CN116297020A - Lithium battery slurry viscosity prediction method and system and electronic equipment thereof - Google Patents
Lithium battery slurry viscosity prediction method and system and electronic equipment thereof Download PDFInfo
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- 239000002002 slurry Substances 0.000 title claims abstract description 219
- 238000000034 method Methods 0.000 title claims abstract description 43
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 35
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 35
- 238000003756 stirring Methods 0.000 claims abstract description 22
- 238000012937 correction Methods 0.000 claims abstract description 21
- 238000012549 training Methods 0.000 claims description 24
- 238000005070 sampling Methods 0.000 claims description 19
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 abstract description 2
- 238000002156 mixing Methods 0.000 description 10
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- 239000011248 coating agent Substances 0.000 description 8
- 230000000694 effects Effects 0.000 description 5
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 229910001416 lithium ion Inorganic materials 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 239000000853 adhesive Substances 0.000 description 2
- 230000001070 adhesive effect Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
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- 238000004519 manufacturing process Methods 0.000 description 2
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- 239000002245 particle Substances 0.000 description 2
- 239000013543 active substance Substances 0.000 description 1
- 238000005054 agglomeration Methods 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 239000006256 anode slurry Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000006257 cathode slurry Substances 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 239000006258 conductive agent Substances 0.000 description 1
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- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 229910002804 graphite Inorganic materials 0.000 description 1
- 239000010439 graphite Substances 0.000 description 1
- 239000011344 liquid material Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
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- 239000007774 positive electrode material Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004537 pulping Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
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- 238000006467 substitution reaction Methods 0.000 description 1
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Abstract
The invention belongs to the technical field of lithium battery slurry concentration measurement, and relates to a lithium battery slurry viscosity prediction method, a lithium battery slurry viscosity prediction system and electronic equipment thereof, which comprise the following steps: collecting operation parameter data of a stirrer in the stirring process of target slurry within a preset time period; acquiring the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry; determining a correction coefficient of a slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; and acquiring a prediction time period in the real-time prediction request, and predicting the second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model. The method has the advantages that the viscosity of the slurry in the stirrer can be accurately predicted, and the condition that the viscosity of the slurry is too high or too low during stirring is avoided.
Description
Technical Field
The invention belongs to the technical field of lithium battery slurry concentration measurement, and relates to a lithium battery slurry viscosity prediction method, a lithium battery slurry viscosity prediction system and electronic equipment thereof.
Background
The positive electrode slurry and the negative electrode slurry are one of important raw materials for manufacturing the positive electrode and the negative electrode of the lithium ion battery, wherein the positive electrode slurry is generally composed of an adhesive, a conductive agent, a positive electrode material and the like, and the negative electrode slurry is composed of an adhesive, graphite carbon powder and the like. The preparation of the anode slurry and the cathode slurry comprises a series of processes of mixing, dissolving, dispersing and the like between liquid and between liquid and solid materials, and the whole process is accompanied with the changes of temperature, viscosity, environment and the like. In the positive and negative electrode slurries, the dispersibility and uniformity of the granular active substances directly influence the movement of lithium ions between two electrodes of the battery, so that the mixing and dispersion of the slurries of the pole piece materials, namely the slurry mixing quality of the slurries, are important in the production of the lithium ion battery.
The viscosity of the slurry is an important characterization index of the quality of the composite slurry, and the viscosity of the slurry does not influence the performance of the battery cell, but the viscosity has great influence on the stability of the slurry and the subsequent coating process. When the viscosity of the slurry is high, particles are not easy to settle, the stability and uniformity of the slurry are relatively good, but too high viscosity can cause poor fluidity of the slurry, and the coating effect is affected. Of course, too low viscosity is not feasible, and the problems of poor slurry stability, particle agglomeration, difficult drying during coating, cracking of the coating, inconsistent surface density and the like are easily caused when the viscosity is too low. Therefore, the viscosity of the slurry can directly influence the quality of the coating of the battery pole piece, and it is important to accurately acquire the viscosity data of the slurry in time when the slurry combination operation is performed. However, the current method for testing the viscosity of the slurry is to take a sample once after completion of slurry mixing and test a viscosity value under a rotational viscometer, and has the following drawbacks: (1) sampling test can be performed only after the slurry mixing is completed, if the viscosity exceeds the standard, reworking is performed, and the process cannot be monitored and adjusted in time; (2) two persons are matched in each test, one person operates equipment to open the tank, and one person samples and tests, so that time and labor are wasted, and a certain safety risk exists; (3) the tested slurry cannot be returned to the slurry mixing barrel in time, aggregation is easy, and the risk that foreign matters fall into the slurry mixing barrel is increased due to frequent tank opening. In conclusion, the detection of the viscosity of the existing lithium battery slurry is inconvenient, and the detection cost is high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method, a system and electronic equipment for predicting the viscosity of lithium battery slurry, which can accurately predict the viscosity of slurry in a stirrer, and avoid the occurrence of the condition that the viscosity of the slurry is too high or too low during stirring, thereby improving the effect of a lithium battery during coating.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides a method for predicting viscosity of lithium battery slurry, comprising the following steps:
collecting operation parameter data of a stirrer in a stirring process of target slurry in a preset time period, wherein the operation parameter data of the stirrer comprises the following components: rotational speed, torque, current, power, and temperature;
acquiring the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry;
determining a correction coefficient of a slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity;
and acquiring a prediction time period in the real-time prediction request, and predicting the second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.
