Preparation system and method of cathode material, single cell and electric vehicle

Abstract

This application provides a cathode material preparation system, method, single-cell battery, and electric vehicle. It relates to the field of battery fabrication technology. The preparation system uses response surface methodology (RSM) analysis to obtain the abnormal process state and load fluctuation state of the air jet mill; it adjusts the feeding frequency and grinding gas pressure using a proportional-integral-derivative (PID) control algorithm to obtain adjustment parameters, and collects the updated staged current matching the adjustment parameters; based on the abnormal process state, load fluctuation state, and updated staged current, it performs coordinated adaptation adjustments using RSM to obtain multiple target process parameters. This approach, through the coupling of multiple process parameters and RSM analysis, can accurately identify the abnormal process state and load fluctuation state during the cathode material grinding process, allowing for targeted adjustments to the process parameters, achieving dynamic optimization of the cathode material, and thus improving its performance.

Description

Technical Field

[0001] This application relates to the field of battery manufacturing technology, and in particular to a cathode material preparation system, method, single cell battery, and electric vehicle. Background Technology

[0002] The performance of the cathode material in a battery directly affects the battery's performance, and the manufacturing process of the cathode material is crucial in the battery's fabrication.

[0003] In related technologies, a single parameter is used to control the operation of an air jet mill during the preparation of cathode materials. The air jet mill is used to ultrafinely pulverize and classify the raw materials to control the particle size distribution of the cathode material.

[0004] However, this control method suffers from low accuracy, resulting in poor performance of the cathode material. Summary of the Invention

[0005] This application provides a cathode material preparation system, method, single cell, and electric vehicle to improve the performance of cathode materials.

[0006] In a first aspect, embodiments of this application provide a cathode material preparation system. This system is used to optimize process parameters and prepare cathode materials based on the optimized parameters. The cathode material preparation system is configured to perform the following steps: acquiring multiple real-time process parameters and real-time cathode material particle size during the operation of the air jet mill using multimodal sensors deployed at key measuring points of the mill. These real-time process parameters include staged current, staged frequency, feeding frequency, and grinding gas pressure. Then, a response surface model is used to perform a synergistic analysis of the multiple real-time process parameters and the real-time cathode material particle size to obtain the process variation parameters of the air jet mill. In both normal and load fluctuation states, the response surface model represents the mapping relationship between process parameters and process effect values. Based on the graded current and the preset current range, the feeding frequency and the grinding gas pressure are adjusted using a proportional-integral-derivative control algorithm to obtain adjustment parameters, and the updated graded current matching the adjustment parameters is collected. Based on the abnormal process state, the load fluctuation state, and the updated graded current, the adjustment parameters and the graded frequency are coordinated and adjusted using the response surface model to obtain multiple target process parameters. These multiple target process parameters are used to indicate the execution of a pulverization process on the electrode raw materials to obtain the target cathode material.

[0007] Secondly, embodiments of this application provide a method for preparing a cathode material, comprising: acquiring multiple real-time process parameters and real-time cathode material particle size during the operation of an air jet mill using multimodal sensors deployed at key measuring points of the mill; the multiple real-time process parameters including staged current, staged frequency, feeding frequency, and grinding gas pressure; performing synergistic analysis of the multiple real-time process parameters and the real-time cathode material particle size using a response surface model to obtain the process anomaly state and load fluctuation state of the air jet mill, wherein the response surface model represents the mapping relationship between process parameters and process effect values; adjusting the feeding frequency and the grinding gas pressure according to the staged current and a preset current range using a proportional-integral-derivative control algorithm to obtain adjustment parameters, and acquiring an updated staged current matching the adjustment parameters; and performing synergistic adaptation adjustment of the adjustment parameters and the staged frequency according to the process anomaly state, the load fluctuation state, and the updated staged current using a response surface model to obtain multiple target process parameters, wherein the multiple target process parameters are used to indicate the execution of a pulverization process on the electrode raw materials to obtain the target cathode material.

[0008] Thirdly, embodiments of this application provide a single-cell battery, including a positive electrode, a negative electrode, a separator, and an electrolyte. The positive electrode includes a positive electrode material, which is prepared by the positive electrode material preparation system described in the first aspect.

[0009] Fourthly, embodiments of this application provide a battery pack comprising at least two individual cells as described in the third aspect, wherein each individual cell is electrically connected to the other.

[0010] Fifthly, embodiments of this application provide a battery pack, including a housing and at least two battery packs as described in the fourth aspect, each battery pack being disposed within the housing and electrically connected to each other.

[0011] Sixthly, embodiments of this application provide an electric vehicle that includes at least the battery pack described in the fifth aspect.

[0012] In a seventh aspect, embodiments of this application provide an electrical device that includes at least a single battery cell as described in the third aspect.

[0013] Eighthly, this application provides an apparatus for preparing a cathode material, comprising: a data acquisition module for acquiring multiple real-time process parameters and real-time cathode material particle size during the operation of the air jet mill using multimodal sensors deployed at key measuring points of the air jet mill, wherein the multiple real-time process parameters include a grading current, a grading frequency, a feeding frequency, and a grinding gas pressure; an analysis module for performing a synergistic analysis of the multiple real-time process parameters and the real-time cathode material particle size using a response surface model to obtain the process anomaly state and load fluctuation state of the air jet mill, wherein the response surface model represents the mapping relationship between process parameters and process effect values; an adjustment module for adjusting the feeding frequency and the grinding gas pressure according to the grading current and a preset current range using a proportional-integral-derivative control algorithm to obtain adjustment parameters, and acquiring an updated grading current matching the adjustment parameters; and a generation module for performing a synergistic adaptation adjustment of the adjustment parameters and the grading frequency according to the process anomaly state, the load fluctuation state, and the updated grading current using a response surface model to obtain multiple target process parameters, wherein the multiple target process parameters are used to indicate the execution of a pulverization process on the electrode raw materials to obtain a target cathode material.

[0014] Ninthly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0015] The memory stores computer-executed instructions;

[0016] The processor executes computer execution instructions stored in the memory, causing the processor to perform the implementation method described in the second aspect above.

[0017] In a tenth aspect, embodiments of this application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the embodiments of the second aspect above.

[0018] Eleventhly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the implementation methods described in the second aspect above.

[0019] The embodiments of this application provide a cathode material preparation system, method, single cell, and electric vehicle. Through the coupling of multiple process parameters and the analysis of response surface model, abnormal process states and load fluctuation states during the cathode material grinding process can be accurately identified, allowing for targeted adjustments to process parameters to achieve dynamic optimization of the cathode material and thus improve its performance. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of a method for preparing a cathode material provided in this application embodiment;

[0022] Figure 2 A schematic flowchart illustrating a method for preparing a cathode material according to an embodiment of this application;

[0023] Figure 3 A schematic flowchart illustrating another method for preparing a cathode material provided in this application embodiment;

[0024] Figure 4 A schematic diagram illustrating production status analysis provided for embodiments of this application;

[0025] Figure 5 A schematic diagram illustrating parameter adjustment provided in an embodiment of this application;

[0026] Figure 6 A schematic flowchart illustrating the method for screening process parameters provided in this application embodiment;

[0027] Figure 7 This is a schematic diagram of a cathode material preparation apparatus provided in an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of the structure of another cathode material preparation apparatus provided in the embodiments of this application;

[0029] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0030] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0031] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0032] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0033] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the display interface provided in the embodiments of this application is merely an example, and the display interface may include more or less content.

