Method and device for evaluating insulation life of battery energy storage device and electronic equipment

Abstract

The invention provides a method and device for evaluating the insulation life of a battery energy storage device and electronic equipment, and belongs to the technical field of batteries, and the method comprises the steps: obtaining target data which comprises the operation condition data of the battery energy storage device and parameters related to the insulation performance of the battery energy storage device; and determining the insulation life of the battery energy storage device according to the target data. According to the technical scheme, the insulation life of the battery energy storage device under the combined action of various factors can be determined, and the insulation life of the battery energy storage device under the actual application environment can be determined more accurately. The method provided by the invention can be suitable for different operating conditions and different insulating media, namely, can be suitable for different application environments and different products, and has relatively good expandability.

Description

Technical Field

[0001] The present application belongs to the field of battery technology, and in particular relates to a method, device and electronic equipment for evaluating the insulation life of a battery energy storage device. Background Art

[0002] With the development of new energy technologies, battery energy storage devices are widely used, for example, in grid power supply. Battery energy storage devices possess insulation properties, which gradually degrade over long-term operation. If this insulation fails, the risk of a short circuit between the positive and negative electrodes of the energy storage cells increases, potentially causing serious failures such as battery fires. Therefore, evaluating the insulation life of battery energy storage devices is crucial.

[0003] Current insulation life assessment methods mainly evaluate life based on aging models. However, existing aging models cover fewer factors that affect insulation life in battery energy storage devices, resulting in low accuracy in the insulation life of the assessed battery energy storage devices. Summary of the Invention

[0004] In view of the above technical problems, the embodiments of the present application provide a method, device, and electronic device for evaluating the insulation life of a battery energy storage device, which comprehensively consider the impact of multiple factors on the insulation life of the battery energy storage device and can improve the accuracy of evaluating the insulation life of the battery energy storage device.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the insulation life of a battery energy storage device, the method comprising:

[0006] Acquiring target data, where the target data includes operating condition data of the battery energy storage device and parameters related to insulation performance of the battery energy storage device;

[0007] Determine the insulation life of the battery energy storage device based on the target data.

[0008] In the above technical solution, the operating condition data can reflect the impact of the environment on the insulation life of the insulating medium in the battery energy storage device. The parameters related to the insulation performance of the battery energy storage device can include multiple parameters. Therefore, by determining the insulation life of the battery energy storage device based on the target data, it is possible to determine the insulation life of the battery energy storage device under the combined effects of multiple factors, and to more accurately determine the insulation life of the battery energy storage device in actual application environments. The method provided in this application is applicable to different operating conditions and different insulating media, that is, to different application environments and different products, and has good scalability.

[0009] In some embodiments, the operating condition data of the battery energy storage device includes the temperature and voltage acting on the insulating medium in the battery energy storage device, and the parameters related to the insulation performance of the battery energy storage device include the structural parameters of the insulating medium and the parameters related to the battery cell structure in the battery energy storage device.

[0010] Determining the insulation life of a battery energy storage device based on target data involves determining the insulation life of the battery energy storage device based on the temperature and voltage acting on the insulating medium within the battery energy storage device, the structural parameters of the insulating medium, and parameters related to the battery cell structure. During actual operation of a battery energy storage device, the operating temperature and voltage both affect the insulation performance of the insulating medium. Structural parameters of the insulating medium can include its thickness, which can affect the breakdown voltage and the heat dissipation capacity of the battery cell. Therefore, these structural parameters can significantly impact insulation performance. A parameter related to the battery cell structure within the battery energy storage device can be the electric field nonuniformity coefficient. The arc-shaped region between the side where the aluminum shell of the battery cell is bonded to the insulating medium and the side of the aluminum shell forms a circular arc. During charging and discharging, this arc-shaped region (also known as the R-corner region of the aluminum shell) creates electric field distortion, potentially leading to partial discharge. The impact of electric field distortion on insulation performance can be represented by the electric field nonuniformity coefficient. This allows for more accurate determination of the insulation life of a battery energy storage device under actual application conditions.

[0011] In some embodiments, the operating condition data of the battery energy storage device include the temperature and voltage acting on the insulating medium in the battery energy storage device, and the parameters related to the insulation performance of the battery energy storage device include the structural parameters, electrical performance parameters, parameters related to the interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device.

[0012] According to the target data, the insulation life of the battery energy storage device is determined, including: determining the insulation life of the battery energy storage device based on the temperature and voltage acting on the insulating medium in the battery energy storage device, the structural parameters of the insulating medium, the electrical performance parameters, the parameters related to the interface defects of the insulating medium, and the parameters related to the battery cell structure in the battery energy storage device. One side of the insulating medium is in close contact with the battery cell shell, and the other side is in close contact with the bottom of the electrical box. During the processing of the bonding interface, impurities and bubbles are easily introduced, forming interface defects, thereby forming charge traps; under the action of voltage (such as high-voltage DC operating conditions), the space charge captured by the trap accumulates for a long time and triggers local discharge, which will accelerate the aging and failure of the insulating medium. Therefore, the parameters related to the interface defects of the insulating medium can affect the insulation performance of the insulating medium. This can more accurately determine the insulation life of the battery energy storage device in the actual application environment.

[0013] In some embodiments, the operating condition data of the battery energy storage device include the temperature, voltage and mechanical load acting on the insulating medium in the battery energy storage device, and the parameters related to the insulation performance of the battery energy storage device include the structural parameters, electrical performance parameters, parameters related to the interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device.

[0014] The insulation life of the battery energy storage device is determined based on the target data, including the temperature, voltage, and mechanical load acting on the insulating medium in the battery energy storage device, the structural parameters and electrical performance parameters of the insulating medium, parameters related to interface defects of the insulating medium, and parameters related to the battery cell structure in the battery energy storage device. This allows for a more accurate determination of the insulation life of the battery energy storage device in actual application environments.

[0015] In some embodiments, the parameter related to the cell structure in the battery energy storage device is the electric field non-uniformity coefficient k E , in the contact plane area between the cell and the insulating medium, k E =1; in the area where there is an angle between the contact surface between the cell and the insulating medium and the side surface of the cell, k E >1.

[0016] In some embodiments, the above method further includes: when the insulation life of the battery energy storage device does not meet a preset threshold, adjusting parameters related to the insulation performance of the battery energy storage device according to the difference between the insulation life of the battery energy storage device and the preset threshold.

[0017] In some embodiments, determining the insulation life of a battery energy storage device based on target data includes: inputting the target data into a first model and outputting the insulation life of the battery energy storage device, wherein the first model is pre-trained using operating condition data and parameters related to the insulation performance of the battery energy storage device.

[0018] In the embodiment of the present application, based on the Endicott model framework and combined with the aforementioned factors affecting insulation performance, four key assumptions are proposed to improve the Endicott model, thereby obtaining a first model in which multiple factors act together. The first model can reflect the law of change of the insulation life of the insulating medium under the joint action of multiple factors, and can more accurately predict the insulation life of the insulating medium in actual application environments.

[0019] In a second aspect, an embodiment of the present application further provides a device for evaluating the insulation life of a battery energy storage device, comprising:

[0020] an acquisition module, configured to acquire target data, the target data including operating condition data of the battery energy storage device and parameters related to the insulation performance of the battery energy storage device;

[0021] The processing module is used to determine the insulation life of the battery energy storage device according to the target data.

[0022] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0023] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions. When the computer instructions are executed on a computer, the computer executes the method described in the first aspect.

[0024] In a sixth aspect, an embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program product runs on a computer, it implements the method described in the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 This is a schematic diagram of the process of a method for evaluating the insulation life of a battery energy storage device provided in an embodiment of the present application;

[0027] Figure 2 This is a schematic structural diagram of an insulating specimen provided in an embodiment of the present application;

[0028] Figure 3 This is a schematic structural diagram of an accelerated aging test device provided in an embodiment of the present application;

[0029] Figure 4 This is a schematic diagram of changes in insulation life prediction values under different voltages provided by an embodiment of the present application;

[0030] Figure 5 This is a schematic diagram of changes in insulation life under different insulation layer thicknesses provided in an embodiment of the present application;

[0031] Figure 6 This is a schematic diagram of changes in insulation life under different battery non-uniformity coefficients provided in an embodiment of the present application;

[0032] Figure 7This is a schematic diagram of the change of insulation life under different adhesive layer thicknesses and trap center energy levels provided in an embodiment of the present application;

[0033] Figure 8 It is a structural diagram of a device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0036] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly indicate the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is two or more, unless otherwise specifically defined.

[0037] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0039] As the scale of renewable energy power generation continues to increase, the demand for large-scale battery energy storage devices on the grid side is also increasing. The insulation safety and reliability of battery energy storage devices have become important research issues.

[0040] The battery energy storage device includes energy storage cells, an electrical box, and an insulating medium between the energy storage cells and the electrical box. The insulating layer (such as polyester PET film, also known as blue film) in the insulating medium is bonded to the cell shell by adhesive on one side, and bonded to the cold plate at the bottom of the electrical box by structural adhesive (such as polyurethane PU) on the other side. In the insulation design of the battery energy storage device, the voltage and strong electric field are mainly borne by the insulating medium between the cell and the electrical box. The insulation performance of the insulating medium will gradually decrease during long-term operation. If the insulation performance fails, the risk of short circuit between the positive and negative poles of the energy storage cell will increase, which may cause serious faults such as battery fire. Therefore, the operating life of the battery energy storage device depends to a large extent on the insulation life of the insulating medium between the cell and the electrical box. In order to ensure the long-term safe operation of the battery energy storage device, it is of great significance to accurately evaluate the insulation life of the insulating medium in the battery energy storage device during the design stage and factory stage before the equipment is put into operation.

[0041] Insulation life is primarily assessed based on aging models. For example, an aging model based on Arius's law is used to predict insulation life. This method uses a constant-temperature thermal aging test using the elongation at break of polyester insulation as the test parameter to determine the insulation material's test life and corresponding temperature values. The least-squares method is then used to fit the test life and corresponding temperature values to derive a life prediction formula for polyester insulation used in busbar trunking. Finally, the insulation material life formula is used to predict the insulation life at the rated temperature. However, this method only considers thermal aging factors caused by temperature changes, and the aging model considers fewer factors affecting insulation life. In real-world environments, the insulation medium of battery energy storage devices is subject to the combined influence of multiple aging factors. Therefore, the accuracy of the insulation life assessment method using this method is relatively low.

[0042] In view of this, the embodiments of the present application propose a method for evaluating the insulation life of a battery energy storage device. This method comprehensively considers the impact of multiple factors on the insulation life, such as the multi-layer structure of the insulating medium, interface defects, and electro-thermal-mechanical aging during operating conditions. This method can improve the accuracy of the insulation life assessment of the battery energy storage device. In addition, the insulation parameters can be designed according to the actual product, which has good scalability.

[0043] The battery energy storage device mentioned in the embodiments of this application can be used for high-voltage energy storage as well as low-voltage energy storage. Among them, high-voltage direct current energy storage is the main trend of future energy storage development. The battery energy storage device can be used in industrial, residential, commercial, power grid and other fields.

[0044] The detailed description is given below. Figure 1 This is a flow chart of a method for evaluating the insulation life of a battery energy storage device proposed in an embodiment of the present application, the method comprising S101-S102.

[0045] S101, acquiring target data, where the target data includes operating condition data of a battery energy storage device and parameters related to insulation performance of the battery energy storage device.

[0046] Target data can be stored in a local device, server, or other device with storage capabilities. When evaluating insulation life, target data can be obtained from a device with storage capabilities.

[0047] Among them, the operating condition data is determined based on the actual operating environment. The operating condition data may include at least one of the temperature, voltage, and mechanical load acting on the insulating medium. During the actual operation of the battery energy storage device, the operating temperature, operating voltage, and mechanical load will all affect the insulation performance of the insulating medium. The operating temperature refers to the surface temperature of the battery cell where the insulating medium is located. The operating voltage refers to the DC voltage after the battery energy storage device is in stable operation. The mechanical load refers to the load borne by the insulating medium in the actual environment. The load is caused by the expansion and contraction of the battery. In addition, the charging and discharging process will also cause temperature changes and mechanical load cycles.

[0048] Parameters related to the insulation performance of battery energy storage devices are determined based on the device's production process design and parameter measurements. These parameters include one or more of the following: structural parameters of the insulating medium, parameters related to interface defects within the insulating medium, electrical performance parameters of the insulating medium, and parameters related to the structure of the battery cells within the device.

