Intelligent winding temperature control method of lithium battery automatic winding machine

Abstract

The application provides an intelligent winding temperature control method of a lithium battery automatic winding machine, comprising the following steps: according to the working environment characteristics of the lithium battery automatic winding machine, a plurality of rapid response temperature sensors are arranged on the winding machine to collect temperature data of each part of the battery cell in real time, and the collected temperature data is transmitted to a temperature intelligent control system; after receiving the temperature data, the temperature intelligent control system preliminarily classifies each material based on a material thermal response model and a preset program instruction, and the classification basis comprises thermal physical properties of the material, such as thermal conductivity, specific heat capacity and density, which is used for subsequent accurate control of the temperature of different materials; the system compares the temperature data with the material of the pole piece and the material thermal response database data information acquired in real time, and judges the range where the temperature of each part of the battery cell is located.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to an intelligent winding temperature control method for a lithium battery automatic winding machine. BACKGROUND

[0002] In the production process of a lithium battery automatic winding machine, the thermal response characteristics of the material are closely related to the winding quality. However, the prior art is difficult to accurately control the initial temperature of the pole piece material and the temperature change during winding, resulting in damage to the microstructure of the pole piece and affecting the performance and life of the battery. To solve this problem, a comprehensive material thermal response database and model need to be built, but there is a challenge in how to quickly and accurately obtain the thermal response parameters of different materials. At the same time, how to effectively combine the thermal response model with real-time winding process parameters to achieve intelligent pre-heating and dynamic temperature control is also a technical difficulty. In addition, factors such as winding speed and cell size will affect the temperature distribution, and how to achieve accurate partition temperature control and temperature fluctuation management under complex and changing production conditions, as well as how to balance the temperature control accuracy and response speed, are all technical problems that need to be solved. These interrelated challenges together constitute a complex technical problem that requires the consideration of multiple disciplines such as material science, thermodynamics, and intelligent control to overcome. SUMMARY

[0003] The present application provides an intelligent winding temperature control method for a lithium battery automatic winding machine, which comprises:

[0004] Step S101, according to the working environment characteristics of the lithium battery automatic winding machine, a plurality of rapid response temperature sensors are arranged on the winding machine to collect real-time temperature data of each part of the cell, and the collected temperature data is transmitted to a temperature intelligent control system;

[0005] Step S102, after receiving the temperature data, the temperature intelligent control system classifies each material based on the material thermal response model and pre-set program instructions, and the classification basis includes the thermal conductivity, specific heat capacity, and density thermal physical properties of the material, which is used for subsequent accurate temperature control of different materials. The system compares the temperature data with the material thermal response database data information and the pole piece material to determine the temperature range of each part of the cell;

[0006] Step S103, when the temperature of a certain part of the cell is lower than the lower limit of the optimal winding temperature range obtained by the material thermal response model, the system generates a temperature control curve according to the thermal response database data information to control the heating device in the corresponding area to increase the heating power until the temperature of the detection part reaches the optimal winding temperature range. When the temperature of a certain part of the cell is higher than the upper limit of the optimal winding temperature range obtained by the material thermal response model, the system controls the cooling device in the corresponding area to increase the refrigeration power until the temperature of the detection part decreases to within the optimal winding temperature range.

[0007] In step S104, the temperature intelligent control system starts the preheating or precooling program based on the material thermal response model according to the electrode material property database and winding process parameters. The system combines the winding process parameters to obtain a control strategy and automatically matches the corresponding temperature control curve. The output parameters of the pre-temperature control program are synchronized to the temperature intelligent control system. The system dynamically optimizes the control strategy of the heating and cooling device to make the initial temperature of the material approach the optimal temperature state. This pre-temperature control process provides stable initial conditions for the subsequent speed and temperature dual closed-loop control.

[0008] Step S105: The intelligent temperature control system obtains the operating speed parameters of the winding machine and adjusts the control strength of the heating and cooling device based on the speed-temperature correlation data in the material thermal response model. The system uses speed and temperature as two mutually influencing variables and achieves coordinated regulation of speed and temperature through linkage control. Based on the operating status of the linkage control and the control curve obtained from the material thermal response model, the final operating parameters of each part of the battery cell are obtained, achieving dual closed-loop control of speed and temperature, thereby improving the temperature control accuracy of the battery cell.

[0009] In step S106, the intelligent temperature control system adopts a partition control strategy based on the cell size and preset information about the temperature distribution of cells of different sizes in the material thermal response model. The system divides the cell into multiple control partitions based on the geometric characteristics and thermal distribution characteristics of the cell. Each partition is equipped with an independent temperature sensor and heating and cooling device. The number and boundaries of the partitions are determined taking into account the thermal conductivity characteristics and temperature gradient distribution of the cell. The system independently controls the temperature of each partition based on the real-time temperature data of each partition and determines the operating status information of each partition. This partition control strategy can more accurately respond to temperature differences in different parts of the cell.

[0010] In step S107, the intelligent temperature control system monitors the temperature fluctuation amplitude and makes a judgment based on the safe temperature fluctuation range data in the material thermal response model. When the temperature fluctuation in a certain area exceeds the preset temperature fluctuation threshold, the system adjusts the temperature regulation strength of the detection area and determines the operation strategy information of the detection area. This control method can prevent sudden temperature changes from adversely affecting the performance of the electrode material, reduce the probability of electrode breakage and wrinkling defects, and stabilize the winding quality of the battery cell.

