CN115098704A - Battery pole piece thickness prediction method, device and equipment and readable storage medium - Google Patents
Battery pole piece thickness prediction method, device and equipment and readable storage medium Download PDFInfo
- Publication number
- CN115098704A CN115098704A CN202211015976.7A CN202211015976A CN115098704A CN 115098704 A CN115098704 A CN 115098704A CN 202211015976 A CN202211015976 A CN 202211015976A CN 115098704 A CN115098704 A CN 115098704A
- Authority
- CN
- China
- Prior art keywords
- pole piece
- thickness
- battery pole
- mechanism model
- production
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/02—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness
- G01B21/08—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring length, width, or thickness for measuring thickness
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/38—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Computational Linguistics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Library & Information Science (AREA)
- Battery Electrode And Active Subsutance (AREA)
Abstract
The invention discloses a method, a device and equipment for predicting the thickness of a battery pole piece and a readable storage medium, and relates to the technical field of battery pole piece production, wherein the method for predicting the thickness of the battery pole piece comprises the following steps: obtaining production input parameters on a current battery pole piece production line, and taking the production input parameters as pole piece characteristic numbers of battery pole pieces; inputting the pole piece characteristic number into a preset industrial mechanism model; and generating the predicted thickness of the battery pole piece through a preset industrial mechanism model. In the production process of the battery pole piece, production input parameters of each production device are collected, and the thickness of the produced battery pole piece is predicted by inputting the production input parameters into a preset industrial mechanism model. It can be understood that, in this embodiment, the prediction of the thickness of the battery pole piece is realized, and a technician can adjust the device in advance based on the prediction result, thereby avoiding the problem that the device for adjusting the battery pole piece actually produced wastes production materials.
Description
Technical Field
The invention relates to the technical field of battery pole piece production, in particular to a method, a device and equipment for predicting the thickness of a battery pole piece and a readable storage medium.
Background
At present, in the lithium battery industry, the thickness of a pole piece directly influences the product performance. Therefore, the manufacturing process of the pole piece is particularly important in the battery manufacturing process. And the pole piece manufacturing process stage can be subdivided into five processes of slurry preparation, slurry coating, pole piece rolling, pole piece slitting and pole piece drying. For the thickness of the battery pole piece, at present, a laser thickness measuring instrument is mainly used for measuring in a coating and rolling process, and the thickness of a thin film material is measured by utilizing a laser triangulation distance measuring principle. The operation mode mainly measures the thickness of the electrode plate after rolling and before rolling so as to find out the defects. After the defects are found, personnel are required to comprehensively judge the factors of each device and material, and then the device is adjusted. However, the whole detection process is delayed, and the intervention and guidance on the production process can not be carried out by finding out the abnormality in advance, thereby causing unnecessary material waste.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device and equipment for predicting the thickness of a battery pole piece and a readable storage medium, and aims to solve the technical problems that hysteresis exists in the current detection process of the thickness of the battery pole piece, and the material waste is caused because the abnormality cannot be found in advance to intervene and guide the production process.
In order to achieve the above object, the present invention provides a method for predicting the thickness of a battery pole piece, which comprises the following steps:
obtaining production input parameters on a current battery pole piece production line, and taking the production input parameters as pole piece characteristic numbers of battery pole pieces;
inputting the pole piece characteristic number into a preset industrial mechanism model;
and generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
Further, the battery pole piece production line comprises a stirrer, a coating machine and a roller press, the step of obtaining the production input parameters on the current battery pole piece production line and taking the production input parameters as the pole piece characteristic number of the battery pole piece comprises the following steps:
and taking the production input parameter of the stirrer as a stirrer characteristic number, taking the production input parameter of the coater as a coater characteristic number, and taking the production input parameter of the roller press as a roller press characteristic number, wherein the pole piece characteristic number comprises the stirrer characteristic number, the coater characteristic number and the roller press characteristic number.
Further, the preset industrial mechanism model includes a stirrer mechanism model, a coater mechanism model and a roller press mechanism model, and the step of inputting the characteristic number into the preset industrial mechanism model includes:
inputting the characteristic number of the stirrer into the stirrer mechanism model, inputting the characteristic number of the coating machine into the coating machine mechanism model, and inputting the characteristic number of the roller press into the roller press mechanism model.
Further, the step of generating the predicted thickness of the battery pole piece through a preset industrial mechanism model includes:
generating a first thickness through the stirrer mechanism model, generating a second thickness through the coater mechanism model, and generating a third thickness through the roller press mechanism model;
multiplying the first thickness by a preset first influence factor to obtain a first predicted thickness, multiplying the second thickness by a preset second influence factor to obtain a second predicted thickness, and multiplying the third thickness by a preset third influence factor to obtain a third predicted thickness;
and taking the sum of the first predicted thickness, the second predicted thickness and the third predicted thickness as the predicted thickness of the battery pole piece.
Further, the step of generating a first thickness by the stirrer mechanism model, generating a second thickness by the coater mechanism model, and generating a third thickness by the roller press mechanism model includes:
obtaining a first thickness data set according to a plurality of first classifiers in the mechanism model of the stirring machine, and generating a first thickness based on the distribution condition of each data in the first thickness data set;
obtaining a second thickness data set according to a plurality of second classifiers in the mechanism model of the coating machine, and generating the second thickness based on the distribution condition of each data in the second thickness data set;
and obtaining a third thickness data set according to a plurality of third classifiers in the roller press mechanism model, and generating the third thickness based on the distribution condition of each data in the third thickness data set.
Further, after the step of generating the predicted thickness of the battery pole piece through the preset industrial mechanism model, the method further includes:
obtaining production output parameters on the battery pole piece production line;
and generating an adjustment suggestion of the battery pole piece production line based on a preset battery pole piece knowledge map, the production output parameters and the predicted thickness.
