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

Abstract

The invention relates to an intelligent winding control method of a lithium battery automatic cutting and winding machine in the field of winding machines, which aims at the inertia effect of a material in a high-speed winding process, establishes a material dynamic model, comprehensively considers the quality, rigidity and damping parameters of the material, predicts the dynamic response characteristics of the material in the winding process, and controls the winding speed of the material. A feed-forward compensation mechanism is introduced into a control system, and control instruction correction is performed on an execution mechanism according to a prediction result of a material dynamic model, so that the influence of a material inertia effect on position control precision is inhibited; in a man-machine interaction interface of the winding machine, information display and operation guide functions are provided, key parameters and equipment state information in the winding process are displayed in real time, monitoring and management by operators are facilitated, a perfect data recording and tracing system is established, production process data of each roll of battery cells are completely recorded and stored, and the production efficiency of the battery cells is improved. A traceable quality management chain is formed, and data support is provided for product quality analysis and improvement.

Description

Technical Field

[0001] The present invention relates to the field of winding machines, in particular to the field of lithium battery winding, and specifically to an intelligent winding control method of an automatic cutting and winding machine for lithium batteries. Background Art

[0002] During the winding process of the lithium battery automatic cutting and winding machine, a key technical issue is how to ensure that the anode sheet, separator and cathode sheet always maintain precise positional relationship and tension control during high-speed winding. The difficulty of this problem is that as the winding progresses, the diameter of the core will continue to increase, which will cause changes in the linear speed and tension of the material. If these changes cannot be adjusted and compensated in time, it may cause the material to be dislocated, wrinkled or broken, thus affecting the quality and performance of the battery cell.

[0003] In addition, during high-speed winding, the inertia and vibration of the material will also interfere with the position control. The mechanical structure and transmission system of the winder must have sufficient rigidity and stability to suppress these interferences. At the same time, the tension control system needs to have the ability to respond quickly and adjust accurately to adapt to changes in material properties and winding processes.

[0004] Finally, the winding process also needs to be closely coordinated with upstream and downstream processes, such as the supply of materials and the transfer of bare cells. Any abnormality in any link 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 to coordinate the process flow, as well as real-time monitoring and rapid response capabilities for abnormal situations. Only by solving these key problems can we ensure that the lithium battery automatic cutting and winding machine can stably and efficiently produce high-quality cell products. Summary of the invention

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

[0006] The object of the present invention is achieved in the following ways:

[0007] An intelligent winding control method for an automatic cutting and winding machine for lithium batteries, the intelligent winding method comprising the following steps:

[0008] S101, according to the pre-established position relationship model of the anode sheet, the separator and the cathode sheet, the position deviation data of the three materials in the winding process are obtained in real time, the position deviation data are filtered by the Kalman filter algorithm to obtain an accurate position deviation estimation value, and the estimation value is compared with a preset 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 an accurate position relationship;

[0009] S102, in order to solve the problem of material line speed change caused by the continuous increase of the core diameter during the winding process, an adaptive control algorithm is used to dynamically adjust the material unwinding speed and winding tension according to the real-time measured core diameter data. By combining feedforward compensation and feedback correction, the material line speed is precisely controlled to ensure that the material tension is kept within the preset target range during the entire winding process to avoid material relaxation or excessive stretching;

[0010] S103, in the mechanical structure design of the winding machine, the finite element analysis method is used to optimize the rigidity and stability of key components, and the anti-interference ability of the whole machine is improved by reasonably arranging the transmission system and supporting structure. Active vibration reduction technology is introduced in the transmission system, and the vibration signal in the winding process is detected in real time by the vibration sensor. The vibration signal is analyzed and processed by the adaptive filtering algorithm, and the corresponding compensation control instructions are generated. The actuator is driven to actively suppress the vibration and reduce 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;

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

[0012] S105, for the tension control system, an intelligent control strategy based on fuzzy proportional integral differential is adopted. According to the characteristic parameters of the material and the requirements of the winding process, the parameters of the proportional integral differential 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 the fuzzy rule library to adapt to the tension control requirements under different working conditions. A tension sensor is introduced to monitor the material tension in real time. The measurement data of multiple sensors are comprehensively analyzed through a data fusion algorithm to obtain the optimal estimated value of the material tension, which is input into the control system as a feedback signal to achieve precise control of the tension.

