Method for preparing positive electrode sheet of lithium ion battery, positive electrode sheet and lithium ion battery

Abstract

The application discloses a positive electrode sheet preparation method of a lithium ion battery, a positive electrode sheet and the lithium ion battery, and is applied to the technical field of battery manufacturing, so as to solve the problem that in the prior art, only carbon black conductive agents are selected according to specific surface areas, which leads to the fact that the electronic conduction of the positive electrode sheet and the ion transmission cannot be coordinated, the solid-state diffusion impedance is increased, and the rate performance is limited. Specifically, a compression oil absorption value and a specific surface area of a carbon black conductive agent to be selected are obtained, a ratio of the compression oil absorption value to the specific surface area is taken as a current structure characteristic parameter; according to the current structure characteristic parameter, a structure category of the carbon black conductive agent to be selected and a predicted solid-state diffusion impedance characteristic are determined; when the predicted solid-state diffusion impedance characteristic meets a preset condition, a target addition amount of the carbon black conductive agent to be selected is determined according to the structure category, so as to prepare the positive electrode sheet. In this way, the structure characteristic parameter is differentiated and added, the electronic ion transmission is optimized, the solid-state diffusion impedance is reduced, and the rate performance is improved.

Description

Technical Field

[0001] This application relates to the field of battery manufacturing technology, and in particular to a method for preparing a positive electrode sheet for a lithium-ion battery, the positive electrode sheet, and the lithium-ion battery. Background Technology

[0002] As an important electrochemical energy storage device, the performance of the positive electrode sheet in lithium-ion batteries has a decisive influence on the overall electrochemical performance of the battery. The positive electrode sheet typically includes positive electrode active material, binder, and conductive agent. Among them, carbon black, as an indispensable conductive agent, plays a crucial role in constructing an efficient electronic conductive network between the positive electrode active material particles and between the particles and the current collector, thereby reducing the internal resistance of the electrode and improving the rate performance of the battery.

[0003] Currently, the selection of carbon black conductive agents is mainly based on the key parameter of specific surface area (SSA), assuming that carbon black with a high specific surface area can provide more conductive contact points, thereby improving the electronic conductivity of the electrode. However, relying solely on specific surface area for the selection and addition of conductive agents ignores the impact of the structural integrity of carbon black secondary aggregates on ion transport channels. Carbon black with a high specific surface area is often accompanied by a low degree of secondary aggregate structure. While providing more electronic contact points, it irreversibly adsorbs and binds a large amount of electrolyte, forming a highly tortuous porous structure that severely hinders the solid-state diffusion process of lithium ions within the positive electrode active material particles. This results in the inability to coordinate and optimize electron conduction and ion transport within the positive electrode, leading to a sharp increase in solid-state diffusion resistance. When the battery operates at high rates, the electrode reaction rate is controlled in series by multiple steps, including charge transfer and solid-state diffusion. According to electrode process kinetics, the sharp increase in solid-state diffusion impedance makes the solid-state diffusion process a rate-controlling step under high-rate conditions. The diffusion overpotential will dominate the overall polarization of the battery, resulting in a significant reduction in the voltage plateau and a sharp drop in discharge capacity under high-rate charge and discharge conditions, which severely limits the rate performance. Summary of the Invention

[0004] This application provides a method for preparing a positive electrode sheet for a lithium-ion battery, a positive electrode sheet, and a lithium-ion battery, in order to solve the problem in the prior art where carbon black conductive agent is selected solely based on specific surface area, resulting in a lack of coordination between electronic conduction and ion transport in the positive electrode sheet, leading to increased solid-state diffusion impedance and thus limiting rate performance.

[0005] The technical solutions provided in this application are as follows: On one hand, embodiments of this application provide a method for preparing a positive electrode sheet for a lithium-ion battery, comprising: Obtain the compression oil absorption value and specific surface area of ​​the candidate carbon black conductive agent; The ratio of compression oil absorption value to specific surface area is used as the current structural characteristic parameter of the candidate carbon black conductive agent. Based on the current structural characteristic parameters, determine the structural category of the candidate carbon black conductive agent and predict its solid-state diffusion impedance characteristics; When the predicted solid-state diffusion impedance characteristics meet the preset conditions, the target addition amount of the candidate carbon black conductive agent is determined according to the structure category, so that the candidate carbon black conductive agent can be added to the positive electrode slurry to prepare the positive electrode sheet according to the target addition amount.

[0006] Optionally, based on the current structural characteristic parameters, determine the structural category and predict the solid-state diffusion impedance characteristics of the candidate carbon black conductive agent, including: The structural category of the candidate carbon black conductive agent is determined based on the current structural characteristic parameters and structural characteristic thresholds. By inputting the current structural characteristic parameters into the relationship model corresponding to the structural category of the candidate carbon black conductive agent, the predicted solid-state diffusion impedance characteristics are obtained.

[0007] Optionally, before determining the structural category of the candidate carbon black conductive agent based on the current structural characteristic parameters and structural characteristic thresholds, the following steps are also included: Obtain target rate performance data and target operating temperature range data for the positive electrode sheet; Based on the correspondence between the target magnification performance data and the preset magnification levels, determine the magnification adjustment coefficient; The temperature adjustment coefficient is determined based on the correspondence between the target operating temperature range data and the preset temperature sensitivity level. The adjustment factor is determined based on the rate adjustment coefficient and the temperature adjustment coefficient; The preset baseline structural feature threshold is adjusted according to the adjustment factor to obtain the structural feature threshold.

[0008] Optionally, the structure category includes a high-structure category and a low-structure category; the current structural feature parameters are input into the relationship model corresponding to the structure category to obtain the predicted solid-state diffusion impedance characteristics, including: When the structure category of the carbon black conductive agent to be selected is high structure degree category, the current structural feature parameters are input into the exponential decay model corresponding to the high structure degree category to obtain the predicted solid-state diffusion impedance characteristics. When the structural category of the candidate carbon black conductive agent is low structure degree, the current structural feature parameters are input into the linear boundary model corresponding to the low structure degree category to obtain the predicted solid-state diffusion impedance characteristics.

[0009] Optionally, the exponential decay model and the linear boundary model are established in advance in the following way: Solid-state diffusion impedance characteristics corresponding to different structural feature parameters obtained by electrochemical impedance spectroscopy and relaxation time distribution analysis of each positive electrode sample were obtained; wherein, each positive electrode sample was prepared by adding multiple carbon black conductive agent samples with different structural feature parameters into the positive electrode slurry according to a preset addition amount. Based on the range of structural characteristic parameters and solid-state diffusion impedance characteristics corresponding to the high-structure category, an exponential decay model is fitted. Based on the range of structural characteristic parameters and solid-state diffusion impedance characteristics corresponding to the low-structure category, a linear boundary model is fitted.

