Battery life model building method, battery life model using method and related device

Abstract

The invention provides a battery life model building method, a battery life model using method and a related device, and relates to an energy storage battery technology. The building method comprises the following steps: building a lithium battery quasi-two-dimensional model P2D model; introducing a microcosmic attenuation mechanism equation corresponding to a preset microcosmic attenuation mechanism into the P2D model to obtain an attenuation mechanism model; the preset microcosmic attenuation mechanism comprises an SEI membrane growth mechanism, a lithium precipitation mechanism and a gas production mechanism; according to the attenuation mechanism model and the capacity calibration result, carrying out decoupling calculation on capacity loss under different working conditions to obtain first capacity loss caused by lithium content loss and second capacity loss caused by active material loss; and based on a preset function, fitting the data set containing the working conditions and the corresponding first capacity loss and second capacity loss to obtain a battery life model. According to the method, the accuracy of the battery life model is improved.

Description

Technical Field

[0001] The present application relates to energy storage battery technology, and in particular to a battery life model construction method, use method and related devices. Background Art

[0002] As the core component of battery energy storage technology, lithium batteries have become the first choice for power batteries in electric vehicles due to their advantages such as high energy density, no memory effect, light weight and small size. However, lithium-ion batteries begin to experience an aging process after formation. When the battery performance decays to a certain extent, the performance of the entire vehicle will be severely reduced, unable to meet normal driving needs, and even cause safety issues such as thermal runaway.

[0003] In order to avoid the above-mentioned safety issues, it is proposed in the known technology to establish a battery life model for lithium batteries for analyzing and predicting battery life. Specifically, generally, a long-term aging experiment is first conducted to obtain the cycle life of the lithium battery under different working conditions to obtain a large amount of aging data. Secondly, based on the large amount of aging data, a battery life model for the lithium battery is established. However, since the aforementioned process of obtaining aging data is a time-consuming and labor-intensive process, the known technology is more inclined to establish a battery life model based on a microscopic attenuation mechanism through a small amount of experimental data.

[0004] However, due to the time-varying and spatial distribution characteristics of the battery aging mechanism and the complex coupling correlations between various aging side reactions, there is a defect that it is impossible to decouple the influence of aging side reactions and achieve accurate assessment of the battery cell life in actual application scenarios. Summary of the invention

[0005] The present application provides a battery life model construction method, usage method and related devices to decouple the effects of aging side reactions and achieve accurate assessment of battery cell life in actual application scenarios.

[0006] In one aspect, the present application provides a method for building a battery life model, the method comprising:

[0007] Establish a quasi-two-dimensional model P2D model of lithium battery;

[0008] The microscopic attenuation mechanism equation corresponding to the preset microscopic attenuation mechanism is introduced into the P2D model to obtain an attenuation mechanism model; the preset microscopic attenuation mechanism includes SEI film growth mechanism, lithium precipitation mechanism and gas production mechanism;

[0009] According to the attenuation mechanism model and the capacity calibration results under different working conditions, the capacity loss under different working conditions is decoupled and calculated to obtain a first capacity loss caused by lithium content loss and a second capacity loss caused by active material loss; the capacity calibration results are obtained by performing life simulation on the lithium battery after setting different working conditions;

[0010] Based on a preset function, a data set including the operating condition and the corresponding first capacity loss and second capacity loss is fitted to obtain a battery life model.

[0011] In a possible implementation, the capacity loss under different operating conditions is decoupled and calculated according to the attenuation mechanism model and the capacity calibration results under different operating conditions to obtain a first capacity loss caused by lithium content loss and a second capacity loss caused by active material loss, including:

[0012] For any of the operating conditions, obtaining microscopic parameters corresponding to the SEI film growth mechanism and the lithium precipitation mechanism, and calculating the local current density corresponding to the SEI film growth mechanism and the lithium precipitation mechanism respectively according to the microscopic parameters and the attenuation mechanism model;

[0013] According to the local current density corresponding to the SEI film growth mechanism and the local current density corresponding to the lithium precipitation mechanism, respectively determining the thickness of the SEI film and the thickness of the lithium precipitation;

[0014] Calculating a first capacity loss under the operating condition according to the thickness of the SEI film and the thickness of the precipitated lithium;

[0015] The total capacity loss under the operating condition is determined according to the capacity calibration result, and the total capacity loss is subtracted from the first capacity loss to obtain a second capacity loss under the operating condition.

[0016] In one possible implementation method, the microscopic parameters are obtained by any one of the preset methods, including setting based on experience, aging calibration, and disassembling the lithium battery cell for characterization testing; the microscopic parameters corresponding to the SEI film growth mechanism include side reaction rate constant, EC diffusion coefficient, transfer coefficient, overpotential of SEI film side reaction, EC bulk and interface concentration, SEI film resistance, and side reaction equilibrium potential; the microscopic parameters corresponding to the lithium precipitation mechanism include lithium deposition exchange current density, transfer coefficient, lithium precipitation equilibrium potential, overpotential, and electrode porosity.

[0017] In a possible implementation, the fitting of the data set including the operating condition and the corresponding first capacity loss and second capacity loss based on a preset function to obtain a battery life model includes:

[0018] Determine, according to the data set, a first attenuation curve and a second attenuation curve of the lithium battery under different operating conditions; the first attenuation curve is obtained by the first capacity loss, and the second attenuation curve is obtained by the second capacity loss;

[0019] Fitting the first attenuation curve and the second attenuation curve according to the preset function to obtain an aging attenuation function; the preset function includes a first variation function for describing a variation of the first capacity loss, and a second variation function for describing a variation of the second capacity loss;

[0020] An assumption is made on the variation law of the aging attenuation function with the operating conditions to obtain an aging data set, so as to obtain the battery life model according to the aging data set; the aging data set includes the attenuation law of the lithium battery under different operating conditions.

