Dynamic control method and system of battery pole piece infrared-hot air combined drying equipment

Abstract

The invention discloses a dynamic control method and system for battery pole piece infrared-hot air combined drying equipment, and the method comprises the steps: obtaining multi-dimensional characteristic parameters of a battery pole piece in an ideal drying process, and building an ideal drying process model of the battery pole piece according to the multi-dimensional characteristic parameters; performing simulation control on the ideal drying process model to obtain a simulation control record, extracting simulation information in the simulation control record based on a predetermined moisture content interval to obtain a simulation output quantity, and then comparing the simulation output quantity with real-time characteristic parameters collected in an actual drying process to obtain a simulation result; a target comparison deviation of a target time zone is obtained, the target time zone has a corresponding relation with the collection time sequence of the real-time characteristic parameters, and finally, dynamic control over the target time zone is conducted on the infrared heating power, the hot air temperature and the hot air speed according to the target comparison deviation. The technical effect of improving the temperature and moisture content control precision, the drying uniformity and the energy utilization efficiency in the battery pole piece drying process is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of drying technology control, and particularly to a dynamic control method and system for a battery pole piece infrared-hot air combined drying equipment. BACKGROUND

[0002] For the development of drying technology, there are three recognized indicators, namely, the drying operation ensures product quality, the drying operation does not pollute the environment, and the drying process should achieve the purpose of energy saving as much as possible.

[0003] The existing battery pole piece infrared-hot air combined drying equipment mainly adopts a control strategy based on fixed parameters, by presetting some key parameters and keeping them unchanged to realize the control and adjustment of the drying process. However, in the battery pole piece drying process, the state of the pole piece is complex and variable, such as the moisture content of the pole piece, the surface temperature of the pole piece and the internal temperature gradient, etc. The preset fixed parameters are difficult to adapt to the actual operation, and thus the infrared-hot air combined drying method has obvious deficiencies in the control of the stability of the pole piece quality and the effective use of energy. Therefore, it is necessary to improve the above problems through a dynamic control method, system and equipment. SUMMARY

[0004] The purpose of the present application is to provide a dynamic control method and system for a battery pole piece infrared-hot air combined drying equipment, to overcome the problem of fixed control parameters in the existing battery pole piece drying process, which is difficult to adapt to the dynamic changes of the pole piece state, so as to realize real-time and self-adaptive optimization control of the drying process.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows: The dynamic control method for the battery pole piece infrared-hot air combined drying equipment comprises the following steps: Collecting multi-dimensional characteristic parameters of battery pole pieces of the same formula in an ideal drying process; Establishing an ideal drying process model based on the characteristic parameters, and obtaining simulation information of the ideal drying process through simulation; Extracting the simulation information in the simulation control record based on a predetermined moisture content interval, to obtain a simulation output; In the actual drying process, comparing the simulation output with the real-time characteristic parameters of the battery pole piece actually collected, to obtain a comparison deviation of the target parameters, and using the previous parameters as a reference in the subsequent comparison; According to the target comparison deviation, dynamically controlling the infrared heating power, hot air temperature and hot air speed.

[0006] Preferably, the multi-dimensional characteristic parameters include the pole piece surface temperature, the pole piece bottom surface temperature, the pole piece surface solvent vapor partial pressure and the environmental temperature and humidity parameters.

[0007] Preferably, simulation information is extracted from the simulation control record based on a predetermined humidity range to obtain simulation output quantities, including: Extract the electrode temperature parameters, solvent pressure parameters, and ambient temperature and humidity parameters from the simulation information; Based on the sampling of the electrode temperature parameters, solvent pressure parameters, and ambient temperature and humidity parameters within the predetermined humidity range, the parameter time series is obtained. The timing of the parameters is used as the simulation output.

[0008] Preferably, before comparing the simulated output with the actual characteristic parameters, the following steps are included: Obtain the parameter records of the adjacent previous drying cycle in the actual drying process, and record them as the preceding parameters; The simulation timing corresponding to the preceding parameters is obtained by matching the simulation output, and is recorded as the baseline simulation parameters.

[0009] Preferably, the simulated output is compared with the actual feature parameters to obtain the target comparison deviation, including: Based on the principle of uniform sampling, the benchmark simulation parameters and the actual parameters are sampled sequentially to obtain a first sample set and a second sample set; corresponding points are matched one by one in the sample set to obtain the corresponding deviation value; and a target comparison deviation is generated based on the deviation value.

[0010] Preferably, the dynamic control includes: When the target comparison deviation exceeds the predetermined threshold, the infrared heating power, hot air temperature and wind speed of each infrared heating device are adjusted according to the type of deviation. When the deviation between each target is within the allowable range, maintain the existing control parameters.