[0010] Optionally, the target addition amount of the selected carbon black conductive agent can be determined based on the structural category, including: When the structure category is high structure degree, the regular addition amount is determined as the target addition amount; When the structure category is low structure degree category, the current structure feature parameters are input into the optimal addition amount prediction model to obtain the reduction addition amount corresponding to the candidate carbon black conductive agent, and the reduction addition amount is used as the target addition amount.

[0011] Optionally, the target addition amount can be determined based on the relationship curve between solid-state diffusion impedance characteristics and addition amount, including: Input the current structural characteristic parameters into the optimal addition amount prediction model to obtain the relationship curve between the solid diffusion impedance characteristics and the addition amount of the candidate carbon black conductive agent; The amount added corresponding to the minimum point of the solid diffusion impedance characteristic in the relationship curve is taken as the amount to be reduced, and the amount to be reduced is determined as the target amount to be added.

[0012] Optionally, the optimal addition amount prediction model can be pre-established in the following ways: For each structural feature parameter in the low structure degree category, the solid-state diffusion impedance characteristics corresponding to different addition amounts are obtained by electrochemical impedance spectroscopy and relaxation time distribution analysis of each positive electrode sample corresponding to the carbon black conductive agent sample with structural feature parameters. Among them, each positive electrode sample corresponding to the carbon black conductive agent sample with structural feature parameters is prepared by adding the carbon black conductive agent sample with structural feature parameters to the positive electrode slurry according to different addition amounts. Based on each structural feature parameter and the different addition amounts and solid-state diffusion impedance characteristics corresponding to each structural feature parameter, the relationship curve between the solid-state diffusion impedance characteristics and the addition amount corresponding to each structural feature parameter is fitted. An optimal addition amount prediction model is established based on the relationship curve between the solid diffusion impedance characteristics and the addition amount corresponding to each structural characteristic parameter.

[0013] Optionally, an optimal addition amount prediction model can be established based on the relationship curve between the solid-state diffusion impedance characteristics and the addition amount corresponding to each structural characteristic parameter, including: For each structural feature parameter in the low structure degree category, based on the relationship curve between the solid diffusion impedance characteristic corresponding to the structural feature parameter and the amount of additive, the amount of additive reduction corresponding to the minimum point of each relationship curve is determined. Based on the structural characteristic parameters and their corresponding reduction amounts, curve fitting is performed to obtain the optimal addition amount prediction model.

[0014] On the other hand, embodiments of this application provide a positive electrode sheet for a lithium-ion battery, which is prepared using the above-described positive electrode sheet preparation method.

[0015] On the other hand, embodiments of this application provide a lithium-ion battery, including: the above-mentioned positive electrode sheet.

[0016] The beneficial effects of the embodiments of this application are as follows: In this embodiment, by obtaining the compression oil absorption value and specific surface area of ​​the candidate carbon black conductive agent and using their ratio as the current structural characteristic parameter, the limitation of prior art in evaluating conductive agents solely based on specific surface area is overcome. This current structural characteristic parameter simultaneously reflects the structural integrity and surface characteristics of the carbon black secondary aggregates, enabling the selection of conductive agents to achieve synergistic optimization of electronic conductivity and ion transport characteristics. By determining the structural category and corresponding predicted solid-state diffusion impedance characteristics based on the current structural characteristic parameters, a priori prediction of the solid-state diffusion behavior of carbon black conductive agents in the positive electrode is achieved. When the predicted solid-state diffusion impedance characteristics meet preset conditions, the target addition amount is determined based on the structural category differences, achieving synergy between electronic conductivity network construction and ion transport channel optimization, avoiding the imbalance between electronic conductivity and ion transport caused by a single addition amount strategy. As a result, the solid-state diffusion impedance inside the positive electrode is reduced, and the coupling polarization between the charge transfer process and the solid-state diffusion process is alleviated, thereby improving the battery's voltage plateau maintenance capability and discharge capacity under high-rate charge and discharge conditions, and enhancing rate performance.

[0017] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic flowchart outlining the preparation method of the positive electrode sheet of the lithium-ion battery in the embodiments of this application. Figure 2 This is a schematic diagram illustrating the specific process of the structural feature threshold determination method in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the specific process of establishing the exponential decay model and the linear boundary model in the embodiments of this application; Figure 4 This is a table of test data for each positive electrode sample in the embodiments of this application; Figure 5 This is a graph representing the relationship curves of the exponential decay model in the embodiments of this application; Figure 6 This is a graph representing the relationship curves of the linear boundary model in the embodiments of this application; Figure 7 This is a schematic diagram illustrating the specific process of the optimal addition amount prediction model method in the embodiments of this application; Figure 8 This is a graph showing the relationship between the amount added and the solid-state diffusion impedance characteristics in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and beneficial effects of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Currently, in the field of lithium-ion battery cathode electrode preparation technology, carbon black, as an indispensable conductive agent, plays a crucial role in constructing a highly efficient electronic conductive network between cathode active material particles and between particles and current collectors. This reduces electrode internal resistance and improves battery rate performance. Regarding the selection of carbon black as a conductive agent, it is generally believed that the higher the specific surface area of ​​carbon black, the more conductive contact points it can provide, resulting in better electronic conductivity of the electrode and ultimately better battery rate performance. However, experimental data shows that selecting conductive agents based on the linear logic of "high specific surface area = high conductivity = high rate performance" has significant drawbacks: under the same addition amount, the rate performance of batteries prepared with ultra-high specific surface area carbon black (such as SSA > 700 m² / g) does not show a significant advantage compared to some high specific surface area carbon blacks (such as SSA around 150 m² / g); similarly, the rate performance of high specific surface area carbon black (such as SSA around 150 m² / g) also does not show a significant advantage compared to low specific surface area carbon black (SSA < 90 m² / g). The linear logic of "high specific surface area = high conductivity = high rate performance" ignores the impact of the structural integrity of carbon black secondary aggregates on ion transport channels. Specifically, carbon black with excessively high specific surface area is often accompanied by low secondary aggregate structure. While providing more electron contact points and improving electronic conductivity, this type of carbon black irreversibly adsorbs and binds a large amount of electrolyte, forming a highly tortuous porous structure. This severely hinders the solid-state diffusion of lithium ions within the positive electrode active material particles, leading to a sharp increase in solid-state diffusion resistance. Furthermore, excessively high specific surface area exacerbates interfacial side reactions. The excessively high solid-liquid interface continuously catalyzes electrolyte decomposition, forming a thicker, higher-resistance cathode electrolyte interfacial film, consuming active lithium, and impairing battery cycle life.

