Battery cycle performance evaluation method and system, electronic equipment and storage medium

Abstract

The invention discloses a battery cycle performance evaluation method and system, electronic equipment and a storage medium, and belongs to the technical field of batteries. The evaluation method comprises the following steps: obtaining physicochemical index parameters of the cathode material after circulation, wherein the physicochemical index parameters comprise a particle size distribution change rate, a pH change absolute value, a total amount of residual alkali and a lattice distortion degree; processing the physicochemical index parameters based on a preset composite degradation index model to obtain a composite degradation index; and evaluating the performance degradation stage of the battery according to the composite degradation index. According to the method, the composite degradation index model based on multi-dimensional physical and chemical index linkage of the positive electrode material is established, and the indexes are coupled into a unified composite degradation index through nonlinear transformation and normalization processing, so that a dominant degradation mechanism is dynamically captured in different cycle stages, the accuracy and reliability of evaluation are remarkably improved, and the evaluation efficiency is improved. And an effective technical means is provided for rapid screening and life prediction of battery materials.

Description

Technical Field

[0001] This invention belongs to the field of battery technology, specifically relating to a battery cycle performance evaluation method and system, electronic equipment, and storage medium. Background Technology

[0002] Lithium-ion batteries have been widely used in consumer electronics, electric vehicles, and large-scale energy storage systems due to their high energy density and long cycle life. Performance degradation during battery cycling is a key factor affecting its lifespan and reliability; therefore, accurate and rapid assessment of battery state of health (SOH) is crucial.

[0003] Currently, the industry primarily relies on electrochemical testing methods to assess the state of harmonic decay (SOH) of batteries, especially the degradation of cathode materials. The most commonly used methods include: 1. Capacity decay method: This assesses SOH by monitoring the capacity retention rate of the battery during continuous charge-discharge cycles. However, this method requires complete long-term cycle testing, often taking weeks or even months, making it inefficient and unable to meet the needs of rapid material screening and evaluation in R&D and production. 2. Electrochemical impedance spectroscopy (EIS) analysis: This infers the internal state of the battery by measuring changes in its impedance. However, EIS mainly reflects changes at the battery interface (such as the SEI film) and is insufficiently sensitive to structural degradation of the bulk cathode material, such as particle breakage and lattice distortion, making it difficult to reveal the intrinsic failure mechanism of the material.

[0004] Furthermore, to improve evaluation efficiency, some studies have attempted to use a single physicochemical parameter of the cathode material as a degradation indicator, such as using only the change in the median particle size (D50) or particle size distribution width (Dspan) to predict capacity decay. However, battery degradation is a complex process involving multiple coupled factors. A single parameter cannot comprehensively reflect the coordinated degradation behavior of the material in multiple dimensions such as physical structure, chemical composition, and crystal structure, resulting in poor universality of prediction models, large errors, and unreliable prediction results.

[0005] Therefore, there is an urgent need in this field for a battery cycle performance evaluation method to improve the reliability of battery state assessment and accelerate the research and development process of battery materials.

[0006] It should be noted that this part of the present invention only provides background technology related to the present invention, and does not necessarily constitute prior art or known technology. Summary of the Invention

[0007] This invention provides a battery cycle performance evaluation method and system, electronic device, and storage medium, which at least solves the problems of large prediction model error and low prediction accuracy in the prior art.

[0008] To achieve the above objectives, in a first aspect, the present invention provides a method for evaluating battery cycle performance, comprising the following steps: S100. Obtain the physicochemical parameters of the cathode material after cycling. The physicochemical parameters include the particle size distribution change rate, the absolute value of pH change, the total amount of residual alkali, and the degree of lattice distortion. S200. Based on the preset composite degradation index model, the physicochemical index parameters are processed to obtain the composite degradation index. S300: Assess the battery's performance degradation stage based on the composite degradation index.

[0009] Preferably, step S200 specifically includes: S202. The particle size distribution change rate is subjected to a first normalization process, and then a first nonlinear transformation is performed to obtain the first characteristic term. S204. Based on the first coefficient, the absolute value of pH change is processed to obtain the second characteristic term; S206. The total residual alkali is subjected to a second normalization process to obtain the third characteristic term; S208. Based on the second coefficient, the lattice distortion degree is processed to obtain the fourth characteristic term; S210. Based on the first characteristic term, the second characteristic term, the third characteristic term, and the fourth characteristic term, the composite degradation index is calculated.

[0010] Preferably, step S204 further includes processing the absolute value of pH change based on the first coefficient and the third coefficient to obtain a second characteristic term.

[0011] Preferably, step S208 further includes processing the lattice distortion degree based on the second coefficient and the fourth coefficient to obtain the fourth characteristic term.

[0012] Preferably, step S300 specifically includes: When the composite degradation index is less than the first preset threshold, the battery is determined to be in a healthy state. When the composite degradation index is not less than the second preset threshold, the battery is determined to be in a damaged state. When the composite degradation index is between the first preset threshold and the second preset threshold, the battery is determined to be in a sub-healthy state. The first preset threshold is less than the second preset threshold.

[0013] Preferably, the battery cycle performance evaluation method further includes step S400: evaluating the battery's health status value based on the composite degradation index.

[0014] Preferably, step S400 specifically includes: performing a second nonlinear transformation on the fifth coefficient and the composite degradation index, and then performing a third normalization process to obtain the battery's health status value.

[0015] In a second aspect, the present invention provides a battery cycle performance evaluation system for implementing the above method, comprising: The data acquisition module is used to acquire the physicochemical parameters of the cathode material after cycling. The processing and calculation module is used to process the physicochemical index parameters based on the preset composite degradation index model to obtain the composite degradation index. The evaluation output module is used to assess the stage of battery performance degradation based on the composite degradation index.

[0016] Thirdly, the present invention provides an electronic device, comprising: Processor; and The memory stores executable code, which, when executed by the processor, causes the processor to perform the methods described above.

[0017] Fourthly, the present invention provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the above-described method.