[0011] A dynamic control system for infrared-hot air combined drying of battery electrodes, the system comprising: The ideal drying process model building module is used to collect multi-dimensional characteristic parameters of battery electrodes during the ideal drying process and to build an ideal drying process model. The simulation control module is used to simulate and control the ideal drying process model and obtain simulation control records. The simulation output extraction module extracts simulation information from the simulation control record based on a predetermined humidity range to obtain the simulation output. The actual parameter acquisition module extracts parameter information of the actual drying process according to the predetermined moisture content range to obtain actual multidimensional feature parameters; The comparison analysis module is used to compare the simulation output with the actual collected multidimensional feature parameters to obtain the target comparison deviation; The dynamic control module is used to dynamically adjust the infrared heating power, hot air temperature, and hot air speed according to the target comparison deviation.

[0012] Preferably, the multidimensional characteristic parameters include electrode surface temperature, electrode bottom temperature, electrode surface solvent vapor partial pressure, and ambient temperature and humidity parameters.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a dynamic control method for a battery electrode infrared-hot air combined drying device.

[0014] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a dynamic control method for a battery electrode infrared-hot air combined drying device.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a dynamic control method for an infrared-hot air combined drying equipment for battery electrodes. The method involves acquiring multi-dimensional characteristic parameters of the battery electrodes during an ideal drying process, establishing an ideal drying process model based on these parameters, performing simulation control on the model to obtain simulation control records, extracting simulation information from the records based on a predetermined moisture content range to obtain simulation output quantities, comparing these output quantities with real-time characteristic parameters collected during the actual drying process to obtain a target comparison deviation for a target time zone. The target time zone corresponds to the acquisition sequence of the real-time characteristic parameters. Finally, the infrared heating power, hot air temperature, and hot air velocity are dynamically controlled according to the target comparison deviation within the target time zone. This method achieves the technical effects of improving the accuracy of temperature and moisture content control, drying uniformity, and energy utilization efficiency during the battery electrode drying process. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a flowchart illustrating the dynamic control method for an infrared-hot air combined drying equipment for battery electrodes provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the dynamic control system for an infrared-hot air combined drying equipment for battery electrodes provided in an embodiment of this application.

[0019] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] like Figure 1 As shown, the present invention provides a dynamic control method for a battery electrode infrared-hot air combined drying equipment, specifically including the following steps: include: Step S100: Collect multidimensional characteristic parameters of the drying process, and establish an ideal drying process model for the battery electrode based on the multidimensional characteristic parameters. The multidimensional characteristic parameters include at least the electrode moisture content, temperature, and ambient temperature and humidity parameters.

[0023] Specifically, multidimensional characteristic parameters of the drying process are collected through sensors and data acquisition cards, including but not limited to: solvent partial pressure on the upper surface of the electrode (collected by a pressure sensor), temperature of the upper surface of the electrode (collected by a temperature sensor), temperature of the lower surface of the electrode (collected by a temperature sensor on the roller surface), ambient temperature (collected by a temperature sensor), ambient humidity (collected by a wet-bulb and dry-bulb hygrometer), radiation intensity of the infrared lamp (collected by a radiation intensity sensor), and moisture content of the electrode (collected by an online quality measurement system). The collected multidimensional characteristic parameters are filtered, denoised, and normalized to ensure data accuracy and consistency. Based on the collected multidimensional characteristic parameters, a multidimensional parameter model of the ideal drying process is established using mathematical modeling methods (such as system identification and machine learning) to describe the dynamic characteristics of the multidimensional parameters changing with moisture content during the ideal drying process. For example, the system identification method models the system using input and output data to obtain the dynamic characteristics of the multidimensional parameters changing with moisture content. Machine learning methods use algorithms such as neural networks or support vector machines (SVM) to train the model based on historical data and predict the dynamic characteristics of the multidimensional parameters changing with moisture content. The moisture content is calculated as follows: (1) In the formula, It represents the ratio of the free moisture content to be removed at a certain moment to the initial total free moisture content; ， and Let represent the initial, equilibrium, and arbitrary drying time t of the electrode, respectively, in g / g. The moisture content of the electrode after drying is... much smaller and And since it is approximately 0, it can be simplified to .

[0024] Step S200: Simulate and control the ideal drying process model to obtain simulation control records.