[0021] From the theoretical analysis of porous electrodes, electrode performance is determined by the effective electronic conductivity σ. eff and effective ion diffusion coefficient D eff This was decided jointly. Traditional high specific surface area, low compression oil absorption value carbon black, although it may increase σ... eff However, due to its blocking of pores and adsorption of electrolyte, it severely reduces D. eff According to the tortuosity theory, low-compression-absorption carbon black, with its high specific surface area and "potato-like" structure, forms a highly tortuous labyrinthine pore structure, severely hindering ion transport; while high-compression-absorption carbon black forms a well-developed chain-like network that helps construct low-torsional-absorption pore channels, improving D... effTherefore, simply pursuing specific surface area cannot achieve synergistic optimization of electronic conduction and ion transport. When the battery operates at high rates, the electrode reaction rate is controlled in series by multiple steps, including charge transfer, ion diffusion (especially solid-phase diffusion), and interfacial impedance. According to electrode process kinetics, the diffusion step tends to become the rate-controlling step as the current increases. The Butler-Volmer equation shows that the battery's overpotential is mainly dominated by diffusion overpotential, making solid-state diffusion impedance a limiting factor. Existing technologies suffer from significant solid-state diffusion impedance due to improper selection of conductive agents, resulting in severe coupling polarization between charge transfer and solid-state diffusion processes. This leads to a significant reduction in the voltage plateau and a sharp decrease in discharge capacity under high-rate charge-discharge conditions, limiting rate performance. Existing technologies lack effective remedial measures when faced with poor-performing carbon black conductive agents. The conventional approach is to further increase the amount of conductive agent or replace it with another product with a high specific surface area. This not only fails to effectively improve solid-state diffusion impedance but also leads to a contradiction between raw material costs and slurry solid content control, resulting in a blind, inefficient, and costly optimization process.

[0022] In summary, current technologies select carbon black conductive agents solely based on specific surface area, lacking evaluation indicators based on intrinsic structural characteristics and a systematic method for differentiated addition amounts. This leads to an imbalance between electron conduction and ion transport in the positive electrode, resulting in excessively high solid-state diffusion resistance. When faced with carbon blacks with poor structural properties, the only recourse is to blindly increase the dosage or change materials through trial and error, leading to inefficient and costly optimization processes. Therefore, there is an urgent need for a positive electrode preparation method that can overcome the technological bias that high specific surface area equates to high performance, and solve the problems of high solid-state diffusion resistance and limited high-rate performance caused by selecting carbon black conductive agents solely based on specific surface area in current technologies.

[0023] To address the aforementioned technical problems, this application provides a method for preparing a positive electrode sheet for a lithium-ion battery. (See attached document.) Figure 1 As shown, the general flow of the method for preparing the positive electrode of a lithium-ion battery provided in this application embodiment is as follows: Step 101: Obtain the compression oil absorption value and specific surface area of ​​the carbon black conductive agent to be selected.

[0024] In practical applications, the Compressed Oil Absorption Number (COA) characterizes the structural integrity and oil absorption capacity of carbon black secondary aggregates under pressure. The COA can be determined using the compressed DBP (dibutyl phthalate) absorption method. Specific surface area characterizes the sum of the external surface area and the internal pore surface area of ​​carbon black particles and aggregates. Specific surface area can be determined using the nitrogen adsorption method.

[0025] Step 102: Use the ratio of compression oil absorption value to specific surface area as the current structural characteristic parameter of the carbon black conductive agent to be selected.

[0026] In practical applications, the unit for compressible oil absorption is mL / 100g, and the unit for specific surface area is m² / g. The current structural characteristic parameter is the compressible oil absorption value divided by the specific surface area. The current structural characteristic parameter reflects both the structural integrity and surface properties of the carbon black secondary aggregates, and can characterize the comprehensive ability of carbon black in the positive electrode to balance electron conduction and ion transport. The unit for the current structural characteristic parameter is mL / 100m².

[0027] Step 103: Based on the current structural characteristic parameters, determine the structural category of the candidate carbon black conductive agent and predict its solid-state diffusion impedance characteristics.

[0028] In practical applications, candidate carbon black conductive agents are classified into different structural categories based on their current structural characteristic parameters. A larger current structural characteristic parameter indicates that the carbon black has a high degree of secondary aggregate structure, capable of forming a well-developed three-dimensional chain network, thus classifying the candidate carbon black conductive agent as a high-structure category. Conversely, a smaller current structural characteristic parameter indicates a lower degree of secondary aggregate structure, exhibiting a dispersed granular or "potato-like" structure, thus classifying the candidate carbon black conductive agent as a low-structure category. Predicted solid-state diffusion impedance characteristics characterize the magnitude of solid-state diffusion resistance of lithium ions within the positive electrode active material particles. High-structure category carbon black, due to its ability to construct low-torsion ion transport channels, corresponds to a lower predicted solid-state diffusion impedance characteristic; low-structure category carbon black, due to its adsorption of electrolyte and pore blocking effect, corresponds to a higher predicted solid-state diffusion impedance characteristic. Based on the correspondence between structural characteristic parameters and solid-state diffusion impedance characteristics, the solid-state diffusion impedance characteristics of the candidate carbon black conductive agent in the positive electrode sheet are predicted, i.e., the predicted solid-state diffusion impedance characteristics.

[0029] Step 104: When the predicted solid-state diffusion impedance characteristics meet the preset conditions, determine the target addition amount of the candidate carbon black conductive agent according to the structure category, so as to prepare the positive electrode sheet by adding the candidate carbon black conductive agent into the positive electrode slurry according to the target addition amount.

[0030] In practical applications, the preset condition is that the predicted solid-state diffusion resistance characteristic is lower than a preset upper limit value. This upper limit value is predetermined based on the battery's target rate performance requirements and the characteristics of the positive electrode active material, representing an acceptable level of solid-state diffusion resistance. When the predicted solid-state diffusion resistance characteristic meets this preset condition, it indicates that the candidate carbon black conductive agent meets the performance requirements for positive electrode preparation, and the step of determining the target addition amount based on the structure category continues. When the predicted solid-state diffusion resistance characteristic does not meet this preset condition, the candidate carbon black conductive agent is deemed not to meet the performance requirements and must be excluded from use. The target addition amount refers to the proportion of carbon black conductive agent added relative to the mass of the positive electrode active material, determined based on the structure category of the candidate carbon black conductive agent. When the predicted solid-state diffusion resistance characteristic meets this preset condition, for high-structure categories, the target addition amount is determined based on the requirement to fully utilize the candidate carbon black conductive agent's ability to construct a three-dimensional conductive network; for low-structure categories, the target addition amount is determined based on the requirement to balance the construction of the electronic conductive network and the ion transport blocking effect, so that the candidate carbon black conductive agent provides the necessary electronic pathways while minimizing solid-state diffusion resistance. After determining the target addition amount, calculate the required mass of carbon black conductive agent based on the target addition amount; weigh each component according to the calculated actual weight value of carbon black conductive agent, the predetermined mass of positive electrode active material, and the corresponding ratio of binder; add the weighed carbon black conductive agent, positive electrode active material and binder to solvent, and form a uniform positive electrode slurry by high-speed dispersion or vacuum stirring; coat the positive electrode slurry onto the surface of the current collector, and obtain the positive electrode sheet after drying and rolling.

