CN117609846A - Battery performance parameter acquisition method and device, electronic equipment and storage medium - Google Patents
Battery performance parameter acquisition method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses a method and device for acquiring battery performance parameters, electronic equipment and a storage medium. The method for acquiring the battery performance parameters comprises the following steps: classifying production history data of a plurality of batteries according to preset parameter categories to obtain first data; classifying the first data according to a preset performance level and performance parameters in the first data to obtain second data; extracting matching data matched with the battery design parameters from the second data according to the battery design parameters input by a user; and acquiring the battery performance parameters matched with the battery design parameters according to the performance parameters in the matched data. The battery performance parameter acquisition method provided by the embodiment of the application can automatically acquire the battery performance parameter, saves labor cost, shortens time consumption of the battery performance parameter acquisition process, and improves accuracy of the battery performance parameter acquisition result.
Description
Technical Field
The present disclosure relates to the field of battery technologies, and in particular, to a method and an apparatus for acquiring a battery performance parameter, an electronic device, and a storage medium.
Background
Along with the development of science and technology, the development period of the power battery is shorter and shorter, and the accurate and rapid acquisition of the battery performances corresponding to parameters such as a research and development design system or a substitution system directly influences the development period, cost and battery quality of the battery. In the related art, the corresponding battery performance is determined by manually analyzing the production history data of the plurality of batteries, so that the determination of the battery performance corresponding to each parameter in the production history data of the plurality of batteries is very difficult, and has long time consumption and low accuracy.
The statements are to be understood as merely provide background information related to the present application and may not necessarily constitute prior art.
Disclosure of Invention
In view of the above-mentioned problems that in the related art, corresponding battery performance is determined by manually analyzing production history data of a plurality of batteries, and determination of battery performance corresponding to each parameter in the production history data of the plurality of batteries is very difficult, and takes a long time and has low accuracy, the present application provides a method, an apparatus, an electronic device, and a storage medium for acquiring battery performance parameters, so as to automatically acquire battery performance parameters, shorten time consumption in a battery performance parameter acquiring process, and improve acquisition accuracy of battery performance parameters.
In a first aspect of an embodiment of the present application, a method for acquiring a battery performance parameter is provided, including:
classifying production history data of a plurality of batteries according to preset parameter categories to obtain first data;
classifying the first data according to a preset performance level and performance parameters in the first data to obtain second data;
extracting matching data matched with the battery design parameters from the second data according to the battery design parameters input by a user;
and acquiring the battery performance parameters matched with the battery design parameters according to the performance parameters in the matched data.
According to the battery performance parameter acquisition method, the battery performance parameters can be automatically acquired, the labor cost is saved, the time consumption of the battery performance parameter acquisition process is shortened, the accuracy of the battery performance parameter acquisition result is improved, and therefore the following conditions in the related technology are greatly improved: the determination of the battery performance corresponding to each parameter in the production history data of the plurality of batteries is very difficult, the corresponding battery performance is determined by manually analyzing the production history data of the plurality of batteries, and the time consumption is long and the accuracy is low.
In some embodiments of the present application, the classifying the production history data of the plurality of batteries according to the preset parameter class to obtain the first data includes:
comparing third data in the production history data of the plurality of batteries with each preset parameter category to determine a parameter category corresponding to the third data, wherein the third data is any one of the production history data of the plurality of batteries;
and marking the corresponding class label on the third data according to the class label corresponding to the preset parameter class and the parameter class corresponding to the third data to obtain marked data, wherein the marked data corresponding to each data in the production history data of the plurality of batteries form the first data. The production history data is classified, so that the data can be conveniently managed and inquired; through the corresponding class label on the data label, the data management and the query are conveniently realized according to the class label, and the convenience of the data management and the query is further improved.
In some embodiments of the present application, the grading the first data according to a preset performance level and a performance parameter in the first data to obtain second data includes:
Comparing the first performance parameter in the first data with each preset performance level to determine a performance level corresponding to the first performance parameter, wherein the first performance parameter is any performance parameter in the first data;
searching all parameters of a first battery from the first data, wherein the first battery is a battery corresponding to the first performance parameter;
determining a performance level label of the first performance parameter according to a preset performance level label and a performance level corresponding to the first performance parameter;
and marking all parameters of the first battery with the performance level label of the first performance parameter, wherein the first data marked with the performance level label is the second data.
By grading the first data, the data is convenient to manage and inquire according to the performance level; the corresponding performance level label on the data label is convenient for realizing data management and inquiry according to the performance level label, and the convenience of data management and inquiry is further improved.
In some embodiments of the present application, the extracting, from the second data, data matching the battery design parameter according to the battery design parameter input by the user includes:
Comparing the keywords in the battery design parameters input by the user with the category labels and the performance level labels of the second data to obtain labels matched with the keywords;
and extracting the data corresponding to the label matched with the keyword from the second data to obtain the data matched with the battery design parameter.
And through matching the keywords with the labels, the data corresponding to the labels matched with the keywords are extracted from the second data, so that the data retrieval and extraction efficiency is further improved, and the accuracy of the data retrieval and extraction is improved.
In some embodiments of the present application, the obtaining, according to the performance parameter in the matching data, a battery performance parameter matching the battery design parameter includes:
and under the condition that the matching data comprises a plurality of same-category performance parameters, the plurality of same-category performance parameters are processed according to a preset algorithm to obtain battery performance parameters matched with the battery design parameters, so that the accuracy of the battery performance parameters matched with the battery design parameters can be further improved.
In some embodiments of the present application, the processing the plurality of performance parameters of the same category according to a preset algorithm to obtain a battery performance parameter matched with the battery design parameter includes:
And obtaining an average value of the plurality of same-category performance parameters, wherein the average value is a battery performance parameter matched with the battery design parameter. And the average value is used as the battery performance parameter, so that the accuracy of the battery performance parameter matched with the battery design parameter is further improved.
