Heat-based lithium battery pole piece rolling mill roller type regulation method and system

Abstract

This invention relates to the field of lithium battery electrode rolling technology, and provides a method and system for controlling the roll shape of a lithium battery electrode rolling mill based on heat. The method includes the following steps: S1, establishing a finite element mechanism model of the rolls; S2, performing simulation using the finite element mechanism model of the rolls; S3, initial screening of thermocouple placement based on the comprehensive contribution of temperature; S4, eliminating redundant thermocouple placement based on data clustering analysis; S5, determining the optimal combination of thermocouple placements; S6, constructing a dynamic thermal field matrix; S7, constructing a data-driven model; S8, obtaining jointly predicted thermal crown through joint prediction; S9, adjusting the electromagnetic coil power and the edge water cooling flow rate. The system includes: a roll heating device, an edge water cooling device, a temperature sensing network, a control platform, a control execution unit, and a lithium battery electrode rolling mill. This invention achieves real-time dynamic compensation of the roll shape by accurately predicting the thermal crown of the rolls, thereby improving the stability of the electrode thickness.

Description

Technical Field

[0001] This invention relates to the field of lithium battery electrode rolling technology, specifically to a method and system for adjusting the roll shape of a lithium battery electrode rolling mill based on heat. Background Technology

[0002] With the rapid development of new energy vehicles and energy storage technologies, the demand for lithium batteries is constantly increasing, and the manufacturing precision requirements for lithium battery electrodes are also becoming increasingly stringent. In the production process of lithium battery electrodes, the rolling process is one of the key steps. Its purpose is to control the rolling process to achieve appropriate thickness and compaction density of the electrode, thereby improving the mechanical stability and electrochemical performance of the electrode. However, traditional roll profile control technology faces difficulties in controlling the lateral thickness uniformity when producing wide-width electrodes. This is because the deflection of the rolls is more pronounced during the rolling process of wide-width electrodes, making it difficult to guarantee the uniformity of the electrode thickness. Furthermore, existing technologies lack effective thermal management methods, making it impossible to accurately model and compensate for thermal errors during the roll profile control process, thus affecting the accuracy and stability of roll profile control.

[0003] Currently, electrode widths can reach 2000 mm. When using wide-width electrode rolling, the change in roll gap shape due to thermal deformation can cause significant thickness differences between the edges and the center, severely impacting battery performance. Traditional hydraulic bending roll technology has limited adjustment range, suffers from control lag, and high energy consumption, especially under high loads. During operation, the rolls undergo non-uniform thermal deformation due to electromagnetic induction heating, bearing friction, and ambient temperature fluctuations. Lithium-ion battery electrode rolling is temperature-sensitive; studies show that a temperature difference exceeding 5°C on the roll body will cause electrode thickness fluctuations, severely affecting the required transverse thickness consistency.

[0004] The existing technology has the following problems:

[0005] Insufficient lateral stiffness: Significant flexural deformation of the rolls during wide-width roll forming results in a thickness deviation of more than ±3 mm between the edge and center of the electrode sheet;

[0006] Thermal error accumulation: During continuous rolling, the combined effect of frictional heat and electromagnetic heating causes non-uniform thermal deformation, and traditional steady-state models cannot predict dynamic working conditions in real time.

[0007] Control lag: Hydraulic bending rollers require hundreds of tons of external force, resulting in slow response speed and high energy consumption.

[0008] In existing technologies, CN117762084A proposes a thermal error compensation model based on a temperature sensor, but it does not address the multi-field coupling effect of wide-width rolling; CN114019909A uses an LSTM model to predict thermal errors, but lacks co-optimization with the mechanistic model. Therefore, a method is needed to ensure the thickness accuracy and stability of lithium battery electrodes during rolling by dynamically adjusting the roller shape. Summary of the Invention

[0009] To address the shortcomings of the existing technologies, this application integrates electromagnetic induction heating, thermo-mechanical coupling modeling, and data-driven dynamic control of the roller profile, proposing a heat-based roller profile control method and system for lithium battery electrode roll presses. The control method includes the following steps:

[0010] S1. Establish the finite element mechanism model of the rolls of the lithium battery electrode roll press;

[0011] S2, simulation was performed using the finite element mechanism model of the rolling mill rolls;

[0012] Transient simulation was performed using the finite element mechanism model of the roll based on the working parameters, and the temperature cloud map of the roll and the first thermal crown at each point along the axial direction of the roll body were obtained during the entire cycle from initial heating to steady-state maintenance.

[0013] S3, preliminary screening of thermocouple placement based on comprehensive temperature contribution;

[0014] By calculating the temperature gradient contribution and lateral temperature difference contribution of each potential thermocouple placement point, a weighted comprehensive contribution is obtained. Based on the comprehensive contribution, potential thermocouple placement points are initially screened to obtain the initial screening of thermocouple placement points.

[0015] S4, based on data clustering analysis, redundant thermocouple placement is eliminated in the initial screening;

[0016] Fuzzy clustering was used to screen the initial thermocouple placements, resulting in clustered thermocouple placements.

[0017] S5, determine the optimal thermocouple placement combination;

[0018] The optimal thermocouple placement combination was obtained by perturbation simulation using the Monte Carlo iterative optimization method for clustered thermocouple placement.

[0019] S6, construct the dynamic thermal field matrix of the lithium battery electrode roll press roll;

[0020] S7, building a data-driven model;

[0021] The data-driven model uses a dynamic thermal field matrix as input and outputs the second thermal convexity at each thermocouple placement point.

[0022] S8, joint predicted thermal convexity is obtained through joint prediction;

[0023] Based on the first and second thermal convexities at the thermocouple points in the optimal thermocouple placement combination, the corresponding joint predicted thermal convexity is calculated using a weighted fusion formula.

[0024] S9, adjusts the electromagnetic coil power and the side water cooling flow rate;

[0025] Based on the joint predicted thermal convexity obtained in S8, the control and execution unit adopts a combination of feedforward control and feedback control to adjust the electromagnetic coil power and the side cooling flow rate, so as to realize real-time dynamic compensation of the roll profile of the lithium battery electrode rolling mill.

[0026] Preferably, the establishment of the finite element mechanism model of the lithium battery electrode rolling mill rolls in S1 specifically includes:

[0027] S11, Establish the mechanism model of the rolling mill rolls for lithium battery electrode rolls;

[0028] The mechanistic model includes the thermal differential equation describing the transient heat conduction process inside the rolls of the lithium battery electrode rolling mill and the equation for calculating the radial expansion of the roll surface;

[0029] S12, Establish a three-dimensional axisymmetric transient thermal-structural coupled finite element model;

[0030] First, a three-dimensional digital model of the roll is created or imported into the finite element method using software. Then, material properties are assigned to the roll model. Next, meshing is performed on the roll surface and near-surface region to obtain a three-dimensional axisymmetric transient thermo-structural coupled finite element model. Finally, thermo-mechanical boundary conditions and loads are applied to the three-dimensional axisymmetric transient thermo-structural coupled finite element model, with the parameters derived from the mechanism model being applied as boundary conditions.

[0031] Preferably, the initial screening of thermocouple placement based on the overall temperature contribution in S3 specifically includes:

[0032] S31, Setting potential thermocouple points: Multiple potential thermocouple points are pre-set in high density on the two-dimensional cross-section of the roll in the axial and radial directions. The potential thermocouple points are evenly distributed on the two-dimensional cross-section of the roll in the axial and radial directions, forming a potential thermocouple point matrix.