[0031] This application's technical solution overcomes the limitation of existing technologies that evaluate conductive agents solely based on specific surface area by obtaining the compression oil absorption value and specific surface area of ​​the candidate carbon black conductive agent and using their ratio as the current structural characteristic parameter. This current structural characteristic parameter simultaneously reflects the structural integrity and surface characteristics of the carbon black secondary aggregates, enabling the selection of conductive agents to achieve synergistic optimization of electronic conductivity and ion transport characteristics. By determining the structural category and corresponding predicted solid-state diffusion impedance characteristics based on the current structural characteristic parameters, a priori prediction of the solid-state diffusion behavior of carbon black conductive agents in the positive electrode is achieved. When the predicted solid-state diffusion impedance characteristics meet preset conditions, the target addition amount is determined based on the structural category differences, achieving synergy between electronic conductivity network construction and ion transport channel optimization, avoiding the imbalance between electronic conductivity and ion transport caused by a single addition amount strategy. As a result, the solid-state diffusion impedance inside the positive electrode is reduced, and the coupling polarization between the charge transfer process and the solid-state diffusion process is alleviated, thereby improving the battery's voltage plateau maintenance capability and discharge capacity under high-rate charge and discharge conditions, and enhancing rate performance.

[0032] In one possible implementation, the structural category of the candidate carbon black conductive agent and the predicted solid-state diffusion impedance characteristics are determined based on the current structural characteristic parameters. This can be achieved, but is not limited to, the following methods: First, the structural category of the candidate carbon black conductive agent is determined based on the current structural characteristic parameters and structural characteristic thresholds.

[0033] Then, the current structural feature parameters are input into the relationship model corresponding to the structural category of the carbon black conductive agent to be selected, and the predicted solid-state diffusion impedance characteristics are obtained.

[0034] In practical applications, the structural feature threshold is a boundary value used to distinguish the integrity level of the secondary aggregate structure of carbon black. The structural feature threshold can be determined in advance based on experimental data or adaptively adjusted according to battery performance requirements and the characteristics of the positive electrode active material. When the current structural feature parameter is greater than or equal to the structural feature threshold, the candidate carbon black conductive agent is determined to belong to the high-structure category, indicating that the candidate carbon black conductive agent has a well-developed chain network structure; when the current structural feature parameter is less than the structural feature threshold, the candidate carbon black conductive agent is determined to belong to the low-structure category, indicating that the secondary aggregate structure of the candidate carbon black conductive agent is underdeveloped. The preset structural feature threshold can be set to 1.2 mL / 100m² or 2 mL / 100m². After determining the structure category, the current structural feature parameter is input into the relational model corresponding to the structure category to obtain the predicted solid-state diffusion impedance characteristics output by the relational model. The relational model is a mapping function established based on physical mechanisms and experimental data, characterizing the quantitative correlation between the current structural feature parameter and the solid-state diffusion impedance characteristics. Specifically, the preset structural feature threshold can be set to 1.2 mL / 100m². For carbon black conductive agent A, its current structural characteristic parameter of 0.6 mL / 100 m² is less than or equal to the threshold, so it is determined to be in the low structure degree category; for carbon black conductive agent B, its current structural characteristic parameter of 2 mL / 100 m² is greater than the threshold, so it is determined to be in the high structure degree category.

[0035] In this way, by introducing structural feature thresholds, rapid classification of the selected carbon black conductive agents is achieved. A quantitative correlation between structural feature parameters and predicted solid-state diffusion impedance is established through a relational model, significantly improving the efficiency and controllability of the preparation process. By substituting the current structural feature parameters into the corresponding relational model, the prediction and calculation of solid-state diffusion impedance characteristics can be completed without preparing actual electrodes, enabling prior performance evaluation.

[0036] In one possible implementation, see [reference] Figure 2 As shown, before determining the structural category of the candidate carbon black conductive agent based on the current structural feature parameters and structural feature thresholds, the following steps are also included: Step 201: Obtain the target rate performance data and target operating temperature range data of the lithium-ion battery.

[0037] In practical applications, the target rate performance data of lithium-ion batteries characterizes the charge / discharge rate specifications that the positive electrode must meet, such as 1C, 3C, 5C, or higher rates. Target rate performance data reflects the battery design's requirements for instantaneous current carrying capacity. Target operating temperature range data characterizes the designed operating temperature range of the lithium-ion battery, such as the room temperature range (25℃), low temperature range (-20℃ to 0℃), or ultra-low temperature range (below -20℃). The target operating temperature range reflects the difference in sensitivity of temperature conditions to lithium-ion solid-state diffusion kinetics; the lower the temperature, the smaller the solid-state diffusion coefficient, and the more sensitive the diffusion resistance is to temperature.

[0038] Step 202: Determine the rate adjustment coefficient based on the correspondence between the target rate performance data and the preset rate level.

[0039] In practical applications, a mapping relationship is pre-established between rate performance data and preset rate levels, as well as between rate levels and rate adjustment coefficients. The preset rate levels are based on the battery charge / discharge rate range; for example, the discharge rate range corresponding to a standard rate level is 0.5C-2C, the high rate level is 2C-5C, and the ultra-high rate level is greater than 5C. The standard rate level corresponds to a rate adjustment coefficient of 1.0, the high rate level to 1.2-1.5, and the ultra-high rate level to 1.6-2.0. The target rate performance data is matched with the preset rate levels to determine its corresponding rate level range, and the rate adjustment coefficient is determined using table lookup or interpolation. A higher target rate performance requirement corresponds to a larger rate adjustment coefficient, indicating that a higher density carbon black is needed to maintain ion transport capability under high current density conditions.

[0040] Step 203: Determine the temperature adjustment coefficient based on the correspondence between the target operating temperature range data and the preset temperature sensitivity level.

