Battery cell state identification method and device based on sensing data and storage medium

Abstract

The invention discloses a sensing data-based battery cell state identification method and device and a storage medium, and relates to the technical field of battery management. The method comprises the following steps: acquiring identity information of a battery cell to be identified, wherein the identity information is coded through a two-dimensional code attached to a battery cell body; decoding the two-dimensional code, and extracting a first type of state information; synchronously acquiring an impedance response curve of the to-be-identified battery cell under the current working condition; inputting the first type of state information and the impedance response curve into a conjoint analysis model to generate a health state comprehensive evaluation result of the to-be-identified battery cell; and outputting a state conclusion of the battery cell based on the health state comprehensive evaluation result. The invention aims to provide reliable technical support for each link of battery full life cycle management.

Description

Technical Field

[0001] This application belongs to the field of battery management technology, and in particular relates to a method, device and storage medium for cell status identification based on sensor data. Background Technology

[0002] In scenarios such as battery management systems (BMS), secondary utilization detection, and recycling sorting, quickly and accurately identifying the health status of battery cells is crucial for ensuring system safety, assessing residual value, and achieving refined management. Existing technologies mainly rely on two types of methods: direct analysis based on sensor data or static management based on identification information.

[0003] Direct analysis based on sensor data, such as collecting physical signals like voltage, current, temperature, and electrochemical impedance spectroscopy of battery cells, and inferring their health status through pre-set algorithms or models, is direct but lacks understanding of the individual cell's history. Cells of the same model can exhibit significant differences in aging paths and characterization due to variations in production batches, initial performance, and historical usage environments (such as prolonged exposure to high temperatures or high-rate charging / discharging), leading to insufficient accuracy and low reliability in assessments. Static management based on identification information, such as recording basic information like cell model and production date using QR codes or RFID tags, is a better approach.

[0004] Therefore, overcoming the two major shortcomings of sensor data evaluation methods—high misjudgment rate due to lack of individual historical information and weak evaluation capability of identification information management methods due to being detached from real-time physical conditions—is an urgent technical problem to be solved. Summary of the Invention

[0005] In view of this, the embodiments of this application provide a cell status identification method, device and storage medium based on sensor data. By organically coordinating three core technologies, namely, QR code extraction of identity information, real-time impedance curve acquisition and multi-source information joint analysis, the deep integration of individual historical information and real-time sensor data is achieved in cell status identification. This results in higher accuracy, reliability and practical value than single data source evaluation methods, providing reliable technical support for all aspects of battery life cycle management.

[0006] This application provides a method for identifying the state of a battery cell based on sensor data, including the following steps: The identity information of the battery cell to be identified is obtained, and the identity information is encoded by a QR code attached to the battery cell body; The QR code is decoded to extract the first type of status information; Simultaneously acquire the impedance response curve of the battery cell to be identified under the current operating conditions; The first type of state information and the impedance response curve are input into the joint analysis model to generate a comprehensive health status assessment result of the cell to be identified. Based on the comprehensive health status assessment results, the status conclusion of the battery cell is output.

[0007] In one embodiment, the first type of status information includes at least the nominal specifications of the battery cell, production batch information, and historical service records.

[0008] In one embodiment, the step of inputting the first type of state information and the impedance response curve into a joint analysis model to generate a comprehensive health status assessment result for the cell to be identified includes: Based on the lifetime model identifier parsed from the first type of state information, the corresponding reference impedance evolution model is invoked; The impedance response curve is compared with the theoretical curve of the reference impedance evolution model at the corresponding aging stage. Based on the comparison results, the characteristic difference degree representing the current aging state of the battery cell is calculated, and the characteristic difference degree of the current aging state of the battery cell is used as the comprehensive health status assessment result of the battery cell to be identified.

[0009] In one embodiment, comparing the impedance response curve with the theoretical curve of the reference impedance evolution model at the corresponding aging stage includes: Extract the factory calibration parameters of the battery cell from the first type of status information; Based on the factory calibration parameters, the impedance response curve is preprocessed by normalization. The normalized impedance response curve is compared with the theoretical curve.