[0033] S32, Quantifying the overall contribution of potential thermocouple placements: based on the temperature gradient contribution of each potential thermocouple placement. and the contribution of transverse temperature difference Receive a comprehensive contribution rating;

[0034] S33, preliminary screening of potential thermocouple locations: After forcibly retaining potential thermocouple locations located at a specified roller width, preliminary screening is then conducted based on the overall contribution to obtain the initial thermocouple locations.

[0035] Preferably, determining the optimal thermocouple placement combination in S5 specifically involves:

[0036] S51 performs dynamic thermal field disturbance;

[0037] Dynamic thermal field perturbation is performed in the finite element mechanism model of the roll. Multiple perturbation scenarios are constructed through random factors. Temperature cloud map is generated for each perturbation scenario through the finite element mechanism model of the roll, and Gaussian noise is superimposed to simulate measurement uncertainty.

[0038] S52, using Monte Carlo iterative optimization to determine the optimal point placement;

[0039] Under the premise of meeting the compliance rate requirements, the Monte Carlo iterative optimization method is used to select the scheme with the fewest clustered thermocouple placement points as the optimal thermocouple placement point. Specifically, a subset is randomly selected from the clustered thermocouple placement points, and the perturbation simulation is repeated multiple times for each subset. The error compliance rate of each subset is calculated, and the subset with the fewest clustered thermocouple placement points that meets the compliance rate is selected as the optimal thermocouple placement point combination.

[0040] Preferably, the dynamic thermal field matrix for constructing the lithium battery electrode roll press rolls in step S6 is specifically as follows:

[0041] ;

[0042] in, For dynamic thermal field matrix, These are the first thermocouple to the second thermocouple. The temperature of the thermocouple after Kalman filtering, The optimal number of thermocouples in the thermocouple placement combination. The speed of the rolling mill rolls. For rolling force, The total power of the coil, This refers to the cooling flow rate at the edges. For ambient temperature, For heating voltage, To maintain voltage, For forced convection heat transfer coefficient, This refers to the cooling water temperature.

[0043] Preferably, the data-driven model in S7 has a self-correction mechanism, specifically:

[0044] When the prediction error exceeds the threshold continuously during the use of the data-driven model, the model self-correction mechanism is triggered to fine-tune the parameters of the data-driven model and update the fusion weights through the optimization algorithm.

[0045] Preferably, in step S8, the corresponding joint predicted thermal convexity is calculated using a weighted fusion formula based on the first and second thermal convexities at the thermocouple points in the optimal thermocouple placement combination. Specifically:

[0046] ;

[0047] in, To jointly predict the thermal convexity matrix, This is the first thermal convexity matrix predicted by the finite element mechanism model of the roll. The second thermal convexity matrix is ​​predicted by the data-driven model. The weighting coefficients are used; the finite element mechanism model of the rolls in S2 obtains the first thermal convexity at the thermocouple points in the optimal thermocouple point combination, forming the first thermal convexity matrix; the data-driven model in S7 obtains the second thermal convexity at the corresponding thermocouple points, forming the second thermal convexity matrix; the joint predicted thermal convexity matrix is ​​composed of the joint predicted thermal convexity at the thermocouple points in the optimal thermocouple point combination.

[0048] for Joint predicted thermal convexity at time step, weighting coefficient use Weighting coefficients at different times , Weighting coefficients at different times The dynamic adjustment rule is: initial settings =0.5, when Time setting =0.3; when At that time, set =0.7;

[0049] for time and Matrix difference metric:

[0050] ;

[0051] in, and They represent In the optimal thermocouple placement combination at time, the first The first thermal convexity value predicted by the finite element mechanism model of the roll at the thermocouple location and the second thermal convexity value predicted by the data-driven model. This represents the number of thermocouples in the optimal thermocouple placement combination.

[0052] Preferably, the adjustment of the electromagnetic coil power and the edge water cooling flow rate in S9 specifically includes:

[0053] S91, adjusts the power of the electromagnetic coil;

[0054] Thermal convexity deviation:

[0055] ;

[0056] in, For the first Thermal convexity deviation at each thermocouple placement point For the first Joint prediction of thermal convexity at each thermocouple location For the first The desired thermal convexity at each thermocouple placement point;

[0057] The power of the electromagnetic coil is adjusted in real time by the first PID controller:

[0058] ;

[0059] in, for Time according to the first The coil power adjustment is obtained from the thermal convexity deviation at each thermocouple placement point. The proportional coefficient of the first PID controller; The integral coefficient of the first PID controller; The derivative coefficients of the first PID controller; yes Time of the first Thermal convexity deviation at each thermocouple placement point Indicates the first At each thermocouple placement point The instantaneous value of the thermal convexity deviation at any given moment. This indicates that the thermal convexity deviation is between 0 and... Integrate within a given time period;

[0060] right thermocouple points To perform fusion, a fusion function is used. Implementation, input is thermocouple points The output is the total power regulation of the electromagnetic coil. ;

[0061] S92, adjusts the side water cooling flow rate;

[0062] The side water cooling flow rate is adjusted by a second PID controller, and the control equation is:

[0063] ;

[0064] in, This represents the real-time flow rate of the side cooling water. This represents the deviation between the measured edge temperature and the target value. This is the measured temperature value at the edge, which is the thermocouple measurement value with the largest absolute value on the axial coordinate. For the target value, This indicates the deviation between the measured and target values ​​of the edge temperature. The instantaneous value at a given moment. This indicates that the deviation between the measured edge temperature and the target value is within 0 to... Integrate within a given time period; The proportional coefficient of the second PID controller; The integral coefficient of the second PID controller; The derivative coefficients of the second PID controller are given.

[0065] This invention also discloses a heat-based roll profile control system for a lithium battery electrode roll press, comprising the following devices: a roll heating device, an edge water cooling device, a temperature sensing network, a control platform, a control execution unit, and a lithium battery electrode roll press; the roll heating device is used to heat the rolls of the lithium battery electrode roll press; the edge water cooling device is used to cool the rolls of the lithium battery electrode roll press; the temperature sensing network consists of thermocouples embedded in the rolls of the lithium battery electrode roll press, used to detect the roll temperature at the location of the thermocouples; the control platform controls the temperature of the rolls from the roll heating device and the edge water cooling device. The control execution unit and the lithium battery electrode roll press receive system parameters and control the operation of the roll heating device, the edge water cooling device, and the lithium battery electrode roll press by setting system parameters. The control execution unit obtains the total adjustment amount of the electromagnetic coil power of the roll heating device and the edge water cooling flow rate of the edge water cooling device by jointly predicting the thermal crown and the measured value of the thermocouple. The control platform controls the operation of the roll heating device and the edge water cooling device respectively according to the total adjustment amount of the electromagnetic coil power and the edge water cooling flow rate obtained from the control execution unit, so as to realize the roll shape control of the lithium battery electrode roll press.

[0066] Preferably, the roll heating device is installed in the inner hole of the roll and includes an electromagnetic coil and an insulating ceramic support. The copper electromagnetic coil is arranged axially in the inner hole of the roll and is fixed by the insulating ceramic support, maintaining a distance from the inner wall of the roll. The electromagnetic coil is connected to the control platform.

[0067] The edge water cooling device is also in the inner hole of the roll. It uses copper tubes to achieve water cooling heat dissipation. The copper tubes are located at both ends of the inner hole of the roll at both ends of the electromagnetic coil. Organosilicon is used to fill the gap between the copper tubes and the inner wall of the roll.

[0068] The temperature sensing network consists of thermocouples embedded in the rolls of the lithium battery electrode rolling mill, used to detect the temperature at the location of the thermocouples.