[0041] In practical applications, a mapping relationship is pre-established between the target operating temperature range data and the preset temperature sensitivity level, as well as a mapping relationship between the temperature sensitivity level and the temperature adjustment coefficient. The preset temperature sensitivity level is based on the degree of influence of temperature on the lithium-ion solid-state diffusion coefficient. For example, the room temperature level corresponds to a temperature range of 20℃ to 35℃, such as 25℃, and the corresponding temperature adjustment coefficient is 1.0; the low temperature level corresponds to a temperature range of -20℃ to 20℃, and the corresponding temperature adjustment coefficient is 1.2-1.5; the ultra-low temperature level corresponds to a temperature range below -20℃, and the corresponding temperature adjustment coefficient is 1.6-2.0. The target operating temperature range data is matched with the preset temperature sensitivity level to determine its corresponding temperature sensitivity level, and the temperature adjustment coefficient is determined by table lookup or interpolation. The lower the target operating temperature, the larger the temperature adjustment coefficient, indicating a significant increase in solid-state diffusion impedance under low-temperature conditions, requiring carbon black with higher structural density to reduce solid-state diffusion impedance and compensate for the attenuation of ion transport performance at low temperatures.

[0042] Step 204: Determine the adjustment factor based on the rate adjustment coefficient and the temperature adjustment coefficient.

[0043] In practical applications, the multiplier adjustment coefficient and the temperature adjustment coefficient are integrated into an adjustment factor through a weighted summation method; specifically, the adjustment factor can be expressed as: Adjustment factor = α × rate adjustment coefficient + γ × temperature adjustment coefficient; Where α is the rate weighting coefficient and γ is the temperature weighting coefficient, and α + γ = 1. The weighting coefficients are set according to the battery application type and environmental emphasis. For high-power lithium-ion batteries, such as power tools and start-stop power supplies, since the high-rate discharge requirement is the main concern, the rate weighting coefficient α is set to a higher value, and the temperature weighting coefficient γ is set to a lower value, making the adjustment factor more sensitive to changes in rate demand. For low-temperature application lithium-ion batteries, such as polar energy storage and cold-region electric vehicles, since ensuring ion transport in low-temperature environments is the main concern, the rate weighting coefficient α is set to a lower value, and the temperature weighting coefficient γ is set to a higher value, making the adjustment factor more sensitive to the influence of temperature conditions. For conventional power lithium-ion batteries, such as electric vehicles operating at room temperature, considering both rate and temperature adaptability, the rate weighting coefficient α and the temperature weighting coefficient γ can be set equally or differentiated according to the specific vehicle platform.

[0044] Step 205: Adjust the preset baseline structural feature threshold according to the adjustment factor to obtain the structural feature threshold.

[0045] In practical applications, the preset benchmark structural feature threshold is multiplied by the adjustment factor to obtain the structural feature threshold applicable to the current battery system. For high-rate and diffusion-sensitive systems, the structural feature threshold is increased relative to the benchmark structural feature threshold, thereby selecting carbon black with a better structure. For conventional room-temperature energy storage systems, the structural feature threshold is maintained at the benchmark structural feature threshold or slightly adjusted to balance cost and performance.

[0046] For example, for a lithium-ion battery system applied to commercial electric buses with a target rate performance of 3C discharge and a target operating temperature range of -15℃ to 45℃: First, 3C is determined to be a high rate level, with a corresponding rate adjustment coefficient of 1.3; the temperature adjustment coefficient for -15℃ to 45℃ is determined to be 1.2. Considering that commercial electric buses need to balance rate performance and temperature adaptability, the rate weighting coefficient α is set to 0.5, and the temperature weighting coefficient γ is set to 0.5, resulting in an adjustment factor of 0.5 × 1.3 + 0.5 × 1.2 = 1.25. Multiplying the preset baseline structural characteristic threshold of 1.2 mL / 100 m² by the adjustment factor 1.25 yields a structural characteristic threshold of 1.50 mL / 100 m² suitable for this system.

[0047] Thus, by incorporating target rate performance data and target operating temperature range data from lithium-ion batteries, a dynamic adaptive adjustment mechanism for structural characteristic thresholds was established. Through a pre-established mapping relationship, a quantitative conversion from application requirements to technical parameters was achieved, enabling the preparation method to be compatible with the technical needs of different application scenarios. For high-power dynamic systems or low-temperature application systems, the threshold can be increased to ensure the structural quality of carbon black and compensate for the degradation of diffusion kinetics at low temperatures; for conventional room-temperature systems, the threshold can be appropriately relaxed to optimize costs. This adaptive mechanism significantly improves the engineering applicability and flexibility of the preparation method, avoiding over-screening or under-screening problems caused by a single fixed standard.

[0048] In one possible implementation, the structure categories include high-structure categories and low-structure categories. The current structural characteristic parameters are input into the relationship model corresponding to the structure category to obtain the predicted solid-state diffusion impedance characteristics. This can be achieved, but is not limited to, the following methods: When the structure category of the carbon black conductive agent to be selected is high structure degree, the current structural characteristic parameters are input into the exponential decay model corresponding to the high structure degree category to obtain the predicted solid-state diffusion impedance characteristics.

[0049] When the structural category of the candidate carbon black conductive agent is low structure degree, the current structural feature parameters are input into the linear boundary model corresponding to the low structure degree category to obtain the predicted solid-state diffusion impedance characteristics.

[0050] In practical applications, when the candidate carbon black conductive agent is determined to belong to the high-structure category, the current structural characteristic parameters are input into the exponential decay model. The exponential decay model is a function that rapidly decreases and approaches an asymptote as the structural characteristic parameter value increases. Its physical meaning is that once the carbon black secondary aggregate structure reaches a certain integrity, the marginal effect of further increasing the structure on reducing solid-state diffusion resistance gradually weakens, and the impedance value tends to a relatively low limiting level. This reflects the characteristic that high-structure carbon black can form efficient ion transport channels by constructing a well-developed chain network. By substituting the current structural characteristic parameter value into this model, the quantified predicted solid-state diffusion resistance characteristics are calculated. When the candidate carbon black conductive agent is determined to belong to the low-structure category, the current structural characteristic parameters are input into the linear boundary model. The linear boundary model is a function that approximately linearly increases as the structural characteristic parameter value decreases. Its physical meaning is that the less developed the carbon black secondary aggregate structure within this range, the greater the tortuosity of the formed pore structure, the stronger the adsorption and binding of the electrolyte, and the solid-state diffusion resistance deteriorates approximately linearly. This reflects the direct correlation between structural defects and ion transport resistance in low-structure carbon black. By substituting the current structural characteristic parameter values ​​into the model, the quantitative predicted solid-state diffusion impedance characteristics can also be obtained.

[0051] In this way, by adopting corresponding relational models for different structural categories, mathematical correlations were established between the current structural characteristic parameters and the solid-state diffusion impedance characteristics, reflecting the differences in ion transport mechanisms among different structural categories of carbon black. By substituting the current structural characteristic parameters into the corresponding relational models, the predicted solid-state diffusion impedance characteristics can be calculated without preparing actual electrodes, realizing the prediction of the material's electrochemical performance. This provides data support for subsequent process decisions and avoids the subjectivity of traditional experience-based judgments.

