CN117436281B - Method, device and storage medium for improving accuracy of simulation result of lithium battery - Google Patents
Method, device and storage medium for improving accuracy of simulation result of lithium battery Download PDFInfo
- Publication number
- CN117436281B CN117436281B CN202311657529.6A CN202311657529A CN117436281B CN 117436281 B CN117436281 B CN 117436281B CN 202311657529 A CN202311657529 A CN 202311657529A CN 117436281 B CN117436281 B CN 117436281B
- Authority
- CN
- China
- Prior art keywords
- sem
- particle size
- lithium battery
- particle
- obtaining
- 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.)
- Active
Links
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 61
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 61
- 238000004088 simulation Methods 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000002245 particle Substances 0.000 claims abstract description 237
- 238000001878 scanning electron micrograph Methods 0.000 claims abstract description 35
- 239000007773 negative electrode material Substances 0.000 claims abstract description 23
- 239000007774 positive electrode material Substances 0.000 claims abstract description 23
- 239000010405 anode material Substances 0.000 claims abstract description 16
- 239000010406 cathode material Substances 0.000 claims abstract description 16
- 238000012360 testing method Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims description 6
- 239000000843 powder Substances 0.000 claims description 4
- 239000008187 granular material Substances 0.000 claims description 3
- 230000001172 regenerating effect Effects 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 description 11
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 4
- 239000000463 material Substances 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 102220470087 Ribonucleoside-diphosphate reductase subunit M2_S20A_mutation Human genes 0.000 description 1
- 229910021383 artificial graphite Inorganic materials 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 150000002500 ions Chemical group 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 229910021382 natural graphite Inorganic materials 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- 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)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Battery Electrode And Active Subsutance (AREA)
Abstract
The invention discloses a method, a device and a storage medium for improving the accuracy of a simulation result of a lithium battery, wherein the method comprises the following steps: obtaining more than three key particle size values in particle size distribution information of positive and negative electrode materials of the lithium battery, wherein the particle size value comprises a particle size value D of a maximum particle D100, and the particle size distribution information is obtained through a laser particle size test; obtaining the maximum particle diameter D1 in SEM images of the anode and cathode materials of the lithium batteries in the same batch; equivalent D1 to D, obtain the corrected critical particle size value of the lithium battery positive and negative electrode material, regenerate the particle model of the lithium battery positive and negative electrode material according to the preset distribution rule based on the corrected critical particle size value; based on the regenerated particle model, a heterogeneous electrochemical model of the lithium battery is used for simulation prediction. The particle size distribution value of the anode and cathode material particles can be obtained quickly by the method, and the simulation time is saved and an accurate simulation result is obtained by selecting part of the key particle size values and generating a model based on a preset curve.
Description
Technical Field
The embodiment of the invention provides a method, a device, a storage medium and electronic equipment for improving the accuracy of a lithium battery simulation result, in particular to a method, a device, a storage medium and electronic equipment for improving the accuracy of the lithium battery simulation result by selectively and accurately inputting geometric characteristic information of particles.
Background
In order to accurately simulate the secondary ion battery, research and development personnel gradually change from the traditional homogeneous model to a heterogeneous model with more accurate prediction. The currently prevailing heterogeneous electrochemical model includes: three-dimensional models, mesoscale models, particle stacking models and the like, wherein the models need to acquire geometric characteristic information of positive and negative active particles of the lithium battery before calculation so as to accurately predict the electrochemical performance of the battery. For the positive and negative electrode active particle materials, more accurate particle geometric characteristic information is input, and the obtained simulation result is more accurate, but more particle aggregate characteristic information is input, which means that the simulation calculation amount is continuously increased.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides a method, a device, a storage medium and electronic equipment for improving the accuracy of a lithium battery simulation result, in particular to a method, a device, a storage medium and electronic equipment for improving the accuracy of the lithium battery simulation by selectively and accurately inputting geometric characteristic information of particles.
To achieve the above object:
the first aspect of the invention provides a method for improving the accuracy of a simulation result of a lithium battery, which comprises the following steps:
obtaining more than three key particle size values in particle size distribution information of positive and negative electrode materials of the lithium battery, wherein the particle size value comprises a particle size value D of a maximum particle D100, and the particle size distribution information is obtained through a laser particle size test;
obtaining the maximum particle diameter D1 in SEM images of the anode and cathode materials of the lithium batteries in the same batch;
equivalent D1 to D, obtain the corrected critical particle size value of the lithium battery positive and negative electrode material, regenerate the particle model of the lithium battery positive and negative electrode material according to the preset distribution rule based on the corrected critical particle size value;
based on the regenerated particle model, a heterogeneous electrochemical model of the lithium battery is used for simulation prediction.
The accurate acquisition of the particle size distribution rule of the anode and cathode materials of the lithium battery has important significance for the accuracy of simulation prediction results. Because the test efficiency of laser granularity test equipment is higher, can once only test a large amount of samples fast, this application passes through laser granularity test equipment at first, based on a large amount of samples, acquires the granule particle diameter distribution information in the sample fast. Representative key particle size values are selected from the particle size distribution information.
However, the particle size value of larger particles in a sample of material obtained by a laser particle size testing apparatus tends to deviate greatly from the actual particle size. Therefore, a small amount of positive and negative electrode material samples are scanned through SEM equipment, and the accurate particle size value D1 of the maximum particles is obtained. And replacing the maximum particle diameter D in the laser particle size test result based on the obtained maximum particle diameter D1 to obtain a corrected key particle diameter value reflecting the actual particle diameter of the material.
The particle model of the anode and cathode materials is generated through the corrected key particle size value, is a simplified model of a sphere, a rectangle, an ellipse or other geometric shapes generated based on the particle size and the original appearance of the particles, is different from anode and cathode material particles with irregular surfaces, has a relatively regular shape, can simplify simulation calculation, but does not influence the accuracy of simulation results. After the particle model is obtained, a particle model corresponding to the original positive and negative particles is generated based on the corrected particle size and quantity distribution curve, or a normal distribution curve or a Weber distribution curve for simulation.
Through the mode, the particle size distribution value of the anode and cathode material particles can be obtained quickly. In addition, by selecting part of the key particle diameter values and generating a model based on a preset curve, a large amount of simulation time is saved, and an accurate simulation result is obtained.
The particle size in this application is the longest diameter of the particles measured.
As a further improvement, the step of obtaining the maximum particle diameter D1 in the SEM image of the anode and cathode materials of the lithium battery in the same batch further comprises:
obtaining powder samples in the same batch as the laser granularity test, and preparing more than two SEM parallel samples;
obtaining the largest particles in an SEM parallel sample under a first multiple through the SEM;
the largest particles in the SEM parallel samples were measured by SEM at the second magnification to give the largest particle diameter D1.
The particle size of the positive and negative electrode material particles is usually very accurate by SEM, but a large number of particles cannot be measured at a time, and the particle size distribution information of the positive and negative electrode material cannot be obtained quickly, but the particle size of the largest particle is positioned and measured efficiently by this way. The particle size D1 of the largest particles can be rapidly located and measured by first locating the largest particles at a first smaller multiple and then measuring the particle size of the largest particles at a second larger multiple.
As a further improvement, the step of obtaining, by SEM, at a first magnification, the largest particles in the SEM parallel samples further comprises:
obtaining SEM images of more than two SEM parallel samples under a first multiple through SEM;
based on the obtained SEM image, obtaining the morphology of all particles in an SEM parallel sample;
based on the obtained morphology of the particles, the largest particles in the SEM parallel samples were obtained.
Because SEM can only obtain the particle size information of a small amount of particles at a time, more than two samples can be prepared, and the minimum requirement of the sample amount can be met so as to obtain the accurate maximum particle size.
Further, the step of obtaining the largest particles in the SEM parallel sample based on the obtained particle morphology further comprises:
obtaining the morphology features of each particle in a sample SEM image;
the largest particles in each SEM image were identified and compared to obtain the largest particles in all SEM images.
After the SEM image is obtained by SEM equipment, the morphology of each particle can be obtained by the existing general algorithm, and the maximum particle size in the SEM image can be obtained by the algorithm based on the morphology.
The particles in the SEM images can obtain the basic morphology through a conventional algorithm, and then the maximum particles in each SEM image can be obtained through comparing the sizes of the particles through the algorithm, and the particle sizes of the maximum particles in each SEM image are respectively compared to obtain the maximum particles.
As a further improvement, the first multiple is 1500 to 2500 times and the second multiple is 4000 to 15000 times.
The applicant found that the maximum particles in the SEM images obtained by the above-described first magnification can be rapidly and accurately located, and that the particle size value D1 of the maximum particles can be accurately measured by the SEM images obtained by the above-described second magnification.
As a further improvement, the obtained particle size value at least further comprises three particle size values respectively positioned in three sections of D5-D15, D45-D55 and D85-D95.
In a heterogeneous electrochemical model of a lithium battery, a plurality of key particle size values are required to be obtained to generate a distribution curve, so that the particle size distribution situation of positive and negative electrode materials in reality is accurately described, and the applicant finds that the particle sizes of three particles in the interval are input in addition to the particle size value of the maximum particle D100, and a relatively accurate result can be obtained through simulation.
Wherein D represents the diameter of the powder particles, for example, D50 is the average particle diameter or median diameter, and represents the particle diameter at which the cumulative particle distribution is 10%, that is, the particle volume content of particles smaller than this particle diameter is 10% of the total particles, and D100 represents the particle diameter at which the cumulative particle distribution is 100%.
In a second aspect of the present invention, there is provided a device for improving accuracy of simulation results of a lithium battery, comprising:
the first acquisition unit is used for acquiring more than three key particle size values in particle size distribution information of positive and negative electrode materials of the lithium battery, wherein the particle size value comprises a particle size value D of a maximum particle D100, and the particle size distribution information is acquired through a laser particle size test;
the second acquisition unit is used for acquiring the maximum particle diameter D1 in the SEM image of the positive and negative electrode material samples of the lithium batteries in the same batch;
the model generation unit is used for equivalently converting D1 into D to obtain corrected positive and negative electrode material critical particle size values of the lithium battery, and regenerating a particle model of the positive and negative electrode material of the lithium battery according to a preset distribution rule based on the corrected critical particle size values;
and the simulation unit is used for performing simulation prediction by using a heterogeneous electrochemical model of the lithium battery based on the regenerated particle model.
In a third aspect of the present invention, there is provided a computer storage medium for storing network platform generated data and a program for processing the network platform generated data; when the program is read and executed by a processor, the method for improving the accuracy of the simulation result of the lithium battery by adopting any one of the above steps is executed.
In a fourth aspect of the present invention, there is provided an electronic apparatus comprising: a processor; and the memory is used for storing a program for processing data generated by the network platform, and the program, when being read and executed by the processor, executes the method for improving the accuracy of the simulation result of the lithium battery by adopting any one of the above methods.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments, as illustrated in the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a particle size distribution diagram measured by a laser particle size testing apparatus according to an embodiment of the present invention;
figure 2 shows an SEM image and a particle size distribution of the largest particles of an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
the first aspect of the invention provides a method for improving the accuracy of a simulation result of a lithium battery, which comprises the following steps:
s01, obtaining 0.03g of lithium iron phosphate particles (trademark: lithium source, model: S20A), measuring the particle size of the lithium iron phosphate particles by a laser particle size tester to obtain the particle size distribution information of the lithium iron phosphate particles as shown in FIG. 1, and obtaining four key particle size values of 0.434 μm for D10, 1.032 μm for D50, 3.074 μm for D90 and 18.664 μm for D100, wherein the particle size value of D100 is set as D;
besides the D100 particle size value, the three obtained key particle size values are respectively located in three sections of D5-D15, D45-D55 and D85-D95, and the particle size values of the sections can be obtained to draw a relatively accurate particle size distribution curve.
It should be noted that, at present, both positive and negative electrode materials in lithium ion batteries can be used in the present method, for example: the positive electrode material may be NCM523, NCM622, NCM811, NCA, LMO, LCO, or the like, and the negative electrode material may be artificial graphite, natural graphite, or the like, in addition to lithium iron phosphate.
S02, obtaining the maximum particle diameter D1 in the SEM image of the lithium iron phosphate particles in the same batch, wherein the specific method is as follows:
firstly, obtaining 0.01g lithium iron phosphate particle samples in the same batch, preparing three parallel samples, and obtaining the largest particles in the three SEM parallel samples by SEM at 2000 times, wherein the specific method is as follows:
SEM images of three SEM parallel samples were obtained by SEM at a magnification of 2000;
based on the obtained SEM images, obtaining the morphology of all particles in each parallel sample of the SEM;
based on the obtained particle morphology, the largest particles in each SEM parallel sample were obtained.
The specific way to obtain the largest particles is:
obtaining the morphology features of each particle in a sample SEM image;
the largest particles in each SEM image were identified and compared to determine the largest particles in all SEM images.
After the SEM image is obtained by SEM equipment, the morphology of each particle can be obtained by the existing general algorithm, and the largest particle in the SEM image can be obtained by the algorithm based on the morphology.
The algorithm for obtaining the morphology of the particles may be a more general algorithm, such as the algorithm in the open source OpenCV vision recognition library. Such as circular recognition with Circular Hough Transform (CHT), random Hough transform (DHT), random circle detection (Dandomized CiDcle detection, DCD), and so on.
The lithium iron phosphate particles of this example are generally circular in appearance, and therefore employ an algorithm that recognizes prototypes. In the case of other types of material, the longest diameter is identified and compared using a correspondingly shaped algorithm to determine the largest particle.
Then, the largest particles in the SEM parallel samples were measured by SEM at 5000 times, as shown in FIG. 2, to give a largest particle diameter D1 of 9.37. Mu.m.
S03, equivalent D1 to D, obtain corrected key particle size value of lithium iron phosphate particles, and regenerate a particle model of lithium iron phosphate particles according to a preset distribution rule based on the corrected key particle size value;
specifically, the particle size and the number distribution of the anode and cathode materials can accord with a certain curve, such as a normal distribution curve, so that a series of particle models which accord with the distribution curve of the lithium iron phosphate particles are generated based on the key particle size values only by acquiring part of the key particle size values of the lithium iron phosphate particles, and the electrochemical simulation of the heterogeneous model can be realized.
S04, based on the regenerated particle model, performing simulation prediction by using a heterogeneous electrochemical model of the lithium battery.
In the heterogeneous electrochemical model of the lithium battery, the simulation can be accurately performed by describing the particle model of the real particle size and the geometric shape, and all measured particle size information is not needed to be input, so that the simulation process is simplified, and the calculation power consumption is reduced.
In a second aspect of the present invention, there is provided a device for improving accuracy of simulation results of a lithium battery, comprising:
the first acquisition unit is used for acquiring more than three key particle size values in particle size distribution information of positive and negative electrode materials of the lithium battery, wherein the particle size value comprises a particle size value D of a maximum particle D100, and the particle size distribution information is acquired through a laser particle size test;
the second acquisition unit is used for acquiring the maximum particle diameter D1 in the SEM image of the positive and negative electrode material samples of the lithium batteries in the same batch;
the model generation unit is used for equivalently converting D1 into D to obtain corrected critical particle size values of the anode and cathode materials of the lithium battery, and regenerating a particle model of the anode and cathode materials of the lithium battery according to a preset normal distribution rule based on the corrected critical particle size values;
and the simulation unit is used for performing simulation prediction by using a heterogeneous electrochemical model of the lithium battery based on the regenerated particle model.
In a third aspect of the present invention, there is provided a computer storage medium for storing network platform generated data and a program for processing the network platform generated data; when the program is read and executed by a processor, the method for improving the accuracy of the simulation result of the lithium battery by adopting any one of the above steps is executed.
In a fourth aspect of the present invention, there is provided an electronic apparatus comprising: a processor; and the memory is used for storing a program for processing data generated by the network platform, and the program, when being read and executed by the processor, executes the method for improving the accuracy of the simulation result of the lithium battery by adopting any one of the above methods.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (7)
1. The method for improving the accuracy of the simulation result of the lithium battery is characterized by comprising the following steps of:
obtaining more than three key particle size values in particle size distribution information of positive and negative electrode materials of the lithium battery, wherein the particle size value comprises a particle size value D of a maximum particle D100, and the particle size distribution information is obtained through a laser particle size test;
obtaining the maximum particle diameter D1 in SEM images of the anode and cathode materials of the lithium batteries in the same batch;
the method comprises the steps of obtaining the maximum particle diameter D1 in SEM pictures of anode and cathode materials of the lithium batteries in the same batch, and further comprises the following steps:
obtaining powder samples in the same batch as the laser granularity test, and preparing more than two SEM parallel samples;
obtaining the largest particles in an SEM parallel sample under a first multiple through the SEM;
measuring the maximum particles in the SEM parallel sample under a second multiple through the SEM to obtain the maximum particle diameter D1;
wherein, the step is through the SEM, under the first multiple, obtain the biggest granule in the parallel sample of SEM, still include:
obtaining SEM images of more than two SEM parallel samples under a first multiple through SEM;
based on the obtained SEM image, obtaining the morphology of all particles in an SEM parallel sample;
obtaining the maximum particles in the SEM parallel sample based on the obtained particle morphology;
equivalent D1 to D, obtain the corrected critical particle size value of the lithium battery positive and negative electrode material, regenerate the particle model of the lithium battery positive and negative electrode material according to the preset distribution rule based on the corrected critical particle size value;
based on the regenerated particle model, a heterogeneous electrochemical model of the lithium battery is used for simulation prediction.
