Intelligent machine adjustment method and related equipment for double-sided coating of lithium-ion battery electrodes

Abstract

The present application relates to the field of electrode coating technology, and discloses an intelligent machine adjustment method and related equipment for double-sided coating of lithium-ion battery electrodes. The method includes: obtaining coating liquid material parameters and equipment process parameters; inputting coating liquid material parameters and equipment process parameters into a finished product quality prediction network, predicting the quality of the coated finished product, and obtaining predicted values ​​of the finished product quality parameters; performing parameter evaluation on the predicted values ​​of the finished product quality parameters to obtain parameter evaluation results; judging whether the parameter evaluation results meet the preset evaluation conditions; if not, resetting the equipment process parameters according to the parameter evaluation results; if yes, configuring the parameters of the electrode coating equipment so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters. The embodiments of the present application can reduce the complexity of debugging electrode coating equipment to improve the efficiency of battery electrode coating.

Description

Technical Field

[0001] The present application relates to the field of electrode coating technology, and in particular to an intelligent machine adjustment method and related equipment for double-sided coating of lithium-ion battery electrodes. Background Art

[0002] The electrodes in the battery are the components that have the greatest impact on the overall performance of the battery, and electrode coating is the most critical process in battery electrode manufacturing. If the electrodes are uneven due to coating, the overall capacity and performance of the power battery will decrease, and the battery life will be greatly reduced.

[0003] In the related technology, the process of coating both sides of the electrode simultaneously can reduce the non-uniformity of the two sides of the electrode and improve production efficiency, but it also brings difficulties in equipment debugging. Due to the lack of digital quality and control models for the entire process of electrode coating, in the current new product development and product replacement, the coating equipment needs to be manually debugged repeatedly, resulting in a long battery development cycle and high costs. Summary of the invention

[0004] The purpose of this application is to provide an intelligent machine adjustment method and related equipment for double-sided coating of lithium-ion battery electrodes, aiming to reduce the complexity of debugging electrode coating equipment and improve the efficiency of battery electrode coating.

[0005] The present application embodiment provides a method for intelligently adjusting a double-sided coating machine for a lithium-ion battery electrode, comprising:

[0006] Obtain coating liquid material parameters and equipment process parameters;

[0007] Inputting the coating liquid material parameters and the equipment process parameters into a finished product quality prediction network to predict the quality of the coated finished product and obtain predicted values ​​of the finished product quality parameters; the finished product quality prediction network is obtained by training a preset deep neural network using coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters having a mapping relationship;

[0008] Performing parameter evaluation on the predicted value of the finished product quality parameter to obtain a parameter evaluation result;

[0009] Determine whether the parameter evaluation result meets the preset evaluation conditions;

[0010] If not, reset the equipment process parameters according to the parameter evaluation results; return to the step of inputting the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network, predicting the quality of the coated finished product, and obtaining the predicted finished product quality parameters;

[0011] If it meets the requirements, the electrode coating equipment is configured with parameters so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters.

[0012] In some embodiments, the lithium-ion battery electrode double-sided coating intelligent machine adjustment method further includes:

[0013] Sampling parameters of the electrode coating equipment to obtain sample parameters of the coating liquid material, sample parameters of the equipment process and sample parameters of the finished product quality;

[0014] Performing digital twin design on the electrode coating equipment to obtain a digital twin, collecting parameters generated by the digital twin during coating, and supplementing them to the coating liquid material sample parameters, the equipment process sample parameters, and the finished product quality sample parameters;

[0015] The deep neural network is trained using the coating liquid material sample parameters, the equipment process sample parameters and the finished product quality sample parameters to obtain the finished product quality prediction network.

[0016] In some embodiments, the calculation formula for the sampling time interval for parameter sampling of the electrode coating device is:

[0017]

[0018] in, is the sampling time interval, For the The rate of change of the process sample parameters of each equipment over time, is the minimum function, k=1,2,…,o, o is the number of equipment process sample parameters.

