aging model can be improved, and the accuracy of the simulation result is further improved.

In some embodiments, correcting the cell burn-in model based on the target burn-in test data includes:

calling a battery cell aging model to simulate the battery cell performance, and obtaining battery cell performance simulation results in different service life stages;

And correcting the cell aging model based on the target aging test data and the cell performance simulation result.

According to the technical scheme, the battery cell aging model is corrected based on the target aging test data and the battery cell performance simulation result of the battery cell aging model, the characteristics and rules of the battery cell aging process can be accurately captured through the comparison analysis of the target aging test data and the battery cell performance simulation result, and further the battery cell aging model can be corrected more accurately, and the prediction precision of the battery cell aging model is improved.

In some embodiments, correcting the cell burn-in model based on the target burn-in test data and the cell performance simulation results includes:

obtaining target aging test data and an error value of a cell performance simulation result;

determining an objective function based on the error value;

And calling a preset parameter optimizing algorithm to optimize the objective function, and continuously adjusting the attenuation parameter value of the core material of the cell aging model until the optimal solution of the objective function is obtained, and determining the attenuation parameter value of the core material to obtain the corrected cell aging model.

According to the technical scheme provided by the embodiment of the application, the optimal parameter value of the material attenuation parameter of the battery cell aging model can be rapidly determined through the parameter optimizing algorithm based on the target aging test data and the error value of the battery cell performance simulation result, so that the battery cell aging model is more accurate.

In some embodiments, after the cell aging model is called to perform cell performance simulation to obtain the cell performance simulation results of different life stages, the method further includes:

and visually displaying the simulation results of the battery cell performance in different life stages.

According to the technical scheme, through visual display of the battery cell performance simulation result, operators can conveniently and flexibly check and analyze data, and convenience is improved.

In a second aspect, the application provides a method for predicting full life cycle performance of a battery cell. The method comprises the following steps:

acquiring cycle storage working condition data of a battery cell to be tested;

taking the cycle storage working condition data as input, calling a battery cell aging model to conduct full life cycle performance iterative prediction on the battery cell to be tested, and obtaining a full life cycle performance prediction result of the battery cell to be tested;

The battery cell aging model is constructed by adopting the steps in the embodiment of the method for constructing the battery cell aging model.

In the battery cell full life cycle performance prediction method, the battery cell aging model is a model with high simulation accuracy constructed through an automatic model, and the battery cell aging model is coupled with an attenuation mechanism model and an electrochemical model, so that not only can capacity attenuation prediction be realized, but also other deep battery cell attenuation information prediction can be realized, and the effect of realizing the performance prediction of the battery cell under the full life cycle can be achieved. According to the whole scheme, based on the battery cell aging model, accurate performance prediction of the whole life cycle of the battery cell can be realized by only inputting the cycle storage working condition data of the battery cell to be detected, and the accuracy and the efficiency of the performance prediction of the whole life cycle of the battery cell are improved.

In some embodiments, taking the cycle storage working condition data as input, calling the cell aging model to conduct full life cycle performance iterative prediction on the cell to be tested, and obtaining the full life cycle performance prediction result of the cell to be tested includes:

Taking the cycle storage working condition data as input, and calling an electrochemical model to predict the performance so as to obtain a performance prediction result under the current cycle working condition;

taking a performance prediction result as input, and calling an attenuation mechanism model to predict service life attenuation so as to obtain service life attenuation change characteristic data;

And feeding back the life attenuation change characteristic data to the electrochemical model, and returning to the step of calling the electrochemical model to predict the performance of the cycle storage working condition data until the preset iteration stop condition is reached, so as to obtain the performance prediction result of the full life cycle of the battery cell to be tested.

According to the technical scheme, through coupling iteration of the electrochemical model and the attenuation mechanism model, performance prediction of the whole life cycle of the battery cell can be achieved, capacity attenuation data can be obtained, and performance prediction results except the capacity attenuation data can be obtained.

In a third aspect, the application further provides a device for constructing the battery cell aging model. The device comprises:

The data acquisition module is used for acquiring material identification data of the calibrated electrochemical model;

The data matching module is used for matching the electric core material attenuation parameter value corresponding to the material identification data of the electrochemical model from a preset electric core material attenuation parameter library;

the assignment processing module is used for carrying out assignment processing on the core material attenuation parameters of the pre-constructed attenuation mechanism model according to the core material attenuation parameter values to obtain a core aging model;

In a third aspect, the application further provides a device for predicting the full life cycle performance of the battery cell. The device comprises:

the working condition data acquisition module is used for acquiring the cycle storage working condition data of the battery cell to be tested;

the performance prediction module is used for taking the cycle storage working condition data as input, calling the battery cell aging model to conduct full life cycle performance iterative prediction on the battery cell to be tested, and obtaining a full life cycle performance prediction result of the battery cell to be tested;

In a fourth aspect, the present application also provides a computer device. The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the steps in the method embodiment for constructing the cell aging model or the method embodiment for predicting the full life cycle performance of each cell.

In a fifth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has a computer program stored thereon, which when executed by a processor, implements the steps in the above-described embodiments of the method for constructing the cell aging model or the above-described embodiments of the method for predicting the full life cycle performance of each cell.

In a sixth aspect, the application also provides a computer program product. The computer program product comprises a computer program which is executed by a processor to realize the steps in the embodiment of the method for constructing the aging model of each cell or the embodiment of the method for predicting the full life cycle performance of each cell.

The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.

