A battery cycle performance evaluation method and system, electronic device, and storage medium

Abstract

The application 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 positive electrode material after cycling, wherein the physicochemical index parameters comprise a particle size distribution change rate, a pH change absolute value, a total residual alkali amount 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. The application establishes a composite degradation index model based on the linkage of the multi-dimensional physicochemical indexes of the positive electrode material, and through nonlinear transformation and normalization processing, the indexes are coupled into a unified composite degradation index, so that the dominant degradation mechanism is dynamically captured at different cycle stages, the accuracy and reliability of the evaluation are significantly improved, and an effective technical means is provided for the rapid screening and life prediction of battery materials.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of batteries, and particularly relates to a battery cycle performance evaluation method and system, an electronic device, and a storage medium. BACKGROUND

[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. The performance degradation of batteries during cycling is a key factor affecting their service life and reliability, so it is crucial to accurately and quickly evaluate the state of health (SOH) of batteries.

[0003] Currently, the industry mainly relies on electrochemical testing methods to evaluate the SOH of batteries, especially the degradation of positive electrode materials. The most commonly used methods include: 1. Capacity decay method: by monitoring the capacity retention rate of the battery during continuous charge-discharge cycling to evaluate the SOH. However, this method requires complete long-term cycling tests, which often takes weeks or even months, is inefficient, and cannot meet the needs of rapid screening and evaluation of materials in research and production. 2. Electrochemical impedance spectroscopy (EIS) analysis: by measuring the impedance change of the battery to infer its internal state. However, EIS mainly reflects the changes in the battery interface (such as the SEI film), and is not sensitive enough to the structural degradation of the positive electrode material bulk phase, such as particle breakage and lattice distortion, making it difficult to reveal the intrinsic failure mechanism of the material.

[0004] In addition, in order to improve the evaluation efficiency, some studies attempt to use a single physicochemical parameter of the positive electrode material as a degradation indicator, such as using only the change in the median particle size (D50) or particle size distribution width (Dspan) of the material to predict capacity decay. However, the degradation of the battery is a complex process coupled by multiple factors, and a single parameter cannot fully reflect the coordinated degradation behavior of the material in multiple dimensions such as physical structure, chemical composition, and crystal structure, resulting in poor universality and large errors of the prediction model, and unreliable prediction results.

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

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

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

[0008] To achieve the above object, in a first aspect, the application provides a battery cycle performance evaluation method, comprising the following steps:

[0009] S100, obtaining physical and chemical index parameters of the positive electrode material after cycling, the physical and chemical index parameters including a particle size distribution change rate, a pH change absolute value, a total residual base amount, and a lattice distortion degree;

[0010] S200, processing the physical and chemical index parameters based on a preset composite degradation index model to obtain a composite degradation index;

[0011] S300, evaluating a performance degradation stage of the battery according to the composite degradation index.

[0012] Preferably, step S200 specifically comprises:

[0013] S202, performing first normalization processing on the particle size distribution change rate, and then performing first nonlinear transformation to obtain a first feature term;

[0014] S204, processing the pH change absolute value based on a first coefficient to obtain a second feature term;

[0015] S206, performing second normalization processing on the total residual base amount to obtain a third feature term;

[0016] S208, processing the lattice distortion degree based on a second coefficient to obtain a fourth feature term;

[0017] S210, calculating the composite degradation index based on the first feature term, the second feature term, the third feature term, and the fourth feature term.

[0018] Preferably, step S204 further comprises processing the pH change absolute value based on the first coefficient and a third coefficient to obtain the second feature term.

[0019] Preferably, step S208 further comprises processing the lattice distortion degree based on the second coefficient and a fourth coefficient to obtain the fourth feature term.

[0020] Preferably, step S300 specifically comprises:

[0021] when the composite degradation index is less than a first preset threshold, determining that the battery is in a healthy state;

[0022] when the composite degradation index is not less than a second preset threshold, determining that the battery is in a damaged state;

[0023] when the composite degradation index is between the first preset threshold and the second preset threshold, determining that the battery is in a sub-healthy state;

[0024] wherein the first preset threshold is less than the second preset threshold.

[0025] Preferably, the battery cycle performance evaluation method further comprises a step S400 of evaluating a state of health value of the battery according to the composite degradation index.

[0026] Preferably, the step S400 specifically comprises a second non-linear transformation on the fifth coefficient and the composite degradation index, and a third normalization processing to obtain the state of health value of the battery.

[0027] In a second aspect, the present application provides a battery cycle performance evaluation system for implementing the above method, comprising:

[0028] a data acquisition module configured to acquire the physicochemical index parameters of the positive electrode material after the cycle;

[0029] a processing and calculation module configured to process the physicochemical index parameters based on a preset composite degradation index model to obtain a composite degradation index;

[0030] an evaluation output module configured to evaluate a performance degradation stage of the battery according to the composite degradation index.

[0031] In a third aspect, the present application provides an electronic device, comprising:

[0032] a processor; and

[0033] a memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the above method.

[0034] In a fourth aspect, the present application provides a computer readable storage medium having executable codes stored thereon, which, when executed by a processor of an electronic device, causes the processor to execute the above method.

[0035] The present application establishes a composite degradation index model based on the linkage of multiple dimensions of physicochemical indexes of positive electrode materials, and realizes fast and accurate evaluation of the cycle performance of the battery. The method comprehensively considers the synergistic degradation behavior of the positive electrode material in multiple dimensions such as physical structure (particle size distribution change rate), chemical composition (pH change absolute value, total residual alkali amount) and crystal structure (lattice distortion degree), overcomes the limitations of traditional electrochemical methods such as long cycle time and inability to reveal the intrinsic degradation mechanism of the material, and the problems of large prediction error and poor universality of single parameter model. Through non-linear transformation and normalization processing, the indexes are coupled into a unified composite degradation index, so as to dynamically capture the dominant degradation mechanism at different cycle stages, significantly improve the accuracy and reliability of the evaluation, and only need to cycle 100 times, 200 times or 300 times to quickly evaluate the cycle performance of the material, without waiting for the end of the cycle, thereby providing an effective technical means for fast screening and life prediction of battery materials. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0037] Figure 1 The flow chart of the battery cycle performance evaluation method provided by the embodiments of the present application;

[0038] Figure 2 The structural block diagram of the battery cycle performance evaluation system provided by the embodiments of the present application;

[0039] Figure 3 The structural block diagram of the electronic device provided by the embodiments of the present application.