[0013] S106, in the control system of the winding machine, a collaborative scheduling model based on multi-agents is constructed to uniformly manage and optimize the material flow and information flow between the winding process and the upstream and downstream processes. Real-time coordination and dynamic balance between the processes are achieved through intelligent algorithms to ensure the efficient operation of the entire production line. A sound abnormality monitoring and diagnosis mechanism is established to monitor key process parameters and equipment status in real time. Once an abnormality is found, an early warning is triggered and corresponding control measures are taken to reduce the impact of the abnormality on the winding process. In order to realize data visualization and operation control, an intuitive human-computer interaction interface is designed to display key parameters and equipment status information in real time;

[0014] S107, in the human-machine interaction interface of the winding machine, provides information display and operation guidance functions, displays the key parameters and equipment status information of the winding process in real time, facilitates operators to monitor and manage, establishes a complete data recording and traceability system, and completely records and stores the production process data of each roll of battery cells, forming a traceable quality management chain, and providing data support for product quality analysis and improvement.

[0015] Beneficial effects of the present invention: In order to solve the problems of material position deviation, line speed change and vibration interference in the winding process, the present invention adopts the Kalman filter algorithm to estimate the position deviation in real time, and dynamically adjusts the material speed and tension through adaptive control. At the same time, active vibration reduction technology and feedforward compensation mechanism are introduced to suppress the influence of vibration and inertia effects 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 achieve precise control. By constructing a multi-agent collaborative scheduling model, the entire production line is uniformly managed and optimized, and through abnormal monitoring and diagnosis, human-computer interaction interface and data traceability system, comprehensive monitoring and quality management of the winding process are achieved. This method significantly improves the accuracy 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 THE DRAWINGS

[0016] Figure 1 is a schematic diagram of the process of this embodiment; DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0018] In this embodiment, refer 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 separator and the cathode sheet, the position deviation data of the three materials in the winding process are obtained in real time, the position deviation data are filtered by the Kalman filter algorithm to obtain an accurate position deviation estimation value, and the estimation value is compared with a preset 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 an accurate position relationship;

[0020] A pre-established position relationship model of the anode sheet, the diaphragm and the cathode sheet is obtained as a reference for material position control; during the battery winding process, the position deviation data of the anode sheet, the diaphragm and the cathode sheet are collected in real time through sensors; the collected position deviation data is input into the Kalman filter algorithm for noise removal and data smoothing to obtain the position deviation estimation value of the anode sheet, the diaphragm and the cathode sheet; it is determined whether the position deviation estimation value exceeds the preset deviation threshold range, and if it exceeds the deviation threshold range, the position adjustment mechanism is triggered; according to the size and direction of the position deviation estimation value, the actuator is controlled The position of the anode plate, the diaphragm or the cathode plate is adjusted so that the position relationship thereof is consistent with the position relationship model; after the position adjustment, 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 triggering position adjustment mechanism, the control actuator to adjust the position and the acquisition of the position deviation data are repeated until the position relationship of the anode plate, the diaphragm and the cathode plate is always consistent with the position relationship model.

[0021] Specifically, during the battery winding process, the position deviation data of the anode sheet, diaphragm and cathode sheet are collected in real time by a high-precision laser displacement sensor, with a sampling frequency of 1000Hz. The collected position deviation data is input into the Kalman filter algorithm based on adaptive noise covariance estimation for processing. By dynamically adjusting the noise covariance matrix, adaptive estimation of the measurement noise is achieved, thereby obtaining an accurate position deviation estimate with an estimation accuracy better than 1mm. It is determined whether the position deviation estimate exceeds the preset ±5mm deviation threshold range. If it exceeds the threshold, the position adjustment mechanism based on PID control is triggered. According to the size and direction of the position deviation estimate, the actuator driven by the servo motor is controlled to accurately adjust the material position with an adjustment accuracy better than 5mm, so that it is restored to a 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 filter algorithm for processing. If the adjusted position deviation still exceeds the threshold, the incremental PID control law is used to iteratively optimize the adjustment process until the position deviations of the three materials converge to the threshold range, thereby ensuring that the material position relationship during the battery winding process always meets the process requirements and ensures the consistency and reliability of the battery.