Drawings

FIG. 1 is a diagram of an application environment of a method for building a cell aging model in some embodiments of the application;

FIG. 2 is a flow chart of a method for constructing a cell aging model according to some embodiments of the present application;

FIG. 3 is a flow chart of a method for constructing a cell aging model according to other embodiments of the present application;

FIG. 4 is a flowchart of a method for constructing a cell aging model according to still other embodiments of the present application;

FIG. 5 is a detailed flowchart of a method for constructing a cell aging model according to some embodiments of the present application;

FIG. 6 is a detailed flowchart of a method for constructing a cell aging model according to other embodiments of the present application;

FIG. 7 is a flow chart of a method for building a cell aging model according to some embodiments of the application;

FIG. 8 is a flowchart illustrating a method for predicting full life cycle performance of a battery cell according to some embodiments of the present application;

FIG. 9 is a flowchart illustrating a full life cycle performance prediction process for a battery cell according to some embodiments of the present application;

FIG. 10 is a block diagram of a device for modeling cell burn-in accordance with some embodiments of the present application;

FIG. 11 is a block diagram illustrating an apparatus for modeling cell burn-in accordance with further embodiments of the present application;

FIG. 12 is a block diagram illustrating a full life cycle performance prediction apparatus of a battery cell according to some embodiments of the present application;

Fig. 13 is an internal block diagram of a computer device in some embodiments of the application.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In the description of the embodiments of the present application, the term "and/or" is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

In the description of the embodiments of the present application, the term "plurality" means two or more (including two), and similarly, "plural sets" means two or more (including two), and "plural sheets" means two or more (including two).

In recent years, with the rapid development of new energy automobile markets, electric automobiles become a main trend of automobile industry development, and the requirements of users on battery performance are also higher and higher. In order to keep the core competitiveness of the product, research and development investment is continuously increased, the product is continuously updated, and the testing and evaluation of the aging performance of the full life cycle of the battery in the research and development process are an essential part for determining the design scheme.

At present, the evaluation of the full life cycle performance of the battery is to calibrate an aging model based on a large amount of test data, and mainly focuses on the prediction of capacity fading, and less research on changes such as DCR (Direct Current Resistance direct current resistance) mapping, lithium analysis windows and the like is performed. In the process of calibrating the aging model, a simulation engineer with high experience seriously depends on the experience and proficiency to manually establish the cell aging model and debug the calibration model through traditional electrochemical simulation business software to perform simulation calculation. According to the scheme, on one hand, electrochemical simulation business software is high in use threshold and low in intelligent degree, performances (such as DCR MAPPING, a lithium analysis window and the like) of all aspects of the full life cycle of the lithium ion battery are difficult to evaluate, and model simulation accuracy deviation is large; on the other hand, modeling and debugging time is long, the aging performance of the lithium ion battery is tested in various types, and the battery core is required to be manufactured and then tested, so that the manufacturing and testing period is long, and a large amount of resources are consumed.

Based on the above consideration, in order to solve the problems of low intelligent degree and large deviation of model simulation accuracy of the battery cell aging model, a battery cell aging model construction platform is designed, and a battery cell aging model construction method is provided. Specifically, a cell material attenuation parameter library is pre-built for a target cell, and a pre-built attenuation mechanism model is coupled on the basis of an electrochemical model to serve as an initial cell aging model. In the actual model construction process, operators do not need to have professional simulation modeling knowledge, only need to input corresponding electric core information and send electric core aging model construction instructions, a background program can calibrate an electrochemical model according to the input electric core information, and according to material identification data such as material names of the calibrated electrochemical model, electric core material attenuation parameter values corresponding to the material names of the electrochemical model are matched from a preset electric core material attenuation parameter library, finally, according to the electric core material attenuation parameter values, assignment processing is carried out on electric core material attenuation parameters of the pre-constructed attenuation mechanism model, and the electric core aging model capable of realizing electric core full life cycle performance prediction can be obtained.

The method for constructing the battery cell aging model provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. Specifically, the server 104 may be pre-built with a cell material attenuation parameter library and a pre-built with a cell aging model, where the pre-built cell aging model is obtained by coupling a pre-built attenuation mechanism model based on a calibrated electrochemical model. When an operator needs to construct a cell aging model, a model construction instruction is sent to a server 104 through a terminal 102, the server 104 responds to the model construction instruction to acquire the material name of the calibrated electrochemical model, then the cell material attenuation parameter value corresponding to the material name of the electrochemical model is matched from a preset cell material attenuation parameter library according to the material name of the calibrated electrochemical model, and finally, the cell material attenuation parameter of the pre-constructed attenuation mechanism model is subjected to assignment processing according to the cell material attenuation parameter value to obtain the cell aging model. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In some embodiments, as shown in fig. 2, a method for constructing a cell aging model is provided, and the method is applied to the server 104 in fig. 1 for illustration, and includes the following steps:

step S200, material identification data of a calibrated electrochemical model is obtained, wherein the calibrated electrochemical model is a sub-model in a pre-built cell aging model, and the pre-built cell aging model comprises a calibrated electrochemical model and a pre-built attenuation mechanism model which are coupled with each other.

The battery cell aging model is used for simulating chemical reaction and physical reaction of the battery cell in the use process, predicting the performance of the battery cell in each life stage, and obtaining the performance prediction result, namely the aging test condition, of the battery cell in each life stage. The material identification data includes, but is not limited to, identification data such as a material name, a material bar code, and a material number. In this embodiment, description will be made taking material identification data as an example of material names. Specifically, the material names of the calibrated electrochemical model include a cathode material name, an anode material name, an electrolyte material name, and the like.