[0021] In a possible implementation, when the operating condition includes temperature, assuming a change rule of the aging attenuation function with the operating condition to obtain an aging data set, and obtaining the battery life model according to the aging data set, includes:

[0022] Determine the coefficient of the first variation function and the coefficient of the second variation function at different temperatures according to the first variation function, the second variation function and the Arrhenius formula;

[0023] Substituting the coefficient of the first variation function at each temperature into the aging attenuation function to obtain a first aging data set; the first aging data set includes the attenuation law of the first capacity loss of the lithium battery at different temperatures;

[0024] Substituting the coefficient of the second transformation function at each temperature into the aging attenuation function to obtain a second aging data set; the second aging data set includes the attenuation law of the second capacity loss of the lithium battery at different temperatures;

[0025] The battery life model is obtained according to the first aging data set, the second aging data set and the aging attenuation function.

[0026] In a second aspect, the present application provides a battery life model building device, the device comprising:

[0027] Establish a module for establishing a quasi-two-dimensional model P2D model of lithium battery;

[0028] An introduction module is used to introduce a microscopic attenuation mechanism equation corresponding to a preset microscopic attenuation mechanism into the P2D model to obtain an attenuation mechanism model; the preset microscopic attenuation mechanism includes an SEI film growth mechanism, a lithium precipitation mechanism, and a gas production mechanism;

[0029] A calculation module, for performing decoupling calculation on the capacity loss under different operating conditions according to the attenuation mechanism model and the capacity calibration results under different operating conditions, to obtain a first capacity loss caused by lithium content loss and a second capacity loss caused by active material loss; the capacity calibration results are obtained by performing life simulation on the lithium battery after setting different operating conditions;

[0030] The fitting module is used to fit the data set including the operating condition and the corresponding first capacity loss and second capacity loss based on a preset function to obtain a battery life model.

[0031] In a third aspect, the present application provides a method for using a battery life model, the method comprising:

[0032] Obtain operating parameters, input the operating parameters into a preset battery life model to obtain an evaluation result output by the preset battery life model; the preset battery life model is built based on the method described in any one of the first aspects; the evaluation result includes the attenuation law of the battery under the operating parameters

[0033] In a fourth aspect, the present application provides a battery life model using device, the device comprising:

[0034] An evaluation module is used to obtain operating condition parameters and input the operating condition parameters into a preset battery life model to obtain an evaluation result output by the preset battery life model; the preset battery life model is constructed based on the method described in any one of the first aspects.

[0035] In a fifth aspect, the present application provides an electronic device, comprising: at least one processor and a memory;

[0036] The memory stores computer-executable instructions;

[0037] The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method as described in any one of the first aspect or the second aspect above.

[0038] In a sixth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first or second aspects above.

[0039] The present application provides a method for building a battery life model, a method for using the method and a related device. The method of the present application is executed by any electronic device. Specifically, when building a battery life model, the electronic device first establishes a P2D model of a lithium battery; secondly, the microscopic attenuation mechanism equation corresponding to the preset microscopic attenuation mechanism is introduced into the established P2D model to obtain an attenuation mechanism model. Further, the electronic device obtains the capacity calibration results under different working conditions, and decouples and calculates the capacity loss under the corresponding working conditions according to the attenuation mechanism model and the capacity calibration results to obtain the first capacity loss and the second capacity loss. Finally, the electronic device fits the data set containing the working conditions and the corresponding first capacity loss and second capacity loss based on the preset function to obtain a battery life model. In the method of the present application, the different effects of various microscopic attenuation mechanisms on battery aging at different times are taken into account. After decoupling the two main types of capacity losses, the battery life model is obtained after fitting in combination with the working conditions. On the one hand, it is conducive to improving the evaluation accuracy of the battery life model, and on the other hand, it enables the battery life model to be evaluated based on the actual working conditions of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] Figure 1 A schematic diagram of a battery life model construction method provided in an embodiment of the present application Figure 1 ;

[0042] Figure 2 A schematic diagram of a battery life model construction method provided in an embodiment of the present application Figure 2 ;

[0043] Figure 3 A graph showing the relationship between the SOH and the number of cycles of a battery under different temperature conditions provided in an embodiment of the present application;

[0044] Figure 4 A capacity attenuation curve obtained under different temperature conditions provided in an embodiment of the present application;

[0045] Figure 5 A data set of LLI changes at different temperatures provided in an embodiment of the present application;

[0046] Figure 6 A data set of LAM changes at different temperatures provided in an embodiment of the present application;

[0047] Figure 7 A data set of battery SOH changes at different temperatures provided in an embodiment of the present application;

[0048] Figure 8 A schematic diagram of the structure of a battery life model building device provided in an embodiment of the present application;

[0049] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0050] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0052] As energy crisis and environmental issues become increasingly prominent, the power system has developed rapidly. In order to improve power supply reliability, ensure power quality, and access stable renewable energy, battery energy storage technology has been widely used. As the core component of battery energy storage technology, lithium-ion batteries have the advantages of high energy density, no memory effect, light weight, and small size. They are currently the first choice for power batteries in electric vehicles.

[0053] However, lithium batteries also inevitably have aging problems, which run through the entire life cycle of the battery and affect the safety of the battery, and thus affect the safety of electric vehicles. In the known technology, a battery life model for lithium batteries is established to analyze and predict the battery life in order to improve the safety of battery use. Specifically, through long-term aging experiments, the cycle life of the lithium battery under different working conditions is obtained, and the aging data of the lithium battery is obtained, so as to further establish the battery life of the lithium battery based on the aging data. It is understandable that in order to improve the accuracy of the model, it takes a lot of time and effort to obtain the aging data through the aforementioned method, which leads to the known technology being more inclined to establish a battery life model based on a microscopic attenuation mechanism through a small amount of time-delay data.

