CN117521857A - Battery cell lithium analysis method and device, readable storage medium and electronic equipment - Google Patents

Battery cell lithium analysis method and device, readable storage medium and electronic equipment Download PDF

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CN117521857A
CN117521857A CN202410018113.8A CN202410018113A CN117521857A CN 117521857 A CN117521857 A CN 117521857A CN 202410018113 A CN202410018113 A CN 202410018113A CN 117521857 A CN117521857 A CN 117521857A
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cell
lithium
measurement data
lithium analysis
battery cell
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CN117521857B (en
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段诗敏
杨友结
翁文辉
张子格
薛庆瑞
彭翊庭
刘楚君
何俊晨
黄瑶
章羽
刘忠亚
田达
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Contemporary Amperex Technology Co Ltd
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/05—Accumulators with non-aqueous electrolyte
    • H01M10/052—Li-accumulators
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/05—Accumulators with non-aqueous electrolyte
    • H01M10/052—Li-accumulators
    • H01M10/0525—Rocking-chair batteries, i.e. batteries with lithium insertion or intercalation in both electrodes; Lithium-ion batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

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  • Chemical & Material Sciences (AREA)
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  • Electrochemistry (AREA)
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Abstract

The application belongs to the technical field of batteries, and particularly relates to a method and a device for analyzing lithium by using a battery cell, a readable storage medium and electronic equipment. The method comprises the following steps: acquiring fiber bragg grating measurement data of a target cell; processing the fiber bragg grating measurement data of the target battery cell by using a battery cell lithium analysis model to obtain a lithium analysis result of the target battery cell; the cell lithium analysis model is a machine learning model which is obtained by training fiber grating measurement data of a sample cell in advance. By means of machine learning, the common characteristics of various battery cells are learned, but not the characteristics special for certain battery cells, so that the method has wider application range and can be suitable for lithium analysis of various battery cells including round battery cells.

Description

Battery cell lithium analysis method and device, readable storage medium and electronic equipment
Technical Field
The application belongs to the technical field of batteries, and particularly relates to a method and a device for analyzing lithium by using a battery cell, a computer readable storage medium and electronic equipment.
Background
The battery cell lithium precipitation refers to a phenomenon that lithium ions are precipitated on the surface of an anode of the battery cell due to long-term charge and discharge of the lithium ion battery in a low-temperature environment. The whole process of lithium separation of the battery is irreversible, so that the battery can be damaged for a long time, and the safety risk of the battery can be caused.
In the prior art, a more mature method can be used for lithium analysis of the battery cell, but the method is constructed for square battery cells, has limited application range, is only suitable for lithium analysis of square battery cells, and cannot be suitable for lithium analysis of round battery cells and other battery cells.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method and apparatus for analyzing lithium by using a battery cell, a computer readable storage medium, and an electronic device, so as to solve the problem that the application range of the existing method for analyzing lithium by using a battery cell is limited.
A first aspect of embodiments of the present application provides a method for analyzing lithium by battery cell, which may include:
acquiring fiber bragg grating measurement data of a target cell;
processing the fiber bragg grating measurement data of the target battery cell by using a battery cell lithium analysis model to obtain a lithium analysis result of the target battery cell;
the cell lithium analysis model is a machine learning model which is obtained by training fiber grating measurement data of a sample cell in advance.
Through the scheme, the fiber bragg grating measurement data of the sample battery cell can be used in advance for training the machine learning model, so that a battery cell lithium analysis model capable of carrying out battery cell lithium analysis is obtained. Based on the cell lithium analysis model, fiber bragg grating measurement data of the target cell can be processed, so that a lithium analysis result of the target cell is obtained. By means of machine learning, the common characteristics of various battery cells are learned, but not the characteristics special for certain battery cells, so that the method has wider application range and can be suitable for lithium analysis of various battery cells including round battery cells.
In a specific implementation manner of the first aspect, before the processing the fiber bragg grating measurement data of the target battery cell by using a battery cell lithium analysis model to obtain a lithium analysis result of the target battery cell, the method may further include:
acquiring fiber bragg grating measurement data of the sample cell;
determining a lithium analysis tag of the sample cell;
and training an initial machine learning model by taking fiber grating measurement data of the sample cell as input and the corresponding lithium analysis tag as expected output to obtain the trained cell lithium analysis model.
According to the scheme, the fiber bragg grating measurement data of the sample cell and the corresponding lithium analysis tag are used as learning objects of the machine learning model, and the machine learning model can establish the mapping relation between the fiber bragg grating measurement data and the lithium analysis tag through the training process, so that when the new fiber bragg grating measurement data is faced, the corresponding lithium analysis result can be obtained according to the mapping relation.