[0025] Specifically, simulation software (such as MATLAB / Simulink, ANSYS Maxwell, etc.) is used to simulate the established multi-dimensional parameter model of the ideal drying process. In the simulation environment, drying parameters corresponding to different electrode moisture contents, such as infrared radiation power, hot air temperature, and hot air velocity, are obtained, and control algorithms are implemented, for example: PID control, using proportional and integral controllers to adjust infrared radiation power, hot air temperature, and hot air velocity. An improved cosine delay feedback control (ICDFC) is used, utilizing the difference between the output of the controlled system and its own delay of one cycle as the feedback quantity. After passing through a cosine function and feedback control parameters, the control signal is obtained. Input and output data during the simulation process are recorded, including electrode moisture content, temperature, ambient temperature and humidity, infrared radiation power, hot air temperature, and hot air velocity.

[0026] Step S300: Based on the predetermined extracted electrode moisture content, extract the simulation information from the simulation control record to obtain the simulation output quantity.

[0027] Specifically, the predetermined extraction of electrode moisture content is a pre-set interval for extracting data from the simulation control record, for example, extracting data once every 1% change in moisture content. Simulation information from the output terminal is extracted from the simulation control record at the predetermined extraction frequency. The extracted data includes temperature signals, ambient temperature and humidity signals, infrared radiation power values, hot air temperature values, and hot air velocity values. The extracted simulation outputs are stored in a database or file for subsequent processing.

[0028] Step S400: Compare the simulated output with the real-time characteristic parameters collected during the actual drying process to obtain the multidimensional characteristic parameter comparison deviation of the target moisture content range, wherein the target moisture content range and the actual collected moisture content range of the multidimensional characteristic parameters have a corresponding relationship.

[0029] Specifically, the target moisture content range of the simulation output is determined based on the moisture content of the electrode collected during the actual drying process, and the multidimensional feature parameters (first output) of the collected actual drying process and the multidimensional feature parameters (second output) corresponding to the target moisture content range are compared.

[0030] In one possible implementation, before comparing the first and second output values ​​for the corresponding moisture content range, step S400 further includes step S410, acquiring the parameter record of the adjacent previous drying cycle of the actual drying process. This is denoted as the preceding parameter.

[0031] In step S420, within the same moisture content range, the preceding parameters and the multidimensional characteristic parameters (second output) of the ideal drying process provide a clear calculation benchmark for the deviation calculation of the multidimensional characteristic parameters (first output) of the actual drying process corresponding to the multidimensional characteristic parameters (second output) within the target moisture content range. By comparing with the preceding parameters, the dynamic control system can better understand the current trend of change, thereby adjusting the dynamic control strategy to adapt to the changes in the drying process.

[0032] In one possible implementation, step S400 further includes S430, which involves sequentially sampling the first output and the second output based on the electrode moisture content according to the uniform sampling principle, to obtain a first sample set and a second sample set, respectively. Uniform sampling refers to collecting data within a fixed electrode moisture content interval to ensure that the sampling points are uniformly distributed along the electrode moisture content axis.

[0033] Step S430: Based on the interval of electrode moisture content range, obtain the comparison deviation between the first sample set and the second sample set.

[0034] Step S500: Based on the comparison deviation, dynamically control the infrared heating power, hot air temperature, and hot air speed in the target time zone.

[0035] Specifically, new control signals are generated based on the target deviation. For example, the parameters of the PID controller and the feedback control parameters of the ICDFC control strategy are adjusted according to the deviation to reduce it. The generated control signals are then sent to the control unit of the dynamic control system for dynamic adjustment. For instance, by monitoring multi-dimensional characteristic parameters of the actual drying process in real time, the control signals are dynamically adjusted to ensure that the target deviation is controlled within the expected range. For example, the output of the dynamic control system is monitored every 1% moisture content, and the control signals are adjusted based on the real-time multi-dimensional drying characteristics.

[0036] This application employs a method of collecting multi-dimensional feature parameters to establish an ideal drying process model, extracting simulation output based on simulation control records and comparing and analyzing the target comparison deviation, and then performing dynamic control based on the electrode moisture content. This solves the problem that existing electrode drying processes with fixed parameters cannot adapt to complex and variable working conditions, resulting in high energy consumption and unstable electrode quality. This effectively improves drying uniformity and energy utilization, and ensures stable electrode quality.

[0037] In the above text, refer to Figure 1 A dynamic control method for an infrared-hot air combined drying apparatus for battery electrodes according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A dynamic control system for an infrared-hot air combined drying apparatus for battery electrodes according to an embodiment of the present invention is described.

