CN117076826A - Energy storage battery performance evaluation method and device, electronic equipment and storage medium - Google Patents
Energy storage battery performance evaluation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN117076826A CN117076826A CN202311342380.2A CN202311342380A CN117076826A CN 117076826 A CN117076826 A CN 117076826A CN 202311342380 A CN202311342380 A CN 202311342380A CN 117076826 A CN117076826 A CN 117076826A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- performance
- storage battery
- index
- grade
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 287
- 238000011156 evaluation Methods 0.000 title claims abstract description 42
- 239000011159 matrix material Substances 0.000 claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 38
- 238000010276 construction Methods 0.000 claims abstract description 33
- 230000007613 environmental effect Effects 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 19
- 238000012360 testing method Methods 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 11
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000011056 performance test Methods 0.000 description 26
- 230000006870 function Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 238000011160 research Methods 0.000 description 8
- 238000011161 development Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 239000008358 core component Substances 0.000 description 2
- 239000006185 dispersion Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000000306 component Substances 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 208000035475 disorder Diseases 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000001208 nuclear magnetic resonance pulse sequence Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000007493 shaping process Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/02—Computing arrangements based on specific mathematical models using fuzzy logic
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Automation & Control Theory (AREA)
- Health & Medical Sciences (AREA)
- Fuzzy Systems (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Secondary Cells (AREA)
Abstract
The invention belongs to the technical field of energy storage batteries, and discloses an energy storage battery performance evaluation method, an energy storage battery performance evaluation device, electronic equipment and a storage medium; the method comprises the following steps: acquiring full-performance data of an energy storage battery; determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery; analyzing the full-performance data of the energy storage battery, and determining the numerical ranges of the technical indexes in different grades; determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index; and selecting the energy storage battery with the corresponding grade from the energy storage batteries with the determined final performance grade according to the construction requirements of the energy storage system. The invention provides technical support for battery type selection in the construction process of the energy storage system and provides a guiding direction for technical improvement of battery manufacturers.
Description
Technical Field
The invention belongs to the technical field of energy storage batteries, and particularly relates to an energy storage battery performance evaluation method and device, electronic equipment and a storage medium.
Background
The energy storage system is an optimal scheme for solving the problems of renewable energy intermittence, randomness and distributed grid connection, and along with the development of the power system, the application value, commercialization and large-scale development of energy storage are widely focused and accepted by society. However, the current technical research focuses on the early application technology aspects such as system integration, function verification and the like, and the research on the performance evaluation technology of the energy storage battery in operation has not attracted enough attention, and the related research work has not strong pertinence.
Some foreign energy storage battery enterprises and research and development institutions perform basic research work on the research of the relationship between the internal physical and chemical indexes of the battery and the external characteristic parameters of the battery. Two professors of the university of columbia in the united states teach Matthias pre indl and Alan West that a machine learning model is being developed that can more accurately estimate the charge level of a lithium battery with a common estimated error rate of 5% for battery states of charge, and the model goal of this team is an error rate of 1%, but there is no set of solutions for the performance grading of energy storage batteries for energy storage applications.
The domestic research on battery performance evaluation technology is mainly focused on the application fields of standby power supplies and electric automobiles, beijing university of transportation Sun Bingxiang and the like, and research on accurate evaluation technology of a power battery management system is currently being conducted. Zhang Yanru et al, taking a high-capacity lithium iron phosphate battery for energy storage as a research object, analyzed the estimation errors of the traditional battery parameter identification on the dynamic performance of the battery under different temperatures and different current multiplying powers, and put forward a particle swarm parameter identification method under the condition of a composite pulse sequence. But only solves the problem of collecting the front section data, and the performance division of the battery is not related; technical support cannot be provided for battery type selection in the energy storage system construction process.
Disclosure of Invention
The invention aims to provide an energy storage battery performance evaluation method, an energy storage battery performance evaluation device, electronic equipment and a storage medium, which provide technical support for battery model selection in the construction process of an energy storage system and provide a guiding direction for technical improvement of battery manufacturers.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for evaluating performance of an energy storage battery, including:
acquiring full-performance data of an energy storage battery;
determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery;
analyzing the full-performance data of the energy storage battery, and determining the numerical ranges of the technical indexes in different grades;
determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index;
selecting an energy storage battery with a corresponding grade from the energy storage batteries with the determined final performance grade according to the construction requirements of the energy storage system;
the step of acquiring the full-performance data of the energy storage battery specifically comprises the following steps:
all k technical indexes of all test items of the electric performance, the environmental adaptability, the durability and the safety performance of the energy storage battery are tested to obtain the full-performance data of the energy storage battery; the full performance data of the energy storage battery comprises k indexes X 1 ,X 2 ,...,X k Wherein each index X i Comprising n sample data; n is a positive integer;
the step of determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery specifically comprises the following steps:
data normalization:
carrying out standardization processing on the acquired full-performance data of the energy storage battery; for k indices X 1 ,X 2 ,...,X k WhereinThe method comprises the steps of carrying out a first treatment on the surface of the k indexes X 1 ,X 2 ,...,X k Normalized value is Y 1 ,Y 2 ,...,Y k ，Wherein Y is ij Is the ith index X i Normalized data, X, of the jth sample data in (a) ij Indicating the ith index X i The j-th sampling data in (a); min (X) i ) Indicating the ith index X i Is the smallest sample data, max (X i ) Indicating the ith index X i Maximum sampling data in (a);
calculate the ith index X i Information entropy of (2)：
Wherein:
if it isThen define: />；
Determining the weight of each index:
calculating each performance index X through information entropy i Weights of (2)：
，i=1,2,...,k
According to the weight of the performance indexDetermining the electrical performance, environmental suitability, durability and safety performance index>The index of (2) is the corresponding key technical index.