[0010] In one embodiment, the step of inputting the first type of state information and the impedance response curve into a joint analysis model to generate a comprehensive health status assessment result for the cell to be identified further includes: Pre-coded service environment data is parsed from the first type of status information; The service environment data is input into a preset multi-stress aging assessment model to obtain the first aging assessment result; The impedance response curve is input into the impedance-based aging analysis model to obtain the second aging assessment result; The consistency between the first aging assessment result and the second aging assessment result is verified, and the comprehensive health status assessment result of the cell to be identified is corrected or confirmed based on the verification result.

[0011] In one embodiment, the step of correcting or confirming the comprehensive health status assessment result of the cell to be identified based on the verification result includes: If the deviation between the first aging assessment result and the second aging assessment result exceeds the preset tolerance, the assessment result is marked as abnormal. In response to being marked as abnormal, a process for reviewing the associated status of other cells in the same batch is triggered based on the production batch information. Based on the statistical results of the associated status review process, the parameters of the multi-stress aging assessment model are optimized, and the current cell status is reassessed using the optimized model.

[0012] In one embodiment, the step of outputting the state conclusion of the battery cell includes: Generate a dynamic maintenance QR code containing the comprehensive health status assessment results; The dynamic maintenance QR code is written to or associated with the battery cell body and output as a visual status conclusion.

[0013] A second aspect of this application provides a battery cell status identification device based on sensor data, comprising: The acquisition module is used to acquire the identity information of the battery cell to be identified, which is encoded by a QR code attached to the battery cell body; The extraction module is used to decode the QR code and extract the first type of status information; The acquisition module is used to synchronously acquire the impedance response curve of the battery cell to be identified under the current operating conditions; The generation module is used to input the first type of state information and the impedance response curve into the joint analysis model to generate a comprehensive health status assessment result of the cell to be identified. The output module is used to output the status conclusion of the battery cell based on the comprehensive health status assessment results.

[0014] In one embodiment, the first type of status information includes at least the nominal specifications of the battery cell, production batch information, and historical service records.

[0015] In one embodiment, the generation module includes: The calling unit is used to call the corresponding reference impedance evolution model based on the lifetime model identifier parsed from the first type of state information; The comparison unit is used to compare the impedance response curve with the theoretical curve of the reference impedance evolution model at the corresponding aging stage. The calculation unit is used to calculate the characteristic difference degree representing the current aging state of the battery cell based on the comparison results, and use the characteristic difference degree of the current aging state of the battery cell as the comprehensive health status evaluation result of the battery cell to be identified.

[0016] In one embodiment, the comparison unit is specifically used for: Extract the factory calibration parameters of the battery cell from the first type of status information; Based on the factory calibration parameters, the impedance response curve is preprocessed by normalization. The normalized impedance response curve is compared with the theoretical curve.

[0017] In one embodiment, the generation module further includes: The parsing unit is used to parse pre-coded service environment data from the first type of state information; The first obtaining unit is used to input the service environment data into a preset multi-stress aging assessment model to obtain the first aging assessment result. The second obtaining unit is used to input the impedance response curve into the impedance-based aging analysis model to obtain the second aging assessment result; The verification unit is used to verify the consistency between the first aging assessment result and the second aging assessment result, and to correct or confirm the comprehensive health status assessment result of the cell to be identified based on the verification result.

[0018] In one embodiment, the verification unit is specifically used for: If the deviation between the first aging assessment result and the second aging assessment result exceeds the preset tolerance, the assessment result is marked as abnormal. In response to being marked as abnormal, a process for reviewing the associated status of other cells in the same batch is triggered based on the production batch information. Based on the statistical results of the associated status review process, the parameters of the multi-stress aging assessment model are optimized, and the current cell status is reassessed using the optimized model.

[0019] In one embodiment, the output module includes: A generation unit is used to generate a dynamic maintenance QR code containing the comprehensive health status assessment results; The output unit is used to write or associate the dynamic maintenance QR code with the battery cell body and output it as a visual status conclusion.