[0052] In one possible implementation, see [reference] Figure 3 As shown, the exponential decay model and the linear boundary model are established in advance in the following way: Step 301: Obtain the solid-state diffusion impedance characteristics corresponding to different structural feature parameters obtained by electrochemical impedance spectroscopy and relaxation time distribution analysis of each positive electrode sample; wherein, each positive electrode sample is prepared by adding multiple carbon black conductive agent samples with different structural feature parameters into the positive electrode slurry according to a preset addition amount.

[0053] In practical applications, multiple carbon black conductive agent samples covering the entire range from high to low structural characteristic parameters are selected to ensure sufficient distribution breadth and gradient differences in the current structural characteristic parameters. Each carbon black sample is added to the positive electrode slurry according to a preset standard addition amount, and the corresponding positive electrode samples are prepared using the same preparation process to control the consistency of variables other than carbon black structural characteristics. The standard addition amount can be a uniform, conventional addition amount, or it can be set according to different structural categories. Electrochemical impedance spectroscopy (EIS) is performed on each positive electrode sample to obtain impedance response data over a wide frequency range. Further, using Distribution of Relaxation Times (DRT) analysis, the complex impedance spectrum is deconvolved into relaxation processes with different time constants, accurately extracting the mid-to-low frequency characteristic peak intensities representing the solid-state diffusion of lithium ions within the positive electrode active material particles as measured values ​​of the solid-state diffusion impedance characteristics. A dataset of different current structural characteristic parameters and corresponding solid-state diffusion impedance characteristics is established.

[0054] Step 302: Based on the range of structural characteristic parameters and solid-state diffusion impedance characteristics corresponding to the high structural density category, an exponential decay model is fitted.

[0055] In practical applications, a subset of data belonging to the high-structure category is selected from the dataset, i.e., data points whose current structural feature parameters are greater than or equal to a preset structural feature threshold. A nonlinear fitting method is then used to fit an exponential function to the data subset, establishing an exponential decay relationship between the current structural feature parameters and the solid-state diffusion impedance characteristics, thus obtaining the mathematical expression and fitting parameters of the exponential decay model.

[0056] Step 303: Based on the range of structural characteristic parameters and solid-state diffusion impedance characteristics corresponding to the low-structure category, a linear boundary model is fitted.

[0057] In practical applications, a subset of data belonging to the low-structure category is selected from the dataset, i.e., data points whose current structural feature parameters are less than a preset structural feature threshold. A linear regression method is then used to fit this subset of data, establishing a linear relationship between the current structural feature parameters and the solid-state diffusion impedance characteristics, thus obtaining the mathematical expression and fitting parameters of the linear boundary model.

[0058] Thus, through experimental design and data analysis, a physical mechanism-based relationship model was pre-established, providing a reliable foundation for the rapid prediction of candidate carbon black conductive agents. By controlling the consistency of the preparation process, it was ensured that the changes in solid-state diffusion impedance characteristics stemmed solely from differences in carbon black structural properties, improving the model's accuracy. The piecewise establishment of the exponential decay model and the linear boundary model fully considered the differences in conduction mechanisms among different structural types of carbon black, ensuring the scientific validity and applicability of the prediction results. This enabled a priori assessment of the electrochemical performance of carbon black conductive agents, avoiding the subjectivity of traditional empirical judgments.

[0059] In a specific implementation, for example, multiple carbon black samples with current structural characteristic parameters ranging from 0.6 mL / 100 m² to 2.6 mL / 100 m² are selected and added to the positive electrode slurry according to the standard addition amount to prepare electrode samples. The solid-state diffusion impedance characteristics of each sample are measured by EIS testing and DRT analysis as follows: Figure 4 As shown. A preset structural feature threshold of 1.2 mL / 100 m² was used to classify samples into two categories based on this threshold: several samples with parameters ranging from 0.6 to 1.2 mL / 100 m² belonged to the low-structure category, while several samples with parameters ranging from 1.3 to 2.6 mL / 100 m² belonged to the high-structure category. For the high-structure category samples, an exponential decay model was obtained by fitting the data using a nonlinear least squares method. For details, please refer to Figure 5 The curve showing the relationship between the structural characteristic parameters and the predicted solid-state diffusion impedance characteristics, as represented by the exponential decay model, is a function in which the predicted solid-state diffusion impedance characteristics rapidly decrease and approach an asymptote as the structural characteristic parameter values ​​increase. For samples in the low-structure category, a linear boundary model is obtained by linear regression fitting. For details, please refer to Figure 6 The relationship curve between the structural characteristic parameters represented by the linear boundary model and the predicted solid-state diffusion impedance is a function in which the predicted solid-state diffusion impedance increases approximately linearly as the value of the structural characteristic parameters decreases.

[0060] In one possible implementation, determining the target addition amount of the candidate carbon black conductive agent based on its structural category includes: When the structure category is high structure degree, the regular addition amount is determined as the target addition amount.

[0061] When the structure category is low structure degree category, the current structure feature parameters are input into the optimal addition amount prediction model to obtain the reduction addition amount corresponding to the candidate carbon black conductive agent, and the reduction addition amount is determined as the target addition amount.

[0062] In practical applications, the conventional addition amount accounts for 1-1.5% of the total positive electrode mixture. Because high-structure carbon black possesses a well-developed secondary aggregate chain network, it can form a three-dimensional, coherent electronic conductivity network in the positive electrode sheet. Furthermore, the tortuosity of its formed pore structure is low, and it does not significantly hinder lithium-ion transport. Therefore, the conventional addition amount can simultaneously meet the dual requirements of electronic conductivity and ion transport, without the need for additional addition amount optimization. For low-structure categories, the current structural characteristic parameters are input into the optimal addition amount prediction model. Based on internally stored mapping relationships, the model directly outputs the corresponding reduction addition amount through retrieval or interpolation calculation. This reduction addition amount corresponds to the addition amount at the minimum point in the relationship curve between the solid-state diffusion resistance characteristic and the addition amount under this structural characteristic parameter. It is pre-determined through mathematical differentiation or numerical search methods, characterizing the optimal balance between the perfection of the electronic conductivity network and the ion transport blocking effect. This reduction addition amount is typically significantly lower than the conventional addition amount used for high-structure categories, in order to reduce ion transport resistance while maintaining necessary electronic pathways.

[0063] In this way, by implementing a differentiated addition strategy based on structural category, the conductivity of high-structure carbon black is fully utilized and the transport characteristics of low-structure carbon black are optimized and compensated. For high-structure categories, the conventional addition amount simplifies the process decision-making process; for low-structure categories, the optimization based on the relationship curve precisely balances the construction of the electronic conductivity network and the ion transport blocking effect, avoiding the deterioration of solid-state diffusion impedance or waste of conductive agent resources caused by uniform addition amount, and effectively improving the high-rate performance of the positive electrode.