[0069] The control execution unit includes a roll finite element mechanism model submodule, a data-driven model submodule, a joint prediction submodule, a first PID controller, a second PID controller, and a fusion submodule. The roll finite element mechanism model submodule obtains the first thermal crown using finite element simulation based on parameters received from the control platform. The data-driven model submodule obtains the second thermal crown using a data-driven model based on the temperature received from the temperature sensing network and parameters received from the control platform. The joint prediction submodule obtains the jointly predicted thermal crown based on weights, the first thermal crown, and the second thermal crown, and sends the jointly predicted thermal crown to the first PID controller. The first PID controller obtains the electromagnetic coil power adjustment amount based on the jointly predicted thermal crown and the desired thermal crown. The fusion submodule obtains the total electromagnetic coil power adjustment amount based on the electromagnetic coil power adjustment amount. The second PID controller receives the measured values ​​of the edge thermocouples in the temperature sensing network through the control platform, and obtains the edge water cooling flow rate based on the deviation between the measured values ​​of the edge thermocouples and the target values. The first PID controller sends the total electromagnetic coil power adjustment amount to the control platform, and the second PID controller sends the edge water cooling flow rate to the control platform.

[0070] The control platform controls the roll heating device through the total power adjustment of the electromagnetic coil and controls the status of the side water cooling device through the side water cooling flow rate, thereby achieving rapid adjustment and steady-state maintenance of the roll temperature of the lithium battery electrode roll press.

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0072] 1. This application combines a finite element mechanism model and a data-driven model for prediction, fundamentally overcoming the limitations of a single model. Traditional mechanism models lack prediction accuracy under complex dynamic conditions, while pure data models experience a sharp drop in reliability when the conditions exceed historical ranges. The fusion model of this invention combines physical laws with real-time data. The mechanism model ensures extrapolation stability, while the data model improves fitting accuracy under known conditions, thus achieving complementary advantages.

[0073] 2. This application designs an intelligent dynamic weight adjustment strategy. The model is not simply weighted at a fixed rate, but rather the weights are automatically adjusted based on the real-time differences in the prediction results of the two models: when the predictions are consistent, the more accurate data-driven model is emphasized; when significant discrepancies occur, the system automatically switches to the more reliable mechanistic model. This allows the system to enjoy both the high accuracy of data-driven models and the robustness to cope with unknown disturbances, achieving intelligent decision-making based on the situation.

[0074] 3. The joint prediction model in this application has a lower average absolute error in predicting the thermal crown of the roller than the single mechanism model, and has a fast response speed. This improvement in core accuracy lays a reliable foundation for subsequent high-precision real-time control of the roller shape, and ultimately achieves a significant improvement in the accuracy of electrode thickness consistency control. Attached Figure Description

[0075] Figure 1 This is a flowchart of the heat-based lithium battery electrode roll press roller shape control method of the present invention;

[0076] Figure 2 This is a schematic diagram of the rolling mill rolls in an embodiment of the present invention;

[0077] Figure 3 This is a schematic diagram of the system of the heat-based lithium battery electrode roll press roller shape control method of the present invention. Detailed Implementation

[0078] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In this embodiment, the roll specifications are roll diameter D × roll body length L = 400mm × 600mm, inner diameter is 180mm, the main body material is 45 steel which is easy to heat, and the central shaft is made of 304 stainless steel which has relatively low magnetic permeability and is not easily heated. The electromagnetic coil 11 is located inside the roll for heating the roll. Copper pipes 21 of edge water cooling devices are also located at both ends of the roll for cooling the roll. Thermocouples 31 are embedded in the roll for detecting the thermal convexity at their location. Figure 2 As shown.

[0079] This invention discloses a heat-based method for adjusting the roll shape of a lithium battery electrode roll press, such as... Figure 1 As shown, the specific steps include:

[0080] S1. Establish the finite element mechanism model of the rolls of the lithium battery electrode roll press;

[0081] S11, Establish the mechanism model of the rolling mill rolls for lithium battery electrode rolls;

[0082] The mechanistic model includes thermal differential equations describing the transient heat conduction process inside the rolls of the lithium battery electrode rolling mill and equations for calculating the radial expansion of the roll surface.

[0083] The transient heat conduction process inside the roll is described by the Fourier heat conduction differential equation in a two-dimensional axisymmetric cylindrical coordinate system:

[0084] ;

[0085] in, For density, For specific heat capacity, Thermal conductivity, For electromagnetic induction heat source density, For temperature field, For the radial coordinates in the cylindrical coordinate system, These are the axial coordinates in the cylindrical coordinate system. For time. Set boundary conditions, which include at least the heat flux density of the roller surface in contact with the electrode, the forced convection coefficient of water, and the ambient temperature.

[0086] In this embodiment, for 45 steel, , , ), The density of the electromagnetic induction heat source. The boundary condition is set as: heat flux density at contact between the roller surface and the electrode. for The contact heat transfer coefficient According to the electrode material calibration , The surface temperature of the roll. The surface temperature of the lithium battery electrode, and the forced convection coefficient of water. Ambient temperature .

[0087] The equation for calculating the radial expansion of the roller surface is based on the temperature contour map:

[0088] ;

[0089] in, The coefficient of thermal expansion is... Where is the radius of the roll. For ambient temperature, for time The first thermal convexity at the location, For radial position Temperature distribution at that location.

[0090] In this embodiment, the simulation step size is set to 1 second, and the thermal expansion coefficient of 45 steel is... .

[0091] S12, Establish a three-dimensional axisymmetric transient thermal-structural coupled finite element model;

[0092] First, a three-dimensional digital model of the roll is created or imported into the finite element method (FEM) software. Then, material properties are assigned to the roll model. Next, meshing is performed on the roll surface and near-surface region. Finally, thermo-mechanical boundary conditions and loads are applied to this three-dimensional axisymmetric transient thermo-structural coupled finite element model, with parameters derived from the mechanistic model used as boundary conditions. These parameters include the heat flux density at the contact between the roll surface and the electrode, the contact heat transfer coefficient, and the forced convection coefficient of water.

[0093] In the simulation of the three-dimensional axisymmetric transient thermal-structural coupled finite element model, the thermal differential equation of the mechanism model can be used to generate temperature cloud map, and the equation for calculating the radial expansion of the roller surface can be used to obtain the first thermal crown.

[0094] In this embodiment, the three-dimensional digital model of the roll is directly imported. The roll material is 45 steel with a relative permeability of 200, a specific heat capacity of 475 J / (kg·K), and a relative permittivity of 1. The material of the magnetic conductor in the electromagnetic coil is silicon steel sheet with a relative permeability of 7000, a specific heat capacity of 500 J / (kg·K), and a relative permittivity of 1. As the temperature increases, its electrical and thermal conductivity will continuously decrease. Since the magnetic conductor is made of multiple stacked silicon steel sheets, the electrical conductivity of silicon steel cannot be directly used for calculation in the simulation. Equivalent conductivity is usually used for calculation. The specific heat capacity and relative permeability do not change much in this temperature range, so constants are used instead. The coil material is copper, and the central shaft is 304 stainless steel. Both are based on the software's built-in material parameters and are not modified. The mesh is refined on the roll surface and near the surface area. For example, it is refined to 0.5 mm to accurately capture the surface eddy current heating effect and steep temperature gradient generated by electromagnetic induction. Electromagnetic induction heating can be equivalently represented as a heat flux density boundary condition applied to the inner wall of the roller sleeve, the magnitude of which is a function of the coil current. Related, among which, For current density, The conductivity is given. The contact area between the roller surface and the electrode plate adopts the third type of convective heat transfer boundary condition of the mechanistic model. Heat transfer at the contact area between the roller surface and the electrode plate is... The contact heat transfer coefficient The forced convection coefficient of water is calibrated based on the electrode material. Ambient temperature The model considers heat transfer at the roll neck and the potential heating of the central shaft. The external air is set as an infinite element domain to simulate boundary conditions at infinity, making the results more accurate. In actual production, the roll neck is supported by a frame and bearing housing; therefore, the displacement at the roll neck is restricted in the model, constraining its radial variation to zero.