Further, the slurry viscosity prediction model is trained according to the following steps:
inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model, and outputting the slurry viscosity prediction data through the slurry viscosity prediction model, wherein the training data comprises a plurality of groups of model training data, and each group of model training data comprises: operating parameter data of the mixer and actual viscosity data corresponding to the target slurry;
and adjusting model parameters of the slurry viscosity prediction model according to actual viscosity data and slurry viscosity prediction data corresponding to the operation parameter data of the mixer, and continuously executing the step of inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model until a preset training condition is met, so as to obtain a trained slurry viscosity prediction model.
Further, the slurry viscosity prediction model is:
y＝f(R，X k ，X k-1 ，X k-2 ，t)；
wherein y is the first predicted viscosity of the target slurry, R is the formula of the target slurry, and X k For the operational parameter data of the mixer, X, at the kth sampling k-1 X is the operation parameter data of the previous stirrer at the kth sampling k-2 The operation parameter data of the previous stirrer at the time of the k-1 th sampling is obtained, and t is the sampling time interval.
Further, the collecting the operation parameter data of the mixer in the mixing process of the target slurry within the preset time period includes: and collecting operation parameter data of the stirrer at different moments in the same time interval within a preset time period.
Further, detecting the viscosity of the target slurry through a rheometer to obtain the actual viscosity of the target slurry at a plurality of different acquisition moments within the preset time period.
A second aspect of the present invention is to provide a lithium battery slurry viscosity prediction system, including:
the system comprises an acquisition module, a stirring module and a stirring module, wherein the acquisition module is used for acquiring the operation parameter data of a stirrer in the stirring process of target slurry in a preset time period, and the operation parameter data of the stirrer comprise: rotational speed, torque, current, power, and temperature;
the calculation module is used for obtaining the actual viscosity corresponding to the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the mixer so as to obtain the first predicted viscosity of the target slurry;
the determining module is used for determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity;
the viscosity prediction module is used for obtaining a prediction time period in the real-time prediction request and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.
A third aspect of the present invention is to provide an electronic apparatus, including: the electronic device includes a memory, a processor, and a lithium battery paste viscosity prediction program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the lithium battery paste viscosity prediction method.
The invention has the beneficial effects that:
(1) Through collecting the operation parameter data of the stirrer in the stirring process of the target slurry in a preset time period, the operation parameter data of the stirrer comprise: rotational speed, torque, current, power, and temperature; acquiring the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry; determining a correction coefficient of a slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; acquiring a prediction time period in a real-time prediction request, and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and a slurry viscosity prediction model; when the slurry is stirred by the stirrer, the viscosity of the slurry in the stirrer can be accurately predicted, and the condition that the viscosity of the slurry is too high or too low during stirring is avoided, so that the effect of the lithium battery during coating is improved.
(2) The concentration of the slurry in the stirrer can be accurately predicted, so that the conditions of energy consumption increase and cost increase caused by overlong stirring time and overlong pulping process of the lithium battery can be avoided.
Drawings
FIG. 1 is a schematic flow chart of a prediction method in the invention;
FIG. 2 is a schematic diagram of the architecture of the prediction system of the present invention;
fig. 3 is a schematic structural view of an electronic device according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Referring to fig. 1, a first aspect of the present invention provides a method for predicting viscosity of lithium battery slurry, comprising the steps of:
s100: collecting operation parameter data of a stirrer in a stirring process of target slurry in a preset time period, wherein the operation parameter data of the stirrer comprises the following components: rotational speed, torque, current, power, and temperature.