[0034] It should be noted that the cathode material preparation system, method, single cell, and electric vehicle of this application can be used in the field of battery preparation technology, or in any field other than battery preparation. The application fields of the cathode material preparation system, method, single cell, and electric vehicle of this application are not limited.

[0035] Figure 1 This is a schematic diagram illustrating an application scenario of a method for preparing a cathode material according to an embodiment of this application. An example is given based on the illustrated scenario: a pulverization process is performed on the raw materials for the cathode material to obtain the cathode material, and a battery is prepared using the cathode material.

[0036] For example, the preparation process of the cathode material affects the performance of the cathode material, and the performance of the cathode material directly affects the performance of the battery. Therefore, the stability of the cathode material preparation process directly affects the stability of the battery performance.

[0037] In practical applications, air jet mills are the core equipment in the ultrafine grinding process of cathode materials (such as lithium iron phosphate, ternary materials, and lithium cobalt oxide). Air jet mills use high-speed airflow to drive raw material particles through high-speed collisions and friction within the grinding chamber, achieving ultrafine grinding and classification of the raw materials. This process requires extremely high uniformity in the particle size distribution of the finished product, especially strict control over the escape rate of large particles >3μm. The escape of large particles not only leads to poor batch consistency but can also cause serious safety hazards such as battery separator puncture and internal short circuits.

[0038] In related technologies, individual process parameters of grinding are controlled manually to obtain cathode materials.

[0039] However, when raw material batches change, the particle size distribution changes accordingly. If process parameters are not adjusted in time, the load on the cathode material in the grinding zone will change, potentially causing a surge in the escape rate of coarse particles due to insufficient residence time. This leads to frequent batch defects and other problems, affecting the performance of the cathode material.

[0040] To illustrate with a scenario example, simply increasing the grinding air pressure to enhance the pulverizing ability may result in coarse particles being directly entrained and escaping due to excessively high airflow velocity. Conversely, simply decreasing the air pressure to suppress escape may lead to insufficient energy input and particle size not meeting the requirements.

[0041] The method for preparing the cathode material provided in this application aims to solve the above-mentioned technical problems in related technologies.

[0042] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0043] Figure 2 This is a schematic flowchart illustrating a method for preparing a cathode material according to an embodiment of this application. The method includes the following steps:

[0044] S201. By deploying multi-modal sensors at key measuring points of the air jet mill, multiple real-time process parameters and real-time cathode material particle size are collected during the operation of the air jet mill. The multiple real-time process parameters include grading current, grading frequency, feeding frequency, and grinding gas pressure.

[0045] As an example, the implementing entity of this embodiment can be a cathode material preparation system, which is used to optimize process parameters and prepare cathode materials according to the optimized process parameters.

[0046] The working principle of the air jet mill is to use high-speed airflow to impact and grind the raw materials. The ground powder flows with the airflow to the classifying wheel. The classifying wheel forms a centrifugal force field through its rotation speed. Qualified fine-particle cathode materials pass through the classifying wheel and enter the finished product collection end. Large-particle cathode materials that exceed the size limit are thrown back into the grinding chamber by the centrifugal force of the classifying wheel to continue to participate in the grinding.

[0047] In practical applications, large-sized cathode material particles that should have been intercepted by the centrifugal force of the classifying wheel and retained in the grinding chamber for secondary grinding may not be effectively intercepted due to fluctuations in operating conditions or parameter mismatches. These particles may then pass directly through the classifying wheel and mix into the finished cathode material, a problem known as large particle escape. This can lead to severely out-of-size and unevenly distributed particles in the finished cathode material, affecting battery performance.

[0048] For example, by arranging multi-modal sensors at key measuring points such as the grinding chamber, classification mechanism, air path and discharge port of the air jet mill, multiple real-time process parameters and real-time cathode material particle size are collected simultaneously during the operation of the equipment.

[0049] Among them, the grading current is used to directly reflect the load of the positive electrode material in the grinding chamber, the grading frequency corresponds to the rotation speed of the grading wheel, the feeding frequency determines the feeding rate of the positive electrode material, and the grinding air pressure affects the grinding intensity and residence time of the material.

[0050] S202. By using a response surface model to perform a collaborative analysis of multiple real-time process parameters and real-time cathode material particle size, the abnormal process state and load fluctuation state of the air jet mill are obtained. The response surface model represents the mapping relationship between process parameters and process effect values.

[0051] For example, the response surface model integrates multi-dimensional real-time process parameters and granularity collected and converts them into intuitive operating condition conclusions, including abnormal process states and load fluctuation states.

[0052] For example, "process abnormality" refers to a specific type of process abnormality, such as mismatched classifier speed, unstable load, or excessive particle size. "Load" refers to the amount of positive electrode material inside the grinding chamber.

[0053] For example, the load fluctuation state represents the magnitude of the load on the positive electrode material within the grinding chamber and the severity of the load fluctuation.

[0054] Based on the above implementation methods, by converting multi-dimensional parameters into specific working condition problems, the actual grinding state can be accurately reflected, replacing manual experience judgment, reducing subjective errors introduced by humans, and thus improving the performance of cathode materials.

[0055] S203. Based on the graded current and the preset current range, the feeding frequency and grinding air pressure are adjusted using a proportional-integral-derivative control algorithm to obtain the adjustment parameters, and the updated graded current matching the adjustment parameters is collected.

[0056] For example, the magnitude of the grading current directly corresponds to the amount of positive electrode material load within the grinding chamber. After adjusting the process parameters, a new grading current is collected to verify whether the load is stable.

[0057] For example, the proportional-integral-derivative control algorithm uses graded current as the controlled parameter, a preset current range as the control target, and feeding frequency and grinding air pressure as control execution parameters.

[0058] For example, the proportional-integral-derivative (PID) control algorithm calculates the deviation between the graded current and the preset current range in real time. Combined with the immediate deviation adjustment of the proportional term, the steady-state deviation elimination of the integral term, and the suppression of fluctuation trends in the derivative term, it regulates the feeding frequency and grinding air pressure, thereby controlling the load on the cathode material within the grinding chamber. This stabilizes the graded current within the preset range, eliminating large fluctuations in the cathode material load.

[0059] S204. Based on the abnormal process status, load fluctuation status, and updated graded current, the adjustment parameters and graded frequency are adjusted in a coordinated manner through the response surface model to obtain multiple target process parameters. These multiple target process parameters are used to indicate the execution of the crushing process on the electrode raw materials to obtain the target cathode material.

[0060] For example, based on the already stable updated graded current of the load, a secondary collaborative optimization is performed by combining the type of process anomaly, load fluctuation, and the currently stable graded current.

[0061] For example, the secondary collaborative optimization not only makes fine corrections to the feeding frequency and grinding air pressure, but also adjusts the grading frequency simultaneously.

[0062] For example, by simultaneously adapting multiple parameters, the load within the grinding chamber is kept stable while large particles are precisely intercepted and particle size issues are corrected. Based on the final optimized target process parameters, the process is executed through an air jet mill to produce qualified cathode materials with uniform particle size and no large particles escaping.

