Lithium battery SOH change trend prediction method and device and storage medium

Abstract

The invention relates to the technical field of lithium battery SOH (state of health) prediction, and provides a lithium battery SOH change trend prediction method, a lithium battery SOH change trend prediction device, electronic equipment, a vehicle and a computer readable storage medium. According to the lithium battery SOH change trend prediction method, historical battery SOH data of a lithium battery is obtained, and a prediction factor is obtained through fitting by using the SOH data according to a preset fitting strategy. Because the preset fitting strategy is used for describing the influence of the growth of the solid electrolyte interface SEI film in the lithium battery on the equivalent reaction area of the lithium ion in the lithium battery and the influence of the equivalent reaction area of the lithium ion on the reaction rate, the prediction model corresponding to the preset fitting strategy is utilized to predict the reaction rate of the lithium ion in the lithium battery according to the prediction factor obtained by fitting. And the SOH change trend of the lithium battery, which can be predicted, is higher in accuracy and higher in reliability.

Description

Technical Field

[0001] This application belongs to the technical field of lithium battery state of health prediction, and particularly relates to a method for predicting the change trend of lithium battery SOH, a device for predicting the change trend of lithium battery SOH, an electronic device, a vehicle, and a computer-readable storage medium. Background Art

[0002] With the development of energy technologies, lithium batteries are used in more and more fields. For example, in mobile terminals, household energy storage, new energy vehicle fields, etc. To improve the safe operation of lithium batteries and maximize the performance and service life of lithium batteries, it is crucial to accurately predict the state of health (SOH) of lithium batteries.

[0003] However, although the related lithium battery SOH change trend prediction solutions can also predict the SOH change trend of lithium batteries, as the number of full charge and discharge cycles of lithium batteries increases, the deviation of the predicted results also becomes larger and larger, making the predicted SOH change trend deviate significantly from the actual change trend. It can be seen that there is an urgent need to provide a lithium battery SOH change trend prediction solution with higher accuracy. Summary of the Invention

[0004] The purpose of this application is to provide a method for predicting the change trend of lithium battery SOH, a device for predicting the change trend of lithium battery SOH, an electronic device, a vehicle, and a computer-readable storage medium, aiming to provide a lithium battery SOH change trend prediction solution with higher accuracy.

[0005] The first aspect of the embodiment of this application provides a method for predicting the change trend of lithium battery SOH, including:

[0006] Obtain the historical state of health (SOH) data of the lithium battery;

[0007] According to a preset fitting strategy, use the historical SOH data to fit and obtain a prediction factor; wherein, the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate;

[0008] Use the prediction model corresponding to the preset fitting strategy to predict the SOH change trend of the lithium battery according to the prediction factor.

[0009] The second aspect of the embodiment of this application provides a device for generating a reference clock signal, including:

[0010] An acquisition unit, configured to obtain the historical state of health (SOH) data of the lithium battery;

[0011] A fitting unit, configured to fit a prediction factor by using historical SOH data according to a preset fitting strategy; wherein, the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate;

[0012] A prediction unit, configured to predict the SOH change trend of the lithium battery according to the prediction factor by using a prediction model corresponding to the preset fitting strategy.

[0013] In a third aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the electronic device. When the processor executes the computer program, the steps of the method for predicting the SOH change trend of the lithium battery provided in the first aspect above are implemented.

[0014] In a fourth aspect of the embodiments of the present application, a vehicle is provided, including a memory, a processor, and a computer program stored in the memory and executable on the vehicle. When the processor executes the computer program, the steps of the method for predicting the SOH change trend of the lithium battery provided in the first aspect above are implemented.

[0015] In a fifth aspect of the embodiments of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting the SOH change trend of the lithium battery provided in the first aspect above are implemented.

[0016] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0017] For the method for predicting the SOH change trend of a lithium battery provided above, by obtaining the historical state of health (SOH) data of the lithium battery as a fitting data sample, and according to a preset fitting strategy, a prediction factor is obtained by fitting the SOH data. Since the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate, the prediction model corresponding to the preset fitting strategy naturally takes into account the influence of the growth of the SEI film on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. In this way, by using the prediction model corresponding to the preset fitting strategy and according to the predicted factor obtained by fitting, the SOH change trend of the lithium battery predicted under the condition of considering the influence of the growth of the SEI film on the equivalent reaction area of lithium ions in the lithium battery and the influence of the equivalent reaction area of lithium ions on the reaction rate is more accurate and reliable.

[0018] In addition, predicting the SOH change trend of lithium batteries with high accuracy helps improve the safe operation of lithium batteries and maximize their performance and service life. Description of the Drawings

[0019] Figure 1 It is a flowchart of the implementation of a method for predicting the SOH change trend of lithium batteries provided by an embodiment of the present application;

[0020] Figure 2 It is a schematic diagram of the principle of SEI film growth fitted by a method for predicting the SOH change trend of lithium batteries provided by an embodiment of the present application;

[0021] Figure 3 It is a flowchart of the implementation of a method for predicting the SOH change trend of lithium batteries provided by another embodiment of the present application;

[0022] Figure 4 It is a flowchart of the implementation of a method for predicting the SOH change trend of lithium batteries provided by still another embodiment of the present application;

[0023] Figure 5 It is a schematic structural diagram of a device for predicting the SOH change trend of lithium batteries provided by an embodiment of the present application;

[0024] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed Description of the Embodiments

[0025] In order to make the technical problems, technical solutions, and beneficial effects to be solved by the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0026] It should be noted that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, "a plurality" means two or more unless otherwise specifically defined.

