CN116027138B - Consistency test method and system for super capacitor - Google Patents
Consistency test method and system for super capacitor Download PDFInfo
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- CN116027138B CN116027138B CN202310307679.8A CN202310307679A CN116027138B CN 116027138 B CN116027138 B CN 116027138B CN 202310307679 A CN202310307679 A CN 202310307679A CN 116027138 B CN116027138 B CN 116027138B
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Abstract
The application discloses a consistency testing method and system of a super capacitor, which are applied to the technical field of intelligent detection, wherein the method comprises the following steps: and obtaining and comparing a preset preparation process by obtaining target preparation process information of the target super capacitor to obtain a target process test index. And acquiring preset characteristic indexes to perform multi-characteristic acquisition on the target electrode material and the target electrolyte, so as to obtain a characteristic parameter set. And obtaining a test index based on the characteristic parameter set, and then obtaining a target material test index. And carrying out suitability analysis on the electrode material and the electrolyte to obtain a target material adaptation test index. And generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index. The method solves the technical problems that the consistency test of the super capacitor is not guaranteed, and the circuit is unstable and has potential safety hazards due to the fact that the consistency of the super capacitor is not guaranteed in the prior art.
Description
Technical Field
The application relates to the technical field of intelligent detection, in particular to a consistency test method and system for a super capacitor.
Background
The super capacitor is a novel energy storage device, and has the characteristics of long service life, short charging time, environmental protection and the like because the charging and discharging processes of the super capacitor do not involve material change. Because the super capacitor has large capacity, the super capacitor is generally used in parallel, and the consistency requirement on the super capacitor on each line is higher when the super capacitor is used in parallel, so that the stability of the line is ensured. However, during the production and preparation process of the super capacitor, the performance of the super capacitor is greatly different due to the change of production parameters. Therefore, consistency test is required to be carried out on the produced super capacitor so as to ensure consistency when the super capacitor is used in parallel.
Therefore, a method for testing the consistency of the super capacitor is lacked in the prior art, so that the consistency of the super capacitor can not be ensured when the super capacitor is used in parallel, and the technical problem of potential safety hazard exists due to unstable circuit.
Disclosure of Invention
The application provides a consistency testing method and system for a super capacitor, which solve the technical problems that the consistency of the super capacitor cannot be ensured and the circuit is unstable and has potential safety hazard due to the lack of a method for carrying out consistency testing on the super capacitor in the prior art.
The application provides a consistency test method of a super capacitor, which comprises the following steps: acquiring target preparation process information of a target supercapacitor; acquiring a preset preparation process, and comparing the preset preparation process with the target preparation process information to obtain a target process test index; respectively obtaining a target electrode material and a target electrolyte of the target supercapacitor; acquiring a preset characteristic index, and carrying out multi-characteristic acquisition on the target electrode material and the target electrolyte based on the preset characteristic index to respectively obtain a first material characteristic parameter set and a second material characteristic parameter set; obtaining a first material test index based on the first material characteristic parameter set, obtaining a second material test index based on the second material characteristic parameter set, and obtaining a target material test index through weighted calculation; carrying out suitability analysis on the target electrode material and the target electrolyte to obtain a target material adaptation test index; and generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index.
The application also provides a consistency test system of the super capacitor, which comprises the following steps: the process information acquisition module is used for acquiring target preparation process information of the target supercapacitor; the process test index acquisition module is used for acquiring a preset preparation process and comparing the preset preparation process with the target preparation process information to obtain a target process test index; the material acquisition module is used for respectively acquiring a target electrode material and a target electrolyte of the target supercapacitor; the characteristic parameter set acquisition module is used for acquiring preset characteristic indexes, and carrying out multi-characteristic acquisition on the target electrode material and the target electrolyte based on the preset characteristic indexes to respectively obtain a first material characteristic parameter set and a second material characteristic parameter set; the material test index module is used for obtaining a first material test index based on the first material characteristic parameter set, obtaining a second material test index based on the second material characteristic parameter set, and obtaining a target material test index through weighting calculation; the material adaptation test index module is used for carrying out suitability analysis on the target electrode material and the target electrolyte to obtain a target material adaptation test index; the test result acquisition module is used for generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index.
