Intelligent winding control method of lithium battery automatic cutting and winding machine

Abstract

The present application relates to the field of winding machine, and discloses an intelligent winding control method of a lithium battery automatic cutting winding machine, which establishes a material dynamics model aiming at the inertia effect of the material in the high-speed winding process, comprehensively considers the mass, stiffness and damping parameters of the material, predicts the dynamic response characteristics of the material in the winding process, introduces a feedforward compensation mechanism in the control system, corrects the control instruction of the actuator according to the prediction result of the material dynamics model, and thus inhibits the influence of the inertia effect of the material on the position control precision; in the man-machine interface of the winding machine, information display and operation guiding functions are provided, key parameters and equipment state information of the winding process are displayed in real time, the operator is facilitated to monitor and manage, a perfect data recording and tracing system is established, the production process data of each battery cell is recorded and stored completely, a traceable quality management chain is formed, and data support is provided for product quality analysis and improvement.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of winding machines, especially to the field of lithium battery winding, in particular to an intelligent winding control method of a lithium battery automatic cutting winding machine. BACKGROUND

[0002] In the winding process of the lithium battery automatic cutting winding machine, a key technical problem is how to ensure the accurate position relationship and tension control of the anode sheet, the separator and the cathode sheet during high-speed winding. The difficulty of this problem lies in that as the winding proceeds, the diameter of the winding core will continuously increase, which will cause changes in the linear velocity and tension of the materials. If these changes cannot be adjusted and compensated in time, it may lead to material misplacement, wrinkles or breakage, thereby affecting the quality and performance of the battery cell.

[0003] In addition, during high-speed winding, the inertia and vibration of the materials will also interfere with the position control. The mechanical structure and transmission system of the winding machine must have sufficient rigidity and stability to suppress these disturbances. At the same time, the tension control system needs to have the ability of fast response and accurate adjustment to adapt to the changes of material properties and winding process.

[0004] Finally, the winding process also needs to be closely coordinated with the upstream and downstream processes, such as the supply of materials and the transfer of bare battery cells. Any abnormality in any of these links may lead to interruption of the winding process or quality problems; therefore, the control system of the winding machine also needs to have the ability of process flow coordination and scheduling, as well as the ability of real-time monitoring and rapid response to abnormal situations. Only by solving these key problems can the lithium battery automatic cutting winding machine produce high-quality battery cell products stably and efficiently. SUMMARY

[0005] The purpose of the present application is to solve the above defects and provide an intelligent winding control method for a lithium battery automatic cutting winding machine to solve the technical problems in the background art.

[0006] The purpose of the present application is achieved in the following way:

[0007] The intelligent winding control method of the lithium battery automatic cutting winding machine comprises the following steps:

[0008] S101, according to the pre-established position relationship model of the anode sheet, the separator and the cathode sheet, real-time acquisition of the position deviation data of the three materials in the winding process, filtering processing of the position deviation data through Kalman filtering algorithm to obtain accurate position deviation estimation value, comparison of the estimation value with the pre-set position deviation threshold value, if the threshold value is exceeded, the position of the corresponding material is adjusted to ensure that the three materials always maintain accurate position relationship;

[0009] S102, in order to solve the problem of material linear velocity variation caused by the increasing diameter of the winding core during the winding process, an adaptive control algorithm is adopted, which dynamically adjusts the unwinding speed and winding tension of the material according to the real-time measured diameter data of the winding core, realizes the accurate control of the material linear velocity through the combination of feedforward compensation and feedback correction, ensures that the material tension remains within the preset target range during the entire winding process, and avoids material relaxation or excessive stretching;

[0010] S103, in the mechanical structure design of the winding machine, the finite element analysis method is adopted to optimize the rigidity and stability of the key components, the anti-interference ability of the whole machine is improved through reasonable arrangement of the transmission system and support structure, the active damping technology is introduced in the transmission system, the vibration signals during the winding process are detected in real time through the vibration sensor, the adaptive filtering algorithm is used to analyze and process the vibration signals, and the corresponding compensation control command is generated to drive the actuator to actively suppress the vibration, reduce the influence of vibration on material position control, at the same time, considering the dynamic characteristics of the material in high-speed motion, the basic data for establishing the material dynamics model is provided;

[0011] S104, in order to solve the inertia effect of the material in the high-speed winding process, a material dynamics model is established, considering the mass, stiffness and damping parameters of the material, the dynamic response characteristics of the material in the winding process are predicted, a feedforward compensation mechanism is introduced in the control system, the control command of the actuator is corrected according to the prediction result of the material dynamics model, so as to suppress the influence of material inertia effect on position control accuracy;