[0064] In one possible implementation, see [reference] Figure 7 As shown, the optimal addition amount prediction model is established in advance in the following way: Step 701: For each structural feature parameter in the low structure degree category, obtain the solid-state diffusion impedance characteristics corresponding to different addition amounts obtained by electrochemical impedance spectroscopy and relaxation time distribution analysis of each positive electrode sample corresponding to the carbon black conductive agent sample with structural feature parameters; wherein, each positive electrode sample corresponding to the carbon black conductive agent sample with structural feature parameters is prepared by adding the carbon black conductive agent sample with structural feature parameters to the positive electrode slurry according to different addition amounts.

[0065] In practical applications, carbon black conductive agent samples with corresponding structural characteristic parameters are selected or prepared for several representative structural characteristic parameters in the low-structure category. For each structural characteristic parameter, the corresponding carbon black sample is added to the positive electrode slurry at multiple gradient addition amounts, covering a range from insufficient to excessive addition, such as 0.3wt%, 0.4wt%, 0.5wt%, 0.6wt%, and 0.8wt%. Positive electrode samples with different addition amounts corresponding to the structural characteristic parameter are prepared using a uniform preparation process. Electrochemical impedance spectroscopy (EIS) is performed on each positive electrode sample to obtain impedance response data over a wide frequency range. By using relaxation time distribution analysis, the complex impedance spectrum is deconvolved into relaxation processes with different time constants, accurately extracting the mid-to-low frequency characteristic peak intensity G(τ3) characterizing the solid-state diffusion of lithium ions within the positive electrode active material particles, which is then used as the measured value of the solid-state diffusion impedance characteristic. Thus, for each structural characteristic parameter, a data set of corresponding addition amounts and solid-state diffusion impedance characteristics is established.

[0066] Step 702: Based on each structural feature parameter and the different addition amounts and solid-state diffusion impedance characteristics corresponding to each structural feature parameter, fit the relationship curve between the solid-state diffusion impedance characteristics and the addition amount corresponding to each structural feature parameter.

[0067] In practical applications, based on a set of data pairs of each structural characteristic parameter and its corresponding addition amount and solid-state diffusion impedance characteristics, a quadratic function fitting is used to establish the relationship curve of the solid-state diffusion impedance characteristic of that structural characteristic parameter as a function of the addition amount. The relationship curve exhibits a shape with a minimum point, reflecting the dual influence mechanism of the addition amount on the solid-state diffusion impedance.

[0068] Step 703: Establish an optimal addition amount prediction model based on the relationship curve between the solid diffusion impedance characteristics and the addition amount corresponding to each structural characteristic parameter.

[0069] In practical applications, for multiple representative structural characteristic parameters distributed in the low-structure category, based on the relationship curve between the solid-state diffusion impedance characteristics and the addition amount corresponding to each parameter, mathematical optimization methods are used to determine the reduction amount corresponding to the minimum point of each curve. This establishes a mapping relationship between structural characteristic parameters and the reduction amount, forming an optimal addition amount prediction model. This model achieves a direct quantitative mapping from the intrinsic structural parameters of carbon black to the optimal process parameters, avoiding the need for tedious addition amount scanning experiments for each batch of carbon black in actual production. This significantly shortens the process development cycle and improves the controllability and repeatability of the preparation process.

[0070] For example, for a carbon black sample with a structural characteristic parameter of 1.13 mL / 100 m² in the low-structure category, positive electrode samples were prepared at five addition gradients of 2.00%, 2.10%, 2.20%, 2.30%, and 2.40%. EIS testing and DRT analysis revealed solid-state diffusion impedance characteristics corresponding to each addition amount to be 60.85 Ω, 62.50 Ω, 45.99 Ω, 68.61 Ω, and 105.93 Ω, respectively. The relationship curve between the solid-state diffusion impedance characteristic and the addition amount under this structural characteristic parameter was obtained by quadratic function fitting, as shown below. Figure 8 As shown. The minimum point of the curve is determined by analytical differentiation to be located at an addition amount of 2.20%, therefore the corresponding reduction in addition amount for this structural characteristic parameter is 2.20%. Furthermore, the above experiment is repeated for other structural characteristic parameters to obtain the corresponding reduction in addition amount. Based on the above sets of structural characteristic parameters and their corresponding reduction in addition amount, an optimal addition amount prediction model is established using an interpolation method.

[0071] Specifically, an optimal addition amount prediction model is established based on the relationship curves between the solid-state diffusion impedance characteristics corresponding to each structural feature parameter and the addition amount, including: First, for each structural feature parameter in the low structure degree category, based on the relationship curve between the solid diffusion impedance characteristic and the amount of addition corresponding to the structural feature parameter, the amount of addition reduction corresponding to the minimum point of each relationship curve is determined. In practical applications, numerical optimization algorithms are used to search for extreme values ​​on the relationship curve, including but not limited to Newton's iteration method, the golden section search method, or the Brent method, to find the points where the curve's derivative is zero or approximately zero. For relationship curves based on quadratic function fitting, the coordinates of the extreme points can be directly calculated using analytical formulas. During the determination of the minimum point, a data quality control mechanism is introduced to eliminate abnormal fluctuations caused by experimental errors, ensuring that the extreme point is within a reasonable range of the added amount and corresponds to a local minimum of the solid-state diffusion impedance characteristic. This minimum point physically represents the optimal balance between the percolation threshold of the electronic conductivity network and the ion transport blocking effect. The corresponding abscissa value is the amount of additive reduction under this structural characteristic parameter. This amount is typically significantly lower than the conventional amount used in high-structure categories, ensuring that the positive electrode has the necessary electronic pathways while maximizing the release of pore space for ion transport.

[0072] Then, curve fitting is performed based on each structural characteristic parameter and its corresponding reduction amount to obtain the optimal addition amount prediction model.

[0073] In practical applications, a discrete data point set is constructed using structural characteristic parameters as independent variables and the amount of reduced addition as the dependent variable. An appropriate fitting method is selected based on the data distribution characteristics, including but not limited to polynomial fitting, spline interpolation, or nonparametric regression methods, to establish a continuous functional relationship or discrete mapping table between the structural characteristic parameters and the amount of reduced addition. During the fitting process, model validation is implemented to evaluate the model's prediction accuracy, ensuring that the residual of the fitted curve at the training data points is less than a preset threshold and exhibits good smoothness and monotonicity within a reasonable range of the structural characteristic parameters. The established optimal addition prediction model, after inputting the current structural characteristic parameters of the candidate carbon black, can directly output the corresponding amount of reduced addition as the target addition amount, eliminating the need to repeatedly perform electrochemical impedance spectroscopy tests and relaxation time distribution analyses during actual preparation. This achieves rapid, standardized, and low-cost screening of conductive agents and determination of process parameters.

