Intelligent winding temperature control method of lithium battery automatic winding machine

Abstract

The invention provides an intelligent winding temperature control method for an automatic lithium battery winding machine, which comprises the following steps of: arranging a plurality of quick-response temperature sensors on the winding machine according to the working environment characteristics of the automatic lithium battery winding machine, acquiring temperature data of each part of a battery cell in real time, and transmitting the acquired temperature data to an intelligent temperature regulation and control system; after the intelligent temperature regulation and control system receives the temperature data, the materials are preliminarily classified based on a material thermal response model and a preset program instruction, and the classification basis comprises thermal physical properties such as thermal conductivity, specific heat capacity and density of the materials and is used for subsequently and accurately controlling the temperatures of the different materials; and the system compares the temperature data with the pole piece material and the data information of the material thermal response database obtained in real time, and judges the temperature range of each part of the battery cell.

Description

Technical Field

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

[0002] In the production process of lithium battery automatic winding machines, the thermal response characteristics of materials are closely related to the winding quality. However, it is difficult for existing technologies to accurately control the initial temperature of the pole piece material and the temperature change during the winding process, resulting in damage to the pole piece microstructure, affecting battery performance and life. To solve this problem, it is necessary to build a comprehensive material thermal response database and model, but it faces the challenge of 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 the real-time winding process parameters to achieve intelligent pre-temperature adjustment and dynamic temperature control is also a technical difficulty. In addition, factors such as winding speed and battery cell size will affect the temperature distribution. How to achieve accurate zone temperature control and temperature fluctuation management under complex and changeable production conditions, and how to balance temperature control accuracy and response speed are all technical problems that need to be solved urgently. These interrelated challenges together constitute a complex technical problem that requires comprehensive consideration of multidisciplinary knowledge such as materials science, thermodynamics and intelligent control to overcome. Summary of the invention

[0003] The present invention provides an intelligent winding temperature control method for a lithium battery automatic winding machine, the method comprising:

[0004] Step S101, according to 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;

[0005] Step S102: After receiving the temperature data, the temperature intelligent control system preliminarily classifies the materials 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 materials, which are used to accurately control the temperature of different materials in the future. The system compares the temperature data with the pole piece material and the material thermal response database data information obtained in real time to determine the temperature range of each part of the battery cell.

[0006] 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 according to the data information of 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 according to the data information of the thermal response database until the temperature of the detection part drops to within the optimal winding temperature range;

[0007] Step S104: the temperature intelligent control system starts the preheating or precooling program based on the material thermal response model according to the pole piece material characteristic database and the winding process parameters. The system obtains the control strategy in combination with the winding process parameters, automatically matches the corresponding temperature control curve, and synchronizes the output parameters of the pre-temperature control program 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 a stable initial condition for the subsequent speed and temperature dual closed-loop control.

[0008] Step S105, the temperature intelligent control system obtains the operating speed parameters of the winding machine, and adjusts the control strength of the heating and cooling device according to the speed-temperature correlation data in the material thermal response model. The system uses speed and temperature as two mutually influencing variables, and realizes coordinated regulation of speed and temperature through linkage control. According to the operating state of the linkage control and the control curve obtained by the material thermal response model, the final operating parameters of each part of the battery cell are obtained, and the speed and temperature dual closed-loop control is realized to improve the temperature control accuracy of the battery cell;

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

[0010] Step S107, the temperature intelligent 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 the sudden temperature change 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 cell by arranging multiple temperature sensors on the winding machine to collect the temperature data of the battery cell 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 double 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 kept within the optimal winding temperature range. At the same time, the present invention also monitors the temperature fluctuation amplitude to prevent the sudden temperature change from adversely affecting the performance of the pole piece material. This intelligent temperature control method can effectively improve the winding quality of the battery cell, 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 present invention is a flow chart of the intelligent winding temperature control method of the lithium battery automatic winding machine. DETAILED DESCRIPTION