[0012] S105, for the tension control system, an intelligent control strategy based on fuzzy proportional integral derivative is adopted, according to the characteristic parameters of the material and the winding process requirements, the parameters of the proportional integral derivative controller are adaptively adjusted, the response speed and adjustment precision of the tension control system are improved, in specific implementation, the controller parameters are adjusted online by using fuzzy rule base, which adapts to the tension control demand under different working conditions, the tension sensor is introduced to monitor the material tension in real time, the measurement data of multiple sensors are analyzed comprehensively through data fusion algorithm, the optimal estimation value of material tension is obtained as feedback signal input into the control system, realizing the accurate control of tension;

[0013] S106, in the control system of the winding machine, a multi-agent based collaborative scheduling model is constructed, the material flow and information flow between the winding process and the upstream and downstream processes are uniformly managed and optimized, real-time coordination and dynamic balance between processes are realized through intelligent algorithms, efficient operation of the entire production line is ensured, a sound abnormal monitoring and diagnosis mechanism is established, real-time monitoring of key process parameters and equipment status is performed, once an abnormal condition is found, a warning is triggered and corresponding control measures are taken to reduce the impact of the abnormality on the winding process, for data visualization and operation control, an intuitive human-machine interface is designed to display key parameters and equipment status information in real time;

[0014] S107, in the human-machine interface of the winding machine, information display and operation guidance functions are provided, key parameters and equipment status information of the winding process are displayed in real time, operation personnel can monitor and manage conveniently, a complete data recording and tracing system is established, production process data of each battery cell is recorded and stored completely, a traceable quality management chain is formed, providing data support for product quality analysis and improvement.

[0015] The beneficial effects of the present application are: for the problems of material position deviation, line speed variation and vibration interference in the winding process, the present application uses Kalman filter algorithm to estimate the position deviation in real time, and dynamically adjusts the material speed and tension through adaptive control, while introducing active vibration reduction technology and feedforward compensation mechanism to suppress the influence of vibration and inertia effect on position control. In terms of tension control, an intelligent control strategy based on fuzzy PID is adopted, combined with multi-sensor data fusion to realize accurate control. Through the construction of a multi-agent collaborative scheduling model, the entire production line is uniformly managed and optimized, and through abnormal monitoring and diagnosis, human-machine interface and data tracing system, comprehensive monitoring and quality management of the winding process are realized. This method significantly improves the precision and stability of lithium battery winding, effectively solves the key technical problems in high-speed winding, and provides important support for improving the production efficiency and quality of lithium batteries. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The flowchart of the present embodiment is shown in the figure; DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be described in further detail below with reference to the drawings and specific embodiments.

[0018] In this embodiment, reference is made to Figure 1 The specific implementation of the intelligent winding control method includes:

[0019] S101, according to the pre-established position relationship model of the anode sheet, the diaphragm and the cathode sheet, real-time acquisition of the position deviation data of the three materials in the winding process, filtering the position deviation data through Kalman filtering algorithm, obtaining the accurate position deviation estimate value, comparing the estimate value with the preset position deviation threshold value, if exceeding the threshold range, adjusting the position of the corresponding material, ensuring that the three materials always maintain accurate position relationship;

[0020] A position relationship model of an anode sheet, a diaphragm and a cathode sheet is obtained in advance as a reference for material position control. During the battery winding process, a sensor is used to collect position deviation data of the anode sheet, the diaphragm and the cathode sheet in real time. The collected position deviation data is input into a Kalman filtering algorithm for noise removal and data smoothing processing to obtain position deviation estimate values of the anode sheet, the diaphragm and the cathode sheet. It is determined whether the position deviation estimate values exceed a preset deviation threshold range. If the position deviation estimate values exceed the deviation threshold range, a position adjustment mechanism is triggered. According to the size and direction of the position deviation estimate values, an actuator is controlled to adjust the position of the anode sheet, the diaphragm or the cathode sheet so that their position relationship is consistent with the position relationship model. After the position adjustment, the position deviation data of the anode sheet, the diaphragm and the cathode sheet is collected again and input into the Kalman filtering algorithm for processing to determine whether the adjusted position deviation is within the deviation threshold range. If the adjusted position deviation still exceeds the deviation threshold range, the position adjustment mechanism is triggered repeatedly, the actuator is controlled to adjust the position, and the position deviation data is collected again until the position relationship of the anode sheet, the diaphragm and the cathode sheet is always consistent with the position relationship model.

