Method, device, and electronic device for predicting accelerated battery degradation

Abstract

The embodiments of the present application provide a method, apparatus, electronic device, and storage medium for predicting accelerated battery degradation. The method comprises: obtaining battery degradation impact information; establishing a battery degradation model based on the degradation impact information; testing the battery degradation model to obtain test results; calibrating the battery degradation model based on the test results to obtain a calibrated battery degradation model; and predicting accelerated battery degradation based on the calibrated battery degradation model to obtain an accelerated degradation node. Implementing the embodiments of the present application can comprehensively consider the stages of accelerated battery degradation, reduce errors, and make the prediction of accelerated battery degradation more accurate.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, electronic device, and computer-readable storage medium for predicting accelerated battery degradation. Background Art

[0002] The battery degradation rate is constantly changing, and most of the time it slows down first and then accelerates. Existing methods for predicting battery degradation basically only consider the early stage of degradation slowdown, and do not consider the later stage of accelerated degradation.

[0003] Furthermore, the methods, reaction equations, and parameters used in existing technologies are difficult to determine, making it difficult to accurately predict battery life, especially in the case of accelerated battery degradation. Because the trends of accelerated battery degradation in the later stages of life are completely different from those in the early stages, using the same data model for the later stages of accelerated battery degradation or ignoring the later stages of battery degradation will result in significant errors and fail to accurately describe the accelerated battery degradation. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, electronic device and computer-readable storage medium for predicting accelerated battery degradation, which can comprehensively consider the stages of accelerated battery degradation, reduce errors, and make the prediction of accelerated battery degradation more accurate.

[0005] In a first aspect, an embodiment of the present application provides a method for predicting accelerated battery degradation, the method comprising:

[0006] Obtain battery degradation impact information;

[0007] Establishing a battery attenuation model according to the attenuation impact information;

[0008] Testing the battery degradation model to obtain test results;

[0009] Calibrate the battery attenuation model according to the test results to obtain a calibrated battery attenuation model;

[0010] Performing accelerated attenuation prediction on the battery according to the calibrated battery attenuation model to obtain an accelerated attenuation node.

[0011] In the above implementation process, a battery attenuation model is established based on the attenuation impact information, so that the battery attenuation model can take into account the stage of accelerated battery attenuation, reduce the error in the prediction process, avoid only considering the stage of slowed battery attenuation in the prediction process, and make the prediction of accelerated battery attenuation more accurate.

[0012] Furthermore, the step of establishing a battery attenuation model according to the attenuation impact information includes:

[0013] Obtaining overall battery health status data, health status data generated by charge and discharge cycles, and health status data generated by calendar storage based on the attenuation impact information;

[0014] The battery degradation model is established based on the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage.

[0015] In the above implementation process, the battery attenuation model is established by obtaining the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage based on the attenuation impact information, so that the battery health status and health status changes can be fully applied to the battery attenuation model, ensuring the accuracy and comprehensiveness of the battery attenuation model.

[0016] Furthermore, the step of obtaining the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by calendar storage according to the attenuation impact information includes:

[0017] SOH=SOH _cycle +SOH _calendar -1

[0018]

[0019]

[0020] Among them, SOH is the overall health status data of the battery. _cycle Health status data generated by charge and discharge cycles, SOH _calendar is the health status data generated by the calendar storage, k1 is the slow attenuation coefficient in the attenuation impact information, k2 is the fast attenuation coefficient in the attenuation impact information, Q is the cumulative charge and discharge capacity of the battery, a is a constant, C inital is the capacity before battery attenuation, k3 is the calendar attenuation coefficient in the attenuation impact information, E a is the reaction activation energy, R is the gas constant, T ref is the reference temperature corresponding to the calendar attenuation coefficient, and T is the operating temperature.

[0021] In the above implementation process, the attenuation impact information can enable the battery attenuation model to more comprehensively improve the factors affecting the battery attenuation acceleration prediction, so that the error in establishing the battery attenuation model is smaller.

