An automatic control system and method for winding up dry stretch film for lithium batteries

Abstract

This invention discloses an automatic control system and method for dry-process stretch film winding in lithium batteries, belonging to the technical field of lithium battery separator production equipment. The system includes a detection module, a control module, and an execution module. The detection module uses a linear CCD sensor combined with a parallel light source to collect real-time edge position information of the film roll. The control module obtains the edge position deviation through a deviation calculation unit, and then uses a fuzzy-PID composite control algorithm to dynamically adjust the PID parameters according to the deviation and rate of change, and can dynamically fine-tune the winding tension-roll diameter taper curve. The execution module receives instructions from the control module and drives the winding machine's actuator to change the winding tension to achieve deviation correction. This invention solves the problems of existing technologies such as reliance on manual labor, slow response, poor accuracy, and weak adaptability, enabling real-time, automatic, and precise control of winding parameters, improving separator winding quality and production efficiency.

Description

Technical Field

[0001] This invention relates to the field of lithium battery separator production equipment technology, specifically to an automatic control system and method for dry stretch film winding of lithium batteries. Background Technology

[0002] In the lithium battery industry chain, the separator, as a key insulation and ion-conducting component, directly determines the safety, cycle life, and energy density of lithium batteries. Within the separator manufacturing process, the dry stretching process has become one of the mainstream technologies due to the excellent mechanical strength and uniform pore structure of the separators it produces. The winding process, as the final core step of the dry stretching process, is the last line of defense to ensure the quality of the finished separator, and its operational stability plays a decisive role in the final product yield.

[0003] In actual winding operations, membrane roll misalignment is the most common and significantly harmful problem. When the membrane roll misaligns on the winding roller due to uneven edges, it triggers a series of chain quality defects: First, misalignment causes uneven stress in different areas of the membrane roll, resulting in localized tension differences. This structural unevenness will reduce the adhesion between the membrane and the positive and negative electrode sheets in subsequent cell assembly processes, increasing the risk of internal short circuits in the battery. Second, during misalignment, the membrane is prone to friction or compression with equipment components, producing wrinkles and creases. In severe cases, it can directly scratch the microporous structure on the membrane surface, destroying its ion conduction channels and leading to battery performance degradation. Third, if misalignment is not corrected in time, as the membrane roll diameter increases, the deviation will accumulate and may eventually cause the membrane to break, forcing the entire production line to shut down for maintenance. This not only wastes a large amount of raw materials but also results in significant production time losses, causing a double impact on the company's production costs and efficiency.

[0004] Currently, the industry's control technology for dry stretch film winding still faces the following critical issues that urgently need to be addressed: High reliance on manual labor and delayed response: Current winding parameter adjustments largely depend on the on-site experience and judgment of operators. Workers must continuously observe the edge position of the film roll with the naked eye, and manually adjust the winding tension and taper parameters when signs of deviation are detected. This process involves a significant observation-judgment-operation time lag, with response delays typically ranging from several seconds to tens of seconds. In high-speed production lines, large deviations can occur in a short time, making precise correction difficult. Furthermore, the accuracy of manual judgment is easily affected by subjective and objective factors such as worker experience level, fatigue level, and ambient lighting conditions, resulting in large fluctuations in parameter adjustment precision and an inability to guarantee consistent winding quality.

[0005] The dry stretch film winding process is susceptible to interference from multiple dynamic factors, exhibiting poor adaptability to environmental and material conditions and an inability to handle systemic deviations. On one hand, the base film may experience slight width fluctuations during production, which are gradually amplified during winding. On the other hand, changes in temperature and humidity in the production environment affect the physical properties of the diaphragm, and long-term operation can lead to systemic deviations such as roller wear and increased clearance in the transmission mechanism. Existing control technologies lack the ability to perceive and adaptively adjust to these dynamic disturbances in real time, only maintaining winding operations under fixed parameters. When the disturbance exceeds a certain range, it can cause continuous deviation, failing to meet the stable production requirements of high-quality diaphragms.