In this embodiment, the electrochemical model is designed by simulation according to the structure, material and electrochemical parameters of the cell to be tested in advance, and the electrochemical parameter calibration of the electrochemical model is completed. Electrochemical models include, but are not limited to, electrochemical P2D (Pseudo-Two-dimensional) models. The pre-constructed cell aging model is obtained by coupling a pre-constructed attenuation mechanism model on the basis of a calibrated electrochemical model, namely the pre-constructed cell aging model comprises an electrochemical model and a pre-constructed attenuation mechanism model which are mutually coupled, wherein the pre-constructed attenuation mechanism model is a model constructed based on a multi-dimensional cell attenuation mechanism, wherein the parameter values of each cell material attenuation parameter are unknown, the attenuation mechanism comprises attenuation mechanisms such as active material reduction, conductivity reduction and impedance increase caused by electrode material decomposition, flaking or corrosion, and gas, insoluble substances generated by side reaction, impedance increase caused by binder modification and current collector corrosion and other factors which can cause capacity attenuation. The attenuation mechanism model not only can realize the prediction of capacity attenuation, power attenuation and the like of the battery cell, but also can output the information of deep-level battery cell attenuation such as active lithium loss, cathode and anode material loss, cathode and anode interface reaction rate, solid phase diffusion and the like.

In specific implementation, a pre-built cell aging model is deployed in the server. The material name of the calibrated electrochemical model can be obtained when the server receives the model construction instruction.

Step S400, matching the electric core material attenuation parameter value corresponding to the material identification data of the electrochemical model from a preset electric core material attenuation parameter library.

The cell material attenuation parameter library can be constructed based on the cyclic storage aging test data of the cell to be tested. Taking material identification data as material name data as an example, the electric core material attenuation parameter library comprises electric core material names, attenuation parameters corresponding to the electric core material names (namely electric core material attenuation parameters) and specific parameter values of all attenuation parameters. The specific parameter values of all attenuation parameters are determined based on the circularly stored aging test data of a large number of battery cells to be tested. Specifically, the attenuation parameters include attenuation parameters such as a film forming solvent diffusion coefficient of SEI (Solid Electrolyte Interface ), a film forming reaction diffusion coefficient of an additive, an SEI rupture factor, a corresponding activation energy and the like. Specifically, the electric core material attenuation parameter library comprises, but is not limited to, kinetic attenuation parameters such as interface reaction rate constant, solid phase diffusion coefficient and the like of the anode and cathode materials, film forming reaction solvent diffusion coefficient of the cathode SEI, anode and cathode material loss rate, anode and cathode plate elastic modulus and the like.

When the method is implemented, after the material name of the calibrated electrochemical model is obtained, the value of the core material attenuation parameter corresponding to the material name of the electrochemical model can be matched from a preset core material attenuation parameter library according to the material name of the electrochemical model. Specifically, a preset material recommendation program is called, accurate matching is performed in a preset electric core material attenuation parameter library according to a material name field of the electrochemical model, and electric core material attenuation parameter values corresponding to the material name of the electrochemical model are automatically matched.

For example, a data table a is constructed for an electrochemical model, a data table B is constructed for an attenuation mechanism model, the data table a and the data table B have common fields for material names of the electrochemical model, and an association relationship between the data table a and the data table B is established by the material names. The data table B includes a correspondence among material names, attenuation parameters, and specific parameter values of the attenuation parameters. Specifically, a material name field of the electrochemical model is obtained through a data table a, and then data matching is performed in the data table B according to the material name field, so as to match attenuation parameter values corresponding to the material name, such as a diffusion coefficient of an SEI film forming solvent, a diffusion coefficient of an additive film forming reaction, and the like.

And S600, performing assignment processing on the core material attenuation parameters of the pre-constructed attenuation mechanism model according to the core material attenuation parameter values to obtain the cell aging model.

After the specific electric core material attenuation parameter value is matched, the electric core material attenuation parameter of the attenuation mechanism model in the pre-built electric core aging model can be subjected to assignment treatment, and the electric core aging model after the assignment treatment is an effective aging mechanism.

Further, after the cell aging model is constructed, an operator can determine whether the constructed cell aging model needs to be stored in a cell life model library according to requirements.

In the method for constructing the battery cell aging model, different from the traditional mode of manually modeling by relying on a professional simulation engineer, the battery cell aging model is constructed by pre-constructing a battery material attenuation parameter library and coupling an attenuation mechanism model on the basis of an electrochemical model in advance, in the subsequent model construction process, corresponding battery material attenuation parameter values are matched in the preset battery material attenuation parameter library only according to material identification data of the electrochemical model, and then assignment processing is carried out on the battery cell material attenuation parameters of the attenuation mechanism model, so that the battery cell aging model can be obtained. The whole process is high in automation degree without complex data processing, operators can complete rapid modeling without professional simulation knowledge, and an attenuation mechanism model is coupled on the basis of an electrochemical model, so that performance evaluation of the whole life cycle of the battery cell can be realized. In summary, the adoption of the scheme can support the accurate evaluation of the full life cycle performance of the battery cell.

As shown in fig. 3, in some embodiments, before obtaining the material name of the calibrated electrochemical model, further includes: step S100, a model pre-construction instruction is received, the model pre-construction instruction carries electrochemical parameters of a cell to be tested, parameter calibration is carried out on a pre-constructed electrochemical model according to the electrochemical parameters, a calibrated electrochemical model is obtained, and the calibrated electrochemical model is coupled with a pre-constructed attenuation mechanism model, so that a pre-constructed cell aging model is obtained.

The electrochemical parameters comprise electrode potential, solution ion concentration, activation energy parameters, electrode material parameters, electrolyte parameters and the like of the cell to be measured. The cyclic aging test data comprise aging test result data (namely aging test result data of different life stages) of the battery cell to be tested under different cycle numbers or different storage days, and comprise capacity attenuation data, current data and voltage data under a cyclic charge and discharge period, balance potential data of an anode and a cathode, and temperature of the stable battery cell under the cyclic charge and discharge period.