[0021] Specifically, during the battery winding process, the position deviation data of the anode sheet, the separator and the cathode sheet are collected in real time by a high-precision laser displacement sensor, and the sampling frequency is 1000 Hz. The collected position deviation data is input into a Kalman filtering algorithm based on adaptive noise covariance estimation for processing. By dynamically adjusting the noise covariance matrix, adaptive estimation of the measurement noise is realized, so as to obtain an accurate position deviation estimation value with an estimation accuracy better than 1 mm. It is judged whether the position deviation estimation value exceeds the preset ±5 mm deviation threshold range. If the threshold is exceeded, a position adjustment mechanism based on PID control is triggered. According to the size and direction of the position deviation estimation value, the material position is accurately adjusted by the actuator driven by the servo motor, and the adjustment accuracy is better than 5 mm, so that it returns to the state consistent with the pre-established three-dimensional CAD position relationship model. After the position adjustment, the material position deviation data is obtained again and input into the Kalman filtering algorithm for processing. If the adjusted position deviation still exceeds the threshold, the incremental PID control law is iteratively optimized to adjust the process until the position deviation of the three materials converges to the threshold range, so as to ensure that the material position relationship in the battery winding process always meets the process requirements, and the consistency and reliability of the battery are ensured.

[0022] S102, in order to solve the problem of material linear velocity change caused by the increasing diameter of the winding core during the winding process, an adaptive control algorithm is adopted. According to the real-time measured winding core diameter data, the unwinding speed and winding tension of the material are dynamically adjusted. Through the combination of feedforward compensation and feedback correction, the material linear velocity is accurately controlled, and the material tension is ensured to be within the preset target range during the entire winding process, avoiding material relaxation or excessive stretching;

[0023] Obtain real-time winding core diameter data during the winding process, and use the real-time winding core diameter data as the input of the adaptive control algorithm;

[0024] According to the real-time winding core diameter data, the unwinding speed and winding tension parameters to be adjusted are calculated through a preset feedforward compensation model;

[0025] The unwinding speed and winding tension parameters are transmitted to the actuator of the winding equipment, and the actuator dynamically adjusts the unwinding speed and winding tension during the winding process according to the unwinding speed and winding tension parameters;

[0026] Continuously monitor the material tension during the winding process to obtain real-time material tension data;

[0027] Judge whether the real-time material tension data exceeds the preset target tension range;

[0028] If the real-time material tension data exceeds the target tension range, the preset feedback correction model is used to calculate the further adjustment of the unwinding speed and winding tension parameters, and the further adjustment of the unwinding speed and winding tension parameters is transmitted to the execution mechanism;

[0029] The execution mechanism further dynamically adjusts the unwinding speed and winding tension during the winding process according to the further adjustment of the unwinding speed and winding tension parameters until the winding process ends, so that the material tension is always stable within the target tension range.

[0030] Specifically, during the battery winding process, the high-precision laser ranging sensor installed on the winding equipment obtains the core diameter data in real time, and the sampling frequency is 100 Hz. The obtained core diameter data is input into the feedforward compensation model based on the radial basis function neural network (RBFNN), and the nonlinear mapping relationship obtained by training is used to calculate the adjustment of the unwinding speed and winding tension parameters in real time. The calculated control parameters are transmitted to the servo motor and tension controller of the winding equipment through industrial Ethernet to realize dynamic adjustment of the unwinding speed and winding tension, and the adjustment accuracy is better than 1%. At the same time, the tension sensor installed on the material transmission path continuously monitors the material tension, and the sampling frequency is 1000 Hz. The real-time material tension data is compared with the preset target tension range (such as 10N±1N) to determine whether it exceeds the target range. If the real-time material tension exceeds the target range, the feedback correction model based on fuzzy PID control is triggered, and the further adjustment of the unwinding speed and winding tension parameters is calculated according to the size and change rate of the tension deviation through fuzzy reasoning, and the further adjustment of the unwinding speed and winding tension parameters is transmitted to the execution mechanism for dynamic adjustment, and the adjustment accuracy is better than 5%. Through continuous monitoring, comparison and feedback correction, the material tension is ensured to be stable within the target range of 10N±1N during the entire winding process, effectively avoiding the winding quality problems caused by material relaxation or excessive stretching, and improving the consistency and reliability of the battery.

