CN117452219A - Energy storage battery dispersion evaluation method, system, equipment and medium - Google Patents
Energy storage battery dispersion evaluation method, system, equipment and medium Download PDFInfo
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- 238000004146 energy storage Methods 0.000 title claims abstract description 144
- 239000006185 dispersion Substances 0.000 title claims abstract description 110
- 238000011156 evaluation Methods 0.000 title claims abstract description 81
- 238000000034 method Methods 0.000 claims description 73
- 230000008569 process Effects 0.000 claims description 52
- 238000004590 computer program Methods 0.000 claims description 20
- 238000012360 testing method Methods 0.000 claims description 20
- 238000007599 discharging Methods 0.000 claims description 15
- 238000003860 storage Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 3
- 210000000352 storage cell Anatomy 0.000 claims 4
- 230000002035 prolonged effect Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 7
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000012983 electrochemical energy storage Methods 0.000 description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 229910001416 lithium ion Inorganic materials 0.000 description 2
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000010220 Pearson correlation analysis Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000001453 impedance spectrum Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
- G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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Abstract
The invention belongs to the technical field of energy storage, and discloses an energy storage battery dispersion evaluation method, an energy storage battery dispersion evaluation system, an energy storage battery dispersion evaluation device and an energy storage battery dispersion evaluation medium, wherein the energy storage battery dispersion evaluation method comprises the following steps: and acquiring the discharge capacity of each energy storage battery and the parameters of each energy storage battery, calculating the Pearson correlation coefficient of the discharge capacity of each energy storage battery and the parameters of each energy storage battery, taking the parameters of the energy storage battery with the Pearson correlation coefficient larger than or equal to a set value as the parameters for evaluating the dispersion of the energy storage battery, and evaluating the dispersion of the energy storage battery. The invention has multiple covered evaluation parameter types, can evaluate the dispersion degree of the energy storage battery from various angles, and improves the reliability of the dispersion degree evaluation; the parameters for evaluating the dispersion degree of the energy storage battery are screened by adopting the Pearson correlation coefficient, so that the evaluation parameters with low correlation degree are reduced, the confidence coefficient for evaluating the dispersion degree is improved, the service life of the energy storage battery can be prolonged to the greatest extent, and the safety risk is reduced.
Description
Technical Field
The invention belongs to the technical field of energy storage, and particularly relates to an energy storage battery dispersion evaluation method, an energy storage battery dispersion evaluation system, an energy storage battery dispersion evaluation device and an energy storage battery dispersion evaluation medium.
Background
The energy storage technology can improve the continuity and stability of power supply and improve the quality of electric energy, and the electrochemical energy storage technology represented by the lithium ion battery energy storage has good technical advantages in the energy storage field due to the characteristics of high efficiency, flexible configuration and the like. According to incomplete statistics of a CNESA (energy storage professional committee, china Energy Storage Alliance) global energy storage project library, in 2022, the new energy storage newly-increased scale is new and has high history, the power scale is increased by 200% in a same way, and the energy gauge module is increased by 280% in a same way; in the novel energy storage, a lithium ion battery occupies absolute predominance, and the specific gravity reaches 97%.
The electrochemical energy storage system has the advantages that the number of the batteries in the electrochemical energy storage system is large, the scale is large, the use condition is complex, the safety and the service life characteristics of the energy storage batteries are very high, the health state and the residual service life of the battery pack and even the battery cluster are limited by the battery with the worst performance due to the existence of a wooden barrel effect, meanwhile, due to the discreteness of the batteries, part of the batteries can be in a local heated state, local overheating is easy to cause, and thermal runaway is caused under severe conditions, so that the evaluation of the discreteness of the energy storage batteries in the whole life cycle is very important.
At present, parameters such as battery voltage, internal resistance and the like are mainly adopted for evaluating the dispersion degree of the energy storage battery, so that the dispersion degree of the battery is difficult to comprehensively reflect, in addition, the adopted methods mostly adopt extremum, variance and the like of single parameters, the duration of action of the methods is short, and the dispersion can occur again quickly.