[0022] S102, in order to solve the problem of material line speed change caused by the continuous increase of the core diameter during the winding process, an adaptive control algorithm is used to dynamically adjust the material unwinding speed and winding tension according to the real-time measured core diameter data. By combining feedforward compensation and feedback correction, the material line speed is precisely controlled to ensure that the material tension is kept within the preset target range during the entire winding process to avoid material relaxation or excessive stretching;

[0023] Acquiring real-time core diameter data during the winding process, and using the real-time core diameter data as input to an adaptive control algorithm;

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

[0025] The unwinding speed and winding tension parameters are transmitted to an actuator of a winding device, 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 material tension during the winding process and obtain real-time material tension data;

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

[0028] If the real-time material tension data exceeds the target tension range, the unwinding speed and winding tension parameters that need to be further adjusted are calculated through a preset feedback correction model, and the unwinding speed and winding tension parameters that need to be further adjusted are transmitted to the actuator;

[0029] The actuator further dynamically adjusts the unwinding speed and the winding tension during the winding process according to the unwinding speed and the winding tension parameters that need to be further adjusted until the winding process is completed, so that the material tension is always stable within the target tension range.

[0030] Specifically, during the battery winding process, the core diameter data is obtained in real time through a high-precision laser ranging sensor installed on the winding equipment, with a sampling frequency of 100Hz. The core diameter data obtained is input into the feedforward compensation model based on the radial basis function neural network (RBFNN), and the unwinding speed and winding tension parameters that need to be adjusted are calculated in real time through the nonlinear mapping relationship obtained through training. 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, with an adjustment accuracy of better than 1%. At the same time, the material tension is continuously monitored by the tension sensor installed on the material transmission path, with a sampling frequency of 1000Hz. 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. According to the size and change rate of the tension deviation, the unwinding speed and winding tension parameters that need to be further adjusted are calculated through fuzzy reasoning, and are transmitted to the actuator for dynamic adjustment, with an adjustment accuracy of 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 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 key components, and the anti-interference ability of the whole machine is improved by reasonably arranging the transmission system and supporting structure. Active vibration reduction technology is introduced in the transmission system, and the vibration signal in the winding process is detected in real time by the vibration sensor. The vibration signal is analyzed and processed by the adaptive filtering algorithm, and the corresponding compensation control instructions are generated. The actuator is driven to actively suppress the vibration and reduce 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] Obtain the structural parameters and working requirements of the winding machine, and establish the finite element analysis model of the key components of the winding machine;

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

[0034] Determine structural parameters and layout optimization schemes of key components based on the stress-strain distribution and vibration modal data, wherein the optimization schemes include adjusting material selection, cross-sectional dimensions, and reinforcement rib arrangement;

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

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

[0037] Using wavelet analysis and empirical mode decomposition methods to extract features from the preprocessed vibration signal to obtain time-frequency features of the vibration signal;

[0038] Determining the main components and sources of the vibration according to the time-frequency characteristics of the vibration signal;

[0039] Adopting an adaptive filtering algorithm to generate vibration compensation control instructions according to the main components and sources of the vibration;

[0040] The vibration compensation control instruction is converted into a driving signal to drive the actuator of the winding machine, and the winding machine is actively subjected to vibration reduction control to suppress the transmission and amplification of vibration.

[0041] S104, in view of the inertia effect of the material in the high-speed winding process, a material dynamics model is established, the mass, stiffness and damping parameters of the material are comprehensively considered, the dynamic response characteristics of the material in the winding process are predicted, a feedforward compensation mechanism is introduced into the control system, and the control instructions of the actuator are corrected according to the prediction results of the material dynamics model, so as to suppress the influence of the material inertia effect on the position control accuracy;

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

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

[0044] Obtaining the prediction results of the material dynamics model, introducing a feedforward compensation mechanism in the control system, and generating a corrected control instruction according to the prediction results;

[0045] The modified control instruction is transmitted to the actuator, and the movement of the actuator is adjusted and optimized in real time to suppress the influence of the material inertia effect on the position control accuracy;

[0046] Obtaining the prediction error of the material kinetic model, using an adaptive control algorithm, and adjusting the parameters of the feedforward compensation mechanism in real time according to the prediction error to achieve 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 analyzed and experimentally verified, and the design parameters of the control system are further optimized.