In the specific implementation, an operator inputs electrochemical parameters and original cyclic aging test data of a cell to be tested at a man-machine interaction interface of a model construction platform through a terminal, then clicks a 'model construction' button, the terminal generates a model pre-construction instruction, the model pre-construction quality is sent to a server, the server responds to the model pre-construction instruction, and a response calibration method is selected according to the electrochemical parameters and the cyclic aging test data, wherein the calibration method comprises, but is not limited to, a least square method, a nonlinear least square method and the like, parameter calibration is performed on the pre-constructed electrochemical model, parameters of the electrochemical model are optimized to minimize an error value between experimental test data and an electrochemical model predicted value, and finally, parameters of the pre-constructed electrochemical model are determined to obtain the calibrated electrochemical model. And then, coupling the calibrated electrochemical model with a pre-constructed attenuation mechanism model, namely, taking the output of the calibrated electrochemical model as the input of the attenuation mechanism model to influence the output of the attenuation mechanism model, taking the output of the attenuation mechanism model as input data to feed back the output of the attenuation mechanism model to the calibrated electrochemical model, and coupling the output of the attenuation mechanism model and the input data to obtain the cell aging model capable of being used for simulating the cell aging process to be measured. It can be understood that the cyclic aging test data and the electrochemical parameters can be sequentially input by an operator, and specifically, after the cell aging model is constructed, the operator inputs the original cyclic aging test data.

As shown in fig. 4, in some embodiments, the method further comprises: step S120, extracting target aging test data containing attenuation characteristics of different life stages from the cyclic aging test data, and writing the target aging test data into a preset file.

By adopting the embodiment, after the cyclic aging test data of the battery cell to be tested is obtained, the target aging test data containing attenuation characteristics of different life stages in the cyclic aging test data can be extracted. Specifically, the target burn-in test data includes test data including characteristics of a decay signal, such as capacity decay data, current data under a cyclic charge-discharge period, voltage data, and temperature data. Wherein the capacity fade data may be a capacity fade curve. Specifically, the method may call a preset attenuation characteristic extraction degree, extract target aging test data in the extracted cyclic aging test data, and write the extracted target aging test data into a preset file so as to use the target aging test data. The number of the preset files may be one or more.

As shown in fig. 4, in some embodiments, after step S600, further includes: and step S700, receiving a model correction instruction, and correcting the cell aging model based on the target aging test data.

In practical application, after the cell aging model is obtained, the cell aging model at this time can perform simulation calculation on the cell performance, but because the parameter values of the attenuation parameters of each material in the attenuation mechanism model are basic values obtained in a matching manner, the simulation result of the cell performance obtained through the cell aging model may be inaccurate. Therefore, correction of the cell aging model is required.

In the specific implementation, an operator can manually select whether model correction is needed according to actual requirements. If an operator sends a model correction instruction to the server through the terminal, the server calls a preset model correction program, extracts target aging test data from a preset file, and then performs data preprocessing on the target aging test data, including data cleaning, abnormal data point removal, missing value filling and the like, so that the quality of the data is ensured, and the influence on the subsequent model correction process is reduced. And then, correcting the cell aging model through preset correction logic based on the preprocessed target aging test data. The correction process may be performed by performing multiple iterations and adjustments by using different methods, such as parameter adjustment, model structure optimization, etc., to correct the cell aging model. In other embodiments, the cell aging model may be corrected by taking into account that cell aging is a dynamic process, and the complexity of cell aging may be captured using a time series model, a regression model, a neural network model, or the like.

As shown in fig. 5, in some embodiments, step S700 includes: and step S720, calling a battery cell aging model to simulate the battery cell performance, obtaining battery cell performance simulation results in different life stages, and correcting the battery cell aging model based on the target aging test data and the battery cell performance simulation results.

As described in the above embodiment, the constructed cell aging model has the capability of simulating and calculating the cell performance, so the correction process for the cell aging model may be: and (3) calling a battery cell aging model to simulate the battery cell performance, and obtaining battery cell performance simulation results Of the battery cell to be tested in different life stages (namely, different SOH (State Of Health) stages), wherein the battery cell performance simulation results comprise capacity attenuation prediction data, power attenuation prediction data, lithium analysis window prediction data and the like. And then, correcting the cell aging model based on the target aging test data and the cell performance simulation result. Specifically, the target aging test data and the battery cell performance simulation result in different SOH stages are compared and analyzed, the difference between the target aging test data and the battery cell performance simulation result in different SOH stages is determined, and the electrochemical parameters in the battery cell aging model are adjusted according to the difference between the target aging test data and the battery cell performance simulation result, so that the battery cell performance simulation result is more consistent with the target aging test data. Further, the corrected cell aging model can be verified, the corrected cell aging model is called to perform performance simulation, the performance simulation result is compared with a real aging test result, and if the performance simulation result is matched with the aging test data, the cell aging model is characterized to accurately predict the aging process of the cell.

In some embodiments, correcting the cell burn-in model based on the target burn-in test data and the cell performance simulation results includes: obtaining the target aging test data and the error value of the cell performance simulation result, determining a target function based on the error value, calling a preset parameter optimizing algorithm, optimizing the target function, continuously adjusting the cell material attenuation parameter value of the cell aging model until the optimal solution of the target function is obtained, and determining the cell material attenuation parameter value to obtain the corrected cell aging model.

In this embodiment, the correction for the cell aging model may be: aiming at different SOH stages, comparing and analyzing target aging test data and cell performance results, determining error values of the target aging test data and the cell performance simulation results, including a capacity attenuation error value, a loop temperature error value and the like, then constructing an objective function based on the determined error values and following the principle of error value minimization, optimizing the objective function through preset parameter optimizing algorithms including but not limited to a physical optimizing algorithm, a Bayesian optimizing algorithm or a Newton gradient iterative algorithm and the like, continuously adjusting the cell material attenuation parameter values of an attenuation mechanism model in a cell aging model so as to enable the objective function to obtain an optimal solution, determining the material attenuation parameter values corresponding to the optimal solution as final material attenuation parameter values when the objective function obtains the optimal solution, and then carrying out assignment processing on the material attenuation parameters in the cell aging model according to the finally determined material attenuation parameter values to obtain the corrected cell aging model. Specifically, the correction process includes correcting attenuation parameters such as SEI film-forming solvent diffusion coefficient, additive film-forming reaction diffusion coefficient, SEI cracking factor and corresponding activation energy according to the capacity attenuation curve characteristics of each SOH stage; correcting attenuation parameters such as interface reaction rate constant attenuation, solid phase diffusion coefficient attenuation, material loss rate and the like according to the change of the peak position of the dV/dQ curve and the voltage drop of the charge-discharge curve; and correcting the heat exchange coefficient of the core and air according to the temperature after the circulation is stable.