The invention is further improved in that: the step of analyzing the full performance data of the energy storage battery and determining the numerical ranges of the technical indexes in different grades specifically comprises the following steps:
Each performance index is setThe corresponding data are arranged from small to large, and the performance index is adopted +.>Is Z 1 Performance index->Is the theoretical maximum of (2)Optimum index value Z 0 According to Z 0 And Z is 1 Calculating median Z of corresponding performances 1/2 And the upper quartile value Z 1/4 According to the median value Z 1/2 And the upper quartile value Z 1/4 Three intervals were obtained: z is greater than or equal to 1/4 ，[Z 1/2 ,Z 1/4 ) Less than Z 1/2 Each interval corresponds to A, B, C three levels of performance index.
The invention is further improved in that: the step of determining the final performance level of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range and the key technical indexes of each technical index in different levels specifically comprises the following steps:
establishing a fuzzy relation matrix of electrical property, environmental adaptability, durability and safety performance and corresponding performance indexes:
judging a sign for judging that the ith technical index of the energy storage battery to be evaluated falls into the value range of the jth level, and if the ith technical index of the energy storage battery falls into the value range of the jth level +.>=1, otherwise->=0; a is the number of performance index levels, a=3, m=1;
and evaluating the performance basic grade of the energy storage battery according to the key technical indexes:
the key technical indexes are compared in the a-th column of the fuzzy relation matrix, and the performance basic grade of the energy storage battery is determined;
Determining the performance level of the battery according to the fuzzy relation matrix and the maximum membership principle;
H2=[S 1 、S 2 、S 3 ]；
wherein H2The fuzzy relation matrix is adopted, and S is the performance index; s is S 1 The number S of the corresponding technical indexes falling into class A for each performance 2 The number of the technical indexes falling into the B level and S corresponding to each performance 3 The number of the technical indexes falling into the class C corresponding to each performance;
the S item which is not 0 is lower than the basic level of the performance of the energy storage battery, and the basic level is halved to be used as the final performance level; and S items which are not 0 are higher than or equal to the energy storage battery performance basic level, and the basic level is increased by half as the final performance level, otherwise, the energy storage battery performance basic level is maintained to be the final performance level.
The invention is further improved in that: selecting the energy storage battery with the corresponding grade from the energy storage batteries with the final performance grade according to the construction requirements of the energy storage system, wherein the method specifically comprises the following steps:
and selecting the energy storage batteries with the same final performance grade from the energy storage batteries with the final performance grade, and meeting the requirement of the energy storage system construction on the consistency of the energy storage batteries.
The invention is further improved in that: selecting the energy storage battery with the corresponding grade from the energy storage batteries with the final performance grade according to the construction requirements of the energy storage system, wherein the method specifically comprises the following steps:
And selecting the energy storage batteries with the final performance grade A from the energy storage batteries with the final performance grade A, and meeting the requirement of the energy storage system construction on the optimal performance of the energy storage batteries.
In a second aspect, the present invention provides an energy storage battery performance evaluation device, comprising:
the acquisition module is used for acquiring the full-performance data of the energy storage battery;
the computing module is used for determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery;
the analysis module is used for analyzing the full-performance data of the energy storage battery and determining the numerical ranges of the technical indexes in different grades;
the evaluation module is used for determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index;
the selection module is used for selecting the energy storage battery with the corresponding grade from the energy storage batteries with the final performance grade according to the construction requirements of the energy storage system;
the step of acquiring the full-performance data of the energy storage battery by the acquisition module specifically comprises the following steps:
all k technical indexes of all test items of the electric performance, the environmental adaptability, the durability and the safety performance of the energy storage battery are tested to obtain the full-performance data of the energy storage battery; the full performance data of the energy storage battery comprises k indexes X 1 ,X 2 ,...,X k Wherein each index X i Comprising n sample data; n is a positive integer;
the calculation module determines key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery, and specifically comprises the following steps:
data normalization:
carrying out standardization processing on the acquired full-performance data of the energy storage battery; for k indices X 1 ,X 2 ,...,X k WhereinThe method comprises the steps of carrying out a first treatment on the surface of the k indexes X 1 ,X 2 ,...,X k Normalized value is Y 1 ,Y 2 ,...,Y k ，Wherein Y is ij Is the ith index X i Normalized data, X, of the jth sample data in (a) ij Indicating the ith index X i The j-th sampling data in (a); min (X) i ) Indicating the ith index X i Is the smallest sample data, max (X i ) Indicating the ith index X i Maximum sampling data in (a);
calculate the ith index X i Information entropy of (2)：
Wherein:
if it isThen define: />；
Determining the weight of each index:
calculating each performance index X through information entropy i Weights of (2)：
，i=1,2,...,k
According to the weight of the performance indexDetermining the electrical performance, environmental suitability, durability and safety performance index>The index of (2) is the corresponding key technical index.
The invention is further improved in that: the analysis module analyzes the full-performance data of the energy storage battery and determines the numerical ranges of the technical indexes in different grades, and the method specifically comprises the following steps:
Each performance index is setThe corresponding data are arranged from small to large, and the performance index is adopted +.>Is Z 1 Performance index->The theoretical maximum value is the optimal index value Z 0 According to Z 0 And Z is 1 Calculating median Z of corresponding performances 1/2 And the upper quartile value Z 1/4 According to the median value Z 1/2 And the upper quartile value Z 1/4 Three intervals were obtained: z is greater than or equal to 1/4 ，[Z 1/2 ,Z 1/4 ) Less than Z 1/2 Each interval corresponds to A, B, C three levels of performance index.