[0095] S2, simulation was performed using the finite element mechanism model of the rolling mill rolls;

[0096] Transient simulations were performed using a finite element model of the roll based on the operating parameters. This yielded temperature contour maps of the roll throughout the entire cycle from initial heating to steady-state maintenance, as well as the first thermal crown at each point along the roll's axial direction. A temperature contour map consists of the temperatures on the mesh at the same moment during the spatiotemporal evolution of the roll's interior.

[0097] Temperature contour maps serve as an objective basis for subsequent thermocouple placement selection. Thermal crown serves as the basis for subsequent control of electromagnetic coils and cooling water. Operating parameters include heating voltage, rolling force, rotational speed, and cooling conditions.

[0098] S3, preliminary screening of thermocouple placement based on comprehensive temperature contribution;

[0099] By calculating the temperature gradient contribution and lateral temperature difference contribution of each potential thermocouple placement point, a weighted comprehensive contribution is obtained. Based on the comprehensive contribution, potential thermocouple placement points are initially screened to obtain the initial screening of thermocouple placement points.

[0100] S31, Set potential thermocouple placement points; On the two-dimensional cross-section of the roll in the axial and radial directions, multiple potential thermocouple placement points are pre-set at high density. The potential thermocouple placement points are evenly distributed on the two-dimensional cross-section of the roll in the axial and radial directions, forming a potential thermocouple placement point matrix.

[0101] Multiple potential thermocouple points are pre-drilled at a high density on the axial and radial two-dimensional cross-sections of the roll. These potential thermocouple points are evenly distributed across the axial and radial two-dimensional cross-sections of the roll, forming a potential thermocouple point matrix. Because the roll is a rolling cylinder, the position of the thermocouples on the circumference is not considered; only the axial position and installation depth of the thermocouples need to be determined, i.e., the axial and radial two-dimensional cross-sections of the roll. In this embodiment, 40-50 potential thermocouple points are pre-drilled, forming a potential thermocouple point matrix on the roll surface.

[0102] S32, quantify the overall contribution of potential thermocouple placements; based on the temperature gradient contribution of each potential thermocouple placement. and the contribution of transverse temperature difference Receive a comprehensive contribution rating;

[0103] temperature gradient contribution The size of the region where each potential thermocouple point is located is set. The regions of different potential thermocouple points cannot overlap. The axial temperature gradient of the region where each potential thermocouple point is located is obtained from the temperature cloud map. The axial temperature gradient of the roll is the key to affecting the thermal crown. Therefore, potential thermocouple points located in the high gradient region are given a high weight, such as 0.8-1.0. The high gradient region is usually located at the edge of the heating zone or near the cooling zone, while potential thermocouple points in the temperature flat region are given a low weight, such as 0.3-0.5.

[0104] Lateral temperature difference contribution This is used to evaluate the ability of potential thermocouple placements to reflect thickness deviations in the electrode width direction. Potential thermocouple placements at 1 / 4 and 3 / 4 of the roll width are given the highest weights because they are crucial for capturing thickness variations caused by the "edge effect." Potential thermocouple placements at the center of the roll width have a medium weight. The roll edge is an irregular area, and potential thermocouple placements near the edge have the lowest weight. The weights of potential thermocouple placements at other locations are linearly distributed among these locations. For example, the weights at 1 / 4 and 3 / 4 of the roll width can be set to 0.9, the center of the roll width to 0.7, and near the edge to 0.3. Therefore, the weight of a potential thermocouple placement at 3 / 8 of the roll width would be 0.8, and the weight at 1 / 8 of the roll width would be 0.6.

[0105] Calculate the overall contribution C: ,in and For contribution weighting coefficients, Contribution to temperature gradient Contribution to lateral temperature difference; and + =1, > This reflects an emphasis on the formation mechanism of axial thermal convexity.

[0106] S33, perform preliminary screening of potential thermocouple locations; after forcibly retaining potential thermocouple locations located at a specified roller width, perform preliminary screening based on comprehensive contribution to obtain the initial thermocouple locations.

[0107] First, potential thermocouple points located at 1 / 4 and 3 / 4 of the roll width are forcibly retained, as they are crucial for capturing thickness variations caused by the "edge effect." Then, all potential thermocouple points with a combined contribution higher than a preset contribution level are retained. This step effectively eliminates invalid or weak measurement points with low contribution to the thermal field characterization. Preliminary screening of potential thermocouple points yields initial thermocouple points. In this embodiment, the preset contribution level is 0.7. From 40-50 potential thermocouple points, 20-25 potential thermocouple points are retained as initial screening points.

[0108] S4, based on data clustering analysis, redundant thermocouple placement is eliminated in the initial screening;

[0109] Fuzzy clustering was used to screen the initial thermocouple placements, resulting in clustered thermocouple placements.

[0110] S41, construct the temperature feature matrix;

[0111] From the finite element simulation results, the temperature time series data of each initial screening thermocouple placement point within the complete simulation cycle are extracted to form a feature matrix. ,in Represents a real number matrix, For the potential number of thermocouple points, For time steps, the feature matrix The elements in the table represent the temperatures measured at potential thermocouple locations.

[0112] S42, calculate the similarity between the initial screening thermocouple locations;

[0113] Temperature time series similarity: Calculate the Pearson correlation coefficient between the temperature curves of any two initial screening thermocouple locations. . The closer the result is to 1, the more consistent the temperature change patterns of the two initial screening thermocouples are, and the higher the degree of information overlap.

[0114] Spatial proximity: Calculate the Euclidean distance between any two initial screening thermocouple locations. .

[0115] The comprehensive similarity is obtained by fusion: the comprehensive similarity is obtained based on the temperature time series similarity and spatial proximity between the two initial screening thermocouple locations. The comprehensive similarity is defined as follows: Temperature time series similarity is usually given higher weight, for example, by... Set it to between 0.6 and 0.7. This represents the maximum effective distance.

[0116] S43, use fuzzy clustering to screen the initial thermocouple placement;

[0117] A similarity matrix for the initial screening of thermocouple locations is constructed based on the comprehensive similarity. The similarity matrix is ​​then transformed into a fuzzy equivalence matrix using the transitive closure method. The initial selection of measurement points is then divided into several clusters based on dynamic threshold clustering. The goal of dynamic threshold clustering is to retain more initial screening thermocouple locations in high gradient regions where the threshold is low and more clusters are formed. In low gradient regions where the threshold is high, fewer clusters are formed to simplify the initial screening of thermocouple locations.

[0118] Within each cluster, calculate the temperature information contribution of each initial screening thermocouple placement point. :

[0119] ;

[0120] in, and These are weighting coefficients. To calculate the merging variance of the clusters, The temperature variance of the thermocouple placement points in the initial screening. Its spatial coverage area, This represents the optimal spatial coverage potential that a primary screening thermocouple placement can achieve within this cluster.

[0121] Preserve within each cluster The largest initial screening thermocouple point is used as the sole representative, and other initial screening thermocouple points within the cluster are eliminated. This step ensures that only the most representative point is retained from each cluster, resulting in clustered thermocouple points, thus achieving deduplication of the initial screening thermocouple point information. 10-12 potential thermocouple points are retained as initial screening thermocouple points.