In some embodiments of the present application, the processing the plurality of same-category performance parameters to obtain battery performance parameters matching the battery design parameters includes:
removing the maximum value and the minimum value in the plurality of same-category performance parameters to obtain the rest same-category performance parameters;
and obtaining the average value of the remaining same-class performance parameters to obtain the battery performance parameters matched with the battery design parameters, so that the probability of being interfered by data possibly having abnormality is greatly reduced, and the accuracy of the battery performance parameters matched with the battery design parameters is further improved.
In some embodiments of the present application, the acquiring method further includes: and visually displaying the battery performance parameters matched with the battery design parameters, so that the battery performance parameters can be intuitively displayed to a user.
In a second aspect of the embodiments of the present application, there is provided an apparatus for acquiring a battery performance parameter, including:
The classification module is used for classifying the production history data of the batteries according to the preset parameter types to obtain first data;
the performance grading module is used for grading the first data according to a preset performance level and the performance parameters in the first data to obtain second data;
the extraction matching module is used for extracting matching data matched with the battery design parameters from the second data according to the battery design parameters input by a user;
and the battery performance parameter acquisition module is used for acquiring the battery performance parameters matched with the battery design parameters according to the performance parameters in the matched data.
According to the battery performance parameter acquisition device, the battery performance parameters can be automatically acquired, the labor cost is saved, the time consumption of the battery performance parameter acquisition process is shortened, the accuracy of the battery performance parameter acquisition result is improved, and therefore the following conditions in the related technology are greatly improved: the determination of the battery performance corresponding to each parameter in the production history data of the plurality of batteries is very difficult, the corresponding battery performance is determined by manually analyzing the production history data of the plurality of batteries, and the time consumption is long and the accuracy is low.
In a third aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the computer program to implement the method for obtaining the battery performance parameter according to any embodiment of the present application.
The electronic device provided in the third aspect of the embodiments of the present application may achieve the beneficial effects that any one of the embodiments of the first aspect may achieve.
In a fourth aspect of the embodiments of the present application, there is provided a computer readable storage medium having stored thereon a computer program that is executed by a processor to implement the method for obtaining a battery performance parameter according to any of the embodiments of the present application.
The fourth aspect of the embodiments of the present application provides a computer readable storage medium, which can achieve the beneficial effects achieved by any one of the embodiments of the first aspect.
The foregoing description is only an overview of the embodiments of the present application, and may be implemented in accordance with the content of the specification in order to make the technical means of the embodiments of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the embodiments of the present application more comprehensible, the following detailed description of the present application.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the embodiments. The drawings are only for purposes of illustrating embodiments of the present application and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the accompanying drawings.
Fig. 1 is a diagram of a method application scenario for acquiring battery performance parameters in accordance with one or more embodiments.
Fig. 2 is a flow diagram of a method of obtaining battery performance parameters in accordance with one or more embodiments.
Fig. 3 is a flow diagram of classifying production history data for a plurality of batteries according to preset parameter categories in accordance with one or more embodiments.
FIG. 4 is a flow diagram of ranking first data according to a preset performance level and performance parameters in the first data in accordance with one or more embodiments.
Fig. 5 is a flow diagram of extracting data matching battery design parameters from second data according to user-entered battery design parameters in accordance with one or more embodiments.
Fig. 6 is a block diagram of an acquisition device of battery performance parameters in accordance with one or more embodiments.
FIG. 7 is a block diagram of an electronic device in accordance with one or more embodiments.
FIG. 8 is a schematic diagram of a computer-readable storage medium in accordance with one or more embodiments.
Detailed Description
Embodiments of the technical solutions of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical solutions of the present application, and thus are only examples, and are not intended to limit the scope of protection of the present application.
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 particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions.
In the description of the embodiments of the present application, the technical terms "first," "second," etc. are used merely to distinguish between different objects and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, a particular order or a primary or secondary relationship. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
With the development of new energy technology, power batteries have been widely used in various fields. The power battery such as lithium ion power battery has the advantages of high power, high energy density, good environmental protection effect and the like, and has been widely applied to the technical fields of new energy vehicles, consumer electronics, energy storage systems and the like. Power cells can be used, but are not limited to, in electrical devices such as vehicles, boats or aircraft. Taking an electric vehicle as an example, the electric vehicle powered by the power battery has the advantages of good environmental protection effect, low noise, low cost, capability of effectively promoting energy conservation, emission reduction and the like, has huge market application prospect, and is beneficial to economic sustainable development.
Along with the development of science and technology, the development period of the power battery is shorter and shorter, and the accurate and rapid acquisition of the battery performances corresponding to parameters such as a research and development design system or a substitution system directly influences the development period, cost and battery quality of the battery. In the course of battery development design, it is often necessary to determine battery performance corresponding to certain battery design parameters. In the related art, corresponding battery performance is determined by manually analyzing production history data of a plurality of batteries, and determination of battery performance corresponding to each parameter in the production history data of the plurality of batteries is very difficult, and is long in time consumption and low in accuracy, for example, when a large amount of historical development production data is investigated, a large amount of parameter analysis is long in time consumption and easy to generate omission and errors, battery development production efficiency is affected, and high labor cost is generated.