[0022] Furthermore, the step of calibrating the battery attenuation model according to the test results to obtain a calibrated battery attenuation model includes:

[0023] Obtaining the reference temperature corresponding to the battery pre-attenuation capacity, battery health status data, and calendar attenuation coefficient in the test results;

[0024] The battery attenuation model is calibrated according to the battery pre-attenuation capacity, the battery health status data, and a reference temperature corresponding to the calendar attenuation coefficient to obtain the calibrated battery attenuation model.

[0025] In a second aspect, an embodiment of the present application further provides a device for predicting accelerated battery degradation, the device comprising:

[0026] An acquisition module is used to obtain battery attenuation impact information;

[0027] An establishment module, configured to establish a battery attenuation model according to the attenuation impact information;

[0028] A testing module, used to test the battery degradation model and obtain test results;

[0029] a calibration module, configured to calibrate the battery attenuation model according to the test results to obtain a calibrated battery attenuation model;

[0030] The prediction module is used to perform accelerated attenuation prediction on the battery according to the calibrated battery attenuation model to obtain an accelerated attenuation node.

[0031] In the above implementation process, a battery attenuation model is established based on the attenuation impact information, so that the battery attenuation model can take into account the stage of accelerated battery attenuation, reduce the error in the prediction process, avoid only considering the stage of slowed battery attenuation in the prediction process, and make the prediction of accelerated battery attenuation more accurate.

[0032] Furthermore, the establishment module is also used to:

[0033] Obtaining overall battery health status data, health status data generated by charge and discharge cycles, and health status data generated by calendar storage based on the attenuation impact information;

[0034] The battery degradation model is established based on the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage.

[0035] In the above implementation process, the battery attenuation model is established by obtaining the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage based on the attenuation impact information, so that the battery health status and health status changes can be fully applied to the battery attenuation model, ensuring the accuracy and comprehensiveness of the battery attenuation model.

[0036] Furthermore, the establishment module is also used to:

[0037] SOH=SOH _cycle +SOH _calendar -1

[0038]

[0039]

[0040] Among them, SOH is the overall health status data of the battery. _cycle Health status data generated by charge and discharge cycles, SOH _calendar is the health status data generated by the calendar storage, k1 is the slow attenuation coefficient in the attenuation impact information, k2 is the fast attenuation coefficient in the attenuation impact information, Q is the cumulative charge and discharge capacity of the battery, a is a constant, C inital is the capacity before battery attenuation, k3 is the calendar attenuation coefficient in the attenuation impact information, E a is the reaction activation energy, R is the gas constant, T ref is the reference temperature corresponding to the calendar attenuation coefficient, and T is the operating temperature.

[0041] In the above implementation process, the attenuation impact information can enable the battery attenuation model to more comprehensively improve the factors affecting the battery attenuation acceleration prediction, so that the error in establishing the battery attenuation model is smaller.

[0042] Furthermore, the calibration module is also used for:

[0043] Obtaining the reference temperature corresponding to the battery pre-attenuation capacity, battery health status data, and calendar attenuation coefficient in the test results;

[0044] The battery attenuation model is calibrated according to the battery pre-attenuation capacity, the battery health status data, and a reference temperature corresponding to the calendar attenuation coefficient to obtain the calibrated battery attenuation model.

[0045] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in any one of the first aspects when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed on a computer, the computer executes the method as described in any one of the first aspects.

[0047] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a computer, enables the computer to execute the method as described in any one of the first aspects.

[0048] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by practicing the above-mentioned technology of the present disclosure.

[0049] It can be implemented according to the contents of the specification. The following is a detailed description of the preferred embodiments of the present application with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A flowchart of a method for predicting accelerated battery degradation provided in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of the structure of a device for predicting accelerated battery degradation provided in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of the structural composition of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0055] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0056] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0057] Example 1

[0058] Figure 1 This is a flow chart of the method for predicting accelerated battery degradation provided by an embodiment of the present application. Figure 1 As shown, the method includes:

[0059] S1, obtain battery attenuation impact information;

[0060] S2, establishing a battery degradation model based on the degradation impact information;

[0061] S3, testing the battery degradation model and obtaining the test results;

[0062] S4, calibrating the battery attenuation model according to the test results to obtain a calibrated battery attenuation model;

[0063] S5, performing accelerated battery attenuation prediction on the battery according to the calibrated battery attenuation model to obtain an accelerated attenuation node.