[0054] However, the current aging mechanism of lithium batteries has time-varying and spatial distribution characteristics, and the coupling correlation between various aging side reactions is complex. Therefore, it has become an urgent problem to clarify the battery micro-attenuation mechanism, decouple aging side reactions, and realize accurate evaluation of battery cell life in actual application scenarios.

[0055] The present application provides a method for building a battery life model, a method of using the model, and related devices. In the present application, the electronic device first establishes a quasi-two-dimensional model (Pseudo-two-Dimensions, P2D) of a lithium battery, and introduces the microscopic attenuation mechanism equation corresponding to the preset microscopic attenuation mechanism into the P2D model to obtain an attenuation mechanism model. Specifically, the preset microscopic attenuation mechanisms in the present application include SEI film growth mechanism, lithium precipitation mechanism, and gas production mechanism, wherein the SEI film growth mechanism and lithium precipitation mechanism will lead to loss of lithium content (Loss of Lithium Inventory, LLI), and the gas production mechanism will lead to loss of active material (Loss of Active Material, LAM).

[0056] Furthermore, the electronic device decouples and calculates the capacity loss caused by different microscopic attenuation mechanisms under various operating conditions based on the attenuation mechanism model and capacity calibration results, and obtains the first capacity loss caused by lithium content loss and the second capacity loss caused by active material loss. Finally, the electronic device fits the data set including the operating conditions and the first capacity loss and the second capacity loss based on the preset function to obtain the battery life model.

[0057] In the method of the present application, the electronic device decouples and settles the losses caused by different microscopic attenuation mechanisms, and determines the final battery life model based on the data set obtained after decoupling. The method of the present application can effectively reduce the risk of inaccurate battery life models due to the different time periods of each microscopic attenuation mechanism affecting battery aging. In addition, the battery life model is also capable of accurately evaluating the life of lithium batteries under actual operating conditions after inputting actual operating conditions.

[0058] It is understandable that during the migration of lithium ions, lithium ions and other chemical substances in the electrolyte undergo irreversible reactions, resulting in a reduction in lithium ions that cannot be embedded in the negative electrode carbon, which manifests as a loss of capacity. This lithium element loss mechanism is called lithium content loss. If the electrode breaks, part of the electrode peels off, resulting in a reduction in the honeycomb that accommodates lithium ions, or the electrolyte and electrode material C react to cause an unstable carbon structure, it will also lead to a reduction in battery capacity. This lithium element loss mechanism is called active material loss.

[0059] It is understandable that the battery life model building method of the present application is applicable to any type of lithium battery. The following is a detailed description of some embodiments of the present application in conjunction with the accompanying drawings. In the case where the embodiments do not conflict, the following embodiments and features in the embodiments can be combined with each other.

[0060] Figure 1 A schematic diagram of a battery life model construction method provided in an embodiment of the present application Figure 1 ,like Figure 1 As shown, the method of this embodiment includes:

[0061] S101, establishing a quasi-two-dimensional model P2D model of a lithium battery.

[0062] In this embodiment, the electronic device establishes a quasi-two-dimensional model P2D model based on the basic principles of electrochemistry, which includes electrochemical reaction kinetics, conservation of matter and conservation of charge. In this embodiment, two reaction dimensions are mainly considered, namely the x dimension along the thickness direction of the electrode and the r dimension along the radius direction of the electrode particles.

[0063] Specifically, in this embodiment, the electronic device introduces the basic control equations into the P2D model based on the basic electrochemical mechanism of lithium batteries, wherein the basic control equations include the liquid phase material conservation equation, the solid phase material conservation equation, the liquid phase charge conservation equation, the electrode reaction kinetics equation, and the like.

[0064] S102, introducing a microscopic attenuation mechanism equation corresponding to a preset microscopic attenuation mechanism into a P2D model to obtain an attenuation mechanism model.

[0065] Among them, the preset microscopic attenuation mechanisms include SEI film growth mechanism, lithium precipitation mechanism and gas production mechanism.

[0066] In this embodiment, the SEI film growth side reaction can be reduced with the EC solvent on the negative electrode surface, and the process can be expressed as: 2C2H4CO3+2e - +2Li + →(CH2OCH2Li)2↓+C2H4↑. The backflow density of the side reaction of SEI film growth is mainly determined by the diffusion rate of EC solvent to the negative electrode surface and the reaction kinetics, and is calculated by the anode Tafel formula. The microscopic parameters involved include the side reaction rate constant, EC diffusion coefficient, transfer coefficient, overpotential of SEI film side reaction, EC bulk and interface concentration, SEI film resistance, side reaction equilibrium potential, etc.

[0067] Specifically, the microscopic mechanism equation corresponding to the side reaction of SEI film growth is: Where F represents the Faraday constant, R represents the molar gas constant, T is the temperature, and j SEI Used to represent the SEI film side reaction current density, k 0，SEI Used to represent the side reaction rate constant, Used to represent the EC concentration diffused to the negative electrode surface, α c，SEI It is used to represent the transfer coefficient, with a value of 0.5, and η2 is used to represent the overpotential of the side reaction of the SEI membrane.

[0068] In this embodiment, the electrochemical equation of the lithium deposition side reaction is expressed as: Li + +e -→Li(s)↓. The current density of the lithium deposition side reaction can be calculated by the electrode reaction kinetics equation, and the microscopic parameters involved include lithium deposition exchange current density, transfer coefficient, lithium deposition equilibrium potential, overpotential, and electrode porosity. The microscopic mechanism equation corresponding to the lithium deposition mechanism is: Among them, i 0，lpl Used to express the exchange current density of lithium deposition, α c，lpl Used to express the transfer coefficient, U lpl Used to express the equilibrium potential of lithium deposition, F represents the Faraday constant, R represents the molar gas constant, and T represents the temperature. Used to represent overpotential, where is the solid phase electrode potential, is the liquid electrode potential, i is the external applied current, R film It is the negative electrode interface film resistance. When it is less than 0, lithium deposition occurs.