In a specific implementation manner of the first aspect, the determining a lithium analysis tag of the sample cell may include:
Performing disassembly analysis on the sample battery cell to obtain a disassembly analysis result;
and determining the lithium analysis tag according to the disassembly analysis result.
Through the scheme, a battery cell disassembly mode is adopted, so that an accurate analysis result can be obtained, and the accurate analysis result is used as a lithium analysis label of a sample battery cell to carry out machine learning, so that the accuracy of a finally obtained battery cell lithium analysis model can be improved.
In a specific implementation manner of the first aspect, the lithium analysis tag is whether lithium is analyzed;
accordingly, the analysis result of lithium is whether lithium is extracted.
Through the scheme, the binary label whether to precipitate lithium is adopted, and the battery cell lithium precipitation analysis model trained based on the label can effectively analyze whether to precipitate lithium of the target battery cell.
In a specific implementation manner of the first aspect, the lithium analysis tag is a lithium analysis grade;
accordingly, the lithium analysis result is a lithium analysis grade.
According to the scheme, the lithium precipitation grade is used as the label, and the lithium precipitation grade of the target battery cell can be effectively analyzed based on the battery cell lithium precipitation analysis model obtained through label training.
In a specific implementation manner of the first aspect, the obtaining fiber grating measurement data of the target electrical core may include:
Acquiring the original measurement data of the fiber bragg grating of the target cell;
calculating a corresponding center wavelength according to the original measurement data of the fiber bragg grating;
and determining the fiber bragg grating measurement data of the target cell according to the center wavelength.
Through the scheme, the center wavelength corresponding to the original measurement data of the fiber bragg grating can be calculated, and the lithium precipitation condition of the battery cell can be reflected because the center wavelength is related to the lithium precipitation condition of the battery cell, so that the fiber bragg grating measurement data determined according to the center wavelength can be used as an effective basis for lithium precipitation analysis of the battery cell.
In a specific implementation manner of the first aspect, the fiber grating raw measurement data includes wavelengths and signal intensities of a plurality of measurement time points;
the calculating the corresponding center wavelength according to the original measurement data of the fiber bragg grating includes:
performing data fitting on multiple groups of wavelengths and signal intensities of the same measurement time point to obtain a data fitting function;
determining a signal intensity peak value according to the data fitting function;
and determining the wavelength corresponding to the signal intensity peak value as a center wavelength.
Through the scheme, the accurate center wavelength can be calculated by adopting a data fitting mode, so that a reliable basis is provided for subsequent lithium analysis of the battery cell.
In a specific implementation manner of the first aspect, the fiber bragg grating measurement data may include basic data units at K charge and discharge cycles, C measurement channels, and N measurement time points; wherein K, C, N is a positive integer.
By the scheme, comprehensive analysis can be performed on the data of multiple charge and discharge cycles, multiple measurement channels and multiple measurement time points, and the accuracy of the final lithium analysis result is effectively improved.
In a specific implementation manner of the first aspect, the basic data unit may include a center wavelength.
According to the scheme, the lithium analysis of the battery cell can be performed based on the center wavelength, and the lithium analysis condition of the battery cell can be reflected because the center wavelength is related to the lithium analysis condition of the battery cell, so that the data comprising the center wavelength can be used as an effective basis for the lithium analysis of the battery cell.
In a specific implementation manner of the first aspect, the basic data unit further includes an environmental parameter.
According to the scheme, on the basis of carrying out the lithium analysis of the battery cell based on the center wavelength, the lithium analysis of the battery cell can be carried out more comprehensively based on the center wavelength and the environmental parameters by considering that the center wavelength is influenced by the environment, so that the accuracy of the final lithium analysis result is further improved.
In a specific implementation of the first aspect, the environmental parameter includes temperature and/or pressure.
By the scheme, the temperature and/or pressure which have the greatest influence on the center wavelength are taken into consideration, so that the accuracy of a final lithium analysis result is effectively improved.
A second aspect of embodiments of the present application provides a battery cell lithium analysis device, which may include:
the target cell data acquisition module is used for acquiring fiber bragg grating measurement data of the target cell;
the battery cell lithium analysis module is used for processing the fiber bragg grating measurement data of the target battery cell by using a battery cell lithium analysis model to obtain a lithium analysis result of the target battery cell;
the cell lithium analysis model is a machine learning model which is obtained by training fiber grating measurement data of a sample cell in advance.