[0018] This invention achieves rapid and accurate evaluation of battery cycle performance by establishing a composite degradation index model based on the linkage of multiple physicochemical indicators of cathode materials. This method comprehensively considers the synergistic degradation behavior of cathode materials across multiple dimensions, including physical structure (particle size distribution change rate), chemical composition (absolute pH change, total residual alkali), and crystal structure (lattice distortion). It overcomes the limitations of traditional electrochemical methods, such as long cycles and inability to reveal intrinsic degradation mechanisms, as well as the large prediction errors and poor universality of single-parameter models. Through nonlinear transformation and normalization, the various indicators are coupled into a unified composite degradation index, thereby dynamically capturing the dominant degradation mechanism at different cycling stages. This significantly improves the accuracy and reliability of the evaluation, allowing for rapid assessment of material cycle performance after only 100, 200, or 300 cycles, without waiting for the end of the cycle. This provides an effective technical means for rapid screening and lifetime prediction of battery materials. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a battery cycle performance evaluation method provided in an embodiment of the present invention; Figure 2This is a structural block diagram of the battery cycle performance evaluation system provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention.

[0021] Explanation of reference numerals in the attached figures: 100. Battery cycle performance evaluation system; 101. Data acquisition module; 102. Processing and calculation module; 103. Evaluation output module; 200. Electronic device; 201. Memory; 202. Processor. Detailed Implementation

[0022] In this invention, unless otherwise stated, directional terms such as "up," "down," "left," and "right" are generally understood in conjunction with the accompanying drawings and the directions shown in actual applications.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0024] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0025] The endpoints and any values ​​of the ranges disclosed herein are not limited to the precise ranges or values, and should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of the various ranges, the endpoint values ​​of the various ranges and individual point values, and individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed herein. The terms "optional" and "discretionary" mean that they may or may not be included (or may or may not be present).

[0026] like Figure 1 As shown, the present invention provides a method for evaluating battery cycle performance, comprising the following steps: S100. Obtain the physicochemical parameters of the cathode material after cycling. The physicochemical parameters include the particle size distribution change rate, the absolute value of pH change, the total amount of residual alkali, and the degree of lattice distortion. S200. Based on the preset composite degradation index model, the physicochemical index parameters are processed to obtain the composite degradation index. S300: Assess the battery's performance degradation stage based on the composite degradation index.

[0027] During the cycling process of lithium-ion batteries, the performance degradation of the cathode material is one of the key factors affecting the overall battery life. To accurately and efficiently assess the State of Health (SOH) of the battery, this invention proposes an assessment method based on the linkage of multiple physicochemical indicators. The core indicators selected include particle size distribution change rate, absolute pH change, total residual alkali, and lattice distortion. These parameters reflect the degradation behavior of the cathode material during cycling from three dimensions: physical structure, chemical composition, and crystal structure, respectively, and have clear physical significance and strong indicative power.

[0028] The particle size distribution change rate refers to the relative change rate of the particle size distribution width (i.e., Dspan = (D90 - D10) / D50) of the cathode material before and after cycling. Its calculation formula is as follows: ΔDspan=[(Dspan 循环后 -Dspan 初始 ) / Dspan 初始 ]×100%.

[0029] This parameter reflects the changes in particle size distribution caused by breakage, crack propagation, or agglomeration of cathode particles during cycling. As cycling progresses, particle breakage increases the tortuosity of the ion diffusion path, exacerbates polarization, and thus accelerates capacity decay. Therefore, ΔDspan is an important indicator for evaluating the physical structural stability of cathode materials.

[0030] The absolute value of pH change refers to the absolute value of the pH change in the leachate of the cathode material before and after circulation. Its calculation formula is as follows: ΔpH=|pH 循环后 -pH 初始 |

[0031] This parameter is primarily used to characterize changes in the chemical environment of the material surface, especially the accumulation of acidic substances. During cycling, acidic substances such as HF produced by electrolyte decomposition corrode the cathode material, triggering side reactions such as transition metal dissolution and surface reconstruction, which in turn lead to structural damage and capacity loss. Therefore, ΔpH becomes a key indicator reflecting the chemical stability of the cathode interface.

[0032] Total residual alkali refers to the total mass percentage of alkaline substances (mainly LiOH and Li₂CO₃) remaining in the cathode material, calculated as 100% of the cathode material's mass. The presence of residual alkali promotes electrolyte decomposition, consumes active lithium sources, and may cause problems such as gas evolution and impedance increase, severely affecting the battery's cycle life and safety. This parameter directly reflects the quality of the cathode material's manufacturing process and the degree of side reactions during cycling.

[0033] Lattice distortion refers to the change in the ratio of the c-axis to the a-axis (c / a) in the crystal structure of the cathode material before and after cycling, as calculated by X-ray diffraction (XRD). The formula for its calculation is: Δ(c / a)=|(c / a) 循环后 -(c / a) 初始 |

[0034] This parameter reflects the lattice distortion and cation mixing phenomena that occur in layered cathode materials during lithium-ion intercalation / deintercalation. Abnormal changes in the c / a value often indicate decreased structural stability and increased risk of phase transition, and are an important basis for evaluating the integrity of the cathode crystal structure.

[0035] The reason for selecting the above four parameters in this invention is that they comprehensively capture the multi-mechanism synergistic degradation behavior of cathode materials during cycling from three dimensions: physical, chemical, and structural. Traditional methods often rely on a single parameter (such as D50 or Dspan alone) or macroscopic electrical performance testing, which cannot reveal the intrinsic degradation mechanism of materials, resulting in large prediction errors and poor universality. However, by establishing a quantitative coupling relationship between these four parameters and constructing a composite degradation index model, a rapid and accurate assessment of the battery's state of equilibrium (SOH) can be achieved, significantly improving the efficiency of material development and the reliability of state assessment.