[0006] Based on the aforementioned industry pain points, there is an urgent need in this field for a winding control technology solution that integrates real-time detection, intelligent analysis, and automatic adjustment. This solution must possess high-precision membrane roll edge position detection capabilities to capture minute deviations in real time; simultaneously, it must be equipped with advanced intelligent control algorithms to achieve dynamic and precise adjustment of winding parameters, completely replacing manual operation and effectively coping with multiple complex factors such as raw material fluctuations, environmental interference, and equipment aging. This would fundamentally solve the problem of winding deviation, thereby improving the yield of finished separators, reducing production accident rates, and meeting the growing demand for high-quality separators in the lithium battery industry. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide an automatic control system and method for winding lithium battery dry stretch film, so as to solve the problems of existing technologies such as reliance on manual labor, slow response, poor accuracy and weak adaptability.

[0008] To achieve the above objectives, the present invention provides an automatic control system for the winding of lithium battery dry stretch film, characterized in that it includes a detection module, a control module, and an execution module; the detection module is used to detect the actual edge position of the wound film roll in real time; the control module is signal-connected to the detection module and is used to receive the actual edge position data and execute the core control algorithm; the execution module is connected to the control module and the winding machine power system and is used to receive the tension correction command from the control module and drive the winding machine execution mechanism to change the winding tension.

[0009] Furthermore, the detection module employs a linear CCD sensor combined with a parallel light source, or a laser rangefinder array, or an area array industrial camera; when a linear CCD sensor is used in combination with a parallel light source, the parallel light source provides stable illumination, and the linear CCD sensor scans rapidly to obtain high-contrast edge image signals.

[0010] Furthermore, the control module includes a deviation calculation unit and an intelligent control unit; the deviation calculation unit is used to calculate the deviation value (ΔE) between the actual edge position and the preset target edge position; the intelligent control unit is used to execute the core control algorithm and analyze the long-term trend of deviation.

[0011] Furthermore, the intelligent control unit employs a fuzzy-PID composite control algorithm, which dynamically adjusts the proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd) of the PID controller based on the real-time deviation (ΔE) and its rate of change. Traditional PID controllers typically use fixed values ​​for their proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd), achieving good control only under specific conditions, such as a fixed winding speed and stable raw material characteristics. However, the winding process of lithium-ion battery dry-stretch film exhibits nonlinearity. For example, changes in tension demand due to increased film roll diameter, fluctuations in raw material width, and environmental disturbances mean that fixed PID parameters cannot adapt to dynamic conditions, easily leading to overshoot, oscillation, or slow response. Therefore, the core purpose of designing the fuzzy-PID composite control algorithm is to dynamically adjust the PID parameters through fuzzy logic, enabling the controller to adapt to changes in the winding process in real time.

[0012] In the problem of film roll misalignment, the magnitude of the deviation (ΔE), such as the deviation of the film roll edge from the target position and the rate of change of the deviation, reflects the severity and development trend of the misalignment. Traditional PID control cannot distinguish the control requirements corresponding to different deviation characteristics. For example, large deviations require rapid correction, while small deviations require avoiding overshoot. The fuzzy-PID composite control algorithm takes ΔE and its rate of change as input, judges the misalignment state through fuzzy inference, and adjusts the PID parameters accordingly. The aim is to achieve on-demand control, ensuring both rapid response under large deviations and control accuracy under small deviations. Dry stretch film winding is susceptible to interference from slight width variations in raw materials, systematic deviations from long-term equipment operation, and fluctuations in environmental temperature and humidity. These interferences lead to complex and dynamically changing mechanisms of misalignment. The purpose of designing this composite algorithm is to identify and adapt the deviation characteristics caused by different interferences through the nonlinear processing capability of fuzzy logic, and then combine it with the precise adjustment capability of PID control to effectively suppress complex interferences, avoiding the problem that a single control algorithm cannot cope with multiple interference scenarios.