[0019] In some embodiments, the lithium-ion battery electrode double-sided coating intelligent machine adjustment method further includes:

[0020] According to the preset delay parameters, the equipment process sample parameters and the finished product quality sample parameters are associated to obtain a mapping relationship between the equipment process sample parameters and the finished product quality sample parameters;

[0021] The coating liquid material parameters are corrected to obtain corrected coating liquid material parameters,

[0022] The equipment process sample parameters and the corrected coating liquid material parameters are associated to obtain a mapping relationship between the equipment process sample parameters and the coating liquid material parameters.

[0023] In some embodiments, performing parameter evaluation on the predicted value of the finished product quality parameter to obtain a parameter evaluation result includes:

[0024] Inputting the predicted value of the finished product quality parameter into the finished product quality parameter evaluation model, performing finished product quality parameter evaluation, and obtaining the parameter evaluation result;

[0025] The expression of the finished product quality parameter evaluation model is:

[0026]

[0027] in, is the parameter evaluation result, is the predicted value of the quality parameter of the i-th finished product, is the i-th finished product quality target parameter, i=1,2,...,m, and m is the number of finished product quality target parameters.

[0028] In some embodiments, resetting the equipment process parameters according to the parameter evaluation results includes:

[0029] Constructing a continuous equipment process parameter search space according to the mapping relationship between the equipment process sample parameters and the finished product quality sample parameters;

[0030] In the equipment process parameter search space, parameter search is performed with the goal of making the parameter evaluation result approach the preset evaluation condition, and the equipment process parameter is reset according to the search result.

[0031] In some embodiments, the parameter configuration of the electrode coating equipment according to the predicted value of the finished product quality parameter includes:

[0032] Determine the equipment process configuration parameters according to the predicted values ​​of the finished product quality parameters; the equipment process prediction parameters are the equipment process parameters input into the finished product quality prediction network corresponding to the predicted values ​​of the finished product quality parameters;

[0033] Performing parameter interpolation in a parameter region between the current equipment process parameters of the electrode coating equipment and the equipment process configuration parameters to obtain a plurality of equipment process interpolation parameters;

[0034] According to the equipment process interpolation parameters, the electrode coating equipment is configured with parameters for multiple times, so that the electrode coating equipment operates according to the equipment process configuration parameters after the parameter configuration.

[0035] The present application also provides a lithium-ion battery electrode double-sided coating intelligent machine adjustment device, comprising:

[0036] The first module is used to obtain coating liquid material parameters and equipment process parameters;

[0037] The second module is used to input the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network to predict the quality of the coated finished product and obtain the predicted values ​​of the finished product quality parameters; the finished product quality prediction network is obtained by training a preset deep neural network using the coating liquid material sample parameters, the equipment process sample parameters and the finished product quality sample parameters having a mapping relationship;

[0038] The third module is used to perform parameter evaluation on the predicted value of the finished product quality parameter to obtain a parameter evaluation result;

[0039] The fourth module is used to determine whether the parameter evaluation result meets the preset evaluation conditions;

[0040] The fifth module is used to reset the equipment process parameters according to the parameter evaluation results when the parameter evaluation results do not meet the preset evaluation conditions, return to the step of inputting the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network, predict the quality of the coated finished product, and obtain the predicted finished product quality parameters, and when the parameter evaluation results meet the preset evaluation conditions, configure the parameters of the electrode coating equipment so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters.

[0041] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the above-mentioned intelligent adjustment method for double-sided coating of lithium-ion battery electrodes is implemented.

[0042] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned intelligent adjustment method for double-sided coating of lithium-ion battery electrodes is implemented.