[0038] The dynamic control system for an infrared-hot air combined drying equipment for battery electrodes according to an embodiment of the present invention addresses the problems of existing control systems for infrared-hot air combined drying equipment for battery electrodes, which are unable to adapt to complex and variable operating conditions, leading to high energy consumption and unstable electrode quality during the drying process. The system aims to improve drying uniformity and energy utilization, and ensure stable electrode quality. The dynamic control system for the infrared-hot air combined drying equipment for battery electrodes includes: an ideal drying process model establishment module 10, a simulation control module 20, an actual parameter acquisition module 30, a time-series comparison module 40, a simulation output extraction module 50, and a dynamic control module 60.

[0039] The ideal drying process model establishment module 10 is used to collect multi-dimensional characteristic parameters of the electrode moisture content change during the ideal drying process, and establish an ideal drying process model of the battery electrode infrared-hot air combined drying based on the multi-dimensional characteristic parameters; the simulation control module 20 is used to perform simulation control on the ideal drying process model to obtain simulation control records; the simulation output quantity extraction module 30 is used to extract the output simulation information in the simulation control records based on a predetermined moisture content extraction interval to obtain simulation output quantities; the comparison module 40 is used to compare the corresponding first output quantity time sequence and second output quantity in the simulation output quantities to obtain a target comparison deviation; the dynamic control module 50 is used to dynamically control the drying parameters of the battery electrode infrared-hot air combined drying equipment according to the target comparison deviation.

[0040] The specific configuration of the ideal drying process model establishment module 10 will be described in detail below. As mentioned above, the ideal drying process model establishment module 10 may further include: the multidimensional feature parameters include at least the electrode upper surface temperature, lower surface temperature and ambient temperature and humidity parameters at a predetermined moisture content.

[0041] The specific configuration of the actual parameter acquisition module 30 will be described in detail below. As mentioned above, the actual parameter acquisition module 30 may further include: the multidimensional characteristic parameters include at least the electrode upper surface temperature, lower surface temperature and ambient temperature and humidity parameters at a predetermined humidity level.

[0042] The specific configuration of the simulation output extraction module 50 will be described in detail below. As mentioned above, the ideal drying multidimensional parameters in the simulation record are extracted based on the predetermined electrode moisture content interval to obtain the simulation output.

[0043] The specific configuration of the comparison module 40 will be described in detail below. As mentioned above, after considering the deviation between the reference preceding parameters and the ideal drying parameters, the multidimensional characteristic parameters obtained from the actual parameter acquisition are compared with the ideal multidimensional parameters to obtain the comparison deviation. The generated control signal is sent to the control unit of the dynamic control system to achieve dynamic adjustment.

[0044] The dynamic control system for photovoltaic inverters provided in this embodiment of the invention can execute the dynamic control method for infrared-hot air combined drying of battery electrodes provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0045] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0046] Based on the foregoing embodiments, this application also provides an electronic device. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 301, a processor 302, a memory 303, and an output device 304. The processor 302 may be one or more; the memory 303 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0047] The memory 303 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage medium can be, but is not limited to, an infrared or hot air system, device or component, or any combination thereof, used to store software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the dynamic control method for infrared-hot air combined drying of battery electrodes in this embodiment of the invention. The processor 302 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 303, thereby realizing the above-mentioned dynamic control method for infrared-hot air combined drying of battery electrodes.

[0048] The present invention discloses a dynamic control method for a battery electrode infrared-hot air combined drying equipment, the working principle of which is as follows: The device mainly consists of an infrared heating component, a hot air circulation component, a conveying mechanism, a temperature sensor group, a solvent partial pressure sensor group, a data acquisition module, and a central control unit.

[0049] During the electrode drying process, the electrode is continuously conveyed into the drying chamber by the conveying mechanism. The infrared heating component radiates heat to the surface of the electrode, while the hot air circulation component provides hot air from the bottom or side wall to achieve convective heat transfer, so that the moisture and solvent inside the electrode migrate from the inside to the outside and are carried away by the airflow.

[0050] Temperature sensors are positioned on the surface, bottom, and environment of the electrode to monitor the temperature distribution of the electrode in real time; solvent partial pressure sensors are used to detect the solvent vapor concentration in the drying chamber. The central control unit first establishes an ideal drying process model based on ideal drying process data of electrodes with the same formulation, and obtains the temperature field, moisture content, and solvent partial pressure change curves under ideal conditions through simulation.

[0051] In actual drying operation, the control unit receives sensor feedback signals in real time and compares its real-time characteristic parameters with the simulation output of the ideal drying model to calculate the target parameter deviation. Based on the magnitude and trend of the deviation, the system automatically adjusts the infrared heating power, hot air temperature, and hot air velocity to achieve closed-loop dynamic control. When the deviation is within the allowable range, the system maintains the current control parameters; when the deviation exceeds the predetermined threshold, the system performs adaptive adjustment to restore the ideal drying trajectory.