The application also provides an electronic device, comprising:
a memory for storing executable instructions;
and the processor is used for realizing the consistency testing method of the super capacitor when executing the executable instructions stored in the memory.
The embodiment of the application provides a computer readable storage medium which stores a computer program, and when the program is executed by a processor, the consistency testing method of the super capacitor provided by the embodiment of the application is realized.
According to the consistency testing method and system for the super capacitor, the target preparation process information of the target super capacitor is obtained, the preset preparation process is obtained, and comparison is carried out, so that the target process testing index is obtained. And acquiring preset characteristic indexes to perform multi-characteristic acquisition on the target electrode material and the target electrolyte, so as to obtain a characteristic parameter set. And obtaining a test index based on the characteristic parameter set, and then obtaining a target material test index. And carrying out suitability analysis on the electrode material and the electrolyte to obtain a target material adaptation test index. And generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index. The method solves the technical problems that the consistency test of the super capacitor is not guaranteed, and the circuit is unstable and has potential safety hazards due to the fact that the consistency of the super capacitor is not guaranteed in the prior art. The consistency of the super capacitor is tested by comparing the target preparation process information with the preparation processes such as a preset preparation process, so that the consistency of the super capacitor is accurately evaluated, and the potential safety hazard in the use process of the super capacitor is reduced.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
Fig. 1 is a schematic flow chart of a consistency testing method of a supercapacitor according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a preset preparation process obtained by a consistency testing method of a supercapacitor according to an embodiment of the present application;
fig. 3 is a schematic flow chart of acquiring a material characteristic parameter set by using the consistency test method of the supercapacitor provided by the embodiment of the application;
fig. 4 is a schematic structural diagram of a system of a method for testing consistency of a supercapacitor according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system electronic device of a method for testing consistency of a supercapacitor according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a process information acquisition module 11, a process test index acquisition module 12, a material acquisition module 13, a characteristic parameter set acquisition module 14, a material test index module 15, a material adaptation test index module 16, a test result acquisition module 17, a processor 31, a memory 32, an input device 33 and an output device 34.
Description of the embodiments
Examples
The present application will be further described in detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present application more apparent, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a particular ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a particular order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only.
While the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, the modules are merely illustrative, and different aspects of the system and method may use different modules.
A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
As shown in fig. 1, an embodiment of the present application provides a method for testing consistency of a supercapacitor, where the method includes:
s10: acquiring target preparation process information of a target supercapacitor;
s20: acquiring a preset preparation process, and comparing the preset preparation process with the target preparation process information to obtain a target process test index;
s30: respectively obtaining a target electrode material and a target electrolyte of the target supercapacitor;
s40: acquiring a preset characteristic index, and carrying out multi-characteristic acquisition on the target electrode material and the target electrolyte based on the preset characteristic index to respectively obtain a first material characteristic parameter set and a second material characteristic parameter set;
specifically, current preparation process information of the target supercapacitor is obtained, wherein the preparation process comprises all actual preparation processes of the supercapacitor, including the preparation process of the electrode plate, the assembly process of the supercapacitor and the like. The preparation of the electrode plate specifically comprises the steps of slurry preparation, slurry coating, drying, cutting and the like. And then, acquiring a preset preparation process, wherein the preset preparation process is an optimal preparation process of the supercapacitor after optimizing a historical preparation process of the supercapacitor, comparing the preset preparation process with target preparation process information, and comparing the similarity of the preset preparation process and the target preparation process information to obtain a target process test index. Then, the target electrode material and the target electrolyte of the target supercapacitor are respectively obtained. And acquiring preset characteristic indexes, wherein the preset characteristic indexes are all characteristic indexes which are acquired through big data acquisition and analysis and can influence the specific capacity of the super capacitor. And performing multi-feature collection on the target electrode material and the target electrolyte based on preset feature indexes,
as shown in fig. 2, the method S20 provided by the embodiment of the present application further includes:
s21: acquiring historical preparation process data;
wherein the historical preparation process data comprises a plurality of groups of process data of a plurality of super capacitors;
s22: sequentially detecting the capacitance performance of each super capacitor in the plurality of super capacitors, and forming a plurality of capacitance performance parameters;
s23: taking the multiple groups of process data as intelligent optimizing areas and taking the multiple capacitance performance parameters as intelligent optimizing evaluation parameters;
s24: optimizing in the intelligent optimizing area according to the intelligent optimizing evaluation parameters to obtain an optimal process data set;
s25: and taking the optimal process data set as the preset preparation process.