[0040] Explanation of reference signs:

[0041] 100, battery cycle performance evaluation system; 101, data acquisition module; 102, processing calculation module; 103, evaluation output module;

[0042] 200, electronic device; 201, memory; 202, processor. DETAILED DESCRIPTION

[0043] In the present application, the orientation words such as "up", "down", "left", "right" are generally understood in connection with the orientation shown in the drawings and the actual application, unless otherwise stated.

[0044] In addition, the terms "first" and "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0045] In the present application, unless otherwise specifically stated and limited, the "on" or "under" of the first feature to the second feature can be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. Moreover, the "over", "above" and "on" of the first feature to the second feature can be that the first feature is directly above or obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The "under", "below" and "under" of the first feature to the second feature can be that the first feature is directly below or obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0046] The endpoints of the ranges and any values disclosed herein are not limited to the precise values recited as the exact dimensions are not to be understood as being crucial to the application. Any numeric range recited is intended to include all values from the lower value to the upper value. For numeric ranges, the endpoints are included in the ranges, are discrete points in the ranges, and can be combined with other points in the ranges to form one or more new ranges, which are to be considered disclosed herein. The terms "optional" and "optional" mean that the subsequently described element can or can not be present, or can be present or not present.

[0047] As shown in Figure 1 The application provides a battery cycle performance evaluation method, comprising the following steps:

[0048] S100, obtaining the physicochemical index parameters of the positive electrode material after cycling, the physicochemical index parameters including particle size distribution change rate, pH change absolute value, total residual alkali amount and lattice distortion degree;

[0049] S200, processing the physicochemical index parameters based on a preset composite degradation index model to obtain a composite degradation index;

[0050] S300, evaluating the performance degradation stage of the battery according to the composite degradation index.

[0051] During the cycle use of the lithium ion battery, the performance degradation of the positive electrode material is one of the key factors affecting the overall life of the battery. In order to accurately and efficiently evaluate the state of health (SOH) of the battery, the application proposes an evaluation method based on multi-dimensional physicochemical index linkage, and the selected core indexes include particle size distribution change rate, pH change absolute value, total residual alkali amount and lattice distortion degree. These parameters respectively reflect the degradation behavior of the positive electrode material during the cycle process from three dimensions of physical structure, chemical composition and crystal structure, and have clear physical meaning and strong indication.

[0052] 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 positive electrode material before and after cycling, and the calculation formula is:

[0053] ΔDspan=[(Dspan 循环后 -Dspan 初始 ) / Dspan 初始 ]×100%。

[0054] This parameter reflects the change of particle size distribution of the positive electrode particles during the cycle process due to crushing, crack propagation or agglomeration behavior. With the cycle, the particle crushing will increase the tortuosity of the ion diffusion path, aggravate the polarization, and thus accelerate the capacity attenuation. Therefore, ΔDspan is an important index for evaluating the physical structure stability of the positive electrode material.

[0055] The absolute value of pH change refers to the absolute value of the pH value change of the positive electrode material leaching solution before and after cycling, and the calculation formula is:

[0056] ΔpH = |pH 循环后 -pH 初始 |.

[0057] This parameter is mainly used to characterize the change of the chemical environment of the material surface, especially the accumulation of acidic substances. During the cycling process, the acidic substances such as HF produced by the decomposition of the electrolyte will corrode the positive electrode material, trigger the dissolution of transition metals, surface reconstruction and other side reactions, and then cause the damage of the material structure and the loss of capacity. Therefore, ΔpH becomes a key indicator reflecting the chemical stability of the positive electrode interface.

[0058] The total amount of residual alkali refers to the total mass percentage of residual alkali substances (mainly including LiOH and Li2CO3) in the positive electrode material, based on 100% of the mass of the positive electrode material. The presence of residual alkali will promote the decomposition of the electrolyte, consume active lithium sources, and may cause gas evolution, impedance growth and other problems, which seriously affect the cycle life and safety of the battery. This parameter directly reflects the quality of the preparation process of the positive electrode material and the degree of side reactions during the cycling process.

[0059] The lattice distortion degree refers to the change of the ratio of c-axis to a-axis (c / a) in the crystal structure of the positive electrode material calculated by X-ray diffraction (XRD) refinement before and after cycling, and the calculation formula is:

[0060] Δ(c / a) = |(c / a) 循环后 -(c / a) 初始 |.

[0061] This parameter reflects the lattice distortion and cation mixing phenomenon of the layered structure positive electrode material during the lithium ion deintercalation process. Abnormal change of c / a value often means that the structure stability decreases and the risk of phase transition increases, which is an important basis for evaluating the integrity of the positive electrode crystal structure.

[0062] The reason for selecting the above four parameters is that they comprehensively capture the multi-mechanism synergistic degradation behavior of the positive electrode material during the cycling process from the three dimensions of physics, chemistry and structure. Traditional methods often rely on a single parameter (such as only D50 or Dspan) or macroscopic electrical performance test, which cannot reveal the intrinsic degradation mechanism of the material, has large prediction error and poor universality. By establishing the quantitative coupling relationship between the four parameters and constructing the composite degradation index model, the SOH of the battery can be quickly and accurately evaluated, which significantly improves the material research and development efficiency and the reliability of state evaluation.

[0063] In addition, the four parameters have the characteristics of strong measurability and good repeatability, and are suitable for lithium ion batteries of different systems, such as ternary lithium batteries (NCM, NCA), lithium iron phosphate batteries (LFP), lithium cobaltate batteries (LCO), etc. Only by adjusting the appropriate coefficient can the cross-material platform be applied, and it has strong engineering applicability and industrialization prospects.