Further, the step of generating the adjustment suggestion of the battery pole piece production line based on the preset battery pole piece knowledge map, the production output parameter and the predicted thickness comprises the following steps of:
taking the production output parameters and the predicted thickness as retrieval fields, and retrieving from the preset battery pole piece knowledge graph;
and taking the retrieved result as an adjustment suggestion of the battery pole piece production line.
In addition, in order to achieve the above object, the present invention further provides a battery pole piece thickness prediction apparatus, including:
the acquisition module is used for acquiring production input parameters on the current battery pole piece production line and taking the production input parameters as pole piece characteristic numbers of the battery pole pieces;
the input module is used for inputting the pole piece characteristic number to a preset industrial mechanism model;
and the prediction module is used for generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
In addition, in order to achieve the above object, the present invention further provides a battery pole piece thickness prediction apparatus, including: the device comprises a memory, a processor and a battery pole piece thickness prediction program which is stored on the memory and can run on the processor, wherein the battery pole piece thickness prediction program realizes the steps of the battery pole piece thickness prediction method when being executed by the processor.
In addition, in order to achieve the above object, the present invention further provides a readable storage medium, where a battery pole piece thickness prediction program is stored, and when the battery pole piece thickness prediction program is executed by a processor, the steps of the above battery pole piece thickness prediction method are implemented.
The embodiment of the invention provides a method, a device and equipment for predicting the thickness of a battery pole piece and a readable storage medium. Obtaining production input parameters on a current battery pole piece production line, and taking the production input parameters as pole piece characteristic numbers of battery pole pieces; inputting the pole piece characteristic number into a preset industrial mechanism model; and generating the predicted thickness of the battery pole piece through a preset industrial mechanism model. In the production process of the battery pole piece, production input parameters of all production equipment are collected, and the thickness of the produced battery pole piece is predicted by inputting the production input parameters into a preset industrial mechanism model. It can be understood that, in this embodiment, the prediction of the thickness of the battery pole piece is realized, and a technician can adjust the device in advance based on the prediction result, thereby avoiding the problem that the device for adjusting the battery pole piece actually produced wastes production materials.
Drawings
FIG. 1 is a schematic diagram of an apparatus architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for predicting thickness of a battery electrode according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a second embodiment of the method for predicting the thickness of a battery electrode plate according to the present invention;
FIG. 4 is a schematic view of a process flow of the battery plate of the present invention in the method for predicting the thickness of a battery plate of the present invention;
FIG. 5 is a schematic diagram of a frame applied to the battery pole piece thickness prediction method of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The main solution of the embodiment of the invention is as follows: obtaining production input parameters on a current battery pole piece production line, and taking the production input parameters as pole piece characteristic numbers of battery pole pieces; inputting the pole piece characteristic number into a preset industrial mechanism model; and generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
Because the current detection process of the thickness of the battery pole piece has hysteresis, the production process cannot be intervened and guided by discovering abnormality in advance, and the technical problem of material waste is caused.
The invention provides a solution, which is characterized in that in the production process of a battery pole piece, production input parameters of each production device are collected, and the thickness of the produced battery pole piece is predicted by inputting the production input parameters into a preset industrial mechanism model. It can be understood that, in this embodiment, the prediction of the thickness of the battery pole piece is realized, and a technician can adjust the device in advance based on the prediction result, so as to avoid the problem that the device can waste production materials according to the actually produced battery pole piece.
As shown in fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
The device in the embodiment of the invention can be a battery pole production device, and also can be an electronic terminal device with data receiving, data processing and data sending functions, such as a smart phone, a PC, a tablet personal computer, a portable computer and the like.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. The communication bus 1002 is used to implement connection communication among these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001 described previously.
Optionally, the device may also include a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, WiFi modules, and the like. Such as light sensors, motion sensors, and other sensors. In particular, the light sensor may include an ambient light sensor that adjusts the brightness of the display screen based on the intensity of ambient light, and a proximity sensor that turns off the display screen and/or backlight when the mobile device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the mobile terminal is stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration), vibration recognition related functions (such as pedometer and tapping) and the like for recognizing the attitude of the mobile terminal; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein again.
Those skilled in the art will appreciate that the configuration of the apparatus shown in fig. 1 is not intended to be limiting of the apparatus and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a battery pole piece thickness prediction program.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call the battery pole piece thickness prediction program stored in the memory 1005, and perform the following operations:
obtaining production input parameters on a current battery pole piece production line, and taking the production input parameters as pole piece characteristic numbers of battery pole pieces;
inputting the pole piece characteristic number into a preset industrial mechanism model;
and generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
Further, processor 1001 may call a battery pole piece thickness prediction program stored in memory 1005, and also perform the following operations:
the production line of the battery pole piece comprises a stirrer, a coating machine and a roller press, the step of obtaining the production input parameters on the current production line of the battery pole piece and taking the production input parameters as the pole piece characteristic number of the battery pole piece comprises the following steps:
and taking the production input parameter of the stirrer as a stirrer characteristic number, taking the production input parameter of the coater as a coater characteristic number, and taking the production input parameter of the roller press as a roller press characteristic number, wherein the pole piece characteristic number comprises the stirrer characteristic number, the coater characteristic number and the roller press characteristic number.
Further, processor 1001 may call a battery pole piece thickness prediction program stored in memory 1005, and also perform the following operations:
the preset industrial mechanism model comprises a stirrer mechanism model, a coating machine mechanism model and a roller press mechanism model, and the step of inputting the characteristic number into the preset industrial mechanism model comprises the following steps:
inputting the characteristic number of the stirrer into the stirrer mechanism model, inputting the characteristic number of the coating machine into the coating machine mechanism model, and inputting the characteristic number of the roller press into the roller press mechanism model.