[0013] Furthermore, the execution of the fuzzy-PID composite control algorithm includes the following steps: 4.1 Fuzzification Processing Steps: Convert the deviation ΔE and its rate of change ΔEC into fuzzy linguistic variables using a membership function; 4.2 Fuzzy Inference Steps: Based on the preset fuzzy rule base, the fuzzy outputs of the proportional coefficient adjustment amount ΔKp, integral coefficient adjustment amount ΔKi, and differential coefficient adjustment amount ΔKd are obtained by reasoning based on fuzzy linguistic variables. 4.3 Defuzzification steps: Convert the fuzzy outputs of ΔKp, ΔKi, and ΔKd into precise numerical values; 4.4 Parameter adjustment steps: Dynamically update the Kp, Ki, and Kd parameters of the PID controller based on the precise values ​​of ΔKp, ΔKi, and ΔKd.

[0014] Furthermore, in the fuzzification processing step, the fuzzy linguistic variable set for the deviation ΔE and the deviation change rate ΔEC is: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}; the membership function is a triangular membership function or a Gaussian membership function; the defuzzification step uses the centroid method or the maximum membership method to convert the fuzzy output to precise numerical values; the fuzzy rule base used in the fuzzy inference step includes three independent rule tables, corresponding to the inference rules of ΔKp, ΔKi, and ΔKd respectively, with each rule table using the deviation ΔE and the deviation change rate ΔEC as input variables.

[0015] Furthermore, in the parameter adjustment step, the updated PID parameters are calculated according to the following formula: Kp = Kp0 + α p ·ΔKp,Ki = Ki0 + α i ·ΔKi,Kd = Kd0 +α d • ΔKd; Kp0, Ki0, and Kd0 are the initial parameters of the PID controller; α p α i α d This is the corresponding scaling factor.

[0016] Furthermore, the intelligent control unit also includes an online learning module, used to dynamically optimize the rule weights and scaling factors in the fuzzy rule base based on historical control data; the online learning module uses a genetic algorithm, particle swarm optimization algorithm, or neural network algorithm to achieve parameter optimization.

[0017] Furthermore, the intelligent control unit can also dynamically fine-tune the preset winding tension-roll diameter taper curve to compensate for systematic deviations caused by raw materials or equipment.

[0018] Furthermore, the tension correction command received by the execution module is ΔT, and the winding machine execution mechanism includes a servo motor, a magnetic powder clutch, or a frequency converter. The execution module changes the winding tension applied to the film roll by driving the above-mentioned execution mechanism.

[0019] On the other hand, the present invention also provides an automatic control method for winding up lithium battery dry stretch film of the above system, comprising the following steps: S1: System initialization. Specifically, system initialization involves setting the target edge position and loading initial winding parameters, which include initial tension and the taper curve of tension as a function of roll diameter. S2: Edge position detection. During the winding process, the detection module collects the edge position information of the winding film roll in real time. S3: Deviation judgment. Specifically, the control module calculates the edge position deviation (ΔE) through the deviation calculation unit. If the deviation value is within the preset dead zone threshold, the system maintains the current parameter operation; if the deviation exceeds the dead zone threshold, the automatic adjustment program is triggered. S4: Intelligent calculation. Specifically, intelligent calculation involves the control module running a fuzzy-PID composite control algorithm to generate the tension adjustment amount (ΔT), while simultaneously assessing the deviation trend and determining whether to fine-tune the taper curve. S5: Execution control. The execution module drives the winding machine to change the current tension output according to the received adjustment command; S6: Closed-loop feedback. Specifically, closed-loop feedback involves repeating steps S2 to S5 to form real-time closed-loop feedback control, continuously monitoring and adjusting the winding parameters until the winding process ends.