The invention is further improved in that: the evaluation module determines the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical indexes, and specifically comprises the following steps:
establishing a fuzzy relation matrix of electrical property, environmental adaptability, durability and safety performance and corresponding performance indexes:
judging a sign for judging that the ith technical index of the energy storage battery to be evaluated falls into the value range of the jth level, and if the ith technical index of the energy storage battery falls into the value range of the jth level +.>=1, otherwise->=0; a is the number of performance index levels, a=3, m=1;
and evaluating the performance basic grade of the energy storage battery according to the key technical indexes:
the key technical indexes are compared in the a-th column of the fuzzy relation matrix, and the performance basic grade of the energy storage battery is determined;
Determining the performance level of the battery according to the fuzzy relation matrix and the maximum membership principle;
H2=[S 1 、S 2 、S 3 ]；
wherein H2 is a fuzzy relation matrix, and S is a performance index; s is S 1 The number S of the corresponding technical indexes falling into class A for each performance 2 The number of the technical indexes falling into the B level and S corresponding to each performance 3 The number of the technical indexes falling into the class C corresponding to each performance;
the S item which is not 0 is lower than the basic level of the performance of the energy storage battery, and the basic level is halved to be used as the final performance level; and S items which are not 0 are higher than or equal to the energy storage battery performance basic level, and the basic level is increased by half as the final performance level, otherwise, the energy storage battery performance basic level is maintained to be the final performance level.
The invention is further improved in that: the selection module selects the energy storage battery with the corresponding grade from the energy storage batteries with the determined final performance grade according to the construction requirement of the energy storage system, and the method specifically comprises the following steps:
and selecting the energy storage batteries with the same final performance grade from the energy storage batteries with the final performance grade, and meeting the requirement of the energy storage system construction on the consistency of the energy storage batteries.
The invention is further improved in that: the selection module selects the energy storage battery with the corresponding grade from the energy storage batteries with the determined final performance grade according to the construction requirement of the energy storage system, and the method specifically comprises the following steps:
And selecting the energy storage batteries with the final performance grade A from the energy storage batteries with the final performance grade A, and meeting the requirement of the energy storage system construction on the optimal performance of the energy storage batteries.
In a third aspect, the present invention provides an electronic device, including a processor and a memory, where the processor is configured to execute a computer program stored in the memory to implement the energy storage battery performance evaluation method.
In a fourth aspect, the present invention provides a computer readable storage medium storing at least one instruction that when executed by a processor implements the method of evaluating the performance of an energy storage battery.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an energy storage battery performance evaluation method, which comprises the following steps: acquiring full-performance data of an energy storage battery; determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery; analyzing the full-performance data of the energy storage battery, and determining the numerical ranges of the technical indexes in different grades; determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index; and selecting the energy storage battery with the corresponding grade from the energy storage batteries with the determined final performance grade according to the construction requirements of the energy storage system. The invention combines the conventional mathematical method with the energy storage application characteristics to obtain a grade evaluation method which reflects the comprehensive characteristics of the product and comprehensively reflects the application requirements, provides a development direction for the performance improvement of the core component of the energy storage battery, and has the guiding function of industry development; the quality and the safety technical level of core component products such as the energy storage battery are more intuitively identified in a performance grade division mode, the pain points such as insufficient effective information, asymmetric information, difficult technical comparison screening and the like in the application of the energy storage battery are fundamentally solved, important technical support is provided for the stable operation of the power system, technical support is provided for the battery model selection in the construction process of the energy storage system, and a guiding direction is provided for the technical improvement of battery manufacturers.
The basic idea of the entropy weight method is to determine objective weights according to the size of index variability. According to the explanation of the basic principle of the information theory, the information is a measure of the order degree of the system, and the entropy is a measure of the disorder degree of the system; according to the definition of the information entropy, for a certain index, the degree of dispersion of the certain index can be judged by using the entropy value, the smaller the information entropy value is, the larger the degree of dispersion of the index is, the larger the influence (i.e. weight) of the index on the comprehensive evaluation is, and if the values of the certain index are all equal, the index does not play a role in the comprehensive evaluation. The invention calculates the weight of each index by utilizing the information entropy tool, and provides basis for multi-index comprehensive evaluation.
The fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics. The comprehensive evaluation method converts the shaping evaluation into quantitative evaluation according to the membership theory of fuzzy data, namely, fuzzy mathematics is used for carrying out overall evaluation on things or objects limited by various factors. The method has the characteristics of clear results and strong systematicness, can better solve the problems of ambiguity and difficulty in quantification, and is suitable for evaluating the performance grade of the energy storage battery in the energy storage battery performance evaluation method.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a schematic flow chart of an energy storage battery performance evaluation method according to the present invention;
FIG. 2 is a block diagram of an energy storage battery performance evaluation device according to the present invention;
fig. 3 is a block diagram of an electronic device according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the invention. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the invention.
Example 1
The invention provides an energy storage battery performance evaluation method, comprehensively considers the technical level of the existing energy storage battery, simultaneously considers the requirement of energy storage application, quantitatively characterizes the product performance, and provides important support for benign development of the energy storage industry.
The invention classifies the performance of the energy storage battery: the characteristics are classified into electrical properties, environmental suitability, durability and safety properties, and comprehensive properties representing the overall properties.