Further, processor 1001 may call a battery pole piece thickness prediction program stored in memory 1005, and also perform the following operations:
the step of generating the predicted thickness of the battery pole piece through a preset industrial mechanism model comprises the following steps:
generating a first thickness through the stirrer mechanism model, generating a second thickness through the coater mechanism model, and generating a third thickness through the roller press mechanism model;
multiplying the first thickness by a preset first influence factor to obtain a first predicted thickness, multiplying the second thickness by a preset second influence factor to obtain a second predicted thickness, and multiplying the third thickness by a preset third influence factor to obtain a third predicted thickness;
and taking the sum of the first predicted thickness, the second predicted thickness and the third predicted thickness as the predicted thickness of the battery pole piece.
Further, processor 1001 may call a battery pole piece thickness prediction program stored in memory 1005, and also perform the following operations:
the step of generating a first thickness by the stirrer mechanism model, generating a second thickness by the coater mechanism model, and generating a third thickness by the roller press mechanism model includes:
obtaining a first thickness data set according to a plurality of first classifiers in the mechanism model of the stirring machine, and generating a first thickness based on the distribution condition of each data in the first thickness data set;
obtaining a second thickness data set according to a plurality of second classifiers in the mechanism model of the coating machine, and generating a second thickness based on the distribution condition of each data in the second thickness data set;
and obtaining a third thickness data set according to a plurality of third classifiers in the roller press mechanism model, and generating the third thickness based on the distribution condition of each data in the third thickness data set.
Further, processor 1001 may call a battery pole piece thickness prediction program stored in memory 1005, and also perform the following operations:
after the step of generating the predicted thickness of the battery pole piece through the preset industrial mechanism model, the method further comprises:
obtaining production output parameters on the battery pole piece production line;
and generating an adjustment suggestion of the battery pole piece production line based on a preset battery pole piece knowledge map, the production output parameters and the predicted thickness.
Further, processor 1001 may call a battery pole piece thickness prediction program stored in memory 1005, and also perform the following operations:
the preset battery pole piece knowledge graph is built based on the technological parameters of the battery pole piece production line, and the step of generating the adjustment suggestion of the battery pole piece production line based on the preset battery pole piece knowledge graph, the production output parameters and the predicted thickness comprises the following steps:
taking the production output parameters and the predicted thickness as retrieval fields, and retrieving from the preset battery pole piece knowledge graph;
and taking the retrieved result as an adjustment suggestion of the battery pole piece production line.
Referring to fig. 2, a method for predicting the thickness of a battery pole piece according to a first embodiment of the present invention includes:
step S10, obtaining production input parameters on the current battery pole piece production line, and taking the production input parameters as pole piece characteristic numbers of the battery pole pieces;
further, the battery pole piece production line comprises a stirrer, a coating machine and a roller press, the production input parameters of the current battery pole piece production line are obtained, the production input parameters of the stirrer are used as stirrer characteristic numbers, the production input parameters of the coating machine are used as coating machine characteristic numbers, and the production input parameters of the roller press are used as roller press characteristic numbers, wherein the pole piece characteristic numbers comprise the stirrer characteristic numbers, the coating machine characteristic numbers and the roller press characteristic numbers.
Specifically, in this embodiment, the apparatus in the battery pole piece production line mainly includes a stirrer, a coater, and a roller press. And the production input parameters on the production line refer to the input parameters of each device on the production line. All production equipment, environmental parameters, quality detection equipment and the like are interconnected and communicated on the basis of the Internet of things field, so that related operation parameters or detection results are obtained in real time. Referring to fig. 4, a production flow chart of the battery pole piece of the present invention is shown. The method comprises a battery pole piece production process, an input variable and an output variable. The battery level sheet production process is used for reflecting the production process of the battery pole sheet production line. The production process of the battery pole piece comprises the following steps: stirring; detecting components; coating; detecting the size and the thickness; rolling; detecting the size and the thickness; and (6) rolling. Correspondingly, the stirring process is carried out by the stirrer, and the input variables of the stirring process comprise: rotation speed, temperature and humidity, time, stirring method, formula, solid content and the like. The coating process is performed by a coater, and the input variables during the coating process include: tension, temperature and humidity, coating speed, unwinding and winding diameters and deviation correction precision. The rolling process is performed by a roller press, and the input variables of the rolling process include: pressure, pre-adjusting roll gap, coating thickness. In addition, the device also comprises a winding process, and input variables comprise rotating speed, unwinding and winding diameter. The production input parameters include parameter values corresponding to input variables in each process.
And taking the parameter value corresponding to the input variable of the stirring process as the characteristic number of the stirrer. And taking the parameter value corresponding to the input variable in the coating process as the characteristic number of the coating machine. And taking the parameter value corresponding to the input variable in the rolling process as the characteristic number of the roller press. The pole piece characteristic numbers used for predicting the thickness of the pole piece comprise the stirrer characteristic number, the coating machine characteristic number and the rolling machine characteristic number.
Step S20, inputting the pole piece characteristic number to a preset industrial mechanism model;
further, the preset industrial mechanism model comprises a stirrer mechanism model, a coater mechanism model and a roller press mechanism model, the stirrer characteristic number is input into the stirrer mechanism model, the coater characteristic number is input into the coater mechanism model, and the roller press characteristic number is input into the roller press mechanism model.
Specifically, the preset industrial mechanism model in this example includes a stirrer mechanism model, a coater mechanism model, and a roll press mechanism model. And respectively inputting the characteristic number of the stirrer into the stirrer mechanism model, inputting the characteristic number of the coating machine into the coating machine mechanism model, and inputting the characteristic number of the roller press into the roller press mechanism model. Each mechanism model is built based on a classification algorithm, such as a random forest algorithm in Bagging. And training the actual production data of each mechanism model as the training data of the corresponding mechanism model. Referring also to fig. 4, in addition to the input variables and the battery pole piece production process, output variables are included. And the output variables of the blending process include: viscosity, particle size, stability, filterability, rheology. Output variables of the coating process include: size, coating thickness and precision. The output variables of the rolling process include: effective rolling force and rolling thickness. Obtaining the output variable after winding comprises: size and thickness of the pole piece. And the production data used to train the mechanistic model may actually include the input variables described above and/or the output variables described above. The output variable pole piece size and thickness are actually the results to be predicted by the method, so the pole piece size and thickness in the production data can be the labels of the training samples. The training data actually consists of the label (pole piece size and thickness) and the characteristic numbers, wherein the characteristic numbers may consist of production parameters corresponding to input and/or output variables, and in this embodiment, the input parameters of each device are preferably used as the characteristic numbers of the corresponding device.