[0027] Exemplarily, in the related art, the life attenuation of lithium batteries conforms to the Arrhenius formula Q loss =A*exp(-Ea / RT)*t B . Where A is the pre-exponential factor, Ea is the activation energy, T is the temperature, R is the ideal gas constant, t is the number of cycles, and B is the cycle index.

[0028] Based on this, the lifespan attenuation of the lithium battery or the state of health (SOH) of the battery can be 1 - Q loss That is, the following formula can be obtained: SOH = 1 - Q loss = 1 - A * exp(-Ea / RT) * t B Here, by letting K = A * exp(-Ea / RT), the above formula can be simplified to get SOH = 1 - K * t B Among them, the attenuation rate K is a constant. In the case of a small number of lithium battery charge-discharge cycles, the above formula can measure relatively accurate SOH data. However, in the case of a large number of lithium battery charge-discharge cycles, the material of the lithium battery splits to generate a larger reaction area, resulting in an accelerated growth of the SEI, and the accelerated growth of the SEI affects the SOH of the lithium battery. If the above formula is used to measure the SOH, as the number of full charge-discharge cycles of the lithium battery increases, the deviation of the predicted result also becomes larger and larger, making the predicted SOH change trend deviate significantly from the actual change trend.

[0029] To solve the above technical problems, this embodiment provides a method for predicting the SOH change trend of a lithium battery. By obtaining the historical SOH data of the lithium battery as a fitting data sample, according to a preset fitting strategy, a prediction factor is obtained by fitting the SOH data. Since the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate, the prediction model corresponding to the preset fitting strategy naturally takes into account the influence of the growth of the SEI film on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. In this way, using the prediction model corresponding to the preset fitting strategy, based on the predicted factor obtained by fitting, the SOH change trend of the lithium battery can be predicted with higher accuracy and stronger reliability under the consideration of the influence of the growth of the SEI film on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate.

[0030] In addition, predicting a relatively accurate SOH change trend of the lithium battery helps to improve the safe operation of the lithium battery and maximize the performance and service life of the lithium battery.

[0031] A method for predicting the SOH change trend of a lithium battery provided in this embodiment has an execution entity such as an electronic device or a vehicle. Here, taking the execution entity as an electronic device as an example, the electronic device can be an electronic device for detecting the lithium battery, or an electronic device equipped with the lithium battery. Specifically, it can be a processor, a battery management system, etc. in the electronic device.

[0032] In actual use, the electronic device can specifically be an electronic device with data processing functions, such as a television, a mobile phone, a tablet computer, a laptop computer, etc. When the electronic device detects a lithium battery, it can obtain the corresponding SOH change trend by executing the lithium battery SOH change trend prediction method provided in this embodiment. The vehicle can be a new energy vehicle, such as a pure electric vehicle, a hybrid electric vehicle, etc., which is not limited here.

[0033] The following only takes the electronic device as the execution subject as an example, and details a lithium battery SOH change trend prediction method provided in this embodiment through specific implementation manners.

[0034] Figure 1 The flowchart of implementing a lithium battery SOH change trend prediction method provided in an embodiment of the present application is shown. As Figure 1 shown, the lithium battery SOH change trend prediction method includes the following steps:

[0035] 110: Obtain the historical state of health (SOH) data of the lithium battery.

[0036] In 110, the historical state of health (SOH) data of the lithium battery generally refers to the data measured or recorded during the actual use of the lithium battery, that is, the historical SOH data is the actual value of the lithium battery. At the same time, the historical SOH data is also the data corresponding to each cycle of charge and discharge of the lithium battery. Here, the cycle of charge and discharge refers to the full charge and full discharge of the lithium battery, that is, one full charge and then one full discharge operation of the lithium battery is recorded as one cycle. Correspondingly, the historical SOH data is the SOH data recorded during multiple actual cycles of the lithium battery. For example, if the lithium battery cycles N times, the historical SOH data includes N groups of SOH values, where N is an integer greater than 1.

[0037] In specific implementation, the historical SOH data can be manually input into the electronic device by the user, or reported by the battery management system of the lithium battery to the electronic device and recorded by the electronic device.

[0038] For example, the electronic device can display an interface for inputting historical SOH data, and the user inputs the historical SOH data through this interface.

[0039] For another example, the historical SOH data of different lithium batteries can be stored in the electronic device, and the user can select the historical SOH data stored in the electronic device and then import the corresponding historical SOH data.

[0040] It can be understood that in actual use, the electronic device can be communicatively connected to the lithium battery or the energy storage device where the lithium battery is located, so that the lithium battery or the energy storage device where the lithium battery is located can report the historical SOH data to the electronic device. For example, the electronic device can communicate with the battery management system in the lithium battery or the energy storage device where the lithium battery is located, that is, by providing a data reporting interface, to receive the historical SOH data reported by the battery management system.