As shown in fig. 5, in some embodiments, after the cell aging model is called to perform cell performance simulation, the method further includes: step 760, the simulation results of the battery cell performance at different life stages are visually displayed.

In practical application, after the cell aging model is called to simulate the cell performance, the cell performance simulation results in different life stages are obtained, and then the cell performance simulation data can be analyzed to obtain the variation trend of the cell performance parameters in different life stages. Further, a visualization tool can be called, a chart, such as a line graph, a curve graph and the like, representing the change trend of the cell performance parameter is drawn, and visual display is performed on a user interface. In addition, the generated chart can be customized according to actual demands, the color, the line type, the label, the coordinate axis range and the like of the chart can be adjusted, so that the chart is more attractive and readable, man-machine interaction type visualization tools can be enhanced, interactive functions such as mouse hovering prompt, zooming, dragging and clicking are increased, and operators can view and analyze data more flexibly.

In order to make a clearer description of the method for constructing the aging model of the battery cell provided by the application, the following description is made with reference to fig. 6 and a specific embodiment, and the specific embodiment includes the following:

Step S100, a model pre-construction instruction is received, the model pre-construction instruction carries electrochemical parameters of a cell to be tested, parameter calibration is carried out on a pre-constructed electrochemical model according to the electrochemical parameters, a calibrated electrochemical model is obtained, and the calibrated electrochemical model is coupled with a pre-constructed attenuation mechanism model, so that a pre-constructed cell aging model is obtained.

Step S120, extracting target aging test data containing attenuation characteristics of different life stages from the cyclic aging test data, and writing the target aging test data into a preset file.

Step S742, calling a cell aging model to simulate the cell performance, obtaining cell performance simulation results in different life stages, obtaining target aging test data and error values of the cell performance simulation results, determining a target function based on the error values, calling a preset parameter optimizing algorithm, optimizing the target function, continuously adjusting the cell material attenuation parameter values of the cell aging model until an optimal solution of the target function is obtained, determining the cell material attenuation parameter values, and obtaining the corrected cell aging model.

Step 760, the simulation results of the battery cell performance at different life stages are visually displayed.

In particular, referring to fig. 7, a cell aging model may be pre-constructed by a server with a cell material attenuation parameter library and coupled with a pre-constructed attenuation mechanism model based on a calibrated electrochemical model. When an operator needs to construct a cell aging model, electrochemical parameters and cyclic aging test data (corresponding to cyclic/stored test original data in fig. 7) of a cell to be tested are imported on a human-computer interface of a terminal, a model construction instruction is sent to a server, the server responds to the model construction instruction, a preset material recommendation program (namely program 1 in fig. 7) is called, the material name of a calibrated electrochemical model is obtained, and then, according to the material name of the calibrated electrochemical model, a cell material attenuation parameter value corresponding to the material name of the electrochemical model, namely a material attribute, is matched from a preset cell material attenuation parameter library.

Then, the server invokes a preset model building program (i.e. program 2 in fig. 7), and performs assignment processing on the core material attenuation parameters of the pre-constructed attenuation mechanism model according to the core material attenuation parameter values, namely returns the material properties to the program 2, and at this time, the core aging model 1 is obtained. Further, after the cell aging model is constructed, an operator can determine whether the constructed cell aging model needs to be stored in the Life cell library according to requirements.

Then, the server invokes a preset data extraction program (i.e. program 4 in fig. 7) to extract target aging test data including attenuation characteristics of different life stages from the cyclic aging test data, and writes the target aging test data into a preset file for subsequent model correction.

After the cell aging model is constructed, an operator can manually select whether model correction is needed according to requirements. If the operator sends a model correction instruction to the server through the terminal, the server invokes a preset model correction program (i.e. program 3 in fig. 7), extracts target aging test data from the preset file, and performs data preprocessing on the target aging test data, including data cleaning, abnormal data point removal, missing value filling, and the like, so as to ensure the quality of the data and reduce the influence on the subsequent model correction process. And then, based on the preprocessed target aging test data, correcting the cell aging model 1 through preset correction logic to obtain a corrected cell aging model 2.

The specific model correction process may be: aiming at different SOH stages, comparing and analyzing target aging test data and cell performance results, determining error values of the target aging test data and the cell performance simulation results, including a capacity attenuation error value, a loop temperature error value and the like, then constructing an objective function based on the determined error values and following the principle of error value minimization, optimizing the objective function through preset parameter optimizing algorithms including but not limited to a physical optimizing algorithm, a Bayesian optimizing algorithm or a Newton gradient iterative algorithm and the like, continuously adjusting the cell material attenuation parameter values of an attenuation mechanism model in a cell aging model so as to enable the objective function to obtain an optimal solution, determining the material attenuation parameter values corresponding to the optimal solution as final material attenuation parameter values when the objective function obtains the optimal solution, and then carrying out assignment processing on the material attenuation parameters in the cell aging model according to the finally determined material attenuation parameter values to obtain the corrected cell aging model.

Specifically, the correction process includes correcting attenuation parameters such as SEI film-forming solvent diffusion coefficient, additive film-forming reaction diffusion coefficient, SEI cracking factor and corresponding activation energy according to the capacity attenuation curve characteristics of each SOH stage; correcting attenuation parameters such as interface reaction rate constant attenuation, solid phase diffusion coefficient attenuation, material loss rate and the like according to the change of the peak position of the dV/dQ curve and the voltage drop of the charge-discharge curve; and correcting the heat exchange coefficient of the core and air according to the temperature after the circulation is stable.