And step S30, generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
Further, generating a first thickness through the stirrer mechanism model, generating a second thickness through the coater mechanism model, and generating a third thickness through the roller press mechanism model; multiplying the first thickness by a preset first influence factor to obtain a first predicted thickness, multiplying the second thickness by a preset second influence factor to obtain a second predicted thickness, and multiplying the third thickness by a preset third influence factor to obtain a third predicted thickness; and taking the sum of the first predicted thickness, the second predicted thickness and the third predicted thickness as the predicted thickness of the battery pole piece.
Specifically, the stirrer mechanism model can generate a first thickness based on the input stirrer characteristic number, the coater mechanism model can generate a second thickness based on the coater characteristic number, and the roller press mechanism model can generate a third thickness based on the roller press characteristic number. And multiplying the first thickness, the second thickness and the third thickness by corresponding preset influence factors respectively, and then adding the multiplied values to obtain the sum, wherein the sum is used as the predicted thickness of the battery pole piece. The specific calculation formula is as follows: predicted thickness = first thickness × preset first influence factor + second thickness × preset second influence factor + third thickness × preset third influence factor. Wherein the first, second and third influencing factors can be set by a skilled person according to the actual situation.
Further, a first thickness data set is obtained according to a plurality of first classifiers in the stirrer mechanism model, and the first thickness is generated based on the distribution condition of each data in the first thickness data set; obtaining a second thickness data set according to a plurality of second classifiers in the mechanism model of the coating machine, and generating a second thickness based on the distribution condition of each data in the second thickness data set; and obtaining a third thickness data set according to a plurality of third classifiers in the roller press mechanism model, and generating the third thickness based on the distribution condition of each data in the third thickness data set.
Specifically, each mechanistic model includes a plurality of classifiers. The mixer mechanism model is taken as an example for illustration, the mixer mechanism model includes a plurality of first classifiers, and the training samples of each first classifier are different. For example, k sample subsets (the number of samples in each sample subset may be 2/3 of the number of original training sample sets, and the number of samples in a specific subset may be set by a technician as required) are generated based on an original training sample set (where each sample in the original training samples is composed of a label (a pole piece size and a thickness) and a feature number (when a mechanical model of a mixer is trained, the feature number is an input parameter corresponding to each input variable in actual production, all the input variables may be used as features, and a part of the input variables may also be extracted as features)). When there are k sample subsets, the number of corresponding first classifiers is then k. It is understood that the first classifier trained based on different sample subsets does not necessarily have the same classification result when the same feature number is received. After the characteristic number of the stirrer is input into the stirrer mechanism model, k first classifiers can obtain k results based on the characteristic number of the stirrer, and the obtained k results are the first thickness data set. And generating the first thickness based on the distribution of each data in the first thickness data set, specifically, taking the result with the largest occurrence number as the first thickness. Similarly, the second thickness can be obtained based on the coater model, the third thickness can be obtained based on the roller press mechanism model, and the concrete process can refer to the stirrer mechanism model, which is not described herein again. It can be understood that, in this embodiment, an industrial mechanism model corresponding to each device is established, and the prediction results of the mechanism models of the devices are integrated to predict the thickness of the pole piece produced by the battery pole piece production line. And the predicted thickness of the battery pole piece can be used as the basis for adjusting equipment by technicians.
In addition, in the present embodiment, reference may be made to fig. 5 for an overall framework of the method for predicting the thickness of the battery pole piece, where fig. 5 is a schematic diagram of the framework of the method for predicting the thickness of the battery pole piece according to the present invention, and the diagram includes a field hardware layer, a data sensing layer, a data processing layer, and an application layer. The field hardware layer can collect parameters related to field equipment and environment, and uploads the related parameters to the data processing layer through the data sensing layer to be processed. And the application layer predicts the battery pole pieces and generates adjustment suggestions (such as stirring operation optimization, coating operation optimization and roller press operation optimization) of production line equipment based on the mechanism model and each knowledge base (such as an integrated process knowledge base, a stirrer process knowledge base, a coating process knowledge base and a roller press process knowledge base) of the data processing layer.
In the implementation, the production input parameters on the current battery pole piece production line are obtained, and the production input parameters are used as the pole piece characteristic numbers of the battery pole pieces; inputting the pole piece characteristic number into a preset industrial mechanism model; and generating the predicted thickness of the battery pole piece through a preset industrial mechanism model. In the production process of the battery pole piece, production input parameters of each production device are collected, and the thickness of the produced battery pole piece is predicted by inputting the production input parameters into a preset industrial mechanism model. It can be understood that, in this embodiment, the prediction of the thickness of the battery pole piece is realized, and a technician can adjust the device in advance based on the prediction result, so as to avoid the problem that the device can waste production materials according to the actually produced battery pole piece.
Referring to fig. 3, a second embodiment of the method for predicting the thickness of a battery pole piece according to the present invention is provided based on the first embodiment of the method for predicting the thickness of a battery pole piece according to the present invention.
After the step of step S30, the method further includes:
step S301, acquiring production output parameters on the battery pole piece production line;
step S302, based on a preset battery pole piece knowledge graph, the production output parameters and the predicted thickness, an adjustment suggestion of the battery pole piece production line is generated.