[0063] The cathode material preparation method provided in this application uses a response surface model to synergistically analyze multiple real-time process parameters and real-time cathode material particle size, obtaining the process anomaly state and load fluctuation state of the air jet mill. The response surface model represents the mapping relationship between process parameters and process effect values. Based on the stepping current and a preset current range, the feeding frequency and grinding gas pressure are adjusted using a proportional-integral-derivative control algorithm to obtain adjustment parameters, and the updated stepping current matching the adjustment parameters is collected. Based on the process anomaly state, load fluctuation state, and updated stepping current, the adjustment parameters and stepping frequency are synergistically adapted and adjusted using the response surface model to obtain multiple target process parameters. These target process parameters are used to indicate the execution of a pulverization process on the electrode raw materials to obtain the target cathode material. This scheme, through the coupling of multiple process parameters and the analysis of the response surface model, can accurately identify the process anomaly state and load fluctuation state during the cathode material grinding process, allowing for targeted adjustments to the process parameters, achieving dynamic optimization of the cathode material, and thus improving its performance.

[0064] Based on any of the above embodiments, the following, in conjunction with Figure 3 The detailed process of preparing the cathode material is explained.

[0065] Figure 3 This is a schematic flowchart illustrating another method for preparing a cathode material provided in this application embodiment. Figure 3 As shown, the method includes:

[0066] S301. By deploying multi-modal sensors at key measuring points of the air jet mill, multiple real-time process parameters and real-time cathode material particle size are collected during the operation of the air jet mill. The multiple real-time process parameters include grading current, grading frequency, feeding frequency, and grinding gas pressure.

[0067] It should be noted that the execution process of S301 is the same as that of S201, and will not be repeated here.

[0068] S302. Through the mapping relationship of the response surface model, determine the predicted process effect values ​​corresponding to the real-time process parameters and the real-time cathode material particle size.

[0069] For example, by using a pre-built response surface model, the process parameters such as the real-time collected grading current, grading frequency, feeding frequency, and grinding gas pressure, as well as the real-time particle size of the cathode material, are fitted and calculated to output the predicted process effect value corresponding to the real-time operating condition.

[0070] One feasible approach is to establish a response surface model using the following method: First, determine the input and output variables of the response surface model based on the range analysis formula. The input variables are the sample process parameters and the particle size of the sample cathode material, and the output variable is the process effect value. Second, using the input variables as experimental factors, set reasonable value ranges for each input variable, and obtain measured data of the output variables corresponding to multiple combinations of different input variables through orthogonal experimental design. Third, based on the measured data of multiple combinations of different input variables and output variables, construct a quantitative mapping relationship between the input and output variables using a quadratic polynomial fitting method to obtain the response surface model.

[0071] For example, range analysis is used to screen the significance of various parameters affecting the cathode material in air jet milling. The sample process parameters (stage current, feed frequency, grinding gas pressure, stage frequency) and the particle size of the sample cathode material that have a significant impact on the grinding process are determined as input variables of the response surface model.

[0072] Optionally, the range analysis formula can be expressed as follows:

[0073]

[0074] in, This represents the range of the j-th factor. This represents the average score of the j-th factor at the k-th level.

[0075] For example, using the selected input variables as experimental factors, and combining them with the actual production requirements of cathode material grinding, reasonable value ranges for each parameter are set. Through orthogonal experimental design, multiple different combinations of input variables are arranged, and actual grinding experiments are carried out to obtain measured data of the process effect values ​​corresponding to each combination, covering a comprehensive range of working conditions with the fewest number of experiments.

[0076] Using scenario examples, it can be seen that in orthogonal experimental design, the four factors—feeding frequency, classification frequency, grinding air pressure, and classification current—are coupled. The classification current increases with increasing feeding frequency, but this is non-linear and has a critical saturation point. As the grinding air pressure increases, the airflow's material-carrying capacity increases, leading to a decrease in classification current and premature removal of coarse particles. With increasing classification frequency, the cut particle size decreases, resulting in increased return material and a higher classification current; however, excessively high frequencies can cause airflow short-circuiting, leading to artificially high current and decreased grinding efficiency.

[0077] Optionally, the orthogonal experiment coding formula can be represented by the following formula:

[0078]

[0079] in, This represents the encoded value of the j-th factor. ; This represents the actual set value of the j-th factor; This represents the baseline level (zero level) for the j-th factor. This represents the range of change for the j-th factor.

[0080] Optionally, the coding formulas for each factor are shown in Table 1:

[0081] Table 1

[0082]

[0083] For example, based on the measured data of multiple sets of input and output variables obtained from orthogonal experiments, a quadratic polynomial fitting method is used to calculate the mathematical correlation between the input and output variables and form a quantitative mapping relationship.

[0084] Optionally, the response surface model can be represented by the following formula:

[0085]

[0086] in, Represents the response surface model. Represents a constant term. Denotes the coefficient of the linear term. Denotes the coefficient of the quadratic term. ε represents the coefficient of the interaction term, and ε represents the random error term.

[0087] In this feasible implementation, range analysis is used to screen key variables, eliminate redundant parameters, simplify the response surface model structure, and improve the model's prediction accuracy for cathode material grinding conditions. Orthogonal experimental design is employed to obtain sample data, significantly reducing the number of cathode material grinding experiments and material consumption, thus lowering modeling costs. Quadratic polynomial fitting accurately matches the nonlinear relationships of multi-parameter coupling in cathode material grinding, making the mapping relationship of the response surface model more closely match the actual process, thereby accurately identifying the operating conditions for decision-making and improving the performance of the cathode material.

[0088] S303. When the predicted process effect value is not within the preset process effect compliance range, the abnormal process state is determined based on the endpoint values ​​of the predicted process effect value and the preset process effect compliance range.

[0089] For example, if the predicted process effect value is not within the preset process effect compliance range, it indicates that there is a process abnormality causing the predicted process effect value to deviate. The predicted process effect value is then compared with the endpoint value of the preset process effect compliance range, and the difference obtained from the comparison is used to determine the current process abnormality state of the air jet mill.

[0090] Optionally, abnormal process conditions include, but are not limited to, at least one of the following: excessive particle size of cathode material, escape of large particles, abnormal grinding efficiency, and other abnormal problems.

[0091] S304. Determine the air mill load value corresponding to the staged current in the predicted process effect value.

[0092] For example, a quantification index of the graded current is extracted from the predicted process effect value. The mapping relationship between the graded current and the positive electrode material load in the grinding chamber is pre-configured to determine the current positive electrode material load value of the air jet mill. This load value directly reflects the real-time total amount of positive electrode material to be processed in the grinding chamber.

[0093] S305. By comparing the load value of the air jet mill with the preset load compliance range, the load fluctuation state is obtained.

[0094] For example, the load compliance range is a suitable load range pre-set experimentally based on the type, model, and target particle size of the cathode material. A load falling within the compliance range indicates that the amount of cathode material is moderate, the grinding is most stable, the efficiency is highest, and large particles are less likely to escape.

[0095] For example, by comparing the real-time calculated air mill load value with this preset range one by one, four typical operating conditions can be obtained.

[0096] Based on the scenario example, if the load value of the air jet mill consistently falls within the specified range, it is determined that the load is stable and there are no significant fluctuations.