[0036] In addition, these four parameters are characterized by strong measurability and good repeatability, and are applicable to lithium-ion batteries of different systems, such as ternary lithium batteries (NCM, NCA), lithium iron phosphate batteries (LFP), and lithium cobalt oxide batteries (LCO). They can be promoted and applied across material platforms simply by adjusting the appropriate coefficients, and have strong engineering applicability and industrialization prospects.

[0037] Specifically, the above parameters can be measured using mature, standardized large-scale analytical instruments and testing methods. Particle size distribution can be obtained using a laser particle size analyzer. For example, this can include peeling the cathode material from the current collector and dispersing it appropriately, typically using wet dispersion with an organic solvent (such as NMP) or deionized water as the dispersion medium. The particles are ultrasonically treated for 3-5 minutes to ensure sufficient dispersion and prevent agglomeration. The measurement is repeated three times and the average value is taken.

[0038] pH measurement employs a standard pH meter and leachate preparation process; for example, it may include mixing a certain mass (e.g., 1.0 g) of positive electrode material with 10 mL of deionized water, shaking for 30 minutes, allowing it to stand, taking the supernatant to measure the pH value, and repeating this process 3 times to obtain the average value.

[0039] The total residual alkali can be quantitatively analyzed by acid-base titration or automatic potentiometric titration. For example, it may include dissolving the positive electrode material in an appropriate amount of deionized water, titrating it with a 0.01 mol / L HCl standard solution, determining the endpoint using an automatic potentiometric titrator, or performing manual titration using phenolphthalein and methyl orange dual indicators to determine the LiOH and Li2CO3 contents respectively and summing them to obtain the total residual alkali.

[0040] The lattice distortion is based on X-ray diffraction (XRD) technology and is obtained through Rietveld refinement calculation. For example, the test conditions can be: CuKα radiation, scanning range 10°~80°, step size 0.02°, scanning speed 2° / min, and refinement using software such as TOPAS, to obtain lattice parameters a and c by fitting, and to calculate the c / a value and its change Δ(c / a).

[0041] These testing methods are widely used in materials research and the battery industry. The testing process is standardized and the data has high repeatability and reproducibility. This invention does not impose any special limitations.

[0042] Preferably, step S200 specifically includes: S202. The particle size distribution change rate is subjected to a first normalization process, and then a first nonlinear transformation is performed to obtain the first characteristic term. Specifically, the formula for calculating the first characteristic term can be: N1=(ΔDspan / α1)^β; Where N1 is the first characteristic term, ΔDspan is the particle size distribution change rate, α1 is the first reference value in the first normalization process, the first nonlinear transformation is a power function transformation, and β is the exponent in the power function.

[0043] The purpose of normalization is to eliminate dimensional differences and unify indicators with different physical meanings into a comparable order of magnitude. ΔDspan, as a percentage change rate, is significantly affected by factors such as material type and cycling conditions. By dividing by a benchmark value α1 (e.g., 15% commonly used in NCM materials), ΔDspan can be converted into a dimensionless proportional value, facilitating weighted combination with other indicators (such as pH change, residual alkali content, etc.) to form a unified composite degradation index η.

[0044] The first benchmark value α1 represents the performance inflection point threshold of ΔDspan for different cathode materials. It is a material-dependent threshold parameter, and its value is derived through statistical analysis of 120 sets of battery cycle test data. For example, for NCM series cathode materials, when ΔDspan exceeds 15%, the battery capacity decay rate accelerates significantly; therefore, α1 can be set to 15. This value reflects the critical point of physical structure degradation in the material system, possessing a certain degree of universality and adjustability, and is suitable for adaptation to different material systems (such as LFP, LCO, etc.).

[0045] Although the normalized value (ΔDspan / α1) becomes a dimensionless relative value, the relationship remains linear. However, the degradation behavior of battery materials is often nonlinear. Especially in physical degradation mechanisms such as particle breakage and crack propagation, their impact on battery performance exhibits significant nonlinear characteristics. For example, when ΔDspan exceeds a certain critical value (e.g., 15%), its blocking effect on ion diffusion paths increases dramatically, thereby accelerating capacity decay. Therefore, preferably, using a power function transformation (i.e., raising it to the power of β) can better capture this nonlinear degradation behavior, allowing the model to assign higher weights when ΔDspan is large, thus more accurately reflecting its contribution to overall degradation.

[0046] The exponent β in the power function is the nonlinear blocking effect exponent. The value of β is obtained by nonlinear regression analysis of historical data (such as more than 120 sets of battery cycle data). Specifically, it can be determined through the following experimental and fitting process: Select a certain number of batteries of the same type and conduct cycle tests under the same charge and discharge conditions. Measure the particle size distribution change ΔDspan of 120 sets of cathode materials at different cycle numbers, and simultaneously record battery performance indicators (such as capacity retention rate); organize ΔDspan with the corresponding performance degradation (such as using the relative value of capacity degradation as a proxy index of N1), take the logarithm of both sides of the relationship N1=(ΔDspan / α1)^β to obtain the linear relationship lnN1=β·ln(ΔDspan / α1), and then use the least squares method and other fitting methods to perform linear regression on lnN1 and ln(ΔDspan / α1). The resulting slope is the value of β, ultimately establishing the optimal correlation between N1 and actual capacity degradation. For example, if β=1.5, it indicates that the effect of ΔDspan on capacity is a 1.5-power relationship, which is much stronger than a linear relationship. This is consistent with the physical mechanism that the "blocking effect" of particle breakage on ion diffusion paths gradually increases.

[0047] S204. Based on the first coefficient, the absolute value of pH change is processed to obtain the second characteristic term; Specifically, the formula for calculating the second characteristic term can be: N2 = k1 × ΔpH; Where N2 is the second characteristic term, k1 is the first coefficient, and ΔpH is the absolute value of pH change.