Further, the preset battery pole piece knowledge graph is built based on the process parameters of the battery pole piece production line, the production output parameters and the predicted thickness are used as retrieval fields, and retrieval is carried out from the preset battery pole piece knowledge graph; and taking the retrieved result as an adjustment suggestion of the battery pole piece production line.
Specifically, referring to fig. 4, the production output parameters are the parameter values corresponding to the output variables in fig. 4. And when the thickness of the battery pole piece is predicted, the production generation output parameters on the battery pole piece production line can be obtained. And generating an adjustment suggestion of each device on the battery pole piece production line according to the battery pole piece knowledge map, the production output parameters and the predicted thickness. The battery pole piece knowledge graph is built based on technological parameters of a battery pole piece production line.
The construction of the knowledge graph mainly comprises four processes: collecting data; extracting knowledge; linking and fusing knowledge; and (4) knowledge reasoning.
Data acquisition: a large amount of data is the basis for building a knowledge graph. The source of data collection can be public data sets of a network, data sets which are already open in the academic field. In the embodiment, the knowledge map is mainly applied to the production line of the battery pole piece, so that data can be obtained from daily production records.
And (3) knowledge extraction: and (4) preprocessing and roughly processing the data. The entities of the data center are sorted by identification and classification. We then need to perform a relationship extraction. In a knowledge graph, the relationship between entities is often represented by RDF (Resource Description Framework). While in form, RDF is often represented as a triple (Subject Predicate Object). Therefore, in the knowledge extraction process, data needs to be extracted into entity-relationship triples. And extracting the relation between the entities to obtain the minimum unit triple of the knowledge graph, wherein the minimum unit triple is mainly realized by a Piece-Wise-CNN (relation extraction) and a Tensorflow deep learning framework. The specific process is as follows: data preprocessing, namely performing position coding on data, and coding according to the distance between each word in sentences in a data file and an entity; segmenting sentences, namely cutting a piece of text data at two entities once respectively so as to segment the text into three segments; extracting characteristics, namely splicing the position characteristics and the text characteristics, and extracting the characteristics through a Convolutional Neural Network (CNN); and (4) relation classification, wherein the extracted features are spliced and sent into a softmax layer after passing through a maximum pooling layer, and finally classification of a relation is obtained, so that the relation between two entities is obtained.
Knowledge linking and fusion: due to the problem of different data sources, different entities can be further fused and aligned to form a new entity. According to the entity after fusion, the triple set can further learn and reason, and rebuild relations and conflicts. And finally, the data is expressed in the form of RDF to form an undirected graph network. The alignment of the entities can be implemented based on machine classification algorithms, such as support vector machines, neural networks, and the like. The process of knowledge extraction can achieve the goal of obtaining entities, relationships and entity attribute information from unstructured and semi-structured data. However, considering that the data sources are different, knowledge from different data sources may have problems of duplication, missing hierarchy and the like. Therefore, some different entities must be further fused through data fusion to form a new entity. According to the fused entities, the triple set can further learn and reason, and rebuild relations and conflicts. And finally still in RDF form. Knowledge fusion is often achieved by means of entity alignment. Entity alignment is mainly used for solving the problem of inconsistency such as entity conflict, unknown direction, entity repetition and the like in heterogeneous data. After the entities are aligned, a top-level unified knowledge base can be created, so that the multi-source heterogeneous problem of the data is effectively solved. A main process of knowledge base entity alignment, wherein the process 1: and carrying out partition indexing on the data needing to be aligned. The method mainly aims to reduce the computation complexity of the matching and alignment algorithm and improve the algorithm efficiency; and (2) a process: establishing a relationship and a similarity degree between the examples by using a similarity function to obtain a pair matching relationship; and (3) a flow path: and based on the obtained matching relationship, carrying out instance fusion by utilizing an entity alignment algorithm based on machine learning. Entity alignment algorithms based on machine learning can be classified into supervised learning and unsupervised learning depending on whether label data is used. The entity alignment method based on supervised learning mainly converts the entity alignment problem into a classification problem. And classifying the entities, and fusing and matching the entities of the same class. Typical representatives of such methods include decision trees, support vector machines, neural networks, and the like. The entity alignment method based on unsupervised learning is to convert the entity alignment problem into a clustering problem. The main idea is to gather similar entities as much as possible based on the matching relationship between the entities obtained in the flow process 3, thereby performing entity alignment. Common Clustering includes K-means, Gaussian Mixture Model (GMM), and Density-Based Spatial Clustering of Applications with Noise Based on Density (DBSCAN). And (4) a flow chart: and aligning and fusing the entities in the same class based on the classification (or clustering) result obtained in the process 3 to form a final alignment result.
Knowledge reasoning: based on the obtained undirected graph network, the network association analysis can be carried out based on a graph theory method, and the undirected graph network is applied to documents and retrieval. Meanwhile, daily knowledge updating, entity expanding and updating undirected graph of a keyword search engine can be carried out, and the undirected graph is a graph without directions. Since the knowledge graph is mainly formed by three-group splicing, when a relationship exists between two nodes in the graph, the two nodes are connected together by a non-directional edge. Nodes are entities, and undirected edges are relationships. The knowledge-graph can generally be viewed as an undirected graph network. After the knowledge graph is built, performing network association analysis by using an undirected graph breadth-first search algorithm: a) starting from any node A, traversing all the adjacent nodes B and C; b) traversing all the adjacent points D and E by taking the B as a starting point; c) traversing all the adjacent points F and G by taking the C as a starting point; d) and so on.
And taking the obtained production output parameters and the predicted thickness as key fields of retrieval, retrieving in the established battery pole piece knowledge graph, and taking the retrieval result as an adjustment suggestion of the battery pole piece production line at the moment. For example, in a production output parameter where the output viscosity parameter value approaches a lower limit, the predicted thickness is less than the gauge thickness. And the retrieval result from the battery pole piece knowledge graph based on the output viscosity parameter value and the predicted thickness is as follows: and (4) adjusting the speed of the coating machine and the roll gap of the roll squeezer to be wide, and then adjusting the speed of the coating machine to be low and the roll gap of the roll squeezer to be wide as adjustment suggestions. In the above situation, the original manual adjustment strategy may be to adjust the material ratio input by the mixer.