[0064] Specifically, the measurement of the above parameters can be obtained by mature and standardized large-scale analytical instruments and test methods. The particle size distribution can be obtained by a laser particle size analyzer. For example, the positive electrode material can be peeled off from the current collector and dispersed appropriately, usually by wet dispersion, using an organic solvent (such as NMP) or deionized water as a dispersion medium, and ultrasonic treatment for 3-5 minutes to ensure that the particles are fully dispersed and do not agglomerate. The measurement is repeated 3 times to obtain the average value.

[0065] The measurement of pH value adopts a standard pH meter and a leaching liquid preparation process; for example, a certain mass (such as 1.0 g) of positive electrode material can be mixed with 10 mL of deionized water, shaken for 30 minutes, and then the supernatant is measured for pH value. The measurement is repeated 3 times to obtain the average value.

[0066] The total amount of residual alkali can be quantitatively analyzed by acid-base titration or automatic potentiometric titrator; for example, the positive electrode material can be dissolved in a suitable amount of deionized water, and titrated with 0.01 mol / L HCl standard solution. The end point is determined by automatic potentiometric titrator, or manual titration is performed using phenolphthalein and methyl orange double indicators. The contents of LiOH and Li2CO3 are measured and added to obtain the total amount of residual alkali.

[0067] The lattice distortion degree is based on X-ray diffraction (XRD) technology and is obtained by Rietveld refinement calculation; for example, the test conditions can be: CuKα radiation, scanning range 10°-80°, step 0.02°, scanning speed 2° / min, and the software such as TOPAS is used for refinement. The lattice parameters a and c are obtained by fitting, and the value c / a and the change amount Δ(c / a) are calculated.

[0068] These detection methods have been widely used in material research and battery industry, and the detection process is standardized, and the data repeatability and reproducibility are high. The present application does not make special limitations.

[0069] Preferably, step S200 specifically comprises:

[0070] S202, the particle size distribution change rate is subjected to first normalization processing, and then subjected to first nonlinear transformation to obtain a first feature term;

[0071] Specifically, the calculation formula of the first feature term can be:

[0072] N1 = (ADspan / a1)P;

[0073] wherein N1 is the first characteristic term, ADspan is the particle size distribution change rate, a1 is the first reference value in the first normalization processing, the first non-linear transformation is a power function transformation, and b is an index in the power function.

[0074] The purpose of the normalization processing is to eliminate dimensional differences and unify different physical indicators to a comparable order of magnitude range. As a percentage change rate, the numerical range of ADspan is greatly affected by factors such as material type and cycle conditions. By dividing by a reference value a1 (for example, 15% commonly used in NCM materials), ADspan can be converted into a dimensionless ratio value, thereby facilitating the weighted combination with other indicators (such as pH change, residual alkali content, etc.) to form a unified composite degradation index η.

[0075] The first reference value a1 is the performance inflection point threshold of ADspan in different positive electrode materials, which is a material-dependent threshold parameter. Its value is obtained through statistical analysis of 120 sets of battery cycle experiment data. For example, for NCM series positive electrode materials, when ADspan exceeds 15%, the battery capacity decay rate significantly accelerates, so a1 can be set to 15. This value reflects the critical point of physical structure degradation in the material system, and has certain universality and adjustability, which is suitable for adaptation of different material systems (such as LFP, LCO, etc.).

[0076] Although the normalized value (ADspan / a1) becomes a dimensionless relative value, its relationship is still linear, but the degradation behavior of battery materials is often nonlinear. Especially in the physical degradation mechanisms such as particle breakage and crack propagation, their influence on battery performance shows obvious nonlinear characteristics. For example, when ADspan exceeds a certain critical value (such as 15%), its blocking effect on ion diffusion paths will be dramatically enhanced, thereby accelerating capacity decay. Therefore, preferably, a power function transformation (i.e., taking b times) can better capture this nonlinear degradation behavior, so that the model gives higher weight when ADspan is larger, thereby more accurately reflecting its contribution to overall degradation.

[0077] The exponent β in the power function is a nonlinear blocking effect index, and the value of β is obtained by nonlinear regression analysis on historical data (such as more than 120 sets of battery cycle data). Specifically, the following experiment and fitting process can be used to determine: a certain number of batteries of the same type are selected, and the particle size distribution change ΔDspan of 120 sets of positive electrode materials is measured at different cycle times under the same charging and discharging conditions, and the battery performance indicators (such as capacity retention rate) are recorded simultaneously; ΔDspan and the corresponding performance degradation (such as the relative value of capacity decay as a proxy for N1) are arranged, the relationship N1=(ΔDspan / α1)^β is taken on both sides to obtain the linear relationship lnN1=β·ln(ΔDspan / α1), and then the least squares method or other fitting methods are used to linearly regress lnN1 and ln(ΔDspan / α1), and the slope obtained is the value of β, finally establishing the optimal correlation between N1 and the actual capacity decay. For example, if β=1.5, it indicates that the influence of ΔDspan on the capacity is in the 1.5th power relationship, which is much stronger than the linear relationship, which is consistent with the physical mechanism of the gradually increasing "blocking effect" of particle breakage on the ion diffusion path.

[0078] S204, based on the first coefficient, the absolute value of the pH change is processed to obtain a second characteristic term;

[0079] Specifically, the calculation formula of the second characteristic term can be:

[0080] N2=k1×ΔpH;

[0081] Wherein, N2 is the second characteristic term, k1 is the first coefficient, and ΔpH is the absolute value of the pH change.

[0082] The absolute value of the pH change ΔpH is a key indicator reflecting the change of the surface chemical environment of the positive electrode material. During the battery cycle process, acidic substances (such as HF) produced by electrolyte decomposition will erode the positive electrode material, trigger side reactions such as transition metal dissolution and surface reconstruction, and then cause material structure damage and capacity decay. The larger the absolute value of ΔpH, the stronger the acidity of the material surface, the more serious the side reactions, and the more significant the negative impact on the battery life.

[0083] However, as a physical quantity, the numerical range of ΔpH is different from other indicators (such as particle size change rate, residual alkali content, etc.) in dimension and order of magnitude. If directly used in model calculation, it will cause the weight imbalance of some indicators, affecting the accuracy and universality of the model. Therefore, it is necessary to scale ΔpH by the coefficient k1 to convert it into a dimensionless or same order of magnitude value comparable to other characteristic terms, and then participate in the weighted summation of the composite degradation index η.