[0048] The absolute value of pH change, ΔpH, is a key indicator reflecting changes in the chemical environment of the cathode material surface. During battery cycling, acidic substances (such as HF) produced by electrolyte decomposition corrode the cathode material, triggering side reactions such as transition metal dissolution and surface reconstruction, which in turn lead to material structural damage and capacity decay. The larger the absolute value of ΔpH, the stronger the acidity of the material surface, the more severe the side reactions, and the more significant the negative impact on battery life.

[0049] However, ΔpH, as a physical quantity, differs in dimensions and orders of magnitude from other indicators (such as particle size change rate and residual alkali content). Directly using it in model calculations could lead to an imbalance in the weights of certain indicators, affecting the model's accuracy and universality. Therefore, it is necessary to scale ΔpH using the coefficient k1 to transform it into a dimensionless or equivalent value comparable to other characteristic terms, thus enabling it to participate in the weighted summation of the composite degradation index η.

[0050] Furthermore, the introduction of k1 reflects the "sensitivity" of pH changes to battery performance. Experiments show that a one-unit change in pH may correspond to a certain proportion of capacity loss (e.g., 2.3%). The value of k1 is chosen to quantify this relationship, so that the second characteristic term N2 can accurately reflect the contribution of ΔpH to the overall degradation.

[0051] The first coefficient, k1, is the pH change sensitivity coefficient, a material-dependent empirical parameter obtained through regression analysis of a large amount of historical battery cycling data. Specifically, multiple sets of cathode material ΔpH values ​​and actual capacity decay data at different cycle numbers are collected to establish a statistical relationship between ΔpH and capacity loss rate. The optimal k1 value is then fitted using the least squares method or other optimization algorithms to maximize the correlation between N2 and actual degradation behavior.

[0052] For example, analysis of over 120 sets of battery data for NCM cathode materials revealed that each unit change in ΔpH results in an average capacity loss of approximately 2.3%. Therefore, k1 can be set to 2.3, making N2 = 2.3 × ΔpH, thus reasonably reflecting the contribution of pH change in the composite degradation index.

[0053] S206. The total residual alkali is subjected to a second normalization process to obtain the third characteristic term; Specifically, the formula for calculating the third characteristic term can be: N3=R alkali / α2 Where N3 is the third characteristic term, R alkaliα2 represents the total residual alkali, and α2 is the second baseline value in the second normalization treatment.

[0054] The total residual alkali refers to the mass percentage of alkaline substances (mainly LiOH and Li2CO3) remaining in the cathode material. Its value directly reflects the degree of side reactions during the material's preparation or cycling process. However, the baseline value of the total residual alkali and its impact on performance vary significantly among different material systems and under different process conditions. If the original residual alkali content is directly used in model calculations, its dimensions and numerical range will be inconsistent with other indicators (such as ΔDspan, ΔpH, etc.), leading to an imbalance in model weights and reducing the accuracy and universality of predictions.

[0055] Therefore, by performing a second normalization process, dividing the total residual alkali by its typical benchmark value α2 in a specific material system yields a relative proportion value N3=R. alkali / α2. This treatment not only eliminates dimensional differences but also transforms the residual alkali content into a multiple relationship reflecting its relationship relative to normal or critical levels, thus more intuitively demonstrating its impact on battery performance. For example, if N3 > 1, it indicates that the residual alkali content has exceeded the tolerance threshold of the material system, which may lead to significant performance degradation.

[0056] The second benchmark value, α2, represents the critical residual alkali content, which is determined based on statistical analysis of a large amount of experimental data and the characteristics of the material system. Typically, α2 represents the typical residual alkali content of the material in its fresh state or at a certain cycle stage, or the critical value at which performance significantly declines. For example, for high-nickel ternary cathode materials (such as NCM811), regression analysis of over 120 sets of battery data revealed that when the total residual alkali exceeds 0.8 wt%, the battery capacity decay rate accelerates significantly, and interfacial side reactions intensify. Therefore, α2 can be set to 0.8, allowing N3 to numerically and intuitively reflect whether the residual alkali content exceeds the safe range.

[0057] S208. Based on the second coefficient, the lattice distortion degree is processed to obtain the fourth characteristic term; Specifically, the formula for calculating the fourth characteristic term can be: N4 = k2 × Δ(c / a) Where N4 is the fourth characteristic term, k2 is the second coefficient, and Δ(c / a) is the degree of lattice distortion.

[0058] In step S208, the lattice distortion Δ(c / a) is processed to obtain the fourth characteristic term N4. This is achieved by introducing a second coefficient k2 for linear scaling, and the calculation formula is N4 = k2 × Δ(c / a). The reason for this processing method is that the lattice distortion Δ(c / a), as a key indicator reflecting the stability of the crystal structure of the cathode material, has a small numerical range (usually in the thousands to hundreds of percent range). If it is directly weighted and combined with other characteristic terms (such as ΔDspan, ΔpH, etc.), its contribution to the composite degradation index will be severely underestimated due to its small value, and it cannot truly reflect its actual impact on battery performance degradation.

[0059] The second coefficient, k2, is the lattice distortion amplification factor, essentially a sensitivity amplification mechanism. Its purpose is to convert the microstructural change Δ(c / a) into a dimensionless or standardized value comparable to other characteristic terms, thus ensuring that lattice distortion can reasonably and evenly reflect its physical meaning and degradation contribution in the composite degradation index model. The value of k2 is not arbitrarily set but obtained through regression analysis of a large amount of historical battery cycling data. Specifically, multiple sets of Δ(c / a) values ​​and actual capacity decay data of the cathode material at different cycle numbers are collected, a statistical relationship between the two is established, and the optimal k2 value is fitted using the least squares method or other optimization algorithms, resulting in the strongest correlation between N4 and actual capacity decay.

[0060] For example, for high-nickel ternary cathode materials (such as LiNi) 0.8 Co 0.1 Mn 0.1 Analysis of over 120 sets of battery data revealed that a 0.01 unit increase in lattice distortion Δ(c / a) results in an average capacity loss of approximately 0.5%. To reasonably reflect this effect in the composite degradation index, k2 can be set to 50, making N4 = 50 × Δ(c / a). This means that when Δ(c / a) is 0.02, N4 contributes 1.0, which is on a similar order of magnitude to other characteristic terms (such as ΔDspan / 15), thus ensuring that the model can accurately capture the impact of crystal structure degradation on overall performance.