[0014] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. The present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant inventions, rather than to limit the invention. It is also necessary to explain that, for the convenience of description, only the parts related 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. According to 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. The collected temperature data is transmitted to the temperature intelligent 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 sensor and transmitted to the background processing center. After the background processing center receives the temperature data, it calculates the difference ΔT (temperature change) between the actual temperature value and the standard safety threshold according to 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 started. After the risk judgment is started, the machine learning Kalman filter algorithm is used to predict the trend of temperature value changes and obtain the predicted value of the battery cell temperature trend change. According to 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 the 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 the corresponding solution, and the parameters of the control algorithm are tuned to obtain new tuning parameter values.

[0018] S102. After receiving the temperature data, the temperature intelligent control system preliminarily classifies each material based on the material thermal response model and preset program instructions. The classification is based on the thermal physical properties of the material, such as thermal conductivity, specific heat capacity and density, which are used to accurately control the temperature of different materials in the future. The system compares the temperature data with the pole piece material and the material thermal response database data information obtained in real time to determine the temperature range of each part of the battery cell.

[0019] The temperature data of each part of the battery core is obtained, and the temperature data is formatted in a unified manner; according to the established thermophysical parameter database of the pole piece material, the thermal conductivity, specific heat capacity and thermal conductivity coefficient of the pole piece material of different materials under the current temperature conditions are calculated to obtain the battery core thermal performance coefficient distribution cloud map and the specific heat capacity cloud map; the temperature data is combined with the thermal conductivity coefficient, and the internal heat flow distribution of the battery core is calculated according to the Fourier heat conduction law, wherein the Fourier 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 the total heat flow cloud map is obtained; 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 a heat flow change gradient cloud map reflecting the temperature change information of each part of the battery core under different pole piece materials is generated; the heat flow change gradient cloud map is combined with the preset target temperature value and reaction speed instruction of different pole piece materials, and the heat flow change gradient cloud map is calculated according to P The ID control algorithm generates control information for the temperature regulator, wherein 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 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 program instruction control information for the next adjustment range of each electrode target value and each electrode target range, and controls the actuator.

[0020] Specifically, in the lithium battery winding machine, the arrangement of temperature sensors needs to consider the key parts of the battery cell. For example, thermocouple sensors are arranged at the junction of the positive pole ear, the negative pole ear, the diaphragm and the pole piece to collect temperature data in real time. After these data are processed in a unified format, they can be used for subsequent analysis. For the analysis of the thermophysical properties of different pole piece materials, taking lithium iron phosphate material as an example, its thermal conductivity is about 0.8 watts per meter Kelvin and its specific heat capacity is about 1,000 joules per kilogram Kelvin. The thermal conductivity of the ternary material is about 1.2 watts per meter Kelvin and its specific heat capacity is about 800 joules per kilogram Kelvin. These parameter differences will lead to different thermal conductivity characteristics. In the calculation of heat flow distribution, assuming that the cross-sectional area of ​​a certain battery cell is 100 square centimeters and the temperature difference between the two ends is 10 degrees, the heat flow distribution can be calculated according to Fourier's law of heat conduction. When it is found that the temperature rises abnormally somewhere, the heat accumulation area can be intuitively displayed through the heat flow cloud map, which helps to find potential problems in time. In the fusion calculation process of the heat transfer change cloud map, the characteristics of different materials need to be considered. For example, in the cathode material area, due to its good thermal conductivity, the temperature changes more evenly; while in the diaphragm area, due to its relatively poor thermal conductivity, a large temperature gradient may occur. In temperature control, taking the cathode area as an example, if the target temperature is thirty degrees, when the temperature is detected to rise to thirty-five degrees, the control system will adjust according to the preset parameters. The proportional coefficient can be set to 0.5, the integral time is ten seconds, and the differential time is 0.1 second. Precise control is achieved through the coordination of these parameters. In terms of actuator control, a regional control strategy can be adopted. When the temperature in a certain area is too high, the speed of the cooling fan in that area will increase accordingly, while reducing the heating power. For example, when the temperature of the tab is detected to exceed forty degrees, the cooling system in the corresponding area will start the maximum power mode. In real-time temperature monitoring and feedback adjustment, the system will dynamically adjust the control parameters according to the temperature change trend in different areas. If the temperature rise rate is found to exceed 0.5 degrees per minute, the system will intervene in the adjustment in advance to avoid temperature runaway. This predictive control can effectively prevent the occurrence of 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 information of 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 according to the data information of the thermal response database until the temperature of the part drops to 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 battery cell winding process 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 certain part is monitored to be lower than the lower limit of the optimal temperature range, the 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 device is controlled to increase the heating power until the temperature of the part reaches the optimal temperature range; if the temperature of a certain part is monitored to be higher than the upper limit of the optimal temperature range, the 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 cooling power, and the excess heat of the part is taken away through heat conduction and convection until the temperature of the part drops to the optimal temperature range; the temperature data is filtered by using the Kalman filter algorithm to improve the accuracy and stability of temperature monitoring and optimize the temperature control effect.