It should be noted that, because the motion of the mixer is driven by the frequency converter, the frequency converter is controlled by mcu, and the frequency converter has communication capability, the frequency converter is connected to the PLC controller in a communication manner, and the corresponding sensing values are synchronized to the corresponding registers of the PLC. And then read through the communication protocol of the PLC. It should be understood that in the operation parameter data of the stirrer, the current is the actual output current of the frequency converter of the stirrer, the power is the actual output power of the frequency converter of the stirrer, and the temperature is the temperature measured by the temperature sensor inside the motor of the stirrer.
S200: and acquiring the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry.
S300: and determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity.
S400: and acquiring a prediction time period in the real-time prediction request, and predicting the second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.
Through gathering the operating parameter data of target slurry in the stirring process in the preset time period, the operating parameter data of the stirrer comprises: rotational speed, torque, current, power, and temperature; acquiring the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry; determining a correction coefficient of a slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; acquiring a prediction time period in a real-time prediction request, and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and a slurry viscosity prediction model; when the slurry is stirred by the stirrer, the viscosity of the slurry in the stirrer can be accurately predicted, and the condition that the viscosity of the slurry is too high or too low during stirring is avoided, so that the effect of the lithium battery during coating is improved.
In one embodiment, the slurry viscosity prediction model is trained according to the following steps:
inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model, and outputting the slurry viscosity prediction data through the slurry viscosity prediction model, wherein the training data comprises a plurality of groups of model training data, and each group of model training data comprises: operating parameter data of the mixer and actual viscosity data corresponding to the target slurry;
and adjusting model parameters of the slurry viscosity prediction model according to actual viscosity data and slurry viscosity prediction data corresponding to the operation parameter data of the mixer, and continuously executing the step of inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model until a preset training condition is met, so as to obtain a trained slurry viscosity prediction model.
When the slurry viscosity prediction model in the above embodiment is a three-layer neural network model, the accuracy of the prediction of the slurry viscosity prediction model can be improved by using the slurry viscosity prediction model as the three-layer neural network model.
In one embodiment, the slurry viscosity prediction model is:
y＝f(R，X k ，X k-1 ，X k-2 ，t)；
wherein y is the first predicted viscosity of the target slurryDegree, R is the formula of the target slurry, X k For the operational parameter data of the mixer, X, at the kth sampling k-1 X is the operation parameter data of the previous stirrer at the kth sampling k-2 The operation parameter data of the previous stirrer at the time of the k-1 th sampling is obtained, and t is the sampling time interval. By setting the slurry viscosity prediction model to y=f (R, X k ,X k-1 ,X k-2 And t) so that when the concentration of the target slurry is predicted, sampling results of the last two times of the predicted time point are also added into the slurry viscosity prediction model, thereby improving the prediction accuracy of the slurry viscosity prediction model.
In one embodiment, the collecting the operation parameter data of the mixer during the mixing process of the target slurry within the preset time period includes: and collecting operation parameter data of the stirrer at different moments in the same time interval within a preset time period.
In one embodiment, the viscosity of the target slurry is detected by a rheometer, so as to obtain the actual viscosity of the target slurry at a plurality of different collection moments within the preset time period.
In one embodiment, the functional expression of the correction factor for the actual viscosity of the target slurry is:
wherein y is 0 To the actual viscosity of the target slurry,
is the predicted viscosity of the target slurry.
Referring to fig. 2, a second aspect of the present invention is to provide a lithium battery slurry viscosity prediction system, including:
the system comprises an acquisition module, a stirring module and a stirring module, wherein the acquisition module is used for acquiring the operation parameter data of a stirrer in the stirring process of target slurry in a preset time period, and the operation parameter data of the stirrer comprise: rotational speed, torque, current, power, and temperature;
the calculation module is used for obtaining the actual viscosity corresponding to the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the mixer so as to obtain the first predicted viscosity of the target slurry;
the determining module is used for determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity;
the viscosity prediction module is used for obtaining a prediction time period in the real-time prediction request and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.
In one embodiment, the slurry viscosity prediction model is trained according to the following steps:
inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model, and outputting the slurry viscosity prediction data through the slurry viscosity prediction model, wherein the training data comprises a plurality of groups of model training data, and each group of model training data comprises: operating parameter data of the mixer and actual viscosity data corresponding to the target slurry;
and adjusting model parameters of the slurry viscosity prediction model according to actual viscosity data and slurry viscosity prediction data corresponding to the operation parameter data of the mixer, and continuously executing the step of inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model until a preset training condition is met, so as to obtain a trained slurry viscosity prediction model.