[0020] A third aspect of this application provides a battery cell status identification device based on sensor data, characterized in that it includes: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, it implements the steps of the method described in the first aspect above.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0022] The battery cell status identification method based on sensor data provided in this application includes: obtaining the identity information of the battery cell to be identified, wherein the identity information is encoded by a QR code attached to the battery cell body; The QR code is decoded to extract the first type of status information; the impedance response curve of the cell to be identified under the current operating condition is simultaneously acquired; the first type of status information and the impedance response curve are input into a joint analysis model to generate a comprehensive health status assessment result of the cell to be identified; based on the comprehensive health status assessment result, the status conclusion of the cell is output. Through the organic synergy of QR code-based extraction of identity information, real-time impedance curve acquisition, and joint analysis of multi-source information, an assessment bridge connecting the historical identity of the cell and its real-time physical status is constructed. In cell status identification, deep fusion of individual historical information and real-time sensor data is achieved, thereby obtaining higher accuracy, reliability, and practical value than single-source data source assessment methods, providing reliable technical support for all aspects of battery lifecycle management. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a cell status identification method based on sensor data provided in an embodiment of this application; Figure 2 A schematic diagram of the structure of a cell status identification device based on sensor data provided in an embodiment of this application; Figure 3 This is a schematic diagram of a cell status identification device based on sensor data provided in an embodiment of this application. Detailed Implementation

[0025] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0026] Unless otherwise defined, 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 pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple frames" refers to two or more (including two).

[0031] In the description of the embodiments of this application, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0032] This application provides a method for identifying the status of battery cells based on sensor data. By structurally encoding the battery cell identity information and initial status data and writing them into a specific area of ​​a dedicated chip embedded in the battery cell, the method achieves physical binding between the battery cell identity information and the data, fundamentally eliminating the risk of "identity loss" of the battery cell and providing physical protection for traceability across the entire industry chain.

[0033] Please see Figure 1 As shown, Figure 1 This is a flowchart illustrating a cell status identification method based on sensor data, provided in one embodiment of this application. The method is applied to a cell status identification system, which can be integrated into a handheld testing device, an automated sorting production line, or a maintenance terminal of a battery management system. It aims to achieve accurate and efficient identification of the cell's health status by fusing historical cell identification information with real-time physical characteristics.

[0034] Depend on Figure 1 As can be seen, the cell status identification method based on sensor data includes steps S110 to S150. Details are as follows: S110: Obtain the identity information of the battery cell to be identified, wherein the identity information is encoded by a QR code attached to the battery cell body.

[0035] A QR code is attached to the surface of the battery cell or its packaging. This QR code serves as a unique digital identifier throughout the cell's entire lifecycle. In practice, the QR code pattern can be read using an image acquisition device (such as an industrial camera or barcode scanner). The QR code can be encoded using standard or custom formats such as QR Code or Data Matrix, and the stored data is an encoded string that constitutes the battery cell's identity information. By obtaining the identity information of the battery cell to be identified, a strong binding between information and the physical object is ensured, providing a reliable data source for subsequent traceability and correlation analysis.

[0036] S120: Decode the QR code and extract the first type of status information.

[0037] The acquired QR code is decoded to restore its stored original data string. The decoded data string is then parsed according to a predefined data parsing protocol to extract structured first-type status information. This first-type status information includes at least the nominal specifications of the battery cell, production batch information, and historical service records.

[0038] Specifically, the nominal specifications of the battery cell include design parameters such as rated capacity, nominal voltage, and AC internal resistance; production batch information, used to associate common quality characteristics and aging patterns of products in the same batch; and historical service records, including but not limited to summary information such as cumulative cycle count, highest or lowest historical temperature, and main application scenarios (such as energy storage and power). This information is updated and re-encoded into the QR code each time the battery cell undergoes critical maintenance or a change in condition.

[0039] S130: Synchronously acquire the impedance response curve of the battery cell to be identified under the current operating conditions.