Disclosure of Invention
In order to solve the problem that the dispersion degree of the battery is difficult to comprehensively reflect in the prior art, the invention aims to provide an energy storage battery dispersion degree evaluation method, an energy storage battery dispersion degree evaluation system, energy storage battery dispersion degree evaluation equipment and medium.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
an energy storage battery dispersion evaluation method comprises the following steps:
acquiring the discharge capacity of each energy storage battery and each energy storage battery parameter, wherein the energy storage battery parameters comprise alternating current impedance data, voltage parameters in a charging process, voltage parameters in a discharging process, temperature parameters in the charging process and temperature parameters in the discharging process;
calculating the Pearson correlation coefficient of each energy storage battery parameter and the discharge capacity of each energy storage battery;
taking the energy storage battery parameter with the Pearson correlation coefficient larger than or equal to the set value as the parameter for evaluating the dispersion degree of the energy storage battery; and evaluating the dispersion degree of the energy storage battery according to the parameter of the dispersion degree evaluation of the energy storage battery.
Further, the ac impedance data includes real, imaginary, mode and phase angles of impedance at different frequency points.
Further, the ac impedance data is obtained by:
and (3) carrying out charge and discharge test on the battery module by using set constant power, wherein the charge cut-off condition is that any battery voltage in the battery module reaches the set charge voltage, and the discharge cut-off condition is that any battery voltage in the battery module reaches the set discharge voltage, and testing the alternating current impedance of all the single batteries in the battery module when each cycle is carried out, so as to obtain the real part, the imaginary part, the module value and the phase angle of the impedance at a plurality of frequency points in the alternating current impedance test.
Further, selecting a voltage difference of each battery from the beginning of charging to each set time period in each cycle, and recording a period of less than or exceeding the set time period at the end of charging as a set time period to obtain a voltage parameter of the charging process;
the voltage parameter of the discharge process is obtained by the following process: selecting the voltage difference of each battery in each cycle from the beginning of discharge to each set time period, and recording the time period which is less than or exceeds the set time period at the end of discharge as the set time period to obtain the voltage parameter of the discharge process;
the temperature parameter of the charging process is obtained by the following process: selecting the temperature difference of each battery in each cycle from the beginning of charging to each set time period, and recording the time period which is less than or exceeds the set time period at the end of charging as the set time period to obtain the temperature parameter of the charging process;
the temperature parameter of the discharge process is obtained by the following process: and selecting the temperature difference of each battery from the beginning of discharge to each set time period, and recording the time period which is less than or exceeds the set time period at the end of discharge as the set time period to obtain the temperature parameter of the discharge process.
Further, the pearson correlation coefficient is calculated by the following formula:
wherein r is xy For the pearson correlation coefficient, n is the number of data samples, x i For the evaluation parameters to be screened out,for the average value of the evaluation parameters to be screened, y i For the discharge capacity per cycle, +.>I is the average value of discharge capacity, and is the number of data samples.
Further, the set value is 0.8.
Further, the method for evaluating the dispersion of the energy storage battery according to the parameter of the dispersion evaluation of the energy storage battery comprises the following steps: and calculating a dispersion coefficient according to the parameters of the dispersion evaluation of the energy storage battery, wherein if the dispersion coefficient is 0, the energy storage battery is not dispersed, and if epsilon is 1, the energy storage battery is completely dispersed.
Further, the dispersion coefficient ε is calculated by:
wherein X is i And X is an energy storage battery parameter with the Pearson correlation coefficient larger than or equal to a set value for the dispersion evaluation parameter.
An energy storage battery dispersion evaluation system, comprising:
the energy storage battery parameter acquisition module is used for acquiring the discharge capacity of each energy storage battery and each energy storage battery parameter, wherein the energy storage battery parameters comprise alternating current impedance data, the voltage parameter in the charging process, the voltage parameter in the discharging process, the temperature parameter in the charging process and the temperature parameter in the discharging process;
the pearson correlation coefficient calculation module is used for calculating the pearson correlation coefficient of each energy storage battery parameter and the discharge capacity of each energy storage battery;
the energy storage battery dispersion evaluation module takes the energy storage battery parameter with the Pearson correlation coefficient larger than or equal to the set value as the parameter for evaluating the dispersion of the energy storage battery; and evaluating the dispersion degree of the energy storage battery according to the parameter of the dispersion degree evaluation of the energy storage battery.
Further, the ac impedance data includes real, imaginary, mode and phase angles of impedance at different frequency points.
Further, the ac impedance data is obtained by:
and (3) carrying out charge and discharge test on the battery module by using set constant power, wherein the charge cut-off condition is that any battery voltage in the battery module reaches the set charge voltage, and the discharge cut-off condition is that any battery voltage in the battery module reaches the set discharge voltage, and testing the alternating current impedance of all the single batteries in the battery module when each cycle is carried out, so as to obtain the real part, the imaginary part, the module value and the phase angle of the impedance at a plurality of frequency points in the alternating current impedance test.
Further, the pearson correlation coefficient is calculated by the following formula:
wherein r is xy For the pearson correlation coefficient, n is the number of data samples, x i For the evaluation parameters to be screened out,for the average value of the evaluation parameters to be screened, y i For the discharge capacity per cycle, +.>I is the average value of discharge capacity, and is the number of data samples.
Further, the dispersion coefficient ε is calculated by:
wherein X is i And X is an energy storage battery parameter with the Pearson correlation coefficient larger than or equal to a set value for the dispersion evaluation parameter.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method of energy storage battery dispersion evaluation as described above when executing the computer program.
A computer readable storage medium storing a computer program which when executed by a processor implements the energy storage battery dispersion evaluation method as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the evaluation method for the dispersion degree of the energy storage battery, the alternating current impedance data, the voltage parameter of the charging process, the voltage parameter of the discharging process, the temperature parameter of the charging process, the temperature parameter of the discharging process and the discharge capacity of the energy storage battery are used for calculating the Pearson correlation coefficient, and the energy storage battery parameter with the Pearson correlation coefficient being more than or equal to a set value is used as the parameter for evaluating the dispersion degree of the energy storage battery; according to the invention, the Pearson correlation coefficient is adopted to screen the parameters of the dispersion evaluation of the energy storage battery, so that the evaluation parameters with low correlation degree are reduced, and the confidence coefficient of the dispersion evaluation is improved; the invention improves the evaluation precision, is beneficial to accurately regulating and controlling the output strategy, prolongs the service life of the energy storage battery to the greatest extent, reduces the safety risk, and solves the problem of comprehensively reflecting the discrete degree of the battery in the prior art.
Drawings
FIG. 1 is a flow chart of a method of evaluating the dispersion of an energy storage battery according to the present invention;
fig. 2 is a schematic structural diagram of the energy storage battery dispersion evaluation system of the present invention.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. The drawings illustrate preferred embodiments of the invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Referring to fig. 1, the method for evaluating the dispersion of the energy storage battery according to the invention further screens the dispersion evaluation parameter by extracting the parameter of the energy storage battery and utilizing the pearson correlation coefficient, and finally evaluates the dispersion of the energy storage battery by adopting a mean value and mean square error combined mode, and specifically comprises the following steps:
1) Data source
And (3) carrying out charge and discharge test on the battery module with constant power of 0.5P, recording the surface temperature of each battery of the battery module during charge and discharge, wherein the charge cut-off condition is that the voltage of any battery in the battery module reaches 3.65V, the discharge cut-off condition is that the voltage of any battery in the battery module reaches 2.5V, each cycle is carried out for 1 time, the alternating current impedance of all the single batteries in the 1-time module is tested, the range of the alternating current impedance is 1000 Hz-0.1 Hz, and the number of frequency points in the alternating current impedance test is 25.
In general, the number of the recorded alternating current impedance spectrum frequencies is 6-10 under each order of magnitude, 6 frequencies are recorded under each order of magnitude, 25 points are taken in the frequency range of 0.1-1000 Hz, and the more the recorded frequencies are, the longer the test time is.
And selecting a voltage difference of each battery every 30min from the beginning of charging every cycle, and recording the voltage difference at the end of charging for 30min or more than 30min as 30min to obtain the voltage parameters of four charging processes.
The charging curve of the lithium iron phosphate battery presents a very long platform, and the voltage change is not obvious in a short time, so that the time interval is too short, and the condition that the pressure difference is 0 possibly occurs, so that the voltage parameters of four charging processes are taken.
And selecting a voltage difference of each battery every 30min from the beginning of discharge every cycle, and recording the voltage difference at the end of discharge for 30min or more than 30min as 30min to obtain the voltage parameters of four discharging processes.
And selecting a temperature difference of each battery every 30min from the beginning of charging every cycle, and recording the temperature difference at the end of charging for 30min or more than 30min as 30min to obtain temperature parameters of four charging processes.
And selecting the temperature difference of each battery every 30min from the beginning of discharge every cycle, and recording the temperature difference at the end of discharge for 30min or 30min to be 30min, so as to obtain the temperature parameters of four discharging processes.