It should be noted that, in the above embodiment, the slurry viscosity prediction model is a three-layer neural network model, and the accuracy of the prediction of the slurry viscosity prediction model can be improved by using the slurry viscosity prediction model as the three-layer neural network model.
In one embodiment, the slurry viscosity prediction model is:
y＝f(R,X k ,X k-1 ,X k-2 ,t)；
wherein y is the first predicted viscosity of the target slurry, R is the formula of the target slurry, X_k is the operation parameter data of the stirrer at the kth sampling, X_ (k-1) is the operation parameter data of the stirrer at the last sampling, X_ (k-2) is the operation parameter data of the stirrer at the last sampling at the kth-1, and t is the sampling time interval.
By setting the slurry viscosity prediction model to y=f (R, X k ,X k-1 ,X k-2 And t) so that when the concentration of the target slurry is predicted, sampling results of the last two times of the predicted time point are also added into the slurry viscosity prediction model, thereby improving the prediction accuracy of the slurry viscosity prediction model.
In one embodiment, the collecting the operation parameter data of the mixer during the mixing process of the target slurry within the preset time period includes: and collecting operation parameter data of the stirrer at different moments in the same time interval within a preset time period.
In one embodiment, the viscosity of the target slurry is detected by a rheometer, so as to obtain the actual viscosity of the target slurry at a plurality of different collection moments within the preset time period.
The viscosity prediction module is used for predicting the viscosity of the liquid by arranging an acquisition module, a calculation module, a determination module and a viscosity prediction module; the calculation module is used for obtaining the actual viscosity corresponding to the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry; the calculation module is used for obtaining the actual viscosity corresponding to the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry; the determining module is used for determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity; the prediction module is used for obtaining a prediction time period in the real-time prediction request, and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model; when the slurry is stirred by the stirrer, the viscosity of the slurry in the stirrer can be accurately predicted, and the condition that the viscosity of the slurry is too high or too low during stirring is avoided, so that the effect of the lithium battery during coating is improved.
Referring to fig. 3, a third aspect of the present invention is to provide an electronic device, including: the electronic device includes a memory, a processor, and a lithium battery paste viscosity prediction program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the lithium battery paste viscosity prediction method.
The processor is used to implement various control logic for the system, which may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a single-chip, ARM (AcornRISCMachine) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. Also, the processor may be any conventional processor, microprocessor, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP and/or any other such configuration.
The memory is used as a non-volatile computer readable storage medium for storing non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions corresponding to the lithium battery slurry viscosity prediction method in the embodiment of the invention. The processor executes various functional applications of the system and data processing by running non-volatile software programs, instructions and units stored in the memory, i.e., implements the lithium battery slurry viscosity prediction method in the above method embodiments.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to system usage, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, which may be connected to the system via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more units are stored in the memory that, when executed by the one or more processors, perform the lithium battery slurry viscosity prediction method in any of the method embodiments described above, e.g., perform method steps S100 through S400 in fig. 1 described above.
The embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Based on such understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the related art in the form of a software product, which may exist in a computer-readable storage medium, such as ROM/RAM, a base disk, an optical disk, etc., including several instructions for causing a computer electronic device (which may be a personal computer, a server, or a network electronic device, etc.) to execute the method of the respective embodiments or some parts of the embodiments.
Conditional language such as "capable," "possible," or "may," among others, is generally intended to convey that a particular embodiment can include (but other embodiments do not include) particular features, elements, and/or operations unless specifically stated otherwise or otherwise understood within the context of as used. Thus, such conditional language is also generally intended to imply that features, elements and/or operations are in any way required for one or more embodiments or that one or more embodiments must include logic for deciding, with or without input or prompting, whether these features, elements and/or operations are included or are to be performed in any particular embodiment.
What has been described herein in this specification and the drawings includes examples that can provide a lithium battery slurry viscosity prediction method, system, and electronic device thereof. It is, of course, not possible to describe every conceivable combination of components and/or methodologies for purposes of describing the various features of the present disclosure, but it may be appreciated that many further combinations and permutations of the disclosed features are possible. It is therefore evident that various modifications may be made thereto without departing from the scope or spirit of the disclosure. Further, or in the alternative, other embodiments of the disclosure may be apparent from consideration of the specification and drawings, and practice of the disclosure as presented herein. The examples presented in the specification and drawings are to be considered in all respects as illustrative and not restrictive. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions are not intended to depart from the spirit and scope of the various embodiments of the invention, which are also within the spirit and scope of the invention.
