Battery gas production rate prediction method and electronic equipment

Abstract

The invention relates to the technical field of batteries, particularly provides a battery gas production rate prediction method and electronic equipment, and aims to solve the problem that the gas production rate cannot be effectively predicted in the prior art. In order to achieve the purpose, the battery gas production rate prediction method provided by the invention comprises the following steps: acquiring actually measured ion diffusion impedance of a battery; and obtaining the gas production rate of the battery based on the actually measured ion diffusion impedance and a pre-constructed ion diffusion impedance and battery internal pressure correlation model. According to the method, based on the pre-constructed correlation model of the ion diffusion impedance and the internal pressure of the battery, only the actually measured ion diffusion impedance of the battery needs to be obtained, additional equipment such as a pressure sensor does not need to be added, destructive treatment such as crevasse does not need to be carried out on the battery, the gas production rate can be conveniently and efficiently predicted, and the method is suitable for popularization and application. Therefore, risk cells can be identified in advance, and early warning can be carried out in time.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method for predicting battery gas production and an electronic device. Background Art

[0002] The widespread use of new energy vehicles has promoted the rapid development of the field of on-board power batteries. At present, there are many options for battery cells in on-board power battery packs, such as square shell type, soft pack type, blade type, etc. Among them, the soft pack type is relatively easy to have a higher energy density, but the soft pack battery cell will have a variety of side reactions in the battery cell under long-term charge and discharge cycles or poor operating conditions, which will cause gas accumulation inside the soft pack battery cell. In severe cases, it will expand or even rupture in the weak area of ​​the package's tensile strength.

[0003] In response to this, some related technologies focus on how to suppress the gas production of soft-pack batteries, or how to improve the strength of the packaging film of soft-pack batteries to avoid the battery cells from being damaged. However, the accumulation of gas production in soft-pack batteries is currently unavoidable. How to effectively predict the gas production in order to identify risky batteries in advance and provide effective early warning reminders has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the present application is proposed to solve the existing technical problem that the gas production of the battery cannot be effectively predicted.

[0005] In a first aspect, a method for predicting battery gas production is provided, comprising:

[0006] Obtain the measured ion diffusion impedance of the battery;

[0007] Based on the measured ion diffusion impedance and a pre-constructed correlation model between the ion diffusion impedance and the internal pressure of the battery, the gas production of the battery is obtained.

[0008] In some embodiments, the method further comprises constructing a correlation model between the ion diffusion impedance and the internal pressure of the battery by the following steps:

[0009] Perform multiple aging cycle tests on the test battery;

[0010] After each aging cycle test is performed for a preset number of times, the ion diffusion impedance and the internal pressure of the test battery are obtained to obtain multiple sets of data on the ion diffusion impedance and the internal pressure of the battery;

[0011] A correlation model between the ion diffusion impedance and the internal pressure of the battery is constructed based on the multiple sets of data on the ion diffusion impedance and the internal pressure of the battery.

[0012] In some embodiments, performing multiple aging cycle tests on the test battery includes:

[0013] In the charging stage, the test battery is charged in multiple stages until a first preset state of charge is reached;

[0014] In the discharge stage, the test battery that has reached the first preset state of charge is discharged at a constant current until it reaches a second preset state of charge, thereby completing an aging cycle test;

[0015] The charging phase and the discharging phase are repeatedly performed.

[0016] In some embodiments, performing multi-stage charging on the test battery includes:

[0017] The test battery is charged in multiple stages with different constant currents; or, the test battery is charged in multiple stages with different charging rates.

[0018] In some embodiments, obtaining the ion diffusion impedance of the test battery includes:

[0019] Acquiring electrochemical impedance spectroscopy data of the test battery;

[0020] Based on the electrochemical impedance spectroscopy data, the ion diffusion impedance is obtained.

[0021] In some embodiments, obtaining the ion diffusion impedance based on the electrochemical impedance spectroscopy data comprises:

[0022] The electrochemical impedance spectroscopy data are processed using relaxation time distribution analysis software to obtain the ion diffusion impedance.

[0023] In some embodiments, obtaining the internal pressure of the battery includes:

[0024] The pressure at at least one of the positive electrode tab and the negative electrode tab of the test battery is obtained to obtain the internal pressure of the battery.