2. The method for improving accuracy of simulation results of a lithium battery according to claim 1, wherein the step of obtaining the largest particles in the SEM parallel sample based on the obtained morphology of the particles further comprises:
obtaining the morphology features of each particle in a sample SEM image;
the largest particles in each SEM image were identified and compared to obtain the largest particles in all SEM images.
3. The method for improving the accuracy of the simulation result of the lithium battery according to claim 1, wherein the method comprises the following steps: the first multiple is 1500 to 2500 times and the second multiple is 4000 to 15000 times.
4. The method for improving the accuracy of the simulation result of the lithium battery according to claim 1, wherein the method comprises the following steps: the obtained key particle diameter value at least comprises three particle diameters respectively positioned in the intervals of D5-D15, D45-D55 and D85-D95.
5. The device for improving the accuracy of the simulation result of the lithium battery is characterized by comprising:
the first acquisition unit is used for acquiring more than three key particle size values in particle size distribution information of positive and negative electrode materials of the lithium battery, wherein the particle size value comprises a particle size value D of a maximum particle D100, and the particle size distribution information is acquired through a laser particle size test;
the second acquisition unit is used for acquiring the maximum particle diameter D1 in the SEM image of the positive and negative electrode material samples of the lithium batteries in the same batch;
the method comprises the steps of obtaining the maximum particle diameter D1 in SEM pictures of anode and cathode materials of the lithium batteries in the same batch, and further comprises the following steps:
obtaining powder samples in the same batch as the laser granularity test, and preparing more than two SEM parallel samples;
obtaining the largest particles in an SEM parallel sample under a first multiple through the SEM;
measuring the maximum particles in the SEM parallel sample under a second multiple through the SEM to obtain the maximum particle diameter D1;
wherein, the step is through the SEM, under the first multiple, obtain the biggest granule in the parallel sample of SEM, still include:
obtaining SEM images of more than two SEM parallel samples under a first multiple through SEM;
based on the obtained SEM image, obtaining the morphology of all particles in an SEM parallel sample;
obtaining the maximum particles in the SEM parallel sample based on the obtained particle morphology;
the model generation unit is used for equivalently converting D1 into D, obtaining corrected critical particle diameter values of the anode and cathode materials of the lithium battery, and regenerating a particle model of the anode and cathode materials of the lithium battery according to a preset distribution rule based on the corrected critical particle diameter values;
and the simulation unit is used for performing simulation prediction by using a heterogeneous electrochemical model of the lithium battery based on the regenerated particle model.
6. A computer storage medium for storing network platform generated data and a program for processing the network platform generated data; the method is characterized in that the program, when read and executed by a processor, executes a method for improving the accuracy of simulation results of a lithium battery by adopting the method of any one of claims 1 to 4.