In a specific implementation manner of the second aspect, the lithium analysis device may further include:
the sample cell data acquisition module is used for acquiring fiber bragg grating measurement data of the sample cell;
the lithium analysis tag determination module is used for determining a lithium analysis tag of the sample cell;
and the cell lithium analysis model training module is used for training an initial machine learning model by taking fiber grating measurement data of the sample cell as input and the corresponding lithium analysis label as expected output to obtain the trained cell lithium analysis model.
In a specific implementation manner of the second aspect, the lithium analysis tag determination module may specifically be configured to: performing disassembly analysis on the sample battery cell to obtain a disassembly analysis result; and determining the lithium analysis tag according to the disassembly analysis result.
In a specific implementation manner of the second aspect, the lithium analysis tag may be whether lithium is analyzed;
accordingly, the analysis result of lithium precipitation may be whether lithium is precipitated.
In a specific implementation manner of the second aspect, the lithium analysis tag may be a lithium analysis grade;
accordingly, the lithium analysis result may be a lithium analysis grade.
In a specific implementation manner of the second aspect, the target electrical core data acquisition module may include:
the original measurement data acquisition unit is used for acquiring the original measurement data of the fiber bragg grating of the target cell;
the center wavelength calculation unit is used for calculating the corresponding center wavelength according to the original measurement data of the fiber bragg grating;
and the target cell data determining unit is used for determining the fiber bragg grating measurement data of the target cell according to the center wavelength.
In a specific implementation manner of the second aspect, the fiber grating raw measurement data may include wavelengths and signal intensities at a plurality of measurement time points;
The center wavelength calculation unit may specifically be configured to: performing data fitting on multiple groups of wavelengths and signal intensities of the same measurement time point to obtain a data fitting function; determining a signal intensity peak value according to the data fitting function; and determining the wavelength corresponding to the signal intensity peak value as a center wavelength.
In a specific implementation manner of the second aspect, the fiber grating measurement data may include basic data units at K charge-discharge cycles, C measurement channels, and N measurement time points; wherein K, C, N is a positive integer.
In a specific implementation manner of the second aspect, the basic data unit may include a center wavelength.
In a specific implementation manner of the second aspect, the basic data unit may further include an environmental parameter.
In a specific implementation of the second aspect, the environmental parameter may include temperature and/or pressure.
A third aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of any of the above-described cell lithium analysis methods.
A fourth aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any of the above-described methods of cell lithium analysis when the computer program is executed.
A fifth aspect of embodiments of the present application provides a computer program product for, when run on an electronic device, causing the electronic device to perform the steps of any of the above-described cell lithium analysis methods.
The advantages of the second to fifth aspects may be referred to the specific description of the first aspect, and are not repeated here.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of one embodiment of a method for analyzing lithium by battery cells according to the embodiments of the present application;
FIG. 2 is a schematic flow chart of obtaining fiber grating measurement data of a target cell;
FIG. 3 is a schematic illustration of data measurement inside a cell by a fiber Bragg grating sensor;
FIG. 4 is a schematic diagram of a data fitting function;
FIG. 5 is a schematic flow chart of a training process of a cell lithium analysis model;
FIG. 6 is a block diagram of one embodiment of a battery cell lithium analysis device according to an embodiment of the present application;
fig. 7 is a schematic block diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted in context as "when … …" or "upon" or "in response to a determination" or "in response to detection. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application, the terms "first," "second," "third," etc. are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
The battery cell lithium precipitation refers to a phenomenon that lithium ions are precipitated on the surface of an anode of the battery cell due to long-term charge and discharge of the lithium ion battery in a low-temperature environment. The whole process of lithium separation of the battery is irreversible, so that the battery can be damaged for a long time, and the safety risk of the battery can be caused.
In the prior art, a more mature method can be used for lithium analysis of the battery cell, but the method is constructed for square battery cells, has limited application range, is only suitable for lithium analysis of square battery cells, and cannot be suitable for lithium analysis of round battery cells and other battery cells.
Aiming at the problem, the embodiment of the application can use the fiber bragg grating measurement data of the sample cell to train the machine learning model in advance, so as to obtain the cell lithium analysis model capable of carrying out cell lithium analysis. Based on the cell lithium analysis model, fiber bragg grating measurement data of the target cell can be processed, so that a lithium analysis result of the target cell is obtained. By means of machine learning, the common characteristics of various battery cells are learned, but not the characteristics special for certain battery cells, so that the method has wider application range and can be suitable for lithium analysis of various battery cells including round battery cells.