[0122] S5, determine the optimal thermocouple placement combination;

[0123] The optimal thermocouple placement combination was obtained by perturbation simulation using the Monte Carlo iterative optimization method for clustered thermocouple placement.

[0124] S51 performs dynamic thermal field disturbance;

[0125] Dynamic thermal field perturbation is performed in the finite element mechanism model of the roll. Multiple perturbation scenarios are constructed through random factors. Each perturbation scenario generates a temperature cloud map through the finite element mechanism model of the roll and superimposes Gaussian noise to simulate measurement uncertainty. Random factors can be rolling speed fluctuations, electrode thickness deviations, or roll thermal deformation errors, etc.

[0126] In this embodiment, rolling speed fluctuation of ±10%, electrode thickness deviation of ±5%, and roll thermal deformation error of ±1μm are used as random factors to construct 1000 sets of disturbance scenarios. Gaussian noise σ=0.5℃ is superimposed to simulate measurement uncertainty.

[0127] S52, using Monte Carlo iterative optimization to determine the optimal point placement;

[0128] Under the premise of meeting the compliance rate requirements, the scheme with the fewest clustered thermocouple placement points is selected as the final thermocouple placement. A Monte Carlo iterative optimization method is used: a subset is randomly selected from the clustered thermocouple placement points; perturbation simulations are repeated multiple times for each subset; the error compliance rate of each subset is calculated; and the subset with the fewest clustered thermocouple placement points that meets the compliance rate is selected as the optimal thermocouple placement combination. The optimal thermocouple placement combination includes... A thermocouple.

[0129] In this embodiment, Monte Carlo iterative optimization is used. A subset is randomly selected from 10-12 candidate measurement points, and each subset is subjected to 100 perturbation simulations. Under the premise of meeting the compliance rate requirement of ≥95%, this robustness verification was used to finally determine the five thermocouple placement schemes adopted in this invention. These five thermocouple placements have been proven to maintain the system's monitoring and prediction accuracy with a very high probability under various expected operating fluctuations. The specific five optimized thermocouple placement schemes are shown in Table 1:

[0130] Table 1 Optimized placement scheme of 5 thermocouples on the contact section surface.

[0131]

[0132] Axial x: Along the length of the roller body, with the center of the roller body as the origin 0mm;

[0133] Radial y: Perpendicular to the roller surface, with the roller surface as the origin (0mm), and negative inwards;

[0134] The 1kHz sampling data from 5 measurement points is uploaded to the industrial control computer in real time via a slip ring-wireless transmission module, with a timestamp alignment accuracy of ≤0.1ms.

[0135] S6, construct the dynamic thermal field matrix of the lithium battery electrode roll press roll;

[0136] Using the optimal thermocouple placement combination Temperature signals are collected by several thermocouples. Kalman filtering is used to fuse the thermocouple data, eliminating environmental radiation interference. This data is then combined with other features to construct a dynamic thermal field matrix:

[0137] ;

[0138] in, These are the first thermocouple to the second thermocouple. The temperature of the thermocouple after passing through a Kalman filter, The speed of the rolling mill rolls. For rolling force, The total power of the coil, This refers to the cooling flow rate at the edges. For ambient temperature, For heating voltage, To maintain voltage, To achieve the forced convection heat transfer coefficient, This refers to the cooling water temperature.

[0139] In this embodiment, the roll speed The rolling force is 30-80 m / min. The edge cooling flow rate is 0-2000kN. 0-20 L / min, ambient temperature The temperature is 25℃.

[0140] S7, building a data-driven model;

[0141] The data-driven model uses a dynamic thermal field matrix as input and outputs the second thermal convexity at each thermocouple location. The second thermal convexity refers to the thermal convexity at each thermocouple location obtained from the data-driven model, and the second thermal convexity at each thermocouple constitutes a second thermal convexity matrix. Ten thousand sets of data were generated using the finite element mechanism model of the rolling mill, and 500 sets of data were collected from the production site. These were used to form the training data for the data-driven model, which was then used to correct the generalization of the data-driven model.

[0142] In this embodiment, the data-driven model uses a Long Short-Term Memory (LSTM) network, with the following specific structure:

[0143] Input layer: Receives the dynamic thermal field matrix;

[0144] Hidden layers: 2 layers of LSTM, 64 neurons per layer, activation function Tanh, to solve the problem of long temporal dependencies;

[0145] Fully connected layer: maps the LSTM output to thermal convexity prediction values, with the activation function swish;

[0146] Output layer: Second thermal convexity matrix (Unit: μm)

[0147] Training parameters:

[0148] Loss function: MAE (Mean Absolute Error) + RMSE (Root Mean Square Error);

[0149] Optimizer: Adam optimizer, initial learning rate 0.001, decaying by 10% every 50 rounds;

[0150] Regularization: L2 regularization (weight decay coefficient 1e-4);

[0151] Batch size: 256;

[0152] Training epochs: 200 epochs (early stopping method to monitor validation set loss).

[0153] Performance verification: The test set MAE=0.8μm and RMSE=1.2μm are better than the single mechanism model (MAE=2.5μm).

[0154] Dynamic response time: Single prediction time ≤ 5ms, meeting real-time control requirements.

[0155] Preferably, the data-driven model also has a self-correction mechanism: when the prediction error continuously exceeds the threshold during the use of the data-driven model, the model self-correction mechanism is triggered to fine-tune the parameters of the data-driven model and update the fusion weights through optimization algorithms.

[0156] For example, when using LSTM, if the prediction error is detected to exceed ±1.5μm for three consecutive times, the model self-correction mechanism is activated, updating the LSTM network parameters online and re-optimizing the weights. The correction method is as follows: collect the latest 10 minutes of data; then update the LSTM model online: freeze the first 3 layers and fine-tune the weights of the fully connected layers.

[0157] S8, the predicted thermal convexity is obtained through joint prediction;

[0158] Based on the first and second thermal convexities at the thermocouple points in the optimal thermocouple placement combination, the corresponding joint predicted thermal convexity matrix is ​​calculated using a weighted fusion formula; specifically, the first thermal convexity matrix is ​​formed by deriving the first thermal convexity at the thermocouple points in the optimal thermocouple placement combination from the finite element mechanism model of the rolls in S2. Based on the data-driven model in S7, the second thermal convexity at the corresponding thermocouple placement points is used to form the second thermal convexity matrix. The joint predicted thermal convexity matrix is ​​calculated using a weighted fusion formula.

[0159] The outputs of both are weighted and fused to obtain the joint predicted thermal convexity:

[0160] ;

[0161] in, The joint predicted thermal convexity matrix is ​​composed of the joint predicted thermal convexity at the thermocouple locations in the optimal thermocouple placement combination. This is the first thermal convexity matrix predicted by the finite element mechanism model of the roll. The second thermal convexity matrix is ​​predicted by the data-driven model. These are the weighting coefficients. All thermal convexity matrices are... A 3D matrix Let represent the number of thermocouples included in the optimal thermocouple placement combination, where each element is the th element in the optimal thermocouple placement combination. The corresponding values ​​at each thermocouple placement point.

[0162] for Joint predicted thermal convexity at time step, weighting coefficient use Weighting coefficients at different times , Weighting coefficients at different times The dynamic adjustment rules are as follows:

[0163] Initial settings: =0.5;

[0164] Stable operating condition judgment: when It was concluded that the two models showed good predictive consistency and stable operating conditions, emphasizing the data-driven model and setting... =0.3. This is because LSTM is generally more accurate under stable conditions when fully trained.