[0061] S210. Based on the first characteristic term, the second characteristic term, the third characteristic term, and the fourth characteristic term, the composite degradation index is calculated.

[0062] Specifically, η = N1 + N2 + N3 + N4 Where η is the composite degradation index.

[0063] Battery performance degradation during cycling is a complex process involving multiple factors and mechanisms, encompassing changes in physical structure, chemical composition, and crystal structure. These changes do not occur in isolation but rather interact and collectively affect the overall battery performance. For example, particle breakage (physical degradation) may exacerbate interfacial side reactions (chemical degradation), which in turn may further induce lattice distortion (structural degradation). Therefore, relying solely on a single indicator or a simple linear combination is insufficient to accurately capture the essence of this multi-mechanism synergistic degradation.

[0064] This invention quantifies the contribution of different degradation mechanisms by constructing four characteristic terms. The first characteristic term, N1, reflects the nonlinear blocking effect of particle fragmentation on ion diffusion paths; the second characteristic term, N2, reflects the influence of changes in the surface chemical environment (such as the accumulation of acidic substances) on material stability; the third characteristic term, N3, characterizes the promoting effect of residual alkali accumulation on interfacial side reactions; and the fourth characteristic term, N4, captures the negative impact of crystal structure distortion on electrochemical performance. Each of these four characteristic terms has a clear physical meaning and, through normalization, nonlinear transformation, and coefficient scaling, has been converted into dimensionless or standardized values, exhibiting additivity.

[0065] Adding these four characteristics directly to form the composite degradation index η is essentially a manifestation of a multi-mechanism linear superposition model. This model assumes that the effects of each degradation mechanism on overall performance are, to a certain extent, independent and additive. Although there may be some interaction in actual degradation processes, regression analysis of a large amount of historical data (such as over 120 sets of battery cycle data) shows that the linear superposition model can achieve high accuracy (R0). 2 The actual capacity decay behavior was fitted with a value of 0.985, proving that the hypothesis is reasonable and effective in engineering applications.

[0066] Furthermore, the additive model has advantages such as simple structure, high computational efficiency, and strong interpretability, making it easy to promote and apply in practical engineering. By adapting the coefficients to different material systems (such as NCM, LFP, LCO, etc.) (e.g., adjusting α1, k1, α2, k2, etc.), the model can be further extended to achieve accurate evaluation of the cycle performance of various cathode materials.

[0067] Furthermore, although the four characteristic terms (N1, N2, N3, and N4) in the composite degradation index model affect the overall degradation throughout the battery life cycle, their dominance varies significantly across different cycling stages, exhibiting a clear "stage sensitivity." In the early stages of cycling, due to the relatively intact particle structure, chemical side reactions (such as electrolyte decomposition and acid production) become the dominant degradation mechanism, thus the pH change term (N2) contributes the most significantly. As cycling progresses to the middle stage, particle breakage and crack propagation begin, and physical structure changes gradually become dominant, increasing the contribution of the particle size distribution change term (N1). In the later stages of cycling, accumulated lattice distortion and cation mixing intensify, making the crystal structure degradation term (N4) the most prominent. This stage sensitivity is captured thanks to the different mathematical processing methods used for each characteristic term in the model (such as nonlinear transformation of ΔDspan and sensitivity amplification of Δ(c / a), which dynamically reflects the dominant degradation mechanism at different cycling stages, further improving the accuracy of the assessment.

[0068] Preferably, step S204 further includes processing the absolute value of pH change based on the first coefficient and the third coefficient to obtain a second characteristic term.

[0069] Specifically, the formula for calculating the second characteristic term can be: N2 = k3 × k1 × ΔpH; Where N2 is the second characteristic term, k3 is the third coefficient, k1 is the first coefficient, and ΔpH is the absolute value of pH change.

[0070] The first coefficient, k1, is introduced primarily to quantify the sensitivity of battery performance to pH changes. In practical applications, different material systems (such as NCM, LFP, LCO, etc.) or cathode materials under different process conditions may exhibit varying degrees of sensitivity to pH changes. To further improve the model's adaptability and accuracy, a third coefficient, k3, is introduced. k3 is the pH change weight adjustment coefficient, used to further fine-tune the weight of the pH change term in the composite degradation index based on the baseline sensitivity determined by k1. The value of k3 also relies on statistical analysis of a large amount of experimental data, aiming to optimize the model's predictive performance under different material systems according to their characteristics. For example, for material systems that are particularly sensitive to pH changes (such as LCO batteries), k3 can be greater than 1 (e.g., 1.2) to amplify the contribution of pH changes; while for materials that are relatively insensitive to pH changes (such as lithium iron phosphate), k3 can be less than 1 (e.g., 0.8) to appropriately reduce its weight.

[0071] Preferably, step S208 further includes processing the lattice distortion degree based on the second coefficient and the fourth coefficient to obtain the fourth characteristic term.

[0072] Specifically, the formula for calculating the fourth characteristic term can be: N4 = k4 × k2 × Δ(c / a) Where N4 is the fourth characteristic term, k4 is the fourth coefficient, k2 is the second coefficient, and Δ(c / a) is the degree of lattice distortion.

[0073] The introduction of the second coefficient k2 is a sensitivity amplification mechanism, aiming to convert microscopic lattice distortion into a standardized value comparable to other characteristic terms. However, different material systems (such as NCM, LFP, LCO, etc.) exhibit varying degrees of sensitivity to lattice distortion. To further improve the model's cross-material universality and prediction accuracy, a fourth coefficient k4 is introduced. k4 is a lattice distortion weight adjustment coefficient, a material-dependent adjustment coefficient used to further fine-tune the weight of the lattice distortion term in the composite degradation index based on the baseline sensitivity determined by k2. The value of k4 also depends on the statistical analysis of a large amount of experimental data, aiming to optimize the model's predictive performance in different material systems based on their crystal structure characteristics.