[0040] Simultaneously with or within a very short time after acquiring identification information, a specific form of electroexcitation signal (e.g., multi-frequency sinusoidal sweep signal, current pulse sequence) is applied to the same battery cell to be identified. The voltage response across the battery cell is then measured using a high-precision data acquisition circuit. Based on the excitation signal and the voltage response signal, the complex impedance values ​​of the battery cell at a series of characteristic frequency points are obtained through calculation (e.g., Fourier transform). These complex impedance values ​​are plotted or serialized and stored with frequency as the abscissa and impedance magnitude or phase angle as the ordinate, thus forming an impedance response curve. This curve reflects the dynamic characteristics of electrochemical processes such as charge transfer and ion diffusion within the battery cell and is core sensing data for assessing its health status.

[0041] S140: Input the first type of state information and the impedance response curve into the joint analysis model to generate a comprehensive health status assessment result of the cell to be identified.

[0042] The joint analysis model is a software algorithm module that integrates data processing, model matching, cross-validation, and decision-making logic.

[0043] The step of inputting the first type of state information and the impedance response curve into the joint analysis model to generate a comprehensive health status assessment result of the cell to be identified includes: based on the lifetime model identifier parsed from the first type of state information, querying and calling the benchmark impedance evolution model corresponding to the cell's model, chemical system, and production batch from a preset model database; the benchmark impedance evolution model defines a theoretical curve sequence of the cell's impedance response curve changing with the degree of aging, such as by the number of cycles or capacity retention rate, under a standardized path.

[0044] The impedance response curve is compared with the theoretical curve of the reference impedance evolution model at the corresponding aging stage; based on the comparison result, the characteristic difference degree characterizing the current cell aging state is calculated, and the characteristic difference degree of the current cell aging state is used as the comprehensive health status assessment result of the cell to be identified.

[0045] To ensure the effectiveness of the comparison, the impedance response curves acquired in real time need to be preprocessed by normalization. Specifically, the calibration parameters (such as the initial internal resistance value) of the cell at the time of manufacture are extracted from the first type of state information. Using these calibration parameters, the impedance response curve is normalized and corrected, for example, by scaling or shifting to eliminate the influence of initial manufacturing differences between different individual cells on the absolute value of the curve. The normalized impedance response curve is then compared with the theoretical curve corresponding to the estimated aging stage in the reference impedance evolution model. A preset comparison algorithm is used, for example, to calculate the Euclidean distance between the two curves, the dynamic time warping distance, or the impedance difference at preset key characteristic frequencies such as 1Hz and 100Hz. These calculation results are called the characteristic difference degree. This characteristic difference degree quantifies the degree of deviation of the current measured state of the cell from its theoretical health state. This characteristic difference value directly serves as the core quantitative indicator of the comprehensive evaluation result of the health state of the cell to be identified.

[0046] Simultaneously, a parallel verification path is executed. That is, inputting the first type of state information and the impedance response curve into a joint analysis model to generate a comprehensive health status assessment result for the cell to be identified further includes: parsing pre-encoded service environment data, such as historical average temperature and typical discharge rate, from the first type of state information; inputting the service environment data into a preset multi-stress aging assessment model, for example, a semi-empirical model based on the Arrhenius equation and rainflow counting method, which mainly considers the influence of environmental stress on aging, to obtain a first aging assessment result. On the other hand, inputting the impedance response curve into an impedance-based aging analysis model, for example, by extracting the relaxation time distribution characteristics and their correlation with health status, to obtain a second aging assessment result; performing consistency verification between the first aging assessment result and the second aging assessment result, and correcting or confirming the comprehensive health status assessment result of the cell to be identified based on the verification result.

[0047] The step of correcting or confirming the comprehensive health status assessment result of the cell to be identified based on the verification result includes: if the deviation between the first aging assessment result and the second aging assessment result exceeds a preset tolerance, the assessment result is marked as abnormal; in response to being marked as abnormal, a process for reviewing the associated status of other cells in the same batch is triggered based on the production batch information; based on the statistical results of the associated status review process, the parameters of the multi-stress aging assessment model are optimized, and the current cell status is reassessed using the optimized model to generate a corrected comprehensive health status assessment result.