[0041] In all embodiments of the present application, considering the prediction accuracy of the SOH change trend, the historical SOH data of the lithium battery is obtained, specifically, it can be a set of historical SOH data of the lithium battery. That is to say, the historical SOH data can be multiple sets of data corresponding to multiple cycles of the lithium battery. For example, it can be 1000 sets of historical SOH data corresponding to 1000 cycles of the lithium battery. It should be noted that since the historical SOH data represents the actual data of the lithium battery during past use, and in actual use, it is possible that the lithium battery may not be fully charged and discharged every time, so in specific implementation, it can be the detection device of the lithium battery, that is, the electronic device, or the battery management system in the energy storage device where the lithium battery is located, to predict the cycle count of the lithium battery according to the historical charge and discharge data of the lithium battery using a preset estimation algorithm, and when each cycle count is satisfied, record a corresponding set of historical SOH data, and thus a certain amount of historical SOH data can be obtained.

[0042] As an embodiment, step 110 may specifically include:

[0043] Obtain historical SOH data from the test device of the lithium battery; or obtain historical SOH data from the electrical equipment equipped with the lithium battery.

[0044] In this embodiment, the test device of the lithium battery refers to the test tooling of the lithium battery, for example, the electrical cabinet and / or the upper computer used to test the lithium battery. The electronic device can communicate with the electrical cabinet and / or the upper computer, and thus can obtain the historical SOH data. Here, the electrical equipment equipped with the lithium battery refers to the electrical equipment powered by the lithium battery, such as mobile phone terminals, new energy vehicles, robots, etc. Correspondingly, the electronic device can communicate with the electrical equipment, and thus can obtain the historical SOH data.

[0045] In specific implementation, when obtaining historical SOH data from a lithium battery testing device, the electronic device can instruct the testing device to actively report the historical SOH data by opening a data reporting interface for the testing device. Alternatively, the testing device can open a data access interface for the electronic device, and the electronic device can actively access the stored data of the testing device to obtain the historical SOH data. Similarly, when obtaining historical SOH data from an electrical device equipped with a lithium battery, it can also be that the electronic device opens a data reporting interface for the electrical device to instruct the electrical device to actively report the historical SOH data. Or, the electrical device can open a data access interface for the electronic device, and the electronic device can actively access the stored data of the electrical device to obtain the historical SOH data.

[0046] Exemplarily, taking an electrical device equipped with a lithium battery as a vehicle for example, the vehicle can periodically report the historical SOH data during its use to the electronic device through the data reporting interface opened by the electronic device. Specifically, it can be the vehicle processor, the battery management system of the power battery, etc., to periodically report the historical SOH data during the use of the vehicle to the electronic device.

[0047] In another example, the vehicle can open a data access interface for the electronic device. For example, the vehicle processor, the battery management system of the power battery, etc., can provide the electronic device such as a user terminal with the access right to the power battery data, and the user terminal can actively access the vehicle processor and the battery management system of the power battery to obtain the historical SOH data.

[0048] It can be understood that when the execution subject is the electrical device itself equipped with a lithium battery, the electrical device can directly obtain the historical SOH data from the battery management system of the lithium battery, or the electrical device can directly record the corresponding historical SOH data. For example, the vehicle can directly obtain the historical SOH data from the battery management system of the power battery (equipped with a lithium battery).

[0049] It is easy to understand that since in actual use, obtaining historical SOH data is related to the execution subject that actually executes the method of this embodiment, those skilled in the art can obtain historical SOH data in a manner adapted to the execution subject when implementing the above steps on different execution subjects, so it will not be elaborated here.

[0050] 120: According to a preset fitting strategy, use the historical SOH data to fit and obtain a prediction factor; wherein, the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate.

[0051] In a lithium battery, the Solid Electrolyte Interface (SEI) film is the solid electrolyte interface film in a liquid lithium-ion battery and is also known as the SEI film. The SEI film is a passivation layer composed of organic or inorganic products and is formed between the electrode material and the electrolyte of the lithium battery. The SEI film has the characteristics of a solid electrolyte, has good conductivity for Li+ ions, and is also an insulator for electrons.

[0052] It should be noted that since the formation mechanism of the SEI film is through the reaction of organic or inorganic substances in the electrolyte with the negative electrode material of the lithium battery and deposition on the surface of the negative electrode of the lithium battery, the formation process of the SEI film may lead to the loss of lithium ions in the electrolyte and the irreversible loss of battery capacity. Based on this, when fitting the prediction factor using historical SOH data, the influence of the growth of the SEI film on the battery capacity is taken into account, that is, using a preset fitting strategy to describe the influence of the growth of the solid electrolyte interface SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. Thus, the prediction factor obtained by fitting is closer to the actual use scenario and / or influencing factors of the lithium battery and is more suitable for predicting the change trend of the SOH of the lithium battery.

[0053] In this embodiment, since the historical SOH data is the actual data of the lithium battery and the SOH value also changes with the increase in the number of cycles during the actual use of the lithium battery, the historical SOH data can reflect the capacity attenuation trend of the lithium battery from a certain perspective. That is, when there is enough historical SOH data, it can reflect / reflect the influence degree of the growth of the SEI film on the battery capacity and / or service life. It can be understood that since the historical SOH data is actual data, the prediction factor obtained by fitting using the historical SOH data is also a specific value.