[0074] For example, for lithium iron phosphate (LFP) materials, its olivine structure is relatively stable and less sensitive to lattice distortion. Therefore, k4 can be set to a value less than 1 (such as 0.7) to appropriately reduce its weight and avoid overestimating the impact of lattice distortion on the overall degradation.

[0075] The determination of the coefficients (e.g., k1, k2), benchmark values ​​(e.g., α1, α2), and exponents (e.g., β) is based on statistical analysis of the physicochemical properties of battery cathode materials under no less than 120 different cycling conditions and measured SOH data. Linear or nonlinear regression fitting using the least squares method is employed to ensure the model's prediction accuracy (R²). 2 ≥0.985).

[0076] For different cathode material systems, the coefficients can be adjusted within a certain range based on their characteristics, and the specific values ​​are determined by data fitting under that material system.

[0077] Based on the disclosed model structure, parameter meanings, and acquisition methods, those skilled in the art can determine the coefficient values ​​applicable to a specific battery system through fitting conventional experimental data without creative effort, thereby achieving the technical effects of the present invention.

[0078] Preferably, step S300 specifically includes: When the composite degradation index is less than the first preset threshold, the battery is determined to be in a healthy state. When the composite degradation index is not less than the second preset threshold, the battery is determined to be in a damaged state. When the composite degradation index is between the first preset threshold and the second preset threshold, the battery is determined to be in a sub-healthy state. The first preset threshold is less than the second preset threshold.

[0079] In step S300, the battery performance degradation stage is determined based on the composite degradation index η. The core of this method is to compare the calculated η value with a preset threshold, thereby classifying the battery state into three distinct stages: "healthy," "sub-healthy," and "damaged." This classification method is based on the fact that the composite degradation index η is a quantitative indicator calculated through the coupling of multiple physicochemical indicators, and its value is highly correlated with the actual capacity decay and the degree of degradation of internal materials (R0). 2 =0.985). As the cycle progresses, the value of η increases monotonically. Its value comprehensively reflects the cumulative damage of the cathode material in three dimensions: physical structure, chemical composition and crystal structure. Therefore, it can be used as a reliable proxy variable for the overall state of health (SOH) of the battery.

[0080] "Healthy state" refers to a battery that retains most of its initial capacity, with minimal degradation of internal materials, and all key physicochemical indicators remaining within their safety thresholds. The battery is usable and has a long expected lifespan. "Sub-healthy state" indicates that the battery has entered an observable degradation phase. Some key indicators (such as ΔDspan or ΔpH) may be approaching or slightly exceeding critical thresholds. Battery capacity shows significant decline, and while performance has decreased, it can still be used under certain conditions or its performance may be partially restored through repair. "Damaged state" signifies severe degradation of the battery. Irreversible structural damage to internal materials may have occurred (such as extensive particle breakage, severe lattice distortion, and large accumulation of interfacial by-reaction products). Capacity drops sharply, posing significant safety and reliability risks, and the battery should be discarded or recycled.

[0081] The determination of the first and second preset thresholds is not arbitrary, but rather based on a large amount of systematic battery cycle test data and failure analysis, derived through statistical regression and cluster analysis to obtain key critical values. Specifically, hundreds of sets of η values ​​and their corresponding actual capacity retention rates (SOH) for batteries with different cycle numbers and different material systems were collected, and η-SOH scatter plots were plotted and curve fitting was performed. Based on this, the relationship between the distribution of η values ​​and the actual failure modes of the batteries was analyzed to find the inflection point of performance mutation. For example, through the analysis of more than 120 sets of NCM battery data, it was found that when η < 1.0, the battery capacity decays slowly and there are no obvious material failure characteristics. Therefore, the first preset threshold was set to 1.0 as the upper limit of the "healthy" state. When η ≥ 2.5, a large number of particle cracks and phase transition phenomena can be observed by electron microscopy. Therefore, the second preset threshold was set to 2.5 as the starting point of the "damaged" state. When 1.0 ≤ η < 2.5, the battery is in the accelerated degradation period, corresponding to the "sub-healthy" state.

[0082] Preferably, the battery cycle performance evaluation method further includes step S400: evaluating the battery's health status value based on the composite degradation index.

[0083] Preferably, step S400 specifically includes: performing a second nonlinear transformation on the fifth coefficient and the composite degradation index, and then performing a third normalization process to obtain the battery's health status value.

[0084] Specifically, the formula for calculating the battery's state of health value can be: SOH = α3 × exp (C × η) Where SOH is the battery's state of health value, α3 is the third reference value in the third normalization process, C is the decay rate constant, and exp is the power term of the natural constant e.

[0085] In battery cycle performance evaluation, the composite degradation index η, as a quantitative indicator comprehensively reflecting the multi-dimensional degradation behavior of cathode materials, can be used to preliminarily determine the stage of battery performance degradation (such as healthy, sub-healthy, and damaged). However, it is a unitless relative value and does not directly correspond to the battery's actual capacity retention rate (State of Health, SOH). SOH is a widely accepted key indicator in the industry for quantifying the remaining capacity of a battery, defined as the percentage of current capacity to initial capacity. Therefore, to convert the composite degradation index η into a more engineering-meaning SOH value, a quantitative mapping relationship between η and SOH needs to be established. Step S400 is designed to achieve this goal.

[0086] Step S400 converts the composite degradation exponent η into the battery's state of health (SOH) value by introducing a second nonlinear transformation and a third normalization process. The core of this step lies in using a mathematical model to capture the nonlinear decay law between η and SOH. Battery capacity decay during cycling often follows an exponential decay law, i.e., slow initial decay followed by accelerated decay in the middle and later stages. This phenomenon is highly consistent with the cumulative damage behavior of cathode materials under the synergistic effect of multiple mechanisms. Therefore, choosing an exponential function as the form of the second nonlinear transformation has a solid physical basis and data support.