[0084] In addition, the introduction of k1 also reflects the "sensitivity" of the influence of pH change on battery performance. Experiments show that for every 1 unit change in pH, there may be a certain percentage of capacity loss (e.g. 2.3%), and the value of k1 is just to quantify this relationship, so that the second characteristic term N2 can accurately reflect the contribution of ΔpH to the overall degradation.

[0085] The first coefficient k1 is the pH change sensitivity coefficient, which is a material-dependent empirical parameter. Its value is obtained by regression analysis on a large amount of historical battery cycle data. Specifically, a statistical relationship between ΔpH and actual capacity decay data of the positive electrode material under different cycle times is established, and the optimal k1 value is fitted by least squares method or other optimization algorithms, so that the correlation between N2 and the actual degradation behavior is the strongest.

[0086] For example, for NCM positive electrode materials, through the analysis of more than 120 battery data, it is found that every 1 unit change in ΔpH causes an average capacity loss of about 2.3%. Therefore, k1 can be set to 2.3, so that N2 = 2.3 x ΔpH, thereby reasonably reflecting the contribution of pH change in the composite degradation index.

[0087] S206, performing a second normalization processing on the total residual alkali to obtain a third characteristic term;

[0088] Specifically, the calculation formula of the third characteristic term can be:

[0089] N3 = R alkali / α2

[0090] Where N3 is the third characteristic term, R alkali is the total residual alkali, and α2 is the second reference value in the second normalization processing.

[0091] The total residual alkali refers to the mass percentage of residual alkaline substances (mainly including LiOH and Li2CO3) in the positive electrode material, and its numerical value directly reflects the degree of side reactions during material preparation or cycling. However, under different material systems and different process conditions, the reference value of the total residual alkali and its impact on performance vary significantly. If the original residual alkali content is directly used in model calculation, it will be inconsistent with other indicators (such as ΔDspan, ΔpH, etc.) in terms of dimension and numerical range, resulting in unbalanced model weights and reducing the accuracy and universality of the prediction.

[0092] Therefore, through the second normalization processing, the total residual alkali is divided by its typical reference value α2 in a specific material system to obtain a relative proportion value N3 = R alkaliN3= N2 / a2. This processing not only eliminates the dimensional difference, but also converts the residual alkali content into a multiple relationship reflecting its relative to the normal or critical level, so as to more intuitively reflect its influence on the battery performance. For example, if N3>1, it means that the residual alkali content has exceeded the tolerance threshold of the material system, which may cause significant performance degradation.

[0093] The second reference value a2 is the critical content of residual alkali, which is determined by statistical analysis of a large amount of experimental data and the characteristics of the material system. Generally, a2 represents the typical residual alkali content of the material in the fresh state or at a certain cycle stage, or the critical value corresponding to a significant decrease in performance. For example, for high-nickel ternary positive electrode materials (such as NCM811), through regression analysis of more than 120 battery data, it is found that when the total residual alkali content exceeds 0.8wt%, the battery capacity decay rate significantly accelerates, and the interface side reaction intensifies. Therefore, a2 can be set to 0.8, so that N3 intuitively reflects whether the residual alkali content exceeds the safe range in numerical value.

[0094] S208, processing the lattice distortion degree based on the second coefficient to obtain a fourth feature term;

[0095] Specifically, the calculation formula of the fourth feature term can be:

[0096] N4=k2×Δ(c / a)

[0097] Wherein, N4 is the fourth feature term, k2 is the second coefficient, and Δ(c / a) is the lattice distortion degree.

[0098] In step S208, the lattice distortion degree Δ(c / a) is processed to obtain the fourth feature term N4 by introducing the second coefficient k2 to linearly scale it, and the calculation formula is N4=k2×Δ(c / a). The reason for this processing method is that the lattice distortion degree Δ(c / a) as a key indicator reflecting the stability of the crystal structure of the positive electrode material, its numerical range is small (usually in the order of thousandth to hundredth), if directly combined with other feature terms (such as ΔDspan, ΔpH, etc.), its contribution to the composite degradation index will be seriously underestimated due to its small value, and its actual influence on the degradation of battery performance cannot be truly reflected.

[0099] The second coefficient k2 is a lattice distortion amplification coefficient, which is essentially a sensitivity amplification mechanism. The purpose is to convert the microscopic structural change amount Δ(c / a) into a dimensionless or standardized value comparable to other characteristic items in the same order of magnitude, so as to ensure that the 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 is obtained by regression analysis of a large number of historical battery cycle data. Specifically, a plurality of sets of Δ(c / a) values of the positive electrode material and actual capacity attenuation data under different cycle times are collected, a statistical relationship between the two is established, and the optimal k2 value is fitted by the least square method or other optimization algorithm, so that N4 has the strongest correlation with the actual capacity attenuation.

[0100] For example, for high-nickel ternary positive electrode materials (such as LiNi 0.8 Co 0.1 Mn 0.1 O2), through the analysis of more than 120 sets of battery data, it is found that for every 0.01 unit increase in lattice distortion Δ(c / a), the average capacity loss is about 0.5%. In order to reasonably reflect this influence in the composite degradation index, k2 can be set to 50, so that N4=50×Δ(c / a). This means that when Δ(c / a) is 0.02, N4 contributes 1.0, which is similar in order of magnitude to other characteristic items (such as ΔDspan / 15), so as to ensure that the model can accurately capture the influence of crystal structure degradation on the overall performance.

[0101] S210, based on the first characteristic item, the second characteristic item, the third characteristic item and the fourth characteristic item, a composite degradation index is calculated.

[0102] Specifically, η=N1+N2+N3+N4

[0103] Wherein, η is the composite degradation index.

[0104] The performance degradation of the battery during the cycle process is a complex process of multiple factors and multiple mechanism coupling, involving changes in multiple dimensions such as physical structure, chemical composition and crystal structure. These changes do not occur in isolation, but interact with each other and jointly affect the overall performance of the battery. For example, particle crushing (physical degradation) can exacerbate the interface side reaction (chemical degradation), and the chemical side reaction can further induce lattice distortion (structural degradation). Therefore, it is difficult to accurately capture the essence of this multi-mechanism synergistic degradation by relying on a single indicator or simple linear combination.