[0064] In the above implementation process, a battery attenuation model is established based on the attenuation impact information, so that the battery attenuation model can take into account the stage of accelerated battery attenuation, reduce the error in the prediction process, avoid only considering the stage of slowed battery attenuation in the prediction process, and make the prediction of accelerated battery attenuation more accurate.

[0065] In S1, it is necessary to first determine the factors affecting battery degradation, that is, to obtain battery degradation impact information, specifically, based on factors including current, state of charge (SOC), depth of discharge (DOD), temperature, pressure, etc.

[0066] Furthermore, S2 includes:

[0067] Obtain the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage based on the attenuation impact information;

[0068] A battery degradation model is established based on the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by calendar storage.

[0069] In the above implementation process, the battery attenuation model is established by obtaining the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage based on the attenuation impact information, so that the battery health status and health status changes can be fully applied to the battery attenuation model, ensuring the accuracy and comprehensiveness of the battery attenuation model.

[0070] Battery decay characteristics can be divided into rapid decay in the early stages, slow decay in the middle stages, and accelerated decay in the late stages. This embodiment of the application divides battery cycle decay into two parts: a power function term and an exponential function term. The power function term represents deceleration, while the exponential function term represents accelerated decay. Specifically, a battery decay model is established by obtaining overall battery health data, health data generated by charge and discharge cycles, and health data generated by calendar storage.

[0071] Furthermore, the step of obtaining the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage according to the attenuation impact information includes:

[0072] SOH=SOH _cycle +SOH _calendar -1

[0073]

[0074]

[0075] Among them, SOH is the overall health status data of the battery. _cycle Health status data generated by charge and discharge cycles, SOH _calendar is the health status data generated by the calendar storage, k1 is the slow attenuation coefficient in the attenuation impact information, k2 is the fast attenuation coefficient in the attenuation impact information, Q is the cumulative charge and discharge capacity of the battery, a is a constant, C inital is the capacity before battery attenuation, k3 is the calendar attenuation coefficient in the attenuation impact information, E a is the reaction activation energy, R is the gas constant, T ref is the reference temperature corresponding to the calendar attenuation coefficient, and T is the operating temperature.

[0076] In the above implementation process, the attenuation impact information can enable the battery attenuation model to more comprehensively improve the factors affecting the battery attenuation acceleration prediction, so that the error in establishing the battery attenuation model is smaller.

[0077] Furthermore, S4 includes:

[0078] Obtain the reference temperature corresponding to the battery pre-attenuation capacity, battery health status data, and calendar attenuation coefficient in the test results;

[0079] The battery attenuation model is calibrated according to the battery capacity before attenuation, the battery health status data and the reference temperature corresponding to the calendar attenuation coefficient to obtain a calibrated battery attenuation model.

[0080] Before calibrating the battery attenuation model, the battery attenuation model also needs to be tested. Specifically, the ranges of current, SOC, and DOD are determined according to the battery's operating conditions. The temperature and pressure ranges are determined according to the battery thermal management and group design. The attenuation impact information is determined based on the different attenuation sensitivities of different batteries to different factors. An orthogonal experimental design is used to determine the battery attenuation test plan for testing.

[0081] And use the data obtained from the test to determine the battery capacity before attenuation C inital Reference temperature T corresponding to the calendar attenuation coefficient ref , use the least squares method to fit the data, and calculate the slow decay coefficient k1, fast decay coefficient k2, constant a, calendar decay coefficient k3, reaction activation energy E in the decay influence information of the battery decay model. a The parameters are calibrated to obtain the model parameter values ​​under different influencing factors and their combinations, and the error of the battery attenuation model is checked to complete the calibration of the battery attenuation model.

[0082] Example 2

[0083] In order to execute the method corresponding to the above embodiment 1 and achieve the corresponding functions and technical effects, a device for predicting accelerated battery degradation is provided below. Figure 2 As shown, the device includes:

[0084] Acquisition module 1, used to obtain battery attenuation impact information;

[0085] Establishing module 2 for establishing a battery degradation model based on the degradation impact information;

[0086] Test module 3, used to test the battery degradation model and obtain test results;

[0087] Calibration module 4, used to calibrate the battery attenuation model according to the test results to obtain a calibrated battery attenuation model;

[0088] The prediction module 5 is used to perform accelerated battery attenuation prediction on the battery according to the calibrated battery attenuation model to obtain an accelerated attenuation node.