In the embodiment, production output parameters on the battery pole piece production line are obtained; and generating an adjustment suggestion of the battery pole piece production line based on a preset battery pole piece knowledge map, the production output parameters and the predicted thickness. The method comprises the steps of generating a battery pole piece knowledge graph according to output parameters on a weighing production line, the predicted thickness of the battery pole piece and the built battery pole piece knowledge graph, and then, adjusting the production line of the battery pole piece according to the suggestion. Can be used as a reference basis for technical staff to adjust the equipment on the battery pole piece production line.
In addition, the embodiment of the present invention further provides a battery pole piece thickness prediction apparatus, where the battery pole piece thickness prediction apparatus includes:
the acquisition module is used for acquiring production input parameters on the current battery pole piece production line and taking the production input parameters as pole piece characteristic numbers of the battery pole pieces;
the input module is used for inputting the pole piece characteristic number to a preset industrial mechanism model;
and the prediction module is used for generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
Optionally, the battery pole piece production line includes a stirrer, a coater and a roller press, and the obtaining module is further configured to:
and taking the production input parameter of the stirrer as a stirrer characteristic number, taking the production input parameter of the coater as a coater characteristic number, and taking the production input parameter of the roller press as a roller press characteristic number, wherein the pole piece characteristic number comprises the stirrer characteristic number, the coater characteristic number and the roller press characteristic number.
Optionally, the preset industrial mechanism model includes a stirrer mechanism model, a coater mechanism model and a roller press mechanism model, and the input module is further configured to:
inputting the characteristic number of the stirrer into the stirrer mechanism model, inputting the characteristic number of the coating machine into the coating machine mechanism model, and inputting the characteristic number of the roller press into the roller press mechanism model.
Optionally, the prediction module is further configured to:
generating a first thickness through the stirrer mechanism model, generating a second thickness through the coater mechanism model, and generating a third thickness through the roller press mechanism model;
multiplying the first thickness by a preset first influence factor to obtain a first predicted thickness, multiplying the second thickness by a preset second influence factor to obtain a second predicted thickness, and multiplying the third thickness by a preset third influence factor to obtain a third predicted thickness;
and taking the sum of the first predicted thickness, the second predicted thickness and the third predicted thickness as the predicted thickness of the battery pole piece.
Optionally, the prediction module is further configured to:
obtaining a first thickness data set according to a plurality of first classifiers in the stirrer mechanism model, and generating a first thickness based on the distribution condition of each data in the first thickness data set;
obtaining a second thickness data set according to a plurality of second classifiers in the mechanism model of the coating machine, and generating a second thickness based on the distribution condition of each data in the second thickness data set;
and obtaining a third thickness data set according to a plurality of third classifiers in the roller press mechanism model, and generating the third thickness based on the distribution condition of each data in the third thickness data set.
Optionally, the prediction module is further configured to:
obtaining production output parameters on the battery pole piece production line;
and generating an adjustment suggestion of the battery pole piece production line based on a preset battery pole piece knowledge map, the production output parameters and the predicted thickness.
Optionally, the preset battery pole piece knowledge graph is built based on the process parameters of the battery pole piece production line, and the prediction module is further configured to:
taking the production output parameters and the predicted thickness as retrieval fields, and retrieving from the preset battery pole piece knowledge graph;
and taking the retrieved result as an adjustment suggestion of the battery pole piece production line.
The device for predicting the thickness of the battery pole piece, provided by the invention, adopts the method for predicting the thickness of the battery pole piece in the embodiment, and solves the technical problems that the existing detection process of the thickness of the battery pole piece has hysteresis, and the intervention and guidance on the production process can not be performed by discovering the abnormality in advance, so that the material waste is caused. Compared with the prior art, the beneficial effects of the battery pole piece thickness prediction device provided by the embodiment of the invention are the same as those of the battery pole piece thickness prediction method provided by the embodiment, and other technical characteristics of the battery pole piece thickness prediction device are the same as those disclosed by the embodiment method, which are not repeated herein.
In addition, an embodiment of the present invention further provides a device for predicting a thickness of a battery pole piece, where the device for predicting a thickness of a battery pole piece includes: the battery pole piece thickness prediction method comprises a memory, a processor and a battery pole piece thickness prediction program which is stored on the memory and can run on the processor, wherein the battery pole piece thickness prediction program realizes the steps of the battery pole piece thickness prediction method when being executed by the processor.
The specific implementation of the battery pole piece thickness prediction device of the present invention is basically the same as that of each embodiment of the new battery pole piece thickness prediction method, and is not described herein again.
In addition, an embodiment of the present invention further provides a readable storage medium, where a battery pole piece thickness prediction program is stored on the readable storage medium, and the battery pole piece thickness prediction program, when executed by a processor, implements the steps of the battery pole piece thickness prediction method.
The specific implementation of the medium of the present invention is substantially the same as that of each embodiment of the new method for predicting the thickness of the battery pole piece, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for predicting the thickness of a battery pole piece is characterized by comprising the following steps:
obtaining production input parameters on a current battery pole piece production line, and taking the production input parameters as pole piece characteristic numbers of battery pole pieces;
inputting the pole piece characteristic number into a preset industrial mechanism model;
and generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
2. The method for predicting the thickness of the battery pole piece according to claim 1, wherein the battery pole piece production line comprises a stirrer, a coater and a roller press, the step of obtaining the production input parameters on the current battery pole piece production line and using the production input parameters as the pole piece feature number of the battery pole piece comprises the following steps:
and taking the production input parameter of the stirrer as a stirrer characteristic number, taking the production input parameter of the coater as a coater characteristic number, and taking the production input parameter of the roller press as a roller press characteristic number, wherein the pole piece characteristic number comprises the stirrer characteristic number, the coater characteristic number and the roller press characteristic number.