[0069] In this embodiment, the gas production mechanism mainly considers two stages: Stage 1, as the side reaction proceeds, gas inside the battery will continue to be generated, and first occupy part of the gap between the battery shell and the core; Stage 2, when the generated gas fills these gaps, the gas generated later will diffuse into the battery cell, occupy the pores of the electrode, and cause part of the active material to be inactivated. The microscopic parameters involved in the gas production mechanism include several areas, gas production regulation factors, porosity, void ratio, active material volume fraction, etc. The microscopic mechanism equation corresponding to the gas production mechanism is: Among them, V gas is the total volume of generated gas, A0 is the area of ​​the electrode participating in the electrochemical reaction process, is the molar volume of the ideal gas, Gas production regulatory factors.

[0070] In this embodiment, the microscopic parameters corresponding to each microscopic attenuation mechanism are obtained by any of the preset methods, wherein the preset methods include setting based on experience, aging calibration, and characterization testing after disassembling the lithium battery cell. Specifically, among the microscopic parameters obtained by disassembling and characterizing the lithium battery cell, some parameters are microstructural parameters, which mainly refer to parameters related to the electrode structure, including electrode thickness, porosity, material particle size, etc., and the other parameters are reaction kinetic parameters, which mainly refer to kinetic parameters corresponding to the lithium insertion and extraction reaction and mass transfer process, which need to be measured by electrochemical experiments.

[0071] Exemplarily, the microscopic parameters of the lithium battery, including the thickness of the positive and negative electrodes and the separator, the particle radius of the positive and negative electrodes, the porosity of the positive and negative electrodes and the separator, etc., are obtained using a scanning electron microscope (SEM) and post-processing software; the electrochemical parameters such as the diffusion coefficient, exchange current density, and electrode potential of the battery material are obtained using electrochemical characterization methods such as half-cell charge and discharge tests, cyclic voltammetry, AC impedance spectroscopy, and intermittent titration.

[0072] S103, according to the attenuation mechanism model and the capacity calibration results under different operating conditions, the capacity loss under different operating conditions is decoupled and calculated to obtain a first capacity loss caused by lithium content loss and a second capacity loss caused by active material loss.

[0073] It is understandable that the capacity loss of lithium batteries is mainly caused by the loss of lithium content and the loss of active materials. The loss of lithium content is caused by the SEI film growth mechanism and the lithium precipitation mechanism, and the loss of active materials is mainly caused by the gas production mechanism.

[0074] Since the loss of active materials may also be affected by other microscopic attenuation mechanisms, in this embodiment, the electronic device simulates and calculates the first capacity loss caused by the loss of lithium content due to the SEI film growth mechanism and the lithium precipitation mechanism based on the attenuation mechanism model, and further the electronic device determines the total capacity loss based on the capacity calibration results obtained under the corresponding operating conditions, so as to further decouple the second capacity loss caused by the loss of active materials mainly affected by the gas production mechanism based on the total capacity loss and the first capacity loss.

[0075] Among them, in this embodiment, the capacity calibration result is obtained by setting different working conditions and simulating the life of the lithium battery, specifically including the total capacity loss under each working condition. In practical applications, the electronic device can also directly simulate and calculate the capacity loss caused by the loss of active materials caused by the gas production mechanism based on the attenuation mechanism model, and use the capacity loss as the second capacity loss under the corresponding working condition, which is not limited in this embodiment.

[0076] S104, based on a preset function, fitting a data set including operating conditions and corresponding first capacity loss and second capacity loss to obtain a battery life model.

[0077] In this embodiment, the preset function is set based on a large amount of experience, and the preset function specifically includes a first variation function for describing lithium content loss and battery capacity loss, and a second variation function for describing active material loss and battery capacity loss. The electronic device fits the preset function with a data set containing the first capacity loss and the second capacity loss under different operating conditions to obtain a fitted function, and finally combines the fitted function with the apparent operating conditions to obtain a battery life model, so that when the operating conditions are input, the battery life model can obtain the variation law of the first capacity loss and the second capacity loss of the lithium battery under the operating conditions.

[0078] In the method of this embodiment, when building a battery life model, the electronic device first establishes a P2D model of the lithium battery, and then introduces the microscopic attenuation mechanism equations corresponding to the SEI model growth mechanism, lithium precipitation mechanism and gas production mechanism into the established P2D model to obtain an attenuation mechanism model. Furthermore, the electronic device obtains the capacity calibration results under different operating conditions, and decouples the capacity loss under different operating conditions based on the attenuation mechanism model and the capacity calibration results to obtain the first capacity loss and the second capacity loss. Finally, the electronic device fits the data set containing the operating conditions and the corresponding first capacity loss and second capacity loss based on a preset function to obtain a battery life model.

[0079] Through the method of this embodiment, the electronic device can overcome the defect of inaccurate life assessment results caused by the time-varying nature of the battery life model due to the microscopic mechanism, and based on the battery life model of the present application, it can realize the battery cell life model evaluation under corresponding working conditions in actual application scenarios.

[0080] Figure 2 A schematic diagram of a battery life model construction method provided in an embodiment of the present application Figure 2 , the following combination Figure 2 , the method of the embodiment of the present application is described in detail. Specifically, the method of this embodiment is based on the above-mentioned embodiment, and describes in detail the method of obtaining the first capacity loss and the second capacity loss, and the method of obtaining the battery life model based on the preset function. Figure 2 As shown, this embodiment includes the following contents:

[0081] S201, establish a quasi-two-dimensional model P2D model of lithium battery.

[0082] S202, introducing a microscopic attenuation mechanism equation corresponding to a preset microscopic attenuation mechanism into a P2D model to obtain an attenuation mechanism model.

[0083] Specifically, the preset microscopic attenuation mechanisms include SEI film growth mechanism, lithium precipitation mechanism and gas production mechanism.

[0084] S203, for any operating condition, obtaining microscopic parameters corresponding to the SEI film growth mechanism and the lithium precipitation mechanism, and calculating the local current density corresponding to the SEI film growth mechanism and the lithium precipitation mechanism respectively according to the microscopic parameters and the attenuation mechanism model.

[0085] S204, determining the thickness of the SEI film and the thickness of the deposited lithium respectively according to the local current density corresponding to the SEI film growth mechanism and the local current density corresponding to the lithium deposition mechanism.

[0086] S205, calculating the first capacity loss under the operating condition according to the thickness of the SEI film and the thickness of the precipitated lithium.

[0087] Since the local current density of the SEI film growth mechanism determines the growth of the SEI film, and the local current density of the lithium precipitation mechanism determines the growth of the lithium precipitation film thickness, in this embodiment, the thickness of the SEI film and the precipitated lithium is used to indirectly calculate the first capacity loss caused by the loss of lithium content.

[0088] S206, determining a total capacity loss according to the capacity calibration result under the operating condition, and subtracting the total capacity loss from the first capacity loss to obtain a second capacity loss under the operating condition.

[0089] In this embodiment, the electronic device obtains the total capacity loss under the current working condition according to the capacity calibration result, and obtains the second capacity loss under the current working condition by subtracting the total capacity loss from the first capacity loss. In this embodiment, the capacity calibration result is input by the user, and can also be obtained through relevant battery simulation software, which is not limited in this embodiment.

[0090] S207, determining a first attenuation curve and a second attenuation curve of the lithium battery under different operating conditions according to the data set, wherein the first attenuation curve is obtained by the first capacity loss, and the second attenuation curve is obtained by the second capacity loss.

[0091] In this embodiment, the electronic device determines a first attenuation curve according to the first capacity loss under different working conditions included in the data set; and determines a second attenuation curve according to the second capacity loss under different working conditions included in the data set. Specifically, the first attenuation curve and the second attenuation curve are respectively used to describe the relationship between the number of cycles and the corresponding capacity loss of the lithium battery. Exemplarily, the first attenuation curve is used to describe the relationship between the cycle period and the first capacity loss of the lithium battery.

[0092] S208, fitting the first attenuation curve and the second attenuation curve according to a preset function to obtain an aging attenuation function.

[0093] The preset function includes a first variation function for describing a first capacity loss variation situation, and a second variation function for describing a second capacity loss variation situation.

[0094] In this embodiment, the first change function is: LLI = a1*x^b1, wherein LLI is used to represent the first capacity loss, a1 and b1 are coefficients of the first transformation function, and x is the number of cycles. The second change function is: LAM = 0 (x≤G); LAM = a2*(xG)^b2 (x>G), wherein G is the gas production point, a2 and b2 are coefficients of the second transformation function, and x is the number of cycles. In this embodiment, the specific forms of the first change function and the second change function are set based on experience. It can be understood that based on the aging attenuation function, an aging attenuation curve can be obtained, and the gas production point is the point where the slope suddenly increases in the aging attenuation curve, which is used to indicate that the gas production mechanism begins to have a significant impact on the capacity of the battery.

[0095] In this embodiment, the electronic device fits the first attenuation curve and the second attenuation curve according to a preset function to obtain an aging attenuation function.

[0096] S209, making assumptions about the variation of the aging attenuation function with the operating conditions to obtain an aging data set, and obtaining a battery life model according to the aging data set.

[0097] Among them, the aging data set includes the attenuation law of lithium batteries under different working conditions.

[0098] In a possible implementation, if the operating condition includes temperature, an assumption is made on the variation law of the aging attenuation function with the operating condition to obtain an aging data set, so as to obtain a battery life model according to the aging data set, which specifically includes the following steps:

[0099] Step a: determining the coefficient of the first variation function and the coefficient of the second variation function at different temperatures according to the first variation function, the second variation function and the Arrhenius formula;

[0100] Step b: Substituting the coefficient of the first variation function at each temperature into the aging attenuation number to obtain a first aging data set, and substituting the coefficient of the second variation function at each temperature into the aging attenuation function to obtain a second aging data set;

[0101] Step c: Obtain a battery life model according to the first aging data set, the second aging data set and the aging attenuation function.

[0102] Specifically, the electronic device first determines the coefficients of the first change function and the coefficients of the second change function at different temperatures according to the first change function, the second change function and the Arrhenius formula. The coefficients of the first change function are a1 and b1 in the above steps, and the coefficients of the second change function are a2 and b2 in the above steps. The Arrhenius formula is: ln(k) = A-Ea / RT, where A and Ea are unknown coefficients, respectively used to represent the frequency factor and apparent activation energy, T is the thermodynamic temperature, K is the rate constant, and R is the molar gas constant.

[0103] Furthermore, the electronic device substitutes the coefficient of the first variation function at each temperature into the aging attenuation number to obtain a first aging data set, and substitutes the coefficient of the second variation function at each temperature into the aging attenuation function to obtain a second aging data set. The first aging data set includes the attenuation law of the first capacity loss of the lithium battery at different temperatures, and the second aging data set includes the attenuation law of the second capacity loss of the lithium battery at different temperatures. Finally, the electronic device obtains a battery life model based on the first aging data set, the second aging data set and the aging attenuation function.

[0104] It is understandable that the life of the battery will also be affected by the charging and discharging current, voltage range, shelf time, etc. Therefore, when building a battery life model, these operating conditions can also be used, which is not limited in this embodiment.

[0105] In the method provided in this embodiment, the electronic device ignores the influence of the mechanism factors such as the dissolution of the positive transition metal, the particle crushing, and the phase change of the active material on the battery life attenuation, and focuses on the loss of lithium content caused by the SEI film growth mechanism and the lithium precipitation mechanism, and the influence of the loss of active materials mainly caused by the gas production mechanism on the battery life. That is, when building the battery life model, reasonable assumptions are made to simplify the battery aging mode into lithium content loss and active material loss. At the same time, the total capacity loss obtained in the test and the first capacity loss based on the lithium content loss are combined to obtain the second capacity loss caused by the active material loss. The battery life model is more accurate, and due to the reasonable simplification of parameters, the construction process is simple and suitable for battery life evaluation in actual application scenarios.

[0106] For example, when the operating conditions include temperature, it is first assumed that the basic control equations of the P2D model are introduced based on the electrochemical principle as shown in the following table:

[0107]

[0108] Further, assuming that the simulation parameters involved in the above formula are set based on experience, aging calibration, and characterization testing after disassembling the lithium battery cell through the preset method, the specific ones are shown in the following table:

[0109]

[0110] In this embodiment, the electronic device substitutes the above-mentioned microscopic parameters into the microscopic mechanism equations corresponding to the SEI film growth mechanism and the lithium precipitation mechanism in the attenuation mechanism model to obtain the first capacity loss.

[0111] at this time, Figure 3 This is a relationship diagram between the SOH and cycle number of a battery under different temperature conditions provided in an embodiment of the present application. If the electronic device obtains the life simulation results of a lithium battery at different temperatures, the following is obtained: Figure 3 The relationship between the SOH and the number of cycles of a battery under different temperature conditions is shown in FIG. Figure 3 It can be seen that under 25°C conditions, the capacity decays to 80% after about 7700 cycles, and gas production accelerates the decay of the battery after about 7285 cycles; under 35°C conditions, the capacity decays to 80% after about 5000 cycles, and gas production accelerates the decay of the battery after about 4700 cycles; under 45°C conditions, the capacity decays to 80% after about 3500 cycles, and gas production accelerates the decay of the battery after about 3600 cycles.

[0112] On this basis, the electronic device performs model reduction on the attenuation curve obtained by the above-mentioned microscopic attenuation mechanism simulation. Among them, the change function of LAM and the change function of LLI are respectively the second change function and the first change function in the above-mentioned embodiment, which will not be described in detail here.

[0113] Based on the second variation function and the first variation function, the capacity attenuation curve under different temperature conditions can be calculated, as shown in the following example: Figure 4 As shown, Figure 4 This is a capacity decay curve obtained under different temperature conditions provided in the embodiment of the present application. It can be understood that, Figure 4 The relationship between the number of cycles of lithium batteries and battery SOH at 25° C., 35° C., and 45° C. is specifically shown in FIG. 1. It can be understood that the curve in the figure is a fitting calculation value calculated based on the first variation function and the second variation function, and the points in the figure are points in the data set.

[0114] By using the above-mentioned model reduction method, we can make assumptions about how the coefficients in the fitting formula change with the operating conditions (temperature and other factors), thereby obtaining the attenuation law of the battery under different operating conditions and generating an aging data set.

[0115] The sensitivity of the parameters in the fitting formula to temperature is described by the Arrhenius formula. The coefficients of the LAM variation function at 25℃ / 35℃ / 45℃ are substituted into the Arrhenius formula to obtain the unknown coefficients A and Ea. According to this method, the reaction coefficients a and b at different temperatures T can be obtained respectively.

[0116] For the gas production trigger point, the gas production trigger point G at 25°C / 35°C / 45°C is fitted with the corresponding temperature T, and the relationship between the gas production point and the temperature is obtained: f(T)=a*exp(b*T), where a=4.355e+08, b=-0.03689.

[0117] Substituting the coefficients corresponding to different temperatures T into the variation functions of LLI and LAM, we obtain the variation trend of LAM with the number of cycles in the temperature range of [14°C, 60°C].

[0118] Similarly, the coefficient of the LLI variation function is calculated using the Arrhenius formula, and the variation trend of LLI with the number of cycles in the temperature range of [14°C, 60°C] is obtained.

[0119] Specifically, Figure 5 A data set of LLI changes at different temperatures provided in an embodiment of the present application, Figure 6 A data set of LAM changes at different temperatures provided in an embodiment of the present application.

[0120] By bringing the LAM and LLI data sets into the model, we can finally obtain the battery life decay curve dominated by SEI film growth and gas generation mechanism at different temperatures in actual application scenarios. Figure 7 As shown, Figure 7 A data set of battery SOH changes at different temperatures provided in an embodiment of the present application.

[0121] The present application also provides a method for using a battery life model. Specifically, the method includes: obtaining operating condition parameters, inputting the operating condition parameters into a preset battery life model to obtain an evaluation result output by the preset battery life model.

[0122] The preset battery life model is obtained based on the battery life model building method in the above-mentioned embodiment. The evaluation result includes the attenuation law of the battery under the current operating parameters.

[0123] It is understandable that when the preset battery life model is obtained based on an aging data set under different temperature conditions, correspondingly, when the aging data set is used, the input operating condition should be temperature. When the preset battery life model is obtained based on an aging data set under different operating conditions including temperature, correspondingly, when the aging data set is used, the input operating condition should be an operating condition including temperature, and may also include conditions such as charge and discharge current.

[0124] The above-mentioned embodiments introduce a method for building and using a battery life model from the perspective of a method flow. The following embodiments introduce a device for building and using a battery life model from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiments.

[0125] The present application embodiment provides a battery life model building device, Figure 8 A schematic diagram of a battery life model building device provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the device includes a building module 81, an introduction module 82, a calculation module 83 and a fitting module 84, wherein:

[0126] Establishing module 81, used to establish a quasi-two-dimensional model P2D model of lithium battery;

[0127] An introduction module 82 is used to introduce a microscopic attenuation mechanism equation corresponding to a preset microscopic attenuation mechanism into the P2D model to obtain an attenuation mechanism model; the preset microscopic attenuation mechanism includes an SEI film growth mechanism, a lithium precipitation mechanism, and a gas generation mechanism;

[0128] The calculation module 83 is used to perform decoupling calculation on the capacity loss under different operating conditions according to the attenuation mechanism model and the capacity calibration result, and obtain the first capacity loss caused by the loss of lithium content and the second capacity loss caused by the loss of active materials; the capacity calibration result is obtained by performing life simulation on the lithium battery after setting different operating conditions;

[0129] The fitting module 84 is used to fit the data set including the operating conditions and the corresponding first capacity loss and second capacity loss based on a preset function to obtain a battery life model.

[0130] In another possible implementation of the embodiment of the present application, the calculation module 83 is specifically used to:

[0131] For any working condition, the microscopic parameters corresponding to the SEI film growth mechanism and the lithium precipitation mechanism are obtained, and the local current density corresponding to the SEI film growth mechanism and the lithium precipitation mechanism is calculated according to the microscopic parameters and the attenuation mechanism model;

[0132] According to the local current density corresponding to the SEI film growth mechanism and the local current density corresponding to the lithium precipitation mechanism, the thickness of the SEI film and the thickness of the lithium precipitation are determined respectively;

[0133] According to the thickness of the SEI film and the thickness of the precipitated lithium, the first capacity loss under the working condition is calculated;

[0134] The total capacity loss under the operating condition is determined according to the capacity calibration result, and the total capacity loss is subtracted from the first capacity loss to obtain the second capacity loss under the operating condition.

[0135] Another possible implementation method of the embodiment of the present application is that the microscopic parameters are obtained by any one of the preset methods, including setting based on experience, aging calibration, and characterization testing after disassembling the lithium battery cell; the microscopic parameters corresponding to the SEI film growth mechanism include side reaction rate constant, EC diffusion coefficient, transfer coefficient, overpotential of SEI film side reaction, EC bulk and interface concentration, SEI film resistance, and side reaction equilibrium potential; the microscopic parameters corresponding to the lithium precipitation mechanism include lithium deposition exchange current density, transfer coefficient, lithium precipitation equilibrium potential, overpotential, and electrode porosity.

[0136] In another possible implementation of the embodiment of the present application, the fitting module 84 is specifically used to:

[0137] Determine a first attenuation curve and a second attenuation curve of the lithium battery under different operating conditions according to the data set; the first attenuation curve is obtained by the first capacity loss, and the second attenuation curve is obtained by the second capacity loss;

[0138] Fitting the first attenuation curve and the second attenuation curve according to a preset function to obtain an aging attenuation function; the preset function includes a first variation function for describing a variation of the first capacity loss, and a second variation function for describing a variation of the second capacity loss;

[0139] An assumption is made on the variation of the aging attenuation function with the operating conditions to obtain an aging data set, so as to obtain a battery life model based on the aging data set; the aging data set includes the attenuation law of lithium batteries under different operating conditions.

[0140] In another possible implementation of the embodiment of the present application, the fitting module 84 is specifically used to:

[0141] When the operating conditions include temperature, determining the coefficient of the first variation function at different temperatures and the coefficient of the second variation function according to the first variation function, the second variation function and the Arrhenius formula;

[0142] Substituting the coefficient of the first variation function at each temperature into the aging attenuation function to obtain a first aging data set; the first aging data set includes the attenuation law of the first capacity loss of the lithium battery at different temperatures;

[0143] Substituting the coefficient of the second transformation function at each temperature into the aging attenuation function to obtain a second aging data set; the second aging data set includes the attenuation law of the second capacity loss of the lithium battery at different temperatures;

[0144] A battery life model is obtained according to the first aging data set, the second aging data set and the aging attenuation function.

[0145] A battery life model building device provided in an embodiment of the present application is applicable to the embodiment of the above-mentioned life model building method, which will not be described in detail here.

[0146] An embodiment of the present application provides a device for using a battery life model, which includes an evaluation module, which is specifically used to obtain operating condition parameters, input the operating condition parameters into a preset battery life model to obtain an evaluation result output by the preset battery life model; the preset battery life model is built based on a battery life model building method.

[0147] A battery life model usage device provided in an embodiment of the present application is applicable to the above-mentioned usage method embodiment and will not be described in detail here.

[0148] An electronic device is provided in an embodiment of the present application. Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown, Fig. 9 The electronic device shown includes: a processor 91 and a memory 92. The processor 91 and the memory 92 are connected, such as through a bus 93. Optionally, the electronic device may further include a transceiver 94. It should be noted that in actual applications, the transceiver 94 is not limited to one, and the structure of the electronic device does not constitute a limitation on the embodiments of the present application.

[0149] The processor 91 may be a central processing unit 91 (CPU), a general processor 91, a digital signal processor 91 (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 91 may also be a combination that implements computing functions, such as a combination of one or more microprocessors 91, a combination of a DSP and a microprocessor 91, etc.

[0150] The bus 93 may include a path to transmit information between the above components. The bus 93 may be a Peripheral Component Interconnect (PCI) bus 93 or an Extended Industry Standard Architecture (EISA) bus 93, etc. The bus 93 may be divided into an address bus 93, a data bus 93, a control bus 93, etc. For ease of representation, Fig. 9 Only one thick line is used in the figure, but it does not mean that there is only one bus 93 or only one type of bus 93.

[0151] The memory 92 can be a read-only memory 92 (Read Only Memory, ROM) or other types of static storage devices that can store static information and instructions, a random access memory 92 (Random Access Memory, RAM) or other types of dynamic storage devices that can store information and instructions, or it can be an electrically erasable programmable read-only memory 92 (Electrically Erasable Programmable Read Only Memory, EEPROM), a compact disc (Compact Disc Read Only Memory, CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0152] The memory 92 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 91. The processor 91 is used to execute the application code stored in the memory 92 to implement the contents shown in the above method embodiment.

[0153] The present application also provides a computer-readable storage medium, which may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes. Specifically, the computer-readable storage medium stores program instructions, and the program instructions are used to implement the FPGA control method in the above-mentioned embodiments.

[0154] A computer program product is also provided in an embodiment of the present application, including a computer program. When the computer program is executed by a processor, the technical solution of the above-mentioned method embodiment is implemented. The implementation principle and technical effect are similar and will not be repeated here.

[0155] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0156] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for building a battery life model, characterized in that: The method comprises: Establish a quasi-two-dimensional model P2D model of lithium battery; The microscopic attenuation mechanism equation corresponding to the preset microscopic attenuation mechanism is introduced into the P2D model to obtain an attenuation mechanism model; the preset microscopic attenuation mechanism includes SEI film growth mechanism, lithium precipitation mechanism and gas production mechanism; According to the attenuation mechanism model and the capacity calibration results under different working conditions, the capacity loss under different working conditions is decoupled and calculated to obtain a first capacity loss caused by lithium content loss and a second capacity loss caused by active material loss; the capacity calibration results are obtained by performing life simulation on the lithium battery after setting different working conditions; Based on a preset function, a data set including the operating condition and the corresponding first capacity loss and second capacity loss is fitted to obtain a battery life model.

2. The method according to claim 1, characterized in that According to the attenuation mechanism model and the capacity calibration results under different working conditions, the capacity loss under different working conditions is decoupled and calculated to obtain the first capacity loss caused by the loss of lithium content and the second capacity loss caused by the loss of active materials, including: For any of the operating conditions, obtaining microscopic parameters corresponding to the SEI film growth mechanism and the lithium precipitation mechanism, and calculating the local current density corresponding to the SEI film growth mechanism and the lithium precipitation mechanism respectively according to the microscopic parameters and the attenuation mechanism model; Determine the thickness of the SEI film and the thickness of the deposited lithium according to the local current density corresponding to the SEI film growth mechanism and the local current density corresponding to the lithium deposition mechanism; Calculating a first capacity loss under the operating condition according to the thickness of the SEI film and the thickness of the precipitated lithium; The total capacity loss under the operating condition is determined according to the capacity calibration result, and the total capacity loss is subtracted from the first capacity loss to obtain a second capacity loss under the operating condition.

3. The method according to claim 1, characterized in that The microscopic parameters are obtained by any one of the preset methods, including setting based on experience, aging calibration, and characterization testing after disassembling the lithium battery cell; the microscopic parameters corresponding to the SEI film growth mechanism include side reaction rate constant, EC diffusion coefficient, transfer coefficient, overpotential of SEI film side reaction, EC bulk and interface concentration, SEI film resistance, and side reaction equilibrium potential; the microscopic parameters corresponding to the lithium precipitation mechanism include lithium deposition exchange current density, transfer coefficient, lithium precipitation equilibrium potential, overpotential, and electrode porosity.

4. The method according to any one of claims 1 to 3, characterized in that: The step of fitting a data set including the operating condition and the corresponding first capacity loss and second capacity loss based on a preset function to obtain a battery life model includes: Determine, according to the data set, a first attenuation curve and a second attenuation curve of the lithium battery under different operating conditions; the first attenuation curve is obtained by the first capacity loss, and the second attenuation curve is obtained by the second capacity loss; Fitting the first attenuation curve and the second attenuation curve according to the preset function to obtain an aging attenuation function; the preset function includes a first variation function for describing a variation of the first capacity loss, and a second variation function for describing a variation of the second capacity loss; An assumption is made on the variation law of the aging attenuation function with the operating conditions to obtain an aging data set, so as to obtain the battery life model according to the aging data set; the aging data set includes the attenuation law of the lithium battery under different operating conditions.

5. The method according to claim 4, characterized in that When the operating condition includes temperature, assuming a change rule of the aging attenuation function with the operating condition to obtain an aging data set, and obtaining the battery life model according to the aging data set, includes: Determine the coefficient of the first variation function and the coefficient of the second variation function at different temperatures according to the first variation function, the second variation function and the Arrhenius formula; Substituting the coefficient of the first variation function at each temperature into the aging attenuation function to obtain a first aging data set; the first aging data set includes the attenuation law of the first capacity loss of the lithium battery at different temperatures; Substituting the coefficient of the second transformation function at each temperature into the aging attenuation function to obtain a second aging data set; the second aging data set includes the attenuation law of the second capacity loss of the lithium battery at different temperatures; The battery life model is obtained according to the first aging data set, the second aging data set and the aging attenuation function.

6. A method for using a battery life model, characterized in that: The method comprises: Obtain operating condition parameters, and input the operating condition parameters into a preset battery life model to obtain an evaluation result output by the preset battery life model; the preset battery life model is built based on the method described in any one of claims 1-5; the evaluation result includes the attenuation law of the battery under the operating condition parameters.

7. A battery life model building device, characterized in that: The device comprises: Establish a module for establishing a quasi-two-dimensional model P2D model of lithium battery; An introduction module is used to introduce a microscopic attenuation mechanism equation corresponding to a preset microscopic attenuation mechanism into the P2D model to obtain an attenuation mechanism model; the preset microscopic attenuation mechanism includes an SEI film growth mechanism, a lithium precipitation mechanism, and a gas production mechanism; A calculation module, for performing decoupling calculation on the capacity loss under different operating conditions according to the attenuation mechanism model and the capacity calibration results under different operating conditions, to obtain a first capacity loss caused by lithium content loss and a second capacity loss caused by active material loss; the capacity calibration results are obtained by performing life simulation on the lithium battery after setting different operating conditions; The fitting module is used to fit the data set including the operating condition and the corresponding first capacity loss and second capacity loss based on a preset function to obtain a battery life model.

8. A battery life model using device, characterized in that: The device comprises: An evaluation module is used to obtain operating condition parameters and input the operating condition parameters into a preset battery life model to obtain an evaluation result output by the preset battery life model; the preset battery life model is constructed based on the method described in any one of claims 1-5.

9. An electronic device, characterized in that: include: at least one processor and memory; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory, so that the at least one processor performs the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

11. A computer program product, characterized in that The method comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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