[0089] In the above implementation process, a battery attenuation model is established based on the attenuation impact information, so that the battery attenuation model can take into account the stage of accelerated battery attenuation, reduce the error in the prediction process, avoid only considering the stage of slowed battery attenuation in the prediction process, and make the prediction of accelerated battery attenuation more accurate.

[0090] Furthermore, the establishment module 2 is also used to:

[0091] Obtain battery overall health status data, health status data generated by charge and discharge cycles, and health status data generated by calendar storage based on attenuation impact information;

[0092] A battery degradation model is established based on the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by calendar storage.

[0093] In the above implementation process, the battery attenuation model is established by obtaining the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by the calendar storage based on the attenuation impact information, so that the battery health status and health status changes can be fully applied to the battery attenuation model, ensuring the accuracy and comprehensiveness of the battery attenuation model.

[0094] Furthermore, the establishment module 2 is also used to:

[0095] SOH=SOH _cycle +SOH _calendar -1

[0096]

[0097]

[0098] Among them, SOH is the overall health status data of the battery. _cycle Health status data generated by charge and discharge cycles, SOH _calendar is the health status data generated by the calendar storage, k1 is the slow attenuation coefficient in the attenuation impact information, k2 is the fast attenuation coefficient in the attenuation impact information, Q is the cumulative charge and discharge capacity of the battery, a is a constant, C inital is the capacity before battery attenuation, k3 is the calendar attenuation coefficient in the attenuation impact information, E a is the reaction activation energy, R is the gas constant, T ref is the reference temperature corresponding to the calendar attenuation coefficient, and T is the operating temperature.

[0099] In the above implementation process, the attenuation impact information can enable the battery attenuation model to more comprehensively improve the factors affecting the battery attenuation acceleration prediction, so that the error in establishing the battery attenuation model is smaller.

[0100] Furthermore, the calibration module 4 is also used for:

[0101] Obtain the reference temperature corresponding to the battery pre-attenuation capacity, battery health status data, and calendar attenuation coefficient in the test results;

[0102] The battery attenuation model is calibrated according to the battery capacity before attenuation, the battery health status data and the reference temperature corresponding to the calendar attenuation coefficient to obtain a calibrated battery attenuation model.

[0103] The above-mentioned device for predicting accelerated battery degradation can implement the method of the above-mentioned embodiment 1. The options in the above-mentioned embodiment 1 are also applicable to this embodiment and will not be described in detail here.

[0104] The rest of the contents of the embodiment of this application can refer to the contents of the above-mentioned embodiment 1, and will not be repeated in this embodiment.

[0105] Example 3

[0106] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for predicting accelerated battery degradation of embodiment 1.

[0107] Optionally, the above-mentioned electronic device may be a server.

[0108] See Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 31, a communication interface 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to enable direct communication between these components. The communication interface 32 of the device in the embodiment of the present application is used to communicate signaling or data with other node devices. The processor 31 may be an integrated circuit chip with signal processing capabilities.

[0109] The processor 31 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor, or the processor 31 can also be any conventional processor.

[0110] The memory 33 may be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory 33 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 31, the device can perform the above-mentioned operations. Figure 1 The various steps involved in the method embodiment.

[0111] Optionally, the electronic device may further include a storage controller and an input / output unit. The memory 33, storage controller, processor 31, peripheral interfaces, and input / output units are electrically connected to each other, directly or indirectly, to enable data transmission or interaction. For example, these components may be electrically connected to each other via one or more communication buses 34. The processor 31 is configured to execute executable modules stored in the memory 33, such as software function modules or computer programs included in the device.

[0112] The input and output unit is used to provide users with the ability to create tasks and to create optional start time periods or preset execution times for the tasks to enable interaction between the user and the server. The input and output unit can be, but is not limited to, a mouse and keyboard.

[0113] I understand. Figure 3 The structure shown is only for illustration, and the electronic device may also include Figure 3 More or fewer components than shown, or with Figure 3 Different configurations shown. Figure 3 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0114] In addition, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for predicting accelerated battery degradation of embodiment 1.