Aiming at the technical problems in the related art, the embodiment of the application provides a method for acquiring battery performance parameters, which classifies production history data of a plurality of batteries according to preset parameter types to obtain first data, classifies the first data according to preset performance levels and performance parameters in the first data to obtain second data, extracts matching data matched with the battery design parameters from the second data according to the battery design parameters input by a user, acquires the battery performance parameters matched with the battery design parameters according to the performance parameters in the matching data, thereby automatically acquiring the battery performance parameters, saving labor cost, shortening time consumption in a battery performance parameter acquisition process, improving accuracy of acquisition results of the battery performance parameters, reducing occurrence probability of data omission and errors, improving battery research and development production efficiency, and greatly improving the following conditions in the related art: the method has the advantages that the battery performance corresponding to each parameter in the production historical data of the plurality of batteries is difficult to determine, the corresponding battery performance is determined by manually analyzing the production historical data of the plurality of batteries, the time consumption is long, the accuracy is low, when a large amount of historical research and development production data are researched, the time consumption is long, the time consumption of analyzing a large amount of parameters is long, omission and errors are easy to generate, the research and development production efficiency of the batteries is influenced, and the labor cost is high.
The method for acquiring the battery performance parameters can be applied to an application scenario shown in fig. 1, wherein a server side is in communication connection with a user side. The server classifies the production history data of the batteries according to the preset parameter types to obtain first data, and classifies the first data according to the preset performance level and the performance parameters in the first data to obtain second data. The user can send the information containing the battery design parameters input by the user to the server through the user side, and after the server receives the information sent by the user side, the server extracts matching data matched with the battery design parameters from the second data according to the battery design parameters input by the user, and obtains the battery performance parameters matched with the battery design parameters according to the performance parameters in the matching data, so that the battery performance parameters can be sent to the user side for display. The clients may be, but are not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
A method for acquiring a battery performance parameter according to an embodiment of the present application is described below with reference to the accompanying drawings.
Referring to fig. 2, an embodiment of the present application provides a method for obtaining a battery performance parameter, which may include steps S10 to S40:
s10, classifying production history data of a plurality of batteries according to preset parameter categories to obtain first data.
The battery may be, for example, a battery cell, a battery module, a battery pack, or the like. The production history data of a battery includes all data that is involved in the entire process of developing the battery from design to manufacturing the finished battery. The various databases for storing battery development and production related data may be associated to facilitate processing of the production history data for the plurality of batteries.
The preset parameter categories are parameter categories preset for better management of production history data, and are used for classifying the parameters of the data. The preset parameter category may be a parameter category preset according to actual application needs and application experience. In some embodiments, the parameters required to be defined in the process of developing and producing the battery can be logically confirmed, and edited by a logic editor to obtain a plurality of preset parameter categories. The production history data of the plurality of batteries may be categorized according to the plurality of preset parameter categories.
In one example, the preset parameter categories may include, for example, chemical generation, chemical system, process generation, cathode parameters, anode material, anode coating weight, cathode material, cathode coating weight, performance characteristics, energy density characteristics, lifetime characteristics, charge rate, discharge rate, temperature rise, voltage information, electrolyte category, separator type, coating accuracy, tension control accuracy, and the like. Specifically, the preset parameter types can be set according to actual application requirements.
The preset parameter category may include a plurality of different levels, and may specifically be preset according to actual needs. For example, in one specific example, each of three predetermined parameter categories of chemical generation, material, and process parameters has a next level category, the chemical system is the next level category of chemical generation, one chemical generation contains multiple chemical systems, and different chemical generation may contain the same chemical system. The next hierarchical categories of process parameters may include coat weight, compaction density, speed, and temperature. The next level category of materials may include preset parameter categories of cathode, anode, electrolyte, separator and auxiliary materials.
For another example, in one specific example, the anode material and the anode coating weight both belong to the next level category of anode parameters, the cathode material and the cathode coating weight both belong to the next level category of cathode material, and the lifetime characteristic, the charge rate, and the discharge rate are all the next level characteristics of the performance characteristic. The number of levels of the preset parameter class may be set according to actual needs, for example, may be set to only one level, may be set to include two levels or three levels, and the like.
The production history data of the batteries are classified according to the preset parameter types, the obtained classified data are the first data, and the data are classified so as to facilitate data management, and data searching according to search keywords input by a user is facilitated, so that the data searching efficiency is improved.
Referring to fig. 3, in some embodiments, classifying production history data of a plurality of batteries according to a preset parameter category to obtain first data may include steps S101 to S102:
s101, comparing third data in the production history data of the plurality of batteries with each preset parameter type to determine the parameter type corresponding to the third data, wherein the third data is any one of the production history data of the plurality of batteries.
For example, if E-1, E-2, E-3, E-4 and E-5 in the production history data of a plurality of batteries respectively represent one electrolyte, the comparison is performed with each preset parameter class, and it can be determined that five data of E-1, E-2, E-3, E-4 and E-5 respectively correspond to the preset parameter class of the electrolyte class.
In another specific example, graphite a-1, graphite a-2, graphite a-3, and graphite a-4 in the production history data of the plurality of batteries represent one material for manufacturing the anode sheet, respectively, and then the comparison is made with each preset parameter class, it can be determined that graphite a-1, graphite a-2, graphite a-3, and graphite a-4 each correspond to two preset parameter classes of anode material and anode parameter, and that the anode material is a lower class of anode parameter, and it can be determined that graphite a-1, graphite a-2, graphite a-3, and graphite a-4 correspond to the anode material under the anode parameter.
S102, marking the corresponding class label on the third data according to the class label corresponding to the preset parameter class and the parameter class corresponding to the third data to obtain marked data, wherein the marked data corresponding to each data in the production history data of the plurality of batteries form first data.