The execution body of the embodiment of the present application may be an electronic device, including but not limited to any one of a server or a terminal device.
The server may include, but is not limited to, any of an independent physical server, a server cluster made up of multiple physical servers, a distributed system, a cloud server, and the like.
The terminal devices may include, but are not limited to, any of mobile terminal devices (e.g., cell phone, palm top (Personal Digital Assistant, PDA), tablet (Tablet Personal Computer, tablet PC), notebook, smart watch, smart bracelet, etc.) and fixed terminal devices (e.g., desktop, smart panel, computer all-in-one, etc.), etc.
Referring to fig. 1, an embodiment of a method for analyzing lithium by battery cell according to an embodiment of the present application may include:
and step S101, acquiring fiber bragg grating measurement data of a target cell.
In embodiments of the present application, a cell to be subjected to a cell lithium analysis may be referred to as a target cell, which may include, but is not limited to, a round cell, a square cell, or other cell.
Under the condition that a user needs to conduct lithium analysis of the battery cell, an analysis instruction can be sent to the electronic equipment, the electronic equipment receives and responds to the analysis instruction, fiber bragg grating measurement data of the target battery cell are obtained, and subsequent analysis is conducted.
In a specific implementation manner of the embodiment of the application, the electronic device may be provided with an input panel, and when a user needs to perform the lithium analysis of the battery cell, an analysis instruction may be input to the input panel of the electronic device, and the electronic device receives the analysis instruction through the input panel.
In a specific implementation manner of the embodiment of the present application, the electronic device may be provided with a voice recognition module, where, when a user needs to perform the battery cell lithium analysis, voice information may be sent in a voice collection range of the voice recognition module, the voice recognition module collects voice information sent by the user and performs voice recognition on the collected voice information, and determines, according to a recognition result of the voice recognition, that the recognition result includes a case for indicating that the battery cell lithium analysis is performed, for example, a keyword may be "battery cell lithium analysis", "lithium analysis" or "battery cell analysis", and then determines that an analysis instruction for performing the battery cell lithium analysis is received.
In a specific implementation manner of the embodiment of the application, the user may further use the client to send the analysis instruction, where the client is connected to the electronic device through a network, and performs data interaction with the electronic device through the network.
Under the condition that a user needs to conduct battery cell lithium analysis, an analysis instruction can be sent to a client, the client receives and responds to the analysis instruction, the analysis instruction is forwarded to the electronic equipment through a network, and the electronic equipment receives the analysis instruction forwarded by the client.
The client may include, but is not limited to, any of a mobile client (e.g., any of a cell phone client, PDA client, tablet PC client, notebook computer client, smartwatch client, smartband client, or wearable client, etc.), or a fixed client (e.g., a desktop computer client, smartpanel client, etc.), etc.
The Network may include, but is not limited to, any of a ZigBee (ZigBee) Network, a Bluetooth (BT) Network, a wireless fidelity (Wireless Fidelity, wi-Fi) Network, a home internet of things (Thread) Network, a Long Range Radio (LoRa) Network, a Low Power Wide Area Network (LPWAN), an infrared Network, a narrowband internet of things (Narrow Band Internet of Things, NB-IoT), a controller Area Network (Controller Area Network, CAN), a digital living Network alliance (Digital Living Network Alliance, DLNA) Network, a Wide Area Network (Wide Area Network, WAN), a local Area Network (Local Area Network, LAN), a metropolitan Area Network (Metropolitan Area Network, MAN), or a wireless personal Area Network (Wireless Personal Area Network, WPAN), etc.
Fig. 2 is a schematic flow chart of a specific implementation of step S101, where step S101 may include the following procedure:
and S1011, acquiring the original measurement data of the fiber bragg grating of the target cell.
In a specific implementation manner of the embodiment of the present application, as shown in fig. 3, the data measurement may be performed inside the electrical core 302 by using the fiber bragg grating (Fiber Bragg Grating, FBG) sensor 301, and compared with a conventional electronic or mechanical sensor, the FBG sensor has a series of advantages of high sensitivity, no electromagnetic interference, light weight, small volume, corrosion resistance, high voltage resistance, long-distance telemetry, capability of being embedded into an intelligent structure, and the like, and can accurately perform the data measurement inside the electrical core. The data measured by the FBG sensor is referred to herein as fiber grating raw measurement data, which may include, but is not limited to, temperature, pressure, wavelength, signal strength, etc. at a plurality of measurement time points.
In a specific implementation manner of the embodiment of the application, the FBG sensor can perform data measurement at a plurality of measurement positions (i.e. channels) inside the battery cell at the same time, so as to improve the reliability of the data. The specific position distribution of the measurement channels in the battery cell can be set according to practical situations, and the embodiment of the application is not particularly limited.
In a specific implementation manner of the embodiment of the application, the FBG sensor can perform data measurement in the process of multiple charge-discharge cycles of the target battery cell, so as to improve the reliability of the data. During each charge-discharge cycle, data measurements at a plurality of measurement time points are made for each measurement channel. Here, the number of charge-discharge cycles may be denoted as K, the number of measurement channels may be denoted as C, and the number of measurement time points may be denoted as N, where K, C, N is a positive integer, and specific values thereof may be set according to practical situations, for example, K may be set to 100, 200, 300 or other values, C may be set to 8, 16, 32 or other values, and N may be set to 100, 200, 300 or other values.
In order to facilitate subsequent use, the original measurement data of the fiber bragg grating of the target battery cell can be stored in a preset data file, and when the battery cell lithium analysis is needed, the original measurement data of the fiber bragg grating of the target battery cell can be obtained from the data file.
Step S1012, calculating the corresponding center wavelength according to the original measurement data of the fiber bragg grating.
In a specific implementation manner of the embodiment of the present application, for each channel, multiple sets of wavelengths and signal intensities at the same measurement time point may be extracted from the original measurement data of the fiber bragg grating, as shown in table 1:
TABLE 1
After extracting multiple sets of wavelengths and signal intensities at the same measurement time point, it may be subjected to data fitting, resulting in a data fitting function, which is here noted as: y=f (x), where x is the wavelength and y is the signal strength.
Fig. 4 is a schematic diagram of a data fitting function, according to which a signal intensity peak can be determined, and a wavelength corresponding to the signal intensity peak is a center wavelength.
And (3) carrying out the data fitting process on the data of each measuring time point of each channel, so as to obtain the center wavelength of each measuring time point of each channel.
Through the scheme, the accurate center wavelength can be calculated by adopting a data fitting mode, so that a reliable basis is provided for subsequent lithium analysis of the battery cell.
Step S1013, determining the fiber bragg grating measurement data of the target cell according to the center wavelength.
In a specific implementation manner of the embodiment of the application, the fiber bragg grating measurement data can include basic data units of K charge-discharge cycles, C measurement channels and N measurement time points, so that comprehensive analysis can be performed on data of the charge-discharge cycles, the measurement channels and the measurement time points comprehensively, and the accuracy of a final lithium analysis result is effectively improved.
The basic data unit can comprise a center wavelength, and the center wavelength is related to the lithium precipitation condition of the battery cell, so that the lithium precipitation condition of the battery cell can be reflected, and the data comprising the center wavelength can be used as an effective basis for lithium precipitation analysis of the battery cell.
In a specific implementation of an embodiment of the present application, the basic data unit may further include an environmental parameter. On the basis of carrying out the lithium analysis of the battery cell based on the center wavelength, considering that the center wavelength is influenced by the environment, the lithium analysis of the battery cell can be carried out more comprehensively based on the center wavelength and the environment parameters, and the accuracy of the final lithium analysis result is further improved.
The environmental parameters may include, but are not limited to, temperature and/or pressure, and the accuracy of the final lithium analysis result is effectively improved by taking the temperature and/or pressure with the greatest influence on the center wavelength into consideration.
Table 2 shows an example of one possible fiber grating measurement data:
TABLE 2
And S102, processing the fiber bragg grating measurement data of the target battery cell by using a battery cell lithium analysis model to obtain a lithium analysis result of the target battery cell.
The cell lithium analysis model is a machine learning model which is obtained by training fiber grating measurement data of a sample cell in advance. The specific machine learning model to be adopted can be set according to practical situations, and the embodiment of the application is not limited in particular. For example, may include, but is not limited to: decision trees, random forests, XGBoost, etc.
Fig. 5 is a schematic flow chart of a training process of a cell lithium analysis model, and as shown, the training process of the cell lithium analysis model may include:
and step S501, obtaining fiber bragg grating measurement data of a sample cell.
The sample cells may be cells of the same type as the target cells, and the specific number of sample cells may be set according to practical situations, for example, may be set to tens, hundreds or other numbers, which is not specifically limited in the embodiments of the present application.
The process of obtaining the fiber grating measurement data of each sample cell is similar to the process of obtaining the fiber grating raw measurement data of the target cell, the fiber grating raw measurement data of the sample cell can be obtained first, then the corresponding center wavelength is calculated according to the fiber grating raw measurement data of the sample cell, and finally the fiber grating measurement data of the sample cell is determined according to the center wavelength. The fiber bragg grating measurement data of each sample cell has the same data structure as the fiber bragg grating measurement data of the target cell.
The specific process of obtaining the fiber bragg grating measurement data may refer to the detailed description in step S101, which is not repeated in the embodiment of the present application.
And step S502, determining a lithium analysis label of the sample cell.
For each sample cell, the sample cell can be disassembled and analyzed, and the lithium analysis condition of the cell interface is observed, so that a disassembled analysis result is obtained, and then a lithium analysis label can be determined according to the disassembled analysis result.
In a specific implementation manner of the embodiment of the present application, the lithium analysis tag of the sample cell may be whether to analyze lithium, and table 3 shows an example of a possible lithium analysis tag of the sample cell:
TABLE 3 Table 3
Accordingly, the analysis result of lithium precipitation of the target battery cell may be whether lithium precipitation is performed. By adopting a binary label for lithium precipitation, the battery cell lithium precipitation analysis model trained based on the label can effectively analyze whether the target battery cell is lithium precipitation.
In a specific implementation manner of the embodiment of the application, the lithium analysis tag of the sample cell may be a lithium analysis grade.
The lithium precipitation level may be set according to actual conditions, for example, levels such as level 1 (no lithium precipitation), level 2 (medium lithium precipitation), and level 3 (serious lithium precipitation), and other forms of lithium precipitation levels may be set, which are not particularly limited in the embodiment of the present application.
Table 4 shows an example of a possible lithium analysis label for a sample cell:
TABLE 4 Table 4
Accordingly, the lithium analysis result of the target cell may be a lithium analysis grade. By adopting the lithium precipitation grade as a label, the lithium precipitation grade of the target battery cell can be effectively analyzed based on the battery cell lithium precipitation analysis model trained by the label.
By adopting a battery cell disassembly mode, a precise analysis result can be obtained, and the accurate analysis result is used as a lithium analysis label of a sample battery cell to carry out machine learning, so that the accuracy of a finally obtained battery cell lithium analysis model can be improved.
And S503, training an initial machine learning model by taking fiber bragg grating measurement data of the sample battery cell as input and a corresponding lithium analysis tag as expected output to obtain a trained battery cell lithium analysis model.
In the training process, for each sample cell, the fiber grating measurement data of the sample cell can be processed by using a machine learning model to obtain the actual output of the sample cell, and then a preset loss function can be used to calculate a training loss value according to the expected output and the actual output in the sample cell. In the embodiment of the present application, any loss function in the prior art may be selected to calculate the training loss value in a more practical manner, which is not specifically limited in the embodiment of the present application.
After the training loss value is calculated, model parameters of the machine learning model may be adjusted according to the training loss value. In the embodiment of the present application, it is assumed that in an initial state, a model parameter of a machine learning model is W1, and a training loss value is back-propagated to modify the model parameter W1 of the machine learning model, so as to obtain a modified model parameter W2. The next training process is continuously executed after the parameters are modified, in the training process, a training loss value is obtained through recalculation, the training loss value is transmitted reversely to modify the model parameters W2 of the machine learning model, modified model parameters W3 and … … are obtained, and the process is repeated continuously, the model parameters can be modified each time until the preset training condition is met, wherein the training condition can be that the training times reach a preset time threshold, and the time threshold can be set according to actual conditions, for example, the training time threshold can be set to thousands, tens of thousands, hundreds of thousands or even larger values; the training condition can also be that the machine learning model converges; since it may happen that the training times have not reached the time threshold, but the machine learning model has converged, it may result in repeated unnecessary work; or the machine learning model can not be converged all the time, infinite loop can be caused, the training process can not be ended, and based on the two conditions, the training condition can be that the training times reach a time threshold or the machine learning model is converged. And when the training conditions are met, obtaining the trained battery cell lithium analysis model.
Through the process shown in fig. 5, the fiber bragg grating measurement data of the sample cell and the corresponding lithium analysis tag are used as learning objects of the machine learning model, and through the training process, the machine learning model can establish the mapping relationship between the fiber bragg grating measurement data and the lithium analysis tag, so that when the new fiber bragg grating measurement data is faced, the corresponding lithium analysis result can be obtained according to the mapping relationship.
After the training of the cell lithium analysis model is completed, the cell lithium analysis model can be used for processing the fiber grating measurement data of the target cell, so that the lithium analysis result of the target cell is obtained.
In summary, according to the embodiment of the present application, the training of the machine learning model may be performed in advance by using the fiber grating measurement data of the sample battery cell, so as to obtain a battery cell lithium analysis model that may perform the battery cell lithium analysis. Based on the cell lithium analysis model, fiber bragg grating measurement data of the target cell can be processed, so that a lithium analysis result of the target cell is obtained. By means of machine learning, the common characteristics of various battery cells are learned, but not the characteristics special for certain battery cells, so that the method has wider application range and can be suitable for lithium analysis of various battery cells including round battery cells.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Fig. 6 shows a block diagram of an embodiment of a battery cell lithium analysis device according to an embodiment of the present application, corresponding to a battery cell lithium analysis method described in the above embodiment.
In this embodiment, a cell lithium analysis device 6 may include:
the target cell data acquisition module 601 is configured to acquire fiber bragg grating measurement data of a target cell;
the cell lithium analysis module 602 is configured to process the fiber bragg grating measurement data of the target cell by using a cell lithium analysis model to obtain a lithium analysis result of the target cell;
the cell lithium analysis model is a machine learning model which is obtained by training fiber grating measurement data of a sample cell in advance.
In a specific implementation manner of the embodiment of the present application, the lithium analysis device for battery cell may further include:
the sample cell data acquisition module is used for acquiring fiber bragg grating measurement data of the sample cell;
The lithium analysis tag determination module is used for determining a lithium analysis tag of the sample cell;
and the cell lithium analysis model training module is used for training an initial machine learning model by taking fiber grating measurement data of the sample cell as input and the corresponding lithium analysis label as expected output to obtain the trained cell lithium analysis model.
In a specific implementation manner of the embodiment of the present application, the lithium analysis tag determination module may be specifically configured to: performing disassembly analysis on the sample battery cell to obtain a disassembly analysis result; and determining the lithium analysis tag according to the disassembly analysis result.
In a specific implementation manner of the embodiment of the present application, the lithium analysis tag may be whether to analyze lithium;
accordingly, the analysis result of lithium precipitation may be whether lithium is precipitated.
In a specific implementation manner of the embodiment of the present application, the lithium analysis tag may be a lithium analysis grade;
accordingly, the lithium analysis result may be a lithium analysis grade.
In a specific implementation manner of the embodiment of the present application, the target electrical core data acquisition module may include:
the original measurement data acquisition unit is used for acquiring the original measurement data of the fiber bragg grating of the target cell;
The center wavelength calculation unit is used for calculating the corresponding center wavelength according to the original measurement data of the fiber bragg grating;
and the target cell data determining unit is used for determining the fiber bragg grating measurement data of the target cell according to the center wavelength.
In a specific implementation manner of the embodiment of the present application, the raw measurement data of the fiber grating may include wavelengths and signal intensities at a plurality of measurement time points;
the center wavelength calculation unit may specifically be configured to: performing data fitting on multiple groups of wavelengths and signal intensities of the same measurement time point to obtain a data fitting function; determining a signal intensity peak value according to the data fitting function; and determining the wavelength corresponding to the signal intensity peak value as a center wavelength.
In a specific implementation manner of the embodiment of the present application, the fiber grating measurement data may include basic data units at K charge-discharge cycles, C measurement channels, and N measurement time points; wherein K, C, N is a positive integer.
In a specific implementation manner of the embodiment of the present application, the basic data unit may include a center wavelength.
In a specific implementation manner of the embodiment of the present application, the basic data unit may further include an environmental parameter.
In a specific implementation of an embodiment of the present application, the environmental parameter may include temperature and/or pressure.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described apparatus, modules and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Fig. 7 shows a schematic block diagram of an electronic device provided in an embodiment of the present application, and for convenience of explanation, only a portion relevant to the embodiment of the present application is shown.
As shown in fig. 7, the electronic device 7 of this embodiment includes: a processor 70, a memory 71, and a computer program 72 stored in the memory 71 and executable on the processor 70. The steps of the embodiments of the method for analyzing lithium by cell described above, such as steps S101 to S102 shown in fig. 1, are implemented when the processor 70 executes the computer program 72. Alternatively, the processor 70 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 601-602 shown in fig. 6, when executing the computer program 72.
By way of example, the computer program 72 may be partitioned into one or more modules/units that are stored in the memory 71 and executed by the processor 70 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program 72 in the electronic device 7.