[0097] If the load value of the air jet mill exceeds the upper limit of the range multiple times, it is judged as excessive load and overload fluctuation, that is, there is too much positive electrode material in the grinding chamber, which can easily cause insufficient grinding, excessive classification pressure, and large particles to escape.

[0098] If the load value of the air jet mill is repeatedly lower than the lower limit of the range, it is judged as low load or underload fluctuation, that is, there is too little positive electrode material in the grinding chamber, resulting in low grinding efficiency, high energy consumption, and possibly over-grinding of particles.

[0099] Frequent fluctuations in the load value of the air jet mill within the specified range, indicating severe load volatility and a seriously unstable operating condition, will directly lead to uneven particle size, unstable grading current, and loss of control over the quality of the cathode material.

[0100] Below, in conjunction with Figure 4 Explain the production status analysis.

[0101] Figure 4 This is a schematic diagram illustrating production status analysis provided in an embodiment of this application. For example... Figure 4 As shown, real-time process parameters and real-time cathode material particle size are used as inputs. Mapping is performed through the response surface model to obtain the predicted process effect value based on the current production state. By comparing the predicted process effect value with the preset process effect compliance range, abnormal process states are identified. The staged current is extracted from the predicted process effect value, and the current airflow mill load value is calculated based on the staged current. By comparing the airflow mill load value with the preset load compliance range, a fluctuating state is determined.

[0102] Based on the above implementation methods, by coupling analysis of multiple process parameters, the real-time grinding state of the cathode material can be accurately identified, thereby enabling accurate adaptive adjustment of the process parameters.

[0103] One feasible implementation method, after obtaining the process abnormality state and load fluctuation state, also includes: determining the update acquisition frequency based on the process abnormality state and load fluctuation state; and acquiring data through a multimodal sensor based on the update acquisition frequency.

[0104] For example, based on the identified process anomaly type (such as excessive particle size or large particle escape) and the severity of load fluctuations (stable load, small fluctuations, or large jumps), the corresponding update collection frequency is matched and determined.

[0105] For example, the multimodal sensor is controlled to re-perform data acquisition according to the updated acquisition frequency, replacing the fixed acquisition frequency.

[0106] As illustrated by the scenario examples, the more unstable the operating conditions and the more obvious the anomalies, the more frequently the sensors collect data. When the operating conditions are stable and there are no anomalies, the collection frequency should be appropriately reduced.

[0107] In this feasible implementation, high-frequency acquisition is used when there are abnormal processes or drastic load fluctuations. This can quickly and in real time capture transient changes in operating conditions, avoid untimely adjustments due to data lag, and thus improve the performance of cathode materials.

[0108] S306. Determine the material identification of the cathode material and the equipment identification of the air jet mill.

[0109] For example, material identification is used to distinguish the specific type of cathode material (such as lithium cobalt oxide, lithium iron phosphate, etc.). Different cathode materials have different hardness, grinding difficulty, and particle size requirements, and the corresponding grinding load or grading current requirements are also different.

[0110] For example, equipment identification is used to distinguish the equipment model and specifications of air jet mills (e.g., air jet mills with different capacities and different grinding chamber volumes). Different equipment have different load-bearing capacities and grinding efficiencies, and the applicable graded current ranges are also different.

[0111] S307. Determine the corresponding preset current range based on the material and equipment markings.

[0112] For example, this solution pre-determines the corresponding preset current ranges for different combinations of material and equipment labels through experiments. That is, when the positive electrode material is ground by the equipment, the graded current range that can maintain the stability of the positive electrode material load and achieve the best grinding effect.

[0113] The following examples illustrate how to grind lithium cobalt oxide cathode material using a Model A air jet mill, with a preset current range of 10-15A. Similarly, use a Model A air jet mill to grind lithium iron phosphate cathode material, with a preset current range of 8-12A. Use a Model B air jet mill to grind lithium cobalt oxide cathode material, with a preset current range of 12-16A, ensuring the current range is fully compatible with the material and equipment.

[0114] Based on the above implementation methods, the dedicated current range can ensure that the graded current is always maintained within a reasonable range that is suitable for the current working conditions, thereby stabilizing the load of the cathode material in the grinding chamber, avoiding overload and underload problems, and thus improving the performance of the cathode material.

[0115] S308. Based on the graded current and the preset current range, the feeding frequency and grinding air pressure are adjusted using a proportional-integral-derivative control algorithm to obtain the adjustment parameters.

[0116] For example, the real-time collected graded current is compared with the preset current range specifically corresponding to the cathode material and the air jet mill, and the feeding frequency and grinding air pressure are adjusted in a targeted manner through a proportional-integral-derivative control algorithm.

[0117] Below, in conjunction with Figure 5 The parameter adjustment is explained.

[0118] Figure 5 This is a schematic diagram illustrating parameter adjustment provided in an embodiment of this application. Figure 5 As shown, if the classification current is higher than the upper limit of the preset range (overload of the cathode material in the grinding chamber), the proportional-integral-derivative (PID) control algorithm reduces the feeding frequency (i.e., reduces the feed rate) and appropriately increases the grinding gas pressure (i.e., accelerates the discharge of the cathode material), thereby reducing the load in the grinding chamber and causing the classification current to fall back within the range. If the classification current is lower than the lower limit of the preset range (underload of the cathode material in the grinding chamber), the PID control algorithm increases the feeding frequency (i.e., increases the feed rate) and appropriately decreases the grinding gas pressure (i.e., extends the material residence time), thereby increasing the load in the grinding chamber and causing the classification current to rise within the range. After adjustment, the obtained feeding frequency and grinding gas pressure are the adjustment parameters adapted to the current cathode material and air jet mill.

[0119] Based on the above implementation methods, precise proportional-integral-derivative control can stabilize the load inside the grinding chamber, reduce problems such as uneven particle size and large particle escape caused by load fluctuations, thereby improving the performance of the cathode material.

[0120] One feasible implementation method involves adjusting the parameters as follows: calculating the deviation between the graded current and the preset current range; calculating a correction amount using the proportional term of a proportional-integral-derivative (PID) control algorithm, where the correction amount compensates for the deviation of the current cathode material load from the steady-state load; calculating a trend compensation amount using the derivative term of the PID control algorithm to calculate the real-time rate of change of the graded current, where the trend compensation amount compensates for the subsequent trend of the cathode material load; determining the adjustment amount based on the correction amount and the trend compensation amount; and adjusting the feeding frequency and grinding air pressure according to the adjustment amount to obtain the adjustment parameters.

[0121] For example, the difference between the real-time collected graded current and the preset current range is calculated to obtain the current deviation value. This deviation value directly reflects the degree to which the load of the positive electrode material in the current grinding chamber deviates from the ideal steady-state load.

[0122] For example, the proportional term is used to calculate the immediate correction amount based on the above deviation value. The larger the deviation value, the larger the correction amount, which is used to quickly and directly compensate for the deviation of the current cathode material load and quickly narrow the gap between the graded current and the preset range.

[0123] For example, the differential term is used to calculate the real-time rate of change of the graded current to obtain the trend compensation amount. The trend compensation amount is used to predict the subsequent trend of the load on the cathode material. If the graded current rises / falls rapidly, the differential term will output reverse compensation in advance to suppress excessively rapid load fluctuations.