[0020] The beneficial effects of this invention are: (1) Intelligent automatic control: The fuzzy-PID composite control is adopted, which overcomes the disadvantage of fixed parameters of traditional PID. It can automatically adjust the optimal parameters according to the magnitude and trend of deviation, so that the system maintains a fast and stable response characteristic throughout the winding process.

[0021] (2) High precision and high stability: Combining the intelligence of fuzzy control and the precision of PID control, it can effectively suppress small-amplitude random disturbances and large-amplitude deviations, resulting in high control precision and good system stability.

[0022] (3) Proactive compensation: The addition of dynamic fine-tuning function for the taper curve can compensate for systematic deviations caused by raw materials or equipment itself, reflecting a higher level of intelligent control and further improving the winding quality.

[0023] (4) Improve production efficiency: Reduce downtime, membrane breakage and scrap rate caused by deviation, improve production efficiency and product yield, and are suitable for high-speed, high-quality lithium battery separator production lines. Attached Figure Description

[0024] Figure 1 This is a block diagram illustrating the structural principle of the automatic control system for the dry stretch film winding of lithium batteries according to the present invention.

[0025] Figure 2 This is a flowchart of the automatic control method for winding up lithium battery dry stretch film in this invention.

[0026] Figure 3 This is a schematic diagram illustrating the principle of the fuzzy-PID composite control algorithm in this invention. Detailed Implementation

[0027] To make the technical solutions and advantages of the present invention clearer, the present invention and its beneficial effects will be described in further detail below with reference to specific embodiments and accompanying drawings. However, the embodiments of the present invention are not limited thereto. For ease of understanding, the present invention will be described more comprehensively and meticulously below with reference to the accompanying drawings and preferred embodiments. However, the scope of protection of the present invention is not limited to the following specific embodiments.

[0028] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of the invention.

[0029] Unless otherwise specified, all reagents and raw materials used in this invention are commercially available products or products that can be prepared by known methods.

[0030] To better understand the present invention, the following embodiments further illustrate the content of the present invention, but the content of the present invention is not limited to the following embodiments.

[0031] Reference Figure 1 This embodiment describes an automatic control system for the winding of dry-stretched lithium-ion battery film. It includes a detection module, a control module, and an execution module. The specific structure and connection relationships of each module are as follows: Detection module: Used for real-time detection of the actual edge position of the wound film roll. A preferred implementation uses a linear CCD sensor combined with a parallel light source. The parallel light source provides stable and clear illumination, while the linear CCD sensor can scan quickly to acquire high-contrast edge image signals, offering strong anti-interference capabilities and high accuracy. Alternative implementations include laser rangefinder arrays or area array industrial cameras.

[0032] Control module: Connected to the detection module, it receives actual edge position data and executes the core control algorithm. The control module further includes a deviation calculation unit and an intelligent control unit. Deviation calculation unit: used to calculate the deviation value (ΔE) between the actual edge position and the preset target edge position.

[0033] Intelligent control unit: Employing a fuzzy-PID composite control algorithm, this unit dynamically and nonlinearly adjusts the proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd) of the PID controller (proportional-integral-derivative controller) based on the real-time deviation (ΔE) and its rate of change to address the nonlinearity and time-varying nature of the winding process. Specifically, the fuzzy-PID composite control algorithm includes the following steps: Fuzzification processing: The deviation ΔE and its rate of change ΔEC are converted into fuzzy linguistic variables through triangular membership functions or Gaussian membership functions. The set of fuzzy linguistic variables includes: {Negative Large (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Large (PB)}; Fuzzy inference: Based on a preset fuzzy rule base, the fuzzy outputs of the proportional coefficient adjustment amount ΔKp, integral coefficient adjustment amount ΔKi, and differential coefficient adjustment amount ΔKd are obtained by inference based on the fuzzified ΔE and ΔEC; the fuzzy rule base includes three independent rule tables, which correspond to the adjustment strategies of ΔKp, ΔKi, and ΔKd respectively; Defuzzification: The fuzzy outputs of ΔKp, ΔKi, and ΔKd are converted into precise values ​​using the centroid method or the maximum membership method; Dynamic parameter adjustment: The updated PID parameters are calculated using the following formula: Kp = Kp0 + α p ·ΔKp,Ki = Ki0+ α i ·ΔKi,Kd = Kd0 +α d • ΔKd; Kp0, Ki0, and Kd0 are the initial parameters of the PID controller; α p α i α d This is the corresponding scaling factor.