[0087] The third baseline value, α3, serves as the SOH calibration baseline, calibrating the model output to a reasonable SOH dimension (percentage). Typically, in the initial state (η=0), the SOH should be close to 100%. Therefore, the theoretical value of α3 should be 100. However, in practical applications, due to measurement errors, model approximations, and other factors, the initial SOH may be slightly higher or lower than 100%. Through statistical analysis of a large amount of fresh battery data, α3 can be fine-tuned to optimize the model's prediction accuracy in the initial stage. For example, if regression analysis shows that the average initial SOH is 102.3%, α3 can be set to 102.3, making the model more closely reflect actual data.

[0088] The degradation rate constant C is a negative parameter, which physically represents the rate of SOH degradation caused by a unit change in the composite degradation exponent. The value of C is obtained through nonlinear regression analysis of historical battery cycle data. Specifically, multiple sets of η values ​​at different cycle numbers and actual measured SOH data are collected. The optimal C value is then fitted using the least squares method or other optimization algorithms to minimize the error between the predicted and actual SOH. The magnitude of C reflects the sensitivity of the battery system to material degradation: the larger |C| is, the faster the SOH decreases for each unit increase in η, and the more rapidly the battery life degrades.

[0089] The choice of the exponential function exp(C×η) not only conforms to the physical laws of battery capacity decay but also effectively captures the nonlinear characteristics of the degradation process. This function form is chosen primarily based on the following two considerations: First, the loss of active lithium is one of the key factors leading to battery capacity decay. Its loss rate is proportional to the amount of remaining active lithium. This process follows first-order reaction kinetics and can be described by the differential equation dQ / dt=-kQ. Solving this equation reveals that the capacity Q decays exponentially with time (Q=Q0e^(-kQ / dt)). -ktFurthermore, the structural degradation of electrode materials (such as particle breakage and lattice distortion) intensifies with increasing cycle count, and its cumulative effect also exhibits an exponential growth characteristic, leading to an exponential decay of battery capacity. Therefore, the selection of the natural exponential function exp(C×η) has a clear physicochemical basis and can essentially describe the capacity decay behavior under the combined effects of active lithium consumption and material structural degradation. Secondly, the natural exponential function y=e x It exhibits good differentiability and monotonicity. Its derivative equals itself, a property that facilitates mathematical analysis and solutions in parameter fitting and model optimization. Furthermore, when the decay constant C < 0, the function exp(C × η) monotonically decreases with increasing composite degradation exponent η, which is highly consistent with the actual trend of battery performance degradation as degradation intensifies. In the early stages of cycling, the value of η is small, the exponential function changes slowly, and the corresponding capacity decays gradually; as cycling progresses, the value of η increases, the exponential function decays faster, and the corresponding capacity decreases rapidly, which is highly consistent with the actual aging behavior of batteries.

[0090] like Figure 2 As shown, the present invention provides a battery cycle performance evaluation system 100 for implementing the above method, including a data acquisition module 101, a processing and calculation module 102, and an evaluation output module 103; the data acquisition module 101 is used to acquire the physicochemical index parameters of the cathode material after cycling; the processing and calculation module 102 is used to process the physicochemical index parameters based on a preset composite degradation index model to obtain the composite degradation index; the evaluation output module 103 is used to evaluate the performance degradation stage of the battery according to the composite degradation index.

[0091] like Figure 3 As shown, the present invention provides an electronic device 200, including a processor 202 and a memory 201; The processor 202 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0092] Memory 201 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by processor 202 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 201 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, memory 201 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, a high-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0093] The memory 201 stores executable code, which, when processed by the processor 202, can cause the processor 202 to execute part or all of the methods described above.

[0094] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0095] Alternatively, this application may also be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by the processor 202 of the electronic device 200 (or server, etc.), causes the processor 202 to perform part or all of the steps of the above-described method according to this application.

[0096] The present invention will be further illustrated by specific embodiments below. These are exemplary and do not limit the scope of protection of the present invention in any way.

[0097] Example 1: Cycle performance evaluation of NCM811 ternary cathode material 1. Materials and Battery Preparation Commercially available NCM811 cathode material (LiNi) was selected. 0.8 Co 0.1 Mn 0.1 O2), a conductive agent (SuperP), and a binder (PVDF) are mixed at a mass ratio of 96:2:2, coated onto aluminum foil, dried, and then punched into positive electrode sheets. Using lithium metal as the negative electrode, Celgard 2320 as the separator, and 1M LiPF6 in EC:DEC:EMC (1:1:1, vol%) as the electrolyte, CR2032 coin cells are assembled in an argon-protected glove box.

[0098] 2. Cyclic testing and sampling The batteries are charged and discharged at a constant current of 0.5C at 25°C. A batch of batteries is taken out after every 50 or 100 cycles. After disassembly, the positive electrode is removed, cleaned by DMC, vacuum dried, and then subjected to physicochemical index testing.

[0099] 3. Physicochemical index testing Particle size distribution change rate (ΔDspan): The D10, D50, and D90 of the cathode material before and after cycling were measured using a laser particle size analyzer (Malvern Mastersizer 3000). ΔDspan was calculated as follows: ΔDspan = [(Dspan...] 循环后 -Dspan 初始 ) / Dspan 初始 ]×100%.

[0100] Absolute pH change (ΔpH): Immerse the positive electrode material in deionized water, shake, and let stand. Measure the pH value of the supernatant using a pH meter, and calculate ΔpH = |pH|. 循环后 -pH 初始 |

[0101] Total residual alkali (R_alkali): The total content of LiOH and Li2CO3 was determined by acid-base titration.

[0102] Lattice distortion (Δ(c / a)): Diffraction patterns were acquired using an X-ray diffractometer (XRD, Bruker D8 Advance), and the change in c / a value was calculated using Rietveld refinement.