[0048] S150: Based on the comprehensive health status assessment results, output the status conclusion of the battery cell.

[0049] The cell status conclusion is a set of structured decision data, whose content is directly mapped and filled based on the comprehensive health status assessment results, and includes at least: health status level, availability determination, and maintenance recommendations. The health status level is a qualitative or quantitative level (e.g., "healthy," "warning," "abnormal," or a specific SOH value) based on the comprehensive assessment results (such as feature difference or optimized assessment values). The availability determination is the final judgment conclusion based on the above levels, such as continued use, recommended derating, or replacement required. The maintenance recommendations are specific operational guidelines related to the judgment conclusion, such as recommending a re-inspection within three months or adjusting the charging cut-off voltage to a preset volt. To achieve persistence, visualization, and convenient interaction of the status conclusion, the status conclusion (including health status level, availability determination, and maintenance recommendations) is converted into a dynamic maintenance QR code image data according to a predetermined data encoding protocol. This QR code adopts a common matrix QR code format (such as QR Code) to ensure that it can be read by standard scanning devices.

[0050] The dynamic maintenance QR code is applied to the battery cell body through physical or electronic means, achieving a strong binding between the status conclusion and the physical entity. Specific output methods include, but are not limited to: printing the QR code on an adhesive label using a portable printer or label printer and affixing it to a designated position on the battery cell surface; directly engraving the QR code pattern onto the battery cell casing using a laser marking machine or chemical etching process; and for smart battery cells equipped with a display unit (such as an e-ink screen), sending the QR code image data to the display unit for display.

[0051] The generated dynamic maintenance QR code data is associated with the unique identifier of the battery cell (obtained by S110) and stored to update its digital file. The status conclusion and dynamic maintenance QR code data are simultaneously output to downstream systems or users through at least one of the following methods: the status conclusion text information and dynamic maintenance QR code image are displayed intuitively on the screen of the testing equipment or the host computer software interface; or the structured status conclusion data packet is sent to the battery management system (BMS), manufacturing execution system (MES) or asset management system through wired or wireless communication interfaces (such as USB, Bluetooth, Wi-Fi).

[0052] As can be seen from the above analysis, the cell status identification method based on sensor data provided in this application includes: acquiring the identity information of the cell to be identified, wherein the identity information is encoded by a QR code attached to the cell body; decoding the QR code to extract a first type of status information; synchronously acquiring the impedance response curve of the cell to be identified under the current operating condition; inputting the first type of status information and the impedance response curve into a joint analysis model to generate a comprehensive health status assessment result of the cell to be identified; and outputting the status conclusion of the cell based on the comprehensive health status assessment result. Through the organic synergy of QR code extraction of identity information, real-time impedance curve acquisition, and joint analysis of multi-source information, an assessment bridge connecting the historical identity of the cell and its real-time physical status is constructed. In cell status identification, a deep fusion of individual historical information and real-time sensor data is achieved, thereby obtaining higher accuracy, reliability, and practical value than single-source data source assessment methods, providing reliable technical support for all aspects of battery lifecycle management.

[0053] Please see Figure 2 , Figure 2 This is a schematic diagram of a cell status identification device based on sensor data according to an embodiment of this application. The cell status identification device based on sensor data includes modules or units for performing... Figure 1 The steps in the corresponding embodiments. Please refer to the details. Figure 1 The relevant descriptions in the corresponding embodiments are shown below. For ease of explanation, only the parts relevant to this embodiment are shown. See also... Figure 2 A cell status identification device 200 based on sensor data includes: The acquisition module 210 is used to acquire the identity information of the battery cell to be identified, wherein the identity information is encoded by a QR code attached to the battery cell body; Extraction module 220 is used to decode the QR code and extract the first type of status information; The acquisition module 230 is used to synchronously acquire the impedance response curve of the battery cell to be identified under the current operating conditions; The generation module 240 is used to input the first type of state information and the impedance response curve into the joint analysis model to generate a comprehensive health status assessment result of the cell to be identified. The output module 250 is used to output the status conclusion of the battery cell based on the comprehensive health status assessment results.

[0054] In one embodiment, the first type of status information includes at least the nominal specifications of the battery cell, production batch information, and historical service records.

[0055] In one embodiment, the generation module 240 includes: The calling unit is used to call the corresponding reference impedance evolution model based on the lifetime model identifier parsed from the first type of state information; The comparison unit is used to compare the impedance response curve with the theoretical curve of the reference impedance evolution model at the corresponding aging stage. The calculation unit is used to calculate the characteristic difference degree representing the current aging state of the battery cell based on the comparison results, and use the characteristic difference degree of the current aging state of the battery cell as the comprehensive health status evaluation result of the battery cell to be identified.

[0056] In one embodiment, the comparison unit is specifically used for: Extract the factory calibration parameters of the battery cell from the first type of status information; Based on the factory calibration parameters, the impedance response curve is preprocessed by normalization. The normalized impedance response curve is compared with the theoretical curve.

[0057] In one embodiment, the generation module 240 further includes: The parsing unit is used to parse pre-coded service environment data from the first type of state information; The first obtaining unit is used to input the service environment data into a preset multi-stress aging assessment model to obtain the first aging assessment result. The second obtaining unit is used to input the impedance response curve into the impedance-based aging analysis model to obtain the second aging assessment result; The verification unit is used to verify the consistency between the first aging assessment result and the second aging assessment result, and to correct or confirm the comprehensive health status assessment result of the cell to be identified based on the verification result.

[0058] In one embodiment, the verification unit is specifically used for: If the deviation between the first aging assessment result and the second aging assessment result exceeds the preset tolerance, the assessment result is marked as abnormal. In response to being marked as abnormal, a process for reviewing the associated status of other cells in the same batch is triggered based on the production batch information. Based on the statistical results of the associated status review process, the parameters of the multi-stress aging assessment model are optimized, and the current cell status is reassessed using the optimized model.

[0059] In one embodiment, the output module 250 includes: A generation unit is used to generate a dynamic maintenance QR code containing the comprehensive health status assessment results; The output unit is used to write or associate the dynamic maintenance QR code with the battery cell body and output it as a visual status conclusion.

[0060] Please see Figure 3 , Figure 3 This is a schematic diagram of a cell status identification device based on sensor data, provided as an embodiment of this application. Figure 3 It is understood that the cell status identification device 300 based on sensor data includes: a processor 310, a memory 320, and a computer program 330 stored in the memory 320 and executable on the processor 310; when the processor 310 executes the computer program 330, it implements the steps in the above-described embodiments of the cell status identification method based on sensor data, for example... Figure 1 The steps S110 to S150 are shown. Alternatively, when the processor 310 executes the computer program 330, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of modules 210 to 250 are shown.

[0061] For example, the computer program 330 can be divided into one or more modules / units, one or more of which are stored in the memory 320 and executed by the processor 310 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 330 in a cell status identification device based on sensor data. For example, the computer program 330 can be divided into an acquisition module, an extraction module, a collection module, a generation module, and an output module.

[0062] The cell status identification device 300 based on sensor data provided in this embodiment may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that... Figure 3 This is merely an example of a cell status identification device 300 based on sensor data and does not constitute a limitation on the cell status identification device 300 based on sensor data. It may include more or fewer components than shown, or combine certain components, or different components. For example, the cell status identification device 300 based on sensor data may also include input / output devices, network access devices, buses, etc.

[0063] The processor 310 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0064] The memory 320 can be an internal storage unit of the sensor-based cell status identification device 300, such as a hard disk or memory of the sensor-based cell status identification device 300. The memory 320 can also be an external storage device of the sensor-based cell status identification device 300, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the sensor-based cell status identification device 300. Furthermore, the sensor-based cell status identification device 300 can include both internal storage units and external storage devices. The memory 320 is used to store computer programs and other programs and data required by the sensor-based cell status identification device 300. The memory 320 can also be used to temporarily store data that has been output or will be output.