Specifically, historical preparation process data is obtained, wherein the historical preparation process data is the preparation process data adopted in the process of historically preparing the supercapacitor. The historical preparation process data comprises a plurality of groups of process data of the super capacitors, namely the obtained historical preparation process data comprises process data of the super capacitors. And then, sequentially detecting the capacitance performance of each super capacitor in the plurality of super capacitors, acquiring the performance parameters of each super capacitor, and forming a plurality of capacitance performance parameters. Further, a plurality of groups of process data are used as intelligent optimizing areas, and the plurality of capacitance performance parameters are used as intelligent optimizing evaluation parameters. And optimizing in the intelligent optimizing area according to the intelligent optimizing evaluation parameters to obtain an optimal process data set, and obtaining the optimal process data set. And finally, taking the optimal process data as a preset preparation process.
The method S25 provided by the embodiment of the application further comprises the following steps:
s251: randomly acquiring a first process data set in the intelligent optimizing area;
s252: the first super capacitor of the first process data set is matched, and a first capacitance performance parameter is obtained through detection;
s253: acquiring a first preset neighborhood scheme, and acquiring a first neighborhood of the first process data set, wherein the first neighborhood comprises a plurality of groups of neighborhood process data;
s254: acquiring a first neighborhood process data set in the plurality of sets of neighborhood process data, and matching the first neighborhood super capacitor;
s255: detecting and obtaining a first neighborhood capacitance performance parameter of the first neighborhood super capacitor;
s256: and comparing the first capacitance performance parameter with the first neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result.
Specifically, when the optimal process data set is acquired, a first process data set in the intelligent optimizing area is acquired randomly, then a first supercapacitor of the first process data set is matched, and a first capacitance performance parameter is detected. Further acquiring a first preset neighborhood scheme, wherein the first neighborhood scheme is a scheme obtained after the first process data set is expanded, and the first neighborhood scheme comprises a preset expansion range of each data of the first process data set. Taking the example that the first process data set includes data of A, B, C three categories, the corresponding first preset neighborhood scheme is a process data range obtained by respectively expanding preset ranges of the data of A, B, C three categories, so as to obtain a first neighborhood, wherein the first neighborhood data includes multiple groups of neighborhood process data. And acquiring a first neighborhood process data set in the plurality of sets of neighborhood process data, and matching the first neighborhood super capacitor. And detecting and obtaining the first neighborhood capacitance performance parameter of the first neighborhood super capacitor. And comparing the first capacitance performance parameter with the first neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result.
The method S256 provided by the embodiment of the application further includes:
s2561: if the first neighborhood capacitance performance parameter is better than the first capacitance performance parameter, obtaining an iterative optimization instruction;
s2562: obtaining a second neighborhood of the first neighborhood process data set based on the iterative optimization instruction;
s2563: acquiring a second neighborhood process data set of the second neighborhood, and matching a second neighborhood super capacitor;
s2564: detecting and obtaining a second neighborhood capacitance performance parameter of the second neighborhood super capacitor;
s2565: and comparing the first neighborhood capacitance performance parameter with the second neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result.
Specifically, if the first neighborhood capacitance performance parameter is better than the first capacitance performance parameter, an iterative optimization instruction is obtained, wherein the iterative optimization instruction is used for indicating the system to continue optimizing in the intelligent optimization area. And obtaining a second neighborhood of the first neighborhood process data set based on the iterative optimization instruction, wherein the obtaining mode of the second neighborhood is consistent with that of the first neighborhood, and the second neighborhood process data set based on the second neighborhood is matched with the second neighborhood supercapacitor. And detecting the second neighborhood super capacitor to obtain a second neighborhood capacitance performance parameter of the second neighborhood super capacitor. And comparing the first neighborhood capacitance performance parameter with the second neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result. Because the specific optimizing process optimizes the neighborhood of each process data set in the intelligent optimizing area, the acquired optimal process data set is more accurate.
As shown in fig. 3, the method S40 provided by the embodiment of the present application further includes:
s41: acquiring a first preset characteristic index in the preset characteristic indexes;
the first preset characteristic indexes comprise specific surface area, pore size distribution, grain size, element composition and ash content;
s42: acquiring the first material characteristic parameter set based on the first preset characteristic index;
s43: acquiring a second preset characteristic index in the preset characteristic indexes;
wherein the second preset characteristic index comprises component composition and component proportion;
s44: acquiring the second material characteristic parameter set based on the second preset characteristic index;
the first material characteristic parameter set refers to a set of characteristic parameters of the target electrode material, and the second material characteristic parameter set refers to a set of characteristic parameters of the target electrolyte.
Specifically, a first preset characteristic index in preset characteristic indexes is obtained, wherein the first preset characteristic index is a factor characteristic index of a target electrode material, which can influence the consistency of the supercapacitor, and comprises specific surface area, pore size distribution, diameter particle size, element composition and ash content. And then, acquiring the first material characteristic parameter set based on a first preset characteristic index, namely acquiring the first material characteristic parameter set of the target electrode material. Further, a second preset characteristic index in the preset characteristic indexes is obtained, wherein the second preset characteristic index is a factor characteristic index of the target electrolyte, which can influence the consistency of the supercapacitor. The second preset characteristic index comprises component composition and component proportion. And acquiring a second material characteristic parameter set of the target electrolyte according to a second preset characteristic index. The first material characteristic parameter set refers to a set of characteristic parameters of the target electrode material, and the second material characteristic parameter set refers to a set of characteristic parameters of the target electrolyte.
The method S47 provided by the embodiment of the application further includes:
s471: sequentially constructing a first material characteristic index set of the target electrode material and a second material characteristic index set of the target electrolyte;
s472: performing union operation on the first material characteristic index set and the second material characteristic index set to obtain a material characteristic index set;
s473: extracting a first index in the material characteristic index set, and carrying out correlation analysis on the first index and specific capacity to obtain a correlation analysis result;
s474: and according to the correlation analysis result, if the first index is obviously correlated with the specific capacity, adding the first index to the preset characteristic index.
Specifically, before acquiring preset characteristic indexes, and performing multi-characteristic acquisition on the target electrode material and the target electrolyte based on the preset characteristic indexes to respectively obtain a first material characteristic parameter set and a second material characteristic parameter set, sequentially constructing the first material characteristic index set of the target electrode material and the second material characteristic index set of the target electrolyte, and then performing union operation on the first material characteristic index set and the second material characteristic index set, namely combining the acquired first material characteristic index set and the acquired second material characteristic index set to obtain a material characteristic index set. All the indexes of the first material characteristic index set and the second material characteristic index set are contained in the material characteristic index set. And then, extracting a first index in the material characteristic index set, and carrying out correlation analysis on the first index and the specific capacity to obtain a correlation analysis result. When the correlation analysis is performed, correlation analysis means in the prior art, such as statistical analysis software, can be adopted to determine the correlation of the first index to the influence of the capacity according to the first index and the corresponding specific capacity. And finally, according to a correlation analysis result, if the first index is obviously correlated with the specific capacity, adding the first index to the preset characteristic index. And if the first index is weakly related or not significantly related to the specific capacity, eliminating the first index.
S50: obtaining a first material test index based on the first material characteristic parameter set, obtaining a second material test index based on the second material characteristic parameter set, and obtaining a target material test index through weighted calculation;
s60: carrying out suitability analysis on the target electrode material and the target electrolyte to obtain a target material adaptation test index;
s70: and generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index.
Specifically, a first material testing index of the target supercapacitor is obtained based on a first material characteristic parameter set, a second material testing index of the target supercapacitor is obtained based on a second material characteristic parameter set, wherein the first material testing index and the second material testing index are similarity ratios of the first material characteristic parameter set and the second material characteristic parameter set to the first material characteristic and the second material characteristic parameter in a preset preparation process, and the target material testing index is obtained through weighted calculation according to the weight ratio of the preset first material testing index and the second material testing index. And carrying out adaptability analysis on the target electrode material and the target electrolyte to obtain the adaptation degree of the electrode material and the target electrolyte, wherein the electrolyte and the electrode material which are not mutually adapted can have serious influence on the performance of the supercapacitor because the different electrolytes and the electrode material are not completely adapted. And when the adaptation degree of the electrode materials and the target electrolyte is obtained, analyzing the specific adaptation degree of each electrode material and the corresponding target electrolyte in a big data mode to obtain a target material adaptation test index. And finally, performing product operation according to the target process test index, the target material test index and the target material adaptation test index to obtain an operation result, and generating a target consistency test result of the target supercapacitor according to the operation result. The consistency of the super capacitor is tested by comparing the target preparation process information with the preparation processes such as a preset preparation process, so that the consistency of the super capacitor is accurately evaluated, and the potential safety hazard in the use process of the super capacitor is reduced.