[0043] The beneficial effects of the present application are as follows: by inputting coating liquid material parameters and equipment process parameters into a finished product quality prediction network, a finished product quality prediction network is obtained by training a preset deep neural network with coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters having a mapping relationship, so as to obtain predicted values ​​of finished product quality parameters, and by performing parameter evaluation on the predicted values ​​of finished product quality parameters, when the parameter evaluation results do not meet preset evaluation conditions, the equipment process parameters are reset according to the parameter evaluation results, and when the parameter evaluation results meet the preset evaluation conditions, the electrode coating equipment is parameter configured so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters. Since the finished product quality parameters are predicted by the finished product quality prediction network, the finished product quality prediction network is trained using coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters that have a mapping relationship, the predicted values ​​of the predicted finished product quality parameters have a mapping relationship with the coating liquid material parameters and equipment process parameters currently input into the finished product quality prediction network, and the corresponding finished product quality parameters can be predicted before configuring the electrode coating equipment, which can reduce the complexity of debugging the electrode coating equipment and improve the efficiency of battery electrode coating. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of the intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes provided in the first embodiment of the present application.

[0045] Figure 2 This is a flow chart of the intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes provided in the second embodiment of the present application.

[0046] Figure 3 This is a flow chart of the intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes provided in the third embodiment of the present application.

[0047] Figure 4 It is a flowchart of the specific method of step S106 provided in an embodiment of the present application.

[0048] Figure 5 It is a structural schematic diagram of the intelligent machine adjustment device for double-sided coating of lithium-ion battery electrodes provided in an embodiment of the present application.

[0049] Figure 6 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown can be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second" and the like in the specification, claims and drawings are used to distinguish similar objects, and are not used to describe a specific order or sequence.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0053] See also Figure 1 , Figure 1 Flow chart of the intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes provided in the embodiment of the present application. In some embodiments, Figure 1 The method may specifically include but is not limited to steps S101 to S106. Figure 1 These six steps are introduced in detail.

[0054] Step S101, obtaining coating liquid material parameters and equipment process parameters.

[0055] It should be noted that the coating liquid material parameters include relevant parameters associated with the rheological properties of the coating liquid, including parameters such as the coating liquid density, the coating liquid viscosity and the coating liquid kinematic viscosity. The equipment process parameters include relevant parameters associated with the coating process of the electrode coating equipment, including parameters such as the coating flow rate, cavity pressure, die pressure and coating speed.

[0056] Step S102, inputting the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network, predicting the quality of the coated finished product, and obtaining the predicted values ​​of the finished product quality parameters.

[0057] Among them, the finished product quality prediction network is obtained by training a preset deep neural network using coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters with a mapping relationship.

[0058] It should be noted that the finished product quality parameters include relevant parameters associated with the coating quality indicators of the coated finished product, including parameters such as electrode thickness, surface density and porosity after coating and drying.

[0059] It can be understood that the coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters are respectively the pre-collected coating liquid material parameters, equipment process parameters and finished product quality parameters. The collected coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters can be stored in the database according to the corresponding mapping relationship, and then the preset deep neural network is trained using the coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters with the mapping relationship to obtain the finished product quality prediction network.

[0060] In step S102, the coating liquid material parameters and the equipment process parameters are input into the finished product quality prediction network, and the coating liquid material parameters and the equipment process parameters input into the finished product quality prediction network are used to perform feature extraction and extract the mapping relationship between the coating liquid material sample parameters, the equipment process sample parameters and the finished product quality sample parameters. The finished product quality parameters are predicted based on the extracted mapping relationship and the input coating liquid material parameters and equipment process parameters to obtain the predicted values ​​of the finished product quality parameters.

[0061] See also Figure 2 ,exist Figure 1 Based on the embodiment, the parameter adjustment method may also include but is not limited to steps S201 to S203. Figure 2 These three steps are introduced in detail.

[0062] Step S201, sampling parameters of the electrode coating equipment to obtain coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters.

[0063] Step S202, perform digital twin design on the electrode coating equipment to obtain a digital twin, collect parameters generated by the digital twin during coating, and add them to the coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters.

[0064] Step S203, using the coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters, a deep neural network is trained to obtain a finished product quality prediction network.