[0103] 4. Calculation of Composite Degradation Index The composite degradation index model described in this invention is adopted:

[0104] 5. Criteria for Determining Battery Performance Degradation Stages When η < 1, the battery is considered to be in a healthy state. When 1≤η<2.5, the battery is judged to be in a sub-healthy state; When η≥2.5, the battery is determined to be in a damaged state.

[0105] 6. Calculation of battery health status value The specific calculation formula is as follows:

[0106] 7. Results Table 1 below shows the specific prediction results. The measured SOH was tested using the capacity decay method. The formula for calculating the SOH error is: SOH error = [(predicted SOH - measured SOH) / measured SOH] * 100%.

[0107] Table 1

[0108] As can be seen from Table 1, the model-predicted SOH agrees well with the measured SOH, with a small average error, and The value increases monotonically with the number of cycles, accurately reflecting the evolution of the battery from a healthy state to a damaged state.

[0109] Example 2: Cycle performance evaluation of LFP lithium iron phosphate cathode material 1. Materials and Testing Commercially available LFP cathode material was selected, and the battery assembly method was the same as in Example 1. The cycle test conditions were 1 C charge-discharge, and samples were taken for testing every 100 cycles.

[0110] 2. Physicochemical index testing Same as Example 1.

[0111] 3. Model Adaptation LFP materials are less sensitive to lattice distortion; adjust the model coefficients as follows:

[0112] 4. Criteria for Determining Battery Performance Degradation Stages Same as Example 1.

[0113] 5. Calculation of battery health status value Same as Example 1.

[0114] 6. Results Table 2 below shows the specific prediction results: Table 2

[0115] As can be seen from Table 2, the composite degradation index model after coefficient adjustment also shows excellent prediction accuracy for LFP materials. The predicted SOH is in good agreement with the measured SOH, with a small average error. Moreover, the η value increases monotonically with the number of cycles, accurately reflecting the evolution of LFP batteries from healthy to damaged state, proving the good applicability of the method of the present invention to different cathode material systems.

[0116] Comparative Example: Single Particle Size Parameter Prediction Method Using the same batch of NCM811 battery data as in Example 1, only ΔDspan was substituted into the composite degradation index model to predict SOH. The results are shown in Table 3 below: Table 3

[0117] As shown in Table 3, after 300 cycles, the comparative example yielded an η value of 1.20, corresponding to a sub-healthy battery state. In Example 1, the η value was 0.84, corresponding to a healthy battery state. This demonstrates that the comparative example, using only the η value of ΔDspan, cannot comprehensively reflect the battery's condition, and may lead to significant misjudgments in battery state assessment in certain situations. Furthermore, compared to Example 1, the SOH error of the comparative example's single particle size parameter prediction method is significantly increased, and the error continues to accumulate and expand with increasing cycle count, failing to accurately reflect the actual degradation state of the battery. This proves the limitations of relying solely on a single physicochemical indicator for performance evaluation.

[0118] The preferred embodiments of the present invention have been described in detail above; however, the present invention is not limited thereto. Within the scope of the inventive concept, various simple modifications can be made to the technical solutions of the present invention, including combinations of various technical features in any other suitable manner. These simple modifications and combinations should also be considered as the content disclosed in the present invention and are all within the protection scope of the present invention.

Claims

1. A method for evaluating battery cycle performance, characterized in that, Includes the following steps: S100. Obtain the physicochemical parameters of the cathode material after cycling, including the particle size distribution change rate, absolute value of pH change, total residual alkali and lattice distortion degree. S200. The physicochemical index parameters are processed based on a preset composite degradation index model to obtain the composite degradation index. S300. Assess the performance degradation stage of the battery based on the composite degradation index.

2. The battery cycle performance evaluation method according to claim 1, characterized in that, Step S200 specifically includes: S202. The particle size distribution change rate is subjected to a first normalization process, and then a first nonlinear transformation is performed to obtain the first characteristic term. S204. Based on the first coefficient, the absolute value of pH change is processed to obtain the second characteristic term; S206. The total residual alkali is subjected to a second normalization process to obtain the third characteristic term; S208. Based on the second coefficient, the lattice distortion degree is processed to obtain the fourth characteristic term; S210. Based on the first characteristic term, the second characteristic term, the third characteristic term, and the fourth characteristic term, the composite degradation index is calculated.

3. The battery cycle performance evaluation method according to claim 2, characterized in that, Step S204 further includes processing the absolute value of pH change based on the first coefficient and the third coefficient to obtain the second characteristic term.

4. The battery cycle performance evaluation method according to claim 2, characterized in that, Step S208 also includes processing the lattice distortion degree based on the second and fourth coefficients to obtain the fourth characteristic term.

5. The battery cycle performance evaluation method according to claim 1, characterized in that, Step S300 specifically includes: When the composite degradation index is less than the first preset threshold, the battery is determined to be in a healthy state. When the composite degradation index is not less than the second preset threshold, the battery is determined to be in a damaged state. When the composite degradation index is between the first preset threshold and the second preset threshold, the battery is determined to be in a sub-healthy state. Wherein, the first preset threshold is less than the second preset threshold.

6. The battery cycle performance evaluation method according to claim 1, characterized in that, The battery cycle performance evaluation method further includes step S400: evaluating the battery's health status value based on the composite degradation index.

7. The battery cycle performance evaluation method according to claim 6, characterized in that, Step S400 specifically includes: performing a second nonlinear transformation on the fifth coefficient and the composite degradation index, followed by a third normalization process to obtain the battery's health status value.

8. A battery cycle performance evaluation system for implementing the method as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire the physicochemical parameters of the cathode material after cycling. The processing and calculation module is used to process the physicochemical index parameters based on a preset composite degradation index model to obtain the composite degradation index. An evaluation output module is used to evaluate the performance degradation stage of the battery based on the composite degradation index.

9. An electronic device, characterized in that, include: processor; as well as A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores executable code that, when executed by a processor of an electronic device, causes the processor to perform the method as described in any one of claims 1 to 7.

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