[0025] In some embodiments, constructing the correlation model between the ion diffusion impedance and the internal pressure of the battery based on the multiple sets of data of the ion diffusion impedance and the internal pressure of the battery includes:

[0026] Using any one of a polynomial regression model, a support vector regression model and a Bayesian regression model, a correlation model between the ion diffusion impedance and the internal pressure of the battery is constructed based on the multiple sets of data of the ion diffusion impedance and the internal pressure of the battery.

[0027] In a second aspect, the present application provides an electronic device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the battery gas production prediction method described in any one of the above items is implemented.

[0028] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored in the computer-readable storage medium, wherein the program codes are suitable for being loaded and run by a processor to execute any of the above-mentioned methods for predicting battery gas production.

[0029] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0030] In the implementation of the technical solution provided by the present application, the measured ion diffusion impedance of the battery is obtained; based on the measured ion diffusion impedance and the pre-constructed ion diffusion impedance and battery internal pressure correlation model, the gas production of the battery is obtained. This method is based on a pre-constructed ion diffusion impedance and battery internal pressure correlation model and only needs to obtain the measured ion diffusion impedance of the battery, without adding additional equipment such as pressure sensors, and does not require destructive treatment such as rupture of the battery. It can conveniently and efficiently predict the gas production, and thus facilitate early identification of risky cells and timely warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The disclosure of the present application will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. Among them:

[0032] Figure 1 This is a schematic flow chart of the main steps of a method for predicting battery gas production provided by an embodiment of the present application;

[0033] Figure 2 is a relaxation time distribution diagram provided by a specific example of this application;

[0034] Figure 3 It is a schematic flow chart of a method for constructing a correlation model between ion diffusion impedance and internal pressure of a battery provided in an embodiment of the present application;

[0035] Figure 4 It is a structural schematic diagram of a battery internal pressure acquisition device provided in the present application;

[0036] Figure 5 It is a schematic diagram of the ion diffusion impedance and battery internal pressure curve obtained by fitting the polynomial regression model provided by the specific example of this application;

[0037] Figure 6 It is a schematic diagram of the structure of an electronic device provided by a specific example of this application. DETAILED DESCRIPTION

[0038] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0039] The battery gas production prediction method provided in the present application is implemented based on a pre-constructed model of the correlation between ion diffusion impedance and battery internal pressure. Therefore, it is only necessary to obtain the measured ion diffusion impedance of the battery to obtain the current internal pressure of the battery, and the current internal pressure of the battery is used to characterize the gas production of the battery.

[0040] Among them, the association between ion diffusion impedance and internal pressure of the battery, that is, gas production, is realized by pre-constructing a correlation model between ion diffusion impedance and internal pressure of the battery. The association between ion diffusion impedance and gas production is based on: ion diffusion impedance Rcp is mainly composed of solid-phase and liquid-phase ion diffusion. In soft-pack cells, solid-phase diffusion impedance accounts for a small proportion and changes little during the entire life cycle of the cell, while liquid-phase ion diffusion impedance changes greatly, so that liquid-phase ion diffusion impedance has a greater impact on the entire ion diffusion impedance Rcp. For example, in the early stage of the cycle, the electrolyte is unstable, and a variety of electrolyte additives form a large number of side reactions with the positive and negative electrode interfaces, affecting the diffusion coefficient of active lithium (Li+) ions in the liquid phase, thereby affecting the size of the entire ion diffusion impedance. At the same time, after the initial unstable stage of the cycle, the internal components of the electrolyte have stabilized, and the film layer on the surface of the positive and negative electrodes has been stably formed. At this time, the liquid-phase ion diffusion impedance is mainly determined by the distance between the pole pieces, and the gas production will affect the distance between the pole pieces to a certain extent, thereby affecting the size of the ion diffusion impedance Rcp. Based on this, in the present application, by correlating the ion diffusion impedance with the gas production, a correlation model between the ion diffusion impedance and the internal pressure of the battery is obtained.

[0041] See attached Figure 1 , Figure 1 1 is a flow chart of the main steps of a method for predicting battery gas production provided by an embodiment of the present application, which may include the following steps S11 and S12.

[0042] Step S11: Obtaining the measured ion diffusion impedance of the battery.

[0043] In some implementations, step S11 may specifically include:

[0044] Obtain the measured electrochemical impedance spectroscopy data of the battery;

[0045] Based on the measured electrochemical impedance spectroscopy data, the measured ion diffusion impedance is obtained.