[0065] In step S201, a parameter acquisition system may be established, and parameters of the electrode coating equipment may be sampled by the parameter acquisition system when the electrode coating equipment is running, so as to obtain coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters.

[0066] In some embodiments, parameter collection of coating liquid material sample parameters may be performed by testing the coating liquid of the positive and negative electrodes using a capillary rheometer or a rotational rheometer to obtain rheological data of how the viscosity of the coating liquid varies with equipment process parameters such as temperature, shear rate, and active particle concentration. Parameter collection of equipment process sample parameters may be performed by using a multi-point measurement technology and a coating liquid particle concentration and velocity measurement device and software inside the coating die to collect parameters such as the coating liquid flow rate, pressure, and velocity field inside the coating die to obtain relevant parameters associated with the coating process of the electrode coating equipment. Parameter collection of finished product quality sample parameters may be performed by using a laser high-frequency and high-resolution thickness measurement technology to achieve micron-level precise measurement of the thickness after electrode coating and drying, using X-ray and β-ray measurement devices and software to measure the surface density of the positive and negative electrode coatings of the battery electrodes, and using non-destructive and non-contact infrared thermal imaging technology and devices to achieve precise measurement of the coating porosity.

[0067] In a specific embodiment, the parameter sampling of the electrode coating device is performed by the parameter acquisition system, and the calculation formula for the sampling time interval for sampling the parameters of the electrode coating device is:

[0068]

[0069] in, is the sampling time interval, For the The rate of change of the process sample parameters of each equipment over time, is the minimum function, k=1,2,…,o, o is the number of equipment process sample parameters.

[0070] In some embodiments, the collected coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters are continuously fed back to adjust the equipment operation status, and the collected coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters are stored in a value database to update the finished product quality prediction network in real time, so as to obtain more accurate prediction values ​​of the finished product quality parameters, so as to select the optimal equipment process parameters and configure them to the electrode coating equipment.

[0071] In step S202, a digital twin is established on the physical basis of the electrode coating equipment. A digital twin scene corresponding to the working state of the electrode coating equipment can be generated based on the data acquired in real time and using computer real-time simulation calculation. The parameters generated by the digital twin coating are collected in real time in the digital twin scene by the computer. The types of collected parameters are coating liquid material parameters, equipment process parameters and finished product quality parameters. The collected parameters are then supplemented to the coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters and stored in a database. The parameter quantity of the coating liquid material sample parameters, the equipment process sample parameters and the finished product quality sample parameters can be expanded to reduce manual machine adjustment.

[0072] In step S203, the coating liquid material sample parameters and the equipment process sample parameters are input into a preset deep neural network, so that the neural network outputs the corresponding predicted values ​​of the finished product quality sample parameters, and the deep neural network is iteratively trained according to the deviation between the finished product quality sample parameters and the predicted values ​​of the finished product quality sample parameters. The training is terminated when the deviation between the finished product quality sample parameters and the predicted values ​​of the finished product quality sample parameters is less than the preset deviation threshold, thereby obtaining a finished product quality prediction network.

[0073] See also Figure 3 ,exist Figure 1 Based on the embodiment, the parameter adjustment method may also include but is not limited to step S301 to step S303. Figure 3 These three steps are introduced in detail.

[0074] Step S301 , according to the preset delay parameters, the equipment process sample parameters and the finished product quality sample parameters are associated to obtain a mapping relationship between the equipment process sample parameters and the finished product quality sample parameters.

[0075] Step S302, correcting the coating liquid material parameters to obtain corrected coating liquid material parameters.

[0076] Step S303 , associating the equipment process sample parameters with the corrected coating liquid material parameters to obtain a mapping relationship between the equipment process sample parameters and the coating liquid material parameters.

[0077] In step S301, based on the digital twin principle, a digital twin designed based on the electrode coating equipment can be used to carry out coating production using a coating liquid with constant coating liquid material parameters. By adjusting the equipment process sample parameters, the time when the equipment process sample parameters are adjusted and the time when the finished product quality sample parameters change are recorded, the delay parameters are determined, thereby determining the mapping relationship between the equipment process sample parameters and the finished product quality sample parameters.