Further, the visual tool can be called, and the visual display post-processing is carried out on the battery cell performance simulation results in different life stages, so that operators can conveniently view and analyze data. The operator can input the cycle storage working condition data of the battery cell to be tested, set corresponding boundary conditions, predict the performance of the battery cell to be tested in the whole life cycle, and perform post-processing such as visual display on the performance prediction result of the whole life cycle.

Based on the same inventive concept, the application also provides a battery cell full life cycle performance prediction method, which can be applied to the application environment shown in fig. 1. Specifically, the server 104 is deployed with a cell aging model, and the cell aging model may be constructed according to the above-mentioned cell aging model construction method. The method comprises the steps that an operator inputs cycle storage working condition data of a battery cell to be tested on a user interface through a terminal 102, sets boundary conditions such as cycle number or storage days, and the like, sends a battery cell full life cycle performance prediction instruction to a server 104 through the terminal 102, the server 104 responds to the instruction to obtain the cycle storage working condition data of the battery cell to be tested, and then calls a battery cell aging model to conduct full life cycle performance iterative prediction on the battery cell to be tested by taking the cycle storage working condition data as input to obtain a performance prediction result of the full life cycle of the battery cell to be tested. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In some embodiments, as shown in fig. 8, a method for predicting full life cycle performance of a battery cell is provided, and the method is applied to the server 104 in fig. 1 for illustration, and includes the following steps:

Step S800, acquiring the cycle storage working condition data of the battery cell to be tested.

The cycle storage working condition data refer to working condition parameters of the core to be tested under the cycle charge-discharge period or different storage days. The method specifically comprises the steps of circle number, storage days, boundary conditions, cell performance parameters, dynamic working condition parameters and the like. The boundary condition comprises the calculation of dynamic performance such as direct current impedance test after how many circles are circulated or how many days are stored. The battery core performance parameters comprise temperature, humidity, charging current, cut-off working voltage, charge and discharge capacity and the like.

In the implementation, after the cell aging model is constructed, an operator inputs cycle storage working condition data of the cell to be tested on a human-computer interface of the terminal, sets the cycle number, the storage days and corresponding boundary conditions, and sends a full life cycle performance prediction instruction to the server through the terminal. And the server responds to the instruction and acquires the cycle storage working condition data of the battery cell to be tested.

And step S900, taking the cycle storage working condition data as input, calling a battery cell aging model to conduct full life cycle performance iterative prediction on the battery cell to be tested, and obtaining a full life cycle performance prediction result of the battery cell to be tested.

The performance prediction result of the full life cycle of the battery cell to be measured comprises battery cell performance parameters of different SOH stages, such as anode potential, current and voltage information, change information of a lithium precipitation window, capacity attenuation data and the like under different SOH stages. In this embodiment, the cell aging model is constructed by adopting the embodiment of the method for constructing a cell aging model, and includes a calibrated electrochemical model and a calibrated attenuation mechanism model that are coupled to each other.

In the implementation, after the cycle storage working condition data of the battery cell to be tested is obtained, the cycle storage working condition data is taken as input, the constructed battery cell aging model is called to conduct performance iteration prediction of the full life cycle, and in the prediction process, the electrochemical model and the attenuation mechanism model are subjected to continuous coupling iteration prediction to obtain battery cell performance prediction results in different SOH stages.

In the battery cell full life cycle performance prediction method, the battery cell aging model is a model with high simulation accuracy constructed through an automatic model, and the battery cell aging model is coupled with an attenuation mechanism model and an electrochemical model, so that not only can capacity attenuation prediction be realized, but also other deep battery cell attenuation information prediction can be realized, and the effect of realizing the performance prediction of the battery cell under the full life cycle can be achieved. According to the whole scheme, based on the battery cell aging model, accurate performance prediction of the whole life cycle of the battery cell can be realized by only inputting the cycle storage working condition data of the battery cell to be detected.

As shown in fig. 9, in some embodiments, step S900 includes:

Step S920, taking the cycle storage working condition data as input, and calling an electrochemical model to predict the performance so as to obtain a performance prediction result under the current cycle working condition;

Step S940, taking a performance prediction result as input, and calling an attenuation mechanism model to predict the service life attenuation so as to obtain service life attenuation change characteristic data;

step S960, the life attenuation change characteristic data is fed back to the electrochemical model, and the step S920 is returned.

And step S980, stopping iteration if a preset iteration stopping condition is met, and obtaining a performance prediction result of the full life cycle of the battery cell to be tested.

The life attenuation change characteristic data comprises change characteristic data of parameters such as anode and cathode efficiency, battery cell capacity, maximum and minimum lithium intercalation quantity of the anode and the cathode, porosity of an anode/isolating film/thickness of a pole piece, solid phase volume fraction of the anode and the cathode, interface reaction of the anode and the cathode, solid phase diffusion parameters and the like.

In specific implementation, taking an electrochemical model as an electrochemical P2D model as an example, after the cycle storage working condition data is input into a cell aging model, firstly, the electrochemical P2D model carries out electrochemical simulation of a first circle according to the input cycle working condition parameters to obtain a performance simulation result, the performance simulation result comprises anode potential, current, voltage information and the like, then, the performance simulation result comprising anode potential, current, voltage information and the like is input into an attenuation mechanism model, the attenuation mechanism model carries out SEI active lithium loss, lithium analysis simulation, anode and cathode material loss, expansion force simulation and the like according to the anode potential, current, voltage and the like and a certain cycle step length, the life attenuation change characteristic data is output to an electrochemical P2D model, the electrochemical P2D model further carries out performance simulation calculation based on the received life attenuation change characteristic data, the process is repeated, the electrochemical P2D models in different SOH stages are obtained through continuous coupling iteration, multi-dimensional performance prediction of the whole life cycle can be achieved based on the electrochemical P2D models, dynamic working conditions and boundary conditions in different SOH stages, and performance prediction results of the whole life cycle of the battery cell to be tested are obtained, and the performance prediction results comprise capacity attenuation prediction data, power attenuation parameters, lithium precipitation windows, DCR MAPPING, imax mapping and other data. Furthermore, the visual tool can be called to visually display the performance prediction result of the whole life cycle of the battery cell, so that an operator can intuitively know data and view the data.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a battery cell aging model construction device for realizing the battery cell aging model construction method and a battery cell full life cycle performance prediction device for realizing the battery cell full life cycle performance prediction method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitations in the embodiments of the device for constructing the cell aging model provided below may be referred to as the limitations of the method for constructing the cell aging model, and the specific limitations in the embodiments of the device for predicting the full life cycle performance of the cell provided above may be referred to as the limitations of the method for predicting the full life cycle performance of the cell described above, which are not repeated herein.

In some embodiments, as shown in fig. 10, there is provided a cell aging model construction apparatus 800, including: a data acquisition module 810, a data matching module 820, and an assignment processing module 830, wherein:

The data acquisition module 810 is configured to acquire material identification data of the calibrated electrochemical model.

And the data matching module 820 is used for matching the electric core material attenuation parameter value corresponding to the material identification data of the electrochemical model from a preset electric core material attenuation parameter library.

And the assignment processing module 830 is configured to perform assignment processing on the core material attenuation parameter of the pre-constructed attenuation mechanism model according to the core material attenuation parameter value, so as to obtain a core aging model.

As shown in fig. 11, in some embodiments, the apparatus further comprises: the model pre-construction module 802 is configured to receive a model pre-construction instruction, where the model pre-construction instruction carries electrochemical parameters and cyclic aging test data of a to-be-tested battery cell, perform parameter calibration on a pre-constructed electrochemical model according to the electrochemical parameters and the cyclic aging test data to obtain a calibrated electrochemical model, and couple the calibrated electrochemical model with the pre-constructed attenuation mechanism model to obtain a pre-constructed battery cell aging model.

In some embodiments, the apparatus further includes a data extraction module 804, configured to extract target burn-in test data from the cyclic burn-in test data, and write the target burn-in test data into a preset file.

In some embodiments, the apparatus further comprises a model correction module 840 for receiving model correction instructions to correct the cell burn-in model based on the target burn-in data.

In some embodiments, the correction module 840 is further configured to invoke the cell aging model to perform cell performance simulation, obtain cell performance simulation results of different life stages, and correct the cell aging model based on the target aging test data and the cell performance simulation results.

In some embodiments, the correction module 840 is further configured to obtain the target aging test data and an error value of the cell performance simulation result, determine an objective function based on the error value, call a preset parameter optimization algorithm, optimize the objective function, continuously adjust the cell material attenuation parameter value of the cell aging model until an optimal solution of the objective function is obtained, determine the cell material attenuation parameter value, and obtain the corrected cell aging model.

As shown in fig. 11, in some embodiments, the apparatus further includes a visual display module 850 for visually displaying the results of the cell performance simulation at different life stages.

In some embodiments, as shown in fig. 12, a battery cell full life cycle performance prediction apparatus 900 is provided, comprising: a working condition data acquisition module 910 and a performance prediction module 920, wherein:

The working condition data acquisition module 910 is configured to acquire cycle storage working condition data of the to-be-tested battery cell;

the performance prediction module 920 is configured to call a cell aging model to perform full life cycle performance iterative prediction on the to-be-measured cell by using the cycle storage condition data as input, so as to obtain a performance prediction result of the to-be-measured cell in the full life cycle, where the cell aging model is constructed by adopting the steps in the embodiment of the method for constructing the cell aging model.

In the technical scheme of the embodiment of the application, the battery cell aging model is a model with high simulation accuracy constructed by an automatic model, and the battery cell aging model is coupled with an attenuation mechanism model and an electrochemical model, so that not only can capacity attenuation prediction be realized, but also other deep battery cell attenuation information prediction can be realized, and the effect of realizing performance prediction under the whole life cycle of the battery cell can be realized. According to the whole scheme, based on the battery cell aging model, accurate performance prediction of the whole life cycle of the battery cell can be realized by only inputting the cycle storage working condition data of the battery cell to be detected, and the accuracy and the efficiency of the performance prediction of the whole life cycle of the battery cell are improved.

In some embodiments, the performance prediction module 920 is further configured to use the cycle storage operating mode data as input, call the electrochemical model to perform performance prediction, obtain a performance prediction result under the current cycle operating mode, use the performance prediction result as input, call the attenuation mechanism model to perform life attenuation prediction, obtain life attenuation change feature data, feed back the life attenuation change feature data to the electrochemical model, and return to the step of calling the electrochemical model to perform performance prediction on the cycle storage operating mode data until a preset iteration stop condition is reached, thereby obtaining a performance prediction result of the full life cycle of the battery cell to be tested.

The modules in the cell aging model device and the cell full life cycle performance prediction device can be all or partially realized by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In some embodiments, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 13. 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. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing data such as a cell material attenuation parameter library, a cell aging model, cycle aging test data and the like. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor is used for realizing a cell aging model construction method or a cell full life cycle performance prediction method.

It will be appreciated by those skilled in the art that the structure shown in FIG. 13 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In some embodiments, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described embodiments of the method for constructing the cell aging model or the above-described embodiments of the method for predicting the full life cycle performance of each cell.

In some embodiments, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the steps of the above-described embodiments of the method for building a cell aging model or the above-described embodiments of the method for predicting full life cycle performance of each cell.