[0034] Furthermore, the intelligent control unit may also include an online learning module for dynamically optimizing the rule weights and scaling factors in the fuzzy rule base based on historical control data. The optimization method may employ genetic algorithms, particle swarm optimization, or neural network algorithms.

[0035] Meanwhile, this unit can also analyze the long-term trend of deviation and dynamically fine-tune the preset winding tension-roll diameter taper curve to achieve forward-looking control.

[0036] Execution module: Connected to the control module and the winding machine power system, it receives tension correction commands (ΔT) from the control module and drives the winding machine's actuators (such as servo motors, magnetic powder clutches, frequency converters, etc.) to change the winding tension applied to the film roll, thereby correcting deviation.

[0037] Reference Figure 2This is a flowchart of the automatic control method for winding lithium battery dry stretch film according to the present invention. Based on the above system, the method includes the following steps: Step S1: System Initialization Set the target edge position, such as the pixel point or physical position where the edge of the film roll should be based on the production standard, and load the initial winding parameters, including the initial tension value and the taper curve of tension changing with the roll diameter (this curve defines the theoretical value of winding tension under different roll diameters to ensure uniform force during the film roll winding process).

[0038] Step S2: Real-time detection of the actual edge position of the membrane roll Detection modules, such as linear CCD sensors combined with parallel light sources, laser rangefinder arrays, or area array industrial cameras, can acquire real-time information on the actual edge position of the film roll during the winding process. Taking a linear CCD sensor as an example, it can quickly scan and acquire high-contrast edge image signals when used with a parallel light source, providing accurate data for subsequent deviation calculations.

[0039] Step S3: Calculate the edge position deviation ΔE and determine whether it exceeds the dead zone threshold. The deviation calculation unit of the control module compares the detected actual edge position with the target edge position and calculates the edge position deviation ΔE. Then it determines whether the deviation value is within the preset dead zone threshold. If it is within the dead zone threshold, the system maintains the current winding parameters; if it exceeds the dead zone threshold, the automatic adjustment program is triggered, and the process proceeds to step S4.

[0040] Step S4: Run the fuzzy-PID composite control algorithm Figure 3 This is a schematic diagram illustrating the principle of the fuzzy-PID composite control algorithm, specifically: Step 4.1: Calculation of Deviation and Rate of Change of Deviation First, the actual edge position of the membrane roll is obtained through the detection module and compared with the preset target edge position to calculate the system deviation e, that is, the edge position deviation ΔE.

[0041] At the same time, the rate of change of the system deviation e is calculated to obtain the deviation change rate ec, that is, the deviation change rate ΔEC.

[0042] Step 4.2: Blur Reduction Processing The precise numerical quantities of system deviation *e* and deviation change rate *ec* are converted into linguistic variables in fuzzy sets. For example, the fuzzy subset of deviation *e* is defined as {negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), positive large (PB)}, and the deviation change rate *ec* is defined similarly. Membership functions, such as triangular membership functions or Gaussian membership functions, are used to determine the membership degrees of *e* and *ec* to each fuzzy subset.