[0078] In step S302, the material parameters of the coating liquid are corrected, specifically, the material parameters of each component of the coating liquid are homogenized to obtain the corrected material parameters of the coating liquid, wherein the coating liquid solvent, active particles, binder and conductive agent are composed. For example, the expression of the homogenization process is:

[0079]

[0080]

[0081] Where, ρ is the density of the coating liquid, ρ s is the solvent density, ρ p is the particle density, is the total volume of the coating liquid, V s is the solvent volume, V p is the particle volume, μ is the coating liquid viscosity, μ s is the solvent viscosity.

[0082] In step S303, the equipment process sample parameters and the corrected coating liquid material parameters are associated, specifically, the flow field is solved according to the corrected coating liquid material parameters and the laminar Navier-Stokes equation, and the flow behavior of the coating liquid made of a specific material under the specific equipment process sample parameters is determined to obtain a mapping relationship between the equipment process sample parameters and the coating liquid material parameters. For example, the expression for solving the flow field is:

[0083]

[0084] Where, ρ is the density of the coating liquid, is the velocity derivative, is the coating pressure, is the volume external force, and μ is the viscosity of the coating liquid.

[0085] Step S103, performing parameter evaluation on the predicted value of the finished product quality parameter to obtain a parameter evaluation result.

[0086] In step S103, a finished product quality parameter evaluation model is established, and the required finished product quality target parameters are set. The finished product quality parameter evaluation model is used to perform parameter evaluation on the predicted values ​​of the finished product quality parameters to obtain parameter evaluation results that characterize the deviation between the finished product quality target parameters and the finished product quality parameters.

[0087] In a specific embodiment, step S103 specifically includes: inputting the predicted value of the finished product quality parameter into the finished product quality parameter evaluation model, performing finished product quality parameter evaluation, and obtaining a parameter evaluation result.

[0088] Among them, the expression of the finished product quality parameter evaluation model is:

[0089]

[0090] in, is the parameter evaluation result, is the predicted value of the quality parameter of the i-th finished product, is the i-th finished product quality target parameter, i=1,2,...,m, and m is the number of finished product quality target parameters.

[0091] Step S104, determining whether the parameter evaluation result meets the preset evaluation conditions.

[0092] If not, execute step S105; if compliant, execute step S106.

[0093] In step S104, it may be a preset quality parameter evaluation threshold. When the parameter evaluation result is greater than the quality parameter evaluation threshold, the parameter evaluation result meets the preset evaluation condition. When the parameter evaluation result is not greater than the quality parameter evaluation threshold, the parameter evaluation result does not meet the preset evaluation condition.

[0094] Step S105: reset the equipment process parameters according to the parameter evaluation results.

[0095] After executing step S105, return to step S102.

[0096] In step S105, according to the parameter evaluation result, a parameter search is performed in the continuous equipment process parameters by using the gradient descent method to find the equipment process parameters that may obtain the optimal finished product quality parameters, and the found equipment process parameters are used to replace the current equipment process parameters.

[0097] In a specific embodiment, step S105 specifically includes: constructing a continuous equipment process parameter search space based on the mapping relationship between equipment process sample parameters and finished product quality sample parameters, performing parameter search within the equipment process parameter search space with the goal of making the parameter evaluation results approach preset evaluation conditions, and resetting the equipment process parameters based on the search results.

[0098] Among them, the expression for searching equipment process parameters to obtain search results is:

[0099]

[0100]

[0101] in, is the nth equipment process parameter, is the quality parameter of the i-th finished product, is the correlation function between the i-th finished product quality parameter and the equipment process parameter, i=1,2,...,m, m is the number of finished product quality target parameters, and n is the number of equipment process parameters.

[0102] Step S106, configuring parameters of the electrode coating equipment so that the electrode coating equipment operates according to equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters.