The method S70 provided by the embodiment of the application further comprises the following steps:
s71: judging whether the target electrode material belongs to a first preset material or not;
s72: if the target super capacitor belongs to the super capacitor, generating a detection instruction, and detecting whether a second preset material exists in the target super capacitor according to the detection instruction;
s73: if the second preset material exists, a metering instruction is generated, and the actual addition amount of the second preset material is obtained according to the metering instruction;
s74: and adjusting the target consistency test result according to the actual addition amount.
Specifically, whether the target electrode material belongs to a first preset material is judged, wherein the first preset material is activated carbon. If the target super capacitor belongs to the field, a detection instruction is generated, and whether a second preset material exists in the target super capacitor or not is detected according to the detection instruction, wherein the second preset material is a graphene material. If the second preset material exists, a metering instruction is generated, and the actual addition amount of the second preset material is obtained according to the metering instruction. The graphene is added to promote the performance of the active carbon, so that the conductivity and the comprehensive performance of the supercapacitor are improved, and if the target electrode material in the supercapacitor is the active carbon, the comprehensive performance of the supercapacitor can be improved due to the addition of the graphene. And finally, adjusting the target consistency test result according to the actual addition amount, and acquiring the actual addition amount and the corresponding performance amplification result according to the big data when adjusting, so as to adjust the target consistency test result, thereby further improving the accuracy of the target consistency test result.
According to the technical scheme provided by the embodiment of the application, the target preparation process information of the target super capacitor is obtained. And acquiring a preset preparation process, and comparing the preset preparation process with the target preparation process information to obtain a target process test index. And respectively obtaining a target electrode material and a target electrolyte of the target supercapacitor. Acquiring a preset characteristic index, and carrying out multi-characteristic acquisition on the target electrode material and the target electrolyte based on the preset characteristic index to respectively obtain a first material characteristic parameter set and a second material characteristic parameter set. And obtaining a first material testing index based on the first material characteristic parameter set, obtaining a second material testing index based on the second material characteristic parameter set, and obtaining a target material testing index through weighting calculation. And carrying out suitability analysis on the target electrode material and the target electrolyte to obtain a target material adaptation test index. And generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index. The method solves the technical problems that the consistency test of the super capacitor is not guaranteed, and the circuit is unstable and has potential safety hazards due to the fact that the consistency of the super capacitor is not guaranteed in the prior art. The consistency of the super capacitor is tested by comparing the target preparation process information with the preset preparation process, so that the accurate evaluation of the consistency of the super capacitor is realized, and the potential safety hazard in the use process of the super capacitor is reduced.
Examples
Based on the same conception as the consistency testing method of the super capacitor in the foregoing embodiment, the present application also provides a system of the consistency testing method of the super capacitor, which can be implemented by hardware and/or software, and can be generally integrated in an electronic device, for executing the method provided by any embodiment of the present application. As shown in fig. 4, the system includes:
the process information acquisition module 11 is used for acquiring target preparation process information of the target supercapacitor;
a process test index obtaining module 12, configured to obtain a preset preparation process, and compare the preset preparation process with the target preparation process information to obtain a target process test index;
a material acquisition module 13, configured to acquire a target electrode material and a target electrolyte of the target supercapacitor respectively;
the feature parameter set acquisition module 14 is configured to acquire a preset feature index, and perform multi-feature acquisition on the target electrode material and the target electrolyte based on the preset feature index, so as to obtain a first material feature parameter set and a second material feature parameter set respectively;
the material test index module 15 is configured to obtain a first material test index based on the first material characteristic parameter set, obtain a second material test index based on the second material characteristic parameter set, and obtain a target material test index through weighted calculation;
a material adaptation test index module 16, configured to perform suitability analysis on the target electrode material and the target electrolyte to obtain a target material adaptation test index;
the test result obtaining module 17 is configured to generate a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index.