[0054] It is easy to understand that fitting the prediction factor using historical SOH data according to the preset fitting strategy is to take into account the influence of the growth of the SEI film on the battery capacity in the prediction process of SOH. That is, the influence of the growth of the SEI film on the battery capacity can be described by the preset fitting strategy in two parts. One part is the influence of the growth of the solid electrolyte interface SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the other part is the influence of the equivalent reaction area of lithium ions on the reaction rate. Based on this, when the historical SOH data can reflect / reflect the influence degree of the growth of the SEI film on the battery capacity and / or service life, fitting the prediction factor using historical SOH data according to the preset fitting strategy can, from the perspective of specific values and mathematical relationships, describe the influence degree of the growth of the SEI film on the battery capacity and / or service life.

[0055] In specific implementation, the preset fitting strategy can be a fitting model, and the specific prediction factor values are obtained by fitting according to the historical SOH data using this fitting model. Alternatively, the preset fitting strategy can include a regular expression characterizing the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate.

[0056] As an example, the preset fitting strategy can include a fitting model. In this fitting model, the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate are described by specific equations. Among them, the unknowns in the equations are the arithmetic factors related to the SEI film, that is, the prediction factors. Correspondingly, a data fitting tool can be used to fit this system of equations according to the historical SOH data to obtain the prediction factors, that is, to obtain the specific values corresponding to the arithmetic factors related to the SEI film.

[0057] It can be understood that in the system of equations, when the historical SOH data are known numbers and the number of historical SOH data is large, taking the arithmetic factors related to the SEI film as unknowns, each set of historical SOH data can be substituted into the system of equations, and then multiple systems of equations can be obtained. When the number of arithmetic factors related to the SEI film is much smaller than the number of groups of historical SOH data, a data fitting tool can completely be used to make the unknowns in the system of equations converge based on the large amount of historical SOH data, and then the arithmetic factors related to the SEI film, that is, the prediction factors, can be obtained.

[0058] As another example, the preset fitting strategy includes a regular expression characterizing the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. Combining this regular expression with the existing SOH calculation formula, the specific values of the unknowns in this regular expression, that is, the arithmetic factors related to the SEI film, that is, the prediction factors, can be calculated using the historical SOH data.

[0059] Exemplarily, as a possible implementation, the electronic device can display an interface for the user to configure a preset fitting strategy. For example, it can display an interface for the user to configure an equation system or a model. The user configures the corresponding fitting equation system or fitting model through this interface, and then can implement the configuration and / or modification operation of the preset fitting strategy. Of course, in actual implementation, different fitting strategies can also be pre-configured in the electronic device for the user to select. The common feature of different fitting strategies is that they can all characterize the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. The differences include at least one of the number of prediction factors, the weights of the prediction factors, and the degree of influence.

[0060] It is easy to understand that in specific implementation, since the preset fitting strategy describes or characterizes the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate, in specific implementation, an existing or known arithmetic model or equation system, etc. can also be used to implement the preset fitting strategy, as long as it is ensured that the corresponding prediction factors can be calculated using historical SOH data. Therefore, the specific implementation method of the preset fitting strategy will not be elaborated here.

[0061] 130: Use the prediction model corresponding to the preset fitting strategy to predict the SOH change trend of the lithium battery according to the prediction factors.

[0062] In 130, the prediction model corresponds to the preset fitting strategy, that is, the preset fitting strategy describes the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. Correspondingly, the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate can also be reflected in the prediction model.

[0063] It is easy to understand that since the prediction factors are actual values obtained by fitting historical SOH data according to the preset fitting strategy, and because the prediction model corresponds to the preset fitting strategy, the prediction model naturally takes into account the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. Substituting the prediction factors as specific values into the prediction model for calculation can realize the prediction operation of the SOH change trend of the lithium battery under the condition of considering the influence of the growth of the SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate.

[0064] As an example of a possible implementation of the combination 120, in a possible implementation of 130, when the user selects or configures a preset fitting strategy in the electronic device, the prediction model is determined accordingly. That is, the prediction model can change with the actual change of the preset fitting strategy. For example, if the preset fitting strategy is a fitting equation set, increasing or decreasing the factors to be fitted in the fitting equation set can correspondingly modify (increase or decrease) the prediction factors in the prediction model.

[0065] In specific implementation, the electronic device can directly substitute the actual values of the prediction factors obtained in step 120 into the prediction model corresponding to the preset fitting strategy for calculation, and then measure the change trend of the SOH of the lithium battery. Here, the SOH change trend generally refers to multiple groups of predicted SOH data, that is, the change trend of the SOH value of the lithium battery with the usage time / cycle number.

[0066] For example, the historical SOH data can be the data of the lithium battery after 1000 cycles. Correspondingly, the predicted SOH change trend can specifically be the SOH data of several groups corresponding to several cycle numbers after the lithium battery has cycled 1000 times. Here, the several cycle numbers can specifically be 2000 times, 3000 times, 4000 times, 5000 times... etc. In actual prediction, the corresponding prediction cycle number can be configured based on the specifications of the lithium battery or actual design requirements, which is not limited here.

[0067] In the above solution, by obtaining the historical state of health (SOH) data of the lithium battery as the fitting data sample, according to the preset fitting strategy, the prediction factors are obtained by fitting the SOH data. Since the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate, the prediction model corresponding to the preset fitting strategy naturally takes into account the influence of the growth of the SEI film on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. In this way, using the prediction model corresponding to the preset fitting strategy, according to the fitted prediction factors, the change trend of the SOH of the lithium battery can be predicted with higher accuracy and stronger reliability under the condition of considering the influence of the growth of the SEI film on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate.