The overall performance is characterized by full performance, including electrical performance, environmental suitability, durability, and safety performance;
the electrical performance comprises an initial charge and discharge energy test, a power characteristic test, a multiplying power charge and discharge performance test and an energy retention and energy recovery capability test;
the environment adaptability comprises a high-temperature charge and discharge performance test, a low-temperature charge and discharge performance test and a high-altitude initial charge and discharge energy test;
durability performance includes storage performance test and cycle performance test;
safety performance includes electrical safety performance tests (e.g., overcharge performance tests, overdischarge performance tests, overload performance tests, short circuit performance tests, insulating performance tests, and pressure resistance performance tests), mechanical safety performance tests (e.g., extrusion performance tests, drop performance tests, vibration performance tests, liquid cooling pipeline pressure resistance performance tests), environmental safety performance tests (including salt spray performance tests, alternating wet heat performance tests, high altitude insulating performance tests, high altitude pressure resistance performance tests), thermal safety performance tests (including adiabatic temperature rise performance tests, thermal runaway performance tests, and thermal runaway diffusion performance tests);
Referring to fig. 1, the invention provides a method for evaluating performance of an energy storage battery, comprising the following steps:
s1, acquiring full-performance data of an energy storage battery;
the method comprises the steps that performance data corresponding to all k technical indexes of all test items of the electric performance, the environmental adaptability, the durability and the safety performance of the energy storage battery are respectively acquired in the full-performance data acquisition of the energy storage battery; the final obtained full performance data includes k indexes X 1 ,X 2 ,... ,X k Wherein each index X i Including n sample data.
S2, calculating index weights based on the full-performance data of the energy storage battery obtained in the step S1 through an entropy weight method to obtain key technical indexes; the method specifically comprises the following steps:
s21, data standardization:
carrying out standardization processing on the acquired full-performance data of the energy storage battery; for k indices X in full performance data 1 ,X 2 ,...,X k WhereinThe method comprises the steps of carrying out a first treatment on the surface of the For k indexes X 1 ,X 2 ,...,X k Normalized value is Y 1 ,Y 2 ,...,Y k ，/>Wherein Y is ij Is the ith index X i Normalized data, X, of the jth sample data in (a) ij Indicating the ith index X i The j-th sampling data in (a); min (X) i ) Indicating the ith index X i Is the smallest sample data, max (X i ) Indicating the ith index X i Is the largest sample data in the sample data.
S22, obtaining information entropy of each index:
calculate the ith index X i Information entropy of (2)；
Wherein:
if it isThen define: />；
S23, determining the weight of each index:
calculating the information entropy of each index to be E according to the above formula 1 ,E 2 ,...,E k . Calculating each performance finger through information entropyLabel X i Weights of (2)：
，i=1,2,...,k
According to the weight of the performance indexDetermining the electrical performance, environmental suitability, durability and safety performance index>The index of (2) is the corresponding key technical index. For example, the weight of "the ratio of the average value of the range of initial charge energy to the average value of initial charge energy of the test sample" representing the uniformity in the initial charge-discharge energy test in the electric properties>And determining the maximum as a key technical index of the electrical performance.
S3, determining numerical ranges of all technical indexes in different grades by analyzing the battery full-performance actual measurement data;
each performance index obtained in the step S2 is processedThe corresponding data are arranged from small to large, and the performance index is adopted +.>Is Z 1 Performance index->The theoretical maximum value is the optimal index value Z 0 According to Z 0 And Z is 1 Calculating median Z of corresponding performances 1/2 And the upper quartile value Z 1/4 According to the median value Z 1/2 And the upper quartile value Z 1/4 Three intervals were obtained: z is greater than or equal to 1/4 ，[Z 1/2 ,Z 1/4 ) Less than Z 1/2 Each interval corresponds to A, B, C three levels of performance index; taking battery efficiency as an example: the highest efficiency is 98%; measured minimum value Z 1 =90%；Z 1/2 =94%，Z 1/4 =96%。
S4, determining the performance grade of the energy storage battery through the fuzzy relation matrix and the weight, wherein the method comprises the following specific steps:
s41, establishing a fuzzy relation matrix of electrical performance, environmental adaptability, durability performance and safety performance and corresponding performance indexes:
judging a sign for judging that the ith technical index of the energy storage battery to be evaluated falls into the value range of the jth level, and if the ith technical index of the energy storage battery falls into the value range of the jth level +.>=1, otherwise->=0；
a is the number of performance index levels, a is equal to or greater than 2, the first column is the highest level, the a is the lowest level, in the invention, a is 3, and M is 1.
Examples: the value of the technical index of the energy storage battery to be evaluated, namely the ratio of the extremely poor average value of the initial charging energy of the test sample to the average value of the initial charging energy, falls into the interval corresponding to the B grade defined by S3, N in the formula 11 /M、N 13 The value of/M is 0, N 12 The value of/M is 1.
S42, evaluating the battery performance basic grade according to the key technical indexes:
and (3) starting comparison of the key technical indexes obtained in the step (S2) in the a-th column of the fuzzy relation matrix, wherein if any one of the indexes in the a-th column is greater than 0, the battery performance is in a grade A, if all the indexes in the a-th column are 0, the battery performance is in a grade B, if any one of the indexes in the a-1 th column is greater than 0, the indexes in the a-2 th column are continuously compared, if all the indexes in the a-1 th column are 0, and if any one of the indexes in the a-2 th column is greater than 0, the battery performance is in a grade C.
S43, determining the performance grade of the battery according to the fuzzy relation matrix and the maximum membership degree principle, wherein the performance grade comprises electric performance, environmental adaptability, durability and safety performance grade.
H2=[S 1 、S 2 、S 3 ...、S a ]In the invention, a is 3;
in the formula, H is a fuzzy relation matrix, and S is a performance index. S is S 1 The number S of the corresponding technical indexes falling into class A for each performance 2 The number of the technical indexes falling into the B level and S corresponding to each performance 3 The number of the technical indexes falling into the class C corresponding to each performance;
the S item which is not 0 is lower than the basic level, and the basic level is halved to be used as the final performance level; if the S item other than 0 is higher than or equal to the basic level, the performance index higher than the basic level exceeds the corresponding technical index by half, the performance level of the energy storage battery is taken as the basic level and is increased by half to be taken as the final performance level, and if the basic level is B, the performance index is increased to be B+; otherwise, maintaining the energy storage battery performance basic grade as the final performance grade. The performance of the energy storage battery is divided into 7 grades from high to low, namely A, B +, B, B-, C and C respectively.
S5, selecting a required energy storage battery from the energy storage batteries with the performance grade determined in the step S4 according to the requirements in the construction process of the energy storage system; for example, the performance consistency of the demand batteries may be all selected for class B energy storage batteries; if the energy storage battery is required to be built in a special extremely cold environment, the energy storage batteries with the grade of A can be selected completely so as to meet the requirements of environmental adaptation to the maximum extent.
Example 2
Referring to fig. 2, the present invention provides an energy storage battery performance evaluation device, which includes:
the acquisition module is used for acquiring the full-performance data of the energy storage battery;
the computing module is used for determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery;
the analysis module is used for analyzing the full-performance data of the energy storage battery and determining the numerical ranges of the technical indexes in different grades;
the evaluation module is used for determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index;
and the selection module is used for selecting the energy storage battery with the corresponding grade from the energy storage batteries with the final performance grade according to the construction requirements of the energy storage system.
In a specific embodiment, the step of acquiring the full performance data of the energy storage battery by the acquisition module specifically includes:
all k technical indexes of all test items of the electric performance, the environmental adaptability, the durability and the safety performance of the energy storage battery are tested to obtain the full-performance data of the energy storage battery; the full performance data of the energy storage battery comprises k indexes X 1 ,X 2 ,...,X k Wherein each index X i Comprising n sample data; n is a positive integer.
In a specific embodiment, the step of determining key technical indicators in each performance of the energy storage battery by the calculation module based on the acquired full performance data of the energy storage battery specifically includes:
s21, data standardization:
carrying out standardization processing on the acquired full-performance data of the energy storage battery; for k indices X 1 ,X 2 ,...,X k WhereinThe method comprises the steps of carrying out a first treatment on the surface of the k indexes X 1 ,X 2 ,...,X k Normalized value is Y 1 ,Y 2 ,...,Y k ，Wherein Y is ij Is the ith index X i Normalized data, X, of the jth sample data in (a) ij Indicating the ith index X i The j-th sampling data in (a); min (X) i ) Indicating the ith index X i Is the smallest sample data, max (X i ) Indicating the ith index X i Maximum sampling data in (a);
s22, calculating the ith index X i Information entropy of (2)：
Wherein:
if it isThen define: />；
S23, determining the weight of each index:
calculating each performance index X through information entropy i Weights of (2)：
，i=1,2,...,k
According to the weight of the performance indexDetermining the electrical performance, environmental suitability, durability and safety performance index>The index of (2) is the corresponding key technical index.
In a specific embodiment, the step of analyzing the full performance data of the energy storage battery by the analysis module to determine the numerical ranges of the technical indexes in different grades specifically includes:
Each performance index is setThe corresponding data are arranged from small to large, and the performance index is adopted +.>Is Z 1 Performance index->The theoretical maximum value is the optimal index value Z 0 According to Z 0 And Z is 1 Calculating median Z of corresponding performances 1/2 And the upper quartile value Z 1/4 According to the median value Z 1/2 And the upper quartile value Z 1/4 Three intervals were obtained: z is greater than or equal to 1/4 ，[Z 1/2 ,Z 1/4 ) Less than Z 1/2 Each interval corresponds to A, B, C three levels of performance index.
In a specific embodiment, the step of determining the final performance level of the energy storage battery by the evaluation module through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different levels and the key technical index specifically includes:
s41, establishing a fuzzy relation matrix of electrical performance, environmental adaptability, durability performance and safety performance and corresponding performance indexes:
judging a sign for judging that the ith technical index of the energy storage battery to be evaluated falls into the value range of the jth level, and if the ith technical index of the energy storage battery falls into the value range of the jth level +.>=1, otherwise->=0; a is the number of performance index levels, a=3, m=1;
s42, evaluating the performance basic grade of the energy storage battery according to the key technical indexes:
the key technical indexes are compared in the a-th column of the fuzzy relation matrix, and the performance basic grade of the energy storage battery is determined;
S43, determining the performance grade of the battery according to the fuzzy relation matrix and the maximum membership rule;
H2=[S 1 、S 2 、S 3 ]；
wherein H2 is a fuzzy relation matrix, and S is a performance index; s is S 1 The number S of the corresponding technical indexes falling into class A for each performance 2 The number of the technical indexes falling into the B level and S corresponding to each performance 3 The number of the technical indexes falling into the class C corresponding to each performance;
the S item which is not 0 is lower than the basic level of the performance of the energy storage battery, and the basic level is halved to be used as the final performance level; and S items which are not 0 are higher than or equal to the energy storage battery performance basic level, and the basic level is increased by half as the final performance level, otherwise, the energy storage battery performance basic level is maintained to be the final performance level.