Further, the process test index acquisition module 12 is further configured to:
acquiring historical preparation process data;
wherein the historical preparation process data comprises a plurality of groups of process data of a plurality of super capacitors;
sequentially detecting the capacitance performance of each super capacitor in the plurality of super capacitors, and forming a plurality of capacitance performance parameters;
taking the multiple groups of process data as intelligent optimizing areas and taking the multiple capacitance performance parameters as intelligent optimizing evaluation parameters;
optimizing in the intelligent optimizing area according to the intelligent optimizing evaluation parameters to obtain an optimal process data set;
and taking the optimal process data set as the preset preparation process.
Further, the process test index acquisition module 12 is further configured to:
randomly acquiring a first process data set in the intelligent optimizing area;
the first super capacitor of the first process data set is matched, and a first capacitance performance parameter is obtained through detection;
acquiring a first preset neighborhood scheme, and acquiring a first neighborhood of the first process data set, wherein the first neighborhood comprises a plurality of groups of neighborhood process data;
acquiring a first neighborhood process data set in the plurality of sets of neighborhood process data, and matching the first neighborhood super capacitor;
detecting and obtaining a first neighborhood capacitance performance parameter of the first neighborhood super capacitor;
and comparing the first capacitance performance parameter with the first neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result.
Further, the process test index acquisition module 12 is further configured to:
if the first neighborhood capacitance performance parameter is better than the first capacitance performance parameter, obtaining an iterative optimization instruction;
obtaining a second neighborhood of the first neighborhood process data set based on the iterative optimization instruction;
acquiring a second neighborhood process data set of the second neighborhood, and matching a second neighborhood super capacitor;
detecting and obtaining a second neighborhood capacitance performance parameter of the second neighborhood super capacitor;
and comparing the first neighborhood capacitance performance parameter with the second neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result.
Further, the feature parameter set obtaining module 14 is further configured to:
acquiring a first preset characteristic index in the preset characteristic indexes;
the first preset characteristic indexes comprise specific surface area, pore size distribution, grain size, element composition and ash content;
acquiring the first material characteristic parameter set based on the first preset characteristic index;
acquiring a second preset characteristic index in the preset characteristic indexes;
wherein the second preset characteristic index comprises component composition and component proportion;
acquiring the second material characteristic parameter set based on the second preset characteristic index;
the first material characteristic parameter set refers to a set of characteristic parameters of the target electrode material, and the second material characteristic parameter set refers to a set of characteristic parameters of the target electrolyte.
Further, the feature parameter set obtaining module 14 is further configured to:
sequentially constructing a first material characteristic index set of the target electrode material and a second material characteristic index set of the target electrolyte;
performing union operation on the first material characteristic index set and the second material characteristic index set to obtain a material characteristic index set;
extracting a first index in the material characteristic index set, and carrying out correlation analysis on the first index and specific capacity to obtain a correlation analysis result;
and according to the correlation analysis result, if the first index is obviously correlated with the specific capacity, adding the first index to the preset characteristic index.
Further, the test result obtaining module 17 is further configured to:
judging whether the target electrode material belongs to a first preset material or not;
if the target super capacitor belongs to the super capacitor, generating a detection instruction, and detecting whether a second preset material exists in the target super capacitor according to the detection instruction;
if the second preset material exists, a metering instruction is generated, and the actual addition amount of the second preset material is obtained according to the metering instruction;
and adjusting the target consistency test result according to the actual addition amount.
The included units and modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
Examples
Fig. 5 is a schematic structural diagram of an electronic device provided in a third embodiment of the present application, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present application. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present application. As shown in fig. 5, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 5, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 5, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to a method for testing the consistency of supercapacitors in embodiments of the present application. The processor 31 executes various functional applications of the computer device and data processing by running software programs, instructions and modules stored in the memory 32, i.e. implements a method of testing the consistency of the super capacitor as described above.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, while the application has been described in connection with the above embodiments, the application is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the application, which is set forth in the following claims.