[0124] For example, the immediate correction of the proportional term is superimposed with the trend compensation of the derivative term, and the final parameter adjustment is determined by combining the current deviation with the future trend.

[0125] Optionally, a pre-configured ratio for the coordinated adjustment of feeding frequency and grinding air pressure can be established. Sub-adjustments for both feeding frequency and grinding air pressure are determined based on the adjustment amount and the ratio, and adjustments are made according to these sub-adjustments.

[0126] In this feasible implementation method, a composite adjustment logic combining correction amount and trend compensation amount is adopted, which is more stable and accurate than single ratio adjustment, thereby improving the accuracy of cathode material.

[0127] One feasible implementation method is to determine the adjustment amount by: determining the first weight corresponding to the correction amount and the second weight corresponding to the trend compensation amount based on the deviation value and the real-time rate of change; and weighting and summing the correction amount and the trend compensation amount according to the first weight and the second weight to obtain the adjustment amount.

[0128] For example, the corresponding weights are dynamically assigned based on the magnitude of the deviation between the graded current and the preset current range, as well as the real-time rate of change of the graded current.

[0129] With the help of scenario examples, the correction amount calculated by the proportional term is assigned the first weight. The larger the deviation value, the more serious the deviation of the cathode material load from the steady state. The higher the first weight, the more priority is given to quickly correcting the deviation.

[0130] The trend compensation amount calculated by the differential term is assigned a second weight. The faster the real-time change rate of the graded current, the more violent the load fluctuation trend of the cathode material, and the higher the second weight, giving priority to predicting and suppressing fluctuations.

[0131] In this feasible implementation, the weights are dynamically and adaptively allocated, rather than being superimposed in a fixed ratio. This allows the calculation of the adjustment amount to better match the real-time working conditions of the cathode material grinding process, thereby improving the performance of the cathode material.

[0132] S309. Based on the abnormal process status, load fluctuation status, and updated graded current, the adjustment parameters and graded frequency are adjusted in a coordinated manner through the response surface model to obtain multiple target process parameters. These multiple target process parameters are used to indicate the execution of the crushing process on the electrode raw materials to obtain the target cathode material.

[0133] One feasible implementation method is to collect and update the graded current by: controlling the operation of the air mill by adjusting parameters; and collecting the graded current as the update graded current after the air mill has reached a steady state.

[0134] For example, the adjusted parameters (i.e., the adjusted feeding frequency and grinding air pressure) are sent to the control system of the air jet mill, allowing the mill to operate according to the adjusted parameters. Current is then collected after the mill reaches a steady state.

[0135] Optionally, the steady state can be determined based on whether the graded current is stable.

[0136] With the example of the scenario, when the air mill is first adjusting the parameters, the feed rate of the positive electrode material and the grinding and discharge efficiency have not yet been matched, the load of the positive electrode material in the chamber is still in a fluctuating transition state, and the graded current will show a brief jump. At this time, no data is collected.

[0137] Once the air jet mill reaches a steady state, meaning the feeding, grinding, and discharging processes become stable, the load on the positive electrode material in the grinding chamber no longer changes drastically, and the classifying current remains stable without significant fluctuations, the classifying current at this point is collected and used as the updated classifying current.

[0138] In this feasible implementation, data is collected after the steady state is reached, eliminating transient fluctuations after parameter switching and avoiding the collection of false or abrupt graded current data. This ensures the accuracy of updating the graded current and thus improves the performance of the cathode material.

[0139] One feasible implementation method involves collaborative adaptation and adjustment, including: inputting process anomalies, load fluctuations, and updated grading current into the response surface model; adjusting the feeding frequency and grinding gas pressure in the adjustment parameters according to the coupling relationship between parameters, obtaining compensated feeding frequency and compensated grinding gas pressure to match the cathode material load with the operating conditions and maintain the updated grading current within a preset current range; adjusting the grading frequency through the response surface model to match the current load state and eliminate process anomalies related to cathode material particle size, obtaining a compensated grading frequency; and determining the compensated feeding frequency, compensated grinding gas pressure, and compensated grading frequency as multiple target process parameters.

[0140] For example, the identified process anomalies (such as excessive particle size of cathode material, escape of large particles), load fluctuations, and updated graded currents after steady-state operation of the air jet mill are all input into the response surface model.

[0141] For example, the coupling relationship is the correlation between the feeding frequency, grinding air pressure, positive electrode material load, and grading current.

[0142] For example, the response surface model adapts and compensates the adjustment parameters based on the coupling relationship. The compensated feeding frequency and the compensated grinding gas pressure are obtained, so that the load of the positive electrode material in the grinding chamber is completely matched with the current operating conditions. At the same time, the updated grading current is stably maintained within the preset current range to avoid load fluctuations from interfering with particle size control.

[0143] For example, the response surface model combines the current cathode material load state to specifically adapt and compensate the classification frequency. The classification frequency directly corresponds to the classification wheel speed, which determines the magnitude of the centrifugal force of the classification wheel. By matching the speed, large cathode material particles can be intercepted, eliminating particle size-related process anomalies such as particle size exceeding the standard and large particle escape, ultimately obtaining a compensated classification frequency adapted to the current operating conditions.

[0144] Based on scenario examples, the process involves first stabilizing the load on the cathode material and the graded current, then specifically eliminating particle size-related process anomalies, taking into account both load stability and finished product particle size compliance, thereby solving the problems of large particle escape and uneven particle size.

[0145] In this feasible implementation, collaborative optimization is performed based on parameter coupling relationship, avoiding the limitations of independent adjustment of single parameters, avoiding interference with the classification effect when adjusting feed or air pressure, or causing load fluctuations when adjusting classification frequency, thus greatly improving parameter adaptability and thereby improving the performance of cathode material.

[0146] One feasible implementation method, after obtaining multiple target process parameters, the cathode material preparation method further includes: collecting multiple measured process effect values ​​corresponding to multiple target process parameters; comprehensively scoring the multiple measured process effect values ​​through a multi-objective weighted scoring model to obtain a target score, wherein the multi-objective weighted scoring model is used to perform weighted calculations on the escape prevention index, capacity index, energy consumption index, and current stability according to production needs to obtain a comprehensive score; and updating the response surface model based on the multiple target process parameters and the target score to obtain an updated response surface model.

[0147] For example, after the air jet mill operates with multiple target process parameters (optimized feed frequency, grinding air pressure, and classification frequency), the corresponding measured process effect values ​​are collected. These effect values ​​represent the actual production results.

[0148] For example, the measured process performance values ​​are input into a multi-objective weighted scoring model. The model calculates a weighted sum of the anti-escape index, capacity index, energy consumption index, and current stability based on production priorities, ultimately yielding a quantified target score. A higher score indicates better performance of the current target process parameters in actual production.

[0149] Optionally, production priorities may include escape prevention mode, capacity priority mode, etc.

[0150] Optionally, the measured process effect values ​​can be converted into standard scores according to the characteristics of the process effect. The escape prevention scoring formula can be:

[0151]

[0152] in, This indicates the escape prevention index score. This represents the escape rate penalty coefficient. This indicates the percentage of coarse particles (e.g., larger than 3μm) by mass.

[0153] Optionally, the capacity scoring formula can be:

[0154]

[0155] in, This represents the capacity index score, where Q represents the measured cathode material processing capacity per unit time. This indicates the maximum capacity of the air jet mill.