[0043] Step 4.3: Fuzzy Reasoning Based on a pre-established fuzzy rule base, the fuzzified values ​​'e' and 'ec' are input into the fuzzy inference engine. The fuzzy inference engine, following fuzzy logic reasoning methods (such as the Mamdani inference method), derives fuzzy sets of the adjustment amounts ΔKp (proportional coefficient adjustment), ΔKi (integral coefficient adjustment), and ΔKd (derivative coefficient adjustment) of the PID controller parameters. The fuzzy rule base contains three independent rule tables, corresponding to the adjustment logic of ΔKp, ΔKi, and ΔKd, respectively.

[0044] Step 4.4: Defuzzification The fuzzy sets of ΔKp, ΔKi, and ΔKd obtained from fuzzy inference are converted into precise numerical values. Commonly used defuzzification methods include the centroid method and the maximum membership method. Specific PID parameter adjustment values ​​are obtained through defuzzification.

[0045] Step 4.5: PID controller parameter adjustment and control signal generation By combining the defuzzified values ​​ΔKp, ΔKi, and ΔKd with the initial parameters (Kp0, Ki0, Kd0) of the PID controller, the adjusted PID parameters are obtained: Kp = Kp0 + α p ·ΔKp,Ki = Ki0 + α i ·ΔKi,Kd = Kd0 +α d • ΔKd; Kp0, Ki0, and Kd0 are the initial parameters of the PID controller; α p α i α d This is the corresponding proportional factor. The PID controller calculates the control signal u, i.e., the control signal for the winding tension adjustment ΔT, based on the system deviation ΔE and the adjusted parameters.

[0046] Step 6: Control signal execution and system output feedback The control signal u is sent to the execution module, which drives the actuator (such as a servo motor) of the winding machine to adjust the winding tension. The operating status of the winding machine, i.e., the system output y, such as the actual change in the position of the film roll edge, is fed back through the detection module. This is used for display output and also re-enters the deviation calculation stage, forming a closed-loop control that continuously and dynamically adjusts the winding process.

[0047] Step S5: The execution module adjusts the winding tension. The execution module receives the tension adjustment amount ΔT command from the control module and drives the actuator of the winding machine, such as a servo motor, magnetic powder clutch or frequency converter, to change the winding tension applied to the film roll, thereby correcting the film roll deviation.

[0048] Step S6: Determine if the winding process is complete. If the winding process is completed, such as when the film roll reaches the preset diameter or winding length, the process ends; if it is not completed, return to step S2 and repeat the above closed-loop process of detection-calculation-judgment-control, continuously monitor and adjust the winding process in real time until the winding process ends.

[0049] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An automatic control system for the winding of dry-stretched lithium battery film, characterized in that, It includes a detection module, a control module, and an execution module; the detection module is used to detect the actual edge position of the winding film roll in real time; the control module is signal-connected to the detection module and is used to receive the actual edge position data and execute the core control algorithm; the execution module is connected to the control module and the winding machine power system and is used to receive the tension correction command from the control module and drive the winding machine execution mechanism to change the winding tension.

2. The automatic control system for winding lithium battery dry stretch film according to claim 1, characterized in that, The detection module uses a linear CCD sensor combined with a parallel light source, or a laser rangefinder array, or an area array industrial camera. When a linear CCD sensor is used in combination with a parallel light source, the parallel light source provides stable illumination, and the linear CCD sensor scans quickly to obtain high-contrast edge image signals.

3. The automatic control system for winding lithium battery dry stretch film according to claim 1, characterized in that, The control module includes a deviation calculation unit and an intelligent control unit; the deviation calculation unit is used to calculate the deviation value (ΔE) between the actual edge position and the preset target edge position; the intelligent control unit is used to execute the core control algorithm and analyze the long-term trend of deviation.

4. The automatic control system for winding lithium battery dry stretch film according to claim 3, characterized in that, The intelligent control unit adopts a fuzzy-PID composite control algorithm, which can dynamically adjust the proportional coefficient (Kp), integral coefficient (Ki), and derivative coefficient (Kd) of the PID controller according to the real-time deviation (ΔE) and its rate of change.