[0048] Specifically, in order to establish a material dynamics model, it is first necessary to obtain parameters such as material mass, stiffness and damping. By measuring the linear density of the wound material, the mass parameter of the material can be obtained as 0.5 kg / m. The stress-strain curve of the material is tested using a tensile testing machine, and the stiffness parameter of the material is 100 GPa. Through the free vibration attenuation experiment, the damping ratio of the material is measured to be 0.2. Substitute these parameters into the lumped parameter model to establish the dynamic differential equation of the material. The Runge-Kutta method is used to numerically solve the differential equation to obtain the time domain response curve of the material position, velocity and acceleration. By analyzing the peak and steady-state values ​​of the curve, it is predicted that the maximum displacement of the material at a winding speed of 10 m / s is 2 mm, and the velocity fluctuation amplitude is 2 m / s. According to the prediction results, a feedforward compensator is designed in the control system to generate a corrected control instruction.

[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 into the actuator, and the position tracking control of the actuator is realized through the PID control algorithm. At the same time, the adaptive control strategy is introduced, and the gradient descent method is used to adjust the feedforward compensation coefficient in real time. The learning rate is set to 05 to continuously improve the compensation accuracy. The simulation model of the control system is built through MATLAB / Simulink, and the control effect under different working conditions is analyzed. The simulation results show that after the introduction of the material dynamics model and feedforward compensation, the material position control error is reduced by more than 50%. Finally, the experimental verification is carried out on the actual winding machine. When the winding speed is 10m / s, the material position control accuracy is improved to 1mm, 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 differential is adopted. According to the characteristic parameters of the material and the requirements of the winding process, the parameters of the proportional integral differential 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 the fuzzy rule library to adapt to the tension control requirements under different working conditions. A tension sensor is introduced to monitor the material tension in real time. The measurement data of multiple sensors are comprehensively analyzed through a data fusion algorithm to obtain the optimal estimated value of the material tension, which is input into the control system as a feedback signal to achieve precise control of the tension.

[0051] According to the material characteristic parameters and winding process requirements, the fuzzy rule library is used to adjust the controller parameters online. The material tension is monitored in real time by introducing a tension sensor, and the measurement data of multiple sensors are obtained. The data fusion algorithm is used for comprehensive analysis to obtain the optimal estimate of the material tension, which is input into the control system as a feedback signal to achieve precise control of the tension, improve the system's response speed and adjustment accuracy, and adapt to the control requirements under different working conditions.

[0052] S106, in the control system of the winding machine, a collaborative scheduling model based on multi-agents is constructed to uniformly manage and optimize the material flow and information flow between the winding process and the upstream and downstream processes. Real-time coordination and dynamic balance between the processes are achieved through intelligent algorithms to ensure the efficient operation of the entire production line. A sound abnormality monitoring and diagnosis mechanism is established to monitor key process parameters and equipment status in real time. Once an abnormality is found, an early warning is triggered and corresponding control measures are taken to reduce the impact of the abnormality on the winding process. In order to realize data visualization and operation control, an intuitive human-computer interaction interface is designed to display key parameters and equipment status information in real time;

[0053] Obtain the production process flow and equipment layout information of the winding machine, and build a multi-agent collaborative scheduling model, which maps material flow and information flow into interactive behaviors and constraints between agents;

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

[0055] Constructing an abnormality monitoring and diagnosis knowledge base, wherein the abnormality monitoring and diagnosis knowledge base includes characteristic information of various abnormal situations and corresponding diagnosis rules;

[0056] Use machine learning algorithms to analyze and model the data collected by sensors and data acquisition devices, and use pattern recognition and anomaly detection technology to determine whether there are abnormalities 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, the cause of the abnormality is inferred according to the diagnostic rules in the abnormal monitoring and diagnosis knowledge base, and the relevant process parameters or equipment status are automatically adjusted according to the type and severity of the abnormality.

[0058] Specifically, multiple agents are deployed on the winding machine production line, such as material supply agent, winding control agent, quality inspection agent, etc. Reinforcement learning algorithms such as PPO are used to train the agents, and the reward function is set as the weighted sum of production efficiency and product qualification rate. Communication and collaboration between agents are realized through the MQTT protocol, and model parameters are adjusted dynamically. Sensors such as vibration, temperature, and pressure are installed on key equipment, and the sampling frequency is set to 100Hz. PCA and SVM algorithms are used to extract features and detect anomalies in the collected data. When the detection accuracy reaches more than 95%, the diagnostic rules are added to the knowledge base. When an abnormality 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 visualization interface is built, and WebSocket is used to transmit equipment status and production parameters in real time, and REST API is provided for remote calls. Cluster analysis is performed on historical data. When the production efficiency is increased by 10% and the product qualification rate is increased 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 have been significantly improved, and intelligent production has been realized.

[0059] S107, in the human-machine interaction interface of the winding machine, provides information display and operation guidance functions, displays the key parameters and equipment status information of the winding process in real time, facilitates operators to monitor and manage, establishes a complete data recording and traceability system, and completely records and stores the production process data of each roll of battery cells, forming a traceable quality management chain, and providing data support for product quality analysis and improvement.

[0060] Winding machines are key equipment in lithium-ion battery production, and real-time monitoring and recording of their operating data are crucial to ensuring battery cell quality.

[0061] By obtaining the real-time operation data of the winding machine, it is possible to achieve precise control of the production process, improve production efficiency, and provide data support for subsequent quality traceability.

[0062] Real-time operation data includes key parameters such as winding tension, speed, temperature, pressure, and other state information such as equipment alarm information, fault information, etc. 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 the winding process 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 limit alarms of the tension can be set. When the tension exceeds the set range, the system will automatically alarm to remind the operator to deal with it in time. These data can be transmitted to the human-machine interface (HMI) via communication protocols such as industrial Ethernet and OPC UA. These parameters and status information can be displayed in real time on the HMI in the form of charts, numbers, etc., so that the operator can intuitively understand the operating status of the winding machine. For example, the real-time winding speed curve can be displayed on the HMI, and the operator can judge whether the winding process is stable by the curve.

[0064] The production process data of each roll of battery cells needs to be fully recorded and stored in order to establish a data traceability system. The production process data includes raw material information, exemplary pole piece suppliers, batch numbers, thickness, etc.; process parameters, exemplary winding tension, speed, temperature, etc.; and quality inspection results, exemplary appearance inspection, size inspection, electrical performance test, etc. These data can be stored and managed through a database.

[0065] Exemplarily, a relational database (such as MySQL) can be used to store these data, and a data table can be established to record the production information of each roll of battery cells.

[0066] Each roll of battery cells will have a unique identification code, exemplified by a QR code, which is used to associate all its production data.

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

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

[0069] By analyzing these data, we can find the reason why the internal resistance is high.

[0070] For example, it may be a problem with the raw material batch or an improper winding tension setting. Suppose a roll of battery cells has a problem of too fast capacity decay during the charge and discharge test. The production process data of the battery cell can be retrieved from the database through the identification code of the battery cell.

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

[0072] By comparing and analyzing with other battery cells using the same negative electrode material batch A, it was found that the capacity decay of these battery cells was normal.

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

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

[0075] Based on the analysis results, corresponding improvement measures can be formulated, such as adjusting the winding speed to a normal range and focusing on monitoring the subsequent production of battery cells, which can effectively improve the quality of battery cells and reduce production costs.

[0076] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention is disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes by using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent changes and modifications made to the above embodiments according to the technology of the present invention are within the scope of the technical solution of the present invention without departing from the content of the technical solution of the present invention.