[0156] Optionally, the energy consumption rating formula can be:

[0157]

[0158] in, This indicates the energy consumption index score. E represents the power consumption per unit output of the baseline, while E represents the power consumption per unit output of the actual measured output.

[0159] Optionally, the current stability scoring formula can be:

[0160]

[0161] in, Indicates the current stability score. This indicates the penalty coefficient for current deviation. This represents the measured graded current. This indicates the target current value.

[0162] Optionally, the overall score can be expressed using the following formula:

[0163]

[0164] in, This indicates the overall score. This indicates the weight of each measured process effect value. .

[0165] Optionally, the scores for each effect value can be represented in Table 2:

[0166] Table 2

[0167]

[0168] Calculate the overall score based on each effect value: point.

[0169] Optionally, weights can be dynamically allocated based on production needs. For example, for production needs that strictly control runaway, the weights can be increased. The value. To address production demands prioritizing capacity, the value can be increased. The value of .

[0170] Optionally, constraints can be set for the weight allocation, which can be expressed by the following formula:

[0171]

[0172] in, This represents the dynamically adjusted weights, ensuring that the sum of the dynamically adjusted weights is 1, and constraining the range of each weight. The upper and lower limits of the constraints satisfy the basic feasibility of production.

[0173] In this feasible implementation, multiple target process parameters are used as inputs, and target scores are combined with the evaluation results to update and train the original response surface model. This allows the response surface model to match the actual operating conditions, and the subsequently predicted process parameters will be more in line with actual production needs, thereby improving the performance of the cathode material.

[0174] Figure 6 This is a flowchart illustrating a method for screening process parameters provided in an embodiment of this application. The method includes the following steps:

[0175] S601, Orthogonal experimental design.

[0176] For example, based on load-energy balance pre-experiments to determine factor levels, for air jet mills, the feasible region of four factors was determined through pre-experiments, as shown in Table 3:

[0177] Table 3

[0178]

[0179] To illustrate with scenario examples, the 70 A midpoint corresponds to the ideal load for high-density steady-state grinding, at which point the power density is approximately 12 kW / m³, maximizing the particle collision frequency; the 80 A upper limit corresponds to 85% of the equipment's rated current, leaving a 10% safety margin to prevent sudden material blockage; and the 480 kPa air pressure midpoint, combined with high load, can form a low-pressure, high-efficiency system.

[0180] Below, Table 4 provides examples of orthogonal representations:

[0181] Table 4

[0182]

[0183] Optionally, the actual current can be adjusted to within ±5 A of the target value by fine-tuning the feeding frequency or air pressure, and the actual steady-state parameters can be recorded; current fluctuation < ±5 A for 5 minutes is considered to have reached the target load state; the test sequence can be randomly determined to eliminate batch differences in raw materials.

[0184] Optionally, if it is necessary to accurately capture the four-factor interaction effect (such as the antagonistic effect of air pressure and current), a full factorial design (81 groups) can be used to: verify the optimal ridge line of 480 kPa, 45 Hz, 47 Hz, and 75 A on the four-dimensional response surface; draw the current-feed interaction diagram and determine the boundary conditions of the 70-80 A platform.

[0185] S602, Data Acquisition and Normalization.

[0186] Below are examples of data measurement for four targets, as shown in Table 5:

[0187] Table 5

[0188]

[0189] Optionally, current stability can be expressed by the following formula:

[0190]

[0191] in, Indicates the standard deviation of current. This represents the k-th current reading. This represents the average of n current readings, where n represents the number of samples.

[0192] Optionally, the current stability score can also be expressed by the following formula:

[0193]

[0194] in, This represents another formula for scoring current stability. This represents the penalty coefficient.

[0195] S603. Analysis of experimental results.

[0196] For example, the results were analyzed using range analysis, and the results are shown in Table 6:

[0197] Table 6

[0198]

[0199] Table 6 leads to the following conclusions: The grading current is the primary factor preventing escape: when the current is <60 A, the escape rate is relatively high regardless of air pressure; when the current is >70 A, the escape rate is relatively low at low pressure (480 kPa). The density effect in the 70-80 A range occurs because this current range corresponds to a dense-phase grinding bed in the grinding zone, where coarse particles are buffered and encapsulated by fine particles, repeatedly pulverized in the jet zone, rather than being directly entrained by the airflow. The negative correlation effect of air pressure is also observed: under high loads of 70-80 A, higher air pressure and faster airflow velocity increase the probability of coarse particles inertially penetrating the grading wheel.

[0200] For example, Table 7 illustrates the interaction effect between current and feed:

[0201] Table 7

[0202]

[0203] Based on Table 7, the following conclusions can be drawn: In the 70-80 A plateau region, a high-scoring plateau is formed in the 45 Hz feeding range, indicating that this current range is robust to feeding fluctuations. Under the current upper limit constraint, at 50 Hz feeding, 80 A approaches the overload boundary, current stability decreases, and the score actually decreases. The optimal solution is locked; 45 Hz feeding and 75 A current represent the optimal point, at which point the system is insensitive to raw material particle size fluctuations.

[0204] For example, Table 8 shows the data for dynamically adjusted weights:

[0205] Table 8

[0206]

[0207] Based on the above implementation method, through the visual control of dynamic load, the abstract material concentration in the grinding zone is transformed into an intuitive ammeter reading. Operators, without prior experience, can adjust the feed to maintain optimal conditions simply by observing the current. A 70-80A range is set, corresponding to high-density steady-state grinding, significantly reducing the escape rate and maintaining high production capacity. Through a multi-objective balanced weight configuration, Pareto optimality is achieved for anti-escape, production capacity, energy consumption, and stability, with a comprehensive score of 96.8. Under the optimal parameter combination (45 Hz feed, 47 Hz grading, 480 kPa air pressure, 75 A current), it has an adaptive buffering capability against raw material particle size fluctuations (D50±2 μm), eliminating the need for frequent adjustments.

[0208] This application provides a single-cell battery, including a positive electrode, a negative electrode, a separator, and an electrolyte. The positive electrode includes a positive electrode material, which is prepared by a positive electrode material preparation system.

[0209] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0210] This application provides a battery pack comprising at least two of the above-described individual cells, each of which is electrically connected to the other.

[0211] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0212] This application provides a battery pack, including a housing and at least two battery packs as described above, each battery pack being disposed within the housing and electrically connected to each other.

[0213] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0214] This application provides an electric vehicle that includes at least the aforementioned battery pack.

[0215] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0216] This application provides an electrical device that includes at least the aforementioned single battery cell.

[0217] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material. Figure 7 This is a schematic diagram of a cathode material preparation apparatus provided in an embodiment of this application. Figure 7 As shown, the cathode material preparation device 70 may include: a data acquisition module 71, an analysis module 72, an adjustment module 73, and a generation module 74.

[0218] The acquisition module 71 is used to acquire multiple real-time process parameters and real-time cathode material particle size during the operation of the air jet mill by using multi-modal sensors deployed at key measuring points of the air jet mill. The multiple real-time process parameters include grading current, grading frequency, feeding frequency and grinding gas pressure.