[0023] Specifically, the thermal response database stores the thermophysical property parameters of different pole piece materials at different temperatures, including thermal conductivity, specific heat capacity, etc. Taking lithium iron phosphate, a common lithium-ion battery positive electrode material, as an example, its optimal operating temperature range is 25 to 40 degrees Celsius. When the temperature is too low, the material activity is insufficient, and when it is too high, it will accelerate 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 temperature is detected to be 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 plate, which can accurately adjust the power output according to the temperature control curve to achieve a steady rise in temperature. The generation of the temperature control curve takes into account the thermal response characteristics of the material. Taking 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 increase the temperature from 18 degrees Celsius to 25 degrees, and generates a step-type or gradual control curve. When it is detected that the temperature in a certain area rises to 45 degrees Celsius, exceeding the upper limit of the optimal range, the system will start the corresponding cooling device. The cooling system can be air-cooled or liquid-cooled to remove excess heat through forced convection. Taking the air-cooled 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 two stages of prediction and correction. Taking the temperature data of a certain measuring point as an example, the raw data collected by the sensor may fluctuate due to external interference. After Kalman filtering, a smoother and more stable temperature curve can be obtained, which effectively avoids frequent start and stop of the control system due to data fluctuations. The entire temperature control process forms a closed-loop feedback system, which ensures that the temperature of each part of the battery cell is always maintained within the optimal range through real-time monitoring, analysis and comparison, control and adjustment, and data optimization. This precise temperature control can not only improve the manufacturing quality of the battery cell, but also extend the battery life and improve product performance.

[0024] S104. The temperature intelligent control system starts the preheating or precooling program based on the material thermal response model according to the pole piece material characteristic database and winding process parameters. The system obtains the control strategy based on the winding process parameters 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.

[0025] 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 pole piece 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 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, and uses the data in the database combined with the process parameters to construct a dynamic PID control algorithm to obtain the PID coefficient and 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, performs comparative feedback and adjusts it to the optimal temperature state to obtain the initial temperature value of the dual closed-loop control.