7. An electronic device, comprising: a processor; a memory for storing a program for processing data generated by a network platform, which when read and executed by the processor, performs a method for improving accuracy of simulation results of a lithium battery using a method as claimed in any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311657529.6A CN117436281B (en) | 2023-12-06 | 2023-12-06 | Method, device and storage medium for improving accuracy of simulation result of lithium battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311657529.6A CN117436281B (en) | 2023-12-06 | 2023-12-06 | Method, device and storage medium for improving accuracy of simulation result of lithium battery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117436281A CN117436281A (en) | 2024-01-23 |
| CN117436281B true CN117436281B (en) | 2024-03-22 |
Family
ID=89551665
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311657529.6A Active CN117436281B (en) | 2023-12-06 | 2023-12-06 | Method, device and storage medium for improving accuracy of simulation result of lithium battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117436281B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111931339A (en) * | 2020-07-06 | 2020-11-13 | 清华大学 | Method for determining structural parameters of electrochemical model electrode layer of lithium ion battery |
| CN112883531A (en) * | 2019-11-29 | 2021-06-01 | 比亚迪股份有限公司 | Lithium ion battery data processing method, computer device and storage medium |
| CN113405954A (en) * | 2021-05-10 | 2021-09-17 | 上海市计量测试技术研究院 | Method for determining value of wide-distribution solid particle size standard substance applied to dry dispersion system of laser diffraction particle size analyzer |
| CN114169233A (en) * | 2021-11-30 | 2022-03-11 | 欣旺达电动汽车电池有限公司 | Method, system, equipment and storage medium for optimizing particle size of material particles |
| WO2023092799A1 (en) * | 2021-11-23 | 2023-06-01 | 苏州易来科得科技有限公司 | Method and apparatus for marking internal micro-structure of electrochemical apparatus |
| CN116305912A (en) * | 2023-03-13 | 2023-06-23 | 上海玫克生储能科技有限公司 | Method and device for generating spatial position distribution of lithium battery active material particles |
-
2023
- 2023-12-06 CN CN202311657529.6A patent/CN117436281B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112883531A (en) * | 2019-11-29 | 2021-06-01 | 比亚迪股份有限公司 | Lithium ion battery data processing method, computer device and storage medium |
| CN111931339A (en) * | 2020-07-06 | 2020-11-13 | 清华大学 | Method for determining structural parameters of electrochemical model electrode layer of lithium ion battery |
| CN113405954A (en) * | 2021-05-10 | 2021-09-17 | 上海市计量测试技术研究院 | Method for determining value of wide-distribution solid particle size standard substance applied to dry dispersion system of laser diffraction particle size analyzer |
| WO2023092799A1 (en) * | 2021-11-23 | 2023-06-01 | 苏州易来科得科技有限公司 | Method and apparatus for marking internal micro-structure of electrochemical apparatus |
| CN114169233A (en) * | 2021-11-30 | 2022-03-11 | 欣旺达电动汽车电池有限公司 | Method, system, equipment and storage medium for optimizing particle size of material particles |
| CN116305912A (en) * | 2023-03-13 | 2023-06-23 | 上海玫克生储能科技有限公司 | Method and device for generating spatial position distribution of lithium battery active material particles |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117436281A (en) | 2024-01-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113254382B (en) | Data processing system for constructing digital numerical value fusion device based on supercomputer | |
| CN116502112A (en) | New energy power supply test data management method and system | |
| CN113252701B (en) | Cloud edge cooperation-based power transmission line insulator self-explosion defect detection system and method | |
| CN112990330A (en) | User energy abnormal data detection method and device | |
| Joos et al. | Virtual Electrode Design for Lithium‐Ion Battery Cathodes | |
| Nagda et al. | Image-based 3D characterization and reconstruction of heterogeneous battery electrode microstructure | |
| CN112327194B (en) | Lithium battery capacity diving identification method and device | |
| CN114460469A (en) | Battery state analysis method, system and terminal based on voltage and current | |
| CN115393181A (en) | Training and generating method of head portrait generating model with beautiful and romantic style and electronic equipment | |
| CN116756351A (en) | Power battery pack data storage and health assessment method based on vision technology | |
| CN117930012A (en) | Battery consistency assessment method and device, computer equipment and storage medium | |
| CN109800477B (en) | Lithium ion battery three-factor inconsistency prediction method and device | |
| Lyu et al. | A semiparametric clustering method for the screening of retired Li-ion batteries from electric vehicles | |
| CN116311462A (en) | Facial image restoration and recognition method combining context information and VGG19 | |
| CN115238804A (en) | Spot welding data filling method and device based on generation countermeasure network and storage medium | |
| CN115409317A (en) | Transformer area line loss detection method and device based on feature selection and machine learning | |
| CN117436281B (en) | Method, device and storage medium for improving accuracy of simulation result of lithium battery | |
| Yanmin et al. | Research on ear recognition based on SSD_MobileNet_v1 network | |
| CN114596302A (en) | PCB defect detection method, system, medium, equipment and terminal | |
| CN117173100B (en) | Polymer lithium ion battery production control system and method thereof | |
| Miloevic | Fractal analysis of two dimensional images: parameters of the space-filling and shape | |
| CN110703038B (en) | Harmonic impedance estimation method suitable for fan access power distribution network | |
| CN116908693A (en) | Method, system, medium and equipment for evaluating performance grade of battery energy storage system | |
| CN116595752A (en) | Homogeneous random particle size distribution method of pseudo two-dimensional model | |
| CN116487737A (en) | Lithium battery matching method and device based on multiple clustering |
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 |