5. The automatic control system for winding lithium battery dry stretch film according to claim 4, characterized in that, Execution of the fuzzy-PID composite control algorithm Includes the following steps: 4.1 Fuzzification Processing Steps: Convert the deviation ΔE and its rate of change ΔEC into fuzzy linguistic variables using a membership function; 4.2 Fuzzy Inference Steps: Based on the preset fuzzy rule base, the fuzzy outputs of the proportional coefficient adjustment amount ΔKp, integral coefficient adjustment amount ΔKi, and differential coefficient adjustment amount ΔKd are obtained by reasoning based on fuzzy linguistic variables. 4.3 Defuzzification steps: Convert the fuzzy outputs of ΔKp, ΔKi, and ΔKd into precise numerical values; 4.4 Parameter adjustment steps: Dynamically update the Kp, Ki, and Kd parameters of the PID controller based on the precise values ​​of ΔKp, ΔKi, and ΔKd.

6. The automatic control system for winding lithium battery dry stretch film according to claim 5, characterized in that, In the fuzzification process, the fuzzy linguistic variable set for the deviation ΔE and the deviation change rate ΔEC is: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}; the membership function is a triangular membership function or a Gaussian membership function; the defuzzification process uses the centroid method or the maximum membership method to convert the fuzzy output to a precise numerical value; the fuzzy rule base used in the fuzzy inference process includes three independent rule tables, corresponding to the inference rules of ΔKp, ΔKi, and ΔKd respectively, with each rule table using the deviation ΔE and the deviation change rate ΔEC as input variables.

7. The automatic control system for winding lithium battery dry stretch film according to claim 5, characterized in that, In the parameter adjustment step, the updated PID parameters are calculated according to the following formula: Kp = Kp0 + α p ·ΔKp,Ki = Ki0 + α i ·ΔKi,Kd = Kd0 +α d • ΔKd; Kp0, Ki0, and Kd0 are the initial parameters of the PID controller; α p α i α d This is the corresponding scaling factor.

8. The automatic control system for winding lithium battery dry stretch film according to claim 7, characterized in that, The intelligent control unit also includes an online learning module, which is used to dynamically optimize the rule weights and scaling factors in the fuzzy rule base based on historical control data; the online learning module uses a genetic algorithm, particle swarm optimization algorithm or neural network algorithm to optimize parameters.

9. The automatic control system for winding lithium battery dry stretch film according to claim 1, characterized in that, The intelligent control unit can also dynamically fine-tune the preset winding tension-roll diameter taper curve to compensate for systematic deviations caused by raw materials or equipment; the tension correction command received by the execution module is ΔT, and the winding machine execution mechanism includes a servo motor, a magnetic powder clutch or a frequency converter. The execution module changes the winding tension applied to the film roll by driving the above-mentioned execution mechanism.

10. An automatic control method for winding lithium battery dry stretch film based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: S1: System initialization. Specifically, system initialization involves setting the target edge position and loading initial winding parameters, which include initial tension and the taper curve of tension as a function of roll diameter. S2: Edge position detection. During the winding process, the detection module collects the edge position information of the winding film roll in real time. S3: Deviation judgment. Specifically, the control module calculates the edge position deviation (ΔE) through the deviation calculation unit. If the deviation value is within the preset dead zone threshold, the system maintains the current parameter operation; if the deviation exceeds the dead zone threshold, the automatic adjustment program is triggered. S4: Intelligent calculation. Specifically, intelligent calculation involves the control module running a fuzzy-PID composite control algorithm to generate the tension adjustment amount (ΔT), while simultaneously assessing the deviation trend and determining whether to fine-tune the taper curve. S5: Execution control. The execution module drives the winding machine to change the current tension output according to the received adjustment command; S6: Closed-loop feedback. Specifically, closed-loop feedback involves repeating steps S2 to S5 to form real-time closed-loop feedback control, continuously monitoring and adjusting the winding parameters until the winding process ends.

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