Claims (7)
1. The consistency testing method of the super capacitor is characterized by comprising the following steps of:
acquiring target preparation process information of a target supercapacitor;
obtaining a preset preparation process, and comparing the preset preparation process with the target preparation process information to obtain a target process test index, wherein the method comprises the following steps:
acquiring historical preparation process data;
wherein the historical preparation process data comprises a plurality of groups of process data of a plurality of super capacitors;
sequentially detecting the capacitance performance of each super capacitor in the plurality of super capacitors, and forming a plurality of capacitance performance parameters;
taking the multiple groups of process data as intelligent optimizing areas and taking the multiple capacitance performance parameters as intelligent optimizing evaluation parameters;
optimizing in the intelligent optimizing area according to the intelligent optimizing evaluation parameters to obtain an optimal process data set;
taking the optimal process data set as the preset preparation process;
respectively obtaining a target electrode material and a target electrolyte of the target supercapacitor;
acquiring a preset characteristic index, and carrying out multi-characteristic acquisition on the target electrode material and the target electrolyte based on the preset characteristic index to respectively obtain a first material characteristic parameter set and a second material characteristic parameter set, wherein the method comprises the following steps:
sequentially constructing a first material characteristic index set of the target electrode material and a second material characteristic index set of the target electrolyte;
performing union operation on the first material characteristic index set and the second material characteristic index set to obtain a material characteristic index set;
extracting a first index in the material characteristic index set, and carrying out correlation analysis on the first index and specific capacity to obtain a correlation analysis result;
according to the correlation analysis result, if the first index is significantly related to the specific capacity, adding the first index to the preset feature index includes:
acquiring a first preset characteristic index in the preset characteristic indexes;
the first preset characteristic indexes comprise specific surface area, pore size distribution, grain size, element composition and ash content;
acquiring the first material characteristic parameter set based on the first preset characteristic index;
acquiring a second preset characteristic index in the preset characteristic indexes;
wherein the second preset characteristic index comprises component composition and component proportion;
acquiring the second material characteristic parameter set based on the second preset characteristic index;
wherein the first material characteristic parameter set refers to a set of characteristic parameters of the target electrode material, and the second material characteristic parameter set refers to a set of characteristic parameters of the target electrolyte;
obtaining a first material test index based on the first material characteristic parameter set, obtaining a second material test index based on the second material characteristic parameter set, and obtaining a target material test index through weighted calculation;
carrying out suitability analysis on the target electrode material and the target electrolyte to obtain a target material adaptation test index;
and generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index.
2. The method according to claim 1, wherein optimizing in the intelligent optimizing area according to the intelligent optimizing evaluation parameter to obtain an optimal process data set comprises:
randomly acquiring a first process data set in the intelligent optimizing area;
the first super capacitor of the first process data set is matched, and a first capacitance performance parameter is obtained through detection;
acquiring a first preset neighborhood scheme, and acquiring a first neighborhood of the first process data set, wherein the first neighborhood comprises a plurality of groups of neighborhood process data;
acquiring a first neighborhood process data set in the plurality of sets of neighborhood process data, and matching the first neighborhood super capacitor;
detecting and obtaining a first neighborhood capacitance performance parameter of the first neighborhood super capacitor;
and comparing the first capacitance performance parameter with the first neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result.
3. The method of claim 2, wherein comparing the first capacitive performance parameter to the first neighbor capacitive performance parameter and determining the optimal process data set based on the comparison result comprises:
if the first neighborhood capacitance performance parameter is better than the first capacitance performance parameter, obtaining an iterative optimization instruction;
obtaining a second neighborhood of the first neighborhood process data set based on the iterative optimization instruction;
acquiring a second neighborhood process data set of the second neighborhood, and matching a second neighborhood super capacitor;
detecting and obtaining a second neighborhood capacitance performance parameter of the second neighborhood super capacitor;
and comparing the first neighborhood capacitance performance parameter with the second neighborhood capacitance performance parameter, and determining the optimal process data set according to a comparison result.
4. The method of claim 1, further comprising, after said generating a target conformance test result for said target supercapacitor based on said target process test index, said target material test index, and said target material adaptation test index:
judging whether the target electrode material belongs to a first preset material or not;
if the target super capacitor belongs to the super capacitor, generating a detection instruction, and detecting whether a second preset material exists in the target super capacitor according to the detection instruction;
if the second preset material exists, a metering instruction is generated, and the actual addition amount of the second preset material is obtained according to the metering instruction;
and adjusting the target consistency test result according to the actual addition amount.
5. A consistency test system for a supercapacitor, comprising:
the process information acquisition module is used for acquiring target preparation process information of the target supercapacitor;
the process test index acquisition module is used for acquiring a preset preparation process and comparing the preset preparation process with the target preparation process information to obtain a target process test index, and comprises the following steps:
acquiring historical preparation process data;
wherein the historical preparation process data comprises a plurality of groups of process data of a plurality of super capacitors;
sequentially detecting the capacitance performance of each super capacitor in the plurality of super capacitors, and forming a plurality of capacitance performance parameters;
taking the multiple groups of process data as intelligent optimizing areas and taking the multiple capacitance performance parameters as intelligent optimizing evaluation parameters;
optimizing in the intelligent optimizing area according to the intelligent optimizing evaluation parameters to obtain an optimal process data set;
taking the optimal process data set as the preset preparation process;
the material acquisition module is used for respectively acquiring a target electrode material and a target electrolyte of the target supercapacitor;
the characteristic parameter set acquisition module is used for acquiring a preset characteristic index, carrying out multi-characteristic acquisition on the target electrode material and the target electrolyte based on the preset characteristic index, respectively obtaining a first material characteristic parameter set and a second material characteristic parameter set, and comprises the following steps:
sequentially constructing a first material characteristic index set of the target electrode material and a second material characteristic index set of the target electrolyte;
performing union operation on the first material characteristic index set and the second material characteristic index set to obtain a material characteristic index set;
extracting a first index in the material characteristic index set, and carrying out correlation analysis on the first index and specific capacity to obtain a correlation analysis result;
according to the correlation analysis result, if the first index is significantly related to the specific capacity, adding the first index to the preset feature index includes:
acquiring a first preset characteristic index in the preset characteristic indexes;
the first preset characteristic indexes comprise specific surface area, pore size distribution, grain size, element composition and ash content;
acquiring the first material characteristic parameter set based on the first preset characteristic index;
acquiring a second preset characteristic index in the preset characteristic indexes;
wherein the second preset characteristic index comprises component composition and component proportion;
acquiring the second material characteristic parameter set based on the second preset characteristic index;
wherein the first material characteristic parameter set refers to a set of characteristic parameters of the target electrode material, and the second material characteristic parameter set refers to a set of characteristic parameters of the target electrolyte;
the material test index module is used for obtaining a first material test index based on the first material characteristic parameter set, obtaining a second material test index based on the second material characteristic parameter set, and obtaining a target material test index through weighting calculation;
the material adaptation test index module is used for carrying out suitability analysis on the target electrode material and the target electrolyte to obtain a target material adaptation test index;
the test result acquisition module is used for generating a target consistency test result of the target supercapacitor according to the target process test index, the target material test index and the target material adaptation test index.
6. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor for implementing a method of testing the consistency of a supercapacitor according to any one of claims 1 to 4 when executing executable instructions stored in said memory.
7. A computer readable medium on which a computer program is stored, characterized in that the program, when executed by a processor, implements a method for testing the consistency of a supercapacitor according to any one of claims 1 to 4.
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| CN116954088B (en) * | 2023-09-20 | 2023-12-12 | 南通新丰威机械科技有限公司 | Online monitoring method and system for mixer |
| CN117153310B (en) * | 2023-10-31 | 2023-12-29 | 南通江海储能技术有限公司 | Optimization method and system for high-temperature-resistant welding buckle type supercapacitor |
| CN117148251B (en) * | 2023-10-31 | 2024-03-29 | 南通江海储能技术有限公司 | Voltage compensation method and system based on aging clamp of super capacitor |
| CN117169770B (en) * | 2023-11-01 | 2024-01-26 | 南通江海储能技术有限公司 | On-line monitoring method and system for health state of super capacitor |
| CN117990865B (en) * | 2024-04-07 | 2024-06-21 | 金石(天津)科技发展有限公司 | Composite film bag performance test method and system based on three-layer co-extrusion blown film |
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