The class label corresponding to the preset parameter class comprises the name of the preset parameter class or the corresponding number. The class label corresponding to each data can be determined according to the class label corresponding to the preset parameter class and the parameter class corresponding to each data, and then the class label corresponding to each data label is provided, for example, the preset parameter class corresponding to the graphite A-1 comprises an anode parameter and an anode material, the class label of the anode parameter is S61, the class label of the anode material is D32, and the graphite A-1 can be marked with both the S61 and the D32 labels. Corresponding data can be labeled through a logic editor, and corresponding category labels are marked on the data labels so as to facilitate data retrieval according to the category labels.
Marking each data in the production history data of the plurality of batteries with a corresponding category label to obtain marked data which is the first data. The production history data is classified, so that the data can be conveniently managed and inquired; through the corresponding class label on the data label, the data management and the query are conveniently realized according to the class label, and the convenience of the data management and the query is further improved.
And S20, grading the first data according to the preset performance level and the performance parameters in the first data to obtain second data.
The performance parameters are parameters for characterizing the performance of the battery, such as battery life, charge rate, discharge rate, temperature rise, energy density, etc. The preset performance level includes a category preset according to a certain performance parameter for classification, for example, two performance levels of long life and short life can be set according to the service life of the battery, and the long life and the short life respectively correspond to a life interval; according to the speed of the charging speed, two performance levels of fast charging and slow charging can be set, and the fast charging and the slow charging fully correspond to a charging speed interval respectively; the energy density can be divided into three energy density levels, namely a large energy density, a medium energy density and a small energy density according to the energy density, and the large energy density, the medium energy density and the small energy density respectively correspond to one energy density interval. The preset performance level can be specifically preset according to the actual application requirement.
Referring to fig. 4, in some embodiments, the step of grading the first data according to the preset performance level and the performance parameter in the first data to obtain the second data may include steps S201 to S204:
s201, comparing a first performance parameter in the first data with each preset performance level to determine a performance level corresponding to the first performance parameter, wherein the first performance parameter is any performance parameter in the first data.
Each preset performance level corresponds to a preset level condition, the performance parameter meeting the level condition is the performance parameter corresponding to the preset performance level, the level condition can be a numerical interval, for example, the performance parameter of which the value belongs to is the performance parameter corresponding to the preset performance level. For example, the preset performance level may be correspondingly provided with a numerical interval, and the value of one performance parameter (for convenience of description, the performance parameter may be referred to as a first performance parameter) is compared with the numerical interval of one preset performance level, and if the value of the first performance parameter falls within the numerical interval, it may be determined that the first performance parameter corresponds to the preset performance level; if the value of the first performance parameter does not belong to the numerical value interval, comparing the value of the first performance parameter with the numerical value interval of another preset performance level until the performance level corresponding to the first performance parameter is found.
For example, the lifetime interval corresponding to the lifetime of the preset performance level is greater than or equal to 5 years, the lifetime interval corresponding to the short lifetime of the preset performance level is greater than or equal to 0 years and less than 5 years, and if the value of one performance parameter is 6 years of battery lifetime, the value of the performance parameter falls into the lifetime interval corresponding to the lifetime, and it can be determined that the performance level corresponding to the performance parameter is long lifetime.
S202, all parameters of a first battery are searched out from the first data, wherein the first battery is a battery corresponding to the first performance parameter.
Each performance parameter in the first data represents the performance of one battery correspondingly, and the battery represented by the performance parameter is the battery corresponding to the performance parameter. For example, assuming that the performance parameter a is a parameter indicating the performance of the battery B, the battery B is the battery corresponding to the performance parameter a. For convenience of description, the battery corresponding to the first performance parameter is referred to as a first battery, the first battery corresponding to the first performance parameter is first determined, and then all parameters of the first battery are found from the first data.
S203, determining a performance level label of the first performance parameter according to the preset performance level label and the performance level corresponding to the first performance parameter.
The performance level label can be a number or a text. For example, the performance level label of the long-life performance level can be the number LL1, the word long-life can also be directly used as the performance level label, and the specific form of the performance level label can be set according to the actual application requirement.
For example, if the performance level corresponding to the first performance parameter is a long-life performance level and the performance level label corresponding to the long-life performance level is LL1, it is determined that the performance level label of the first performance parameter is LL1.
S204, marking all parameters of the first battery with performance level labels of the first performance parameters, wherein the first data marked with the performance level labels are second data.
With the above example, if the performance level of the first performance parameter is long-life and the performance level label is LL1, all the parameters of the first battery are labeled with LL 1. After all the data in the first data are marked with the performance level label, the second data are obtained, that is, the second data are the first data marked with the performance level label. The corresponding parameters can be labeled through a logic editor, and all the parameters of the first battery are labeled with the performance level labels of the first performance parameters so as to facilitate data retrieval according to the performance level labels.
And grading the first data and marking the data obtained after the performance level labels are marked as second data. By grading the first data, the data is convenient to manage and inquire according to the performance level; the corresponding performance level label on the data label is convenient for realizing data management and inquiry according to the performance level label, and the convenience of data management and inquiry is further improved.
S30, extracting matching data matched with the battery design parameters from the second data according to the battery design parameters input by the user.
The matching data includes all parameters of the battery produced and manufactured using the battery design parameters entered by the user. When a user designs a battery, the performance of the designed battery is predicted, and the design parameters of the battery are used as search keywords, so that the search sentences input by the user contain the battery design parameters, and matching data can be extracted from the second data according to the battery design parameters. These matching data may include, for example, all parameters of the battery corresponding to the battery design parameters entered by the user. The battery corresponding to the battery design parameters input by the user is a battery produced by using the battery design parameters input by the user.
For example, if the battery design parameter inputted by the user is E-3, all parameters of the battery manufactured by using E-3 are extracted from the second data.