[0031] S103, in the mechanical structure design of the winding machine, the finite element analysis method is used to optimize the rigidity and stability of the key components, the transmission system and the support structure are reasonably arranged to improve the anti-interference ability of the whole machine, the active damping technology is introduced into the transmission system, the vibration signal during the winding process is detected in real time through the vibration sensor, the vibration signal is analyzed and processed by using the adaptive filtering algorithm, the corresponding compensation control instruction is generated, and the execution mechanism is driven to actively suppress the vibration, thereby reducing the influence of the vibration on the material position control. At the same time, the dynamic characteristics of the material in high-speed motion are considered to provide basic data for the next step of establishing the material dynamics model;

[0032] The structural parameters and working requirements of the winding machine are obtained, and the finite element analysis model of the key components of the winding machine is established;

[0033] Perform stress-strain distribution and vibration modal analysis on the finite element analysis model to obtain stress-strain distribution and vibration modal data of the key components;

[0034] Determine the structure parameters and layout optimization scheme of the key components according to the stress-strain distribution and vibration modal data, the optimization scheme including adjusting material selection, cross-sectional size, and reinforcement layout;

[0035] Install multiple vibration sensors at vibration-sensitive points of the winding machine to collect vibration signals in real time during the winding process;

[0036] Preprocess the collected vibration signals to filter out high-frequency noise and interference components in the vibration signals to obtain preprocessed vibration signals;

[0037] Perform feature extraction on the preprocessed vibration signals using wavelet analysis and empirical mode decomposition methods to obtain time-frequency features of the vibration signals;

[0038] Determine the main components and sources of vibration according to the time-frequency features of the vibration signals;

[0039] Generate vibration compensation control instructions based on the main components and sources of vibration using an adaptive filtering algorithm;

[0040] Convert the vibration compensation control instructions into drive signals to drive the actuators of the winding machine and perform active vibration control to suppress the transmission and amplification of vibration.

[0041] S104, for the inertia effect of the material during high-speed winding, a material dynamics model is established, considering the mass, stiffness, and damping parameters of the material, to predict the dynamic response characteristics of the material during winding, and a feedforward compensation mechanism is introduced into the control system to modify the control instructions for the actuators based on the prediction results of the material dynamics model, thereby suppressing the influence of material inertia effect on position control accuracy;

[0042] Obtain dynamic response characteristic data of the material during high-speed winding, including mass, stiffness, and damping parameters of the material;

[0043] Establish a material dynamics model based on the dynamic response characteristic data, which is used to predict the position, velocity, and acceleration state variables of the material during high-speed winding;

[0044] Obtain the prediction results of the material dynamics model, introduce a feedforward compensation mechanism into the control system, and generate modified control instructions based on the prediction results;

[0045] The modified control command is delivered to the actuator, and the motion of the actuator is adjusted and optimized in real time to suppress the influence of material inertia effect on position control accuracy.

[0046] The prediction error of the material dynamics model is obtained, and an adaptive control algorithm is used to adjust the parameters of the feedforward compensation mechanism in real time according to the prediction error, realizing adaptive optimization of the control system.

[0047] According to the material dynamics model and the adaptive control algorithm, the high-speed winding process is simulated and experimentally verified, and the design parameters of the control system are further optimized.

[0048] Specifically, to establish the material dynamics model, first, the mass, stiffness and damping parameters of the material need to be obtained. By measuring the linear density of the wound material, the mass parameter of the material is obtained as 05 kg / m. The stress-strain curve of the material is tested by a tensile testing machine, and the stiffness parameter of the material is obtained as 100 GPa. Through free vibration decay experiment, the damping ratio of the material is measured as 02. Substitute these parameters into the lumped parameter model to establish the dynamics differential equation of the material. The Runge-Kutta method is used to solve the differential equation numerically, and the time-domain response curves of the material position, velocity and acceleration are obtained. By analyzing the peak value and steady value of the curve, the maximum displacement of the material at a winding speed of 10 m / s is predicted as 2 mm, and the velocity fluctuation amplitude is 2 m / s. According to the prediction result, a feedforward compensator is designed in the control system to generate a modified control command.

[0049] The compensator uses the least squares method to fit the feedforward compensation function according to historical data, and the compensation coefficient is 8. The compensated control command is input to the actuator, and the position tracking control of the actuator is realized through the PID control algorithm. At the same time, an adaptive control strategy is introduced, and the gradient descent method is used to adjust the feedforward compensation coefficient in real time, with a learning rate of 05, so that the compensation accuracy is continuously improved. A simulation model of the control system is built by MATLAB / Simulink to analyze the control effect under different working conditions. The simulation results show that after introducing the material dynamics model and the feedforward compensation, the material position control error is reduced by more than 50%. Finally, experimental verification is carried out on the actual winding machine, and the material position control accuracy is improved to 1 mm at a winding speed of 10 m / s, which proves the effectiveness of the proposed control method.