[0219] Analysis module 72 is used to perform collaborative analysis of multiple real-time process parameters and real-time cathode material particle size through response surface model to obtain the process abnormal state and load fluctuation state of air jet mill. The response surface model represents the mapping relationship between process parameters and process effect values.

[0220] The adjustment module 73 is used to adjust the feeding frequency and grinding air pressure according to the graded current and the preset current range through a proportional-integral-derivative control algorithm to obtain adjustment parameters, and to collect the updated graded current matching the adjustment parameters.

[0221] The generation module 74 is used to coordinately adapt and adjust the adjustment parameters and the grading frequency through the response surface model according to the abnormal process state, load fluctuation state and updated grading current, to obtain multiple target process parameters. These multiple target process parameters are used to indicate the execution of the crushing process on the electrode raw materials to obtain the target cathode material.

[0222] Optionally, the acquisition module 71 can perform... Figure 2 S201 in the embodiment.

[0223] Optionally, analysis module 72 can execute... Figure 2 S202 in the embodiment.

[0224] Optionally, adjustment module 73 can be executed. Figure 2 S203 in the embodiment.

[0225] Optionally, the generation module 74 can be executed. Figure 2 S204 in the embodiment.

[0226] It should be noted that the cathode material preparation apparatus shown in the embodiments of this application can perform the technical solutions shown in the above method embodiments, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0227] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0228] In one possible implementation, the analysis module 72 is specifically used for:

[0229] By using the mapping relationship of the response surface model, the predicted process effect values ​​corresponding to the real-time process parameters and the real-time cathode material particle size are determined.

[0230] When the predicted process effect value is not within the preset process effect compliance range, the abnormal process state is determined based on the endpoint values ​​of the predicted process effect value and the preset process effect compliance range.

[0231] Determine the air mill load value corresponding to the staged current in the predicted process effect value;

[0232] By comparing the load value of the air jet mill with the preset load range, the load fluctuation state is obtained.

[0233] In one possible implementation, the adjustment module 73 is specifically used for:

[0234] Determine the material identification for the cathode material and the equipment identification for the air jet mill;

[0235] Determine the corresponding preset current range based on the material and equipment markings;

[0236] Based on the graded current and the preset current range, the feeding frequency and grinding air pressure are adjusted using a proportional-integral-derivative control algorithm to obtain the adjustment parameters.

[0237] In one possible implementation, the adjustment module 73 is specifically used for:

[0238] Calculate the deviation between the graded current and the preset current range;

[0239] The proportional term of the proportional-integral-derivative control algorithm is used to calculate the correction value from the deviation value. The correction value is used to compensate for the deviation of the current cathode material load from the steady-state load.

[0240] The trend compensation amount is calculated by using the differential term of the proportional-integral-derivative control algorithm to calculate the real-time rate of change of the graded current. The trend compensation amount is used to compensate for the subsequent change trend of the cathode material load.

[0241] The adjustment amount is determined based on the correction amount and the trend compensation amount;

[0242] The feeding frequency and grinding air pressure are adjusted according to the adjustment amount to obtain the adjustment parameters.

[0243] In one possible implementation, the adjustment module 73 is specifically used for:

[0244] Based on the deviation value and the real-time rate of change, determine the first weight corresponding to the correction amount and the second weight corresponding to the trend compensation amount;

[0245] The adjustment amount is obtained by weighting and summing the correction amount and the trend compensation amount according to the first and second weights.

[0246] In one possible implementation, the adjustment module 73 is specifically used for:

[0247] The operation of the airflow mill is controlled by adjusting parameters;

[0248] After the air mill reaches a steady state, the staged current is collected as the update staged current.

[0249] In one possible implementation, the generation module 74 is specifically used for:

[0250] The abnormal process conditions, load fluctuation conditions, and updated graded current input response surface models are included.

[0251] Based on the coupling relationship between parameters, the feeding frequency and grinding gas pressure in the adjustment parameters are adapted and adjusted by the response surface model to obtain the compensated feeding frequency and the compensated grinding gas pressure, so as to match the load of the cathode material with the working conditions and maintain the updated grading current within the preset current range.

[0252] The grading frequency is adapted and adjusted using a response surface model to match the current load conditions and eliminate process anomalies related to cathode material particle size, resulting in a compensated grading frequency.

[0253] The compensated feeding frequency, compensated grinding air pressure, and compensated classification frequency were determined as multiple target process parameters.

[0254] Figure 8 This is a schematic diagram of the structure of another cathode material preparation apparatus provided in an embodiment of this application. Figure 7 Based on the illustrated embodiments, as Figure 8 As shown, the cathode material preparation apparatus 70 further includes an update module 75, a construction module 76, and an optimization module 77.

[0255] Update module 75, used for:

[0256] The update collection frequency is determined based on process anomalies and load fluctuations.

[0257] Data is acquired using a multimodal sensor based on the update frequency.

[0258] Module 76 is used for:

[0259] The input and output variables of the response surface model are determined based on the range analysis formula. The input variables are the sample process parameters and the particle size of the sample cathode material, and the output variable is the process effect value.

[0260] Using input variables as experimental factors, reasonable value ranges for each input variable are set, and measured data of output variables corresponding to multiple sets of different input variable combinations are obtained through orthogonal experimental design.

[0261] Based on multiple sets of different combinations of input variables and measured data of output variables, a quantitative mapping relationship between input variables and output variables is constructed using a quadratic polynomial fitting method, thus obtaining a response surface model.

[0262] Optimization module 77 is used for:

[0263] Collect multiple measured process effect values ​​corresponding to multiple target process parameters;

[0264] The target score is obtained by comprehensively scoring multiple measured process effect values ​​through a multi-objective weighted scoring model. The multi-objective weighted scoring model is used to calculate the anti-escape index, capacity index, energy consumption index and current stability based on production needs to obtain a comprehensive score.

[0265] The response surface model is updated based on multiple target process parameters and target scores to obtain the updated response surface model.

[0266] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 9 As shown, the electronic device includes:

[0267] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods of the above embodiments.

[0268] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0269] The memory 292, as a non-volatile computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above-described method embodiments.

[0270] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0271] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0272] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in the foregoing embodiments.

[0273] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0274] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in the foregoing embodiments.

[0275] Based on the above implementation methods, through the coupling of multiple process parameters and the analysis of response surface models, abnormal process states and load fluctuation states in the cathode material grinding process can be accurately identified, so as to make targeted adjustments to the process parameters, achieve dynamic optimization of the cathode material, and thus improve the performance of the cathode material.

[0276] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0277] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages, which do not necessarily complete at the same time but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential but can be alternated or carried out in turn with other steps or at least some of the sub-steps or stages of other steps.

[0278] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0279] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0280] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. The processor can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC. The storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0281] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0282] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0283] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0284] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A system for preparing a positive electrode material, characterized in that, The cathode material preparation system is used to optimize process parameters and prepare cathode materials according to the optimized process parameters. The cathode material preparation system is configured to perform the following steps: By deploying multi-modal sensors at key measuring points of the air jet mill, multiple real-time process parameters and real-time cathode material particle size are collected during the operation of the air jet mill. The multiple real-time process parameters include grading current, grading frequency, feeding frequency, and grinding gas pressure. By using a response surface model to perform a synergistic analysis of the multiple real-time process parameters and the real-time cathode material particle size, the abnormal process state and load fluctuation state of the air jet mill are obtained. The response surface model represents the mapping relationship between process parameters and process effect values. Based on the graded current and the preset current range, the feeding frequency and the grinding air pressure are adjusted using a proportional-integral-derivative control algorithm to obtain adjustment parameters, and the updated graded current matching the adjustment parameters is collected. Based on the abnormal process state, the load fluctuation state, and the updated graded current, the adjustment parameters and the graded frequency are coordinated and adjusted using a response surface model to obtain multiple target process parameters. These multiple target process parameters are used to indicate the execution of a crushing process on the electrode raw materials to obtain the target cathode material.

