Battery SOH analysis method and application thereof

Abstract

The invention relates to the technical field of battery SOH analysis, in particular to a battery SOH analysis method and application thereof. The battery SOH analysis method comprises the following steps: establishing a dependency relationship of capacity fading on cycle times under different conditions; the conditions include at least one of a charging rate, a temperature, an SOC interval, and a ratio of a negative electrode capacity to a positive electrode capacity; the capacity fading comprises capacity fading caused by SEI film growth and capacity fading caused by lithium deposition; optimizing the dependency relationship to obtain an SEI film growth aging semi-empirical model QSEI and a lithium deposition aging semi-empirical model QLi under different conditions; and adding the QSEI and the QLi under the same condition to obtain a cycle life model Qtotal. According to the method, the cycle life of the lithium ion battery can be quickly and accurately predicted by establishing the lithium battery SEI growth and lithium deposition two-stage cycle aging prediction model, and the turning point of the two stages of the cycle aging process can be quickly identified.

Description

Technical Field

[0001] The present invention relates to the technical field of battery state of health analysis, and in particular to a battery SOH analysis method and application thereof. Background Art

[0002] With the rapid popularization of electric vehicles, people are paying more and more attention to battery safety issues. Power batteries are not only the main power source and core components of electric vehicles, but also the main technical barrier to their rapid development. Lithium batteries are widely used in mobile phones, electric vehicles, mobile power supplies and other fields due to their high energy density, high cycle life and high environmental protection.

[0003] At present, there are some problems in the process of characterizing the microscopic morphology and structure of lithium-ion battery materials. For example, many battery components, such as charged electrode sheets, lithium metal electrodes, organic liquid electrolytes, solid electrolyte interface (SEI), etc., are not resistant to electron beam irradiation, so the data information obtained is affected by the surrounding environmental factors during the sample preparation and observation process, and cannot be true and reliable; on the other hand, the state of lithium-ion batteries after electrochemical cycling is usually observed using conventional scanning electron microscopes, which means that the battery after electrochemical cycling is disassembled, separated and cleaned before characterization. In this process, contamination is easily generated, which is out of the most realistic state of the reaction; in addition, the means commonly used to characterize the microscopic morphology of electrodes, such as scanning electron microscopes and atomic force microscopes, are limited by the nature of the characterization means themselves, and can only observe the surface of the material, but cannot observe the morphology and structure inside the electrode material.

[0004] The difficulty in sample preparation and the harsh environmental requirements of lithium-ion battery materials have put forward higher requirements for analytical characterization equipment. As an important micro-nanoscale precision processing equipment, the Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) system can realize functions such as fixed-point etching, deposition, and in-situ analysis.

[0005] Traditional thermal runaway analysis of power batteries is to analyze the power battery cells after thermal runaway. This kind of analysis inevitably causes damage to the battery cells during the analysis process. At the same time, in the process of dissecting the battery cells, interference factors are inevitably introduced, which leads to misleading and interference in the analysis of the true cause, which is neither scientific nor precise. At the same time, due to the drastic changes in the physical and chemical forms of the above-mentioned battery cells, the above-mentioned dissection analysis methods have limitations such as complex methods, insufficient detection capabilities, and low accuracy, resulting in various problems in the process of power battery fault diagnosis, thereby generating a large amount of personnel costs, time costs and equipment investment. Thermal runaway of power batteries is strongly related to the aging of lithium-ion batteries and the damage of SEI membranes. The aging process and remaining life prediction of lithium-ion batteries are of great significance to the safety of battery applications. The aging process of lithium batteries can be divided into calendar aging and cycle aging. Calendar aging refers to the aging of batteries in storage, which is mainly affected by the battery health state (SOH) and temperature; cycle aging refers to the capacity decay of batteries during cyclic charge and discharge. The battery cycle aging process is complex and is mainly affected by many factors such as temperature, rate and SOH. These factors are coupled with each other inside the battery, and the resulting processes such as solid electrolyte interface (SEI) growth and lithium deposition are the main mechanisms of battery aging.