In some embodiments, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the cell aging model construction method embodiments or the cell full life cycle performance prediction method embodiments described above.

It should be noted that, the data (including, but not limited to, data for analysis, stored data, displayed data, etc.) related to the present application are all information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. The method for constructing the cell aging model is characterized by comprising the following steps of:

Receiving a model pre-construction instruction, wherein the model pre-construction instruction carries electrochemical parameters and cyclic aging test data of a cell to be tested;

According to the electrochemical parameters and the cyclic aging test data, parameter calibration is carried out on the pre-constructed electrochemical model, and a calibrated electrochemical model is obtained;

Coupling the calibrated electrochemical model with a pre-constructed attenuation mechanism model to obtain a pre-constructed cell aging model, wherein the pre-constructed attenuation mechanism model is a model constructed based on a multi-dimensional cell attenuation mechanism;

Acquiring material identification data of a calibrated electrochemical model;

calling a preset material recommendation program, and matching a core material attenuation parameter value corresponding to the material identification data of the electrochemical model from a preset core material attenuation parameter library;

Calling a preset model building program, and performing assignment processing on the electric core material attenuation parameters of the pre-constructed attenuation mechanism model according to the electric core material attenuation parameter values to obtain an electric core aging model;

Receiving a model correction instruction, correcting the battery cell aging model based on target aging test data containing attenuation characteristics of different life stages in the cyclic aging test data, wherein the correction process comprises correcting a film forming solvent diffusion coefficient of SEI according to capacity attenuation curve characteristics of different SOH stages, correcting a heat exchange coefficient of the battery cell to be tested and air according to temperature after cyclic stabilization, and the target aging test data comprises the capacity attenuation curve characteristics and the temperature of each SOH stage.

2. The method according to claim 1, wherein the method further comprises:

extracting target aging test data containing attenuation characteristics of different life stages from the cyclic aging test data;

And writing the target aging test data into a preset file.

3. The method of claim 1, wherein correcting the cell burn-in model based on the target burn-in data comprises:

calling the battery cell aging model to simulate the battery cell performance, and obtaining battery cell performance simulation results in different service life stages;

4. The method of claim 3, wherein the correcting the cell aging model based on the target aging test data and the cell performance simulation results comprises:

Obtaining the target aging test data and an error value of the battery cell performance simulation result;

determining an objective function based on the error value;

and calling a preset parameter optimizing algorithm to optimize the objective function, continuously adjusting the core material attenuation parameter value of the cell aging model until the optimal solution of the objective function is obtained, and determining the core material attenuation parameter value to obtain the corrected cell aging model.

5. The method of claim 3, wherein after the calling the cell aging model to perform cell performance simulation to obtain the cell performance simulation results of different life stages, further comprising:

6. A method for predicting full life cycle performance of a battery cell, the method comprising:

acquiring cycle storage working condition data of a battery cell to be tested;

taking the cycle storage working condition data as input, and calling a battery cell aging model to conduct full life cycle performance iterative prediction on the battery cell to be tested to obtain a full life cycle performance prediction result of the battery cell to be tested;

The battery cell aging model is constructed by adopting the battery cell aging model construction method according to any one of claims 1 to 5.

7. The method for predicting full life cycle performance of a battery cell according to claim 6, wherein the step of taking the cycle storage condition data as input, and calling a battery cell aging model to perform full life cycle performance iterative prediction on the battery cell to be measured, the step of obtaining a full life cycle performance prediction result of the battery cell to be measured comprises:

taking the cycle storage working condition data as input, and calling the electrochemical model to predict the performance so as to obtain a performance prediction result under the current cycle working condition;

Taking the performance prediction result as input, and calling the decay mechanism model to predict the service life decay to obtain service life decay change characteristic data;

And feeding back the life attenuation change characteristic data to the electrochemical model, and returning to the step of calling the electrochemical model to predict the performance of the cycle storage working condition data until a preset iteration stop condition is reached, so as to obtain a performance prediction result of the full life cycle of the battery cell to be detected.

8. A device for constructing a cell aging model, the device comprising:

The model pre-construction module is used for receiving a model pre-construction instruction, wherein the model pre-construction instruction carries electrochemical parameters and cyclic aging test data of a battery cell to be tested, parameter calibration is carried out on a pre-constructed electrochemical model according to the electrochemical parameters and the cyclic aging test data to obtain a calibrated electrochemical model, the calibrated electrochemical model is coupled with a pre-constructed attenuation mechanism model to obtain a pre-constructed battery cell aging model, and the pre-constructed attenuation mechanism model is a model constructed based on a multi-dimensional battery cell attenuation mechanism;

The data matching module is used for calling a preset material recommendation program and matching a core material attenuation parameter value corresponding to the material identification data of the electrochemical model from a preset core material attenuation parameter library;

the assignment processing module is used for calling a preset model building program, and carrying out assignment processing on the electric core material attenuation parameters of the pre-constructed attenuation mechanism model according to the electric core material attenuation parameter values to obtain an electric core aging model;

The model correction module is used for receiving a model correction instruction, correcting the battery cell aging model based on target aging test data containing attenuation characteristics of different life stages in the cyclic aging test data, correcting a film forming solvent diffusion coefficient of SEI according to capacity attenuation curve characteristics of different SOH stages, correcting a heat exchange coefficient of the battery cell to be tested and air according to temperature after cyclic stabilization, and the target aging test data comprise the capacity attenuation curve characteristics and the temperature of each SOH stage.

9. A battery cell full life cycle performance prediction apparatus, the apparatus comprising:

The performance prediction module is used for calling a battery cell aging model to conduct full life cycle performance iterative prediction on the battery cell to be tested by taking the cycle storage working condition data as input, so as to obtain a full life cycle performance prediction result of the battery cell to be tested;

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 5, or 6 to 7 when the computer program is executed.

11. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5, or 6 to 7.

12. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5, or 6 to 7.