Claims (7)
1. The lithium battery slurry viscosity prediction method is characterized by comprising the following steps:
collecting operation parameter data of a stirrer in a stirring process of target slurry in a preset time period, wherein the operation parameter data of the stirrer comprises the following components: rotational speed, torque, current, power, and temperature;
acquiring the actual viscosity of the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the stirrer so as to obtain the first predicted viscosity of the target slurry;
determining a correction coefficient of a slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity;
and acquiring a prediction time period in the real-time prediction request, and predicting the second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.
2. The method for predicting the viscosity of a lithium battery slurry according to claim 1, wherein the slurry viscosity prediction model is trained according to the following steps:
inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model, and outputting the slurry viscosity prediction data through the slurry viscosity prediction model, wherein the training data comprises a plurality of groups of model training data, and each group of model training data comprises: operating parameter data of the mixer and actual viscosity data corresponding to the target slurry;
and adjusting model parameters of the slurry viscosity prediction model according to actual viscosity data and slurry viscosity prediction data corresponding to the operation parameter data of the mixer, and continuously executing the step of inputting the operation parameter data of the mixer in the training data into the slurry viscosity prediction model until a preset training condition is met, so as to obtain a trained slurry viscosity prediction model.
3. The method for predicting the viscosity of a lithium battery slurry according to claim 1, wherein the slurry viscosity prediction model is as follows:
y＝f(R,X k ,X k-1 ,X k-2 ,t)；
wherein y is the first predicted viscosity of the target slurry, R is the formula of the target slurry, and X k For the operational parameter data of the mixer, X, at the kth sampling k-1 X is the operation parameter data of the previous stirrer at the kth sampling k-2 The operation parameter data of the previous stirrer at the time of the k-1 th sampling is obtained, and t is the sampling time interval.
4. The method for predicting viscosity of lithium battery slurry according to claim 1, wherein the step of collecting the operation parameter data of the stirrer during stirring of the target slurry within the preset time period comprises the steps of: and collecting operation parameter data of the stirrer at different moments in the same time interval within a preset time period.
5. The method for predicting the viscosity of a lithium battery slurry according to claim 4, wherein the viscosity of the target slurry is detected by a rheometer to obtain the actual viscosity of the target slurry at a plurality of different collection times within the preset time period.
6. A lithium battery slurry viscosity prediction system, comprising:
the system comprises an acquisition module, a stirring module and a stirring module, wherein the acquisition module is used for acquiring the operation parameter data of a stirrer in the stirring process of target slurry in a preset time period, and the operation parameter data of the stirrer comprise: rotational speed, torque, current, power, and temperature;
the calculation module is used for obtaining the actual viscosity corresponding to the target slurry in a preset time period, and calculating the viscosity of the target slurry based on a pre-established slurry viscosity prediction model according to the operation parameter data of the mixer so as to obtain the first predicted viscosity of the target slurry;
the determining module is used for determining a correction coefficient of the slurry viscosity prediction model according to the actual viscosity and the first predicted viscosity;
the viscosity prediction module is used for obtaining a prediction time period in the real-time prediction request and predicting second predicted viscosity of the target slurry in the prediction time period based on the correction coefficient and the slurry viscosity prediction model.
7. An electronic device, comprising: the electronic device comprising a memory, a processor, and a lithium battery slurry viscosity prediction program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the lithium battery slurry viscosity prediction method of any of claims 1-5.
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| CN117110143A (en) * | 2023-10-24 | 2023-11-24 | 钛玛科（北京）工业科技有限公司 | Lithium battery slurry viscosity on-line detection method and device |
| CN117160336A (en) * | 2023-11-02 | 2023-12-05 | 南通凯赛生化工程设备有限公司 | Rotating speed control method and system of material mixer |
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| CN117110143A (en) * | 2023-10-24 | 2023-11-24 | 钛玛科（北京）工业科技有限公司 | Lithium battery slurry viscosity on-line detection method and device |
| CN117110143B (en) * | 2023-10-24 | 2024-02-02 | 钛玛科（北京）工业科技有限公司 | Lithium battery slurry viscosity on-line detection method and device |
| CN117160336A (en) * | 2023-11-02 | 2023-12-05 | 南通凯赛生化工程设备有限公司 | Rotating speed control method and system of material mixer |
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