[0026] Specifically, according to the pole piece material characteristic database, the system first obtains the material's thermophysical parameters, such as specific heat capacity, thermal conductivity, etc. For example, the specific heat capacity of a certain lithium battery positive electrode material is 0.8J / (g·K) and the thermal conductivity is 1.5W / (m·K). These parameters are the basis for establishing a thermal response model and directly affect the temperature change rate of the material during heating or cooling. When establishing a thermal response model, the system will simulate the temperature change curves of different materials under specific conditions based on the acquired thermophysical parameters. Assuming that the theoretical heating time of a material is 10 minutes, from 20°C to 60°C, the system will record this process to provide a theoretical basis for subsequent temperature control. If the current ambient temperature is significantly different from the set target temperature, for example, the ambient temperature is 25°C and the target temperature is 60°C, the system will combine the winding process parameters (such as the winding speed is 5m / min) and the equipment operation status (such as the current station is in the preheating stage) to output the pre-temperature adjustment program control information to ensure that the temperature quickly reaches the target value. After receiving the real-time temperature information transmitted by the temperature control unit, the temperature intelligent control system uses the data in the database and the process parameters to build a dynamic PID control algorithm. Assuming that the PID coefficients are Kp=2, Ki=0.1, and Kd=0.05, the system will adjust the heating or cooling power according to these coefficients to ensure accurate temperature control. Based on the coefficients obtained by the PID control algorithm and combined with the temperature control curve, the system matches the heating or cooling rate. For example, if the target heating rate is 2℃ / min, the system will adjust the heating power according to the PID coefficient to ensure that the actual heating rate is close to this target value. Finally, the temperature intelligent control system outputs optimized control instructions to the heating and cooling device, and the device adjusts the power in real time according to the instructions to ensure that the actual temperature of the material reaches the optimal state. For example, if the current temperature is 55℃ and the target temperature is 60℃, the system will instruct the heating device to increase the power until the temperature reaches 60℃. Through this series of steps, the system can accurately control the temperature during the winding process of the battery cell, ensure the optimization of material performance, and improve product quality and production efficiency. This dual closed-loop control mechanism not only improves the accuracy of temperature control, but also enhances the stability and reliability of the system.

[0027] S105. The temperature intelligent control system obtains the operating speed parameters of the winding machine, and adjusts the control strength of the heating and cooling device according to the speed-temperature correlation data in the material thermal response model. The system uses speed and temperature as two mutually influencing variables, and realizes the coordinated regulation of speed and temperature through linkage control. According to the operating status of linkage control and the control curve obtained by the material thermal response model, the final operating parameters of each part of the battery cell are obtained, and the speed and temperature dual closed-loop control is realized to improve the temperature control accuracy of the battery cell.

[0028] The running speed data v of the winding machine is obtained, and the temperature data of each part of the equipment are collected to form a temperature matrix T. The time variable is defined as t, and the initial time 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 between the theoretical value and the actual value is set to ΔT. If the actual deviation |TTtheoretical value| is greater than ΔT, it is determined that the temperature T1 at time t1 has a control demand. Based on the thermal response model, the control intensity function F is obtained. F is a function of time t and speed v, expressed as F(t,

[0029] v); obtain the control curve C in the thermal response model of the material, the state function with the control force L 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)), v1 is the speed at the moment t1; after a time Δt, t2=t1+Δt, the speed v2 and temperature T2 at the moment t2 are obtained; if the speed v2 is not equal to the speed v1 at the previous moment, the moment t2 is the speed change point; according to the thermal response model of the material, the control curve C2 corresponding to the speed v2 at the moment t2 is obtained, and it is judged whether the temperature T2 at this time meets the preset interval requirements; if T2 meets the preset interval requirements, an operating parameter matrix P is obtained; if T2 does not meet the preset interval requirements, the force is adjusted to H, and the size of H is |T2 theoretical value-T2|; by continuously collecting speed data and temperature data, it is judged 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.

[0030] Specifically, in the temperature intelligent control system, the running speed data v of the winding machine is first obtained. For example, assuming that the current running speed of the winding machine is 5 meters per minute. At the same time, the temperature data of each part of the equipment is collected to construct the temperature matrix T. Assuming that the initial time t0 is 0 seconds, the temperatures of various parts of the equipment are 30°C, 32°C, 28°C, etc., respectively, forming the initial temperature matrix T0. According to the pre-established thermal response model of the pole piece material, the theoretical temperature value Ttheoretical at the speed v is extracted. For example, if the model predicts that at a speed of 5 meters per minute, the theoretical temperature of each part should be 35°C, 37°C, and 33°C. The deviation threshold ΔT between the theoretical value and the actual value is set to 2°C, and the actual deviation is expressed as |TTtheoretical value|. If the temperature T1 (such as 34°C, 36°C, 31°C) at a certain time t1 (such as 10 seconds) deviates from the theoretical value by more than 2°C, the alarm mechanism is activated to determine that there is a need for control at this moment. Based on the thermal response model, the control intensity function F(t, v) is obtained, which represents the control intensity 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, indicating that a heating intensity of 12% is required for the first stage of regulation. 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, the control curve C2 corresponding to the speed v2 at the moment t2 is obtained. If T2 meets the preset interval (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 interval. 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 / 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 performance 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 can dynamically adjust the heating or cooling intensity at each time node according to the deviation between the actual temperature and the theoretical value to ensure that the temperature always meets the process requirements. This not only improves the stability and controllability of the production process, but also significantly improves product quality and production efficiency. For example, in actual production, the initial temperature of a batch of pole piece materials during the winding process is 30°C and the target temperature is 35°C. According to the thermal response model, the system predicts that it will take 10 minutes to heat to reach the target temperature at a speed of 5 meters per minute. In actual operation, the system collects temperature data every 5 seconds and finds that the temperature is 32°C at 10 seconds, with a deviation of 3°C. The heating program is started and the heating intensity is increased by 5%. At 15 seconds, the temperature rises to 34°C, with a deviation of 1°C, and the heating intensity is continued to be adjusted to 2%. Finally, at 20 seconds, the temperature stabilizes at 35°C and reaches the target state. This dynamic control mechanism achieves precise temperature control through real-time data acquisition and model prediction, ensures the stability of the production process and the consistency of product quality, and significantly improves production efficiency and economic benefits.

[0032] S106. The temperature intelligent control system adopts a partition control strategy based on the preset information about the temperature distribution of cells of different sizes in the cell size and the material thermal response model. The system divides the cell into multiple control partitions according to the geometric characteristics and thermal distribution characteristics of the cell, and each partition is equipped with an independent temperature sensor and heating and cooling device. The determination of the number and boundaries of the partitions takes 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.

[0033] Establish a thermal response model based on the size and material of the battery cell. Collect the thermal conduction data of the battery cell according to the established thermal response model, and collect the thermal distribution data inside the battery cell. Obtain the thermal distribution data of the battery cell for analysis. If the maximum temperature difference in the thermal distribution data is greater than the set threshold, obtain the temperature gradient value of the battery cell, and determine the number of control zones based on this gradient value. Divide the battery cell control zones according to the number of control zones, deploy temperature sensors in the battery cell control zones, and monitor the temperature of each control zone. According to the temperature data of the control zone and the set target temperature, judge the operating state of each control zone, and determine whether to start the heating or cooling components of each control zone based on the judgment results. According to the thermal response model, obtain the thermal response time, and calibrate the time of the temperature data collected by the temperature sensor in each control zone. Update the thermal response model parameters based on the temperature sensor values ​​of each control zone and the operating state information.