[0068] In addition, predicting a relatively accurate change trend of the SOH of the lithium battery helps to improve the safe operation of the lithium battery and maximize the performance and service life of the lithium battery.

[0069] As an embodiment, the preset fitting strategy includes a fitting equation set corresponding to the prediction factors. Correspondingly, step 120 may include:

[0070] The fitting equation group is called by the formula fitting tool, and the historical SOH data is substituted into the fitting equation group to solve the prediction factors to obtain the prediction factors.

[0071] In this embodiment, the fitting equation group can be a set of mathematical equations prepared in advance based on the influence of the growth of the SEI film on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate. In the fitting equation group, the formula factors related to the SEI film can be set to form a set of simultaneous equations in the form of mathematical formulas.

[0072] In the specific implementation, the formula fitting tool can be a professional mathematical software tool, such as the function fitting tool cftool in MATLAB, which can realize a variety of linear and nonlinear function fitting. By calling cftool to customize the fitting equation group, the actual numerical approximation of each prediction factor in the equation group can be realized. For example, the rstool in MATLAB can be used to achieve fitting through multivariate binomial regression, or the polynomial fitting tool polyfit can be used to flexibly handle polynomials of arbitrary orders to achieve fitting. It can be understood that in the fitting equation group, when the historical SOH data is a known number and the number of historical SOH data is large, the formula factors related to the SEI film are used as unknown numbers, and each group of historical SOH data can be substituted into the equation group, and then multiple equation groups can be obtained. When the number of formula factors related to the SEI film is much smaller than the number of groups of historical SOH data, the data fitting tool can be used to converge the unknown numbers in the equation group based on more historical SOH data, and then the formula factors related to the SEI film, that is, the prediction factors, can be obtained. Therefore, which formula fitting tool to use can be selected according to actual conditions, and it will not be repeated here.

[0073] As an embodiment, the above step: using the formula fitting tool to call the fitting equation group, and substituting the historical SOH data into the fitting equation group to solve the prediction factor to obtain the prediction factor includes:

[0074] Use the equation fitting tool to call the following equation group:

[0075] SOH1=1-K×t B

[0076] K=A×S

[0077] S=1-C×t D

[0078] Solve the prediction factors based on historical SOH data to obtain the prediction factors; where SOH1 is the historical SOH data; K is the reaction rate; t is the number of cycles of the lithium battery; S is the equivalent reaction area of lithium ions in the lithium battery; A is the reaction rate per unit area corresponding to S; B is the reaction rate cycle constant; C is the equivalent area growth rate of the SEI film; D is the area growth cycle constant of the SEI film.

[0079] In this embodiment, the reaction rate K can be understood as the overall reaction rate of the lithium battery. Since A is the reaction rate per unit area corresponding to S and S is the equivalent reaction area of lithium ions in the lithium battery, the reaction rate K can be expressed as the product of A and S. For the equivalent reaction area S of lithium ions in the lithium battery, the greater the number of cycles of the lithium battery, the higher the degree of influence by the growth of the SEI film. Based on this, a relational expression related to the equivalent reaction area S of lithium ions in the lithium battery can be established according to the growth rate of the SEI film, that is, the equivalent area growth rate C of the SEI film, and the area growth cycle constant D of the SEI film: S = 1 - C×t D . Here, since the growth of the SEI film is on the periphery of the material particles, that is, it affects the equivalent reaction area of lithium ions. Let the original equivalent reaction area of lithium ions in the lithium battery be 1, then the equivalent reaction area S of lithium ions can be made equal to 1 - C×t D , that is, the expression of S is obtained.

[0080] It should be noted that both the equivalent area growth rate C of the SEI film and the area growth cycle constant D of the SEI film can be measured by conducting SEI film growth experiments on lithium ions or obtained by looking up tables. In this way, when establishing the above fitting equations, the historical SOH data and the equivalent area growth rate C of the SEI film and the area growth cycle constant D of the SEI film can also be used to verify their effectiveness.

[0081] As an embodiment, step 130 may include:

[0082] Determine the prediction model according to the fitting equations. Substitute the prediction factors into the prediction model to predict the SOH change trend of the lithium battery.

[0083] In this embodiment, since the prediction factors are obtained by fitting the historical SOH data according to the fitting equations, the prediction factors can be substituted into the fitting equations for calculation. Based on this, in order to enable the prediction model to also perform calculations according to the prediction factors, there is also a corresponding relationship or association relationship between the prediction model and the fitting equations. In specific implementation, a mapping list between the fitting equations and the prediction model can be established in advance. When determining the preset model according to the fitting equations, the prediction model corresponding to the fitting equations can be determined by querying this mapping list.

[0084] It is easy to understand that in order for the prediction model to predict the SOH change trend of the lithium battery based on the predictors, in addition to the arithmetic expressions of the fitting equations, the prediction model can also include a function of the number of cycles t. In this way, by substituting the predictors into the prediction model, the SOH data that changes with the increase in the number of cycles during the future use of the lithium battery can be predicted, that is, the SOH change trend.