[0103] In step S106, the equipment process parameters of the electrode coating equipment are configured as the equipment process parameters currently input into the finished product quality prediction network, so that the electrode coating equipment operates according to the configured equipment process parameters to produce a finished product with the predicted value of the finished product quality parameter.

[0104] See also Figure 4 , Figure 4 is a flowchart of a specific method of step S106 provided in an embodiment of the present application. Step S106 may specifically include but is not limited to steps S401 to S403. Figure 4 These three steps are introduced in detail.

[0105] Step S401, determining equipment process configuration parameters according to the predicted values ​​of finished product quality parameters.

[0106] The equipment process prediction parameters are equipment process parameters input into the finished product quality prediction network corresponding to the predicted values ​​of the finished product quality parameters.

[0107] Step S402 , performing parameter interpolation in a parameter region between the current equipment process parameters of the electrode coating equipment and the equipment process configuration parameters to obtain a plurality of equipment process interpolation parameters.

[0108] Step S403, performing multiple parameter configurations on the electrode coating equipment according to the equipment process interpolation parameters, so that the electrode coating equipment operates according to the equipment process configuration parameters after the parameter configuration.

[0109] In step S401 to step S403, the equipment process parameters input into the finished product quality prediction network when outputting the predicted values ​​of the finished product quality parameters are determined according to the predicted values ​​of the finished product quality parameters, that is, the equipment process configuration parameters serving as parameter configuration targets. In order to avoid drastic changes and fluctuations in equipment state, parameter interpolation is performed in the parameter region between the current equipment process parameters of the electrode coating equipment and the equipment process configuration parameters, and a plurality of equipment process interpolation parameters between the current equipment process parameters and the equipment process configuration parameters are obtained. The electrode coating equipment is sequentially configured to operate at a plurality of equipment process interpolation parameters, and finally to operate at the equipment process configuration parameters, to ensure that the switching process of the equipment process parameters can be smoothly transitioned, and the electrode coating equipment operates in a stable process state.

[0110] See also Figure 5 The present application also provides a lithium-ion battery electrode double-sided coating intelligent machine adjustment device, which can implement the above-mentioned lithium-ion battery electrode double-sided coating intelligent machine adjustment method, and the device includes:

[0111] The present application also provides a lithium-ion battery electrode double-sided coating intelligent machine adjustment device, comprising:

[0112] The first module 501 is used to obtain coating liquid material parameters and equipment process parameters;

[0113] The second module 502 is used to input the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network to predict the quality of the coated finished product and obtain the predicted value of the finished product quality parameters; the finished product quality prediction network is obtained by training a preset deep neural network using the coating liquid material sample parameters, the equipment process sample parameters and the finished product quality sample parameters having a mapping relationship;

[0114] The third module 503 is used to perform parameter evaluation on the predicted value of the finished product quality parameter to obtain a parameter evaluation result;

[0115] The fourth module 504 is used to determine whether the parameter evaluation result meets the preset evaluation condition;

[0116] The fifth module 505 is used to reset the equipment process parameters according to the parameter evaluation results when the parameter evaluation results do not meet the preset evaluation conditions, return to the step of inputting the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network, perform coating finished product quality prediction, and obtain predicted finished product quality parameters. When the parameter evaluation results meet the preset evaluation conditions, the electrode coating equipment is parameterized so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters.

[0117] The specific implementation of the intelligent machine adjustment device for double-sided coating of lithium-ion battery electrodes is basically the same as the specific implementation of the intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes mentioned above, and will not be repeated here.

[0118] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment.

[0119] Refer to the following Figure 6 The electronic device 600 according to this embodiment of the present disclosure is described. Figure 6 The electronic device 600 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0120] like Figure 6As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0121] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned lithium-ion battery electrode double-sided coating intelligent machine adjustment method section of this specification.

[0122] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0123] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0124] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0125] The electronic device 600 may also communicate with one or more external devices 600' (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0126] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned intelligent adjustment method for double-sided coating of lithium-ion battery electrodes is implemented.