2. The system according to claim 1, characterized in that, The step of performing a synergistic analysis of the multiple real-time process parameters and the real-time cathode material particle size using a response surface methodology to obtain the process anomaly state and load fluctuation state of the air jet mill specifically includes: The predicted process effect values ​​corresponding to the real-time process parameters and the real-time cathode material particle size are determined by the mapping relationship of the response surface model. When the predicted process effect value is not within the preset process effect compliance range, the abnormal process state is determined based on the endpoint values ​​of the predicted process effect value and the preset process effect compliance range. Determine the air mill load value corresponding to the staged current in the predicted process effect value; The load fluctuation state is obtained by comparing the airflow mill load value with the preset load compliance range.

3. The system according to claim 1, characterized in that, The step of adjusting the feeding frequency and grinding air pressure according to the graded current and the preset current range, using a proportional-integral-derivative control algorithm to obtain adjustment parameters, specifically includes: Determine the material identifier of the positive electrode material and the equipment identifier of the air jet mill; The corresponding preset current range is determined based on the material identifier and the equipment identifier; Based on the graded current and the preset current range, the feeding frequency and the grinding air pressure are adjusted using a proportional-integral-derivative control algorithm to obtain the adjustment parameters.

4. The system according to claim 3, characterized in that, The step of adjusting the feeding frequency and grinding air pressure according to the graded current and the preset current range, and obtaining the adjustment parameters through a proportional-integral-derivative control algorithm, specifically includes: Calculate the deviation between the graded current and the preset current range; The correction amount is calculated from the deviation value using the proportional term of the proportional-integral-derivative control algorithm. The correction amount is used to compensate for the deviation of the current cathode material load from the steady-state load. The trend compensation amount is calculated by using the differential term of the proportional-integral-derivative control algorithm to calculate the real-time rate of change of the graded current. The trend compensation amount is used to compensate for the subsequent trend of change of the load on the cathode material. The adjustment amount is determined based on the correction amount and the trend compensation amount; The feeding frequency and the grinding air pressure are adjusted according to the adjustment amount to obtain the adjustment parameters.

5. The system according to claim 4, characterized in that, The step of determining the adjustment amount based on the correction amount and the trend compensation amount specifically includes: Based on the deviation value and the real-time rate of change, determine the first weight corresponding to the correction amount and the second weight corresponding to the trend compensation amount; The adjustment amount is obtained by weighting and summing the correction amount and the trend compensation amount according to the first weight and the second weight.

6. The system according to claim 5, characterized in that, The step of collecting the updated graded current matching the adjusted parameters specifically includes: The operation of the airflow mill is controlled by adjusting the parameters mentioned above; After the air mill reaches a steady state, the graded current is collected as the updated graded current.

7. The system according to claim 1, characterized in that, The step of coordinating and adjusting the adjustment parameters and the graded frequency using a response surface model to obtain multiple target process parameters specifically includes: The process anomaly state, the load fluctuation state, and the updated graded current are input into the response surface model. Based on the coupling relationship between parameters, the feeding frequency and grinding gas pressure in the adjustment parameters are adapted and adjusted by the response surface model to obtain the compensated feeding frequency and the compensated grinding gas pressure, so as to match the load of the cathode material with the working conditions and maintain the updated graded current within the preset current range. The graded frequency is adapted and adjusted using the response surface model to match the current load state and eliminate process anomalies related to cathode material particle size, thus obtaining the compensated graded frequency. The compensated feeding frequency, the compensated grinding air pressure, and the compensated grading frequency are determined as the multiple target process parameters.

8. The system according to claim 1, characterized in that, The cathode material preparation system is also configured to perform the following steps: The update collection frequency is determined based on the abnormal process status and the load fluctuation status. Data is acquired using the multimodal sensor according to the update acquisition frequency.

9. The system according to any one of claims 1-8, characterized in that, The cathode material preparation system is also configured to perform the following steps: The input and output variables of the response surface model are determined according to the range analysis formula. The input variables are the sample process parameters and the particle size of the sample cathode material, and the output variable is the process effect value. Using the input variables as experimental factors, a reasonable range of values ​​for each input variable is set, and measured data of output variables corresponding to multiple sets of different combinations of input variables are obtained through orthogonal experimental design. Based on the multiple sets of different input variable combinations and the measured data of the output variable, a quantitative mapping relationship between the input variables and the output variables is constructed using a quadratic polynomial fitting method, thereby obtaining the response surface model.

10. The system according to claim 9, characterized in that, The cathode material preparation system is also configured to perform the following steps: Collect multiple measured process effect values ​​corresponding to the multiple target process parameters; The target score is obtained by comprehensively scoring the multiple measured process effect values ​​through a multi-objective weighted scoring model. The multi-objective weighted scoring model is used to calculate the anti-escape index, capacity index, energy consumption index and current stability based on production needs to obtain a comprehensive score. Based on the multiple target process parameters and the target score, the response surface model is updated to obtain the updated response surface model.

11. A method for preparing a positive electrode material, characterized in that, include: By deploying multi-modal sensors at key measuring points of the air jet mill, multiple real-time process parameters and real-time cathode material particle size are collected during the operation of the air jet mill. The multiple real-time process parameters include grading current, grading frequency, feeding frequency, and grinding gas pressure. By using a response surface model to perform a synergistic analysis of the multiple real-time process parameters and the real-time cathode material particle size, the abnormal process state and load fluctuation state of the air jet mill are obtained. The response surface model represents the mapping relationship between process parameters and process effect values. Based on the graded current and the preset current range, the feeding frequency and the grinding air pressure are adjusted using a proportional-integral-derivative control algorithm to obtain adjustment parameters, and the updated graded current matching the adjustment parameters is collected. Based on the abnormal process state, the load fluctuation state, and the updated graded current, the adjustment parameters and the graded frequency are coordinated and adjusted using a response surface model to obtain multiple target process parameters. These multiple target process parameters are used to indicate the execution of a crushing process on the electrode raw materials to obtain the target cathode material.

12. A single-cell battery, characterized in that, It includes a positive electrode, a negative electrode, a separator, and an electrolyte. The positive electrode includes a positive electrode material, which is prepared by the positive electrode material preparation system according to any one of claims 1-10.

13. A battery pack, characterized in that, It includes at least two individual cells as described in claim 12, each of which is electrically connected to the other.

14. A battery pack, characterized in that, It includes a housing and at least two battery packs as described in claim 13, each of the battery packs being disposed within the housing and electrically connected to each other.

15. An electric vehicle, characterized in that, It includes at least the battery pack as described in claim 14.

16. An electrical appliance, characterized in that, It includes at least the single cell as described in claim 12.

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