[0165] Abnormal operating condition judgment: When Significant discrepancies between models are considered to usually indicate drastic changes in operating conditions or entry into unlearned regions, emphasizing mechanistic models and setting... =0.7. Because the mechanistic model is based on physical principles, it has greater extrapolation reliability when the operating conditions exceed the range of historical data.

[0166] for time and The matrix difference metric, in this embodiment, uses the mean absolute error (MAE) as the matrix difference metric to represent the overall difference between two matrices. The specific formula is as follows:

[0167] ;

[0168] in, and They represent In the optimal thermocouple placement combination at time, the first The first thermal convexity value predicted by the finite element mechanism model of the roll at the thermocouple location and the second thermal convexity value predicted by the data-driven model. denoted as the number of thermocouples in the optimal thermocouple placement combination.

[0169] S9, adjusts the electromagnetic coil power and the side water cooling flow rate;

[0170] Based on the joint predicted thermal convexity obtained in S8, the control and execution unit adopts a combination of feedforward control and feedback control to adjust the electromagnetic coil power and the side cooling flow rate, so as to realize real-time dynamic compensation of the roll profile of the lithium battery electrode rolling mill.

[0171] S91, adjusts the power of the electromagnetic coil;

[0172] Feedforward control is based on the joint thermal convexity prediction value output by the joint prediction model. Predictive adjustment: The thermal convexity deviation at each thermocouple placement point is obtained based on the joint predicted thermal convexity matrix, and then the electromagnetic coil power adjustment amount is obtained through the first PID controller to adjust the coil power in real time.

[0173] Thermal convexity deviation:

[0174] ;

[0175] in, For the first Thermal convexity deviation at each thermocouple placement point For the first The joint predicted thermal convexity at the thermocouple placement points, which is the first value in the joint predicted thermal convexity matrix. One element, For the first The desired thermal convexity is preset at each thermocouple placement point.

[0176] The power of the electromagnetic coil is adjusted in real time by the first PID controller:

[0177] ;

[0178] in, for Time according to the first The coil power adjustment is obtained from the thermal convexity deviation at each thermocouple placement point. The proportional coefficient of the first PID controller; The integral coefficient of the first PID controller; The derivative coefficients of the first PID controller are... for Time of the first Thermal convexity deviation at each thermocouple placement point Indicates the first At each thermocouple placement point The instantaneous value of the thermal convexity deviation at any given moment. This indicates that the thermal convexity deviation is between 0 and... Integrating within a given time frame.

[0179] when This indicates that the heating power needs to be increased at this point. This indicates that heating power needs to be reduced at this point, requiring either contraction or reliance on cooling. In this embodiment, the three coefficients are set as: proportional coefficient... Integral coefficient Differential coefficients These three coefficients were determined through on-site calibration and optimization.

[0180] right thermocouple points To perform fusion, a fusion function is used. Implementation, input is thermocouple points The output is the total power regulation of the electromagnetic coil. .

[0181] The fusion function in this embodiment Use the following function:

[0182] ;

[0183] in, The dominant demand point power is all The value with the largest absolute value; The average demand intensity is all Calculate the average of the absolute values ​​in the equation; As the dominant factor, it is set to 0.6 in this embodiment.

[0184] S92, adjusts the side water cooling flow rate;

[0185] Feedback control, based on real-time measured temperature deviation, supplements and corrects the feedforward control to overcome model errors and unknown disturbances. The side water cooling flow rate is adjusted by a second PID controller; the control equation is:

[0186] ;

[0187] in, This represents the real-time flow rate of the side cooling water. The deviation between the measured edge temperature value and the target value is represented by the thermocouple measurement value with the largest absolute value of the axial coordinate of the measured edge temperature. This indicates the deviation between the measured and target values ​​of the edge temperature. The instantaneous value at a given moment. This indicates that the deviation between the measured edge temperature and the target value is within 0 to... Integrate within a given time period; The proportional coefficient of the second PID controller; The integral coefficient of the second PID controller; The derivative coefficients of the second PID controller are given.

[0188] The first PID controller for adjusting the real-time power of the coil and the second PID controller for adjusting the real-time flow rate of the side cooling water in this application , , In this application, the three coefficients of the first PID controller and the second PID controller use the same value. Those skilled in the art will know that the three coefficients of these two PID controllers can also use different values ​​after on-site calibration and optimization.

[0189] For example, the cooling water temperature is set at 9℃. When the edge temperature exceeds the set value, such as 17℃, the second PID controller output is used. When the edge temperature rises due to disturbance, the feedback control can significantly increase the cooling flow within one second, achieving rapid "peak shaving," effectively suppressing edge thermal expansion, and ensuring the final control accuracy.

[0190] This application also discloses a heat-based roller profile control system for a lithium battery electrode roll press, such as... Figure 3 As shown, it includes: a roll heating device 1, an edge water cooling device 2, a temperature sensing network 3, a control platform 4, a control execution unit 5, and a lithium battery electrode roll press 6; the roll heating device 1 is used to heat the rolls of the lithium battery electrode roll press; the edge water cooling device 2 is used to cool the rolls of the lithium battery electrode roll press; the temperature sensing network 3 consists of thermocouples embedded in the rolls of the lithium battery electrode roll press, used to detect the roll temperature at the location of the thermocouples; the control platform 4 controls the roll heating device 1, the edge water cooling device 2, the control execution unit 5, and the lithium battery electrode roll press 6. The roller press 6 receives system parameters and controls the operation of the roll heating device 1, the edge water cooling device 2, and the lithium battery electrode roll press 6 by setting these parameters. The control execution unit 5 obtains the total adjustment of the electromagnetic coil power of the roll heating device 1 and the edge water cooling flow rate of the edge water cooling device 2 by jointly predicting the thermal crown and the measured values ​​of the thermocouples. The control platform 4 controls the operation of the roll heating device and the edge water cooling device respectively based on the total adjustment of the electromagnetic coil power and the edge water cooling flow rate obtained from the control execution unit 5, thereby achieving roller profile control of the lithium battery electrode roll press. Specifically:

[0191] A roll heating device 1 is installed inside the roll bore and includes an electromagnetic coil and an insulating ceramic support. A copper electromagnetic coil is arranged axially along the inner wall of the roll bore. The electromagnetic coil is fixed to the inner wall of the roll by the insulating ceramic support to maintain a distance from the inner wall to avoid direct contact and short circuit. The electromagnetic coil is connected to a control platform. In this embodiment, the copper electromagnetic coil arranged axially along the inner wall of the roll sleeve has a diameter of 8mm and 20 turns. The coil spacing is determined according to the formula... Dynamic adjustment, among which For the length of the roller, This refers to the number of coils. The electromagnetic coil is fixed by an insulating ceramic bracket, with a distance of 10mm between it and the inner wall of the roller sleeve.

[0192] The edge water cooling device 2 is also located in the inner hole of the roll, such as Figure 2As shown, water cooling is achieved using copper tubes. The copper tubes are located at both ends of the inner hole of the roll at both ends of the electromagnetic coil. Organosilicon is used to fill the gap between the copper tube and the inner wall of the roll. Specifically, in order to make the copper tube 21 fit tightly against the inner wall of the roll, organosilicon with high thermal conductivity and high fluidity is used to fill the gap, reduce the contact thermal resistance, increase the heat exchange capacity of the copper tube for cooling, so as to remove excess heat for cooling protection, avoid coil overheating failure, and thus ensure stable operation for a long time.