[0065] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0066] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0067] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0068] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0069] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0070] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0071] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0072] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying the state of a battery cell based on sensor data, characterized in that, The method includes: The identity information of the battery cell to be identified is obtained, and the identity information is encoded by a QR code attached to the battery cell body; The QR code is decoded to extract the first type of status information; Simultaneously acquire the impedance response curve of the battery cell to be identified under the current operating conditions; The first type of state information and the impedance response curve are input into the joint analysis model to generate a comprehensive health status assessment result of the cell to be identified. Based on the comprehensive health status assessment results, the status conclusion of the battery cell is output.

2. The method according to claim 1, characterized in that, The first type of status information includes at least the nominal specifications of the battery cell, production batch information, and historical service records.

3. The method according to claim 2, characterized in that, The step of inputting the first type of state information and the impedance response curve into a joint analysis model to generate a comprehensive health status assessment result for the cell to be identified includes: Based on the lifetime model identifier parsed from the first type of state information, the corresponding reference impedance evolution model is invoked; The impedance response curve is compared with the theoretical curve of the reference impedance evolution model at the corresponding aging stage. Based on the comparison results, the characteristic difference degree representing the current aging state of the battery cell is calculated, and the characteristic difference degree of the current aging state of the battery cell is used as the comprehensive health status assessment result of the battery cell to be identified.

4. The method according to claim 3, characterized in that, The step of comparing the impedance response curve with the theoretical curve of the reference impedance evolution model at the corresponding aging stage includes: Extract the factory calibration parameters of the battery cell from the first type of status information; Based on the factory calibration parameters, the impedance response curve is preprocessed by normalization. The normalized impedance response curve is compared with the theoretical curve.

5. The method according to claim 2, characterized in that, The step of inputting the first type of state information and the impedance response curve into the joint analysis model to generate a comprehensive health status assessment result for the cell to be identified further includes: Pre-coded service environment data is parsed from the first type of status information; The service environment data is input into a preset multi-stress aging assessment model to obtain the first aging assessment result; The impedance response curve is input into the impedance-based aging analysis model to obtain the second aging assessment result; The consistency between the first aging assessment result and the second aging assessment result is verified, and the comprehensive health status assessment result of the cell to be identified is corrected or confirmed based on the verification result.

6. The method according to claim 5, characterized in that, The step of correcting or confirming the comprehensive health status assessment result of the cell to be identified based on the verification result includes: If the deviation between the first aging assessment result and the second aging assessment result exceeds the preset tolerance, the assessment result is marked as abnormal. In response to being marked as abnormal, a process for reviewing the associated status of other cells in the same batch is triggered based on the production batch information. Based on the statistical results of the associated status review process, the parameters of the multi-stress aging assessment model are optimized, and the current cell status is reassessed using the optimized model.

7. The method according to claim 2, characterized in that, The output of the cell's state conclusion includes: Generate a dynamic maintenance QR code containing the comprehensive health status assessment results; The dynamic maintenance QR code is written to or associated with the battery cell body and output as a visual status conclusion.

8. A cell status identification device based on sensor data, characterized in that, include: The acquisition module is used to acquire the identity information of the battery cell to be identified, which is encoded by a QR code attached to the battery cell body; The extraction module is used to decode the QR code and extract the first type of status information; The acquisition module is used to synchronously acquire the impedance response curve of the battery cell to be identified under the current operating conditions; The generation module is used to input the first type of state information and the impedance response curve into the joint analysis model to generate a comprehensive health status assessment result of the cell to be identified. The output module is used to output the status conclusion of the battery cell based on the comprehensive health status assessment results.

9. A battery cell status identification device based on sensor data, characterized in that, include: Processor, memory, and computer programs stored in said memory and executable on said processor; When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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