3. The method for predicting the thickness of the battery pole piece according to claim 2, wherein the preset industrial mechanism model comprises a stirrer mechanism model, a coater mechanism model and a roller press mechanism model, and the step of inputting the characteristic number into the preset industrial mechanism model comprises the steps of:
inputting the characteristic number of the stirrer into the stirrer mechanism model, inputting the characteristic number of the coating machine into the coating machine mechanism model, and inputting the characteristic number of the roller press into the roller press mechanism model.
4. The method of claim 3, wherein the step of generating the predicted thickness of the battery pole piece via a pre-defined industrial mechanism model comprises:
generating a first thickness through the stirrer mechanism model, generating a second thickness through the coater mechanism model, and generating a third thickness through the roller press mechanism model;
multiplying the first thickness by a preset first influence factor to obtain a first predicted thickness, multiplying the second thickness by a preset second influence factor to obtain a second predicted thickness, and multiplying the third thickness by a preset third influence factor to obtain a third predicted thickness;
and taking the sum of the first predicted thickness, the second predicted thickness and the third predicted thickness as the predicted thickness of the battery pole piece.
5. The method of predicting the thickness of a battery pole piece according to claim 4, wherein the step of generating a first thickness by the blender mechanism model, a second thickness by the coater mechanism model, and a third thickness by the roller press mechanism model comprises:
obtaining a first thickness data set according to a plurality of first classifiers in the stirrer mechanism model, and generating a first thickness based on the distribution condition of each data in the first thickness data set;
obtaining a second thickness data set according to a plurality of second classifiers in the mechanism model of the coating machine, and generating a second thickness based on the distribution condition of each data in the second thickness data set;
and obtaining a third thickness data set according to a plurality of third classifiers in the roller press mechanism model, and generating the third thickness based on the distribution condition of each data in the third thickness data set.
6. The method of predicting the thickness of a battery pole piece of claim 5, wherein after the step of generating the predicted thickness of the battery pole piece through a pre-set industrial mechanism model, the method further comprises:
obtaining production output parameters on the battery pole piece production line;
and generating an adjustment suggestion of the battery pole piece production line based on a preset battery pole piece knowledge map, the production output parameters and the predicted thickness.
7. The method for predicting the thickness of the battery pole piece according to claim 6, wherein the preset battery pole piece knowledge graph is built based on process parameters of the battery pole piece production line, and the step of generating the adjustment suggestion of the battery pole piece production line based on the preset battery pole piece knowledge graph, the production output parameters and the predicted thickness comprises:
taking the production output parameters and the predicted thickness as retrieval fields, and retrieving from the preset battery pole piece knowledge graph;
and taking the retrieved result as an adjustment suggestion of the battery pole piece production line.
8. A battery pole piece thickness prediction device is characterized by comprising:
the acquisition module is used for acquiring production input parameters on the current battery pole piece production line and taking the production input parameters as pole piece characteristic numbers of the battery pole pieces;
the input module is used for inputting the pole piece characteristic number to a preset industrial mechanism model;
and the prediction module is used for generating the predicted thickness of the battery pole piece through a preset industrial mechanism model.
9. A battery pole piece thickness prediction apparatus, characterized by comprising: a memory, a processor, and a battery pole piece thickness prediction program stored on the memory and executable on the processor, the battery pole piece thickness prediction program when executed by the processor implementing the steps of the battery pole piece thickness prediction method of any one of claims 1 to 7.
10. A readable storage medium, wherein a battery pole piece thickness prediction program is stored on the readable storage medium, and when executed by a processor, the battery pole piece thickness prediction program implements the steps of the battery pole piece thickness prediction method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211015976.7A CN115098704B (en) | 2022-08-24 | 2022-08-24 | Battery pole piece thickness prediction method, device and equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211015976.7A CN115098704B (en) | 2022-08-24 | 2022-08-24 | Battery pole piece thickness prediction method, device and equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115098704A true CN115098704A (en) | 2022-09-23 |
| CN115098704B CN115098704B (en) | 2023-01-06 |
Family
ID=83301534
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211015976.7A Active CN115098704B (en) | 2022-08-24 | 2022-08-24 | Battery pole piece thickness prediction method, device and equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115098704B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1819308A (en) * | 2006-03-15 | 2006-08-16 | 上海南都能源科技有限公司 | Lithium ion battery pole piece with high multiplying power and production thereof |
| CN106099046A (en) * | 2016-08-19 | 2016-11-09 | 深圳市信宇人科技有限公司 | Lithium is from the continuous complete automatic assembly line of battery pole piece |
| CN108199005A (en) * | 2018-01-03 | 2018-06-22 | 浙江衡远新能源科技有限公司 | The rolling method and equipment of a kind of battery pole piece |
| US10047717B1 (en) * | 2018-02-05 | 2018-08-14 | Energystics, Ltd. | Linear faraday induction generator for the generation of electrical power from ocean wave kinetic energy and arrangements thereof |