[0049] Among these, the structural parameters of the insulating medium may include its thickness, which can be specifically divided into the thickness of the insulating layer and the thickness of the adhesive layer. The insulating layer and adhesive layer can also be combined into a single layer. The insulating layer or adhesive layer can be a single material or a composite of multiple materials. The thickness of the insulating medium can affect the breakdown voltage, the heat dissipation capacity of the battery cell, and therefore significantly influence the insulating performance of the insulating medium.

[0050] The insulating medium is bonded to the core shell on one side and to the bottom of the electrical box on the other. During the processing of the bonding interface, impurities and bubbles are easily introduced, forming interfacial defects, which in turn form charge traps. Under voltage (such as high-voltage DC operating conditions), the trapped space charge accumulates over a long period of time and triggers partial discharge, which accelerates the aging and failure of the insulating medium. Therefore, parameters related to the interfacial defects of the insulating medium can affect the insulation performance of the insulating medium.

[0051] In some embodiments, the electrical performance parameters of the insulating medium include the dielectric constant of the adhesive layer material. Considering that the adhesive layer is more susceptible to defects such as bubbles and impurities than the insulating layer, and the insulating performance of the adhesive layer is generally lower than that of the insulating layer, space charge primarily accumulates in the adhesive layer region, thereby enhancing the electric field in the adhesive layer region. The dielectric constant refers to the ability of a material to retain charge, and the dielectric constant of the adhesive layer material has a significant relationship with the electric field enhancement in the adhesive layer region.

[0052] In some embodiments, a parameter related to the structure of the battery cell in the battery energy storage device can be an electric field non-uniformity coefficient. The side where the battery cell aluminum shell is bonded to the insulating medium and the side of the battery cell aluminum shell are arc-shaped. During charging and discharging, the arc-shaped area (also called the R corner area of the battery cell aluminum shell) forms electric field distortion, which may cause local discharge. The impact of electric field distortion on insulation performance can be expressed by the electric field non-uniformity coefficient.

[0053] S102: Determine the insulation life of the battery energy storage device according to the target data.

[0054] After the target data is obtained, the target data can be input into a pre-trained aging model, or a pre-trained neural network model, or other methods to determine the insulation life of the battery energy storage device.

[0055] In the above technical solution, the operating condition data can reflect the impact of the environment on the insulation life of the insulating medium in the battery energy storage device. The parameters related to the insulation performance of the battery energy storage device can include multiple parameters. Therefore, by determining the insulation life of the battery energy storage device based on the target data, it is possible to determine the insulation life of the battery energy storage device under the combined effects of multiple factors, and to more accurately determine the insulation life of the battery energy storage device in actual application environments. The method provided in this application is applicable to different operating conditions and different insulating media, that is, to different application environments and different products, and has good scalability.

[0056] In some embodiments, the operating condition data of the battery energy storage device includes the temperature and voltage of an insulating medium acting on the battery energy storage device, and parameters related to the insulation performance of the battery energy storage device include structural parameters of the insulating medium and parameters related to the structure of battery cells in the battery energy storage device. Determining the insulation life of the battery energy storage device based on the target data includes determining the insulation life of the battery energy storage device based on the temperature and voltage of the insulating medium acting on the battery energy storage device, the structural parameters of the insulating medium, and parameters related to the structure of battery cells in the battery energy storage device.

[0057] Taking the pre-trained aging model as an example, the relationship between insulation life and temperature, voltage, structural parameters of the insulating medium, and parameters related to the structure of the battery cell in the battery energy storage device can be expressed by the following formula (1).

[0058]

[0059] Where L is the insulation life, T is the temperature, U is the voltage, h is the Planck constant, k is the Boltzmann constant, d1 is the thickness of the insulation layer, d2 is the thickness of the adhesive layer, and k E is the electric field inhomogeneity coefficient, and α, ΔG, and β are the coefficients obtained by fitting.

[0060] After obtaining the temperature T, voltage U, the structural parameters d1 and d2 of the insulating medium, and the electric field non-uniformity coefficient k E Then, the temperature T, voltage U, structural parameters d1 and d2 of the insulating medium, and the electric field non-uniformity coefficient k can be calculated. E The aging model represented by equation (1) is input to determine the insulation life of the battery energy storage device.

[0061] In some embodiments, in the planar area of contact between the battery cell and the insulating medium, k E =1; in the area where there is an angle between the contact surface between the cell and the insulating medium and the side surface of the cell, k E >1, and the specific value can be obtained through the corresponding electric field simulation.

[0062] In some embodiments, the operating condition data of the battery energy storage device includes the temperature and voltage of the insulating medium acting on the battery energy storage device, and parameters related to the insulation performance of the battery energy storage device include structural parameters, electrical performance parameters, parameters related to interface defects of the insulating medium, and parameters related to the structure of the battery cells in the battery energy storage device. Determining the insulation life of the battery energy storage device based on the target data includes: determining the insulation life of the battery energy storage device based on the temperature and voltage of the insulating medium acting on the battery energy storage device, the structural parameters, electrical performance parameters, parameters related to interface defects of the insulating medium, and parameters related to the structure of the battery cells in the battery energy storage device.

[0063] Taking the pre-trained aging model as an example, the relationship between insulation life and temperature, voltage, structural parameters of the insulating medium, electrical performance parameters, parameters related to interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device can be expressed by the following formula (2).

[0064]

[0065] Where L is the insulation life, T is the temperature, U is the voltage, h is the Planck constant, k is the Boltzmann constant, d1 is the thickness of the insulation layer, d2 is the thickness of the adhesive layer, and k E is the electric field inhomogeneity coefficient, E ρis the additional electric field caused by the interface defects of the insulating medium, and α, ΔG, β1 and β2 are the coefficients obtained by fitting.

[0066] Among them, the parameters related to the interface defects of the insulating medium can be expressed by the trap center energy level E of the insulating medium. tc , trap center energy level E tc The corresponding trap state density N tc Under the action of voltage, space charge is captured by charge traps, i.e., charge is trapped; then, under the continuous action of electric field and temperature, the charge will undergo de-trapping and migration, which will affect the insulation performance of the insulating medium. The energy required for de-trapping and migration is the energy level. Different charge traps require different energies, and the number of traps at different energy levels (i.e., trap state density) is different. The energy level with the largest trap state density is the trap center energy level. E tc With N tc It can be measured using either the isothermal decay current method or the thermally stimulated current method. The thermally stimulated current method measures the external circuit current generated by the detrapping and migration of charges within the medium during heating to study its trap energy distribution characteristics. The isothermal decay current method uses DC charging to deposit a certain amount of charge on the material surface and measures the change in charge over time to study the internal charge transport characteristics and trap distribution characteristics.

[0067] The effects of interface defects on insulation life are considered in the embodiments of this application. It is assumed that the externally injected space charge is first captured by the trap state in the insulating layer that is subjected to a greater voltage drop, i.e., the charge is trapped; then, under the continuous action of the electric field and temperature, the charge is de-trapped and migrates. Considering that the glue layer is more prone to defects such as bubbles and impurities than the insulating layer, and the insulating performance of the glue layer is usually lower than that of the insulating layer, it is assumed that the space charge mainly accumulates in the glue layer area, thereby causing the electric field in the glue layer area to be enhanced. The additional electric field E introduced by the space charge is ρ It satisfies Gauss's theorem under electrostatic field, as shown in formula (3).

[0068]

[0069] Where ρ is the space charge density, x is the coordinate along the thickness of the adhesive layer, and ε² is the dielectric constant of the adhesive layer material. The space charge density distribution ρ(x) of an insulating sample can be measured using the electroacoustic pulse test method. This method is used to measure space charge. Its basic principle is to apply an electric pulse disturbance source to electrodes at both ends of the dielectric. This pulsed electric field acts on the space charge in the dielectric and on the electrode interface, generating acoustic pulses. Receiving and measuring these acoustic pulses provides information on the internal space charge distribution of the dielectric.

[0070] According to the space charge transfer theory, E ρ It can also be estimated in a simplified manner according to formula (4).

[0071]

[0072] Among them, ε2 is the dielectric constant of the adhesive layer material, E tc is the trap center energy level of the insulating medium, N tc The energy level is E tc The trap state density corresponding to the charge trap. The term is related to the charge trapping probability. If the simplified estimation method of formula (4) is adopted, formula (2) can be rewritten as formula (5).

[0073]

[0074] Where α, ΔG, β'1 and β2 are the coefficients obtained by fitting.

[0075] In the embodiment of the present application, after obtaining the temperature T, voltage U, structural parameters d1 and d2 of the insulating medium, dielectric constant ε2 of the glue layer material, and energy level of the trap center E tc , trap state density N tc , and the electric field nonuniformity coefficient k E These parameters can then be input into the aging model represented by equation (5) to determine the insulation life of the battery energy storage device.

[0076] In some embodiments, the operating condition data of the battery energy storage device includes the temperature, voltage, and mechanical load acting on the insulating medium in the battery energy storage device, and the parameters related to the insulation performance of the battery energy storage device include the structural parameters, electrical performance parameters, parameters related to the interface defects of the insulating medium, and parameters related to the structure of the battery cells in the battery energy storage device. Determining the insulation life of the battery energy storage device based on the target data includes: determining the insulation life of the battery energy storage device based on the temperature, voltage, and mechanical load acting on the insulating medium in the battery energy storage device, the structural parameters, electrical performance parameters, parameters related to the interface defects of the insulating medium, and parameters related to the structure of the battery cells in the battery energy storage device. This can more accurately determine the insulation life of the battery energy storage device in actual application environments.

[0077] Taking the pre-trained aging model as an example, the relationship between insulation life and temperature, voltage, mechanical load, structural parameters of the insulating medium, electrical performance parameters, parameters related to the interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device can be expressed by the following formula (6).

[0078]

[0079] Among them, L is the insulation life, T is the temperature, U is the voltage, S is the pressure on the insulating medium, h is the Planck constant, k is the Boltzmann constant, d1 is the thickness of the insulating layer, d2 is the thickness of the adhesive layer, k E is the electric field inhomogeneity coefficient, E ρ is the additional electric field caused by the interface defects of the insulating medium, and α, ΔG, β1, β2, and γ are the coefficients obtained by fitting.

[0080] In the embodiment of the present application, after obtaining the temperature T, voltage U, pressure S, structural parameters d1 and d2 of the insulating medium, dielectric constant ε2 of the glue layer material, and energy level E of the trap center, tc , trap state density N tc , and the electric field nonuniformity coefficient k E These parameters can then be input into the aging model represented by equation (6) to determine the insulation life of the battery energy storage device.

[0081] The process of obtaining the aging model is described in detail below.

[0082] Before training the model, insulation specimens are prepared based on the actual insulation materials and structures used in battery energy storage devices. Accelerated aging tests are performed on the insulation specimens under different test conditions, and insulation life test values are obtained through accelerated aging tests. The operating conditions under different test conditions are used to simulate real-world environments. Parameters related to the insulation performance of the insulation medium can be obtained by testing the insulation specimens, and insulation life test values are obtained through accelerated aging tests. This allows the generation of test condition data, parameters related to the insulation performance of the insulation medium, and insulation life test values for each insulation specimen, ultimately resulting in multiple sets of sample data.

[0083] The following combination Figure 2 and Figure 3 The accelerated aging test used in the examples of this application is described below. This test involves two steps: sample preparation and testing. Sample preparation is related to the insulating medium's processing technology, material selection, and multilayer structure. The test also includes test equipment, operating condition setup, and data processing.

[0084] As an example, Figure 2 The structure of an insulating sample provided by the embodiment of the present application is shown. According to the actual situation of the battery energy storage device, a multi-layer composite insulating sample is prepared, such as Figure 2 As shown in the figure, the insulation specimen consists of an upper metal sheet, an insulating layer, a glue layer, and a lower metal sheet.

[0085] The upper metal sheet is made of the same material as the cell casing. Preferably, it can be 3003 aluminum alloy. The insulating layer can be made of PET polyester blue film, epoxy resin coating, or the like. The adhesive layer can be a polymer adhesive such as polyurethane. The lower metal sheet is made of the same material as the cooling plate at the bottom of the electrical box and can be sprayed with insulating paint.

[0086] The thickness of each layer and the total thickness of the insulation sample are consistent with the design of the battery energy storage device, and the length and width can be adjusted according to the test conditions. Preferably, the insulation sample can be a square piece with a length and width of 10 cm.

[0087] Figure 3 An accelerated aging test device provided in an embodiment of the present application is shown, including a housing, a cylindrical upper electrode, a cylindrical lower electrode, an electrode adjustment module, an oil bath circulation temperature control module, a high-voltage DC power supply, a test control module and a host computer.

[0088] The shell is preferably made of a transparent insulating material, such as a thick acrylic plate, to facilitate observation of the internal breakdown. The top of the shell can be opened for placement / sampling and has good sealing.

[0089] The height of the upper cylindrical electrode can be adjusted by the electrode adjustment module, and the position of the lower cylindrical electrode is fixed. The insulating sample is placed between the upper and lower cylindrical electrodes. The upper and lower cylindrical electrodes have the same diameter and chamfer. Preferably, the electrode diameter can be 25mm.

[0090] The electrode adjustment module can control the static load pressure applied to the insulating sample by adjusting the height position of the electrode on the cylinder, and can display the pressure between the electrode on the cylinder and the insulating sample in real time.

[0091] The oil bath circulation temperature control module achieves temperature control by circulating the oil bath and displays the temperature in real time. The oil bath can be filled with silicone oil and the insulation sample is immersed in the oil bath to prevent surface flashover of the insulation sample.

[0092] The test control module controls the high-voltage DC power supply to apply a DC voltage to the upper and lower electrodes of the cylinder, specifically controlling the power on / off and voltage amplitude. The test control module also monitors and records the DC voltage waveform between the electrodes. If a breakdown occurs, it automatically shuts off the high-voltage DC power supply and records the breakdown time. After the high-voltage DC power supply is shut off, the test control module sends control instructions to the electrode adjustment module, which controls the movement of the upper cylindrical electrode, separating it from the insulating specimen by a certain distance.

[0093] The test control module also has a breakdown short-circuit protection function; in addition, the test control module can exchange information with the host computer, making it convenient for testers to obtain and save information such as voltage waveform, breakdown time, mechanical load pressure, etc. in real time on the host computer, and adjust experimental parameters and control the test process on the host computer.

[0094] During an insulation aging test, an insulating sample is placed between upper and lower cylindrical electrodes. The temperature and mechanical load are set, and a DC voltage is applied. After the insulating sample breaks down, the high-voltage DC power supply is turned off, the sample is removed, and the corresponding temperature, mechanical load, DC voltage, voltage waveform, and breakdown time are recorded. The breakdown time is obtained by reading the voltage waveform. Breakdown refers to the destruction of the insulating dielectric structure and its conversion to a conductor. The breakdown time is the total time from the start of pressure application to complete breakdown.

[0095] At different set temperatures and static loads (i.e., normal stress), different levels of DC voltage were applied to the insulation sample to obtain multiple sets of operating data under different test conditions. Due to the randomness of the breakdown process, the present embodiment repeated the test under the same operating conditions, that is, repeatedly tested new samples with the same temperature, load, and voltage.

[0096] Preferably, the test temperature grouping is set to no less than 3 groups, and the upper temperature limit is not higher than the glass transition temperature of the insulating medium. The test voltage grouping is set to no less than 3 groups, and the upper voltage limit is lower than the short-time (600s) breakdown voltage of the insulating sample. The applied load grouping is set to no less than 2 groups, and the stress range is close to the actual working conditions of the energy storage device to be evaluated, and avoids causing macroscopic damage to the insulating structure. Test 10 samples or more under each working condition. Considering the possibility of damage to the specimens in the accelerated aging test, the more specimens prepared, the better.

[0097] In the embodiment of the present application, the life test value of the sample is determined by the breakdown time. The breakdown time statistics of multiple test samples under the same test conditions are obtained as follows:

[0098] Assuming that the relationship between breakdown probability and breakdown time obeys the two-parameter Weibull distribution law, the relationship between breakdown time and breakdown probability is fitted according to formula (7), and parameters A and B are obtained, thereby obtaining a specific breakdown time distribution function.

[0099]

[0100] Where F is the breakdown probability and t is the breakdown time (unit: hours). A and B are unknown coefficients, which are determined by fitting the test data. The breakdown time statistics corresponding to a breakdown probability of 63.2% are calculated as the insulation life test value L under the corresponding temperature, load and voltage. m(Unit: hour), as shown in formula (8).

[0101] L m =A (8)

[0102] Combine Figure 2 and Figure 3 In the illustrated embodiment, insulation life test values of the sample under different temperatures, different voltages, and different mechanical loads, as well as performance parameters of the sample itself, can be obtained, thereby obtaining multiple sets of sample data.

[0103] It should be noted that the samples corresponding to multiple data sets can include samples with different insulation materials and structures, corresponding to different test conditions. Insulation materials can include polyester blue film coating, epoxy resin coating, epoxy resin powder coating, etc. Multiple sets of sample data can be stored in a storage device such as a local device or a server. When training a model, multiple sets of sample data can be obtained from a storage device.

[0104] After obtaining multiple sets of sample data, a pre-set model can be fitted and solved based on the multiple sets of sample data; a first model can be obtained based on the results of the fitting and solving. Specifically, the pre-set model is fitted and solved based on the multiple sets of sample data to obtain values of undetermined coefficients in the model. The accuracy of the model solution is tested. If the accuracy does not meet a threshold, the fitting and solving is continued, and the values of the undetermined coefficients in the model are updated until the accuracy meets the threshold, thereby obtaining final values of the undetermined coefficients, thereby obtaining the first model, i.e., the aging model.

[0105] In the embodiment of the present application, based on the Endicott model framework and in combination with the aforementioned factors affecting insulation performance, four key assumptions are proposed to improve the Endicott model, thereby obtaining an aging model in which multiple factors act together.

[0106] Considering the combined electrical and thermal aging, the Endicott model describes the relationship between the insulation life L and the temperature T and the electric field E, as shown in Equation (9).

[0107]

[0108] Where h is Planck's constant, k is the Boltzmann constant, and α, ΔG, w1, and w2 are all unknown coefficients. In particular, according to chemical reaction rate theory, the physical meaning of the ΔG parameter is the free energy of the thermal aging reaction.

[0109] The following describes the aging model under four key assumptions based on the mechanisms of action of different aging factors.

[0110] In some embodiments, when only thermal aging acts alone, the aging model is as shown in equation (10).

[0111]

[0112] In some embodiments, a voltage U is applied based on thermal aging, and an aging model represents the relationship between insulation life and temperature, voltage, structural parameters of the insulating medium, and parameters related to the structure of the battery cell in the battery energy storage device, as shown in formula (11).

[0113]

[0114] Where L is the insulation life, T is the temperature, U is the voltage, h is the Planck constant, k is the Boltzmann constant, d1 is the thickness of the insulation layer, d2 is the thickness of the adhesive layer, and k E is the electric field inhomogeneity coefficient, and α, ΔG, and β are the coefficients to be fitted and solved.

[0115] In some embodiments, the aging model represents the relationship between insulation life and temperature, voltage, structural parameters of the insulating medium, electrical performance parameters, parameters related to interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device. The first relationship is shown in formula (12).

[0116]

[0117] Where L is the insulation life, T is the temperature, U is the voltage, h is the Planck constant, k is the Boltzmann constant, d1 is the thickness of the insulation layer, d2 is the thickness of the adhesive layer, and k E is the electric field inhomogeneity coefficient, E ρ is the additional electric field caused by the interface defects of the insulating medium, and α, ΔG, β1 and β2 are the coefficients to be fitted and solved.

[0118] According to the space charge transfer theory, E ρ It can also be estimated in a simplified manner according to formula (13).

[0119]

[0120] Among them, ε2 is the dielectric constant of the adhesive layer material, E tc is the trap center energy level of the insulating medium, N tc The energy level is E tc The trap state density corresponding to the charge trap. The term is related to the charge trapping probability. If the simplified estimation method of formula (13) is adopted, formula (12) can be rewritten as formula (14).

[0121]

[0122] Among them, α, ΔG, β'1 and β2 are unknown coefficients.

[0123] In some embodiments, the aging model represents the relationship between insulation life and temperature, voltage, mechanical load, structural parameters of the insulating medium, electrical performance parameters, parameters related to interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device.

[0124] The present embodiment considers the impact of mechanical load on insulation life. It is assumed that the mechanical load caused by the charge-discharge cycle is equivalent to a static load of pressure S intermittently applied to the insulating medium, with the load cycle being consistent with the energy storage device's operating cycle. Furthermore, it is assumed that the aging effect caused by the mechanical load is independent of other factors. The aging model is shown in Equation (15).

[0125]

[0126] Wherein, L represents insulation life, T represents temperature, U represents voltage, S represents the pressure on the insulating medium, h represents Planck constant, k represents Boltzmann constant, d1 represents insulation layer thickness, d2 represents adhesive layer thickness, and k represents the insulation layer thickness. E is the electric field inhomogeneity coefficient, E ρ is the additional electric field caused by the interface defects of the insulating medium, and α, ΔG, β1, β2 and γ are the coefficients to be fitted and solved.

[0127] The aging model after improving the Endicott model based on different key assumptions includes multiple undetermined coefficients. Multiple groups of sample data are fitted with the aging model, and the optimal values of the undetermined coefficients can be obtained based on the fitting results.

[0128] The fitting calculation process is essentially a multi-parameter nonlinear programming problem. A better solution algorithm can be selected from a variety of solutions, such as the steepest descent method, least squares method, Levenberg-Marquardt algorithm, artificial neural network algorithm, etc.

[0129] Preferably, the classic nonlinear generalized reduced gradient method can be used, with the optimization goal of maximizing the goodness-of-fit R-squared to obtain the optimal value of the undetermined coefficient. Preferably, if the goodness-of-fit R-squared is less than 0.95, the number of test data sets can be increased, for example, by obtaining more insulation life test values under more temperatures, more voltages, or more mechanical loads.

[0130] After obtaining the aging model, the aging model can be used to predict the insulation life of the actual battery energy storage device. It should be noted that in actual applications, if a certain influencing factor does not need to be taken into account, the corresponding exponential term can be deleted in Equation (15) or the coefficient to be solved can be set to 0. For example, if mechanical load is not considered, Equation (14) can be used to predict the insulation life. If interface defects are not considered, Equation (16) can be used to predict the insulation life.

[0131]

[0132] In the above technical solution, because the aging model can reflect the changing patterns of insulation life under the combined effects of multiple factors, using the aging model to predict insulation life can more accurately predict the insulation life of battery energy storage devices in actual application environments. Furthermore, the aging model is applicable to different operating conditions and different insulation media, that is, different application environments and different products, and has good scalability.

[0133] In some embodiments, the operating condition data further includes daily operating hours.

[0134] In the aforementioned embodiment, the insulation lifespan determined in step S102 is calculated in hours, denoted as L1. In practical applications, the battery energy storage device may operate for less than 24 hours per day. Lifespan loss during operating periods is cumulative and irreversible, while lifespan loss during non-operating periods is negligible. Therefore, according to equation (17), L1 can be converted to insulation lifespan calculated in years, denoted as L2.

[0135]

[0136] The following is an example of the aging model training and application process with reference to Tables 1 to 5. Table 1 shows the insulation life test value L obtained through the accelerated aging test. m When no voltage is applied, the insulation life is equivalent to the thermal aging life. According to the standard GB / T 13542.2, the end of the life can be taken as the reduction of the material's elongation at break by 50%.

[0137] Table 1 Insulation life test values L obtained from accelerated aging test m

[0138]

[0139]

[0140] Table 2 shows the performance parameters of the insulation specimens at different temperatures and voltages in the accelerated aging test. In this example, no load aging was performed in the accelerated aging test. Therefore, the above equation (14) can be used as the aging model, or the above equation (15) can be used as the aging model, with γ being 0 in equation (15). For example, substituting the data in Tables 1 and 2 into equation (15) yields multiple equations. By fitting and solving, the undetermined coefficients of the aging model can be obtained, as shown in Table 3.

[0141] Table 2 Performance parameters of insulation samples

[0142]

[0143] Table 3 Undetermined coefficients of the aging model obtained by fitting

[0144]

[0145] Therefore, substituting the fitting results shown in Table 3 into formula (15), the aging model can be expressed as formula (18).

[0146]

[0147] The fitting results shown in Table 3 correspond to a goodness of fit R-square greater than 0.99, indicating that the fitting results are relatively accurate.

[0148] When predicting the insulation life, the actual operating conditions (rated conditions) are determined according to the actual application environment of the battery energy storage device. Table 4 shows the set values for the two rated conditions. Before the battery energy storage device leaves the factory, the insulation structure and performance parameters of the insulating medium of the product to be tested are determined, as shown in Table 5. Substituting the data in Tables 4 and 5 into the aging model shown in formula (18), the insulation life prediction values under the two rated conditions can be calculated. Figure 4 As shown in the figure, the predicted insulation life under Class 1 rated operating conditions is 24.9 years, and the predicted insulation life under Class 2 rated operating conditions is 19.44 years. This means that at an operating temperature of 50°C, when the rated voltage increases from 1500V to 2500V, the predicted insulation life decreases from 24.9 years to 19.44 years, a decrease of approximately 22%.

[0149] It should be understood that the mechanical load on the bottom of the electrical box is considered to be low and negligible only in this example, which does not mean that the mechanical load is 0 in actual conditions.

[0150] Table 4 Actual operating conditions

[0151]

[0152] Table 5 Insulation structure and performance parameters of insulating medium

[0153]

[0154] In summary, the embodiments of the present application prepare insulating specimens based on the actual insulating materials and structures in the battery energy storage device, and conduct accelerated aging tests under group settings of different temperatures, different voltages, and different mechanical loads. The test data is processed according to the Weilblull statistical distribution law, and the obtained test results are used as the insulation life under accelerated aging conditions; based on the influence of multiple factors on the aging of the insulating medium, quantitative modeling is performed. Specifically, the multiple factors include temperature, voltage, thickness of the insulating layer / glue layer, space charge accumulated at defects in the bonding interface, electric field non-uniformity at the R corner of the battery cell, and mechanical loads caused by charge and discharge cycles. Based on the Endicott model framework, four key assumptions are proposed to improve the model and obtain an aging model under the influence of multiple factors. By adopting an appropriate nonlinear programming algorithm, the accelerated aging test results are fitted with the corresponding temperature, voltage, mechanical load and other known performance parameters and structural parameters to obtain the values of the unknown coefficients in the aging model, and the accuracy of the model solution is tested by the goodness of fit. In practical applications, the rated operating conditions and the performance parameters and structural parameters of the insulating medium in the actual battery energy storage device are substituted into the established aging model to predict the insulation life of the insulating medium in the actual battery energy storage device, which can more accurately predict the insulation life of the battery energy storage device in the actual application environment.

[0155] The validity and applicability of the aging model are also described in the examples of this application. Figures 5 to 7 As shown, Figure 5 shows how insulation life varies with insulation thickness, Figure 6 shows the variation of insulation life with electric field non-uniformity coefficient, Figure 7 The figure shows how the insulation life varies with the thickness of the adhesive layer, and how the insulation life varies with the energy level of the trap center. These variations were obtained through testing, and it can be seen that factors such as the thickness of the insulation layer, the electric field non-uniformity coefficient, the thickness of the adhesive layer, and the energy level of the trap center can affect the insulation life.

[0156] In some embodiments, when the insulation life of the battery energy storage device does not meet a preset threshold, parameters related to the insulation performance of the battery energy storage device can be adjusted according to the difference between the insulation life of the battery energy storage device and the preset threshold.

[0157] The preset threshold value may be a minimum value of the insulation life or other set value. When the insulation life of the battery energy storage device determined according to the target data is less than the preset threshold value, the values of parameters related to the insulation performance of the battery energy storage device in multiple factors may be continuously adjusted according to the difference between the insulation life of the battery energy storage device and the preset threshold value. The insulating medium may be improved according to the values of the adjusted factors so that the insulation life of the insulating medium is greater than the preset threshold value.

[0158] For example, according to the above-mentioned changes in insulation life with different factors, it can be seen that increasing the thickness of the insulation layer from the existing 0.11mm to 0.3mm only increases the insulation life by 9%. Therefore, when designing insulation, it is necessary to carefully consider thickening the insulation layer. When the electric field non-uniformity coefficient is about 4, it is an extremely non-uniform electric field. Figure 5 It can be seen that the insulation life is less than 20 years at this time, and when the R-angle electric field non-uniformity coefficient is less than 2, the insulation life is significantly increased to 25-30 years. Therefore, the electric field distribution should be improved to meet the slightly non-uniform electric field conditions. Figure 6 It can be seen that enhancing the trap energy level is beneficial to suppressing the migration of carriers, and the insulation life can be significantly improved; reducing the thickness of the glue layer can reduce the local electric field distortion under the same space charge density, which is also beneficial to increasing the insulation life. Combining the above two factors, while improving the trap energy level, the glue layer is thinned, and the insulation life can be further increased to more than 30 years.

[0159] When improving the insulating medium, the insulation life can be extended by taking the following measures.

[0160] In some cases, the electric field distribution in the R corner area can be improved. Specifically, the occurrence of defects such as burrs and pits in the aluminum shell can be avoided as much as possible, and consideration can be given to spraying a gradient insulating coating in the R corner area.

[0161] In some cases, under the premise of meeting the bonding strength, the thickness of the adhesive layer can be reduced and the processing technology can be improved to avoid the introduction of defects such as microbubbles and impurities, thereby suppressing the space charge effect and reducing the space charge density accumulated in the adhesive layer.

[0162] In some cases, the space charge generation pathway can be suppressed. Specifically, the upper and lower surfaces of the insulating layer can be short-circuited to eliminate the space charge generated during the raw material processing process; frictional static electricity can be reduced during the lamination process to avoid contact with workpieces that have a large difference in electronegativity from the insulating layer; material modification methods such as inorganic nanocoatings can be introduced to increase the charge injection barrier and regulate the trap energy level center of the insulating layer to shift toward deep traps.

[0163] The embodiments of the present application comprehensively consider the effects of the insulation structure, interface defects, and electro-thermal-mechanical aging in operating conditions, which can reflect the law of change of the insulation life of the insulating medium under the joint action of multiple factors, and can more accurately predict the insulation life of the insulating medium in the actual application environment. Among them, a number of insulation design parameters such as the thickness of the insulating layer, the thickness of the glue layer, the R-angle electric field non-uniformity, the spatial charge density distribution, etc. are specific and quantifiable, and can be combined with the actual product design. Therefore, this method can be applied to the life verification and design optimization of the insulating medium. The aging model can be applied to different operating conditions and different insulating media, that is, it can be applied to different application environments and different products, and has good scalability. The scope of application of the sample testing, model establishment, parameter solution, and data processing and analysis methods involved in the above method includes but is not limited to polyester blue film coating, epoxy resin coating, epoxy resin powder spraying and other insulation methods, which helps to guide the selection of insulating materials and process improvements.