Claims

1. An intelligent winding control method for an automatic cutting and winding machine for lithium batteries, characterized in that: The intelligent winding method comprises the following steps: S101, according to the pre-established position relationship model of the anode sheet, the separator and the cathode sheet, the position deviation data of the three materials in the winding process are obtained in real time, the position deviation data are filtered by the Kalman filter algorithm to obtain an accurate position deviation estimation value, and the estimation value is compared with a preset 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 an accurate position relationship; S102, in order to solve the problem of material line speed change caused by the continuous increase of the core diameter during the winding process, an adaptive control algorithm is used to dynamically adjust the material unwinding speed and winding tension according to the real-time measured core diameter data. By combining feedforward compensation and feedback correction, the material line speed is precisely controlled to ensure that the material tension is kept within the preset target range during the entire winding process to avoid material relaxation or excessive stretching; S103, in the mechanical structure design of the winding machine, the finite element analysis method is used to optimize the rigidity and stability of key components, and the anti-interference ability of the whole machine is improved by reasonably arranging the transmission system and supporting structure. Active vibration reduction technology is introduced in the transmission system, and the vibration signal in the winding process is detected in real time by the vibration sensor. The vibration signal is analyzed and processed by the adaptive filtering algorithm, and the corresponding compensation control instructions are generated. The actuator is driven to actively suppress the vibration and reduce 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; S104, in view of the inertia effect of the material in the high-speed winding process, a material dynamics model is established, the mass, stiffness and damping parameters of the material are comprehensively considered, the dynamic response characteristics of the material in the winding process are predicted, a feedforward compensation mechanism is introduced into the control system, and the control instructions of the actuator are corrected according to the prediction results of the material dynamics model, so as to suppress the influence of the material inertia effect on the position control accuracy; S105, for the tension control system, an intelligent control strategy based on fuzzy proportional integral differential is adopted. According to the characteristic parameters of the material and the requirements of the winding process, the parameters of the proportional integral differential 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 the fuzzy rule library to adapt to the tension control requirements under different working conditions. A tension sensor is introduced to monitor the material tension in real time. The measurement data of multiple sensors are comprehensively analyzed through a data fusion algorithm to obtain the optimal estimated value of the material tension, which is input into the control system as a feedback signal to achieve precise control of the tension. S106, in the control system of the winding machine, a collaborative scheduling model based on multi-agents is constructed to uniformly manage and optimize the material flow and information flow between the winding process and the upstream and downstream processes. Real-time coordination and dynamic balance between the processes are achieved through intelligent algorithms to ensure the efficient operation of the entire production line. A sound abnormality monitoring and diagnosis mechanism is established to monitor key process parameters and equipment status in real time. Once an abnormality is found, an early warning is triggered and corresponding control measures are taken to reduce the impact of the abnormality on the winding process. In order to realize data visualization and operation control, an intuitive human-computer interaction interface is designed to display key parameters and equipment status information in real time; S107, in the human-machine interaction interface of the winding machine, provides information display and operation guidance functions, displays the key parameters and equipment status information of the winding process in real time, facilitates operators to monitor and manage, establishes a complete data recording and traceability system, and completely records and stores the production process data of each roll of battery cells, forming a traceable quality management chain, and providing data support for product quality analysis and improvement.

2. The intelligent winding control method of the automatic cutting and winding machine for lithium batteries according to claim 1 is characterized in that: In the step S101, a pre-established position relationship model of the anode sheet, the separator and the cathode sheet is obtained as a reference for material position control; 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 sensors; Inputting the collected position deviation data into a Kalman filter algorithm for noise removal and data smoothing to obtain position deviation estimates of the anode sheet, the diaphragm, and the cathode sheet; Determine whether the position deviation estimate exceeds a preset deviation threshold range, and if so, trigger a position adjustment mechanism; According to the magnitude and direction of the position deviation estimation value, controlling the actuator to adjust the position of the anode sheet and the diaphragm or the cathode sheet so that the position relationship thereof 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 are obtained 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 triggering position adjustment mechanism and the control actuator are repeatedly executed to adjust the position and obtain the position deviation data again until the position relationship between the anode plate, the diaphragm and the cathode plate is always consistent with the position relationship model.

3. The intelligent winding control method of the automatic cutting and winding machine for lithium batteries according to claim 1 is characterized in that: In the step S102, real-time core diameter data during the winding process is acquired, and the real-time core diameter data is used as input of the adaptive control algorithm; According to the real-time core diameter data, the unwinding speed and winding tension parameters that need to be adjusted are calculated through a preset feedforward compensation model; The unwinding speed and winding tension parameters are transmitted to an actuator of a winding device, and the actuator dynamically adjusts the unwinding speed and winding tension during the winding process according to the unwinding speed and winding tension parameters; Continuously monitor material tension during the winding process and obtain real-time material tension data; Determining whether the real-time material tension data exceeds a 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 further adjusted are calculated through a preset feedback correction model, and the unwinding speed and winding tension parameters that need to be further adjusted are transmitted to the actuator; The actuator further dynamically adjusts the unwinding speed and the winding tension during the winding process according to the unwinding speed and the winding tension parameters that need to be further adjusted until the winding process is completed, so that the material tension is always stable within the target tension range.