[0034] Specifically, the establishment of the thermal response model of the battery cell needs to consider the geometric dimensions, material properties and thermal conduction characteristics of the battery cell. Taking a cylindrical battery cell as an example, its dimensions include diameter and height, and its materials include positive electrode material, negative electrode material, diaphragm and electrolyte. Through the theory of heat conduction, a relationship model between the temperature distribution inside the battery cell and the external heat source can be established. The thermal distribution data collection is achieved by arranging temperature sensors on the surface and inside the battery cell. For example, ten temperature sensors are evenly arranged on the surface of the battery cell, and five temperature sensors are arranged inside to form a three-dimensional temperature distribution monitoring network. The collected data includes the temperature value of each point and its change trend over time. In the temperature gradient analysis, it is assumed that the highest temperature on the surface of the battery cell is 45 degrees, the lowest temperature is 35 degrees, and the temperature difference is 10 degrees. If the temperature difference threshold is set to 8 degrees, it is necessary to divide the control area. According to the temperature gradient distribution characteristics, the area with large temperature difference can be divided into independent control areas to achieve precise control. When dividing the control area, it is necessary to consider the area where the temperature gradient changes drastically. Taking a cylindrical battery cell as an example, it can be divided into three control areas: upper, middle and lower. Each control zone is equipped with two temperature sensors to monitor the temperature distribution in the area. The operation state judgment is based on the comparison between the actual temperature and the target temperature of each control zone. If the temperature of a control zone is lower than the target value, such as the actual temperature is 35 degrees and the target temperature is 40 degrees, the heating device of the area is started. On the contrary, if the temperature exceeds the target value, the cooling device is started. The thermal response time calibration takes into account the thermal inertia characteristics of the material. For example, after the heating device is started, it may take ten seconds to observe a significant temperature rise. Therefore, the temperature data collection time needs to be adjusted accordingly according to the thermal response characteristics to ensure that the collected data accurately reflects the control effect. The model parameter update is realized through the feedback of actual operation data. If the temperature rise rate of a control zone deviates from the model prediction value under a specific heating power, it is necessary to adjust the parameters such as the thermal conductivity coefficient of the zone. Through continuous data collection and parameter optimization, the model accuracy is continuously improved. In practical applications, it is also necessary to consider the influence of factors such as ambient temperature changes and the charging and discharging state of the battery cell on the temperature distribution. By establishing a complete thermal response model, the temperature of the battery cell can be accurately controlled to improve product quality and production efficiency.

[0035] S107. The temperature intelligent control system monitors the temperature fluctuation amplitude and makes judgments 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 area and determines the operation strategy information of the area. This control method can prevent the adverse effect of sudden temperature changes on the performance of the pole piece material, reduce the probability of pole piece fracture and wrinkling defects, and stabilize the winding quality of the battery cell.

[0036] According to the material thermal response model, the safe temperature range is set and the temperature area is divided. The temperature sensor data of each area during the battery cell winding process is collected to obtain a temperature data information sequence. The temperature data information sequence is processed. If the temperature fluctuation in the current area exceeds the preset fluctuation threshold, the fluctuation warning information is generated according to the historical fluctuation data. The fluctuation warning information and the preset material thermal response model data are integrated and calculated by the difference algorithm to determine the result of the material performance change. According to the obtained material performance change result, it is compared with the pre-established material performance database. If the material performance change exceeds the material performance threshold range, the trained XGBoost machine learning algorithm, support vector machine machine learning algorithm, and decision tree learning algorithm are used to generate multiple cooling strategies for the current area. The performance data of multiple cooling strategies are judged. If the performance data of multiple cooling strategies are inconsistent, the voting method is used to output the optimal cooling strategy information. Combined with the optimal cooling strategy information and the pre-set regional control data, the operation strategy information of the area is generated, and the adjusted operation strategy data is obtained. The actuator is controlled according to the adjusted operation strategy data, and the control instructions of the area are issued to the production and processing equipment.

[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 principle 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, according to 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 temperature intelligent control system preliminarily classifies the materials 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 materials, which are used to accurately control the temperature of different materials in the future. The system compares the temperature data with the pole piece material and the material thermal response database data information obtained in real time 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 according to the data information of 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 according to the data information of the thermal response database until the temperature of the detection part drops to within the optimal winding temperature range; Step S104: the temperature intelligent control system starts the preheating or precooling program based on the material thermal response model according to the pole piece material characteristic database and the winding process parameters. The system obtains the control strategy in combination with the winding process parameters, automatically matches the corresponding temperature control curve, and synchronizes the output parameters of the pre-temperature control program 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 a stable initial condition for the subsequent speed and temperature dual closed-loop control. Step S105, the temperature intelligent control system obtains the operating speed parameters of the winding machine, and adjusts the control strength of the heating and cooling device according to the speed-temperature correlation data in the material thermal response model. The system uses speed and temperature as two mutually influencing variables, and realizes coordinated regulation of speed and temperature through linkage control. According to the operating state of the linkage control and the control curve obtained by the material thermal response model, the final operating parameters of each part of the battery cell are obtained, and the speed and temperature dual closed-loop control is realized to improve the temperature control accuracy of the battery cell; Step S106, the temperature intelligent control system adopts a partition control strategy according to the preset information about the temperature distribution of cells of different sizes in the cell size and the material thermal response model. The system divides the cell into multiple control partitions according to the geometric characteristics and thermal distribution characteristics of the cell. Each partition is equipped with an independent temperature sensor and a heating and cooling device. The number and boundaries of the partitions are determined in consideration of the thermal conductivity characteristics and temperature gradient distribution of the cell. The system independently controls the temperature of each partition according to the real-time temperature data of each partition, and determines the operating status information of each partition. This partition control strategy can more accurately deal with the temperature difference of different parts of the cell. Step S107, the temperature intelligent 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 the sudden temperature change 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 value according to the safety threshold value; The temperature difference value and the temperature change amount are transmitted to the determination module. After the determination module receives the temperature difference value and the temperature change amount, if the temperature change amount is greater than zero, the risk determination is initiated; After starting the risk assessment, the machine learning Kalman filter algorithm is used to predict the temperature 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 a potential thermal runaway risk is greater than a set risk probability threshold, a corresponding solution is obtained for the thermal runaway model pre-established for battery startup, and new tuning parameter values ​​are obtained by tuning the parameters of the control algorithm.

3. The method according to claim 1, characterized in that The step S102 includes: Obtain the temperature data of each part of the battery cell and process the temperature data in a unified format; According to the established thermophysical parameter database of pole piece materials, the thermal conductivity and specific heat capacity of different materials and the thermal conductivity coefficient of pole piece materials under the current temperature conditions are calculated to obtain the distribution cloud map of thermal performance coefficient and specific heat capacity cloud map of the battery cell; Combine the temperature data with the thermal conductivity coefficient, and 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 to generate a heat flow change gradient cloud map reflecting the temperature change information of each part of the battery cell under different electrode materials; The heat flux change gradient cloud map is combined with the preset target temperature values ​​of different electrode materials and the reaction speed instruction, and the control information of the temperature regulator is generated according to the PID control algorithm, wherein 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, characterized in that: The step S103 includes: According to the material thermal response model in the thermal response database, determine the optimal temperature range of each part during the battery cell winding process; 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 monitored 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 thermal response database information, 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 according to the thermal response database information; Control the cooling device to increase the cooling power, and remove the 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, characterized in that The step S104 includes: According to the pole piece material characteristic database, the system obtains the material's thermal physical parameters and thermal conductivity; According to the obtained thermal physical parameters and thermal conductivity, a thermal response model of each pole piece 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 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; 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 combined with 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 rate of heating or cooling, 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, performs comparative feedback and adjusts it 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 the 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 thermal distribution data of the battery cell for analysis. If the maximum temperature difference in the thermal distribution data is greater than the set threshold, obtain the temperature gradient value of the battery cell, and determine the number of control zones based on the gradient value. Divide the cell control area according to the number of control areas, deploy temperature sensors in the cell control area, and monitor the temperature of each control area; Through 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 the 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 areas; 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, generate fluctuation warning information 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 performance change results are compared with the pre-established material performance database. When the material performance change exceeds the material performance threshold range, the trained XGBoost machine learning algorithm, support vector machine machine learning algorithm and decision tree learning algorithm are used 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. By 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 according to the adjusted operation strategy data to issue control instructions for the monitoring area to the production and processing equipment.

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