[0011] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0012] The present invention discloses an intelligent temperature control method for an automatic winding machine for lithium batteries. The method realizes precise control of the temperature of the battery cells by arranging multiple temperature sensors on the winding machine to collect the temperature data of the battery cells in real time, and combining the material thermal response model, the pole piece material characteristic database and the winding process parameters. The present invention adopts strategies such as pre-temperature adjustment, speed and temperature dual closed-loop control and partition control to dynamically optimize the control strength of the heating and cooling device, so that the temperature of each part of the battery cell is always maintained within the optimal winding temperature range. At the same time, the present invention also monitors the temperature fluctuation amplitude to prevent the adverse effect of sudden temperature changes on the performance of the pole piece material. This intelligent temperature control method can effectively improve the winding quality of the battery cells, reduce the probability of defects such as pole piece breakage and wrinkling, and is of great significance to improving the production efficiency and product performance of lithium batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The figure is a flow chart of the intelligent winding temperature control method of the lithium battery automatic winding machine of the present invention. DETAILED DESCRIPTION

[0014] To further understand the content of the present invention, the present invention is described in detail with reference to the accompanying drawings and examples. The present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0015] like Figure 1 The intelligent winding temperature control method of the lithium battery automatic winding machine of this embodiment may specifically include:

[0016] S101. Based on the working environment characteristics of the lithium battery automatic winding machine, multiple fast-response temperature sensors are installed on the winding machine to collect real-time temperature data from various parts of the battery cell. The collected temperature data is transmitted to the intelligent temperature control system.

[0017] Multiple response sensors are distributed on the winding machine to collect the temperature values ​​of each part of the battery cell. The battery cell temperature values ​​are collected by the sensors and transmitted to the background processing center. After receiving the temperature data, the background processing center calculates the difference ΔT (temperature change) between the actual temperature value and the standard safety threshold based on the safety threshold. The temperature difference ΔT is transmitted to the judgment module. After the judgment module receives the temperature difference ΔT, if ΔT is greater than zero, the risk judgment is initiated. After the risk judgment is initiated, the machine learning Kalman filter algorithm is used to predict the temperature value change trend and obtain the predicted value of the battery cell temperature trend change. Based on the predicted value of the battery cell temperature trend change, the support vector machine (SVM) method is used to determine whether there is a potential probability of thermal runaway risk. If the probability of potential thermal runaway risk is greater than the set risk probability threshold, the thermal runaway model pre-established for battery startup is used to obtain corresponding solutions, and at the same time, the parameters of the control algorithm are tuned to obtain new tuning parameter values.

[0018] S102: After receiving temperature data, the intelligent temperature control system performs a preliminary classification of each material based on the material thermal response model and pre-programmed instructions. This classification is based on thermophysical properties such as thermal conductivity, specific heat capacity, and density, which are subsequently used to precisely control the temperature of each material. The system compares the temperature data with the electrode material and the real-time data from the material thermal response database to determine the temperature range of each component of the battery cell.

[0019] Obtain temperature data of various parts of the battery cell and perform format unification processing on the temperature data; calculate the thermal conductivity, specific heat capacity and thermal conductivity coefficient of the electrode material of different materials under the current temperature conditions according to the established thermophysical parameter database of the electrode material, and obtain the battery cell thermal performance coefficient distribution cloud map and specific heat capacity cloud map; combine the temperature data with the thermal conductivity coefficient, and calculate the internal heat flow distribution of the battery cell according to Fourier's heat conduction law, wherein the Fourier's heat conduction law is Q=-kA(dT / dx), Q represents the heat conduction rate, k is the thermal conductivity of the material, A represents the cross-sectional area of ​​heat conduction, dT / dx represents the temperature gradient, and obtain the total heat flow cloud map; fuse the total heat flow cloud map and the specific heat capacity cloud map, calculate the heat transfer change cloud map by pixel, and generate a heat flow change gradient cloud map reflecting the temperature change information of each part of the battery cell under different electrode materials; combine the heat flow change gradient cloud map with the preset target temperature value of different electrode materials and the reaction speed instruction, according to P The ID control algorithm generates control information for the temperature regulator, where the PID control algorithm formula is: u(t)=Kpe(t)+Ki∫e(t)dt+Kdde(t) / dt, u(t) represents the control output, e(t) represents the deviation at the current moment, Kp, Ki and Kd are the proportional, integral and differential gains respectively; if the reaction speed is different from the reaction speed instruction, the amplitude and direction of the program instruction adjustment are obtained; the control information is sent to each actuator, and each actuator performs heating and heat dissipation according to the control information; the detector obtains real-time temperature data of each electrode, integrates the temperature electrode target value and the range value of the program instruction, obtains the next adjustment range of the program instruction control information for each electrode target value and each electrode target range, and controls the actuator.