[0074] Thus, by establishing an optimal addition prediction model covering the entire range of low-structure carbon black categories, prior prediction of the optimal addition amount for carbon black with different structural characteristic parameters was achieved. Different structural characteristic parameters correspond to different impedance characteristics and addition amount relationship curves, providing complete data support for the refined preparation of low-structure carbon black. The pre-established experimental database avoids the need for tedious addition amount optimization experiments for each candidate carbon black conductive agent during actual preparation, significantly shortening the process development cycle and improving the efficiency and repeatability of the preparation method.

[0075] Based on the above embodiments, this application provides a positive electrode sheet for a lithium-ion battery, which is prepared using the above-described positive electrode sheet preparation method.

[0076] Based on the above embodiments, this application provides a lithium-ion battery, which includes at least the above-mentioned positive electrode sheet.

[0077] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0078] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0079] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0080] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for preparing a positive electrode sheet for a lithium-ion battery, characterized in that, include: Obtain the compression oil absorption value and specific surface area of ​​the candidate carbon black conductive agent; The ratio of the oil absorption value to the specific surface area is used as the current structural characteristic parameter of the candidate carbon black conductive agent. Based on the current structural characteristic parameters, determine the structural category and predicted solid-state diffusion impedance characteristics of the candidate carbon black conductive agent; When the predicted solid-state diffusion impedance characteristics meet the preset conditions, the target addition amount of the candidate carbon black conductive agent is determined according to the structure category, so that the candidate carbon black conductive agent is added to the positive electrode slurry to prepare the positive electrode sheet according to the target addition amount.

2. The method for preparing the positive electrode sheet of a lithium-ion battery as described in claim 1, characterized in that, The step of determining the structural category and predicted solid-state diffusion impedance characteristics of the candidate carbon black conductive agent based on the current structural characteristic parameters includes: The structural category of the candidate carbon black conductive agent is determined based on the current structural feature parameters and structural feature thresholds. The current structural feature parameters are input into the relationship model corresponding to the structural category of the candidate carbon black conductive agent to obtain the predicted solid-state diffusion impedance characteristics.

3. The method for preparing the positive electrode sheet of a lithium-ion battery as described in claim 2, characterized in that, Before determining the structural category of the candidate carbon black conductive agent based on the current structural feature parameters and structural feature threshold, the method further includes: Obtain target rate performance data and target operating temperature range data for lithium-ion batteries; Based on the correspondence between the target performance data and the preset performance levels, the performance adjustment coefficient is determined; Based on the correspondence between the target operating temperature range data and the preset temperature sensitivity level, the temperature adjustment coefficient is determined; The adjustment factor is determined based on the rate adjustment coefficient and the temperature adjustment coefficient; The preset baseline structural feature threshold is adjusted according to the adjustment factor to obtain the structural feature threshold.

4. The method for preparing the positive electrode sheet of a lithium-ion battery as described in claim 2, characterized in that, The structure categories include high-structure categories and low-structure categories; the step of inputting the current structural feature parameters into the relationship model corresponding to the structure category to obtain the predicted solid-state diffusion impedance features includes: When the structural category of the candidate carbon black conductive agent is high structure degree, the current structural feature parameters are input into the exponential decay model corresponding to the high structure degree to obtain the predicted solid-state diffusion impedance characteristics. When the structural category of the candidate carbon black conductive agent is low structure degree, the current structural feature parameters are input into the linear boundary model corresponding to the low structure degree to obtain the predicted solid-state diffusion impedance characteristics.

5. The method for preparing the positive electrode sheet of a lithium-ion battery as described in claim 4, characterized in that, The exponential decay model and the linear boundary model are established in advance in the following way: Solid-state diffusion impedance characteristics corresponding to different structural feature parameters obtained by electrochemical impedance spectroscopy and relaxation time distribution analysis of each positive electrode sample were obtained; wherein, each positive electrode sample was prepared by adding multiple carbon black conductive agent samples with different structural feature parameters into the positive electrode slurry according to a preset addition amount. The exponential decay model is obtained by fitting the structural characteristic parameter range and solid-state diffusion impedance characteristics corresponding to the high structure degree category. The linear boundary model is obtained by fitting the structural characteristic parameter range and solid-state diffusion impedance characteristics corresponding to the low structure degree category.

6. The method for preparing the positive electrode sheet of a lithium-ion battery according to any one of claims 1-5, characterized in that, Determining the target addition amount of the candidate carbon black conductive agent based on the structural category includes: When the structure category is a high-structure category, the regular addition amount is determined as the target addition amount; When the structure category is a low structure degree category, the current structure feature parameters are input into the optimal addition amount prediction model to obtain the reduction addition amount corresponding to the candidate carbon black conductive agent, and the reduction addition amount is used as the target addition amount.

7. The method for preparing the positive electrode sheet of a lithium-ion battery as described in claim 6, characterized in that, The optimal addition amount prediction model is established in advance through the following methods: For each structural feature parameter in the low structure degree category, the solid-state diffusion impedance characteristics corresponding to different addition amounts are obtained by electrochemical impedance spectroscopy and relaxation time distribution analysis of each positive electrode sample corresponding to the carbon black conductive agent sample possessing the structural feature parameter; wherein, each positive electrode sample corresponding to the carbon black conductive agent sample possessing the structural feature parameter is prepared by adding the carbon black conductive agent sample possessing the structural feature parameter to the positive electrode slurry in different addition amounts; Based on each structural feature parameter and the different addition amounts corresponding to each structural feature parameter and the solid-state diffusion impedance characteristics, a relationship curve between the solid-state diffusion impedance characteristics and the addition amount corresponding to each structural feature parameter is fitted. The optimal addition amount prediction model is established based on the relationship curve between the solid diffusion impedance characteristics corresponding to each structural characteristic parameter and the addition amount.

8. The method for preparing the positive electrode sheet of a lithium-ion battery as described in claim 7, characterized in that, The optimal addition amount prediction model is established based on the relationship curve between the solid-state diffusion impedance characteristics corresponding to each structural feature parameter and the addition amount, including: For each structural feature parameter in the low structure degree category, based on the relationship curve between the solid diffusion impedance characteristic corresponding to the structural feature parameter and the amount of addition, the amount of addition reduction corresponding to the minimum point of each relationship curve is determined. Based on the structural feature parameters and their corresponding reduction amounts, curve fitting is performed to obtain the optimal addition amount prediction model.

9. A positive electrode sheet for a lithium-ion battery, characterized in that, The positive electrode sheet is prepared by the positive electrode sheet preparation method as described in any one of claims 1-8.

10. A lithium-ion battery, characterized in that, include: The positive electrode sheet as described in claim 9.

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