[0164] The above describes the insulation life evaluation method of the battery energy storage device proposed in the embodiment of the present application. The following describes the relevant devices and equipment.

[0165] Figure 8 An evaluation device 800 for the insulation life of a battery energy storage device provided in an embodiment of the present application is shown, including an acquisition module 810 and a processing module 820 .

[0166] The acquisition module 810 is used to acquire target data, where the target data includes operating condition data of the battery energy storage device and parameters related to the insulation performance of the battery energy storage device.

[0167] The processing module 820 is used to determine the insulation life of the battery energy storage device according to the target data.

[0168] In some embodiments, the device 800 for evaluating the insulation life of a battery energy storage device further includes a storage module 830 for storing target data.

[0169] In some embodiments, the processing module 820 is specifically used to determine the insulation life of the battery energy storage device based on the temperature and voltage of the insulating medium acting on the battery energy storage device, the structural parameters of the insulating medium, and parameters related to the battery cell structure in the battery energy storage device.

[0170] In some embodiments, the processing module 820 is specifically used to determine the insulation life of the battery energy storage device based on the temperature and voltage of the insulating medium acting on the battery energy storage device, the structural parameters of the insulating medium, the electrical performance parameters, the parameters related to the interface defects of the insulating medium, and the parameters related to the battery cell structure in the battery energy storage device.

[0171] In some embodiments, the processing module 820 is specifically used to determine the insulation life of the battery energy storage device based on the temperature, voltage and mechanical load acting on the insulating medium in the battery energy storage device, the structural parameters of the insulating medium, the electrical performance parameters, the parameters related to the interface defects of the insulating medium, and the parameters related to the battery cell structure in the battery energy storage device.

[0172] In some embodiments, the processing module 820 is specifically configured to input target data into a first model and output the insulation life of the battery energy storage device. The first model is pre-trained using operating data and parameters related to the insulation performance of the battery energy storage device.

[0173] In some embodiments, the processing module 820 is further configured to adjust parameters related to the insulation performance of the battery energy storage device according to a difference between the insulation life of the battery energy storage device and the preset threshold when the insulation life of the battery energy storage device does not meet a preset threshold.

[0174] It should be understood that the apparatus 800 of the embodiment of the present application can be implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), wherein the PLD can be a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof. The aforementioned method can also be implemented by software, and the apparatus 800 and its various modules can also be software modules.

[0175] An embodiment of the present application also provides a device including: a memory and a processor, wherein the memory stores a computer program, and the processor implements the aforementioned model training method or insulation life prediction method when executing the computer program.

[0176] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid state drive (SSD).

[0177] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for evaluating the insulation life of a battery energy storage device, characterized in that: The method comprises: Acquiring target data, the target data including operating condition data of the battery energy storage device and parameters related to insulation performance of the battery energy storage device; The insulation life of the battery energy storage device is determined according to the target data.

2. The method according to claim 1, characterized in that The operating condition data of the battery energy storage device includes the temperature and voltage of the insulating medium acting on the battery energy storage device, and the parameters related to the insulation performance of the battery energy storage device include the structural parameters of the insulating medium and the parameters related to the cell structure of the battery energy storage device; Determining the insulation life of the battery energy storage device according to the target data includes: The insulation life of the battery energy storage device is determined based on the temperature and voltage acting on the insulating medium in the battery energy storage device, the structural parameters of the insulating medium, and parameters related to the cell structure in the battery energy storage device.

3. The method according to claim 1, characterized in that The operating condition data of the battery energy storage device includes the temperature and voltage acting on the insulating medium in the battery energy storage device, and the parameters related to the insulation performance of the battery energy storage device include the structural parameters and electrical performance parameters of the insulating medium, parameters related to the interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device; Determining the insulation life of the battery energy storage device according to the target data includes: The insulation life of the battery energy storage device is determined based on the temperature and voltage acting on the insulating medium in the battery energy storage device, the structural parameters of the insulating medium, the electrical performance parameters, parameters related to the interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device.

4. The method according to claim 1, wherein The operating condition data of the battery energy storage device includes the temperature, voltage, and mechanical load acting on the insulating medium in the battery energy storage device. The parameters related to the insulation performance of the battery energy storage device include the structural parameters and electrical performance parameters of the insulating medium, parameters related to interface defects of the insulating medium, and parameters related to the cell structure in the battery energy storage device. Determining the insulation life of the battery energy storage device according to the target data includes: The insulation life of the battery energy storage device is determined based on the temperature, voltage and mechanical load acting on the insulating medium in the battery energy storage device, the structural parameters of the insulating medium, the electrical performance parameters, parameters related to the interface defects of the insulating medium, and parameters related to the battery cell structure in the battery energy storage device.

5. The method according to claim 2, characterized in that The parameter related to the cell structure in the battery energy storage device is the electric field non-uniformity coefficient k E , the plane area of contact between the battery cell and the insulating medium, k E =1; In the area where there is an angle between the contact surface between the battery core and the insulating medium and the side surface of the battery core, k E >1.

6. The method according to claim 1, characterized in that The method further comprises: When the insulation life of the battery energy storage device does not meet a preset threshold, parameters related to the insulation performance of the battery energy storage device are adjusted according to the difference between the insulation life of the battery energy storage device and the preset threshold.

7. The method according to any one of claims 1 to 6, characterized in that Determining the insulation life of the battery energy storage device according to the target data includes: The target data is input into a first model to output the insulation life of the battery energy storage device, where the first model is pre-trained using operating condition data and parameters related to the insulation performance of the battery energy storage device.

8. A device for evaluating the insulation life of a battery energy storage device, characterized in that: include: an acquisition module, configured to acquire target data, wherein the target data includes operating condition data of the battery energy storage device and parameters related to the insulation performance of the battery energy storage device; A processing module is used to determine the insulation life of the battery energy storage device according to the target data.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which, when executed on a computer, enable the computer to perform the method according to any one of claims 1 to 7.

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