[0020] Specifically, in lithium battery winding machines, the placement of temperature sensors must consider key locations within the battery cell. For example, thermocouple sensors can be placed at locations such as the positive and negative electrode tabs, at the interface between the separator and the electrode sheet, to collect real-time temperature data. This data, after being standardized, can be used for subsequent analysis. Regarding the thermophysical properties of different electrode materials, for example, lithium iron phosphate (LIFP) has a thermal conductivity of approximately 0.8 watts per meter Kelvin and a specific heat capacity of approximately 1,000 joules per kilogram Kelvin. In contrast, the thermal conductivity of ternary materials is approximately 1.2 watts per meter Kelvin and a specific heat capacity of approximately 800 joules per kilogram Kelvin. These differences in parameters lead to different thermal conductivity characteristics. In heat flow distribution calculations, assuming a battery cell with a cross-sectional area of ​​100 square centimeters and a temperature difference of 10 degrees across its two ends, the heat flow distribution can be calculated using Fourier's law of heat conduction. If an abnormal temperature rise is detected at a specific location, a heat flow cloud map can visually display the area of ​​heat accumulation, helping to promptly identify potential problems. The fusion calculation of heat transfer change cloud maps requires consideration of the characteristics of different materials. For example, in the cathode material area, due to its good thermal conductivity, the temperature changes more uniformly. In contrast, in the separator area, due to its relatively poor thermal conductivity, a large temperature gradient may occur. For temperature control, taking the cathode area as an example, if the target temperature is 30°C, when the temperature is detected to rise to 35°C, the control system will adjust according to preset parameters. The proportional coefficient can be set to 0.5, the integral time to 10 seconds, and the differential time to 0.1 second. These parameters are coordinated to achieve precise control. For actuator control, a zone-based control strategy can be adopted. When the temperature in a particular zone is too high, the cooling fan speed in that zone will be increased accordingly, while the heating power will be reduced. For example, if the tab temperature exceeds 40°C, the cooling system in that zone will enter maximum power mode. In real-time temperature monitoring and feedback control, the system dynamically adjusts control parameters based on the temperature trends in different zones. If the temperature rise rate exceeds 0.5°C per minute, the system will intervene preemptively to prevent temperature runaway. This predictive control effectively prevents thermal runaway accidents.

[0021] S103. When the temperature of a certain part of the battery cell is lower than the lower limit of the optimal winding temperature range obtained by the material thermal response model, the system generates a temperature control curve based on the data in the thermal response database and controls the heating device in the corresponding area to increase the heating power until the temperature of the part reaches the optimal winding temperature range. When the temperature of a certain part of the battery cell is higher than the upper limit of the optimal winding temperature range obtained by the material thermal response model, the system controls the cooling device in the corresponding area to increase the cooling power based on the data in the thermal response database until the temperature of the part falls within the optimal winding temperature range.

[0022] According to the material thermal response model in the thermal response database, the optimal temperature range of each part in the winding process of the battery cell is determined; the temperature data of each part of the battery cell is monitored in real time, and the monitored temperature data is compared with the optimal temperature range; if the temperature of a part is lower than the lower limit of the optimal temperature range, a heating device corresponding to the part is determined according to the information of the thermal response database, and a temperature control curve is generated; according to the generated temperature control curve, the heating power of the heating device is controlled to be increased until the temperature of the part reaches the optimal temperature range; if the temperature of a part is higher than the upper limit of the optimal temperature range, a cooling device corresponding to the part is determined according to the information of the thermal response database; the cooling device is controlled to increase the refrigeration power, and through heat conduction and convection, the excessive heat of the part is taken away until the temperature of the part decreases to the optimal temperature range; the Kalman filtering algorithm is used to filter the temperature data, so as to improve the accuracy and stability of temperature monitoring and optimize the temperature control effect.

[0023] Specifically, the thermal response database stores the thermal physical property parameters of different pole piece materials at different temperatures, including thermal conductivity, specific heat capacity, etc. Taking the common lithium ion battery positive material lithium iron phosphate as an example, its optimal working temperature range is 25-40 degrees Celsius. When the temperature is too low, the material activity is insufficient, and when the temperature is too high, it will accelerate the aging. The real-time temperature monitoring system collects temperature data through thermocouple sensors arranged at key positions of the battery cell. Taking the center area of the battery cell as an example, if the detected temperature is 18 degrees Celsius, which is lower than the lower limit of the optimal range, the system will start the corresponding heating device. The heating device uses a thin film heating sheet, which can accurately adjust the power output according to the temperature control curve to achieve smooth temperature rise. The generation of the temperature control curve takes into account the thermal response characteristics of the material. Taking the lithium iron phosphate material as an example, its thermal conductivity is about 1.2 watts per meter Kelvin, and its specific heat capacity is about 1,000 joules per kilogram Kelvin. Based on these parameters, the system calculates the heating power and time required to raise the temperature from 18 degrees Celsius to 25 degrees Celsius, and generates a step or gradual control curve. When the temperature of a certain area is detected to rise to 45 degrees Celsius, which exceeds the upper limit of the optimal range, the system will start the corresponding cooling device. The cooling system can use air cooling or liquid cooling to remove excess heat through forced convection. Taking the air cooling system as an example, when the temperature exceeds the standard, the controller will gradually increase the fan speed according to the temperature deviation to increase the heat dissipation. The Kalman filter algorithm effectively removes random noise in the temperature data through prediction and correction stages. Taking the temperature data of a certain measuring point as an example, the original data collected by the sensor may fluctuate due to external interference, and after Kalman filter processing, a more smooth and stable temperature curve can be obtained, effectively avoiding the frequent start and stop of the control system due to data fluctuations. The whole temperature control process forms a closed-loop feedback system, through real-time monitoring, analysis and comparison, control and adjustment, and data optimization, etc. to ensure that the temperature of each part of the battery cell always maintains within the optimal range. This precise temperature control not only improves the quality of battery cell manufacturing, but also prolongs the service life of the battery and improves product performance.