[0105] The present application quantifies the contribution degree of different degradation mechanisms by constructing four characteristic terms. The first characteristic term N1 reflects the nonlinear blocking effect of particle breakage on ion diffusion path; the second characteristic term N2 embodies the influence of surface chemical environment change (such as acid accumulation) on material stability; the third characteristic term N3 represents the promotion 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. These four characteristic terms each have a clear physical meaning, and through normalization, nonlinear transformation and coefficient scaling, they have been converted into dimensionless or standardized values, with additivity.

[0106] The four characteristic terms are directly added to form a composite degradation index η, which is essentially a linear superposition model of multiple mechanisms. This model assumes that the influence of each degradation mechanism on the overall performance is independent and additive to some extent. Although there may be some interaction in the actual degradation process, regression analysis of a large amount of historical data (such as 120 battery cycle data) shows that the linear superposition model can accurately fit the actual capacity decay behavior with high precision (R 2 =0.985), proving that the assumption is reasonable and effective within the scope of engineering application.

[0107] In addition, the additive model has the advantages of simple structure, high computational efficiency and strong interpretability, making it easy to apply in practical engineering. By adapting the coefficients (such as adjusting α1, k1, α2, k2) for different material systems (such as NCM, LFP, LCO, etc.), the model can further expand its universality and accurately evaluate the cycle performance of various positive electrode materials.

[0108] Further, although the four characteristic terms (N1, N2, N3, N4) in the composite degradation index model will affect the overall degradation throughout the battery life cycle, their dominant degree in different cycle stages differs significantly, showing obvious "stage sensitivity". In the early stage of cycling, since the particle structure is relatively complete, chemical side reactions (such as electrolyte decomposition acid) become the dominant degradation mechanism, so the contribution of the pH change term (N2) is most significant; as the cycle progresses to the middle stage, particle breakage and crack propagation occur, and physical structure changes gradually become dominant, so the contribution of the particle size distribution change term (N1) increases; in the later stage of cycling, accumulated lattice distortion and cation mixing intensify, and the crystal structure degradation term (N4) has the most significant impact. The capture of this stage sensitivity is due 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 can dynamically reflect the dominant degradation mechanism in different cycle stages, further improving the evaluation accuracy.

[0109] Preferably, step S204 further comprises a pH change absolute value processing based on the first coefficient and the third coefficient, to obtain a second characteristic term.

[0110] Specifically, the calculation formula of the second characteristic term can be:

[0111] N2=k3 x k1 x ΔpH

[0112] wherein N2 is the second characteristic term, k3 is the third coefficient, k1 is the first coefficient, and ΔpH is the pH change absolute value.

[0113] The introduction of the first coefficient k1 is mainly used to quantify the sensitivity of the pH change to the battery performance. In actual application, the sensitivity of the positive electrode material under different material systems (such as NCM, LFP, LCO, etc.) or different process conditions to the pH change may be different. In order to further improve the adaptability and accuracy of the model, the third coefficient k3 is introduced. k3 is a pH change weight adjustment coefficient, which is used to further fine-tune the weight of the pH change term in the composite degradation index on the basis of the baseline sensitivity determined by k1. The value of k3 also depends on the statistical analysis of a large amount of experimental data, and its purpose is to optimize the prediction performance of the model under different material systems according to the characteristics of the material system. For example, for a material system (such as LCO battery) that is particularly sensitive to pH change, k3 can be greater than 1 (such as 1.2) to amplify the contribution of pH change; and for a material (such as lithium iron phosphate) that is relatively insensitive to pH change, k3 can be less than 1 (such as 0.8) to appropriately reduce its weight.

[0114] Preferably, step S208 further comprises a lattice distortion degree processing based on the second coefficient and the fourth coefficient, to obtain a fourth characteristic term.

[0115] Specifically, the calculation formula of the fourth characteristic term can be:

[0116] N4=k4 x k2 x Δ(c / a)

[0117] wherein N4 is the fourth characteristic term, k4 is the fourth coefficient, k2 is the second coefficient, and Δ(c / a) is the lattice distortion degree.

[0118] The introduction of the second coefficient k2 is a sensitivity amplification mechanism, which aims to convert the microscopic lattice distortion variable into a standardized value comparable to other characteristic terms in the same order of magnitude. However, different material systems (such as NCM, LFP, LCO, etc.) have different sensitivities to lattice distortion. To further improve the cross-material universality and prediction accuracy of the model, the fourth coefficient k4 is introduced. k4 is a lattice distortion degree weight adjustment coefficient, which is 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, and its purpose is to optimize the prediction performance of the model for different material systems according to their crystal structure characteristics.

[0119] For example, for lithium iron phosphate (LFP) material, its olivine structure is relatively stable and less sensitive to lattice distortion, so 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 overall degradation.

[0120] The determination of the coefficients (such as k1, k2), reference values (such as a1, a2), and exponents (such as b) is based on statistical analysis of not less than 120 sets of battery cathode material physical and chemical indicators and measured SOH data under different cycle states, and linear or nonlinear regression fitting is performed using the least squares method to ensure the prediction accuracy (R 2 ≥0.985).

[0121] For different cathode material systems, the coefficients can be adjusted within a certain range based on their characteristics, and the specific values are determined through data fitting for this material system.

[0122] Based on the above disclosed model structure, parameter meaning, and acquisition method, a person skilled in the art can determine the coefficient values suitable for a specific battery system through conventional experimental data fitting without creative labor, thereby achieving the technical effects of the present application.

[0123] Preferably, step S300 specifically comprises:

[0124] When the composite degradation index is less than the first preset threshold, the battery is determined to be in a healthy state;

[0125] When the composite degradation index is not less than the second preset threshold, the battery is determined to be in a damaged state;

[0126] 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;

[0127] Wherein, the first preset threshold is less than the second preset threshold.

[0128] In step S300, the performance degradation stage of the battery is determined based on the composite degradation index η, which is essentially to compare the calculated η value with the preset threshold value, thereby dividing the battery state into three clear stages of "healthy", "sub-healthy" and "damaged". The basis of this division is that the composite degradation index η is a quantitative index calculated by coupling multiple dimensional physicochemical indicators, and the numerical value has a high correlation (R 2 =0.985) with the actual capacity attenuation and the internal material degradation degree of the battery. As the cycle progresses, the η value monotonically increases, and its value comprehensively reflects the cumulative damage degree of the positive electrode material in three dimensions of physical structure, chemical composition and crystal structure, and therefore can be used as a reliable proxy variable of the overall health state (SOH) of the battery.

[0129] The "healthy state" means that the battery still retains most of its initial capacity, the internal material degradation is slight, all key physicochemical indicators do not exceed their safety threshold, the battery can be used normally and has a long expected life. The "sub-healthy state" indicates that the battery has entered an observable degradation period, some key indicators (such as ΔDspan or ΔpH) may have approached or slightly exceeded the critical threshold, the battery capacity has decreased significantly, the performance has decreased but can still be used under certain conditions, or the performance can be partially restored through repair means. The "damaged state" means that the battery has undergone serious degradation, the internal material may have suffered irreversible structural damage (such as a large number of particle breakage, severe lattice distortion, a large amount of interface byproduct accumulation, etc.), the capacity has decreased sharply, and there is a significant safety and reliability risk, which should be discarded or recycled.

[0130] The determination of the first and second preset thresholds is not arbitrary, but is based on a large amount of systematic battery cycle test data and failure analysis, and the key threshold values are obtained by statistical regression and cluster analysis. Specifically, hundreds of groups of η values and their corresponding actual capacity retention rates (SOH) of batteries with different cycle times and different material systems are collected, an η-SOH scatter plot is drawn and curve fitting is performed. On this basis, the relationship between the distribution of η value and the actual failure mode of the battery is analyzed, and the inflection point of performance mutation is found. For example, through the analysis of more than 120 groups of NCM battery data, it is found that when η<1.0, the battery capacity decreases slowly and there is no obvious material failure characteristic, so the first preset threshold is set to 1.0 as the upper limit of the "healthy" state. When η≥2.5, a large number of particle cracks and phase change phenomena are observed by electron microscopy, so the second preset threshold is 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.

[0131] Preferably, the battery cycle performance evaluation method further comprises step S400: evaluating the health state value of the battery according to the composite degradation index.

[0132] Preferably, the step S400 specifically comprises: performing a second nonlinear transformation on the fifth coefficient and the composite degradation index, and then performing a third normalization processing to obtain the state of health value of the battery.

[0133] Specifically, the calculation formula of the state of health value of the battery can be:

[0134] SOH = a3 x exp(C x η)

[0135] Wherein, SOH is the state of health value of the battery, a3 is a third reference value in the third normalization processing, C is an attenuation rate constant, and exp is a power term of the natural constant e.

[0136] In the battery cycle performance evaluation, the composite degradation index η, as a quantitative index comprehensively reflecting the multi-dimensional degradation behavior of the positive electrode material, can be used to preliminarily judge the performance degradation stage (such as health, sub-health, damage) of the battery, but it is a relative value without unit and does not directly correspond to the actual capacity retention rate (State of Health, SOH) of the battery. SOH is a widely accepted key index in the industry for quantifying the remaining capacity of the battery, which is defined as the percentage of the current capacity to the initial capacity. Therefore, in order to convert the composite degradation index η into the SOH value with more engineering significance, a quantitative mapping relationship between η and SOH needs to be established, and the step S400 is designed to achieve this goal.

[0137] The step S400 converts the composite degradation index η into the state of health value SOH of the battery by introducing the second nonlinear transformation and the third normalization processing. The core is to capture the nonlinear decay law between η and SOH by using a mathematical model. The capacity decay of the battery in the cycle process often obeys the exponential decay law, that is, the initial decay is slow, and the middle and late stages are accelerated. This phenomenon is highly consistent with the cumulative damage behavior of the positive electrode material under the synergistic action of multiple mechanisms. Therefore, selecting the exponential function as the form of the second nonlinear transformation has a solid physical basis and data support.

[0138] The third reference value a3 is the SOH calibration reference value, which is used to calibrate the model output to a reasonable SOH dimension (percentage). Under normal circumstances, when the battery is in the initial state (η = 0), the SOH should be close to 100%. Therefore, the theoretical value of a3 should be 100. However, in actual application, due to measurement error, model approximation and other factors, the initial SOH may be slightly higher or lower than 100%. Through statistical analysis of a large number of fresh battery data, a3 can be fine-tuned to optimize the prediction accuracy of the model in the initial stage. For example, regression analysis shows that the average value of the initial SOH is 102.3%, and a3 can be set to 102.3, so that the model is more consistent with the actual data.

[0139] The attenuation rate constant C is a negative parameter, and its physical meaning is the SOH attenuation rate caused by the unit composite degradation index change. The value of C is obtained by nonlinear regression analysis on historical battery cycle data. Specifically, a plurality of η values and actual measured SOH data under different cycle times are collected, and the least square method or other optimization algorithm is used to fit the optimal C value, so that the error between the predicted SOH and the actual SOH is minimized. The size of C reflects the sensitivity of the battery system to material degradation: the larger |C| is, the faster the SOH decreases when η increases by one unit, and the more rapidly the battery life attenuates.

[0140] The selection of the exponential function exp(C×η) not only conforms to the physical law of battery capacity attenuation, but also effectively captures the nonlinear characteristics in the degradation process. The selection of this function form is mainly based on the following two considerations: first, the loss of active lithium is one of the key factors leading to battery capacity attenuation. Its loss rate is proportional to the amount of remaining active lithium, and this process follows first-order reaction kinetics, which can be described by the differential equation dQ / dt=-kQ. The solution can obtain the natural exponential decay law of capacity Q with time (Q=Q0e -kt ). In addition, the structural degradation of electrode materials (such as particle breakage and lattice distortion) intensifies with the increase of cycle number, and its cumulative effect also presents an exponential growth characteristic, thereby leading to an exponential decay of battery capacity. Therefore, the selection of the natural exponential function exp(C×η) has a clear physical and chemical basis, and can essentially describe the capacity attenuation behavior under the joint action of active lithium consumption and material structure degradation. Second, the natural exponential function y=e x has good differentiability and monotonicity. Its derivative is equal to itself, which facilitates mathematical analysis and solution in parameter fitting and model optimization. At the same time, when the attenuation constant C<0, the function exp(C×η) decreases monotonically with the increase of the composite degradation index η, which is highly consistent with the actual trend of battery performance degradation with the intensification of degradation. In the early stage of cycling, the η value is small, and the exponential function changes gently, corresponding to a slow capacity attenuation; as the cycle progresses, the η value increases, and the exponential function attenuates rapidly, corresponding to a rapid capacity decline, which is highly consistent with the actual battery aging behavior.

[0141] As shown in Figure 2 , the present application provides a battery cycle performance evaluation system 100 for implementing the above method, comprising 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 positive electrode 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 a composite degradation index; and the evaluation output module 103 is used to evaluate the performance degradation stage of the battery according to the composite degradation index.

[0142] AsFigure 3 As shown, the present application provides an electronic device 200, comprising a processor 202 and a memory 201;

[0143] The processor 202 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0144] The memory 201 can include various types of storage units, such as system memory, read-only memory (ROM) and permanent storage device. Among them, the ROM can store static data or instructions required by the processor 202 or other modules of the computer. The permanent storage device can be a read-write storage device. The permanent storage device can be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a mass storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device can be a removable storage device (such as a floppy disk, an optical drive). The system memory can be a read-write storage device or a volatile read-write storage device, such as a dynamic random access memory. The system memory can store some or all of the instructions and data required by the processor during runtime. In addition, the memory 201 can include a combination of any computer readable storage medium, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic disks and / or optical disks. In some embodiments, the memory 201 can include a read and / or write removable storage device, such as a compact disc (CD), a read-only digital versatile disc (such as DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a min SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. The computer readable storage medium does not include a carrier wave and a transient electronic signal transmitted through wireless or wired transmission.

[0145] The memory 201 stores executable code, which when processed by the processor 202, can cause the processor 202 to perform part or all of the above-mentioned methods.

[0146] Furthermore, the method according to the present application can also be implemented as a computer program or computer program product comprising computer program code instructions for executing some or all of the steps of the above method according to the present application.

[0147] Alternatively, the present application can also be implemented as a computer readable storage medium (or non-transitory machine readable storage medium or machine readable storage medium) having stored thereon executable code (or computer program or computer instruction code) which, when executed by the processor 202 of the electronic device 200 (or server, etc.), causes the processor 202 to perform some or all of the steps of the above method according to the present application.

[0148] The present application will be further described below through specific examples, which are exemplary and do not have any limitation on the protection scope of the present application.

[0149] Example 1: Cycle performance evaluation of NCM811 ternary positive electrode material

[0150] 1. Material and battery preparation

[0151] Commercial NCM811 positive electrode material (LiNi 0.8 Co 0.1 Mn 0.1 O2) was selected and mixed with conductive agent (Super P) and binder (PVDF) at a mass ratio of 96:2:2, coated on aluminum foil, and then punched into positive electrode sheets after drying. With metal lithium as the negative electrode, Celgard 2320 as the separator, and 1M LiPF6in EC:DEC:EMC (1:1:1, vol%) as the electrolyte, CR2032 type button batteries were assembled in an argon glove box.

[0152] 2. Cycle test and sampling

[0153] The batteries were cycled at 0.5C constant current at 25℃, and a batch of batteries was taken out every 50 or 100 cycles. After disassembly, the positive electrode sheets were taken out, washed with DMC, vacuum dried, and then tested for physicochemical indicators.

[0154] 3. Physicochemical indicator test

[0155] Particle size distribution change rate (ΔDspan): The D10, D50, and D90 of the positive electrode material before and after cycling were tested using a laser particle size analyzer (Malvern Mastersizer 3000), and ΔDspan=[(Dspan 循环后 -Dspan 初始 ) / Dspan 初始 ]×100% was calculated.

[0156] pH change absolute value (ΔpH): the positive electrode material was immersed in deionized water, shaken and then left to stand, the pH value of the supernatant was measured using a pH meter, and ΔpH = |pH 循环后 -pH 初始 |.

[0157] Total residual alkali (R_alkali): the total content of LiOH and Li2CO3 was determined by acid-base titration.

[0158] Lattice distortion degree (Δ(c / a)): the diffraction pattern was collected using an X-ray diffractometer (XRD, Bruker D8 Advance), and the c / a value change was calculated by Rietveld refinement.

[0159] 4. Composite degradation index calculation

[0160] The composite degradation index model described in the application is used:

[0161]

[0162] 5. Battery performance degradation stage determination standard

[0163] When η < 1, the battery is determined to be in a healthy state;

[0164] When 1 ≤ η < 2.5, the battery is determined to be in a sub-healthy state;

[0165] When η ≥ 2.5, the battery is determined to be in a damaged state.

[0166] 6. Calculation of the health state value of the battery

[0167] The specific calculation formula is:

[0168]

[0169] 7. Results

[0170] Table 1 below is the specific prediction results, wherein the test method of the measured SOH is the capacity attenuation method, and the SOH error calculation formula is: SOH error = [(predicted SOH - measured SOH) / measured SOH] * 100%.

[0171] Table 1

[0172]

[0173] As can be seen from Table 1, the model predicted SOH is in good agreement with the measured SOH, the average error is small, and The value monotonically increases with the increase of the cycle number, accurately reflecting the state evolution of the battery from healthy to damaged.

[0174] Example 2: Cycle performance evaluation of LFP lithium iron phosphate positive electrode material

[0175] 1. Materials and tests

[0176] Commercial LFP positive electrode material was selected, and the battery was assembled in the same way as in Example 1. The cycle test conditions were 1 C charge and discharge, and the sample was tested every 100 cycles.