[0085] As an embodiment, determining the prediction model according to the fitting equations includes: performing equivalent substitution on the fitting equations to obtain the prediction model.

[0086] In this embodiment, the way to determine the prediction model is to directly perform equivalent substitution on the fitting equations, that is, transform the fitting equations to obtain the prediction model.

[0087] Taking the fitting equations in the above embodiment as an example, based on the arithmetic expressions: SOH1 = 1 - K×t B 、K = A×S and S = 1 - C×t D Performing equivalent substitution, we can get:

[0088] SOH2 = 1 - A(1 - C×t D )×t B , where, in order to distinguish from SOH1 representing historical SOH data, SOH2 is used in the prediction model to represent the predicted SOH data.

[0089] It is easy to understand that corresponding to the predictors in the fitting equations, K in the prediction model is the reaction rate; t is the number of cycles of the lithium battery; S is the equivalent reaction area of lithium ions in the lithium battery; A is the reaction rate per unit area corresponding to S; B is the reaction rate cycle constant; C is the equivalent area growth rate of the SEI film; D is the area growth cycle constant of the SEI film. Here, since t in the prediction model represents the number of cycles and is a variable, in specific implementation, different predicted SOH data can be obtained by changing the value of t, and thus the SOH change trend can be obtained.

[0090] Figure 2 FIG. shows a schematic diagram of the principle of SEI film growth fitted by a method for predicting the SOH change trend of a lithium battery according to an embodiment of the present application. In Figure 2Among them, BOL represents the relationship between the reaction area (the black part in the middle region) of the negative electrode particles of the lithium battery at the initial life and the SEI film area (the gray part on the periphery); MOL represents the relationship between the reaction area (the black part in the middle region) of the negative electrode particles of the lithium battery at the middle life and the SEI film area (the gray part on the periphery); EOL represents the relationship between the reaction area (the black part in the middle region) of the negative electrode particles of the lithium battery at the end of life and the SEI film area (the gray part on the periphery).

[0091] In all embodiments of the present application, when considering the response of the SEI film to the SOH data of the lithium battery, a preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate.

[0092] Specifically, considering the cracking of the negative electrode particles of the lithium battery, as Figure 2 shown, in the MOL stage, the negative electrode particles of the lithium battery split from 1 particle in the BOL stage into 2 particles, and the particle radius also decreases from R in the BOL stage to 0.79R, the area decreases from S to 1.26S / 2, and the volume decreases from V to V / 2. From the MOL stage to the EOL stage, the particle radius also decreases from 0.79R in the MOL stage to 0.63R, the area decreases from S to 1.59S / 4, and the volume decreases from V / 2 to V / 4. Since an SEI film grows around each particle, the overall growth trend of the SEI film shows an accelerating state. Therefore, it can be determined that the growth of the SEI film has a negative impact on the equivalent reaction area of lithium ions in the lithium battery, that is, it reduces the equivalent reaction area of lithium ions in the lithium battery. Furthermore, when the equivalent reaction area of lithium ions decreases, it will inevitably have a negative impact on the reaction rate, that is, it reduces the reaction rate. In Figure 2 it, the gray SEI film will accelerate the consumption of more active lithium, resulting in an accelerated decrease in the capacity of the lithium battery, that is, the capacity of the lithium battery shows a diving phenomenon. Based on this, a corresponding fitting equation set can be constructed as a preset fitting strategy to at least characterize the above two influence paths. Similarly, a prediction model corresponding to the equation set of the fitting strategy is constructed, and then the influence of the SEI film growth on the capacity of the lithium battery as shown in Figure 4 can be expressed by a mathematical formula, so that the SOH change trend of the lithium battery can be predicted by using this prediction model, which is convenient for users to learn about the service life of the lithium battery, and can provide data reference for formulating corresponding retirement strategies.

[0093] Figure 3 FIG. shows a flowchart of the implementation of a method for predicting the SOH change trend of a lithium battery provided by another embodiment of the present application. As Figure 3 shown, compared with Figure 1The difference in this embodiment is that a method for predicting the SOH change trend of a lithium battery provided in this embodiment further includes step 210 after step 130.

[0094] 210: Generate a curve graph of the SOH change trend according to the SOH change trend.

[0095] In this embodiment, the SOH change trend is specifically the specific data obtained by using the prediction model to predict the SOH data of the lithium battery. Here, since the SOH data is related to the number of cycles of the lithium battery, that is, each SOH data corresponds to a specific value of the number of cycles t, therefore, generating a curve graph of the SOH change trend according to the SOH change trend can specifically be a curve graph of the SOH data changing with the number of cycles.

[0096] In specific implementation, the curve graph of the SOH change trend can be generated according to the SOH change trend by calling a drawing tool. It is easy to understand that since the SOH data is related to the number of cycles of the lithium battery, that is, each SOH data corresponds to a specific value of the number of cycles t, therefore, the number of cycles t can be used as the abscissa and the SOH value can be used as the ordinate, and then the curve graph of the SOH change trend can be drawn. In actual implementation, the user can select an existing drawing tool according to actual needs or application requirements, which will not be elaborated here.

[0097] In the above solution, after obtaining the SOH change trend, that is, the specific SOH value, generating a curve graph of the SOH change trend can intuitively express the attenuation of the SOH value with the number of cycles, which can provide a reference for the user and facilitate the user to choose to follow or execute the corresponding retirement strategy when using the lithium battery subsequently, so as to delay the aging rate of the lithium battery and extend the service life of the lithium battery.