[0046] In some embodiments, obtaining the measured electrochemical impedance spectroscopy data of the battery can specifically be to perform an electrochemical impedance spectroscopy (EIS) test on the battery and at least obtain the relationship between the electrochemical impedance and the frequency of the battery. The measured electrochemical impedance spectroscopy data can be characterized by the relationship between the electrochemical impedance and the frequency.

[0047] In some embodiments, obtaining the measured ion diffusion impedance based on the measured electrochemical impedance spectroscopy data may specifically include: processing the measured electrochemical impedance spectroscopy data using relaxation time distribution analysis software to obtain the ion diffusion impedance.

[0048] In some embodiments, the measured electrochemical impedance spectroscopy data in the frequency domain can be converted into time domain data using Distributed Resistance-Time Constant (DRT) analysis software to obtain a relaxation time distribution graph, and the measured ion diffusion impedance Rcp can be identified from the relaxation time distribution graph.

[0049] The relaxation time distribution analysis software is used to convert the measured electrochemical impedance spectroscopy data in the frequency domain into time domain data. The relaxation time distribution diagram can be obtained by obtaining the real part Z(ω) of the battery electrochemical impedance through EIS measurement, and combining the following expression through deconvolution to obtain the relaxation time distribution function g(τ):

[0050]

[0051] The boundary conditions are set as:

[0052]

[0053] Where Z(ω) is the real part of the electrochemical impedance obtained by EIS measurement; R0 is the ohmic impedance, R pol is the polarization impedance, g(τ) is the objective function of DRT, τ is the relaxation time, j is the complex unit, and ω is the angular frequency.

[0054] Then, based on the relaxation time distribution function, a relaxation time distribution function graph is obtained. By analyzing the relaxation time distribution function graph, at least the polarization impedance can be obtained. In some cases, ion diffusion impedance is an important component of polarization impedance. In the relaxation time distribution graph, different peaks represent different kinetic processes. By analyzing the shape, position and intensity of the peaks, the kinetic processes related to ion diffusion can be identified. The time constants and corresponding impedance values ​​of these processes can reflect the rate and resistance of ion diffusion. If there is a peak directly related to ion diffusion in the relaxation time distribution graph, the ion diffusion impedance can be estimated by the time constant and impedance value of the peak, see Figure 2As shown, Figure 2 It is a relaxation time distribution diagram provided by a specific example of the present application, and the peak S4 in the low-frequency region can be extracted, and the peak area of ​​the peak is the measured ion diffusion impedance Rcp.

[0055] In other embodiments, other EIS data processing software in the art may be used to process the measured electrochemical impedance spectroscopy data to obtain the measured ion diffusion impedance Rcp.

[0056] Step S12: Based on the measured ion diffusion impedance and the pre-constructed ion diffusion impedance and battery internal pressure correlation model, the gas production of the battery is obtained.

[0057] Based on the above description, it can be known that in the present application, the internal pressure of the battery can be used to characterize the gas production of the battery. Step S12 can be specifically based on the measured ion diffusion impedance and the pre-constructed ion diffusion impedance and battery internal pressure correlation model to predict the internal pressure of the current battery, that is, to characterize the gas production of the battery, which can be used for subsequent identification of battery damage risks and timely warning.

[0058] In some embodiments, Figure 1 On the basis of the corresponding embodiment, the method provided in the embodiment of the present application may further include pre-constructing a correlation model between ion diffusion impedance and internal pressure of the battery. Figure 3 As shown, Figure 3 It is a schematic flow chart of a method for constructing a correlation model between ion diffusion impedance and internal pressure of a battery provided in an embodiment of the present application, which may include steps S31 to S33.

[0059] Step S31: performing multiple aging cycle tests on the test battery.

[0060] Each aging test cycle of the aging cycle test, that is, an aging cycle test, may include a charging stage and a discharging stage. The aging test is to simulate the change in battery performance under different environments and conditions.

[0061] In some embodiments, step S31 may specifically include:

[0062] In the charging stage, the test battery is charged in multiple stages until a first preset state of charge is reached;

[0063] In the discharge stage, the test battery that has reached the first preset state of charge is discharged at a constant current until it reaches the second preset state of charge, completing an aging cycle test;

[0064] The charging phase and the discharging phase are repeated.

[0065] The first preset state of charge and the second preset state of charge can be flexibly set according to requirements.

[0066] In some embodiments, multi-stage charging of the test battery can be specifically as follows: multi-stage charging of the test battery with different constant currents. In some embodiments, the constant current of the multiple stages can be reduced in sequence. As an example, the test battery can be charged in three stages, wherein the three stages are: charging to 50% SOC (State of Charge) with a constant current of 120A, then charging to 80% SOC with a constant current of 80A, and finally switching to 33.3A constant current charging to 95% SOC, and charging is completed.

[0067] In other embodiments, multi-stage charging of the test battery may be specifically as follows: multi-stage charging of the test battery at different charging rates. The charging rate may be used to characterize the charging rate. The charging rate is a measure of charging speed, which indicates the current value required for the battery to be charged to its rated capacity within a specified time. Generally represented by C, the charging rate C = charging current (A) / battery rated capacity (Ah). For example, for a battery with a capacity of 100Ah, if charged with a current of 100A, the charging rate is 1C; if charged with a current of 200A, the charging rate is 2C. As an example, the test battery may be charged in three stages, wherein the test battery is charged at charging rates of 0.5C, 1C and 1.5C in the three stages, respectively, to a first preset charge state.

[0068] In other embodiments, aging cycle tests may be performed on the test battery at different test temperatures to obtain the characteristics of the test battery under different environmental conditions and enrich the ion diffusion impedance and battery internal pressure data.

[0069] In some embodiments, after the charging stage is finished, the battery may be left to stand for a preset time to ensure that the polarization process inside the battery is completed and will not affect the data obtained during the discharging stage.

[0070] Step S32: After each aging cycle test is performed for a preset number of times, the ion diffusion impedance and the internal pressure of the test battery are obtained to obtain multiple sets of data on the ion diffusion impedance and the internal pressure of the battery.

[0071] The preset number of times can be set according to the demand, and the preset number of times is less than the total number of aging cycle tests. As an example, step S31 can be to perform 300 aging cycle tests on the test battery, and the preset number of times can be 50, that is, after each 50 aging cycle tests, the ion diffusion impedance and the internal pressure of the test battery are obtained, and a set of ion diffusion impedance and internal pressure data of the battery corresponding to the 50th, 100th, 150th, 200th, 250th and 300th times are obtained respectively, so that a total of 6 sets of ion diffusion impedance and internal pressure data of the battery can be obtained.

[0072] In some embodiments, obtaining the ion diffusion impedance of the test battery in step S32 includes:

[0073] Obtain electrochemical impedance spectroscopy data of the test battery;

[0074] Based on the electrochemical impedance spectroscopy data, the ion diffusion impedance was obtained.

[0075] In some embodiments, obtaining the electrochemical impedance spectroscopy data of the test battery can specifically be to perform an electrochemical impedance spectroscopy test on the test battery and at least obtain the relationship between the electrochemical impedance and frequency of the test battery. The electrochemical impedance spectroscopy data can be characterized by the relationship between the electrochemical impedance and frequency.

[0076] In some embodiments, obtaining the ion diffusion impedance based on the electrochemical impedance spectroscopy data may specifically include: processing the electrochemical impedance spectroscopy data using relaxation time distribution analysis software to obtain the ion diffusion impedance.

[0077] In some embodiments, the frequency domain electrochemical impedance spectroscopy data can be converted into time domain data using the Distributed Resistance-Time Constant (DRT) analysis software to obtain a relaxation time distribution diagram, and the ion diffusion impedance Rcp can be identified from the relaxation time distribution diagram. In other embodiments, other EIS data processing software in the art can also be used to process the electrochemical impedance spectroscopy data to obtain the ion diffusion impedance Rcp.

[0078] It should be noted that, in some embodiments, after performing the aging cycle test for a preset number of times, the ion diffusion impedance may be obtained multiple times, and the final ion diffusion impedance may be determined based on the multiple obtained ion diffusion impedances to improve the effectiveness of the test.

[0079] In some embodiments, obtaining the internal pressure of the battery in step S32 may include: obtaining the pressure at at least one of the positive electrode tab and the negative electrode tab of the test battery to obtain the internal pressure of the battery.