The electronic device 7 may include, but is not limited to, any of a server or a terminal device, etc. The server may include, but is not limited to, any of an independent physical server, a server cluster made up of multiple physical servers, a distributed system, a cloud server, and the like. The terminal devices may include, but are not limited to, any of mobile terminal devices (e.g., cell phone, palm top (Personal Digital Assistant, PDA), tablet (Tablet Personal Computer, tablet PC), notebook, smart watch, smart bracelet, etc.) and fixed terminal devices (e.g., desktop, smart panel, computer all-in-one, etc.), etc.
It will be appreciated by those skilled in the art that fig. 7 is merely an example of the electronic device 7 and is not meant to be limiting of the electronic device 7, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the electronic device 7 may further include input-output devices, network access devices, buses, etc.
The processor 70 may be a central processing unit (Central Processing Unit, CPU) or may be another general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the electronic device 7, such as a hard disk or a memory of the electronic device 7. The memory 71 may be an external storage device of the electronic device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the electronic device 7. The memory 71 is used for storing the computer program as well as other programs and data required by the electronic device 7. The memory 71 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each method embodiment described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable storage medium may include content that is subject to appropriate increases and decreases as required by jurisdictions and by jurisdictions in which such computer readable storage medium does not include electrical carrier signals and telecommunications signals.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims (14)

1. A method of analyzing lithium by cell, comprising:
acquiring fiber bragg grating measurement data of a target cell;
processing the fiber bragg grating measurement data of the target battery cell by using a battery cell lithium analysis model to obtain a lithium analysis result of the target battery cell;
the cell lithium analysis model is a machine learning model which is obtained by training fiber grating measurement data of a sample cell in advance.
2. The method for analyzing lithium by using a cell according to claim 1, wherein before processing the fiber bragg grating measurement data of the target cell by using a cell lithium analysis model to obtain a lithium analysis result of the target cell, the method further comprises:
Acquiring fiber bragg grating measurement data of the sample cell;
determining a lithium analysis tag of the sample cell;
and training an initial machine learning model by taking fiber grating measurement data of the sample cell as input and the corresponding lithium analysis tag as expected output to obtain the trained cell lithium analysis model.
3. The method of claim 2, wherein the determining the lithium analysis tag of the sample cell comprises:
performing disassembly analysis on the sample battery cell to obtain a disassembly analysis result;
and determining the lithium analysis tag according to the disassembly analysis result.
4. The method according to claim 2, wherein the lithium analysis tag is whether lithium is analyzed;
accordingly, the analysis result of lithium is whether lithium is extracted.
5. The method of claim 2, wherein the lithium analysis tag is a lithium analysis grade;
accordingly, the lithium analysis result is a lithium analysis grade.
6. The method for analyzing lithium by battery cell according to claim 1, wherein the obtaining the fiber bragg grating measurement data of the target battery cell comprises:
Acquiring the original measurement data of the fiber bragg grating of the target cell;
calculating a corresponding center wavelength according to the original measurement data of the fiber bragg grating;
and determining the fiber bragg grating measurement data of the target cell according to the center wavelength.
7. The method of claim 6, wherein the raw measurement data of the fiber grating includes wavelengths and signal intensities at a plurality of measurement time points;
the calculating the corresponding center wavelength according to the original measurement data of the fiber bragg grating includes:
performing data fitting on multiple groups of wavelengths and signal intensities of the same measurement time point to obtain a data fitting function;
determining a signal intensity peak value according to the data fitting function;
and determining the wavelength corresponding to the signal intensity peak value as a center wavelength.
8. The method according to any one of claims 1 to 7, wherein the fiber bragg grating measurement data includes basic data units at K charge-discharge cycles, C measurement channels, N measurement time points; wherein K, C, N is a positive integer.
9. The method of claim 8, wherein the elementary data units comprise a center wavelength.
10. The method of claim 9, wherein the basic data unit further comprises environmental parameters.
11. The method of claim 10, wherein the environmental parameter comprises temperature and/or pressure.
12. A cell lithium analysis device comprising:
the target cell data acquisition module is used for acquiring fiber bragg grating measurement data of the target cell;
the battery cell lithium analysis module is used for processing the fiber bragg grating measurement data of the target battery cell by using a battery cell lithium analysis model to obtain a lithium analysis result of the target battery cell;
the cell lithium analysis model is a machine learning model which is obtained by training fiber grating measurement data of a sample cell in advance.
13. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the cell lithium analysis method according to any one of claims 1 to 11.
14. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method for lithium analysis of a battery cell according to any one of claims 1 to 11 when the computer program is executed.
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