[0127] The embodiments of the present application provide a method, device, equipment and storage medium for intelligent adjustment of double-sided coating of lithium-ion battery electrodes. By inputting coating liquid material parameters and equipment process parameters into a finished product quality prediction network, a finished product quality prediction network is obtained by training a preset deep neural network with coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters having a mapping relationship to obtain a coating finished product quality prediction network, so as to obtain predicted values ​​of finished product quality parameters. Parameter evaluation is performed on the predicted values ​​of finished product quality parameters. When the parameter evaluation results do not meet preset evaluation conditions, the equipment process parameters are reset according to the parameter evaluation results. When the parameter evaluation results meet the preset evaluation conditions, the electrode coating equipment is parameter configured so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters. Since the finished product quality parameters are predicted by the finished product quality prediction network, the finished product quality prediction network is trained using coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters that have a mapping relationship, the predicted values ​​of the predicted finished product quality parameters have a mapping relationship with the coating liquid material parameters and equipment process parameters currently input into the finished product quality prediction network, and the corresponding finished product quality parameters can be predicted before configuring the electrode coating equipment, which can reduce the complexity of debugging the electrode coating equipment and improve the efficiency of battery electrode coating.

[0128] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above method according to the implementation of the present disclosure.

[0129] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0130] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0131] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0132] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0133] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0134] The exemplary embodiments of the present disclosure are specifically shown and described above. It should be understood that the present disclosure is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present disclosure is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended claims.

Claims

1. A method for intelligent machine adjustment for double-sided coating of lithium-ion battery electrodes, characterized in that: include: Obtain coating liquid material parameters and equipment process parameters; Inputting the coating liquid material parameters and the equipment process parameters into a finished product quality prediction network to predict the quality of the coated finished product and obtain predicted values ​​of the finished product quality parameters; the finished product quality prediction network is obtained by training a preset deep neural network using coating liquid material sample parameters, equipment process sample parameters and finished product quality sample parameters having a mapping relationship; Performing parameter evaluation on the predicted value of the finished product quality parameter to obtain a parameter evaluation result; Determine whether the parameter evaluation result meets the preset evaluation conditions; When the parameter evaluation result is greater than the quality parameter evaluation threshold, the parameter evaluation result meets the preset evaluation condition; when the parameter evaluation result is not greater than the quality parameter evaluation threshold, the parameter evaluation result does not meet the preset evaluation condition; If not, resetting the equipment process parameters according to the parameter evaluation results; Returning to the step of inputting the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network, performing coating finished product quality prediction, and obtaining predicted finished product quality parameters; If it meets the requirements, configuring the parameters of the electrode coating equipment so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters; The performing parameter evaluation on the predicted value of the finished product quality parameter to obtain the parameter evaluation result includes: inputting the predicted value of the finished product quality parameter into the finished product quality parameter evaluation model, performing finished product quality parameter evaluation, and obtaining the parameter evaluation result; The expression of the finished product quality parameter evaluation model is: in, is the parameter evaluation result, is the predicted value of the quality parameter of the i-th finished product, is the i-th finished product quality target parameter, i=1,2,...,m, and m is the number of finished product quality target parameters.

2. The intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes according to claim 1, characterized in that: The lithium-ion battery electrode double-sided coating intelligent machine adjustment method also includes: Sampling parameters of the electrode coating equipment to obtain sample parameters of the coating liquid material, sample parameters of the equipment process and sample parameters of the finished product quality; Performing digital twin design on the electrode coating equipment to obtain a digital twin, collecting parameters generated by the digital twin during coating, and supplementing them to the coating liquid material sample parameters, the equipment process sample parameters, and the finished product quality sample parameters; The deep neural network is trained using the coating liquid material sample parameters, the equipment process sample parameters and the finished product quality sample parameters to obtain the finished product quality prediction network.