[0080] The gas generated inside the battery accumulates, and in severe cases, it will expand or even rupture in the weak tensile area of ​​the battery package, such as the lug area. Therefore, testing the pressure at the battery lug position to characterize the internal pressure of the battery is helpful to timely identify the risk of battery damage, so as to provide timely warning.

[0081] In some embodiments, see Figure 4 As shown, Figure 4 It is a structural schematic diagram of a battery internal pressure acquisition device provided in the present application, which includes a handle, a limiter, a rupture needle, an airway, and an air pressure sensor. The rupture needle is inserted into the positive pole ear or negative pole ear position of the battery, and the puncture position is sealed with a sealant, and the pressure sensor is used to continuously record the pressure changes inside the battery.

[0082] Among them, the battery can be specifically a soft-pack battery cell, and the limiter can cooperate with the rupture needle to control the depth of the rupture needle inserted into the battery cell soft pack; the handle has a thread, which can be used to fix the rupture needle; an airway is provided inside the rupture needle, and the airway connects the external air guide tube and the air valve, which is used for emergency exhaust when the local pressure of the battery cell is too high; an air pressure sensor is provided in the airway, which is used to obtain the air pressure changes inside the battery in real time.

[0083] In some embodiments, the material of the rupture needle can be hard insulating plastic, such as PE (Polyethylene), PP (Polypropylene) and PTFE (Polytetrafluoroethylene), etc.; the handle material can be hard plastic or metal, the plastic includes PPS (Poly Phenylene Sulfide), PTFE, HDPE (High Density Polyethylene), PP, etc., and the metal can be stainless steel.

[0084] Step S33: constructing a correlation model between ion diffusion impedance and battery internal pressure based on multiple sets of data on ion diffusion impedance and battery internal pressure.

[0085] In some embodiments, step S33 may specifically be to construct a correlation model between ion diffusion impedance and battery internal pressure based on multiple sets of data on ion diffusion impedance and battery internal pressure using any one of a polynomial regression model, a support vector regression model and a Bayesian regression model.

[0086] When a polynomial regression model is used to construct a correlation model between ion diffusion impedance and battery internal pressure based on multiple sets of data on ion diffusion impedance and battery internal pressure, the correlation model between ion diffusion impedance and battery internal pressure can be expressed by the following expression:

[0087]

[0088] Among them, y represents the internal pressure of the battery, x represents the ion diffusion impedance, β m is the fitting coefficient, m=0,1,2,3……, ε is a constant, and i can correspond to multiple aging cycle tests under different test conditions. It is more efficient and convenient to use the polynomial regression model to construct the correlation model between ion diffusion impedance and internal pressure of the battery.

[0089] As an example, see Figure 5 As shown, Figure 5 It is a schematic diagram of the ion diffusion impedance and battery internal pressure curve obtained by fitting the polynomial regression model provided by the specific example of the present application. The fitting curve is a schematic diagram of the curve corresponding to the correlation model of ion diffusion impedance and battery internal pressure. It shows that the fitting effect is best when it is a 5th-order polynomial regression model, which can ensure that the random error is minimized and there will be no overfitting phenomenon.

[0090] like Figure 5 As shown, the origin represents the original data obtained based on the test battery. Each dot corresponds to a set of data on ion diffusion impedance and internal pressure of the battery. The fitting curve is in good agreement with the original data, and the fitting curve is S-shaped as a whole, proving that in the early stage of the cycle, due to the sufficient internal additives of the electrolyte and the stable SEI (Solid Electrolyte Interface membrane) film on the negative electrode surface formed in the formation stage, there are fewer side reactions, the gas production inside the battery is suppressed, and the local pressure growth rate is slow. However, since there is still a lot of electrolyte between the pole pieces at the beginning of the cycle, the ion diffusion impedance Rcp does not increase significantly, resulting in a larger slope of the Rcp-local pressure fitting curve. In the middle of the cycle, due to the large volume change of the silicon-oxygen negative electrode during the cycle (up to 160%), the SEI on the negative electrode surface is continuously destroyed and regenerated, which inhibits the side reaction between the electrolyte and the negative electrode surface, the gas production of the battery cell is aggravated, and the local pressurization growth rate is accelerated. At the same time, due to the serious gas production, the large distance between the pole pieces, and the obvious growth of the Rcp impedance, the mid-cycle curve shows a trend of lower slope. In the later stage of the cycle, the gas production continued to increase, the Rcp impedance continued to grow, the local pressure increased rapidly, and the slope of the fitting curve showed an increasing trend. The fitting curve equation is as follows:

[0091]

[0092] Based on the above fitting equation, the gas production volume or degree can be predicted. When the ion diffusion impedance increases to about 0.8mΩ, the internal pressure of the battery can increase to about 5225KPa, exceeding the pressure limit of the target battery. It may cause the soft package to rupture and leak, and in severe cases may cause thermal runaway of the battery cell. Therefore, battery risks can be identified in advance and early warning reminders can be given in time.