[0052] Through the above methods, the present invention achieves dynamic and intelligent control of the infrared-hot air combined drying process, which can effectively reduce uneven electrode temperature and local over-drying, improve drying rate and energy utilization efficiency, and ensure the consistency of electrode moisture content distribution and microstructure stability.

[0053] Contents not described in detail in this specification are existing technologies known to those skilled in the art. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic control method for a combined infrared-hot air drying equipment for battery electrodes, characterized in that, Includes the following steps: Collect multidimensional characteristic parameters of battery electrodes with the same formulation during the ideal drying process; An ideal drying process model is established based on the aforementioned characteristic parameters, and simulation information of the ideal drying process is obtained through simulation. The simulation information in the simulation control record is extracted based on the predetermined humidity range to obtain the simulation output. In the actual drying process, the simulated output is compared with the real-time characteristic parameters of the battery electrode that are actually collected to obtain the comparison deviation of the target parameters, and the preceding parameters are used as the benchmark reference in subsequent comparisons. The infrared heating power, hot air temperature, and hot air speed are dynamically controlled based on the target comparison deviation.

2. The dynamic control method for the infrared-hot air combined drying equipment for battery electrodes according to claim 1, characterized in that, The multidimensional characteristic parameters include electrode surface temperature, electrode bottom temperature, electrode surface solvent vapor partial pressure, and ambient temperature and humidity parameters.

3. The dynamic control method for the infrared-hot air combined drying equipment for battery electrodes according to claim 1, characterized in that, Simulation information is extracted from the simulation control record based on a predetermined humidity range to obtain simulation output quantities, including: Extract the electrode temperature parameters, solvent pressure parameters, and ambient temperature and humidity parameters from the simulation information; Based on the sampling of the electrode temperature parameters, solvent pressure parameters, and ambient temperature and humidity parameters within the predetermined humidity range, the parameter time series is obtained. The timing of the parameters is used as the simulation output.

4. The dynamic control method for the infrared-hot air combined drying equipment for battery electrodes according to claim 1, characterized in that, Before comparing the simulated output with the actual characteristic parameters, the following steps are included: Obtain the parameter records of the adjacent previous drying cycle in the actual drying process, and record them as the preceding parameters; The simulation timing corresponding to the preceding parameters is obtained by matching the simulation output, and is recorded as the baseline simulation parameters.

5. The dynamic control method for the infrared-hot air combined drying equipment for battery electrodes according to claim 1, characterized in that, The simulation output is compared with the actual feature parameters to obtain the target comparison deviation, including: Based on the principle of uniform sampling, the benchmark simulation parameters and the actual parameters are sampled sequentially to obtain a first sample set and a second sample set; corresponding points are matched one by one in the sample set to obtain the corresponding deviation value; and a target comparison deviation is generated based on the deviation value.

6. The dynamic control method for the infrared-hot air combined drying equipment for battery electrodes according to claim 1, characterized in that, The dynamic control includes: When the target comparison deviation exceeds the predetermined threshold, the infrared heating power, hot air temperature and wind speed of each infrared heating device are adjusted according to the type of deviation. When the deviation between each target is within the allowable range, maintain the existing control parameters.

7. A dynamic control system for infrared-hot air combined drying of battery electrodes, characterized in that, The system is used to implement the method according to any one of claims 1 to 6, the system comprising: The ideal drying process model building module is used to collect multi-dimensional characteristic parameters of battery electrodes during the ideal drying process and to build an ideal drying process model. The simulation control module is used to simulate and control the ideal drying process model and obtain simulation control records. The simulation output extraction module extracts simulation information from the simulation control record based on a predetermined humidity range to obtain the simulation output. The actual parameter acquisition module extracts parameter information of the actual drying process according to the predetermined moisture content range to obtain actual multidimensional feature parameters; The comparison analysis module is used to compare the simulation output with the actual collected multidimensional feature parameters to obtain the target comparison deviation; The dynamic control module is used to dynamically adjust the infrared heating power, hot air temperature, and hot air speed according to the target comparison deviation.

8. The dynamic control system of the battery electrode infrared-hot air combined drying equipment according to claim 7, characterized in that, The multidimensional characteristic parameters include electrode surface temperature, electrode bottom temperature, electrode surface solvent vapor partial pressure, and ambient temperature and humidity parameters.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic control method for the battery electrode infrared-hot air combined drying equipment as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the dynamic control method for the infrared-hot air combined drying equipment for battery electrodes as described in any one of claims 1 to 6.

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