[0193] The temperature sensing network 3 consists of thermocouples embedded in the rolls of the lithium battery electrode rolling mill, used to detect the temperature at the location of the thermocouples. The number and location of the thermocouples are determined in step S5.

[0194] The control execution unit 5 includes a roll finite element mechanism model submodule, a data-driven model submodule, a joint prediction submodule, a first PID controller, a second PID controller, and a fusion submodule. The roll finite element mechanism model submodule uses finite element simulation to obtain the first thermal convexity based on parameters received from the control platform. The data-driven model submodule uses a data-driven model to obtain the second thermal convexity based on the temperature received from the temperature sensing network and parameters received from the control platform. The joint prediction submodule obtains the jointly predicted thermal convexity based on weights, the first thermal convexity, and the second thermal convexity, and sends the jointly predicted thermal convexity to the first PID controller. The first PID controller obtains the electromagnetic coil power adjustment amount based on the jointly predicted thermal convexity and the desired thermal convexity. The fusion submodule obtains the total electromagnetic coil power adjustment amount based on the electromagnetic coil power adjustment amount. The second PID controller receives the measured values ​​of the edge thermocouples in the temperature sensing network through the control platform and obtains the edge water cooling flow rate based on the deviation between the measured values ​​of the edge thermocouples and the target values. The first PID controller sends the electromagnetic coil power adjustment amount to the control platform, and the second PID controller sends the edge water cooling flow rate to the control platform.

[0195] Control platform 4 controls the state of the roll heating device and the edge water cooling device by setting the electromagnetic coil power adjustment and the edge water cooling flow rate, thereby achieving rapid adjustment and steady-state maintenance of the roll temperature of the lithium battery electrode roll press. During initial roll profile adjustment, the coil operates at 70%-95% of its maximum power, with 80% of maximum power being preferred. The current frequency is 400Hz, which heats the roller surface to the target temperature, such as 100℃, within 5 minutes. During steady-state operation, the power is switched to 20%-40% of the maximum power, preferably 30% (4.5kW), and the frequency is reduced to 5kHz. Combined with edge cooling, the temperature fluctuation is maintained within ±2℃. The joint prediction model reduces the mean absolute error (MAE) of the roller thermal crown prediction from approximately 2.5μm in the single-mechanism model to 0.8μm, with a response time of less than 5 milliseconds. This improvement in core accuracy lays a reliable foundation for subsequent high-precision real-time roller shape control, ultimately achieving electrode thickness consistency control within ±1.5μm.

[0196] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for adjusting the roll shape of a lithium battery electrode roll press based on heat, characterized in that, It includes the following steps: S1. Establish the finite element mechanism model of the rolls of the lithium battery electrode roll press; S2, simulation was performed using the finite element mechanism model of the rolling mill rolls; Transient simulation was performed using the finite element mechanism model of the roll based on the working parameters, and the temperature cloud map of the roll and the first thermal crown at each point along the axial direction of the roll body were obtained during the entire cycle from initial heating to steady-state maintenance. S3, preliminary screening of thermocouple placement based on comprehensive temperature contribution; By calculating the temperature gradient contribution and lateral temperature difference contribution of each potential thermocouple placement point, a weighted comprehensive contribution is obtained. Based on the comprehensive contribution, potential thermocouple placement points are initially screened to obtain the initial screening of thermocouple placement points. S4, based on data clustering analysis, redundant thermocouple placement is eliminated in the initial screening; Fuzzy clustering was used to screen the initial thermocouple placements, resulting in clustered thermocouple placements. S5, determine the optimal thermocouple placement combination; The optimal thermocouple placement combination was obtained by perturbation simulation using the Monte Carlo iterative optimization method for clustered thermocouple placement. S6, construct the dynamic thermal field matrix of the lithium battery electrode roll press roll; S7, building a data-driven model; The data-driven model uses a dynamic thermal field matrix as input and outputs the second thermal convexity at each thermocouple placement point. S8, joint predicted thermal convexity is obtained through joint prediction; Based on the first and second thermal convexities at the thermocouple points in the optimal thermocouple placement combination, the corresponding joint predicted thermal convexity is calculated using a weighted fusion formula. S9, adjusts the electromagnetic coil power and the side water cooling flow rate; Based on the joint predicted thermal convexity obtained in S8, the control execution unit adopts a combination of feedforward control and feedback control to adjust the electromagnetic coil power and the side water cooling flow rate, so as to realize real-time dynamic compensation of the roll profile of the lithium battery electrode roll press.

2. The method for adjusting the roll shape of a lithium battery electrode roll press based on heat according to claim 1, characterized in that, The finite element mechanism model of the lithium battery electrode roll press rolls in S1 is as follows: S11, Establish the mechanism model of the rolling mill rolls for lithium battery electrode rolls; The mechanistic model includes the thermal differential equation describing the transient heat conduction process inside the rolls of the lithium battery electrode rolling mill and the equation for calculating the radial expansion of the roll surface; S12, Establish a three-dimensional axisymmetric transient thermal-structural coupled finite element model; First, a three-dimensional digital model of the roll is created or imported into the finite element method using software. Then, material properties are assigned to the roll model. Next, meshing is performed on the roll surface and near-surface region to obtain a three-dimensional axisymmetric transient thermo-structural coupled finite element model. Finally, thermo-mechanical boundary conditions and loads are applied to the three-dimensional axisymmetric transient thermo-structural coupled finite element model, with the parameters derived from the mechanism model being applied as boundary conditions.

3. The method for adjusting the roll shape of a lithium battery electrode roll press based on heat according to claim 1, characterized in that, The initial screening of thermocouple placement based on the overall temperature contribution in S3 is as follows: S31, Setting potential thermocouple points: Multiple potential thermocouple points are pre-set in high density on the two-dimensional cross-section of the roll in the axial and radial directions. The potential thermocouple points are evenly distributed on the two-dimensional cross-section of the roll in the axial and radial directions, forming a potential thermocouple point matrix. S32, Quantifying the overall contribution of potential thermocouple placements: based on the temperature gradient contribution of each potential thermocouple placement. and the contribution of transverse temperature difference Receive a comprehensive contribution rating; S33, preliminary screening of potential thermocouple locations: After forcibly retaining potential thermocouple locations located at a specified roller width, preliminary screening is then conducted based on the overall contribution to obtain the initial thermocouple locations.

4. The method for adjusting the roll shape of a lithium battery electrode roll press based on heat according to claim 1, characterized in that, Determining the optimal thermocouple placement combination in S5 specifically involves: S51 performs dynamic thermal field disturbance; Dynamic thermal field perturbation is performed in the finite element mechanism model of the roll. Multiple perturbation scenarios are constructed through random factors. Temperature cloud map is generated for each perturbation scenario through the finite element mechanism model of the roll, and Gaussian noise is superimposed to simulate measurement uncertainty. S52, using Monte Carlo iterative optimization to determine the optimal point placement; Under the premise of meeting the compliance rate requirements, the Monte Carlo iterative optimization method is used to select the scheme with the fewest clustered thermocouple placement points as the optimal thermocouple placement point. Specifically, a subset is randomly selected from the clustered thermocouple placement points, and the perturbation simulation is repeated multiple times for each subset. The error compliance rate of each subset is calculated, and the subset with the fewest clustered thermocouple placement points that meets the compliance rate is selected as the optimal thermocouple placement point combination.