[0093] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted schemes are equivalent to the technical schemes described in this application, and therefore will also fall within the scope of protection of this application.

[0094] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of each of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable storage media may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry computer program code.

[0095] Another aspect of the present application also provides a computer-readable storage medium.

[0096] In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium can be configured to store a program for executing the battery gas production prediction method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned battery gas production prediction method. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0097] Another aspect of the present application provides an electronic device.

[0098] In an embodiment of an electronic device according to the present application, the electronic device may include at least one processor; and a memory connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method of any of the above embodiments is implemented. The electronic device of the present application may include a driving device, a smart car, a robot, and the like. Figure 6 , Figure 6 FIG. 6 exemplarily shows that the memory 61 and the processor 62 are communicatively connected via a bus.

[0099] In some embodiments of the present application, the electronic device may further include at least one sensor for sensing information. The sensor is communicatively connected to any type of processor mentioned in the present application. Optionally, the electronic device may further include an autonomous driving system for guiding the electronic device to drive itself or assist driving. The processor communicates with the sensor and / or the autonomous driving system to complete the method of any of the above embodiments.

[0100] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A method for predicting battery gas production, characterized in that: include: Obtain the measured ion diffusion impedance of the battery; Based on the measured ion diffusion impedance and a pre-constructed correlation model between the ion diffusion impedance and the internal pressure of the battery, the gas production of the battery is obtained.

2. The method according to claim 1, characterized in that The method further comprises constructing a correlation model between the ion diffusion impedance and the internal pressure of the battery by the following steps: Perform multiple aging cycle tests on the test battery; After each aging cycle test is performed for a preset number of times, the ion diffusion impedance and the internal pressure of the test battery are obtained to obtain multiple sets of data on the ion diffusion impedance and the internal pressure of the battery; A correlation model between the ion diffusion impedance and the internal pressure of the battery is constructed based on the multiple sets of data on the ion diffusion impedance and the internal pressure of the battery.

3. The method according to claim 2, characterized in that The multiple aging cycle tests on the test battery include: In the charging stage, the test battery is charged in multiple stages until a first preset state of charge is reached; In the discharge stage, the test battery that has reached the first preset state of charge is discharged at a constant current until it reaches a second preset state of charge, thereby completing an aging cycle test; The charging phase and the discharging phase are repeatedly performed.

4. The method according to claim 3, characterized in that The multi-stage charging of the test battery includes: The test battery is charged in multiple stages with different constant currents; or, the test battery is charged in multiple stages with different charging rates.

5. The method according to claim 2, characterized in that: Obtaining the ion diffusion impedance of the test battery includes: Acquiring electrochemical impedance spectroscopy data of the test battery; Based on the electrochemical impedance spectroscopy data, the ion diffusion impedance is obtained.

6. The method according to claim 5, characterized in that The obtaining of the ion diffusion impedance based on the electrochemical impedance spectroscopy data comprises: The electrochemical impedance spectroscopy data are processed using relaxation time distribution analysis software to obtain the ion diffusion impedance.

7. The method according to claim 2, characterized in that Obtaining the internal pressure of the battery includes: The pressure at at least one of the positive electrode tab and the negative electrode tab of the test battery is obtained to obtain the internal pressure of the battery.

8. The method according to claim 2, characterized in that: The step of constructing the correlation model between the ion diffusion impedance and the internal pressure of the battery based on the multiple sets of data of the ion diffusion impedance and the internal pressure of the battery comprises: A polynomial regression model, a support vector regression model and a Bayesian regression model are used to construct a correlation model between the ion diffusion impedance and the internal pressure of the battery based on the multiple sets of data on the ion diffusion impedance and the internal pressure of the battery.

9. An electronic device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the battery gas production prediction method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the battery gas production prediction method according to any one of claims 1 to 8.

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