3. The intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes according to claim 2, characterized in that: The calculation formula for the sampling time interval for parameter sampling of the electrode coating device is: in, is the sampling time interval, For the The rate of change of the process sample parameters of each equipment over time, is the minimum function, k=1,2,…,o, o is the number of equipment process sample parameters.

4. The intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes according to claim 1, characterized in that: The lithium-ion battery electrode double-sided coating intelligent machine adjustment method also includes: According to the preset delay parameters, the equipment process sample parameters and the finished product quality sample parameters are associated to obtain a mapping relationship between the equipment process sample parameters and the finished product quality sample parameters; The coating liquid material parameters are corrected to obtain corrected coating liquid material parameters, The equipment process sample parameters and the corrected coating liquid material parameters are associated to obtain a mapping relationship between the equipment process sample parameters and the coating liquid material parameters.

5. The intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes according to claim 1, characterized in that: The resetting of the equipment process parameters according to the parameter evaluation result includes: Constructing a continuous equipment process parameter search space according to the mapping relationship between the equipment process sample parameters and the finished product quality sample parameters; In the equipment process parameter search space, parameter search is performed with the goal of making the parameter evaluation result approach the preset evaluation condition, and the equipment process parameter is reset according to the search result.

6. The intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes according to claim 1, characterized in that: The parameter configuration of the electrode coating equipment according to the predicted value of the finished product quality parameter includes: Determine the equipment process configuration parameters according to the predicted values ​​of the finished product quality parameters; the equipment process prediction parameters are the equipment process parameters input into the finished product quality prediction network corresponding to the predicted values ​​of the finished product quality parameters; Performing parameter interpolation in a parameter region between the current equipment process parameters of the electrode coating equipment and the equipment process configuration parameters to obtain a plurality of equipment process interpolation parameters; According to the equipment process interpolation parameters, the electrode coating equipment is configured with parameters for multiple times, so that the electrode coating equipment operates according to the equipment process configuration parameters after the parameter configuration.

7. An intelligent machine adjustment device for double-sided coating of lithium-ion battery electrodes, characterized in that: include: The first module is used to obtain coating liquid material parameters and equipment process parameters; The second module is used to input the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network to predict the quality of the coated finished product and obtain the predicted values ​​of the finished product quality parameters; the finished product quality prediction network is obtained by training a preset deep neural network using the coating liquid material sample parameters, the equipment process sample parameters and the finished product quality sample parameters having a mapping relationship; The third module is used to perform parameter evaluation on the predicted value of the finished product quality parameter to obtain a parameter evaluation result; The fourth module is used to determine whether the parameter evaluation result meets the preset evaluation conditions; When the parameter evaluation result is greater than the quality parameter evaluation threshold, the parameter evaluation result meets the preset evaluation condition; when the parameter evaluation result is not greater than the quality parameter evaluation threshold, the parameter evaluation result does not meet the preset evaluation condition; The fifth module is used for resetting the equipment process parameters according to the parameter evaluation results when the parameter evaluation results do not meet the preset evaluation conditions, returning to the step of inputting the coating liquid material parameters and the equipment process parameters into the finished product quality prediction network to predict the quality of the coated finished product and obtain the predicted finished product quality parameters, and when the parameter evaluation results meet the preset evaluation conditions, configuring the parameters of the electrode coating equipment so that the electrode coating equipment operates according to the equipment process parameters corresponding to the predicted values ​​of the finished product quality parameters; The performing parameter evaluation on the predicted value of the finished product quality parameter to obtain the parameter evaluation result includes: inputting the predicted value of the finished product quality parameter into the finished product quality parameter evaluation model, performing finished product quality parameter evaluation, and obtaining the parameter evaluation result; The expression of the finished product quality parameter evaluation model is: in, is the parameter evaluation result, is the predicted value of the quality parameter of the i-th finished product, is the i-th finished product quality target parameter, i=1,2,...,m, and m is the number of finished product quality target parameters.

8. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes as claimed in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the intelligent machine adjustment method for double-sided coating of lithium-ion battery electrodes according to any one of claims 1 to 6 is implemented.

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