[0050] S105, for the tension control system, an intelligent control strategy based on fuzzy proportional integral derivative is adopted, the parameters of the proportional integral derivative controller are adaptively adjusted according to the material characteristic parameters and the winding process requirements, the response speed and the adjustment accuracy of the tension control system are improved, in specific implementation, the controller parameters are adjusted online by using the fuzzy rule base, the tension control requirements under different working conditions are adapted, the tension sensor is introduced to monitor the material tension in real time, the measurement data of multiple sensors are comprehensively analyzed by using the data fusion algorithm, the optimal estimation value of the material tension is obtained, which is input into the control system as the feedback signal, and the accurate control of the tension is realized;

[0051] According to the material characteristic parameters and the winding process requirements, the controller parameters are adjusted online by using the fuzzy rule base, the material tension is monitored in real time by introducing the tension sensor, the measurement data of multiple sensors are obtained, the optimal estimation value of the material tension is obtained by using the data fusion algorithm, which is input into the control system as the feedback signal, the accurate control of the tension is realized, the response speed and the adjustment accuracy of the system are improved, and the control requirements under different working conditions are adapted.

[0052] S106, in the control system of the winding machine, a multi-agent collaborative scheduling model is constructed, the material flow and the information flow between the winding process and the upstream and downstream processes are uniformly managed and optimized, the real-time coordination and dynamic balance between processes are realized by intelligent algorithm, the efficient operation of the whole production line is ensured, a sound abnormal monitoring and diagnosis mechanism is established, the key process parameters and equipment states are monitored in real time, once an abnormal situation is found, a warning is triggered and corresponding control measures are taken, the influence of the abnormality on the winding process is reduced, for realizing data visualization and operation control, an intuitive man-machine interface is designed, and the key parameters and equipment state information are displayed in real time;

[0053] The production process and equipment layout information of the winding machine are obtained, and a multi-agent collaborative scheduling model is constructed, the multi-agent collaborative scheduling model maps the material flow and the information flow into the interaction behavior and the constraint condition between the agents;

[0054] The multi-agent collaborative scheduling model is trained by using a reinforcement learning algorithm, and a trained multi-agent collaborative scheduling model is obtained, the trained multi-agent collaborative scheduling model can dynamically adjust the model parameters according to real-time production data, and real-time acquisition of various parameters in the production process is realized;

[0055] An abnormal monitoring and diagnosis knowledge base is constructed, the abnormal monitoring and diagnosis knowledge base includes feature information and corresponding diagnosis rules of various abnormal situations;

[0056] The machine learning algorithm is used to analyze and model the data collected by the sensors and data acquisition devices. Through pattern recognition and anomaly detection technology, it can determine whether there is an abnormal situation in the production process.

[0057] If an abnormal situation is detected, the early warning mechanism is triggered, and an alarm signal is sent to the control system. At the same time, according to the diagnostic rules in the abnormal monitoring and diagnosis knowledge base, the cause of the abnormality is inferred, and according to the type and severity of the abnormality, the relevant process parameters or equipment state are automatically adjusted.

[0058] Specifically, multiple agents are deployed on the winding machine production line, such as material supply agents, winding control agents, and quality detection agents. Reinforcement learning algorithms such as PPO are used to train the agents, with a reward function set as the weighted sum of production efficiency and product pass rate. Communication and collaboration between agents are achieved through MQTT protocol, and model parameters are dynamically adjusted. Vibration, temperature, and pressure sensors are installed on key equipment, with a sampling frequency of 100 Hz. PCA and SVM algorithms are used for feature extraction and anomaly detection of collected data, and when the detection accuracy reaches 95% or more, diagnostic rules are added to the knowledge base. When an anomaly occurs, an early warning is triggered through the OPC UA protocol, and the winding tension is adjusted to 80% of the rated value. A web-based visualization interface is built, using WebSocket to transmit device status and production parameters in real time, and providing REST API for remote calls. Cluster analysis is performed on historical data, and when production efficiency improves by 10% and product pass rate improves by 5%, optimization suggestions are generated and the scheduling model and control strategy are updated. Through the application of the above technical means, the production efficiency and product quality of the winding machine are significantly improved, realizing intelligent production.

[0059] S107, in the human-machine interface of the winding machine, provide information display and operation guidance functions, real-time display of key parameters and equipment state information in the winding process, convenient for operators to monitor and manage, establish a perfect data record and traceability system, complete record and storage of production process data of each volume of battery cell, form a traceable quality management chain, provide data support for product quality analysis and improvement.

[0060] The winding machine is a key equipment in lithium-ion battery production, and real-time monitoring and recording of its running data is crucial for ensuring the quality of battery cells.

[0061] By obtaining real-time running data of the winding machine, precise control of the production process can be achieved, production efficiency can be improved, and data support can be provided for subsequent quality traceability.

[0062] Real-time operation data includes key parameters such as winding tension, speed, temperature, pressure, etc., as well as state information such as alarm information and fault information of the equipment. These data can be collected in real time through sensors, such as tension sensors, temperature sensors, encoders, etc.