4. The intelligent winding control method of the automatic cutting and winding machine for lithium batteries according to claim 1 is characterized in that: In the step S103, the structural parameters and working requirements of the winding machine are obtained, and a finite element analysis model of key components of the winding machine is established; Performing stress-strain distribution and vibration modal analysis on the finite element analysis model to obtain stress-strain distribution and vibration modal data of key components; Determine structural parameters and layout optimization schemes of key components based on the stress-strain distribution and vibration modal data, wherein the optimization schemes include adjusting material selection, cross-sectional dimensions, and reinforcement rib arrangement; Install multiple vibration sensors at the vibration sensitive points of the winding machine to collect vibration signals during the winding process in real time; Preprocess the collected vibration signal to filter out high-frequency noise and interference components in the vibration signal to obtain a preprocessed vibration signal; Using wavelet analysis and empirical mode decomposition methods to extract features from the preprocessed vibration signal to obtain time-frequency features of the vibration signal; Determining the main components and sources of the vibration according to the time-frequency characteristics of the vibration signal; Adopting an adaptive filtering algorithm to generate vibration compensation control instructions according to the main components and sources of the vibration; The vibration compensation control instruction is converted into a driving signal to drive the actuator of the winding machine, and the winding machine is actively subjected to vibration reduction control to suppress the transmission and amplification of vibration.

5. The intelligent winding control method of the lithium battery automatic cutting and winding machine according to claim 1 is characterized in that: In the step S104, dynamic response characteristic data of the material during high-speed winding is obtained, and the dynamic response characteristic data includes mass, stiffness and damping parameters of the material; Establishing a material dynamics model based on the dynamic response characteristic data, wherein the material dynamics model is used to predict the position, velocity and acceleration state variables of the material during the high-speed winding process; Obtaining the prediction results of the material dynamics model, introducing a feedforward compensation mechanism in the control system, and generating a corrected control instruction according to the prediction results; The modified control instruction is transmitted to the actuator, and the movement of the actuator is adjusted and optimized in real time to suppress the influence of the material inertia effect on the position control accuracy; Obtaining the prediction error of the material kinetic model, using an adaptive control algorithm, and adjusting the parameters of the feedforward compensation mechanism in real time according to the prediction error to achieve adaptive optimization of the control system; According to the material dynamics model and the adaptive control algorithm, the high-speed winding process is simulated and analyzed and experimentally verified, and the design parameters of the control system are further optimized.

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

7. The intelligent winding control method of the lithium battery automatic cutting and winding machine according to claim 1 is characterized in that: In the 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, wherein the multi-agent collaborative scheduling model maps the material flow and information flow into interactive behaviors and constraints between agents; The multi-agent collaborative scheduling model is trained by using a reinforcement learning algorithm to obtain a trained multi-agent collaborative scheduling model, wherein the trained multi-agent collaborative scheduling model can dynamically adjust model parameters according to real-time production data and collect various parameters in the production process in real time; Constructing an abnormality monitoring and diagnosis knowledge base, wherein the abnormality monitoring and diagnosis knowledge base includes characteristic information of various abnormal situations and corresponding diagnosis rules; Use machine learning algorithms to analyze and model the data collected by sensors and data acquisition devices, and use pattern recognition and anomaly detection technology to determine whether there are abnormalities in the production process; 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, the cause of the abnormality is inferred according to the diagnostic rules in the abnormal monitoring and diagnosis knowledge base, and the relevant process parameters or equipment status are automatically adjusted according to the type and severity of the abnormality.

8. The intelligent winding control method of the automatic cutting and winding machine for lithium batteries according to claim 1 is characterized in that: In the step S107, real-time operation data of the winding machine is obtained, and the real-time operation data includes key parameters of the winding process and equipment status information; Transmitting the real-time operation data to a human-machine interaction interface, and displaying key parameters and equipment status information of the winding process in real time on the human-machine interaction interface; Obtaining production process data of each roll of battery cells, wherein the production process data includes raw material information, process parameters and quality inspection results; Completely record and store the production process data of each roll of battery cells and establish a data traceability system; According to the data traceability system, a traceable quality management chain is formed for each roll of battery cells from raw materials to finished products; If the quality of a roll of battery cells needs to be analyzed and improved, the production process data of the battery cells is retrieved from the data traceability system, and data analysis is performed to determine the cause of the quality problem and formulate corresponding improvement measures.

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