| CN109978250A (en) * | 2019-03-20 | 2019-07-05 | 广州中国科学院工业技术研究院 | A kind of qualitative forecasting method of battery pole piece coating production surface density |
| CN110568374A (en) * | 2019-09-02 | 2019-12-13 | 东北电力大学 | Lithium ion battery residual service life prediction method based on consideration of internal and external characteristics |
| CN209991940U (en) * | 2019-06-26 | 2020-01-24 | 深圳市贝特瑞新能源材料股份有限公司 | Battery pole piece thickness variation measuring device and system |
| CN210400238U (en) * | 2019-08-08 | 2020-04-24 | 湖南盖亚中科新能源有限公司 | Online thickness detection device for lithium battery pole piece |
| CN111822517A (en) * | 2020-07-14 | 2020-10-27 | 江苏科瑞德智控自动化科技有限公司 | Lithium battery pole piece rolling mill thickness control system based on cloud platform BP neural network |
| CN114217238A (en) * | 2021-11-24 | 2022-03-22 | 浙江南都电源动力股份有限公司 | Method for predicting cycle life of lithium ion battery |
| CN114442680A (en) * | 2022-04-07 | 2022-05-06 | 东莞海裕百特智能装备有限公司 | Lithium battery pole piece thickness control method and system and readable storage medium |
| CN114566716A (en) * | 2022-04-24 | 2022-05-31 | 东莞海裕百特智能装备有限公司 | Lithium battery thickness online control method and system and readable storage medium |
| CN114819583A (en) * | 2022-04-19 | 2022-07-29 | 深圳先进技术研究院 | Lithium battery formation stage product quality prediction method and system |
-
2022
- 2022-08-24 CN CN202211015976.7A patent/CN115098704B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1819308A (en) * | 2006-03-15 | 2006-08-16 | 上海南都能源科技有限公司 | Lithium ion battery pole piece with high multiplying power and production thereof |
| CN106099046A (en) * | 2016-08-19 | 2016-11-09 | 深圳市信宇人科技有限公司 | Lithium is from the continuous complete automatic assembly line of battery pole piece |
| CN108199005A (en) * | 2018-01-03 | 2018-06-22 | 浙江衡远新能源科技有限公司 | The rolling method and equipment of a kind of battery pole piece |
| US10047717B1 (en) * | 2018-02-05 | 2018-08-14 | Energystics, Ltd. | Linear faraday induction generator for the generation of electrical power from ocean wave kinetic energy and arrangements thereof |
| CN109978250A (en) * | 2019-03-20 | 2019-07-05 | 广州中国科学院工业技术研究院 | A kind of qualitative forecasting method of battery pole piece coating production surface density |
| CN209991940U (en) * | 2019-06-26 | 2020-01-24 | 深圳市贝特瑞新能源材料股份有限公司 | Battery pole piece thickness variation measuring device and system |
| CN210400238U (en) * | 2019-08-08 | 2020-04-24 | 湖南盖亚中科新能源有限公司 | Online thickness detection device for lithium battery pole piece |
| CN110568374A (en) * | 2019-09-02 | 2019-12-13 | 东北电力大学 | Lithium ion battery residual service life prediction method based on consideration of internal and external characteristics |
| CN111822517A (en) * | 2020-07-14 | 2020-10-27 | 江苏科瑞德智控自动化科技有限公司 | Lithium battery pole piece rolling mill thickness control system based on cloud platform BP neural network |
| CN114217238A (en) * | 2021-11-24 | 2022-03-22 | 浙江南都电源动力股份有限公司 | Method for predicting cycle life of lithium ion battery |
| CN114442680A (en) * | 2022-04-07 | 2022-05-06 | 东莞海裕百特智能装备有限公司 | Lithium battery pole piece thickness control method and system and readable storage medium |
| CN114819583A (en) * | 2022-04-19 | 2022-07-29 | 深圳先进技术研究院 | Lithium battery formation stage product quality prediction method and system |
| CN114566716A (en) * | 2022-04-24 | 2022-05-31 | 东莞海裕百特智能装备有限公司 | Lithium battery thickness online control method and system and readable storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 关玉明 等: "锂电池极片不平度研究与辊压机结构优化分析", 《机械科学与技术》 * |
| 杨欢: "基于GA-BP神经网络的锂电池极片厚度控制系统研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115098704B (en) | 2023-01-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110287316A (en) | A kind of Alarm Classification method, apparatus, electronic equipment and storage medium | |
| US20220198326A1 (en) | Spectral data processing for chemical analysis | |
| CN111831826A (en) | Training method, classification method and device of cross-domain text classification model | |
| CN113704389A (en) | Data evaluation method and device, computer equipment and storage medium | |
| CN113765928A (en) | Internet of things intrusion detection method, system, equipment and medium | |
| CN116502807B (en) | Industrial chain analysis application method and device based on scientific and technological knowledge graph | |
| CN114037645A (en) | Coating defect detection method and device for pole piece, electronic equipment and readable medium | |
| CN117056834A (en) | Big data analysis method based on decision tree | |
| CN110009045A (en) | The recognition methods of internet-of-things terminal and device | |
| Andhy Panca Saputra | Waste Object Detection and Classification using Deep Learning Algorithm: YOLOv4 and YOLOv4-tiny | |
| CN115797349A (en) | Defect detection method, device and equipment | |
| Mirtalaie et al. | Sentiment analysis of specific product’s features using product tree for application in new product development | |
| CN114117056B (en) | Training data processing method and device and storage medium | |
| CN111737294A (en) | Data flow classification method based on dynamic increment integration fuzzy | |
| CN115098704B (en) | Battery pole piece thickness prediction method, device and equipment and readable storage medium | |
| CN113657628A (en) | Industrial equipment monitoring method and system, electronic equipment and storage medium | |
| CN113919361A (en) | Text classification method and device | |
| Gao et al. | Intelligent instrument fault diagnosis and prediction system based on digital twin technology | |
| Choi et al. | Identification of emerging business areas for business opportunity analysis: an approach based on language model and local outlier factor | |
| US20230306602A1 (en) | Information generation method and apparatus, electronic device, and computer readable medium | |
| CN115937195A (en) | Surface defect detection method and device and computer equipment | |
| CN114741483A (en) | Data identification method and device | |
| US20230176833A1 (en) | Methods and systems for paper form to web application construction via digital camera visualization | |
| CN117574160B (en) | Tag identification method and device for media information, storage medium and electronic equipment | |
| Zhou et al. | Progress intelligent recognition method of transmission line project based on YOLOv3 algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |