CN113030761A - Method and system for evaluating health state of battery of super-large-scale energy storage power station - Google Patents
Method and system for evaluating health state of battery of super-large-scale energy storage power station Download PDFInfo
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Abstract
A method and a system for evaluating the battery health state of a super-large scale energy storage power station are provided, the method comprises: performing standard deviation analysis on a preset incidence relation model of the evaluation index and the health state of the energy storage battery to obtain a total standard deviation and a sample relative standard deviation; the overall standard deviation and the average number of the external characteristic state data of the battery are subjected to quotient obtaining a variation coefficient; measuring the evaluation index based on the maximum membership principle to obtain the weight of the evaluation index; obtaining the dispersion of each evaluation index according to the sample relative standard deviation, the variation coefficient and the comprehensive proportion set by the weight of each evaluation index; and determining the health state of the battery according to the dispersion. The system comprises: the device comprises a standard difference analysis unit, a variation coefficient unit, a weighting unit, a dispersion unit and an evaluation unit. The method carries out deep evaluation on the health state of the energy storage battery, and carries out fusion analysis on the external characteristic state data of the battery to obtain a comprehensive evaluation result of the health state of the battery.
Description
Technical Field
The invention belongs to the field of power energy storage, and particularly relates to a method and a system for evaluating the health state of a battery of a super-large-scale energy storage power station.
Background
Aiming at the large-scale engineering application of the super-large scale battery energy storage system, due to the fact that inconsistency exists among single batteries in a battery pack of a super-large scale energy storage power station, the state of health (SOH) evaluation of the energy storage battery becomes a key problem of the large-scale energy storage application. The most considerable problem is how much use value the energy storage battery can provide in the later period of operation, so that the health state of the energy storage battery of the super-large scale battery energy storage power station needs to be analyzed and evaluated, the purpose is to prolong the service life of the energy storage battery with the maximum efficiency and reduce the battery safety risk in the super-large scale energy storage power station to the maximum extent. In the existing method for evaluating the health state of the battery, a large amount of experimental data is needed, the requirement on equipment is harsh, or the influence on the service life of the battery is large, so that the method is not suitable for continuous operation. There is a need for a low-cost evaluation method that does not affect the practical use of batteries.
Particularly, with the increasing of the retired amount of the power battery, the goods output of the power battery reaches 94.5GW in 2020 according to statistics, and the market scale of the echelon utilization of the power battery in China reaches 282 billion yuan by 2025 in forecast. Power batteries will also be included in very large scale energy storage power stations with step usage. How to maximize the residual use value of the retired power battery in the energy storage power station is also one of the main problems currently faced. A method for specially analyzing the dispersion of the retired power batteries in the ultra-large-scale energy storage power station is proposed aiming at the characteristics of large performance difference, high dispersion and the like of the retired power batteries in the ultra-large-scale energy storage power station. In the current common method for evaluating the health state of the battery, the measurement and analysis result has strong pertinence, but the cost is high, and the data processing is relatively complex.
Disclosure of Invention
The invention aims to provide a method and a system for evaluating the health state of an energy storage battery of a super-large-scale energy storage power station, aiming at the defect of evaluating the health state of the energy storage battery in the super-large-scale energy storage power station in the prior art.
The invention is realized by adopting the following technical scheme:
a method for evaluating the state of health of a battery of a super-large-scale energy storage power station comprises the following steps:
carrying out standard deviation analysis on a preset incidence relation model of the evaluation indexes and the health state of the energy storage battery to obtain a total standard deviation and a sample relative standard deviation which influence the dispersion of each evaluation index;
the overall standard deviation and the average of the external characteristic state data of the battery are subjected to quotient, and variation coefficients influencing the dispersion of each evaluation index are obtained;
measuring the evaluation index based on the maximum membership principle to obtain the weight of the evaluation index;
obtaining the dispersion of each evaluation index according to the sample relative standard deviation, the variation coefficient and the comprehensive proportion set by the weight of each evaluation index;
and determining the health state of the battery according to the dispersion.
The further improvement of the invention is that the preset incidence relation model of the external characteristic state data of the battery and the health state of the energy storage battery is as follows:
f(x)＝f(q·T·R·Uocv·SOC·t)
wherein T is time, q is battery capacity, T is battery temperature, R is battery internal resistance, Uocv is battery voltage, and SOC is battery state of charge.