In a specific embodiment, the step of selecting, by the selection module, the energy storage battery of the corresponding level from the energy storage batteries of the determined final performance level according to the construction requirement of the energy storage system specifically includes:
and selecting the energy storage batteries with the same final performance grade from the energy storage batteries with the final performance grade, and meeting the requirement of the energy storage system construction on the consistency of the energy storage batteries.
In a specific embodiment, the step of selecting, by the selection module, the energy storage battery of the corresponding level from the energy storage batteries of the determined final performance level according to the construction requirement of the energy storage system specifically includes:
And selecting the energy storage batteries with the final performance grade A from the energy storage batteries with the final performance grade A, and meeting the requirement of the energy storage system construction on the optimal performance of the energy storage batteries.
Example 3
Referring to fig. 3, the present invention further provides an electronic device 100 for implementing the method for evaluating the performance of an energy storage battery; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
The memory 101 may be used to store the computer program 103, and the processor 102 implements the energy storage battery performance evaluation method steps described in embodiment 1 by running or executing the computer program stored in the memory 101 and invoking the data stored in the memory 101. The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data) created according to the use of the electronic device 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one processor 102 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, the processor 102 being a control center of the electronic device 100, the various interfaces and lines being utilized to connect various portions of the overall electronic device 100.
The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for evaluating the performance of an energy storage battery, the processor 102 being executable to implement:
acquiring full-performance data of an energy storage battery;
determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery;
analyzing the full-performance data of the energy storage battery, and determining the numerical ranges of the technical indexes in different grades;
Determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index;
and selecting the energy storage battery with the corresponding grade from the energy storage batteries with the determined final performance grade according to the construction requirements of the energy storage system.
Example 4
The modules/units integrated in the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, and a Read-Only Memory (ROM).
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (12)
1. The energy storage battery performance evaluation method is characterized by comprising the following steps:
acquiring full-performance data of an energy storage battery;
determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery;
analyzing the full-performance data of the energy storage battery, and determining the numerical ranges of the technical indexes in different grades;
determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index;
selecting an energy storage battery with a corresponding grade from the energy storage batteries with the determined final performance grade according to the construction requirements of the energy storage system;
the step of acquiring the full-performance data of the energy storage battery specifically comprises the following steps:
all k technical indexes of all test items of the electric performance, the environmental adaptability, the durability and the safety performance of the energy storage battery are tested to obtain the full-performance data of the energy storage battery; the full performance data of the energy storage battery comprises k indexes X 1 ,X 2 ,...,X k Wherein each index X i Comprising n sample data; n is a positive integer;
the step of determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery specifically comprises the following steps:
Data normalization:
carrying out standardization processing on the acquired full-performance data of the energy storage battery; for k indices X 1 ,X 2 ,...,X k WhereinThe method comprises the steps of carrying out a first treatment on the surface of the k indexes X 1 ,X 2 ,...,X k Normalized value is Y 1 ,Y 2 ,...,Y k ，Wherein Y is ij Is the ith index X i Normalized data, X, of the jth sample data in (a) ij Indicating the ith index X i The j-th sampling data in (a); min (X) i ) Indicating the ith index X i Is the smallest sample data, max (X i ) Indicating the ith index X i Maximum sampling data in (a);
calculate the ith index X i Information entropy of (2)：
Wherein:
if it isThen define: />；
Determining the weight of each index:
calculating each performance index X through information entropy i Weights of (2)：
，i=1,2,...,k
According to the weight of the performance indexDetermining the electrical performance, environmental suitability, durability and safety performance index>The index of (2) is the corresponding key technical index.
2. The method for evaluating the performance of an energy storage battery according to claim 1, wherein the step of analyzing the full performance data of the energy storage battery to determine the numerical ranges of the technical indexes in different levels specifically comprises:
each performance index is setThe corresponding data are arranged from small to large, and the performance index is adopted +.>Is Z 1 Performance index->The theoretical maximum value is the optimal index value Z 0 According to Z 0 And Z is 1 Calculating median Z of corresponding performances 1/2 And the upper quartile value Z 1/4 According to the median value Z 1/2 And the upper quartile value Z 1/4 Three intervals were obtained: z is greater than or equal to 1/4 ，[Z 1/2 ,Z 1/4 ) Less than Z 1/2 Each interval corresponds to A, B, C three levels of performance index.
3. The method for evaluating the performance of an energy storage battery according to claim 2, wherein the step of determining the final performance level of the energy storage battery by fuzzy relation matrix and weight based on the numerical range of each technical index in different levels and key technical indexes specifically comprises:
establishing a fuzzy relation matrix of electrical property, environmental adaptability, durability and safety performance and corresponding performance indexes:
judging a sign for judging that the ith technical index of the energy storage battery to be evaluated falls into the value range of the jth level, and if the ith technical index of the energy storage battery falls into the value range of the jth level +.>=1, otherwise->=0; a is the number of performance index levels, a=3, m=1;
and evaluating the performance basic grade of the energy storage battery according to the key technical indexes:
the key technical indexes are compared in the a-th column of the fuzzy relation matrix, and the performance basic grade of the energy storage battery is determined;
determining the performance level of the battery according to the fuzzy relation matrix and the maximum membership principle;
H2=[S 1 、S 2 、S 3 ]；
Wherein H2 is a fuzzy relation matrix, and S is a performance index; s is S 1 The number S of the corresponding technical indexes falling into class A for each performance 2 The number of the technical indexes falling into the B level and S corresponding to each performance 3 The number of the technical indexes falling into the class C corresponding to each performance;
the S item which is not 0 is lower than the basic level of the performance of the energy storage battery, and the basic level is halved to be used as the final performance level; and S items which are not 0 are higher than or equal to the energy storage battery performance basic level, and the basic level is increased by half as the final performance level, otherwise, the energy storage battery performance basic level is maintained to be the final performance level.
4. The method for evaluating the performance of an energy storage battery according to claim 3, wherein the step of selecting the energy storage battery of the corresponding grade from the energy storage batteries of the determined final performance grade according to the construction requirements of the energy storage system comprises the following steps:
and selecting the energy storage batteries with the same final performance grade from the energy storage batteries with the final performance grade, and meeting the requirement of the energy storage system construction on the consistency of the energy storage batteries.
5. The method for evaluating the performance of an energy storage battery according to claim 3, wherein the step of selecting the energy storage battery of the corresponding grade from the energy storage batteries of the determined final performance grade according to the construction requirements of the energy storage system comprises the following steps:
And selecting the energy storage batteries with the final performance grade A from the energy storage batteries with the final performance grade A, and meeting the requirement of the energy storage system construction on the optimal performance of the energy storage batteries.
6. An energy storage battery performance evaluation device, comprising:
the acquisition module is used for acquiring the full-performance data of the energy storage battery;
the computing module is used for determining key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery;
the analysis module is used for analyzing the full-performance data of the energy storage battery and determining the numerical ranges of the technical indexes in different grades;
the evaluation module is used for determining the final performance grade of the energy storage battery through the fuzzy relation matrix and the weight based on the numerical range of each technical index in different grades and the key technical index;
the selection module is used for selecting the energy storage battery with the corresponding grade from the energy storage batteries with the final performance grade according to the construction requirements of the energy storage system;
the step of acquiring the full-performance data of the energy storage battery by the acquisition module specifically comprises the following steps:
all test items for the electrical performance, environmental adaptability, durability and safety performance of energy storage batteriesTesting the k technical indexes to obtain the full-performance data of the energy storage battery; the full performance data of the energy storage battery comprises k indexes X 1 ,X 2 ,...,X k Wherein each index X i Comprising n sample data; n is a positive integer;
the calculation module determines key technical indexes in each performance of the energy storage battery based on the acquired full-performance data of the energy storage battery, and specifically comprises the following steps:
data normalization:
carrying out standardization processing on the acquired full-performance data of the energy storage battery; for k indices X 1 ,X 2 ,...,X k WhereinThe method comprises the steps of carrying out a first treatment on the surface of the k indexes X 1 ,X 2 ,...,X k Normalized value is Y 1 ,Y 2 ,...,Y k ，Wherein Y is ij Is the ith index X i Normalized data, X, of the jth sample data in (a) ij Indicating the ith index X i The j-th sampling data in (a); min (X) i ) Indicating the ith index X i Is the smallest sample data, max (X i ) Indicating the ith index X i Maximum sampling data in (a);
calculate the ith index X i Information entropy of (2)：
Wherein:
if it isThen define: />；
Determining the weight of each index:
calculating each performance index X through information entropy i Weights of (2)：
，i=1,2,...,k
According to the weight of the performance indexDetermining the electrical performance, environmental suitability, durability and safety performance index>The index of (2) is the corresponding key technical index.
7. The energy storage battery performance evaluation device according to claim 6, wherein the step of analyzing the energy storage battery full performance data by the analysis module to determine the numerical ranges of the technical indexes in different levels specifically comprises:
Each performance index is setThe corresponding data are arranged from small to large, and the performance index is adopted +.>Is Z 1 Performance index->The theoretical maximum value is the optimal index value Z 0 According to Z 0 And Z is 1 Calculating median Z of corresponding performances 1/2 And the upper quartile value Z 1/4 According to the median value Z 1/2 And the upper quartile value Z 1/4 Three intervals were obtained: z is greater than or equal to 1/4 ，[Z 1/2 ,Z 1/4 ) Less than Z 1/2 Each interval corresponds to A, B, C three levels of performance index.
8. The energy storage battery performance evaluation device according to claim 7, wherein the evaluation module determines the final performance level of the energy storage battery by fuzzy relation matrix and weight based on the numerical range of each technical index in different levels and key technical indexes, specifically comprising:
establishing a fuzzy relation matrix of electrical property, environmental adaptability, durability and safety performance and corresponding performance indexes:
judging a sign for judging that the ith technical index of the energy storage battery to be evaluated falls into the value range of the jth level, and if the ith technical index of the energy storage battery falls into the value range of the jth level +.>=1, otherwise->=0; a is the number of performance index levels, a=3, m=1;
and evaluating the performance basic grade of the energy storage battery according to the key technical indexes:
The key technical indexes are compared in the a-th column of the fuzzy relation matrix, and the performance basic grade of the energy storage battery is determined;
determining the performance level of the battery according to the fuzzy relation matrix and the maximum membership principle;
H2=[S 1 、S 2 、S 3 ]；
wherein H2 is a fuzzy relation matrix, and S is a performance index; s is S 1 The number S of the corresponding technical indexes falling into class A for each performance 2 The number of the technical indexes falling into the B level and S corresponding to each performance 3 The number of the technical indexes falling into the class C corresponding to each performance;
the S item which is not 0 is lower than the basic level of the performance of the energy storage battery, and the basic level is halved to be used as the final performance level; and S items which are not 0 are higher than or equal to the energy storage battery performance basic level, and the basic level is increased by half as the final performance level, otherwise, the energy storage battery performance basic level is maintained to be the final performance level.