Referring to fig. 5, in some embodiments, extracting data matching the battery design parameters from the second data according to the battery design parameters input by the user may include steps S301 to S302:
s301, comparing keywords in battery design parameters input by a user with category labels and performance level labels of the second data, and obtaining labels matched with the keywords.
Keywords in the battery design parameters entered by the user may include both character-type keywords and numeric-type keywords. Character-type keywords are keywords composed of characters that cannot participate in arithmetic operations, such as keywords composed of characters such as Chinese characters, english letters, and/or text-type numeric strings, for example, chemical generation is second generation, E-3, graphite A-1, and the like. Numerical keywords, i.e., keywords comprising at least one number that is capable of arithmetic operations; for example, 2C, anode coat weight 0.140, cathode coat weight 0.25 are numerical keywords, where 2, 0.140, and 0.25 are numbers capable of arithmetic operations, and 2C means that the charge rate is such that the battery can be fully charged in half an hour.
Illustratively, comparing the character type keyword with the category label and the performance level label of the second data includes: and searching out the labels which are the same as the character type keywords from all the class labels and all the performance level labels of the second data, wherein the searched labels are the labels matched with the character type keywords. Comparing the numeric key words with class labels and performance level labels of the second data, including: and searching out the labels of which the numerical range contains the numerical value of the numerical keyword from all the class labels and all the performance level labels of the second data, wherein the searched labels are the labels matched with the numerical keyword.
For example, keywords in the battery design parameters included in the search sentence input by the user include "chemical generation system second generation", "E-3", "graphite a-1", "lifetime of 8 years or more", "3C", "LFP-3", "S-4", "anode coating weight 0.140", and "cathode coating weight 0.25", that is, the design parameters of the battery designed by the user are the second generation chemical generation system, electrolyte E-3, anode material graphite a-1, cathode material LFP-3, separator film S-4, anode coating weight 0.140, cathode coating weight 0.25, the desired performance is charging rate of 3C, lifetime of 8 years or more, wherein "chemical generation system second generation", "E-3", "graphite a-1", "LFP-3", and "S-4" are character type keywords, and "lifetime of 8 years or more", "3C", "anode coating weight 0.140", and "cathode coating weight 0.25" are numerical type keywords. Among the keywords included in the search term input by the user, the term "lifetime of 8 years or more" is a keyword indicating a performance characteristic of lifetime in battery performance, the term "3C" is a keyword indicating a performance characteristic of charge rate in battery performance, and the term "chemical generation system second generation", "E-3", "graphite a-1", "LFP-3", "S-4", "anode coating weight of 0.140" and "cathode coating weight of 0.25" are keywords representing category characteristics. And comparing the category labels and the performance level labels of the second data with the keywords, and searching the category labels and the performance level labels corresponding to the keywords from the category labels and the performance level labels of the second data. For example, the performance level label corresponding to the keyword "lifetime of 8 years or more" is "lifetime", the performance level label corresponding to the keyword "3C" is "fast charge", and the category label corresponding to the keyword "LFP-3" includes both "cathode parameter" and "cathode material". For another example, "anode coat weight 0.140", if a label corresponds to a numerical range of anode coat weight of 0.120-0.150, then 0.140 falls within that range, and the label is a label that matches the keyword "anode coat weight 0.140".
S302, extracting data corresponding to the label matched with the keyword from the second data to obtain data matched with the battery design parameter.
According to the foregoing, the marked data obtained by marking each data in the production history data of the plurality of batteries with the corresponding class label is the first data, and the data obtained after marking all the data in the first data with the performance level label is the second data. Thus, each second data is marked with a category label and a performance level label. After determining the label matched with the keyword, determining the battery marked with the label matched with the keyword from the second data, and extracting all parameter data of the battery marked with the label matched with the keyword to obtain data matched with the battery design parameter, namely matching data. The matching data may include second data corresponding to one or more batteries.
Based on the label of each data in the second data, the specific example is carried out by comparing the second data with the keywords "chemical generation system second generation", "E-3", "graphite a-1", "service life more than 8 years", "3C", "LFP-3", "S-4", "anode coating weight 0.140", and "cathode coating weight 0.25", and determining that the battery manufactured by using chemical generation system second generation, E-3, graphite a-1, LFP-3, S-4, anode coating weight 0.120-0.150 (numerical range of anode coating weight 0.140), cathode coating weight 0.20-0.30 (range of cathode coating weight 0.25), namely the battery corresponding to all keywords, and extracting all parameter data of the battery to obtain matching data.
And through matching the keywords with the labels, the data corresponding to the labels matched with the keywords are extracted from the second data, so that the data retrieval and extraction efficiency is further improved, and the accuracy of the data retrieval and extraction is improved.
And S40, acquiring the battery performance parameters matched with the battery design parameters according to the performance parameters in the matched data.
In some embodiments, obtaining the battery performance parameters that match the battery design parameters based on the performance parameters in the matching data may include: and under the condition that the matching data comprises a plurality of same-category performance parameters, processing the plurality of same-category performance parameters according to a preset algorithm to obtain battery performance parameters matched with the battery design parameters. The same-category performance parameter refers to a performance parameter representing the same performance, for example, all parameters representing the life of the battery belong to the same-category performance parameter, all parameters representing the charge rate of the battery belong to the same-category performance parameter, and so on. The battery performance parameters that match the battery design parameters may characterize the predicted outcome of the performance parameters of the battery designed according to the battery design parameters. Therefore, the performance parameters of the battery designed according to the battery design parameters input by the user can be predicted, and a more accurate prediction result is obtained.
When the matching data includes second data corresponding to a plurality of batteries, for a performance parameter of a certain category, there are a plurality of performance parameters of the category in the matching data. For example, assuming that the matching data includes second data corresponding to n (n is a positive integer) batteries, the matching data includes battery life performance parameters of the n batteries, that is, includes n battery life performance parameters, where the n battery life performance parameters belong to the same category of performance parameters. And processing the n battery life performance parameters according to a preset algorithm to obtain battery life performance parameters matched with the battery design parameters. And processing the plurality of same-class performance parameters according to a preset algorithm to obtain battery performance parameters matched with the battery design parameters, so that the accuracy of the obtained battery performance parameters matched with the battery design parameters can be further improved.
Illustratively, processing the plurality of same-category performance parameters according to a preset algorithm to obtain a battery performance parameter matching the battery design parameter may include: and obtaining an average value of the plurality of same-class performance parameters, wherein the average value is a battery performance parameter matched with the battery design parameter.
In the case where the above specific example is accepted, in which the battery life performance parameters of the n batteries are included in the matching data, an average value of the n battery life performance parameters is calculated as the battery life performance parameter that matches the above battery design parameter. And the average value is used as the battery performance parameter, so that the accuracy of the battery performance parameter matched with the battery design parameter is further improved.
Illustratively, processing the plurality of same-category performance parameters to obtain battery performance parameters that match the battery design parameters may include: removing the maximum value and the minimum value in the plurality of same-category performance parameters to obtain the rest same-category performance parameters; and obtaining the average value of the remaining same-class performance parameters to obtain the battery performance parameters matched with the battery design parameters.
With the above specific example in mind, in the case where the battery life performance parameters of the n batteries are included in the matching data, the maximum value and the minimum value of the n battery life performance parameters are removed, and then the average value of the remaining n-2 battery life performance parameters is calculated as the battery life performance parameter matching the above battery design parameter. And after the maximum value and the minimum value are removed, calculating the average value of the remaining same-class performance parameters, so that the probability of being interfered by data with possible anomalies is greatly reduced, and the accuracy of the battery performance parameters matched with the battery design parameters is further improved.
It will be appreciated that other preset algorithms may be used to process the plurality of same-category performance parameters, for example, taking the median of the plurality of same-category performance parameters as the battery performance parameter that matches the battery design parameter, etc.
In some embodiments, the acquiring method may further include: and visually displaying the battery performance parameters matched with the battery design parameters.
And the battery performance parameters matched with the battery design parameters are visually displayed, so that the battery performance parameters can be intuitively displayed to a user.
Specifically, the BI tool is used for processing the battery performance parameters matched with the battery design parameters to obtain and display the results in the form of a graph or a table and the like, so that references can be provided for related personnel more intuitively.
In some embodiments, battery performance parameters that match battery design parameters may be set to be downloaded in an OFFICE document.
In some embodiments, the battery design parameters entered by the user and the matching data matching the battery design parameters may be stored in the production history data of the plurality of batteries in order to expand the production history database.
The foregoing description of various embodiments is intended to highlight differences between the various embodiments, which may be the same or similar to each other by reference, and is not repeated herein for the sake of brevity.
In a specific example, the method for obtaining the battery performance parameter may include the following steps:
comparing third data in the production history data of the plurality of batteries with each preset parameter type to determine the parameter type corresponding to the third data, wherein the third data is any one of the production history data of the plurality of batteries; and marking the corresponding class label on the third data according to the class label corresponding to the preset parameter class and the parameter class corresponding to the third data to obtain marked data, wherein the marked data corresponding to each data in the production history data of the plurality of batteries form first data. The production history data is classified, so that the data can be conveniently managed and inquired; through the corresponding class label on the data label, the data management and the query are conveniently realized according to the class label, and the convenience of the data management and the query is further improved.
Comparing the first performance parameter in the first data with each preset performance level to determine the performance level corresponding to the first performance parameter, wherein the first performance parameter is any performance parameter in the first data; searching all parameters of a first battery from the first data, wherein the first battery is a battery corresponding to the first performance parameter; determining a performance level label of the first performance parameter according to a preset performance level label and a performance level corresponding to the first performance parameter; all parameters of the first battery are marked with performance level labels of first performance parameters, and first data marked with the performance level labels are second data. By grading the first data, the data is convenient to manage and inquire according to the performance level; the corresponding performance level label on the data label is convenient for realizing data management and inquiry according to the performance level label, and the convenience of data management and inquiry is further improved.
Comparing the keywords in the battery design parameters input by the user with the category labels and the performance level labels of the second data to obtain labels matched with the keywords; and extracting data corresponding to the label matched with the keyword from the second data to obtain data matched with the battery design parameter. And through matching the keywords with the labels, the data corresponding to the labels matched with the keywords are extracted from the second data, so that the data retrieval and extraction efficiency is further improved, and the accuracy of the data retrieval and extraction is improved.
Under the condition that the matching data comprises a plurality of same-category performance parameters, removing the maximum value and the minimum value in the plurality of same-category performance parameters to obtain the rest same-category performance parameters; and calculating the average value of the remaining same-class performance parameters to obtain the battery performance parameters matched with the battery design parameters, so that the probability of being interfered by data possibly with abnormality is greatly reduced, and the accuracy of the battery performance parameters matched with the battery design parameters is further improved.
And visually displaying the battery performance parameters matched with the battery design parameters, so that the battery performance parameters can be intuitively displayed to a user.
The battery performance parameter acquisition method can automatically acquire the battery performance parameter, saves labor cost, shortens time consumption of the battery performance parameter acquisition process, and improves accuracy of the battery performance parameter acquisition result.
The foregoing description of various embodiments is intended to highlight differences between the various embodiments, which may be the same or similar to each other by reference, and is not repeated herein for the sake of brevity.
Referring to fig. 6, another embodiment of the present application provides a device for acquiring a battery performance parameter, which may include:
the classification module is used for classifying the production history data of the batteries according to the preset parameter types to obtain first data;
the performance grading module is used for grading the first data according to the preset performance level and the performance parameters in the first data to obtain second data;
the extraction matching module is used for extracting matching data matched with the battery design parameters from the second data according to the battery design parameters input by the user;
and the battery performance parameter acquisition module is used for acquiring the battery performance parameters matched with the battery design parameters according to the performance parameters in the matched data.
In some embodiments, the classification module is further specifically configured to:
comparing third data in the production history data of the plurality of batteries with each preset parameter type to determine the parameter type corresponding to the third data, wherein the third data is any one of the production history data of the plurality of batteries;
and marking the corresponding class label on the third data according to the class label corresponding to the preset parameter class and the parameter class corresponding to the third data to obtain marked data, wherein the marked data corresponding to each data in the production history data of the plurality of batteries form first data.
In some embodiments, the performance ranking module is further specifically configured to:
comparing the first performance parameter in the first data with each preset performance level to determine the performance level corresponding to the first performance parameter, wherein the first performance parameter is any performance parameter in the first data;
searching all parameters of a first battery from the first data, wherein the first battery is a battery corresponding to the first performance parameter;
determining a performance level label of the first performance parameter according to a preset performance level label and a performance level corresponding to the first performance parameter;
All parameters of the first battery are marked with performance level labels of first performance parameters, and first data marked with the performance level labels are second data.
In some embodiments, the extraction matching module is further specifically configured to:
comparing the keywords in the battery design parameters input by the user with the category labels and the performance level labels of the second data to obtain labels matched with the keywords;
and extracting data corresponding to the label matched with the keyword from the second data to obtain data matched with the battery design parameter.
In some embodiments, the battery performance parameter acquisition module may be further specifically configured to: and under the condition that the matching data comprises a plurality of same-category performance parameters, processing the plurality of same-category performance parameters according to a preset algorithm to obtain battery performance parameters matched with the battery design parameters.
In some embodiments, the battery performance parameter acquisition module may be further specifically configured to:
and obtaining an average value of the plurality of same-class performance parameters, wherein the average value is a battery performance parameter matched with the battery design parameter.
In some embodiments, the battery performance parameter acquisition module may be further specifically configured to:
Removing the maximum value and the minimum value in the plurality of same-category performance parameters to obtain the rest same-category performance parameters;
and obtaining the average value of the remaining same-class performance parameters to obtain the battery performance parameters matched with the battery design parameters.
In some embodiments, the acquiring means may further include:
and the display module is used for visually displaying the battery performance parameters matched with the battery design parameters.
The foregoing description of various embodiments is intended to highlight differences between the various embodiments, which may be the same or similar to each other by reference, and is not repeated herein for the sake of brevity.
Another embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for obtaining the battery performance parameter of any of the foregoing embodiments.
Referring to fig. 7, the electronic device 10 may include: processor 100, memory 101, bus 102 and communication interface 103, processor 100, communication interface 103 and memory 101 being connected by bus 102; the memory 101 has stored therein a computer program executable on the processor 100, which when executed by the processor 100 performs the method provided by any of the embodiments described herein.
The memory 101 may include a high-speed random access memory (RAM: random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the device network element and the at least one other network element is achieved through at least one communication interface 103 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
Bus 102 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be divided into address buses, data buses, control buses, etc. The memory 101 is configured to store a program, and the processor 100 executes the program after receiving an execution instruction, and the method disclosed in any of the foregoing embodiments of the present application may be applied to the processor 100 or implemented by the processor 100.
The processor 100 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 100 or by instructions in the form of software. The processor 100 may be a general-purpose processor, and may include a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), and the like; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 101, and the processor 100 reads the information in the memory 101 and, in combination with its hardware, performs the steps of the method described above.
The electronic device provided by the embodiment of the application and the method provided by the embodiment of the application are the same in the invention conception, and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
Another embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program that is executed by a processor to implement the method for acquiring a battery performance parameter of any of the above embodiments. Referring to fig. 8, a computer readable storage medium is shown as an optical disc 20 having a computer program (i.e., a program product) stored thereon, which, when executed by a processor, performs the method provided by any of the embodiments described above.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above-described embodiments of the present application has the same advantageous effects as the method adopted, operated or implemented by the application program stored therein, for the same inventive concept as the method provided by the embodiments of the present application.
It should be noted that:
the term "module" is not intended to be limited to a particular physical form. Depending on the particular application, modules may be implemented as hardware, firmware, software, and/or combinations thereof. Furthermore, different modules may share common components or even be implemented by the same components. There may or may not be clear boundaries between different modules.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may also be used with the examples herein. The required structure for the construction of such devices is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and the above description of specific languages is provided for disclosure of preferred embodiments of the present application.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
The foregoing examples merely represent embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (11)
1. A method for obtaining a battery performance parameter, comprising:
classifying production history data of a plurality of batteries according to preset parameter categories to obtain first data;
classifying the first data according to a preset performance level and performance parameters in the first data to obtain second data;
extracting matching data matched with the battery design parameters from the second data according to the battery design parameters input by a user;
and acquiring the battery performance parameters matched with the battery design parameters according to the performance parameters in the matched data.
2. The method of claim 1, wherein classifying the production history data of the plurality of batteries according to the predetermined parameter category to obtain the first data comprises:
Comparing third data in the production history data of the plurality of batteries with each preset parameter category to determine a parameter category corresponding to the third data, wherein the third data is any one of the production history data of the plurality of batteries;
and marking the corresponding class label on the third data according to the class label corresponding to the preset parameter class and the parameter class corresponding to the third data to obtain marked data, wherein the marked data corresponding to each data in the production history data of the plurality of batteries form the first data.
3. The method according to claim 2, wherein the grading the first data according to the preset performance level and the performance parameter in the first data to obtain the second data includes:
comparing the first performance parameter in the first data with each preset performance level to determine a performance level corresponding to the first performance parameter, wherein the first performance parameter is any performance parameter in the first data;
searching all parameters of a first battery from the first data, wherein the first battery is a battery corresponding to the first performance parameter;
Determining a performance level label of the first performance parameter according to a preset performance level label and a performance level corresponding to the first performance parameter;
and marking all parameters of the first battery with the performance level label of the first performance parameter, wherein the first data marked with the performance level label is the second data.
4. The method of claim 3, wherein the extracting, from the second data, matching data matching the battery design parameter according to the battery design parameter input by the user, comprises:
comparing the keywords in the battery design parameters input by the user with the category labels and the performance level labels of the second data to obtain labels matched with the keywords;
and extracting data corresponding to the label matched with the keyword from the second data to obtain data matched with the battery design parameter.
5. The method according to any one of claims 1 to 4, wherein the obtaining battery performance parameters matching the battery design parameters according to the performance parameters in the matching data includes:
and under the condition that the matching data comprises a plurality of same-category performance parameters, processing the plurality of same-category performance parameters according to a preset algorithm to obtain battery performance parameters matched with the battery design parameters.
6. The method of claim 5, wherein processing the plurality of same-category performance parameters according to a predetermined algorithm to obtain battery performance parameters that match the battery design parameters comprises:
and obtaining an average value of the plurality of same-category performance parameters, wherein the average value is a battery performance parameter matched with the battery design parameter.
7. The method of claim 5, wherein processing the plurality of same-category performance parameters according to a predetermined algorithm to obtain battery performance parameters that match the battery design parameters comprises:
removing the maximum value and the minimum value in the plurality of same-category performance parameters to obtain the rest same-category performance parameters;
and obtaining the average value of the remaining same-class performance parameters to obtain the battery performance parameters matched with the battery design parameters.
8. The method of any one of claims 1-4, wherein the acquiring method further comprises:
and visually displaying the battery performance parameters matched with the battery design parameters.
9. An acquisition apparatus for battery performance parameters, comprising:
The classification module is used for classifying the production history data of the batteries according to the preset parameter types to obtain first data;
the performance grading module is used for grading the first data according to a preset performance level and the performance parameters in the first data to obtain second data;
the extraction matching module is used for extracting matching data matched with the battery design parameters from the second data according to the battery design parameters input by a user;
and the battery performance parameter acquisition module is used for acquiring the battery performance parameters matched with the battery design parameters according to the performance parameters in the matched data.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the method of obtaining the battery performance parameter of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program is executed by a processor to implement the method of acquiring a battery performance parameter according to any one of claims 1-8.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108957331A (en) * | 2018-05-16 | 2018-12-07 | 蔚来汽车有限公司 | Battery performance detection method and battery performance detection system |
| CN109840353A (en) * | 2018-12-28 | 2019-06-04 | 国联汽车动力电池研究院有限责任公司 | Lithium ion battery dual factors inconsistency prediction technique and device |
| CN111289910A (en) * | 2020-03-12 | 2020-06-16 | 中国第一汽车股份有限公司 | Grading method and device for echelon batteries, computer equipment and medium |
| WO2021192844A1 (en) * | 2020-03-27 | 2021-09-30 | 本田技研工業株式会社 | Battery information-processing system and battery information-processing method |
| CN114047452A (en) * | 2022-01-13 | 2022-02-15 | 浙江玥视科技有限公司 | Method and device for determining cycle life of battery |
| CN115526368A (en) * | 2021-06-25 | 2022-12-27 | 比亚迪股份有限公司 | Power battery capacity prediction method, device and equipment |
| CN115587527A (en) * | 2022-08-31 | 2023-01-10 | 广东邦普循环科技有限公司 | Battery life prediction method, system, terminal device and computer readable medium |
| CN116626504A (en) * | 2023-06-14 | 2023-08-22 | 一汽解放汽车有限公司 | Power battery performance determining method, apparatus, computer device and storage medium |
-
2024
- 2024-01-23 CN CN202410089478.XA patent/CN117609846B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108957331A (en) * | 2018-05-16 | 2018-12-07 | 蔚来汽车有限公司 | Battery performance detection method and battery performance detection system |
| CN109840353A (en) * | 2018-12-28 | 2019-06-04 | 国联汽车动力电池研究院有限责任公司 | Lithium ion battery dual factors inconsistency prediction technique and device |
| CN111289910A (en) * | 2020-03-12 | 2020-06-16 | 中国第一汽车股份有限公司 | Grading method and device for echelon batteries, computer equipment and medium |
| WO2021192844A1 (en) * | 2020-03-27 | 2021-09-30 | 本田技研工業株式会社 | Battery information-processing system and battery information-processing method |
| US20220382771A1 (en) * | 2020-03-27 | 2022-12-01 | Honda Motor Co., Ltd. | Battery information processing system and battery information processing method |
| CN115526368A (en) * | 2021-06-25 | 2022-12-27 | 比亚迪股份有限公司 | Power battery capacity prediction method, device and equipment |
| CN114047452A (en) * | 2022-01-13 | 2022-02-15 | 浙江玥视科技有限公司 | Method and device for determining cycle life of battery |
| CN115587527A (en) * | 2022-08-31 | 2023-01-10 | 广东邦普循环科技有限公司 | Battery life prediction method, system, terminal device and computer readable medium |
| CN116626504A (en) * | 2023-06-14 | 2023-08-22 | 一汽解放汽车有限公司 | Power battery performance determining method, apparatus, computer device and storage medium |
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