In a further development of the invention, the battery external characteristic state data are obtained by analyzing a battery operating state characteristic.
In a further development of the invention, the external battery characteristic state data comprise internal battery resistance, battery voltage, battery state of charge, battery temperature and battery capacity.
The invention has the further improvement that the calculation method of the total standard deviation and the sample relative standard deviation which influence the dispersion of each evaluation index is as follows:
wherein the content of the first and second substances,
is the arithmetic mean of x; s is the sample standard deviation; s (σ) is the total standard deviation; n is the number of samples; srelIs the sample relative standard deviation.
The invention is further improved in that the coefficient of variation C affecting the dispersion of each evaluation indexvThe calculation method of (2) is as follows:
Cv＝S(σ)/μ
wherein S (σ) is the overall standard deviation, and μ is the average of the data of the external characteristic state of the battery.
A further improvement of the invention is that the calculation method of the weight ω' of the evaluation index is as follows:
ω’＝(ω1,ω2,···,ω5)
wherein, ω is1,ω2,···,ω5Respectively, discrete weights for each evaluation index.
The further improvement of the invention is that the dispersion of each evaluation index is obtained according to the comprehensive proportion of the sample relative standard deviation, the variation coefficient and the weight of each evaluation index, and the method specifically comprises the following steps:
σSOH＝f(Srel,Cv,ω’)
wherein sigmaSOHFor the magnitude of the dispersion of each evaluation index, SrelIs the relative standard deviation of the samples, CvTo influence the coefficient of variation of the dispersion of each evaluation index, ω' is the weight of the evaluation index.
A system for assessing the state of health of a battery of a very large scale energy storage power station, comprising:
the standard difference analysis unit is used for carrying out standard difference analysis on a preset incidence relation model of the evaluation indexes and the health state of the energy storage battery to obtain a total standard difference and a sample relative standard difference which influence the dispersion of each evaluation index;
the variation coefficient unit is used for dividing the overall standard deviation and the average number of the external characteristic state data of the battery to obtain variation coefficients influencing the dispersion of each evaluation index;
the weighting unit is used for measuring the evaluation index based on the maximum membership principle to obtain the weight of the evaluation index;
the dispersion unit is used for obtaining the dispersion of each evaluation index according to the sample relative standard deviation, the variation coefficient and the comprehensive proportion set by the weight of each evaluation index;
and the evaluation unit is used for determining the state of health of the battery according to the dispersion degree.
A computer-readable storage medium, which stores a computer program that, when executed by a processor, causes the processor to perform the method of assessing state of health of a battery of a very large scale energy storage power station.
The invention has at least the following beneficial technical effects:
according to the method and the system for evaluating the battery health state of the ultra-large-scale energy storage power station, due to the fact that various health grade indexes of the battery are single, the health state of the energy storage battery cannot be comprehensively measured. The invention can provide support for unified scheduling and control strategies of large-scale battery energy storage power stations, and achieves the purpose of improving the effectiveness and rationality of resource utilization.
Furthermore, when the health degree grade of the battery is determined, historical external characteristic state data of the battery are collected, and certain support is provided in the aspect of data.
Furthermore, the method comprises the steps of representing various external characteristic state data of the energy storage battery, including internal resistance of the battery, voltage of the battery, charge state of the battery, temperature of the battery and capacity of the battery, analyzing the incidence relation among the external characteristic state data of the battery, establishing the health degree grade of the battery, mastering the abnormal data amplitude and the occurrence period of the energy storage battery, and determining the dispersion of state parameters of the battery, thereby effectively evaluating the health state of the battery, eliminating the battery with the health state not reaching the standard, fully exerting the use value of the battery in a gradient manner, and providing support for unified scheduling and control strategies of large-scale battery energy storage power stations.
Further, the invention utilizes the coefficient of variation to eliminate the influence of different measurement scales and dimensions.
Drawings
Fig. 1 is a flow chart of a method for evaluating the state of health of a battery of a very large-scale energy storage power station according to the present invention.
Fig. 2 is a block diagram of a system for estimating the state of health of a battery of a very large-scale energy storage power station according to the present invention.
Detailed Description
The invention is further described below with reference to the following figures and examples.
As shown in fig. 1, the method for evaluating the state of health of a battery of a very large scale energy storage power station provided by the invention comprises the following steps:
carrying out standard deviation analysis on a preset incidence relation model of the evaluation indexes and the health state of the energy storage battery to obtain a total standard deviation and a sample relative standard deviation which influence the dispersion of each evaluation index;
the overall standard deviation and the average of the external characteristic state data of the battery are subjected to quotient, and variation coefficients influencing the dispersion of each evaluation index are obtained;
measuring the evaluation index based on the maximum membership principle to obtain the weight of the evaluation index;
obtaining the dispersion of each evaluation index according to the sample relative standard deviation, the variation coefficient and the comprehensive proportion set by the weight of each evaluation index;
and determining the health state of the battery according to the dispersion.
Specifically, by utilizing the data analysis function of a unified scheduling and control system in a large-scale energy storage power station, historical running state data of an integrated energy storage battery is collected, historical external battery characteristic state data of the battery, including internal battery resistance, battery voltage, battery charge state, battery temperature and battery capacity, are obtained, and the incidence relation between the historical external battery characteristic state data and the battery health state is analyzed.
Specifically, a battery voltage and internal resistance tester is used for measuring and obtaining the constant frequency internal resistance R and the open-circuit voltage Uocv of the energy storage battery in different states.
Analyzing data of battery capacity retention rate and capacity recovery rate, performing full-charge experiment for 4 times by applying current with set degree to the battery, and calibrating the capacity to obtain the 4 th discharge capacity, which is marked as Q4After a period of time, the battery was subjected to full charge test 3 times in the same manner to obtain the 1 st discharge capacity Q1And 3 rd discharge capacity Q3And accordingly obtaining the battery capacity retention rate C1＝(Q1/Q4) 100% volume recovery rate C2＝(Q3/Q4)*100％。
The temperature data of the battery is obtained by measuring the temperature change delta T of the battery in the charging and discharging process by using a thermocouple, so that the temperature data of the battery in SOC states at different charging and discharging times is obtained, and the heating characteristic of the battery is further analyzed.
Aiming at the analysis of direct current internal resistance data of the energy storage battery, the battery is adjusted to the optimal SOC state with a proper multiplying power, and after the battery is fully stood, the constant frequency internal resistance R of the battery under 1000Hz is tested1And an open circuit voltage V1Then, the battery is discharged for 25s at a current I with a large multiplying factor, and the voltage at the end of the discharge is recorded as V2Obtaining the direct current internal resistance R of the battery2The calculation method comprises the following steps: r2＝(V1-V2)/I。
Obtaining the data of the battery capacity, selecting to carry out charging or discharging experiments on the battery according to the voltage threshold value of the battery, and calculating the residual capacity q of the battery according to the final charging and discharging time t to obtain the residual capacity q of the battery I t/(100-SOC)0) And dividing the remaining capacity Q of the battery by the nominal capacity Q to obtain a percentage (%) value, namely the health state data of the battery.
Through the characterization analysis of the steps, battery external characteristic state data required by sorting a large number of batteries is obtained, the incidence relation between the health state of the energy storage battery and the battery external characteristic state data is built through analyzing the change rule of various operation parameter data of the energy storage battery in a large-scale energy storage power station, an energy storage battery database is created, the battery data is analyzed based on the energy storage battery database, and an incidence relation model between the health state of the energy storage battery and the battery external characteristic state data is built:
f(x)＝f(q·T·R·Uocv·SOC·t)
wherein T is time, q is battery capacity, T is battery temperature, R is battery internal resistance, Uocv is battery voltage, and SOC is battery state of charge.
In order to obtain the health state of the energy storage battery, firstly, standard deviation analysis is carried out on an incidence relation model f (x) of the health state of the energy storage battery and external characteristic state data of the battery, the standard deviation is full data calculation and can accurately and intuitively reflect the dispersion of the battery data, and the calculation method comprises the following steps:
wherein the content of the first and second substances,
is the arithmetic mean of x; s is the sample standard deviation; s (σ) is the total standard deviation; n is the number of samples; srelIs the sample relative standard deviation.
Secondly, in an incidence relation model f (x) of the state of health of the energy storage battery and the state data of the external characteristic of the battery, when the dispersion of two groups of data is required to be compared, if the difference of the measurement scales of the two groups of data is too large or the data dimensions are different, the standard deviation is directly used for comparison, at the moment, the influence of the measurement scales and the dimensions should be eliminated, and the variation coefficient CvThis can be done by the ratio of the total standard deviation S (σ) to the mean μ of the data of the external characteristic state of the battery, calculated as follows:
Cv＝S(σ)/μ
next, the weight ω' of each evaluation index of the external characteristic state data of the battery is measured by the maximum membership principle, and the dispersion between different evaluation indexes of the battery is comprehensively compared and analyzed, specifically as follows:
and establishing a fuzzy relation matrix R of the consistency index and the consistency performance grade. R is a row and a column, a is the number of consistency evaluation indexes, and b represents the number of the battery health states. Here, b is taken as 4, namely four grades of ABCD, and a is taken as 5, which are sequentially evaluation indexes of the battery capacity, the battery temperature, the battery internal resistance, the battery voltage and the battery state of charge.
Wherein, the R1 st row represents the probability that the battery capacity dispersion of all sampling points is in each battery health state in turn, the probability that the 2 nd behavior battery temperature dispersion is in each battery health state in turn, the probability that the 3 rd behavior battery internal resistance dispersion is in each battery health state in turn, the probability that the 4 th behavior battery voltage dispersion is in each battery health state in turn, and the probability that the 5 th behavior battery charge state dispersion is in each battery health state in turn.
The consistency of the external characteristic state of the battery can be measured by calculating a weight vector of the dispersion of each consistency index, the weight of each index is evaluated by adopting an entropy weight method, and the weight of each index is calculated as follows:
ω’＝(ω1,ω2,···,ω5)
wherein, ω is1Weight, ω, representing the dispersion of battery capacity2Weight, ω, representing the temperature dispersion of the battery3Weight, ω, representing the dispersion of the internal resistance of the battery4Weight, ω, representing the cell voltage dispersion5Weights representing the dispersion of battery states of charge. Screening the weight of the dispersion of each evaluation index based on the maximum membership principle, wherein the weight is as follows:
there are n fuzzy subsets omega on the set domain P1，ω2，···，ωnIf to any x0E is e.g. P, has i0E {1,2 ·, n }, such that:
then x is obtained0Relative membership to Wi0。W1，W2，···，WnAnd x is0To identify the object, the dispersion of different evaluation indices is represented.
σSOH＝f(Srel,Cv,ω’)
In the formula, according to Srel,CvAnd obtaining the dispersion sigma of each evaluation index according to the comprehensive proportion of omega' in different evaluation indexesSOHSize according to the external characteristics of the comparative batteryDispersion sigma of state dataSOHSize, battery state of health rating was assigned to A, B, C and D four levels. Therefore, the evaluation indexes of the battery health state comprise the battery capacity, the battery temperature, the battery internal resistance and the dispersion of the battery voltage, and the value ranges of the evaluation indexes in different levels are further established.
A represents the state of health of the battery as a premium grade, which can be used normally, where σSOH≥95；
B represents that the health state of the battery is good, the battery can be normally used, when the battery is in an operation and maintenance time node, the battery needs to be balanced and maintained, wherein the sigma is more than or equal to 85SOH＜95；
C represents that the state of health of the battery is middle grade, and balanced maintenance is carried out according to the situation, wherein sigma is more than or equal to 80SOH＜85；
D represents that the state of health of the battery is in a poor level and the battery is required to be balanced and maintained immediately, wherein sigmaSOH＜80。
In conclusion, the invention comprehensively evaluates, classifies, screens and cleans the collected and represented battery running state data, filters invalid data, finds out abnormal data and finally realizes the evaluation of the health state of the energy storage battery.
As shown in fig. 2, the system for estimating the state of health of a battery of a very large scale energy storage power station provided by the invention comprises:
the standard difference analysis unit is used for carrying out standard difference analysis on a preset incidence relation model of the evaluation indexes and the health state of the energy storage battery to obtain a total standard difference and a sample relative standard difference which influence the dispersion of each evaluation index; the variation coefficient unit is used for dividing the overall standard deviation and the average number of the external characteristic state data of the battery to obtain variation coefficients influencing the dispersion of each evaluation index; the weighting unit is used for measuring the evaluation index based on the maximum membership principle to obtain the weight of the evaluation index; the dispersion unit is used for obtaining the dispersion of each evaluation index according to the sample relative standard deviation, the variation coefficient and the comprehensive proportion set by the weight of each evaluation index; and the evaluation unit is used for determining the state of health of the battery according to the dispersion degree.
The invention provides an evaluation system for the battery health state of a super-large scale energy storage power station, which comprises: a processor and a memory coupled to the processor, the memory storing a computer program which, when executed by the processor, carries out the steps of the method for assessing the state of health of an energy storage battery.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for evaluating the state of health of a battery of a super-large-scale energy storage power station is characterized by comprising the following steps:
carrying out standard deviation analysis on a preset incidence relation model of the evaluation indexes and the health state of the energy storage battery to obtain a total standard deviation and a sample relative standard deviation which influence the dispersion of each evaluation index;
the overall standard deviation and the average of the external characteristic state data of the battery are subjected to quotient, and variation coefficients influencing the dispersion of each evaluation index are obtained;
measuring the evaluation index based on the maximum membership principle to obtain the weight of the evaluation index;
obtaining the dispersion of each evaluation index according to the sample relative standard deviation, the variation coefficient and the comprehensive proportion set by the weight of each evaluation index;
and determining the health state of the battery according to the dispersion.
2. The method for assessing the state of health of a battery of a very large-scale energy storage power station according to claim 1, wherein the preset correlation model between the external characteristic state data of the battery and the state of health of the energy storage battery is as follows:
f(x)＝f(q·T·R·Uocv·SOC·t)
wherein T is time, q is battery capacity, T is battery temperature, R is battery internal resistance, Uocv is battery voltage, and SOC is battery state of charge.
3. The method of claim 1, wherein the off-battery performance status data is obtained by analyzing battery operating condition characteristics.
4. The method of claim 3, wherein the external battery characteristic state of health data includes internal battery resistance, battery voltage, battery state of charge, battery temperature, and battery capacity.
5. The method for evaluating the state of health of a battery of a very large-scale energy storage power station according to claim 1, characterized in that the method for calculating the total standard deviation and the sample relative standard deviation influencing the dispersion of each evaluation index comprises the following steps:
wherein the content of the first and second substances,
is the arithmetic mean of x; s is the sample standard deviation; s (σ) is the total standard deviation; n is the number of samples; srelIs the sample relative standard deviation.
6. The method of claim 1, wherein the method of assessing the state of health of a battery of a very large scale energy storage power plant affects eachCoefficient of variation C of evaluation index dispersionvThe calculation method of (2) is as follows:
Cv＝S(σ)/μ
wherein S (σ) is the overall standard deviation, and μ is the average of the data of the external characteristic state of the battery.
7. The method for estimating the state of health of a battery of a very large scale energy storage power station according to claim 1, wherein the weight ω' of the estimation index is calculated as follows:
ω’＝(ω1,ω2,···,ω5)
wherein, ω is1,ω2,···,ω5Respectively, discrete weights for each evaluation index.
8. The method for evaluating the state of health of a battery of a very large-scale energy storage power station according to claim 1, wherein the magnitude of dispersion of each evaluation index is obtained according to the comprehensive proportion of the sample relative standard deviation, the coefficient of variation and the weight of each evaluation index, and specifically comprises the following steps:
σSOH＝f(Srel,Cv,ω’)
wherein sigmaSOHFor the magnitude of the dispersion of each evaluation index, SrelIs the relative standard deviation of the samples, CvTo influence the coefficient of variation of the dispersion of each evaluation index, ω' is the weight of the evaluation index.
9. A system for assessing the state of health of a battery of a very large scale energy storage power station, comprising:
the standard difference analysis unit is used for carrying out standard difference analysis on a preset incidence relation model of the evaluation indexes and the health state of the energy storage battery to obtain a total standard difference and a sample relative standard difference which influence the dispersion of each evaluation index;
the variation coefficient unit is used for dividing the overall standard deviation and the average number of the external characteristic state data of the battery to obtain variation coefficients influencing the dispersion of each evaluation index;
the weighting unit is used for measuring the evaluation index based on the maximum membership principle to obtain the weight of the evaluation index;
the dispersion unit is used for obtaining the dispersion of each evaluation index according to the sample relative standard deviation, the variation coefficient and the comprehensive proportion set by the weight of each evaluation index;
and the evaluation unit is used for determining the state of health of the battery according to the dispersion degree.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to carry out a method of assessing the state of health of a battery of a very large scale energy storage power plant according to any one of claims 1 to 8.
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