[0177] 2. Physicochemical index test

[0178] The same as Example 1.

[0179] 3. Model adaptation

[0180] The LFP material is less sensitive to lattice distortion, and the model coefficients are adjusted as follows:

[0181]

[0182] 4. Battery performance degradation stage determination criteria

[0183] The same as Example 1.

[0184] 5. Calculation of the state of health value of the battery

[0185] The same as Example 1.

[0186] 6. Results

[0187] Table 2 below shows the specific prediction results:

[0188] Table 2

[0189]

[0190] As can be seen from Table 2, the composite degradation index model after coefficient adjustment also shows excellent prediction accuracy for LFP material, the predicted SOH is in good agreement with the measured SOH, the average error is small, and the η value monotonically increases with the number of cycles, accurately reflecting the state evolution of LFP battery from healthy to damaged, proving the good applicability of the method of the present application to different positive electrode material systems.

[0191] Comparative example: single particle size parameter prediction method

[0192] The same batch of NCM811 battery data as in Example 1 was used, and only ΔDspan was substituted into the composite degradation index model to predict SOH. The results are as shown in Table 3 below:

[0193] Table 3

[0194]

[0195] 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.

[0196] 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 cycle performance of a battery, the method comprising: The method comprises the following steps: S100, acquiring physical and chemical index parameters of the positive electrode material after cycling, the physical and chemical index parameters comprising a particle size distribution change rate, an absolute value of pH change, total residual alkali amount, and a lattice distortion degree; S200, processing the physical and chemical index parameters based on a preset composite degradation index model to obtain a composite degradation index; Step S200 specifically comprises: S202, performing first normalization processing on the particle size distribution change rate, and then performing first nonlinear transformation to obtain a first characteristic term, the calculation formula of the first characteristic term being: N1=(ΔDspan / α1)^β; wherein N1 is the first characteristic term, ΔDspan is the particle size distribution change rate, α1 is a first reference value in the first normalization processing, the first nonlinear transformation is a power function transformation, and β is an index in the power function; the calculation formula of the particle size distribution change rate ΔDspan being: ΔDspan = [(Dspan 循环后 -Dspan 初始 ) / Dspan 初始 ] x 100%; wherein Dspan 初始 Dspan 循环后 is the particle size distribution width of the positive electrode material after cycling. S204, processing the absolute value of pH change based on a first coefficient to obtain a second characteristic term, the calculation formula of the second characteristic term being: N2=k1×ΔpH; wherein N2 is the second characteristic term, k1 is the first coefficient, and ΔpH is the absolute value of pH change; the calculation formula of the absolute value of pH change ΔpH being: ΔpH = |pH 循环后 -pH 初始 |; wherein pH 初始 is the pH value of the positive electrode material leaching solution before circulation, pH 循环后 is the pH value of the positive electrode material leaching solution after circulation; S206, performing second normalization processing on the total residual alkali amount to obtain a third characteristic term, the calculation formula of the third characteristic term being: N3 = R alkali / α2; wherein N3 is a third feature term, R alkali is the total residual base amount, and a2 is a second reference value in the second normalization process. total amount of residual alkali R alkali is the mass percentage of the alkali substance remaining in the positive electrode material; S208, processing the lattice distortion degree based on a second coefficient to obtain a fourth characteristic term, the calculation formula of the fourth characteristic term being: N4=k2×Δ(c / a); wherein N4 is the fourth characteristic term, k2 is the second coefficient, and Δ(c / a) is the lattice distortion degree; the calculation formula of the lattice distortion degree Δ(c / a) being: Δ(c / a) = |(c / a) 循环后 -(c / a) 初始 |; wherein (c / a) 初始 (c / a)0 is the ratio of the c-axis to the a-axis in the crystal structure of the positive electrode material calculated by X-ray diffraction refinement before cycling, 循环后 (c / a)1 is the ratio of the c-axis to the a-axis in the crystal structure of the positive electrode material calculated by X-ray diffraction refinement after cycling; S210, calculating the composite degradation index based on the first characteristic term, the second characteristic term, the third characteristic term, and the fourth characteristic term, the calculation formula of the composite degradation index being: η=N1+N2+N3+N4; wherein η is the composite degradation index; S300, evaluating the performance degradation stage of the battery according to the composite degradation index.

2. The battery cycle performance evaluation method according to claim 1, characterized by, Step S204 further comprises processing the absolute value of pH change based on the first coefficient and a third coefficient to obtain the second characteristic term.

3. The battery cycle performance evaluation method according to claim 1, characterized by, Step S208 further comprises processing the lattice distortion degree based on the second coefficient and a fourth coefficient to obtain the fourth characteristic term.

4. The battery cycle performance evaluation method according to claim 1, characterized by, Step S300 specifically comprises: when the composite degradation index is less than a first preset threshold, determining that the battery is in a healthy state; when the composite degradation index is not less than a second preset threshold, determining that the battery is in a damaged state; when the composite degradation index is between the first preset threshold and the second preset threshold, determining that the battery is in a sub-healthy state; wherein the first preset threshold is less than the second preset threshold.

5. The battery cycle performance evaluation method according to claim 1, characterized by, The battery cycle performance evaluation method further comprises step S400 of evaluating the health state value of the battery according to the composite degradation index.

6. The battery cycle performance evaluation method according to claim 5, characterized by, Step S400 specifically comprises performing second nonlinear transformation on a fifth coefficient and the composite degradation index, and then performing third normalization processing to obtain the health state value of the battery.

7. A battery cycle performance evaluation system for implementing the method of any one of claims 1 to 6, characterized by, comprises: a data acquisition module configured to acquire physical and chemical index parameters of a positive electrode material after cycling; The processing calculation module is configured to process the physicochemical index parameter based on a preset composite degradation index model to obtain a composite degradation index. The evaluation output module is configured to evaluate a performance degradation stage of the battery according to the composite degradation index.

8. An electronic device, comprising: The method comprises: a processor; and a memory having stored thereon executable code that, when executed by the processor, causes the processor to perform the method of any one of claims 1 to 6. a memory having stored thereon executable code that, when executed by the processor of the electronic device, causes the processor to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, ​

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