CN117930012A - Battery consistency assessment method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of battery energy storage, and discloses a battery consistency assessment method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring time sequence data of each battery in the target battery module, and converting the time sequence data into a feature map; determining dynamic characteristic data corresponding to the target battery module based on the feature map; acquiring voltage data and temperature data of each battery at the same moment, and determining static characteristic data corresponding to a target battery module; according to the invention, the dynamic characteristics of the battery module are effectively reflected according to the time sequence data of each battery, the corresponding static characteristic data is determined according to the voltage data and the temperature data of each battery, so that the consistency state of the battery module is quantized, the consistency evaluation result of the battery is comprehensively reflected according to the dynamic characteristic data and the static characteristic data, and the accuracy of the consistency result of the battery is improved.
Description
Technical Field
The invention relates to the technical field of battery energy storage, in particular to a battery consistency assessment method, a device, computer equipment and a storage medium.
Background
At present, operation and maintenance analysis technologies of energy storage power stations are continuously developed, and the scale of the energy storage power stations is enlarged and the application scenes are increased. Along with the accumulation of the operation time of the energy storage battery and the change of the operation mode, the performance and the state of the energy storage battery correspondingly change, and the differentiation among the batteries can directly lead to the occurrence of a wooden barrel effect of the energy storage power station, so that the operation efficiency of the energy storage power station is reduced. Therefore, assessment of battery consistency is becoming increasingly important.
In the prior art, statistical indexes are often selected as battery consistency measurement standards, and the dynamic characteristics of the batteries cannot be reflected, so that the stability of a battery consistency judgment result is poor, meanwhile, different characteristic indexes have different physical meanings, important characteristics can be ignored by simple weighted summation, and the accuracy of the consistency judgment result is low.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, a computer device and a storage medium for evaluating consistency of batteries, so as to solve the problem of consistency judgment of batteries.
In a first aspect, the present invention provides a battery consistency assessment method, the method comprising:
Acquiring time sequence data of each battery in a target battery module, and converting the time sequence data into a feature map;
Determining dynamic characteristic data corresponding to the target battery module based on the characteristic diagram;
Acquiring voltage data and temperature data of each battery at the same moment, and determining static characteristic data corresponding to a target battery module based on the voltage data and the temperature data;
And calculating and determining a consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module.
According to the invention, the corresponding dynamic characteristic data is determined according to the time sequence data of each battery so as to effectively reflect the dynamic characteristics of the battery module, the corresponding static characteristic data is determined according to the voltage data and the temperature data of each battery so as to quantify the consistency state of the battery module, the consistency evaluation result of the battery is comprehensively reflected through the dynamic characteristic data and the static characteristic data, the accuracy of the consistency result of the battery is improved, and the consistency evaluation result of the battery module is comprehensively reflected.
In an optional embodiment, the calculating and determining the consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module includes:
Respectively calculating the score and the weight corresponding to the dynamic characteristic data and the static characteristic data;
And determining a consistency evaluation result of each battery in the target battery module based on the scores and weights corresponding to the dynamic characteristic data and the static characteristic data.
According to the invention, the importance of each characteristic data is effectively fused by calculating the scores and weights of each dynamic characteristic data and each static characteristic data, so that the simple weighting is replaced, and the accuracy of the evaluation result is improved.
In an alternative embodiment, the converting the time series data into a feature map includes:
setting delay time and embedding dimension, and reconstructing the time sequence data into a phase space according to the delay time and the embedding dimension;
and carrying out polar coordinate conversion on the vector in the phase space, and determining a feature map according to the vector subjected to polar coordinate conversion.
According to the invention, the time series data is processed and converted into the feature map so as to effectively reflect the dynamic characteristics of the battery under different working conditions, and the poor stability of the evaluation result caused by only selecting the statistical index as the dynamic characteristic measurement standard is avoided.
In an optional embodiment, the determining, based on the feature map, dynamic characteristic data corresponding to the target battery module includes:
Carrying out region segmentation on the polar coordinates, and calculating the correlation coefficient of the feature map in each region;
and determining the similarity between each battery in the target battery module based on the correlation coefficient, and determining the average value of the similarity of all batteries as dynamic characteristic data of the target battery module.
According to the invention, the similarity of each battery is determined by calculating the correlation coefficient of the feature map according to the feature map, so that the dynamic characteristics of each battery in the target battery module are reflected.
In an optional embodiment, the determining the static characteristic data corresponding to the target battery module based on the voltage data and the temperature data includes:
Determining the corresponding polar differences, standard deviations and variation coefficients of all batteries in the target battery module based on the voltage data and the temperature data;
And determining the polar difference, the standard deviation and the variation coefficient as static characteristic data corresponding to the target battery module.
The invention reflects the static characteristic data of the battery module by selecting the range, standard deviation and variation coefficient of each battery.
In an alternative embodiment, the calculating the score and the weight corresponding to the dynamic characteristic data and the static characteristic data respectively includes:
Determining the current change interval of each dynamic characteristic data and static characteristic data;
Determining the corresponding scores of the dynamic characteristic data and the static characteristic data according to the corresponding relation between the change interval and the index score and the current change interval;
constructing a fuzzy complementary judgment matrix based on the dynamic characteristic data and the static characteristic data;
And converting the fuzzy complementary judgment matrix into a fuzzy consistency matrix, carrying out row-wise product calculation, root opening calculation and normalization processing on the fuzzy consistency matrix to obtain weight vectors of all components, and determining weights of all dynamic characteristic data and static characteristic data based on the weight vectors of all the components.
According to the invention, the score of each dynamic characteristic data and each static characteristic data is calculated, a fuzzy complementary judgment matrix is constructed according to the dynamic characteristic data and the static characteristic data, the weight corresponding to each dynamic characteristic data and each static characteristic data is obtained by calculating the fuzzy complementary judgment matrix, the importance degree is mapped to a specific numerical value, the importance degree of each characteristic data is measured, and the fuzzy complementary judgment matrix is converted into a fuzzy consistency matrix, so that the consistency verification process is avoided.
In an optional embodiment, the determining the consistency evaluation result of each battery in the target battery module based on the score and the weight corresponding to the dynamic characteristic data and the static characteristic data includes:
Determining the multiplied result of the scores of the dynamic characteristic data and the static characteristic data and the corresponding weights as the consistency score of the target battery module;
And determining the consistency evaluation result of each battery in the target battery module according to the corresponding relation between the preset consistency score interval and the battery health state and the consistency score.
According to the invention, the scores and weights of the characteristic data are multiplied to obtain the consistency score of the battery module so as to comprehensively reflect the consistency state of the battery module, and the consistency evaluation result is reflected according to the consistency score so as to improve the accuracy of the battery consistency evaluation result.
In a second aspect, the present invention provides a battery consistency assessment apparatus comprising:
The acquisition module is used for acquiring time sequence data of each battery in the target battery module and converting the time sequence data into a characteristic diagram;
the first determining module is used for determining dynamic characteristic data corresponding to the target battery module based on the characteristic diagram;
The second determining module is used for acquiring voltage data and temperature data of each battery at the same moment and determining static characteristic data corresponding to the target battery module based on the voltage data and the temperature data;
And the calculation module is used for calculating and determining the consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module.
In a third aspect, the present invention provides a computer device comprising: the battery consistency assessment system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the battery consistency assessment method of the first aspect or any implementation mode corresponding to the first aspect is executed.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery consistency assessment method of the first aspect or any of the embodiments corresponding thereto.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a battery consistency assessment method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another battery consistency assessment method according to an embodiment of the present invention;
FIG. 3 is a flow chart of yet another battery consistency assessment method according to an embodiment of the present invention;
Fig. 4 is an application diagram of a battery consistency assessment method according to an embodiment of the present invention;
fig. 5 is a block diagram of a battery consistency assessment apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Currently, with the rapid development of energy storage technology, battery consistency assessment becomes an important problem in the operation and maintenance of energy storage power stations. The traditional battery consistency assessment method mainly adopts statistical indexes to measure the consistency of batteries, such as the extreme difference, standard deviation, variation coefficient and the like. However, these methods do not sufficiently reflect the dynamic characteristics of the battery, resulting in poor stability of the evaluation result.
The single battery is the most basic component unit of the energy storage power station, and the problems of material proportion difference, manufacturing tolerance and the like exist in the production of the battery, so that the initial performance difference exists before delivery. When the unit cells are grouped, the voltage and current on each unit cell may also exhibit inconsistencies due to the difference in battery capacity and internal resistance. Meanwhile, the temperature difference can be caused by different battery performances, and if the superimposed environmental temperature distribution is inconsistent, the temperature among the single batteries is more inconsistent, so that the reaction rate and the reaction activation energy of substances in the batteries are affected. If the battery cell works at uneven temperature for a long time, the physical property differences of battery capacity, internal resistance and the like can be further enlarged and a positive feedback effect can be formed. So that the increase of the cell inconsistency accelerates the performance degradation of the whole module.
In accordance with an embodiment of the present invention, there is provided a battery consistency assessment method embodiment, it being noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer-executable instructions, and, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order other than that illustrated herein.
In this embodiment, a battery consistency assessment method is provided, which may be used in a mobile terminal, and fig. 1 is a flowchart of a battery consistency assessment method according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
Step S101, time series data of each battery in the target battery module is acquired, and the time series data is converted into a feature map.
In the embodiment of the invention, the sensor collects time series data of each battery, and the time series data collected by the sensor is converted into a characteristic diagram as a first measurement index. The time series data may be converted into the feature map by using a ternary motif field algorithm, and other algorithms capable of implementing data conversion may be used, which is not limited herein.
Step S102, dynamic characteristic data corresponding to the target battery module is determined based on the feature map.
In the embodiment of the invention, the time series data are converted into the feature images to describe the dynamic change characteristics of the battery, and the dynamic characteristic data can be the similarity of the feature images corresponding to different battery monomers in the target battery module.
Step S103, voltage data and temperature data of each battery at the same moment are obtained, and static characteristic data corresponding to the target battery module is determined based on the voltage data and the temperature data.
In the embodiment of the invention, the voltage change data and the temperature change data of each battery in the target battery module at the same moment are obtained, and the static characteristic data comprising the extreme difference, the standard deviation and the variation coefficient are calculated based on the voltage data and the temperature data of the single battery.
Specifically, the difference between the maximum value and the minimum value of the voltage and the temperature of the single battery at the same time, namely, the extremely poor, is calculated, the degree of dispersion of the voltage data and the temperature data of the single battery, namely, the standard deviation is calculated, and the relative degree of dispersion of the voltage data and the temperature data of the single battery, namely, the variation coefficient is calculated.
Step S104, calculating and determining the consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module.
In the embodiment of the invention, the scores of the dynamic characteristic data and the static characteristic data are calculated according to a scoring algorithm, the weights of the dynamic characteristic data and the static characteristic data are calculated by adopting a fuzzy analytic hierarchy process, and the consistency evaluation result of each battery in the target battery module is determined according to the scores and the weights of the dynamic characteristic data and the static characteristic data.
According to the battery consistency assessment method, corresponding dynamic characteristic data are determined according to time sequence data of each battery so as to effectively reflect dynamic characteristics of the battery module, corresponding static characteristic data are determined according to voltage data and temperature data of each battery so as to quantify consistency states of the battery module, consistency assessment results of the batteries are comprehensively reflected through the dynamic characteristic data and the static characteristic data, accuracy of the battery consistency results is improved, and consistency assessment results of the battery module are comprehensively reflected.
In this embodiment, a battery consistency assessment method is provided, which may be used in the mobile terminal described above, and fig. 2 is a flowchart of a battery consistency assessment method according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:
Step S201, time series data of each battery in the target battery module is acquired, and the time series data is converted into a feature map.
Specifically, the converting the time-series data into the feature map in step S201 includes:
in step S2011, a delay time and an embedding dimension are set, and the time-series data is reconstructed into a phase space according to the delay time and the embedding dimension.
Step S2012, the time-series data is converted into a feature map.
In the embodiment of the invention, a sensor is used for collecting time series data, and the time series data is encoded into a characteristic diagram through a ternary motif field algorithm.
Assuming that the time sequence is x= [ x 1,x2,...,xn ], mapping x to a high-dimensional space by utilizing Takens delay embedding theorem, and fully expanding the n-dimensional manifold image in a larger space to realize the recovery of the change process of the prime power system from the known time sequence.
According to Takens delay embedding theorem, delay time and embedding dimension are set, which determine the coordinates and spatial distribution of the reconstructed phase points. In the present application, the delay time and the embedding dimension are set to 1, respectively, to better reflect the battery dynamic characteristics. Reconstructing x into a phase space representation based on delay time and embedding dimension:
transforming the vector in phase space to polar coordinates:
Wherein, R i and θ i are the polar diameter and polar angle corresponding to the ith vector.
And finally, drawing the corresponding coordinates of all vectors in polar coordinates to obtain a feature map.
Most of traditional battery consistency assessment methods only use statistical indexes as measurement standards, and cannot reflect the dynamic characteristics of batteries, so that the stability of assessment results is poor. According to the scheme, accurate depiction of the dynamic characteristics of the battery is realized through the ternary motif field algorithm, the ternary motif field algorithm codes the sensor time sequence as the characteristic diagram, the dynamic characteristics of the battery under different working conditions can be effectively reflected, and the problem that the stability of an evaluation result is poor due to the fact that only statistical indexes are selected as dynamic characteristic measurement standards is avoided.
Step S202, determining dynamic characteristic data corresponding to the target battery module based on the feature map.
Specifically, the step S202 includes:
In step S2021, the polar coordinates are segmented into regions, and the correlation coefficients of the feature maps in the respective regions are calculated.
Step S2022, determining the similarity between the batteries in the target battery module based on the correlation coefficient, and determining the average value of the similarities of all the batteries as the dynamic characteristic data of the target battery module.
In the embodiment of the invention, the dynamic characteristic data of the target battery module is determined by using a histogram comparison method, the histogram comparison method is an efficient and quick image similarity judgment algorithm, the dynamic characteristic data of the target battery module is the similarity of the corresponding feature maps of different battery cells, and the histogram comparison method can measure the similarity of the corresponding feature maps of different battery cells.
The histogram comparison method divides the polar coordinates into a plurality of areas with the same size, counts the data point number in each area, and calculates the correlation coefficient of the two images by using the pearson correlation coefficient. The larger the correlation coefficient is, the higher the similarity between pictures is represented, and the calculation formula is as follows:
Where m is the number of regions, H 1 (i) and H 2 (i) represent the number of data points contained in the ith region of the first and second graphs, respectively, And/>Each region in the first and second graphs contains an average number of data points.
And finally, summing the correlation coefficients of each picture and other pictures to obtain the similarity of the battery and the rest batteries, and averaging the similarity of all the single batteries to represent the consistency of the batteries, wherein the higher the similarity is, the higher the consistency of the batteries is.
Step S203, voltage data and temperature data of each battery at the same time are obtained, and static characteristic data corresponding to the target battery module is determined based on the voltage data and the temperature data.
Specifically, the determining the static characteristic data corresponding to the target battery module based on the voltage data and the temperature data in the step S203 includes:
step S2031, determining the corresponding polar differences, standard deviations and variation coefficients of the batteries in the target battery module based on the voltage data and the temperature data.
Step S2032, determining the range, standard deviation and variation coefficient as the static characteristic data corresponding to the target battery module.
In the embodiment of the invention, the static characteristic data corresponding to the target battery module comprises the extreme difference, the standard deviation and the variation coefficient.
Let the data collected by the battery module at time t be Z t＝[z1,z2,...,zm ], where Z m represents the data of the mth single battery, and m represents the number of single batteries.
Specifically, the Range is calculated, and the Range (Range) represents the difference between the maximum value and the minimum value of the unit cells at the same time, and can be calculated by the following formula:
Range＝max(Zt)-min(Zt) (4)
Calculating standard deviation, wherein the standard deviation (Standard Deviation) is used for measuring the discrete degree of the single battery data, and can be calculated by the following formula:
wherein, The average value of the cell data is shown.
Calculating a coefficient of variation (Coefficient of Variation) for measuring the relative degree of dispersion of the cell data, which can be calculated by the following formula:
The static characteristic data of the battery module are reflected by selecting the polar differences, standard deviations and variation coefficients of the batteries. By calculating the correlation coefficient among different single batteries, the consistency state of the battery module can be quantified, and the accuracy of battery consistency assessment is improved.
Step S204, based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module, the consistency evaluation result of each battery in the target battery module is calculated and determined.
Please refer to step S104 in the embodiment shown in fig. 1 in detail, which is not described herein.
According to the battery consistency assessment method provided by the embodiment, the dynamic characteristics of the battery module are accurately carved by introducing a ternary die body field algorithm. The static characteristics of the battery module are reflected by calculating the range, standard deviation and variation coefficient of the single battery, and the evaluation result is determined according to the dynamic characteristic data and the static characteristic data, so that the stability and accuracy of the battery evaluation result are improved.
In this embodiment, a battery consistency assessment method is provided, which may be used in the mobile terminal described above, and fig. 3 is a flowchart of a battery consistency assessment method according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
Step S301, time series data of each battery in the target battery module is acquired, and the time series data is converted into a feature map.
Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S302, dynamic characteristic data corresponding to the target battery module is determined based on the feature map.
Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S303, voltage data and temperature data of each battery at the same moment are obtained, and static characteristic data corresponding to the target battery module is determined based on the voltage data and the temperature data.
Please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S304, based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module, the consistency evaluation result of each battery in the target battery module is calculated and determined.
In some alternative embodiments, the step S304 includes:
Step S3041, calculating the score and weight corresponding to the dynamic characteristic data and the static characteristic data, respectively.
Step S3042, determining a consistency evaluation result of each battery in the target battery module based on the scores and weights corresponding to the dynamic characteristic data and the static characteristic data.
In the embodiment of the invention, the similarity, the range, the standard deviation and the score corresponding to the variation coefficient of the characteristic map corresponding to the battery are respectively calculated by utilizing a preset threshold value and linear interpolation, and the characteristic data are mapped to the score percent to unify the measurement standard. And multiplying the scores and weights corresponding to the dynamic characteristic data and the static characteristic data to determine the consistency evaluation result of each battery in the target battery module.
The importance of each characteristic data is effectively fused by calculating the scores and weights of each dynamic characteristic data and each static characteristic data, so that simple weighting is replaced, and the accuracy of an evaluation result is improved.
In some alternative embodiments, step S3041 includes:
step S30411, determining the current variation interval in which each of the dynamic characteristic data and the static characteristic data is located.
And step S30512, determining the corresponding scores of the dynamic characteristic data and the static characteristic data according to the corresponding relation between the change interval and the index score and the current change interval.
And step S3043, constructing a fuzzy complementary judgment matrix based on the dynamic characteristic data and the static characteristic data.
Step S30414, converting the fuzzy complementary judgment matrix into a fuzzy consistency matrix, carrying out line-by-line product calculation, root opening calculation and normalization processing on the fuzzy consistency matrix to obtain weight vectors of all components, and determining weights of all dynamic characteristic data and static characteristic data based on the weight vectors of all the components.
In the embodiment of the invention, the consistency state of the battery of the energy storage power station is firstly divided into four grades of health, sub-health, serious and severe. And setting a change interval of each characteristic index in each grade and a corresponding score thereof, and calculating an accurate score for a certain data point by using linear interpolation when the certain data point is positioned in the interval. For example, assuming that the value of the extremely poor parameter is x, x may be mapped into a corresponding score range according to a preset extremely poor score range.
Assuming that the characteristic index parameter value is [ s 1,s2](s1<s2 ] in a certain level change interval, and the corresponding score is [ v 1,v2](v1>v2), the score v corresponding to the data point s in the interval is:
the lower the cell voltage and temperature characteristic index scoring system is shown in tables 1 and 2, the worse the cell uniformity is indicated.
TABLE 1
TABLE 2
And establishing a hierarchical structure model based on the dynamic characteristic data and the static characteristic data. First, a hierarchical model is built, including a target layer, a criteria layer, and a solution layer. In the battery consistency problem, the hierarchical model structure is as follows:
target layer: the battery consistency weighting score.
Criterion layer: feature map similarity, range, standard deviation and coefficient of variation.
Constructing a fuzzy complementary judgment matrix: the fuzzy complementary judgment matrix A is obtained by comparing elements in the criterion layer in pairs. The importance relationship of one factor to another is measured by expert knowledge, and then the importance scale in the corresponding expression is mapped to a specific value. The elements in matrix a should also satisfy the following properties:
And finally obtaining a fuzzy judgment matrix according to the mutual comparison of the similarity, the extremely poor, the standard deviation and the variation coefficient of the feature map:
Where aij is the jth element of the ith row.
The fuzzy matrix is a 4*4 matrix A, and the elements in the matrix A also meet the following properties:
in order to avoid a complex consistency test process of the matrix, the fuzzy complementary judgment matrix A can be converted into a fuzzy consistency matrix A', and then the weight coefficient of each factor is solved, wherein the conversion formula is as follows:
Where n is the order of the matrix, θ i is the polar angle corresponding to the ith vector, and θ j is the polar angle corresponding to the jth vector.
The power product method is selected to determine the weight of the characteristic index, the elements in A' are multiplied according to the rows and then the root number is opened n times, and the vector G is obtained:
The weight vector W can be obtained after the vector G is normalized, so that the weights of four indexes are calculated, wherein the weight value W i corresponding to the ith component is as follows:
the method comprises the steps of calculating the score of each dynamic characteristic data and each static characteristic data, constructing a fuzzy complementary judgment matrix according to the dynamic characteristic data and the static characteristic data, calculating the fuzzy complementary judgment matrix to obtain weights corresponding to the dynamic characteristic data and the static characteristic data, mapping the importance degree to a specific numerical value to measure the importance degree of each characteristic data, and converting the fuzzy complementary judgment matrix into a fuzzy consistency matrix to avoid a consistency verification process.
The fuzzy analytic hierarchy process combines the advantages of qualitative and quantitative analysis, and can decouple the interrelation and restriction relation among the factors. And determining the weight coefficient of each characteristic index by constructing a fuzzy complementary judgment matrix and converting the fuzzy complementary judgment matrix into a fuzzy consistency matrix. Finally, obtaining the total score of the consistency of the batteries through weighted summation, and comprehensively reflecting the consistency state of the batteries.
In some alternative embodiments, step S3042 includes:
step S30421, determining the result of multiplying the scores of the dynamic characteristic data and the static characteristic data and the corresponding weights as the consistency score of the target battery module.
Step S30422, determining the consistency evaluation result of each battery in the target battery module according to the corresponding relation between the preset consistency score interval and the battery health state and the consistency score.
In the embodiment of the invention, weighted summation is carried out according to the scores of the dynamic characteristic data and the static characteristic data, and the weighted summation result is determined as the consistency score total score y of the target battery module.
y＝v1×w1+v2×w2+v3×w3+v4×w4 (14)
Where y is a consistency score total score, v i is the ith characteristic data, and w i represents a weight corresponding to the ith characteristic data, i=1, 2,3,4.
The consistency score may be used to measure the degree of consistency of the battery, with higher consistency scores indicating better consistency of the battery.
And determining a consistency evaluation result and the health state of the battery according to the consistency score interval division standard. For example, if the consistency score is greater than 90, then the state of health of the battery is healthy; if the consistency score is between 70 and 90, the state of health of the battery is sub-healthy; if the consistency score is between 40 and 70, the state of health of the battery is severe; if the uniformity score is less than 40, the state of health of the battery is poor.
According to the scheme, by calculating the scores and weights of the dynamic characteristic data and the static characteristic data, the phenomenon that important features are ignored in an evaluation result due to simple weighted sum is avoided, noise interference cannot be filtered, and accuracy of the evaluation result is reduced. The accuracy of the consistency judgment result of the battery module is improved by capturing the change characteristics and the consistency change process of the single battery.
According to the battery consistency assessment method provided by the embodiment, the scores and the weights of the characteristic data are multiplied to obtain the consistency scores of the battery modules, so that the consistency state of the battery modules is comprehensively reflected, the consistency assessment result is reflected according to the consistency scores, and the accuracy of the battery consistency assessment result is improved.
As shown in fig. 4, the battery similarity corresponding to the time series collected by the sensor is calculated by using the ternary phantom field algorithm, and the range, standard deviation, and variation coefficient of the time series collected by the sensor are calculated by using the statistical characteristics. And calculating scores v 1、v2、v3、v4 corresponding to the similarity, the polar difference, the standard deviation and the variation coefficient respectively by using a parameter threshold and a linear interpolation method, calculating weights W 1、W2、W3、W4 corresponding to the similarity, the polar difference, the standard deviation and the variation coefficient respectively by using a fuzzy analytic hierarchy process, and calculating a battery consistency score based on the scores and the weights of the data.
The embodiment also provides a battery consistency evaluation device, which is used for realizing the above embodiment and the preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a battery consistency evaluating apparatus, as shown in fig. 5, including:
The acquiring module 501 is configured to acquire time series data of each battery in the target battery module, and convert the time series data into a feature map.
The first determining module 502 is configured to determine dynamic characteristic data corresponding to the target battery module based on the feature map.
The second determining module 503 is configured to obtain voltage data and temperature data of each battery at the same time, and determine static characteristic data corresponding to the target battery module based on the voltage data and the temperature data.
And the calculating module 504 is configured to calculate and determine a consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module.
In some alternative embodiments, the computing module 504 includes:
And the first calculation unit is used for calculating the scores and weights corresponding to the dynamic characteristic data and the static characteristic data respectively.
And the first determining unit is used for determining the consistency evaluation result of each battery in the target battery module based on the scores and weights corresponding to the dynamic characteristic data and the static characteristic data.
In some alternative embodiments, the acquisition module 501 includes:
And the phase space reconstruction unit is used for setting delay time and embedding dimension and reconstructing the time sequence data into a phase space according to the delay time and the embedding dimension.
And the second determining unit is used for carrying out polar coordinate conversion on the vector in the phase space and determining a feature map according to the vector subjected to the polar coordinate conversion.
In some alternative embodiments, the first determining module 502 includes:
and the second calculation unit is used for carrying out region segmentation on the polar coordinates and calculating the correlation coefficient of the feature map in each region.
And a third determining unit for determining the similarity between the respective batteries in the target battery module based on the correlation coefficient, and determining an average value of the similarities of all the batteries as dynamic characteristic data of the target battery module.
In some alternative embodiments, the second determining module 503 includes:
and a fourth determining unit, configured to determine, based on the voltage data and the temperature data, a range, a standard deviation, and a variation coefficient corresponding to each battery in the target battery module.
And a fifth determining unit, configured to determine the range, the standard deviation, and the variation coefficient as static characteristic data corresponding to the target battery module.
In some alternative embodiments, the first computing unit includes:
And the first determination subunit is used for determining the current change interval in which each dynamic characteristic data and each static characteristic data are located.
And the second determining subunit is used for determining the scores corresponding to the dynamic characteristic data and the static characteristic data according to the corresponding relation between the change interval and the index score and the current change interval.
And the matrix construction subunit is used for constructing a fuzzy complementary judgment matrix based on the dynamic characteristic data and the static characteristic data.
And the third determination subunit is used for converting the fuzzy complementary judgment matrix into a fuzzy consistency matrix, carrying out line-by-line product calculation, root opening calculation and normalization processing on the fuzzy consistency matrix to obtain weight vectors of all the components, and determining the weights of all the dynamic characteristic data and the static characteristic data based on the weight vectors of all the components.
In some alternative embodiments, the first determining unit includes:
And a fourth determination subunit, configured to determine a result of multiplying the scores of the dynamic characteristic data and the static characteristic data and the corresponding weights as a consistency score of the target battery module.
And the fifth determining subunit is used for determining the consistency evaluation result of each battery in the target battery module according to the corresponding relation between the preset consistency score interval and the battery health state and the consistency score.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The battery consistency assessment apparatus in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application SPECIFIC INTEGRATED Circuit) Circuit, a processor and a memory that execute one or more software or firmware programs, and/or other devices that can provide the above-described functions.
The embodiment of the invention also provides computer equipment, which is provided with the battery consistency evaluation device shown in the figure 5.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 6, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 6.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device further comprises input means 30 and output means 40. The processor 10, memory 20, input device 30, and output device 40 may be connected by a bus or other means, for example in fig. 6.
The input means 30 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer device, such as a touch screen or the like. The output means 40 may comprise a display device or the like.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (10)
1. A method for evaluating battery consistency, the method comprising:
Acquiring time sequence data of each battery in a target battery module, and converting the time sequence data into a feature map;
Determining dynamic characteristic data corresponding to the target battery module based on the characteristic diagram;
Acquiring voltage data and temperature data of each battery at the same moment, and determining static characteristic data corresponding to a target battery module based on the voltage data and the temperature data;
And calculating and determining a consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module.
2. The method according to claim 1, wherein calculating and determining the consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module includes:
Respectively calculating the score and the weight corresponding to the dynamic characteristic data and the static characteristic data;
And determining a consistency evaluation result of each battery in the target battery module based on the scores and weights corresponding to the dynamic characteristic data and the static characteristic data.
3. The method of claim 1, wherein said converting said time series data into a signature comprises:
setting delay time and embedding dimension, and reconstructing the time sequence data into a phase space according to the delay time and the embedding dimension;
and carrying out polar coordinate conversion on the vector in the phase space, and determining a feature map according to the vector subjected to polar coordinate conversion.
4. The method according to claim 3, wherein determining dynamic characteristic data corresponding to the target battery module based on the feature map includes:
Carrying out region segmentation on the polar coordinates, and calculating the correlation coefficient of the feature map in each region;
and determining the similarity between each battery in the target battery module based on the correlation coefficient, and determining the average value of the similarity of all batteries as dynamic characteristic data of the target battery module.
5. The method according to claim 1, wherein the determining the static characteristic data corresponding to the target battery module based on the voltage data and the temperature data includes:
Determining the corresponding polar differences, standard deviations and variation coefficients of all batteries in the target battery module based on the voltage data and the temperature data;
And determining the polar difference, the standard deviation and the variation coefficient as static characteristic data corresponding to the target battery module.
6. The method of claim 2, wherein calculating the score and weight corresponding to the dynamic characteristic data and the static characteristic data, respectively, comprises:
Determining the current change interval of each dynamic characteristic data and static characteristic data;
Determining the corresponding scores of the dynamic characteristic data and the static characteristic data according to the corresponding relation between the change interval and the index score and the current change interval;
constructing a fuzzy complementary judgment matrix based on the dynamic characteristic data and the static characteristic data;
And converting the fuzzy complementary judgment matrix into a fuzzy consistency matrix, carrying out row-wise product calculation, root opening calculation and normalization processing on the fuzzy consistency matrix to obtain weight vectors of all components, and determining weights of all dynamic characteristic data and static characteristic data based on the weight vectors of all the components.
7. The method according to claim 2, wherein determining the consistency evaluation result of each battery in the target battery module based on the scores and weights corresponding to the dynamic characteristic data and the static characteristic data comprises:
Determining the multiplied result of the scores of the dynamic characteristic data and the static characteristic data and the corresponding weights as the consistency score of the target battery module;
And determining the consistency evaluation result of each battery in the target battery module according to the corresponding relation between the preset consistency score interval and the battery health state and the consistency score.
8. A battery consistency assessment apparatus, the apparatus comprising:
The acquisition module is used for acquiring time sequence data of each battery in the target battery module and converting the time sequence data into a characteristic diagram;
the first determining module is used for determining dynamic characteristic data corresponding to the target battery module based on the characteristic diagram;
The second determining module is used for acquiring voltage data and temperature data of each battery at the same moment and determining static characteristic data corresponding to the target battery module based on the voltage data and the temperature data;
And the calculation module is used for calculating and determining the consistency evaluation result of each battery in the target battery module based on the dynamic characteristic data and the static characteristic data corresponding to the target battery module.
9. A computer device, comprising:
A memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions that, upon execution, perform the battery consistency assessment method of any of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery consistency assessment method according to any one of claims 1 to 7.
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| CN118465588A (en) * | 2024-07-10 | 2024-08-09 | 云储新能源科技有限公司 | Internal resistance consistency evaluation method and system for dynamic reconfigurable battery module |
| CN118707382A (en) * | 2024-08-29 | 2024-09-27 | 北京玖行智研交通科技有限公司 | Comprehensive consistency assessment method and device for battery health state |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118465588A (en) * | 2024-07-10 | 2024-08-09 | 云储新能源科技有限公司 | Internal resistance consistency evaluation method and system for dynamic reconfigurable battery module |
| CN118707382A (en) * | 2024-08-29 | 2024-09-27 | 北京玖行智研交通科技有限公司 | Comprehensive consistency assessment method and device for battery health state |
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