[0006] In view of this, the present invention is proposed. Summary of the invention

[0007] The first purpose of the present invention is to provide a battery SOH analysis method. By establishing a two-stage cycle aging prediction model of lithium battery SEI growth and lithium deposition, the cycle life of lithium-ion batteries can be quickly and accurately predicted, and the turning points of the two stages of the cycle aging process can be quickly identified, thereby reducing the loss of personnel and samples caused by large sample experimental testing, as well as the cost and time cost of related testing materials.

[0008] The second purpose of the present invention is to provide an application of a battery SOH analysis method in real-time detection or monitoring of lithium-ion batteries. By constructing an empirical model to predict the two-stage aging process and turning points, the real cause of problems such as thermal runaway of power battery cells can be better found, and the right remedy can be prescribed fundamentally to effectively solve the battery failure problem.

[0009] In order to achieve the above-mentioned purpose of the present invention, the following technical solutions are particularly adopted:

[0010] The present invention first provides a battery SOH analysis method, comprising the following steps:

[0011] Establishing the dependence of capacity decay on the number of cycles under different conditions; wherein the conditions include at least one of the charge rate, temperature, SOC range, and the ratio of the negative electrode capacity to the positive electrode capacity; the capacity decay includes the capacity decay caused by SEI film growth and the capacity decay caused by lithium deposition;

[0012] The dependence is optimized to obtain the SEI film growth aging semi-empirical model Q under different conditions. SEI and the lithium deposition aging semi-empirical model Q Li ;

[0013] The Q under the same conditions SEI And the Q Li Add up and get the cycle life model Q total .

[0014] Furthermore, the method for establishing the dependence of capacity decay on the number of cycles under different conditions includes: performing nonlinear fitting on the charging rate, temperature, SOC range, at least one of the ratio of the negative electrode capacity to the positive electrode capacity, the number of cycles and the capacity decay to obtain a nonlinear fitting function; wherein the independent variables include the charging rate, temperature, SOC range, at least one of the ratio of the negative electrode capacity to the positive electrode capacity, and the number of cycles; and the dependent variable is the capacity decay.

[0015] Furthermore, the method for optimizing the dependency relationship includes: optimizing the nonlinear fitting function using a nonlinear least squares method.

[0016] Furthermore, the type of the nonlinear fitting function includes a power function.

[0017] Furthermore, the type of the nonlinear fitting function includes a power function with a constant term.

[0018] Furthermore, the establishment of the dependence of capacity decay on the number of cycles under different conditions specifically includes: establishing the dependence of capacity decay caused by SEI film growth on the number of cycles under different conditions and the dependence of capacity decay caused by lithium deposition on the number of cycles under different conditions.

[0019] Among them, the method for establishing the dependence of capacity decay caused by SEI film growth on the number of cycles under different conditions includes: establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and charging rate, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and temperature, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and SOC range, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and the ratio of negative electrode capacity to positive electrode capacity, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles, charging rate, temperature, SOC range, and the ratio of negative electrode capacity to positive electrode capacity.

[0020] The method for establishing the dependency of the capacity decay caused by lithium deposition on the number of cycles under different conditions includes: establishing the dependency of the capacity decay caused by lithium deposition on the number of cycles and the charging rate, or establishing the dependency of the capacity decay caused by lithium deposition on the number of cycles and the temperature, or establishing the dependency of the capacity decay caused by lithium deposition on the number of cycles and the SOC range, or establishing the dependency of the capacity decay caused by lithium deposition on the number of cycles and the ratio of the negative electrode capacity to the positive electrode capacity, or establishing the dependency of the capacity decay caused by lithium deposition on the number of cycles, the charging rate, the temperature, the SOC range, and the ratio of the negative electrode capacity to the positive electrode capacity.

[0021] Furthermore, the analysis results based on the cycle life model are verified by experimental data.

[0022] Furthermore, the analysis results based on the cycle life model are verified by focused ion beam scanning electron microscopy.