[0063] For example, the tension sensor can monitor the tension of the pole piece during winding in real time. If the tension is too large or too small, it will affect the performance and safety of the battery cell. The upper and lower limits of the tension alarm can be set. When the tension exceeds the set range, the system will automatically alarm and remind the operator to handle it in time. These data can be transmitted to the human-machine interface (HMI) through communication protocols such as industrial Ethernet and OPCUA. The HMI can display these parameters and state information in the form of charts and numbers in real time, so that the operator can intuitively understand the running state of the winding machine. For example, the HMI can display the real-time winding speed curve, and the operator can judge whether the winding process is stable through the curve.

[0064] The production process data of each battery cell needs to be recorded and stored completely in order to establish a data traceability system. The production process data includes raw material information, such as pole piece supplier, batch number, thickness, etc.; process parameters, such as winding tension, speed, temperature, etc.; and quality detection results, such as appearance detection, size detection, electrical performance test, etc. These data can be stored and managed through a database.

[0065] For example, a relational database (such as MySQL) can be used to store these data and establish a data table to record the production information of each battery cell.

[0066] Each battery cell will have a unique identification code, such as a two-dimensional code, to associate all its production data.

[0067] Based on complete data recording, a traceable quality management chain can be formed from raw materials to finished products for each battery cell.

[0068] For example, if it is found that the internal resistance of a batch of battery cells is too high, the production process data of the batch of battery cells can be queried through the data traceability system, including the batch of raw materials used, the winding process parameters, and the quality detection results.

[0069] By analyzing these data, the reason for the high internal resistance can be found.

[0070] For example, it may be a problem with the batch of raw materials, or it may be caused by improper winding tension setting. Suppose a battery cell has a problem of rapid capacity decay in the charge and discharge test. The production process data of the battery cell can be retrieved from the database through its identification code.

[0071] The data shows that the negative material batch used by the battery cell is A, the winding tension is 10N, and the winding speed is 100m / min.

[0072] Through comparison and analysis with other battery cells using the same negative material batch A, it is found that the capacity attenuation of these battery cells is normal.

[0073] Further analysis of the winding speed of the battery cell shows that the winding speed is higher than the normal range (80-90m / min).

[0074] Therefore, it can be preliminarily judged that the reason for the rapid capacity attenuation of the battery cell may be that the winding speed is too high, causing damage to the electrode structure.

[0075] Based on the analysis results, corresponding improvement measures can be developed, such as adjusting the winding speed to the normal range and monitoring the battery cells produced subsequently. This can effectively improve the quality of the battery cells and reduce production costs.

[0076] The above is only a preferred embodiment of the present application, and is not intended to limit the present application in any form. Although the present application is disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any skilled person in the art can make some changes or modifications to the above disclosed technical content without departing from the scope of the technical solution of the present application, and any simple modification, equivalent change and modification made to the above embodiment within the scope of the technical solution of the present application are all within the scope of the technical solution of the present application.