9. The energy storage battery performance evaluation device according to claim 8, wherein the selecting module selects the energy storage battery of the corresponding grade from the energy storage batteries of the determined final performance grade according to the energy storage system construction requirement, specifically comprising:
and selecting the energy storage batteries with the same final performance grade from the energy storage batteries with the final performance grade, and meeting the requirement of the energy storage system construction on the consistency of the energy storage batteries.
10. The energy storage battery performance evaluation device according to claim 8, wherein the selecting module selects the energy storage battery of the corresponding grade from the energy storage batteries of the determined final performance grade according to the energy storage system construction requirement, specifically comprising:
and selecting the energy storage batteries with the final performance grade A from the energy storage batteries with the final performance grade A, and meeting the requirement of the energy storage system construction on the optimal performance of the energy storage batteries.
11. An electronic device comprising a processor and a memory, the processor configured to execute a computer program stored in the memory to implement the energy storage battery performance evaluation method of any one of claims 1 to 5.
12. A computer-readable storage medium storing at least one instruction that when executed by a processor implements the energy storage battery performance evaluation method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311342380.2A CN117076826B (en) | 2023-10-17 | 2023-10-17 | Energy storage battery performance evaluation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311342380.2A CN117076826B (en) | 2023-10-17 | 2023-10-17 | Energy storage battery performance evaluation method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117076826A true CN117076826A (en) | 2023-11-17 |
| CN117076826B CN117076826B (en) | 2024-01-02 |
Family
ID=88708422
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311342380.2A Active CN117076826B (en) | 2023-10-17 | 2023-10-17 | Energy storage battery performance evaluation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117076826B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109856515A (en) * | 2019-03-20 | 2019-06-07 | 国网电力科学研究院武汉南瑞有限责任公司 | A kind of direct current cables state of insulation judgment method and system |
| CN113030761A (en) * | 2021-04-08 | 2021-06-25 | 中国电力科学研究院有限公司 | Method and system for evaluating health state of battery of super-large-scale energy storage power station |
| US20220003893A1 (en) * | 2019-10-16 | 2022-01-06 | Dalian University Of Technology | Forecast operation method for lowering reservoir flood limited water level considering forecast uncertainty |
| CN114626655A (en) * | 2020-12-13 | 2022-06-14 | 国网吉林省电力有限公司四平供电公司 | Multi-standard comprehensive evaluation method for regional comprehensive energy system |
-
2023
- 2023-10-17 CN CN202311342380.2A patent/CN117076826B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109856515A (en) * | 2019-03-20 | 2019-06-07 | 国网电力科学研究院武汉南瑞有限责任公司 | A kind of direct current cables state of insulation judgment method and system |
| US20220003893A1 (en) * | 2019-10-16 | 2022-01-06 | Dalian University Of Technology | Forecast operation method for lowering reservoir flood limited water level considering forecast uncertainty |
| CN114626655A (en) * | 2020-12-13 | 2022-06-14 | 国网吉林省电力有限公司四平供电公司 | Multi-standard comprehensive evaluation method for regional comprehensive energy system |
| CN113030761A (en) * | 2021-04-08 | 2021-06-25 | 中国电力科学研究院有限公司 | Method and system for evaluating health state of battery of super-large-scale energy storage power station |
Non-Patent Citations (1)
| Title |
|---|
| 孟俊娜;房宁;刘炳胜;薛斌;: "基于直觉模糊集的城镇基础设施项目可持续性评价", 模糊系统与数学, no. 01 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117076826B (en) | 2024-01-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hu et al. | Health prognosis for electric vehicle battery packs: A data-driven approach | |
| Lipu et al. | A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations | |
| Duan et al. | Evaluation of battery inconsistency based on information entropy | |
| Pang et al. | A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression | |
| Zhang et al. | Aging performance characterization and state-of-health assessment of retired lithium-ion battery modules | |
| CN107422266B (en) | Fault diagnosis method and device for high-capacity battery energy storage system | |
| CN107741568B (en) | Lithium battery SOC estimation method based on state transition optimization RBF neural network | |
| CN112818602B (en) | Battery digital twin control method and device based on big data analysis | |
| CN109100655B (en) | Data processing method and device for power battery | |
| CN105807231B (en) | A kind of method and system for remaining battery capacity detection | |
| CN114114039B (en) | Method and device for evaluating consistency of single battery cells of battery system | |
| WO2024036737A1 (en) | Aging state assessment and retirement screening method and system for power battery | |
| CN112485693B (en) | Battery health state rapid evaluation method based on temperature probability density function | |
| Liu et al. | Comparative study of incremental capacity curve determination methods for lithium-ion batteries considering the real-world situation | |
| CN112287980B (en) | Power battery screening method based on typical feature vector | |
| CN103529397A (en) | Method for estimating electric quantity of battery and system for managing electric quantity of battery | |
| Bao et al. | Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries | |
| Cao et al. | Non-invasive characteristic curve analysis of lithium-ion batteries enabling degradation analysis and data-driven model construction: a review | |
| Hu et al. | Performance evaluation strategy for battery pack of electric vehicles: Online estimation and offline evaluation | |
| CN114646888A (en) | Assessment method and system for capacity attenuation of power battery | |
| Wang et al. | Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering | |
| CN117076826B (en) | Energy storage battery performance evaluation method and device, electronic equipment and storage medium | |
| CN110142803B (en) | Method and device for detecting working state of mobile welding robot system | |
| CN116068402A (en) | New energy automobile lithium battery state prediction method, device, equipment and storage medium | |
| Wang et al. | Research on estimation model of the battery state of charge in a hybrid electric vehicle based on the classification and regression tree |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |