CN113281657A - Intelligent assessment retired battery complementary energy classification and echelon utilization method - Google Patents
Intelligent assessment retired battery complementary energy classification and echelon utilization method Download PDFInfo
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Abstract
The invention discloses a method for intelligently evaluating the waste energy classification and echelon utilization of a retired battery, which comprises the following specific steps of: the intelligent evaluation retired battery complementary energy classification and echelon utilization method comprises the steps of firstly carrying out data acquisition on battery complementary energy to be classified and extracting a characteristic data set of the battery complementary energy, determining the classified number of complementary energy by utilizing a mean square error and a function (SSE) in a group, classifying the battery complementary energy according to an improved mean clustering algorithm, and finally distributing echelon utilization environments of batteries including new energy vehicles, power station energy storage batteries, household batteries, battery recycling and the like according to the classified battery complementary energy.
Description
Technical Field
The invention relates to the field of power battery complementary energy assessment, in particular to a method for intelligently assessing retired battery complementary energy classification and echelon utilization.
Background
In recent years, due to the increasing prominence of energy safety problems and environmental protection pressure, new energy automobiles are vigorously developed in all countries around the world. The output of the power battery in China is 7.4GWH in 8 months in 2020, and the output of the new energy power battery in China in 2022 is predicted to break through 200 Gwh. In 2020, the accumulated retired scrap amount of the power batteries of the electric automobiles reaches the scale of 12-17 ten thousand tons, and the number of the retired batteries is so large, so that the judgment of whether the retired power batteries have the echelon utilization value becomes the market demand.
However, due to the lack of accurate and effective evaluation means (equipment and method) for the retired battery, the phenomenon of fish eyes mixing beads occurs in the battery recycling process. Even if the battery can be used for multiple times in a gradient manner, the battery is also treated and processed into raw materials as industrial waste by recycling enterprises, the value of the retired battery is not fully utilized, and the resource waste is caused. Therefore, the development and industrialization of the rapid intelligent evaluation and classification technology of the power battery are carried out, the significance is great for guaranteeing the safe operation of the new energy automobile and carrying out the recycling echelon utilization of the retired power battery, and the method is also one of important links for the green cycle development of the new energy automobile industry in China.
Patent application publication No. CN201911148806.4 discloses a retired battery classification method and system, which are used for classifying batteries by obtaining corresponding cycle number through an open-circuit voltage and cycle number fitting equation obtained by carrying out a cyclic charge-discharge test on sample batteries. However, the cyclic charge-discharge method adopted by the method is long in time consumption, and resource saving and efficiency improvement are still needed to be improved.
Patent application publication No. CN201610856613.4 provides a new energy automobile power type lithium ion battery's the categorised recovery process method of disassembling, carries out monomer battery residual capacity through the shell of artifical power type lithium ion battery of disassembling and detects, retrieves and ore dressing are categorised to the evaporant of drying. The invention can separate the anode and cathode materials of the battery from valuable metals and realize the recovery of organic electrolyte. However, if the number of the batteries to be classified is large, the manual disassembly is time-consuming, and the method only classifies the batteries to be recycled.
Disclosure of Invention
The technical problem mainly solved by the invention is to provide an intelligent assessment retired battery residual energy classification and echelon utilization method, which can rapidly and accurately classify retired battery residual energy, complete echelon utilization of power batteries according to battery residual energy requirements of different environments and improve the utilization rate of the batteries.
In order to solve the technical problems, the invention adopts a technical scheme that: the method for intelligently evaluating the retired battery complementary energy classification and echelon utilization comprises the following specific steps:
s1: firstly, data acquisition is carried out on the complementary energy of the battery to be classified by adopting a big data statistical management technology and the complementary energy is stored in a database;
s2: extracting a characteristic data set of the complementary energy data of the battery pack to be classified in the database by using a sklern library;
s3: determining the number of the optimal power battery complementary energy classifications by using the characteristic data set collected in the step S2 and using the mean square error in the group and the slope change of a function (SSE);
s4: classifying the residual energy of the power battery according to a mean value clustering algorithm which is improved according to the application environment of the residual energy of the power battery;
s5: and allocating the echelon utilization environment of the battery according to the power battery residual energy classified by the algorithm.
Preferably, in step S1, data is acquired from the battery residual energy detection system through the computer ETL tool, and then the data is loaded and saved into the database through conversion and integration.
Preferably, in step S2, a feature vector that can be learned or processed directly by a machine is extracted from the feature data set, and a feature space is formed after transformation.
Preferably, the S3 includes the following specific steps:
s31: analyzing the feature data set extracted in step S2;
s32: selecting the optimal classification number by using a function, and generally classifying the optimal classification number into 2-15 classes;
s33: and determining the classified number of the residual energy of the power battery by adopting a mean square error sum function (SSE) in the group, calculating the sum of the square errors of the distances under each cluster number, and determining the optimal cluster number according to the change of the function slope.
Preferably, the S4 includes the following specific steps:
s41: selecting the corresponding characteristic vectors of 0.00 percent and 100.00 percent of the residual energy of the battery as two mean value vectors u1And u2Then, selecting other clustering centers according to the characteristic vector taking 100/(k-1) as the increment;
s42: calculating the distance between each sample characteristic vector and each mean vector of the collected retired battery residual energy, and selecting the nearest distance to be classified into the corresponding application environment classification Ci（CiNone are empty); according to formula in the obtained k classes
Calculating a new mean vector ui' judging the mean vector u determined by the mean vector of the current power battery residual energy application environment category and the initial characteristic vector1，u2……ui……ukUpdating the difference;
s43: if u isi' with u determined in the previous stepiIf not, the current mean vector uiIs updated to ui', if new calculated ui' with u determined in the previous stepiIf the average value vector is equal, keeping the average value vector unchanged;
s44: repeating the steps from S41 to S43 until the cluster center is not changed any more or the change is negligibly finished, and obtaining the environment application class C of the final power battery residual energy classification1，C2……Ci……Ck。
Preferably, in step S5, the classification environment category of the power battery ladder utilization may also be determined first and then reclassified.
The invention has the beneficial effects that: according to the intelligent assessment retired battery residual energy classification and echelon utilization method, retired battery residual energy can be rapidly and accurately classified, echelon utilization of the power battery is completed according to battery residual energy requirements of different environments, and the battery utilization rate is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a flow chart of a battery complementary energy classification method according to the present invention;
FIG. 2 is a clustering algorithm classification flow chart of the present invention;
FIG. 3 is a graph of the number of battery remaining energy classifications and the sum of squared error and function according to the present invention;
fig. 4 is a diagram of battery remaining energy classification-echelon utilization matching results.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-4, an embodiment of the present invention includes: the retired battery residual energy classification and echelon utilization method comprises the steps of selecting retired battery residual energy classification and echelon utilization as tasks, classifying by adopting a mean value clustering algorithm, and finally matching battery residual energy with echelon utilization of an application environment according to classification results.
The specific embodiment of the invention comprises the following steps:
(1) firstly, data acquisition is carried out on the complementary energy of 100 batteries to be classified by adopting a big data statistics management technology, and the data is loaded and stored in a database through conversion and integration after the data is acquired;
(2) extracting characteristic vectors which can be learned or directly processed by a machine from the collected original characteristic data set of the complementary energy of the battery to be classified by using a sklern library, and forming characteristic space vectors after transformation;
(3) the number of the optimal power battery complementary energy classification is determined by using the slope change of the mean square error sum function (SSE) in the group, the classification is generally classified into 2-15 classes, and specifically, the optimal value of k is determined according to the change of the slope of the function by calculating the distance mean square error sum under the condition that the cluster number k =2, 3, 4, 5, 6, 7. With the increase of the number of clusters, the sum of squared distance errors becomes smaller, it can be seen that when the number of clusters k =2, 3 and 4, the slope of the function curve is smaller, the sum of squared distance errors changes more slowly, and when the number of clusters k =5, the slope suddenly becomes larger, the sum of squared distance errors changes more quickly, and when the number of clusters k =6 and 7, the slope becomes smaller, the sum of squared distance errors changes more slowly, so that the number of the power battery residual energy classifications at this time is 5.
(4) The method comprises the steps of classifying the residual energy of the power battery by adopting a mean value clustering algorithm improved according to the application environment of the residual energy of the power battery, taking feature vectors corresponding to 0.00% and 100.00% of the residual energy of the power battery as two randomly selected mean value vectors u1 and u2, and then selecting other clustering centers according to the feature vectors taking 100/(k-1) as increment. The number of the battery residual energy classifications is 5, so that the feature vectors of 0.00%, 25.00%, 50.00%, 75.00% and 100.00% of the battery residual energy are respectively selected as initial clustering centers u1，u2，u3，u4，u5Respectively calculating the distance between each sample feature vector and each mean vector of the collected retired battery residual energy, and selecting the nearest distance to be classified into the corresponding application environment classification Ci(ii) a According to formula in the obtained k classes
Calculating a new mean vector ui' judging the mean vector u determined by the mean vector of the current power battery residual energy application environment category and the initial characteristic vector1，u2……ui……ukThe difference (c) is updated.
(5) If u isi' with u determined in the previous stepiIf not, the current mean vector uiIs updated to ui', if new calculated ui' with u determined in the previous stepiEqual, the mean vector is kept unchanged. This process is repeated until the cluster centers are no longer updated. Environmental application class C resulting in a final battery residual energy classification1，C2，C3，C4，C5；
| First kind | Second class | Class III | Class IV | Fifth class |
| 3、5、27、29、 54、55、58、 67、70、88、 89、93 | 4、6、7、12、13、28、 32、35、38、41、64、 75、79、80、86、94、 95、96、97 | 10、14、26、29、 30、31、50、51、 52、59、66、71、 81、82、87、90 | 1、2、8、9、11、16、17、18、19、20、21、22、24、 25、33、34、36、37、39、40、42、44、45、46、 47、48、49、53、61、62、65、68、69、72、74、 77、78、83、85、91、92、98、99 | 15、23、43、56、 57、60、73、76、 84、100 |
(6) And finally, allocating echelon utilization environments of the batteries according to the surplus energy types of the power batteries obtained by the clustering algorithm, such as new energy vehicles, power stations, transformer substation energy storage batteries, electric bicycle energy storage batteries, household batteries or waste batteries for recycling and the like. According to the classification result, the first type of battery residual energy is in the range of 83.24% -99.85%, the performance is the highest in the collected batteries, the performance is the best, the service life is longer, and the battery residual energy collection device can be used in the application environment with high power battery residual energy requirement and high performance requirement, such as the fields of Hybrid Electric Vehicles (HEV) or plug-in hybrid electric vehicles (PHEV); the residual energy of the second battery is within the range of 65.76% -80.53%, the battery performance is good, and the battery can be used in application environments with general battery performance requirements, such as power stations, transformer substations, charging and replacing stations and the like; the residual energy of the third battery is in the range of 53.45-60.31%, the battery performance is good, the battery can be used on a new energy electric bicycle, the resource is saved, and the environment is protected; the residual energy of the fourth type of battery is within the range of 20.45-50.96, most of the batteries to be classified belong to the range, the performance is poor, the quantity is large, the battery can be used in application environments with small battery performance requirements and large quantity requirements, such as household energy storage, the application range is wide, and the demand is large; the residual energy of the fourth battery is below 18.88 percent, the battery performance is extremely poor, the availability ratio is low, and the battery is basically unusable, so the battery is used for recycling waste battery raw materials.
In conclusion, the intelligent assessment retired battery residual energy classification and echelon utilization method can rapidly and accurately classify retired battery residual energy, complete echelon utilization of the power battery according to battery residual energy requirements of different environments, and improve battery utilization rate.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (5)
1. An intelligent assessment retired battery complementary energy classification and echelon utilization method is characterized by comprising the following specific steps:
s1: firstly, data acquisition is carried out on the complementary energy of the battery to be classified by adopting a big data statistical management technology and the complementary energy is stored in a database;
s2: extracting a characteristic data set of the complementary energy data of the battery pack to be classified in the database by using a sklern library;
s3: determining the number of the optimal power battery complementary energy classifications by using the characteristic data set collected in the step S2 and using the mean square error in the group and the slope change of a function (SSE);
s4: classifying the residual energy of the power battery according to a mean value clustering algorithm which is improved according to the application environment of the residual energy of the power battery;
s5: and allocating the echelon utilization environment of the battery according to the power battery residual energy classified by the algorithm.
2. The method for intelligently evaluating the classification and echelon utilization of retired battery remaining energy as claimed in claim 1, wherein in step S1, data is obtained from the battery remaining energy detection system through the computer ETL tool, and then loaded and saved into the database through conversion and integration.
3. The method for intelligently evaluating the retired battery after-energy classification and echelon utilization according to claim 1, wherein in step S2, feature vectors that can be learned or directly processed by a machine are extracted from the feature data set, and transformed to form a feature space.
4. The method for intelligently evaluating the retired battery after-energy classification and echelon utilization according to claim 1, wherein the step S3 comprises the following specific steps:
s31: analyzing the feature data set extracted in step S2;
s32: selecting the optimal classification number by using a function, and generally classifying the optimal classification number into 2-15 classes;
s33: and determining the classified number of the residual energy of the power battery by adopting a mean square error sum function (SSE) in the group, calculating the sum of the square errors of the distances under each cluster number, and determining the optimal cluster number according to the change of the function slope.
5. The method for intelligently evaluating the retired battery after-energy classification and echelon utilization according to claim 1, wherein the step S4 comprises the following specific steps:
s41: selecting the corresponding characteristic vectors of 0.00 percent and 100.00 percent of the residual energy of the battery as two mean value vectors u1And u2Then, selecting other clustering centers according to the characteristic vector taking 100/(k-1) as the increment;
s42: calculating the distance between each sample characteristic vector and each mean vector of the collected retired battery residual energy, and selecting the nearest distance to be classified into the corresponding application environment classification Ci（CiNone are empty); according to formula in the obtained k classes
Calculating a new mean vector ui' judging the mean vector u determined by the mean vector of the current power battery residual energy application environment category and the initial characteristic vector1，u2……ui……ukUpdating the difference;
s43: if u isi' with u determined in the previous stepiIf not, the current mean vector uiIs updated to ui', if new calculated ui' with u determined in the previous stepiIf the average value vector is equal, keeping the average value vector unchanged;
s44: repeating the steps from S41 to S43 until the cluster center is not changed any more or the change is neglected to be over, and obtaining the final classification of the residual energy of the power batteryEnvironmental application class C1，C2……Ci……Ck。
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