[0098] Figure 4 The flowchart of implementing a method for predicting the SOH change trend of a lithium battery provided in another embodiment of the present application is shown. As Figure 4 shown, different from Figure 1 Embodiment or Figure 3 Embodiment, the difference in this embodiment is that a method for predicting the SOH change trend of a lithium battery provided in this embodiment further includes steps 310 to 320 after step 130.

[0099] 310: Determine the target cycle inflection point according to the SOH change trend, and the target cycle inflection point is used to indicate the timing of executing the preset retirement strategy.

[0100] 320: Mark the target cycle inflection point on the curve graph corresponding to the SOH change trend.

[0101] In this embodiment, the target cycle inflection point is used to indicate the timing of executing the preset retirement strategy. At the same time, the target inflection point is also a numerical point where the SOH value decays relatively sharply.

[0102] In specific implementation, the target cycle inflection point can be determined by comparing the differences between two adjacent SOH values in the SOH change trend. For example, when the difference between two adjacent SOH values is greater than a preset threshold, it indicates that there is a relatively large capacity attenuation phenomenon in the lithium battery between the two cycles corresponding to the two adjacent SOH values. Therefore, the cycle number corresponding to any one of the two adjacent SOH values can be used as the target cycle inflection point.

[0103] For example, when the first SOH value corresponding to the lithium battery cycle number of 1500 times is 90%, and the second SOH value corresponding to the lithium battery cycle number of 1501 times is 87%, then the cycle number 1500 times or 1501 times can be used as the target cycle inflection point.

[0104] It is easy to understand that the target cycle inflection point is used to indicate the timing of executing the preset retirement strategy, that is, before the arrival of the target cycle inflection point, the preset retirement strategy is executed. Of course, it can also be understood that the target inflection point is used to indicate the maximum cycle number for which the preset retirement strategy is effective.

[0105] In specific implementation, after determining the target cycle inflection point, the target cycle inflection point can be marked on the curve graph corresponding to the SOH change trend by using an icon, which is convenient for users to visually observe the position of the target cycle inflection point. Thus, before the lithium battery is used up to the cycle number corresponding to the target cycle inflection point, the corresponding retirement strategy can be executed to delay the aging rate of the lithium battery and extend the service life of the lithium battery.

[0106] It can be understood that the retirement strategy refers to the usage optimization strategy for extending the service life of the lithium battery. Specifically, it can be achieved by reducing the usage range, such as the SOC range or voltage range, reducing the current, such as restricting high-power charging and discharging, and reducing the temperature, such as enhancing the thermal management constraint. In specific implementation, the specific content of the retirement strategy can be configured according to the actual usage scenario of the lithium battery, so it will not be elaborated here.

[0107] In actual implementation, steps 310 to 320 in this embodiment can also be executed Figure 3 after step 210 in the embodiment, that is, after obtaining the SOH change trend curve graph generated according to the SOH change trend, the target cycle inflection point can be determined, and then the target cycle inflection point can be marked on the SOH change trend curve graph.

[0108] Please refer to Figure 5 , Figure 5The structural schematic diagram of a lithium battery SOH change trend prediction device provided by an embodiment of the present application is shown. In this embodiment, each unit included in the lithium battery SOH change trend prediction device is used to execute Figure 1 each step in the corresponding embodiment. For details, please refer to Figure 1 the relevant descriptions in the corresponding embodiment. For the sake of convenience of description, only the parts related to this embodiment are shown. Refer to Figure 5 , the lithium battery SOH change trend prediction device includes: an acquisition unit 501, a fitting unit 502, and a prediction unit 503. Specifically:

[0109] The acquisition unit 501 is used to acquire historical battery health state SOH data of the lithium battery.

[0110] The fitting unit 502 is used to fit a prediction factor by using the historical SOH data according to a preset fitting strategy; wherein, the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface SEI film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate.

[0111] The prediction unit 503 is used to predict the SOH change trend of the lithium battery according to the prediction model corresponding to the preset fitting strategy and the prediction factor.

[0112] As an embodiment, the lithium battery SOH change trend prediction device further includes:

[0113] A curve generation unit, which is used to generate an SOH change trend curve graph according to the SOH change trend.

[0114] As an embodiment, the lithium battery SOH change trend prediction device further includes:

[0115] A determination unit, which is used to determine a target cycle inflection point according to the SOH change trend, and the target cycle inflection point is used to indicate the timing of executing a preset retirement strategy.

[0116] A marking unit, which is used to mark the target cycle inflection point on the curve graph corresponding to the SOH change trend.

[0117] It can be understood that the improvement points and specific implementation manners related to the present application have been Figures 1 to 4 described in detail in the corresponding embodiment. When specifically implemented, it can be based on the Figures 1 to 4 corresponding embodiment, and make Figure 5 the units in the lithium battery SOH change trend prediction device provided by the embodiment execute each step in the above method embodiment, so details are not described herein again.

[0118] Figure 6 is the structural block diagram of an electronic device provided by an embodiment of the present application. AsFigure 6 As shown, the electronic device 6 of this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, such as a program for predicting the change trend of the SOH of a lithium battery. When the processor 60 executes the computer program 62, the steps in each of the above embodiments of the method for predicting the change trend of the SOH of a lithium battery are implemented, such as Figures 1 to 4 the steps shown. Alternatively, when the processor 60 executes the computer program 62, the functions of each unit in the above Figure 5 corresponding embodiment are implemented. For specific details, please refer to Figure 5 the relevant descriptions in the corresponding embodiments, which will not be elaborated here.

[0119] Exemplarily, the computer program 62 can be divided into one or more units, and the one or more units are stored in the memory 61 and executed by the processor 60 to complete this application. The one or more units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 62 in the electronic device 6. For example, the computer program 62 can be divided into an acquisition unit, a fitting unit, and a prediction unit, and the specific functions of each unit are as described above.

[0120] The electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 6 this is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device may further include input / output devices, network access devices, buses, etc.

[0121] The so-called processor 60 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0122] The memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. The memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk equipped on the electronic device 6, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 61 may also include both an internal storage unit of the electronic device 6 and an external storage device. The memory 61 is used to store the computer program and other programs and data required by the electronic device. The memory 61 may also be used to temporarily store data that has been output or will be output.

[0123] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for predicting the changing trend of the state of health (SOH) of a lithium battery, characterized in that, Including: Obtaining historical State of Health (SOH) data of a lithium battery; According to a preset fitting strategy, using the historical SOH data to fit a prediction factor; wherein, the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate; Using the prediction model corresponding to the preset fitting strategy, predicting the SOH change trend of the lithium battery according to the prediction factor.

2. The method for predicting the SOH change trend of a lithium battery according to claim 1, wherein The obtaining of the historical SOH data of the lithium battery includes: Obtaining the historical SOH data from the test equipment of the lithium battery; or Obtaining the historical SOH data from the electrical equipment equipped with the lithium battery.

3. The method for predicting the SOH change trend of a lithium battery according to claim 1, wherein The preset fitting strategy includes a fitting equation set corresponding to the prediction factor; The step of using the historical SOH data to fit the prediction factor according to the preset fitting strategy includes: Using an arithmetic fitting tool to call the fitting equation set, and substituting the historical SOH data into the fitting equation set to solve for the prediction factor, thereby obtaining the prediction factor.

4. The method for predicting the SOH change trend of a lithium battery according to claim 3, characterized in that, The step of using the arithmetic fitting tool to call the fitting equation set, and substituting the historical SOH data into the fitting equation set to solve for the prediction factor, thereby obtaining the prediction factor, includes: Using an arithmetic fitting tool to call the following equation set: SOH1 = 1 - K×t B K = A × S S = 1 - C×t D Solving for the prediction factor according to the historical SOH data, thereby obtaining the prediction factor; wherein, SOH1 is the historical SOH data; K is the reaction rate; t is the number of charge-discharge cycles of the lithium battery; S is the equivalent reaction area of lithium ions in the lithium battery; A is the reaction rate per unit area corresponding to S; B is the reaction rate cycle constant; C is the equivalent area growth rate of the SEI film; D is the area growth cycle constant of the SEI film.

5. The method for predicting the SOH change trend of a lithium battery according to claim 3, wherein, The step of predicting the SOH change trend of the lithium battery according to the prediction factor using the prediction model corresponding to the preset fitting strategy includes: Determining a prediction model according to the fitting equation set; Substituting the prediction factor into the prediction model to predict the SOH change trend of the lithium battery.

6. The method for predicting the SOH change trend of a lithium battery according to claim 5, wherein, The determining of the prediction model according to the fitting equation set includes: Performing equivalent substitution on the fitting equation set to obtain the prediction model.

7. The method for predicting the SOH change trend of a lithium battery according to any one of claims 1 to 6, characterized in that After the step of predicting the SOH change trend of the lithium battery based on the prediction factor, it further includes: Generating a SOH change trend curve graph according to the SOH change trend; and / or Determining a target cycle inflection point according to the SOH change trend, where the target cycle inflection point is used to indicate the timing of executing a preset retirement strategy; Marking the target cycle inflection point on the curve graph corresponding to the SOH change trend.

8. A lithium battery SOH change trend prediction device, characterized in that Including: An obtaining unit, configured to obtain historical SOH data of a lithium battery; A fitting unit, configured to fit a prediction factor by using the historical SOH data according to a preset fitting strategy; wherein, the preset fitting strategy is used to describe the influence of the growth of the solid electrolyte interface (SEI) film in the lithium battery on the equivalent reaction area of lithium ions in the lithium battery, and the influence of the equivalent reaction area of lithium ions on the reaction rate; A prediction unit, configured to predict the SOH change trend of the lithium battery according to the prediction factor by using a prediction model corresponding to the preset fitting strategy.

9. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored in the memory and executable on the electronic device, wherein when the processor executes the computer program, the steps of the method for predicting the SOH change trend of the lithium battery according to any one of claims 1 to 7 are implemented.

10. A vehicle, characterized in that, Comprising: A memory, a processor, and a computer program stored in the memory and executable on the vehicle, wherein when the processor executes the computer program, the steps of the method for predicting the SOH change trend of the lithium battery according to any one of claims 1 to 7 are implemented.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting the SOH change trend of the lithium battery according to any one of claims 1 to 7 are implemented.

Images