Claims

1. An intelligent winding control method for an automatic lithium battery cutting and winding machine, characterized in that: The intelligent winding control method includes the following steps: S101. Based on the pre-established positional relationship model of the anode sheet, diaphragm and cathode sheet, the positional deviation data of the three materials during the winding process is acquired in real time. The positional deviation data is filtered by the Kalman filter algorithm to obtain an accurate positional deviation estimate. The estimate is compared with the preset positional deviation threshold. If it exceeds the threshold range, the position of the corresponding material is adjusted to ensure that the three materials always maintain an accurate positional relationship. S102 addresses the issue of material linear velocity changes caused by the continuous increase in core diameter during winding. It employs an adaptive control algorithm to dynamically adjust the unwinding speed and winding tension of the material based on real-time measured core diameter data. By combining feedforward compensation and feedback correction, it achieves precise control of material linear velocity, ensuring that the material tension remains within the preset target range throughout the entire winding process and preventing material slack or excessive stretching. In the mechanical structure design of the S103 winding machine, the finite element analysis method is used to optimize the rigidity and stability of key components. By rationally arranging the transmission system and support structure, the anti-interference capability of the whole machine is improved. Active vibration reduction technology is introduced into the transmission system. Vibration sensors detect vibration signals in real time during the winding process. Adaptive filtering algorithms are used to analyze and process the vibration signals to generate corresponding compensation control commands, which drive the actuator to actively suppress vibration and reduce the impact of vibration on material position control. At the same time, the dynamic characteristics of the material in high-speed motion are considered to provide basic data for the next step of establishing a material dynamics model. S104. To address the inertial effect of materials during high-speed winding, a material dynamics model is established, comprehensively considering the material's mass, stiffness, and damping parameters to predict the material's behavior during winding. Based on the dynamic response characteristics, a feedforward compensation mechanism is introduced into the control system. According to the prediction results of the material dynamics model, the control command of the actuator is corrected, thereby suppressing the influence of material inertia effect on position control accuracy. S105, for the tension control system, an intelligent control strategy based on fuzzy proportional-integral-derivative (PID) is adopted. According to the material's characteristic parameters and winding process requirements, the parameters of the PID controller are adaptively adjusted to improve the response speed and adjustment accuracy of the tension control system. In specific implementation, the controller parameters are adjusted online using a fuzzy rule base to adapt to the tension control requirements under different working conditions. Tension sensors are introduced to monitor the material tension in real time. The measurement data from multiple sensors are comprehensively analyzed through a data fusion algorithm to obtain the optimal estimate of the material tension, which is then input into the control system as a feedback signal to achieve precise tension control. S106, in the control system of the winding machine, a multi-agent collaborative scheduling model is constructed to uniformly manage and optimize the material flow and information flow between the winding process and upstream and downstream processes. Through intelligent algorithms, real-time coordination and dynamic balance between processes are achieved to ensure the efficient operation of the entire production line. A sound anomaly monitoring and diagnosis mechanism is established to monitor key process parameters and equipment status in real time. Once an anomaly is detected, an early warning is triggered and corresponding control measures are taken to reduce the impact of the anomaly on the winding process. To achieve data visualization and operation control, an intuitive human-machine interface is designed to display key parameters and equipment status information in real time. The S107 provides information display and operation guidance functions in the human-machine interface of the winding machine. It displays key parameters and equipment status information of the winding process in real time, facilitating monitoring and management by operators, and establishing a complete data recording and traceability system for each roll of battery cells. Production process data is fully recorded and stored to form a traceable quality management chain, providing data support for product quality analysis and improvement.

2. The intelligent winding control method for the automatic cutting and winding machine for lithium batteries according to claim 1, characterized in that: In step S101, a pre-established positional relationship model of the anode plate, diaphragm, and cathode plate is obtained as a reference for material position control. During the battery winding process, the positional deviation data of the anode plate, the separator, and the cathode plate are collected in real time by sensors; The collected position deviation data is input into a Kalman filter algorithm for noise removal and data smoothing to obtain estimated position deviations of the anode plate, the diaphragm, and the cathode plate. Determine whether the estimated position deviation exceeds a preset deviation threshold range. If it does, trigger the position adjustment mechanism. Based on the magnitude and direction of the estimated position deviation, the actuator is controlled to adjust the positions of the anode plate and the diaphragm or the cathode plate so that their positional relationship is consistent with the positional relationship model. After the position is adjusted, the position deviation data of the anode plate, the diaphragm and the cathode plate are acquired again and input into the Kalman filter algorithm for processing to determine whether the adjusted position deviation is within the deviation threshold range. If the adjusted position deviation still exceeds the deviation threshold range, the trigger position adjustment mechanism and the control actuator are repeatedly executed to adjust the position and obtain position deviation data again until the positional relationship of the anode plate, the diaphragm and the cathode plate is always consistent with the positional relationship model.

3. The intelligent winding control method for the automatic cutting and winding machine for lithium batteries according to claim 1, characterized in that: In step S102, real-time core diameter data during the winding process is obtained, and the real-time core diameter data is used as the input of the adaptive control algorithm. Based on the real-time core diameter data, the unwinding speed and winding tension parameters that need to be adjusted are calculated using a preset feedforward compensation model. The unwinding speed and winding tension parameters are transmitted to the actuator of the winding equipment, and the actuator dynamically adjusts the unwinding speed and winding tension during the winding process according to the unwinding speed and winding tension parameters. During the winding process, the material tension is continuously monitored to obtain real-time material tension data; Determine whether the real-time material tension data exceeds the preset target tension range; If the real-time material tension data exceeds the target tension range, the unwinding speed and winding tension parameters that need to be adjusted are calculated through a preset feedback correction model, and the unwinding speed and winding tension parameters that need to be adjusted are transmitted to the actuator. The actuator dynamically adjusts the unwinding speed and winding tension during the winding process according to the required unwinding speed and winding tension parameters until the winding process is completed, so that the material tension remains stable within the target tension range.