[0115] An embodiment of the present application further provides a computer program product, which, when running on a computer, enables the computer to execute the method described in the method embodiment.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a portion of code, and the module, program segment or a portion of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based device that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0117] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0118] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0119] The foregoing is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures.

[0120] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0121] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

Claims

1. A method for predicting accelerated battery degradation, characterized in that: The method comprises: Obtain battery degradation impact information; Establishing a battery attenuation model according to the attenuation impact information; Testing the battery degradation model to obtain test results; Calibrate the battery attenuation model according to the test results to obtain a calibrated battery attenuation model; Performing accelerated attenuation prediction on the battery according to the calibrated battery attenuation model to obtain an accelerated attenuation node; The step of establishing a battery attenuation model according to the attenuation impact information includes: Obtaining overall battery health status data, health status data generated by charge and discharge cycles, and health status data generated by calendar storage based on the attenuation impact information; Establishing the battery attenuation model based on the overall health status data of the battery, the health status data generated by the charge and discharge cycles, and the health status data generated by the calendar storage; The step of obtaining the overall health status data of the battery, the health status data generated by the charge and discharge cycle, and the health status data generated by calendar storage according to the attenuation impact information includes: ； in, For the overall health status of the battery, Health status data generated by the charge and discharge cycle, Health status data generated for calendar storage, is the slow attenuation coefficient in the attenuation impact information, is the rapid attenuation coefficient in the attenuation impact information, is the cumulative capacity of battery charge and discharge, is a constant, is the capacity of the battery before attenuation, is the calendar attenuation coefficient in the attenuation impact information, is the reaction activation energy, is the gas constant, is the reference temperature corresponding to the calendar attenuation coefficient, is the operating temperature.

2. The method for predicting accelerated battery degradation according to claim 1, wherein: The step of calibrating the battery attenuation model according to the test results to obtain a calibrated battery attenuation model includes: Obtaining the reference temperature corresponding to the battery pre-attenuation capacity, battery health status data, and calendar attenuation coefficient in the test results; The battery attenuation model is calibrated according to the battery pre-attenuation capacity, the battery health status data, and a reference temperature corresponding to the calendar attenuation coefficient to obtain the calibrated battery attenuation model.

3. A device for predicting accelerated battery degradation, characterized in that: The device comprises: An acquisition module is used to obtain battery attenuation impact information; An establishment module, configured to establish a battery attenuation model according to the attenuation impact information; A testing module, used to test the battery degradation model and obtain test results; a calibration module, configured to calibrate the battery attenuation model according to the test results to obtain a calibrated battery attenuation model; A prediction module, configured to perform accelerated attenuation prediction on the battery according to the calibrated battery attenuation model to obtain an accelerated attenuation node; The establishment module is also used to: Obtaining overall battery health status data, health status data generated by charge and discharge cycles, and health status data generated by calendar storage based on the attenuation impact information; Establishing the battery attenuation model based on the overall health status data of the battery, the health status data generated by the charge and discharge cycles, and the health status data generated by the calendar storage; The establishment module is also used to: ； in, For the overall health status of the battery, Health status data generated by the charge and discharge cycle, Health status data generated for calendar storage, is the slow attenuation coefficient in the attenuation impact information, is the rapid attenuation coefficient in the attenuation impact information, is the cumulative capacity of battery charge and discharge, is a constant, is the capacity of the battery before attenuation, is the calendar attenuation coefficient in the attenuation impact information, is the reaction activation energy, is the gas constant, is the reference temperature corresponding to the calendar attenuation coefficient, is the operating temperature.

4. The device for predicting accelerated battery degradation according to claim 3, wherein: The calibration module is also used for: Obtaining the reference temperature corresponding to the battery pre-attenuation capacity, battery health status data, and calendar attenuation coefficient in the test results; The battery attenuation model is calibrated according to the battery pre-attenuation capacity, the battery health status data, and a reference temperature corresponding to the calendar attenuation coefficient to obtain the calibrated battery attenuation model.

5. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method for predicting accelerated battery degradation according to any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the method for predicting accelerated battery degradation as claimed in any one of claims 1 to 2.

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