[0024] S104, the temperature intelligent control system starts the pre-heating or pre-cooling program based on the material thermal response model according to the pole piece material characteristics database and the winding process parameters. The system combines the winding process parameters to obtain the control strategy and automatically matches the corresponding temperature control curve. The output parameters of the pre-temperature adjustment program are synchronized to the temperature intelligent control system, and the system dynamically optimizes the control strategy of the heating and cooling devices to make the initial temperature of the material approach the optimal temperature state. This pre-temperature adjustment process provides stable initial conditions for the subsequent speed-temperature double closed-loop control.

[0025] Based on the electrode material characteristic database, the system obtains the material's thermophysical parameters and thermal conductivity. Based on the obtained thermophysical parameters and thermal conductivity, a thermal response model is established for each electrode material to obtain the theoretical heating or cooling time corresponding to each material. If the difference between the current ambient temperature and the set target temperature is greater than the set threshold, the winding speed in the winding process parameters and the operating status information of each station inside the equipment are obtained, and the pre-temperature adjustment program control information is output in combination with the thermal response model. Pre-temperature adjustment is started according to the pre-temperature adjustment program control information, and instructions are sent to the temperature control unit. The real-time temperature information is transmitted through the communication bus and synchronized to the temperature intelligent control system. The temperature intelligent control system receives the real-time temperature information transmitted by the temperature control unit, uses the data in the database combined with the process parameters to construct a dynamic PID control algorithm, obtains the PID coefficient, and obtains the control strategy. The PID coefficient obtained based on the PID control algorithm is combined with the temperature control curve to match the heating or cooling rate, perform temperature matching processing, and obtain optimized control instructions. The intelligent temperature control system outputs optimized control instructions to the heating and cooling device, adjusts the power in real time, and the heating and cooling device performs the adjustment operation to determine the final actual temperature of the material. After comparative feedback, it is adjusted to the optimal temperature state to obtain the initial temperature value of the dual closed-loop control.

[0026] Specifically, based on the electrode material property database, the system first obtains the material's thermophysical properties, such as specific heat capacity and thermal conductivity. For example, the specific heat capacity of a certain lithium battery cathode material is 0.8 J / (g·K) and the thermal conductivity is 1.5 W / (m·K). These parameters form the basis for establishing a thermal response model and directly influence the material's temperature change rate during heating or cooling. When establishing the thermal response model, the system simulates the temperature change curves of different materials under specific conditions based on the obtained thermophysical parameters. Assuming a theoretical heating time of 10 minutes for a material, from 20°C to 60°C, the system records this process, providing a theoretical basis for subsequent temperature control. If the current ambient temperature differs significantly from the set target temperature—for example, the ambient temperature is 25°C and the target temperature is 60°C—the system combines winding process parameters (such as a winding speed of 5 m / min) and equipment operating status (such as the current station is in the preheating phase) to output pre-temperature control information to ensure that the temperature quickly reaches the target value. After receiving real-time temperature information from the temperature control unit, the intelligent temperature control system utilizes database data and process parameters to develop a dynamic PID control algorithm. Assuming the PID coefficients are Kp = 2, Ki = 0.1, and Kd = 0.05, the system adjusts the heating or cooling power accordingly to ensure precise temperature control. Based on the coefficients obtained by the PID control algorithm and the temperature control curve, the system matches the heating or cooling rate. For example, if the target heating rate is 2°C / min, the system adjusts the heating power based on the PID coefficients to ensure the actual heating rate approaches this target. Finally, the intelligent temperature control system outputs optimized control instructions to the heating and cooling devices, which adjust power accordingly to ensure the optimal material temperature. For example, if the current temperature is 55°C and the target temperature is 60°C, the system will instruct the heating device to increase power until the temperature reaches 60°C. Through this series of steps, the system can precisely control the temperature during the battery cell winding process, ensuring optimal material properties and improving product quality and production efficiency. This dual closed-loop control mechanism not only improves temperature control accuracy but also enhances system stability and reliability.

[0027] S105: The intelligent temperature control system obtains the winder's operating speed parameters and adjusts the control intensity of the heating and cooling devices based on the speed-temperature correlation data from the material thermal response model. The system treats speed and temperature as two mutually influencing variables and achieves coordinated regulation of speed and temperature through linkage control. Based on the linkage control's operating status and the control curves derived from the material thermal response model, the final operating parameters of each cell component are determined, achieving dual closed-loop speed and temperature control, thereby improving the accuracy of cell temperature control.