[0023] Furthermore, the sample is continuously sliced ​​and imaged using an ion beam to obtain a series of continuous images, and the analysis results are verified by image analysis and comparison with experimental data.

[0024] Furthermore, the SEI film growth aging semi-empirical model Q is improved by combining the data-driven model. SEI , the lithium deposition aging semi-empirical model Q Li and the cycle life model Q total At least one of .

[0025] The present invention further provides application of the battery SOH analysis method in real-time detection or monitoring of lithium-ion batteries.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] (1) The battery SOH analysis method provided by the present invention can quickly and accurately predict the cycle life of lithium-ion batteries by establishing a two-stage cycle aging prediction model of SEI growth and lithium deposition of lithium batteries, and quickly identify the turning points of the two stages of the cycle aging process, thereby reducing the loss of personnel and samples caused by large sample experimental testing, as well as the cost and time cost of related testing materials.

[0028] (2) The battery SOH analysis method provided by the present invention can better find the root cause of problems such as thermal runaway of power battery cells, and fundamentally solve the battery failure problem by taking appropriate measures.

[0029] (3) The battery SOH analysis method provided by the present invention further verifies the analysis results through FIB-SEM and is applicable to various applications.

[0030] (4) The battery SOH analysis method provided by the present invention can be combined with a data-driven model in subsequent research to improve the empirical model, thereby obtaining a more accurate and efficient empirical model for application in the design and optimization of battery fast charging strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 The data set provided by the present invention for simulation with a 2.05Ah NCM battery;

[0033] Figure 2 A curve diagram of capacity decay caused by SEI film growth and lithium deposition versus cycle number at different charge rates provided by the present invention;

[0034] Figure 3 A graph showing the capacity attenuation caused by SEI film growth and lithium deposition at different temperatures versus cycle number provided by the present invention;

[0035] Figure 4 Different fitting formula forms and their correlation coefficients R provided by the present invention 2 result;

[0036] Figure 5 The fitting coefficient results of the aging curves at different temperatures provided by the present invention;

[0037] Figure 6 The fitting coefficient results of the capacity decay curves at different charging rates provided by the present invention;

[0038] Figure 7 The semi-empirical model Q of SEI film growth aging considering multiple factors provided by the present invention SEI and the lithium deposition aging semi-empirical model Q Li ;

[0039] Figure 8 The continuous images of the FIB-SEM electrode tomographic scanning cross section provided by the present invention. DETAILED DESCRIPTION

[0040] The technical scheme of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments, but it will be understood by those skilled in the art that the following described embodiments are part of embodiments of the present invention, rather than all embodiments, and are only used to illustrate the present invention, and should not be considered as limiting the scope of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of the present invention. If specific conditions are not specified in the embodiments, they are carried out according to normal conditions or conditions recommended by the manufacturer. If the manufacturer is not specified in the reagents or instruments used, they are all conventional products that can be purchased commercially.

[0041] If there is no special explanation, in the present invention, "the first aspect", "the second aspect", "the third aspect", "the fourth aspect", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or quantity, nor can they be understood as implicitly indicating the importance or quantity of the indicated technical features. Moreover, "the first", "the second", "the third", "the fourth", etc. only serve the purpose of non-exhaustive enumeration and description, and it should be understood that they do not constitute a closed limitation on quantity.

[0042] If there is no special explanation, the "include" and "comprising" mentioned in the present invention represent open-ended or closed-ended expressions. For example, the "include" and "comprising" may represent that other components not listed may also be included or only the listed components may be included or only the listed components may be included.

[0043] If there is no special explanation, in the present invention, "one or more" or "at least one" refers to any one, any two or more of the listed items. Among them, "several" refers to any two or more of the listed items.

[0044] In a first aspect, the present invention provides a battery SOH analysis method, which comprises the following steps:

[0045] First, the dependence of capacity decay on cycle number under different conditions is established.

[0046] The conditions include at least one of the following: charging rate, temperature, SOC range, and ratio of negative electrode capacity to positive electrode capacity (N / P), for example, one, two, three or four.

[0047] The capacity decay includes the capacity decay caused by the growth of SEI film and the capacity decay caused by lithium deposition. Wherein, SEI film refers to the solid electrolyte interface film.

[0048] Then the dependence is optimized to make the regression function consistent with the simulated data, and the semi-empirical model Q of SEI film growth aging under different conditions is obtained. SEI and the lithium deposition aging semi-empirical model Q Li .

[0049] Then the Q under the same conditions SEI And the Q Li Add up and get the cycle life model Q total That is, after obtaining the calculation models of the aging process caused by SEI and lithium deposition, the overall aging process of the battery can be obtained by integrating them. total =Q SEI +Q Li .

[0050] For example, if the effect of charging rate is considered, the capacity decay caused by SEI film growth, the number of cycles and the charging rate are fitted and optimized. SEI Q after fitting and optimization based on capacity decay, cycle number and charge rate caused by lithium deposition Li Add up and get the cycle life model Q total .

[0051] If the temperature effect is considered, the capacity decay caused by SEI film growth, the number of cycles and the temperature are fitted and optimized. SEI Q after fitting and optimization based on capacity decay caused by lithium deposition, cycle number and temperature Li Add up and get the cycle life model Q total .

[0052] If the combined effects of charge rate, temperature, SOC range, and the ratio of negative electrode capacity to positive electrode capacity are considered, the capacity decay caused by SEI film growth, cycle number, charge rate, temperature, SOC range, and the ratio of negative electrode capacity to positive electrode capacity are fitted and optimized. SEI Q after fitting and optimization based on capacity decay caused by lithium deposition, number of cycles, charge rate, temperature, SOC range, and ratio of negative electrode capacity to positive electrode capacity Li Add up and get the cycle life model Q total .

[0053] The aging process of lithium-ion batteries can be divided into two stages dominated by the growth of the solid electrolyte interface film (SEI film) and the lithium deposition mechanism. In order to quickly and accurately predict the cycle life of lithium-ion batteries and quickly identify the turning points of the two stages of the aging process, the present invention constructs an empirical model for predicting the two-stage aging process and turning points by analyzing the linear aging dominated by SEI film growth and the accelerated aging dominated by lithium deposition, which can better find the real cause of problems such as thermal runaway of power battery cells, and fundamentally solve the problem of battery failure by prescribing the right medicine.

[0054] Specifically, according to the turning point (Knee point) of the capacity decay curve, the aging process of lithium-ion batteries can be divided into two stages: before the Knee point, the capacity decay curve of the battery is linear decay, and its dominant mechanism is SEI film growth; after the Knee point, the capacity decay curve of the battery is nonlinear decay, and its dominant mechanism is Li deposition. The number of cycles at the Knee point is determined by the relationship between the aging caused by SEI growth and lithium deposition in a unit cycle. Therefore, when considering the two dominant mechanisms of capacity decay, SEI growth and lithium deposition, in the above two stages, the total capacity decay Q of the battery in the two stages is total Capacity decay Q caused by SEI growth and lithium deposition SEI and Q Li Therefore, a two-stage semi-empirical aging model is established to predict the SEI aging and lithium deposition aging processes. The aging processes caused by SEI and Li deposition are fitted respectively.

[0055] In some specific implementations, before establishing the dependency of capacity decay on cycle number under different conditions, basic battery data and model data are first obtained.

[0056] As an example, the method of obtaining battery basic data and model data includes: establishing an electrochemical-thermal coupling model, simulating a 2.05Ah NCM battery, and the data set is as follows: Figure 1 As shown in Figure 2. C represents the charge rate, and N / P represents the ratio of the negative electrode capacity to the positive electrode capacity. Figure 1 Analyze the data and get Figure 2 and Figure 3 .in, Figure 2 The curves of capacity attenuation (i.e., capacity loss) caused by SEI film growth and lithium deposition versus cycle number at different charge rates; Figure 3 The curves of capacity attenuation (i.e., capacity loss) caused by SEI film growth and lithium deposition at different temperatures versus cycle number.

[0057] In some specific embodiments, the method for establishing the dependence of capacity decay on the number of cycles under different conditions includes: performing nonlinear fitting on at least one of the charge rate, temperature, SOC interval, and the ratio of the negative electrode capacity to the positive electrode capacity, the number of cycles, and the capacity decay to obtain a nonlinear fitting function. The independent variables include at least one of the charge rate, temperature, SOC interval, and the ratio of the negative electrode capacity to the positive electrode capacity, and the number of cycles; the dependent variable is the capacity decay.

[0058] The present invention obtains a battery aging trajectory data set of capacity decay caused by battery SEI growth and lithium deposition under different conditions through two-stage electrochemical model simulation calculation, and establishes an empirical model that can quickly and accurately predict the cycle aging process of battery monomers using a two-step nonlinear regression method. This method reduces the loss of personnel and samples caused by large sample experimental tests, as well as the cost and time cost of related testing materials, which is conducive to the promotion and application of this method.

[0059] In some specific implementations, in the process of obtaining the nonlinear fitting function, the best function model is selected by comparing and evaluating different functions. Figure 4 It can be seen that in order to make the overall error of the function smaller and the fitting parameters less, the exponential function class selects the power function with a fixed constant term as the fitting function of battery aging. Figure 4 In, R 2 Represents the degree of fit.

[0060] In some specific implementations, the method for optimizing the dependency relationship includes: optimizing the nonlinear fitting function using a nonlinear least squares method.

[0061] In some specific embodiments, the type of nonlinear fitting function includes a power function.

[0062] In some specific embodiments, the type of the nonlinear fitting function includes a power function with a constant term.

[0063] In some specific embodiments, establishing the dependence of capacity decay on the number of cycles under different conditions specifically includes: establishing the dependence of capacity decay caused by SEI film growth on the number of cycles under different conditions and the dependence of capacity decay caused by lithium deposition on the number of cycles under different conditions.

[0064] Among them, the method for establishing the dependence of capacity decay caused by SEI film growth on the number of cycles under different conditions includes: establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and charging rate, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and temperature, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and SOC range, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles and the ratio of negative electrode capacity to positive electrode capacity, or establishing the dependence of capacity decay caused by SEI film growth on the number of cycles, charging rate, temperature, SOC range, and the ratio of negative electrode capacity to positive electrode capacity.

[0065] The method for establishing the dependency of capacity decay caused by lithium deposition on the number of cycles under different conditions includes: establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and charging rate, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and temperature, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and SOC range, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and the ratio of negative electrode capacity to positive electrode capacity, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles, charging rate, temperature, SOC range, and the ratio of negative electrode capacity to positive electrode capacity.

[0066] Temperature has a significant impact on battery aging. The aging caused by SEI at different temperatures is as follows: Figure 3 As shown in the figure, taking the dependence of capacity decay caused by SEI film growth on cycle number and temperature as an example, the fitting coefficients of aging curves at different temperatures are as follows: Figure 5 As shown, Figure 5 a t and b t are the coefficients of the fitting function.

[0067] When considering the temperature effect, the SEI aging changes with the number of cycles and temperature, that is, the SEI film growth aging semi-empirical model Q SEI (n,t) can be expressed as:

[0068] Q SEI (n,t)=a t n bt -0.6.

[0069] at=0.00171e 0.1031t +0.0547e 0.0226t .

[0070] bt=-7.367×10 -6 ×t 2.548 +0.8188.

[0071] In the above formula, n is the number of cycles; t is the temperature, in °C.

[0072] Furthermore, by applying the above similar method, the dependence of capacity decay caused by SEI film growth on cycle number and charging rate was established, and the battery aging caused by SEI at different rates ( Figure 2 ), the fitting coefficient of the capacity decay curve considering the rate change is as follows Figure 6 As shown, Figure 6 a c and b c are the coefficients of the fitting function.

[0073] Similarly, when considering the effect of charge rate, the SEI-induced aging changes with the number of cycles and charge rate, that is, the SEI film growth aging semi-empirical model QSEI(n,t) can be expressed as:

[0074] Q SEI (n,C)=a c n bc -0.6.

[0075] a c =0.04e 0.9838C .

[0076] b c =-0.0306C 2.4712 +0.8338.

[0077] In the above formula, n is the number of cycles; C is the charge rate.

[0078] Similarly, we can calculate the average Capacity decay Q of battery cycle aging SEI (n, ), capacity decay Q caused by SEI with N / P variation SEI (n,N / P), in order to reduce the error of combining different factors to form a new function, the coefficients of the new function need to be corrected.

[0079] Q SEI (n,t,C, ,DOD,N / P) and Q Li (n,t,C, ,DOD,N / P) respectively represent the aging caused by SEI film growth and lithium deposition when considering multiple factors, such as Figure 7 The figure shows a two-stage battery aging empirical model, where a SEI and b Li are the coefficients of the corresponding fitting functions. Where DOD (Depth of discharge) represents the percentage of battery discharge to the rated capacity of the battery.

[0080] Using a similar method as above, the capacity decay Q caused by SEI considering the N / P change can be calculated: SEI (n,N / P) can be expressed as:

[0081] Q SEI (n,N / P)=a N / P n bN / P -0.6.

[0082] A N / P =0.0166exp(1.8146N / P).

[0083] BN / P =-2.2116(N / P) 0.0724 -3.0178.

[0084] In some specific embodiments, the analysis results based on the cycle life model are verified by experimental data.

[0085] In some specific embodiments, the analysis results based on the cycle life model are verified by focused ion beam scanning electron microscopy (FIB-SEM).

[0086] As a key structural component of lithium-ion batteries, the structural stability of electrode materials directly determines the electrochemical performance of lithium-ion batteries. Since electrode materials are sensitive to air and moisture, and are not resistant to electron beam irradiation, and the electrodes themselves and their chemical environment are constantly changing during the charge and discharge process, it is challenging to characterize their microstructure morphology and structure. As an important micro-nanoscale fine processing equipment, the focused ion beam-scanning electron microscope is an important means of preparing transmission samples and has been widely used in semiconductors, biology and other fields. Further factual verification through FIB-SEM analysis results is conducive to the large-scale promotion and use of the above method.

[0087] Specifically, the experimental data can be used for verification first, and the results of the cyclic aging calculation model and the traditional empirical model can be compared. Finally, the above analysis results can be further verified by FIB-SEM.

[0088] In some specific embodiments, the FIB-SEM tomography method includes: using an ion beam to continuously slice and image the sample to obtain a series of continuous images, such as Figure 8 As shown, the analysis results are verified by image analysis and comparison with experimental data.

[0089] In some specific embodiments, the SEI film growth aging semi-empirical model Q is improved by combining the data-driven model. SEI , the lithium deposition aging semi-empirical model Q Li and the cycle life model Q total At least one of .

[0090] Subsequent research based on the above-mentioned battery SOH analysis method can improve the empirical model in combination with the data-driven model to obtain a more accurate and efficient empirical model, which can be applied to the design and optimization of battery fast charging strategies.

[0091] In a second aspect, the present invention provides an application of the battery SOH analysis method in real-time detection or monitoring of lithium-ion batteries.

[0092] The present invention provides a battery SOH analysis method using an ion beam scanning electron microscope. By analyzing the linear aging dominated by SEI film growth and the accelerated aging dominated by lithium deposition, an empirical model for predicting the two-stage aging process and turning point is constructed, which can better find the real cause of problems such as thermal runaway of power battery cells, and fundamentally solve the battery failure problem by prescribing the right remedy. This is of great significance to the safety of battery applications.

[0093] It can also reduce the loss of personnel and samples caused by large sample experimental testing, as well as the cost and time cost of related testing materials, further making the above method scalable.

[0094] Although the present invention has been illustrated and described with specific embodiments, it should be appreciated that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Those skilled in the art should understand that the technical solutions described in the above embodiments may be modified, or some or all of the technical features thereof may be replaced by equivalents without departing from the spirit and scope of the present invention. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention. Therefore, this means that all such replacements and modifications within the scope of the present invention are included in the appended claims.

Claims

1. A battery SOH analysis method, characterized in that: The steps include: Establishing the dependence of capacity decay on the number of cycles under different conditions; wherein the conditions include at least one of the charge rate, temperature, SOC range, and the ratio of the negative electrode capacity to the positive electrode capacity; the capacity decay includes the capacity decay caused by SEI film growth and the capacity decay caused by lithium deposition; The dependence is optimized to obtain the SEI film growth aging semi-empirical model Q under different conditions. SEI and the lithium deposition aging semi-empirical model Q Li ; The Q under the same conditions SEI and the Q Li Add up and get the cycle life model Q total .

2. The battery SOH analysis method according to claim 1, characterized in that: The method for establishing the dependence of capacity decay on the number of cycles under different conditions includes: performing nonlinear fitting on the charging rate, temperature, SOC range, at least one of the ratio of the negative electrode capacity to the positive electrode capacity, the number of cycles and the capacity decay to obtain a nonlinear fitting function; wherein the independent variables include the charging rate, temperature, SOC range, at least one of the ratio of the negative electrode capacity to the positive electrode capacity, and the number of cycles; and the dependent variable is the capacity decay.

3. The battery SOH analysis method according to claim 2, characterized in that: The method for optimizing the dependency relationship includes: optimizing the nonlinear fitting function using a nonlinear least squares method.

4. The battery SOH analysis method according to claim 2, characterized in that: The type of the nonlinear fitting function includes a power function.

5. The battery SOH analysis method according to claim 4, characterized in that: The type of the nonlinear fitting function includes a power function with a constant term.

6. The battery SOH analysis method according to claim 1, characterized in that: The establishment of the dependence of capacity decay on the number of cycles under different conditions specifically includes: establishing the dependence of capacity decay caused by SEI film growth on the number of cycles under different conditions and the dependence of capacity decay caused by lithium deposition on the number of cycles under different conditions; Wherein, the method for establishing the dependence of the capacity decay caused by SEI film growth on the number of cycles under different conditions includes: establishing the dependence of the capacity decay caused by SEI film growth on the number of cycles and the charging rate, or establishing the dependence of the capacity decay caused by SEI film growth on the number of cycles and the temperature, or establishing the dependence of the capacity decay caused by SEI film growth on the number of cycles and the SOC interval, or establishing the dependence of the capacity decay caused by SEI film growth on the number of cycles and the ratio of the negative electrode capacity to the positive electrode capacity, or establishing the dependence of the capacity decay caused by SEI film growth on the number of cycles, the charging rate, the temperature, the SOC interval, and the ratio of the negative electrode capacity to the positive electrode capacity; The method for establishing the dependency of capacity decay caused by lithium deposition on the number of cycles under different conditions includes: establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and charging rate, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and temperature, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and SOC range, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles and the ratio of negative electrode capacity to positive electrode capacity, or establishing the dependency of capacity decay caused by lithium deposition on the number of cycles, charging rate, temperature, SOC range, and the ratio of negative electrode capacity to positive electrode capacity.

7. The battery SOH analysis method according to claim 1, characterized in that: The analysis results based on the cycle life model are verified by experimental data.

8. The battery SOH analysis method according to claim 7, characterized in that: The analysis results based on the cycle life model were verified by focused ion beam scanning electron microscopy.

9. The battery SOH analysis method according to claim 8, characterized in that: The sample is continuously sliced ​​and imaged using an ion beam to obtain a series of continuous images, and the analysis results are verified by image analysis and comparison with experimental data.

10. The battery SOH analysis method according to claim 1, characterized in that: Combined with data-driven model, the SEI film growth aging semi-empirical model Q is improved SEI , the lithium deposition aging semi-empirical model Q Li and the cycle life model Q total At least one of .

11. Application of the battery SOH analysis method according to any one of claims 1 to 10 in real-time detection or monitoring of lithium-ion batteries.

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