5. The method for adjusting the roll shape of a lithium battery electrode roll press based on heat according to claim 1, characterized in that, The dynamic thermal field matrix for constructing the lithium battery electrode roll press rolls in S6 is specifically as follows: ； in, For dynamic thermal field matrix, These are the first thermocouple to the second thermocouple. The temperature of the thermocouple after Kalman filtering, The optimal number of thermocouples in the thermocouple placement combination. The speed of the rolling mill rolls. For rolling force, The total power of the coil, This refers to the water cooling flow rate at the perimeter. For ambient temperature, For heating voltage, To maintain voltage, For forced convection heat transfer coefficient, This refers to the cooling water temperature.

6. The method for adjusting the roll shape of a lithium battery electrode roll press based on heat according to claim 1, characterized in that, The data-driven model in S7 also has a self-correction mechanism, specifically: When the prediction error exceeds the threshold continuously during the use of the data-driven model, the model self-correction mechanism is triggered to fine-tune the parameters of the data-driven model and update the fusion weights through the optimization algorithm.

7. The method for adjusting the roll shape of a lithium battery electrode roll press based on heat according to claim 1, characterized in that, In step S8, the corresponding joint predicted thermal convexity is calculated using a weighted fusion formula based on the first and second thermal convexities at the thermocouple locations in the optimal thermocouple placement combination. Specifically: ； in, To jointly predict the thermal convexity matrix, This is the first thermal convexity matrix predicted by the finite element mechanism model of the roll. The second thermal convexity matrix is ​​predicted by the data-driven model. The weighting coefficients are used; the finite element mechanism model of the rolls in S2 obtains the first thermal convexity at the thermocouple points in the optimal thermocouple point combination, forming the first thermal convexity matrix; the data-driven model in S7 obtains the second thermal convexity at the corresponding thermocouple points, forming the second thermal convexity matrix; the joint predicted thermal convexity matrix is ​​composed of the joint predicted thermal convexity at the thermocouple points in the optimal thermocouple point combination. for Joint predicted thermal convexity at time step, weighting coefficient use Weighting coefficients at different times , Weighting coefficients at different times The dynamic adjustment rule is: initial settings =0.5, when Time setting =0.3; when At that time, set =0.7; for time and Matrix difference metric: ； in, and They represent In the optimal thermocouple placement combination at time, the first The first thermal convexity value predicted by the finite element mechanism model of the roll at the thermocouple location and the second thermal convexity value predicted by the data-driven model. This represents the number of thermocouples in the optimal thermocouple placement combination.

8. The method for adjusting the roll shape of a lithium battery electrode roll press based on heat according to claim 1, characterized in that, The adjustment of the electromagnetic coil power and the side water cooling flow rate in S9 is specifically as follows: S91, adjusts the power of the electromagnetic coil; Thermal convexity deviation: ； in, For the first Thermal convexity deviation at each thermocouple placement point For the first Joint prediction of thermal convexity at each thermocouple location For the first The desired thermal convexity at each thermocouple placement point; The power of the electromagnetic coil is adjusted in real time by the first PID controller: ； in, for Time according to the first The coil power adjustment is obtained from the thermal convexity deviation at each thermocouple placement point. The proportional coefficient of the first PID controller; The integral coefficient of the first PID controller; The derivative coefficients of the first PID controller; yes Time of the first Thermal convexity deviation at each thermocouple placement point Indicates the first At each thermocouple placement point The instantaneous value of the thermal convexity deviation at any given moment. This indicates that the thermal convexity deviation is between 0 and... Integrate within a given time period; right thermocouple points To perform fusion, a fusion function is used. Implementation, input is thermocouple points The output is the total power regulation of the electromagnetic coil. ; S92, adjusts the side water cooling flow rate; The side water cooling flow rate is adjusted by a second PID controller, and the control equation is: ； in, This represents the real-time flow rate of the side cooling water. This represents the deviation between the measured edge temperature and the target value. This is the measured temperature value at the edge, which is the thermocouple measurement value with the largest absolute value on the axial coordinate. For the target value, This indicates the deviation between the measured and target values ​​of the edge temperature. The instantaneous value at a given moment. This indicates that the deviation between the measured edge temperature and the target value is within 0 to... Integrate within a given time period; The proportional coefficient of the second PID controller; The integral coefficient of the second PID controller; The derivative coefficients of the second PID controller are given.

9. A heat-based lithium battery electrode roll forming control system, wherein the lithium battery electrode roll forming control system is used in the heat-based lithium battery electrode roll forming control method according to any one of claims 1-8, characterized in that, The lithium battery electrode roll forming control system includes a roll heating device, an edge water cooling device, a temperature sensing network, a control platform, a control execution unit, and a lithium battery electrode roll forming machine. The roll heating device heats the rolls of the lithium battery electrode roll forming machine; the edge water cooling device cools the rolls; the temperature sensing network consists of thermocouples embedded in the rolls of the lithium battery electrode roll forming machine, used to detect the roll temperature at the thermocouple locations; the control platform controls the temperature of the rolls from the roll heating device, the edge water cooling device, the control execution unit, and the lithium battery electrode roll forming machine. The battery electrode roll press receives system parameters and controls the operation of the roll heating device, the edge water cooling device, and the lithium battery electrode roll press by setting system parameters. The control execution unit obtains the total adjustment amount of the electromagnetic coil power of the roll heating device and the edge water cooling flow rate of the edge water cooling device by jointly predicting the thermal crown and the measured value of the thermocouple. The control platform controls the operation of the roll heating device and the edge water cooling device respectively according to the total adjustment amount of the electromagnetic coil power and the edge water cooling flow rate obtained from the control execution unit, so as to realize the roll shape control of the lithium battery electrode roll press.

10. The heat-based lithium battery electrode roll forming control system according to claim 9, characterized in that, The roll heating device is installed in the inner hole of the roll and includes an electromagnetic coil and an insulating ceramic support. A copper electromagnetic coil is arranged axially in the inner hole of the roll. The electromagnetic coil is fixed by the insulating ceramic support and maintains a distance from the inner wall of the roll. The electromagnetic coil is connected to the control platform. The edge water cooling device is also in the inner hole of the roll. It uses copper tubes to achieve water cooling heat dissipation. The copper tubes are located at both ends of the inner hole of the roll at both ends of the electromagnetic coil. Organosilicon is used to fill the gap between the copper tubes and the inner wall of the roll. The temperature sensing network consists of thermocouples embedded in the rolls of the lithium battery electrode rolling mill, used to detect the temperature at the location of the thermocouples. The control execution unit includes a roll finite element mechanism model submodule, a data-driven model submodule, a joint prediction submodule, a first PID controller, a second PID controller, and a fusion submodule; The finite element mechanism model submodule of the roll obtains the first thermal crown using finite element simulation based on the parameters received from the control platform; The data-driven model submodule obtains the second thermal convexity using the data-driven model based on the temperature received from the temperature sensing network and the parameters received from the control platform. The joint prediction submodule obtains the joint predicted thermal convexity based on the weights, the first thermal convexity, and the second thermal convexity, and sends the joint predicted thermal convexity to the first PID controller. The first PID controller obtains the electromagnetic coil power adjustment amount based on the joint predicted thermal convexity and the desired thermal convexity. The fusion submodule obtains the total electromagnetic coil power adjustment amount based on the electromagnetic coil power adjustment amount. The second PID controller receives the measured values ​​of the edge thermocouples in the temperature sensing network through the control platform, and obtains the edge water cooling flow rate based on the deviation between the measured values ​​of the edge thermocouples and the target values. The first PID controller sends the total electromagnetic coil power adjustment amount to the control platform, and the second PID controller sends the edge water cooling flow rate to the control platform. The control platform controls the roll heating device through the total power adjustment of the electromagnetic coil and controls the status of the side water cooling device through the side water cooling flow rate, thereby adjusting the roll temperature of the lithium battery electrode roll press.

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