4. The intelligent winding control method for the automatic cutting and winding machine for lithium batteries according to claim 1, characterized in that: In step S103, the structural parameters and working requirements of the winding machine are obtained, and a finite element analysis model of the key components of the winding machine is established. Stress-strain distribution and vibration mode analysis were performed on the finite element analysis model to obtain stress-strain distribution and vibration mode data of key components; Based on the stress-strain distribution and vibration mode data, the structural parameters and layout optimization schemes of key components are determined. The optimization schemes include adjusting material selection, cross-sectional dimensions, and stiffener arrangement. Multiple vibration sensors are installed at vibration-sensitive points on the winding machine to collect vibration signals in real time during the winding process; The collected vibration signals are preprocessed to filter out high-frequency noise and interference components, resulting in preprocessed vibration signals. Wavelet analysis and empirical mode decomposition methods are used to extract features from the preprocessed vibration signal to obtain the time-frequency features of the vibration signal; Based on the time-frequency characteristics of the vibration signal, determine the main components and source of the vibration; An adaptive filtering algorithm is used to generate vibration compensation control commands based on the main components and sources of the vibration. The vibration compensation control command is converted into a drive signal to drive the actuator of the winding machine, thereby performing active vibration reduction control on the winding machine and suppressing the transmission and amplification of vibration.

5. The intelligent winding control method for the automatic cutting and winding machine for lithium batteries according to claim 1, characterized in that: In step S104, dynamic response characteristic data of the material during high-speed winding is obtained, and the dynamic response characteristic data includes the material's mass, stiffness, and damping parameters. Based on the dynamic response characteristic data, a material dynamics model is established. The material dynamics model is used to predict the position, velocity and acceleration state variables of the material during the high-speed winding process. Obtain the prediction results from the material dynamics model and introduce feedforward compensation into the control system. The compensation mechanism generates corrected control commands based on the prediction results; The modified control command is transmitted to the actuator, and the motion of the actuator is adjusted and optimized in real time to suppress the influence of material inertia effect on position control accuracy. The prediction error of the material dynamics model is obtained, and an adaptive control algorithm is used to adjust the parameters of the feedforward compensation mechanism in real time according to the prediction error, so as to realize the adaptive optimization of the control system. Based on the material dynamics model and the adaptive control algorithm, the high-speed winding process was simulated and experimentally verified, and the design parameters of the control system were optimized.

6. The intelligent winding control method for the automatic cutting and winding machine for lithium batteries according to claim 1, characterized in that: In step S105, the controller parameters are adjusted online using a fuzzy rule base according to the material property parameters and winding process requirements. The material tension is monitored in real time by introducing a tension sensor, and the measurement data from multiple sensors are obtained. The data fusion algorithm is used for comprehensive analysis to obtain the optimal estimate of the material tension, which is then used as a feedback signal to be input into the control system to achieve precise tension control, improve the system's response speed and adjustment accuracy, and adapt to the control requirements under different working conditions.

7. The intelligent winding control method for the automatic cutting and winding machine for lithium batteries according to claim 1, characterized in that: In step S106, the production process flow and equipment layout information of the winding machine are obtained, and a multi-agent collaborative scheduling model is constructed. The multi-agent collaborative scheduling model maps the material flow and information flow into the interactive behaviors and constraints between agents. The multi-agent cooperative scheduling model is trained using a reinforcement learning algorithm to obtain a trained multi-agent cooperative scheduling model. The model can dynamically adjust its parameters based on real-time production data and collect various parameters during the production process in real time. An anomaly monitoring and diagnosis knowledge base is constructed, which includes feature information of various anomalies and corresponding diagnostic rules; Machine learning algorithms are used to analyze and model the data collected by sensors and data acquisition devices. Pattern recognition and anomaly detection technologies are used to determine whether there are any abnormalities in the production process. If an anomaly is detected, an early warning mechanism is triggered, sending an alarm signal to the control system. At the same time, the cause of the anomaly is inferred based on the diagnostic rules in the anomaly monitoring and diagnosis knowledge base, and the relevant process parameters or equipment status are automatically adjusted according to the type and severity of the anomaly.

8. The intelligent winding control method for the automatic cutting and winding machine for lithium batteries according to claim 1, characterized in that: In step S107, real-time operating data of the winding machine is obtained, including key parameters of the winding process and equipment status information. The real-time operation data is transmitted to the human-machine interface, and the key parameters of the winding process and equipment status information are displayed in real time on the human-machine interface. Acquire production process data for each roll of battery cells, including raw material information, process parameters, and quality inspection results; Completely record and store the production process data of each roll of battery cell to establish a data traceability system; Based on the data traceability system, a traceable quality management chain is formed for each roll of battery cells, from raw materials to finished products; If it is necessary to analyze and improve the quality of a particular roll of battery cells, then trace back from the aforementioned data. The system retrieves the production process data of the battery cell, performs data analysis, determines the cause of the quality problem, and formulates corresponding improvement measures.

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