[0028] The winding machine running speed data v is acquired, the temperature data of each part of the collection equipment is collected to form a temperature matrix T, a time variable is defined as t, and the initial moment is t0; based on the pre-established material thermal response model, the corresponding temperature theoretical value Ttheoretical value under the running speed v is obtained, and the deviation threshold value between the theoretical value and the actual value is set as AT; if the actual deviation |T-Ttheoretical value| is greater than AT, it is determined that the temperature T1 at the moment t1 has a regulation requirement; based on the thermal response model, a control force function F is obtained, F is a function of time t and speed v, and is expressed as F(t,

[0029] v); the regulation curve C in the thermal response model of the material is acquired, the state function of the control force is expressed as G(L), and a state function matrix is constructed; according to the state function matrix, the control force L2 corresponding to the moment t1 is obtained, L2=G(F(t1, v1)), and v1 is the speed at the moment t1; after a time interval At, t2=t1+At, the speed v2 and the temperature T2 at the moment t2 are acquired; if the speed v2 is not equal to the speed v1 at the previous moment, the moment t2 is a speed change point; according to the thermal response model of the material, the regulation curve C2 corresponding to the speed v2 at the moment t2 is obtained, and it is judged whether the temperature T2 at this moment meets the preset interval requirement; if T2 meets the preset interval requirement, the running parameter matrix P is obtained; if T2 does not meet the preset interval requirement, the adjustment force is H, and the size of H is |T2theoretical value-T2|; by continuously collecting the speed data and the temperature data, it is judged whether the deviation between the actual data and the thermal response model exceeds the preset threshold value, the running parameters at each moment t are obtained, and a running parameter set is generated.

[0030] Specifically, in the intelligent temperature control system, the winding machine's operating speed data v is first acquired. For example, assume the winding machine's current operating speed is 5 meters per minute. Simultaneously, temperature data from various equipment components is collected to construct a temperature matrix T. Assuming the initial time t0 is 0 seconds, the temperatures of various equipment components at this time are 30°C, 32°C, 28°C, and so on, forming the initial temperature matrix T0. Based on the pre-established thermal response model of the electrode material, the theoretical temperature values ​​Ttheoretical at speed v are extracted. For example, if the model predicts that at a speed of 5 meters per minute, the theoretical temperatures of various components should be 35°C, 37°C, and 33°C. A threshold ΔT for the deviation between the theoretical and actual values ​​is set to 2°C, with the actual deviation represented by |TTtheoreticalvalue|. If the temperature T1 at a certain time t1 (e.g., 34°C, 36°C, or 31°C) deviates by more than 2°C from the theoretical value, an alarm mechanism is activated, indicating that a control requirement exists at that moment. Based on the thermal response model, a control force function F(t, v) is derived, which represents the control force at different times and speeds. For example, F (10 seconds, 5 meters / minute) may indicate that the heating intensity needs to be increased by 10%. Obtain the control curve C, construct the state function matrix G (L), and obtain the control intensity L2 corresponding to this moment. Assume that L2 = G (F (10 seconds, 5 meters / minute)) = 0.12, which means that a heating intensity of 12% is required for the first stage of control. After a time Δt (such as 5 seconds), at the moment t2 = t1 + Δt (that is, 15 seconds), obtain the speed v2 (such as 6 meters / minute) and temperature T2 (such as 35°C, 38°C, 32°C) at the moment t2. Determine whether this moment is a speed change point. If v2 is not equal to v1, it is determined to be a speed change point, and linkage control is adopted. According to the thermal response model, obtain the control curve C2 corresponding to the speed v2 at the moment t2. If T2 meets the preset range (such as 34℃-38℃), the operating parameter matrix P is obtained; if not, the heating or cooling force H is used for adjustment, and the size of H is |T2 theoretical value-T2 actual value|, until T2 is adjusted to meet the range. By continuously collecting speed and temperature data, it is determined whether the deviation between the actual data and the thermal response model exceeds the preset threshold, the operating parameters at each moment t are obtained, and an operating parameter set is generated. For example, at 20 seconds, the speed is 6 meters per minute, and the temperature is 36℃, 39℃, and 34℃. If the deviation is within the threshold, the operating parameters at that moment are recorded. The purpose of this series of steps is to ensure that the pole piece material is always in the optimal temperature state throughout the winding process, thereby improving product quality and production efficiency. Through real-time monitoring and dynamic adjustment, the system can respond quickly to temperature changes and avoid unstable material properties caused by temperature fluctuations. For example, if the temperature is too high, the material may deform, and if it is too low, it may affect the bonding effect. The system ensures that the temperature is always within the ideal range through precise control. In specific implementation, assuming that the actual temperature value of a certain workstation is 33°C, the theoretical value is 35°C, the deviation is 2°C, and it exceeds the threshold value by 1°C, the system immediately starts the heating program and increases the heating intensity by 2%.After 5 seconds, the temperature rose to 34.5℃ and the deviation narrowed to 0.5℃, which was still within the threshold. The system continued to monitor to ensure temperature stability.

[0031] In this way, the system dynamically adjusts heating or cooling intensity at each time point based on the deviation between actual and theoretical temperatures, ensuring that temperatures consistently meet process requirements. This not only improves the stability and controllability of the production process, but also significantly enhances product quality and production efficiency. For example, during the winding process of a batch of electrode material, the initial temperature was 30°C, and the target temperature was 35°C. Based on the thermal response model, the system predicted that at a speed of 5 meters per minute, it would take 10 minutes to reach the target temperature. In actual operation, the system collected temperature data every 5 seconds. At 10 seconds, the temperature was 32°C, a deviation of 3°C. The system initiated the heating program and increased the heating intensity by 5%. At 15 seconds, the temperature rose to 34°C, a deviation of 1°C. The heating intensity was further adjusted to 2%. Finally, at 20 seconds, the temperature stabilized at 35°C, achieving the target temperature. This dynamic control mechanism, through real-time data collection and model prediction, achieves precise temperature control, ensuring production process stability and consistent product quality, significantly improving production efficiency and economic benefits.

[0032] S106. The intelligent temperature control system adopts a zoning control strategy based on cell size and preset information about the temperature distribution of cells of different sizes from the material thermal response model. The system divides the cells into multiple control zones based on their geometric characteristics and thermal distribution. Each zone is equipped with independent temperature sensors and heating and cooling devices. The number and boundaries of the zones are determined based on the thermal conductivity characteristics and temperature gradient distribution of the cells. Based on the real-time temperature data of each zone, the system independently controls the temperature of each zone and determines the operating status of each zone. This zoning control strategy can more accurately address temperature differences in different parts of the cell.

[0033] A thermal response model is established based on the size and material of the battery cell. Based on the established thermal response model, the thermal conduction data of the battery cell is collected, and the thermal distribution data inside the battery cell is collected. The thermal distribution data of the battery cell is obtained and analyzed. If the maximum temperature difference in the thermal distribution data is greater than the set threshold, the temperature gradient value of the battery cell is obtained, and the number of control zones is determined based on this gradient value. The battery cell control zones are divided according to the number of control zones, and temperature sensors are deployed in the battery cell control zones to monitor the temperature of each control zone. The operating state of each control zone is judged based on the temperature data of the control zone and the set target temperature. Based on the judgment result, it is determined whether the heating or cooling elements of each control zone are activated. Based on the thermal response model, the thermal response time is obtained, and the time of the temperature data collected by the temperature sensors in each control zone is calibrated. Based on the values ​​of the temperature sensors in each control zone and the operating state information, the thermal response model parameters are updated.

[0034] Specifically, building a cell thermal response model requires considering the cell's geometric dimensions, material properties, and thermal conductivity characteristics. Taking a cylindrical cell as an example, its dimensions include diameter and height, and its materials include the positive electrode material, negative electrode material, separator, and electrolyte. Using heat conduction theory, a model can be established to establish the relationship between the cell's internal temperature distribution and external heat sources. Thermal distribution data is collected by placing temperature sensors on the cell's surface and interior. For example, ten temperature sensors can be evenly spaced on the cell's surface and five on the cell's interior, forming a three-dimensional temperature distribution monitoring network. The collected data includes the temperature values ​​at each point and their temporal trends. For temperature gradient analysis, assume the cell's surface temperature is 45°C (maximum 45°F) and its minimum 35°C (maximum 35°F), with a temperature difference of 10°C. If the temperature difference threshold is set at 8°C (maximum 8°F), control zones must be defined. Based on the temperature gradient distribution characteristics, areas with large temperature differences can be divided into independent control zones for precise control. When defining control zones, consider areas with drastic temperature gradients. For example, a cylindrical cell can be divided into three control zones: upper, middle, and lower. Each control zone is equipped with two temperature sensors to monitor the temperature distribution within the area. The operating status is determined by comparing the actual temperature of each control zone with the target temperature. If the temperature in a control zone falls below the target, for example, the actual temperature is 35 degrees Celsius and the target is 40 degrees Celsius, the heating device in that zone is activated. Conversely, if the temperature exceeds the target, the cooling device is activated. Thermal response time calibration takes into account the thermal inertia of the material. For example, after the heating device is activated, it may take ten seconds for a noticeable temperature rise to be observed. Therefore, the temperature data collection time needs to be adjusted accordingly to the thermal response characteristics to ensure that the collected data accurately reflects the control effect. Model parameter updates are achieved through feedback from actual operating data. If the temperature rise rate in a control zone deviates from the model prediction at a specific heating power, parameters such as the thermal conductivity coefficient in that zone need to be adjusted. Continuous data collection and parameter optimization continuously improve model accuracy. In actual applications, factors such as ambient temperature fluctuations and the charge and discharge status of the battery cells need to be considered to influence the temperature distribution. By establishing a comprehensive thermal response model, precise control of battery cell temperature can be achieved, improving product quality and production efficiency.

[0035] S107: The intelligent temperature control system monitors temperature fluctuations and determines the safe temperature fluctuation range based on the material thermal response model. When the temperature fluctuation in a certain area exceeds the preset temperature fluctuation threshold, the system adjusts the temperature regulation intensity in that area and determines the operating strategy information for that area. This control method prevents sudden temperature changes from adversely affecting the performance of the electrode material, reduces the probability of electrode fracture and wrinkling defects, and stabilizes the winding quality of the battery cell.

[0036] Based on the material thermal response model, a safe temperature range is set and temperature zones are divided. Temperature sensor data is collected from each zone during the cell winding process to obtain a temperature data sequence. This temperature data sequence is processed. If the temperature fluctuation in the current zone exceeds a preset fluctuation threshold, a fluctuation warning message is generated based on historical fluctuation data. This fluctuation warning message is combined with the preset material thermal response model data and calculated using a difference algorithm to determine the resulting material property change. The resulting material property change results are compared with a pre-established material property database. If the material property change exceeds the material property threshold, the trained XGBoost, support vector machine, and decision tree learning algorithms are applied to generate multiple cooling strategies for the current zone. The performance data of multiple cooling strategies is evaluated. If the performance data of the current cooling strategies is inconsistent, a voting method is used to output the optimal cooling strategy information. The optimal cooling strategy information is combined with the pre-set zone control data to generate the zone's operating strategy information. Adjusted operating strategy data is then generated. The actuators are controlled based on this adjusted operating strategy data, issuing control instructions for the production and processing equipment in that zone.

[0037] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the principles of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent winding temperature control method for a lithium battery automatic winding machine, characterized in that: The method comprises: Step S101: Based on the working environment characteristics of the lithium battery automatic winding machine, multiple fast-response temperature sensors are arranged on the winding machine to collect temperature data of various parts of the battery cell in real time, and the collected temperature data is transmitted to the temperature intelligent control system; Step S102: After receiving the temperature data, the intelligent temperature control system preliminarily classifies each material based on the material thermal response model and preset program instructions. The classification is based on the thermal conductivity, specific heat capacity, and density thermophysical properties of the material, which are used to subsequently accurately control the temperature of different materials. The system compares the temperature data with the electrode material and the real-time material thermal response database data to determine the temperature range of each part of the battery cell; Step S103: When the temperature of a certain part of the battery cell is lower than the lower limit of the optimal winding temperature range obtained by the material thermal response model, the system generates a temperature control curve based on the data information in the thermal response database, and controls the heating device in the corresponding area to increase the heating power until the temperature of the detection part reaches the optimal winding temperature range. When the temperature of a certain part of the battery cell is higher than the upper limit of the optimal winding temperature range obtained by the material thermal response model, the system controls the cooling device in the corresponding area to increase the cooling power based on the data information in the thermal response database until the temperature of the detection part drops to within the optimal winding temperature range. In step S104, the temperature intelligent control system starts the preheating or precooling program based on the material thermal response model according to the electrode material property database and winding process parameters. The system combines the winding process parameters to obtain a control strategy and automatically matches the corresponding temperature control curve. The output parameters of the pre-temperature control program are synchronized to the temperature intelligent control system. The system dynamically optimizes the control strategy of the heating and cooling device to make the initial temperature of the material approach the optimal temperature state. This pre-temperature control process provides stable initial conditions for the subsequent speed and temperature dual closed-loop control. Step S105: The intelligent temperature control system obtains the operating speed parameters of the winding machine and adjusts the control strength of the heating and cooling device based on the speed-temperature correlation data in the material thermal response model. The system uses speed and temperature as two mutually influencing variables and achieves coordinated regulation of speed and temperature through linkage control. Based on the operating status of the linkage control and the control curve obtained from the material thermal response model, the final operating parameters of each part of the battery cell are obtained, achieving dual closed-loop control of speed and temperature, thereby improving the temperature control accuracy of the battery cell. In step S106, the intelligent temperature control system adopts a partition control strategy based on the cell size and preset information about the temperature distribution of cells of different sizes in the material thermal response model. The system divides the cell into multiple control partitions based on the geometric characteristics and thermal distribution characteristics of the cell. Each partition is equipped with an independent temperature sensor and heating and cooling device. The number and boundaries of the partitions are determined taking into account the thermal conductivity characteristics and temperature gradient distribution of the cell. The system independently controls the temperature of each partition based on the real-time temperature data of each partition and determines the operating status information of each partition. This partition control strategy can more accurately respond to temperature differences in different parts of the cell. In step S107, the intelligent temperature control system monitors the temperature fluctuation amplitude and makes a judgment based on the safe temperature fluctuation range data in the material thermal response model. When the temperature fluctuation in a certain area exceeds the preset temperature fluctuation threshold, the system adjusts the temperature regulation strength of the detection area and determines the operation strategy information of the detection area. This control method can prevent sudden temperature changes from adversely affecting the performance of the electrode material, reduce the probability of electrode breakage and wrinkling defects, and stabilize the winding quality of the battery cell.

2. The method according to claim 1, characterized in that The step S101 includes: Multiple response sensors are distributed on the winding machine to collect the temperature values ​​of each part of the battery cell; The temperature value of the battery cell is collected by the sensor and transmitted to the background processing center; After receiving the temperature data, the background processing center calculates the temperature change difference between the actual temperature value and the standard safety threshold according to the safety threshold; The temperature difference and temperature change are transmitted to the judgment module. After the judgment module receives the temperature difference and temperature change, if the temperature change is greater than zero, the risk judgment is initiated; After starting the risk assessment, the machine learning Kalman filter algorithm is used to predict the temperature change trend and obtain the predicted value of the battery cell temperature trend change; Based on the predicted value of the battery cell temperature trend change, the support vector machine (SVM) method is used to determine whether there is a potential thermal runaway risk probability; If the probability of potential thermal runaway risk is greater than the set risk probability threshold, the thermal runaway model pre-established for battery startup is used to obtain corresponding solutions, and the parameters of the control algorithm are tuned to obtain new tuning parameter values.

3. The method according to claim 1, characterized in that The step S102 includes: Obtain temperature data of various parts of the battery cell and standardize the format of the temperature data; Based on the established thermophysical parameter database of electrode materials, the thermal conductivity and specific heat capacity of different materials and the thermal conductivity coefficient of the electrode materials under the current temperature conditions are calculated to obtain the distribution cloud map of the thermal performance coefficient and the specific heat capacity cloud map of the battery cell; Combine the temperature data with the thermal conductivity coefficient to calculate the heat flow distribution inside the battery cell according to Fourier's heat conduction law, where Fourier's heat conduction law is Q = -kA (dT / dx), and obtain the total heat flow cloud map; The total heat flow cloud map and the specific heat capacity cloud map are integrated, and the heat transfer change cloud map is calculated by pixel, and the heat flow change gradient cloud map is generated to reflect the temperature change information of each part of the battery cell under different electrode materials; The heat flux gradient cloud map is combined with the preset target temperature values ​​and reaction speed instructions of different electrode materials to generate the control information of the temperature regulator according to the PID control algorithm, where the PID control algorithm formula is: u(t)=Kpe(t)+Ki∫e(t)dt+Kdde(t) / dt; If the reaction speed is different from the reaction speed instruction, the magnitude and direction of the program instruction adjustment are obtained; Send control information to each actuator, and each actuator performs heating and cooling according to the control information; The detector obtains the real-time temperature data of each electrode, integrates the temperature electrode target value and the range value of the program instruction, obtains the program instruction control information of the next adjustment range for each electrode target value and each electrode target range, and controls the actuator.

4. The method according to claim 1, wherein The step S103 includes: Determine the optimal temperature range for each part during the battery cell winding process based on the material thermal response model in the thermal response database; Monitor the temperature data of each part of the battery cell in real time and compare the monitored temperature data with the optimal temperature range; If the temperature of a certain part is detected to be lower than the lower limit of the optimal temperature range, the heating device corresponding to the monitored part is determined according to the information in the thermal response database, and a temperature control curve is generated; According to the generated temperature control curve, the heating device is controlled to increase the heating power until the temperature of the monitored part reaches the optimal temperature range; If the temperature of a certain part is detected to be higher than the upper limit of the optimal temperature range, the cooling device corresponding to the monitored part is determined based on the information in the thermal response database; Control the cooling device to increase the cooling power, and remove excess heat from the monitoring part through heat conduction and convection until the temperature of the monitoring part drops to the optimal temperature range; The Kalman filter algorithm is used to filter the temperature data to improve the accuracy and stability of temperature monitoring and optimize the temperature control effect.

5. The method according to claim 1, wherein The step S104 includes: According to the pole piece material characteristic database, the system obtains the material's thermophysical parameters and thermal conductivity; Based on the obtained thermophysical parameters and thermal conductivity, a thermal response model for each electrode material is established to obtain the theoretical heating or cooling time corresponding to each material; If the difference between the current ambient temperature and the set target temperature is greater than the set threshold, the winding speed and the operating status of each station inside the equipment in the winding process parameters are obtained, and the pre-temperature adjustment program control information is output in combination with the thermal response model; Start pre-temperature adjustment according to the pre-temperature adjustment program control information, send instructions to the temperature control unit, transmit the real-time temperature information through the communication bus, and synchronize it to the temperature intelligent control system; The temperature intelligent control system receives the real-time temperature information transmitted by the temperature control unit, uses the data in the database and the process parameters to build a dynamic PID control algorithm, obtains the PID coefficient, and obtains the control strategy; The PID coefficient obtained based on the PID control algorithm is combined with the temperature control curve to match the heating or cooling rate, perform temperature matching processing, and obtain optimized control instructions; The intelligent temperature control system outputs optimized control instructions to the heating and cooling device, adjusts the power in real time, and the heating and cooling device performs the adjustment operation to determine the final actual temperature of the material. After comparative feedback, it is adjusted to the optimal temperature state to obtain the initial temperature value of the dual closed-loop control.

6. The method according to claim 1, characterized in that The step S106 includes: Establish a thermal response model based on cell size and material; Collect the thermal conduction data of the battery cell and the internal heat distribution data of the battery cell according to the established thermal response model; Obtain the battery cell thermal distribution data for analysis. If the maximum temperature difference in the thermal distribution data is greater than the set threshold, obtain the battery cell temperature gradient value and determine the number of control zones based on this gradient value. Divide the cell control zones according to the number of control zones, deploy temperature sensors in the cell control zones, and monitor the temperature of each control zone; Based on the temperature data of the control zone and the set target temperature, the operating state of each control zone is judged, and according to the judgment result, it is determined whether the heating element or cooling element of each control zone is started; According to the thermal response model, the thermal response time is obtained and the time of the temperature data collected by the temperature sensor in each control area is calibrated; According to the temperature sensor values ​​of each control area and combined with the operating status information, the thermal response model parameters are updated.

7. The method according to claim 1, characterized in that The step S107 includes: According to the material thermal response model, set the safe temperature range and divide the temperature zones; Collect temperature sensor data from each area during the battery cell winding process to obtain a temperature data information sequence; Process the temperature data information sequence. If the temperature fluctuation in the current area exceeds the preset fluctuation threshold, a fluctuation warning message is generated based on the historical fluctuation data. The fluctuation warning information and the preset material thermal response model data are integrated and calculated through the difference algorithm to determine the result of the material performance change; The obtained material property change results are compared with the pre-established material property database. When the material property change exceeds the material property threshold range, the trained XGBoost machine learning algorithm, support vector machine machine learning algorithm, and decision tree learning algorithm are applied to generate multiple cooling strategies for the current area. Determine the performance data of multiple cooling strategies. If the performance data of multiple cooling strategies are inconsistent, use voting to output the optimal cooling strategy information. Combining the optimal cooling strategy information and the pre-set regional control data, the monitoring area operation strategy information is generated to obtain the adjusted operation strategy data. The actuator is controlled based on the adjusted operation strategy data to issue control instructions for the monitoring area to the production and processing equipment.

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