The real part, the imaginary part, the modulus value and the phase angle of the impedance at 25 frequency points in the alternating current impedance test are 100 parameters.
In summary, 116 parameters are initially selected as parameters for evaluating the dispersion of the energy storage battery.
2) Screening evaluation parameters
The pearson correlation coefficient of each parameter and the capacity of each cycle is calculated according to the following formula, wherein the pearson correlation coefficient is 0.8-1.0, is extremely strong correlation, the pearson correlation coefficient is 0.6-0.8, is strong correlation, the pearson correlation coefficient is 0.4-0.6 and is moderately correlated, the pearson correlation coefficient is 0.2-0.4 and is weak correlation, and the pearson correlation coefficient is 0.0-0.2 and is extremely weak correlation or no correlation.
Wherein r is xy For the pearson correlation coefficient, n is the number of data samples, x i For the evaluation parameters to be screened out,for the average value of the evaluation parameters to be screened, y i For the discharge capacity per cycle, +.>Is the average value of discharge capacity, i is the data sampleThis reference number.
And selecting a parameter with the Pearson correlation coefficient of more than 0.8 as a parameter for evaluating the dispersion of the energy storage battery, wherein the parameter is marked as X= [ X1, X2, …, xm ] and m is the number of the parameters for evaluating the dispersion.
3) Evaluation of dispersion
Since the dispersion evaluation parameters are parameters of different dimensions, data normalization processing is performed before the dispersion evaluation.
In order to quantitatively evaluate the dispersion of the energy storage battery, the dispersion evaluation is performed by adopting a mode of combining a mean square error and a mean value, and a dispersion coefficient is defined as epsilon.
Wherein X is i Parameters were evaluated for dispersion.
And the numerator is the mean square error of the parameters, the denominator is the average value, the judgment is carried out according to the dispersion coefficient calculated by the formula, if epsilon is 0, the energy storage battery is not dispersed, and if epsilon is 1, the energy storage battery is completely dispersed.
Referring to fig. 2, another embodiment of the present invention provides an energy storage battery dispersion evaluation system, including:
the energy storage battery parameter acquisition module is used for acquiring the discharge capacity of each energy storage battery and each energy storage battery parameter, wherein the energy storage battery parameters comprise alternating current impedance data, the voltage parameter of the charging process, the voltage parameter of the discharging process, the temperature parameter of the charging process and the temperature parameter of the discharging process; the alternating current impedance data comprises an impedance real part, an impedance imaginary part, an impedance modulus value and an impedance phase angle at different frequency points. Specifically, the ac impedance data is obtained by: and (3) carrying out charge and discharge test on the battery module by using set constant power, wherein the charge cut-off condition is that any battery voltage in the battery module reaches the set charge voltage, and the discharge cut-off condition is that any battery voltage in the battery module reaches the set discharge voltage, and testing the alternating current impedance of all the single batteries in the battery module when each cycle is carried out, so as to obtain the real part, the imaginary part, the module value and the phase angle of the impedance at a plurality of frequency points in the alternating current impedance test.
The Pearson correlation coefficient calculation module is used for calculating the Pearson correlation coefficient of each energy storage battery parameter and the discharge capacity of each energy storage battery; the pearson correlation coefficient is calculated by the following formula:
wherein r is xy For the pearson correlation coefficient, n is the number of data samples, x i For the evaluation parameters to be screened out,for the average value of the evaluation parameters to be screened, y i For the discharge capacity per cycle, +.>I is the average value of discharge capacity, and is the number of data samples.
The dispersion coefficient ε is calculated by:
wherein X is i And X is an energy storage battery parameter with the Pearson correlation coefficient larger than or equal to a set value for the dispersion evaluation parameter.
The energy storage battery dispersion evaluation module is used for taking the energy storage battery parameter with the Pearson correlation coefficient larger than or equal to the set value as the parameter for evaluating the dispersion of the energy storage battery; and evaluating the dispersion degree of the energy storage battery according to the parameter of the dispersion degree evaluation of the energy storage battery.
Another embodiment of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the energy storage battery dispersion evaluation method as described above when executing the computer program.
Another embodiment of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the energy storage battery dispersion evaluation method as described above.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable storage medium may include: any entity or device, medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunications signals, software distribution media, and the like capable of carrying the computer program code. It should be noted that the content of the computer readable medium may be increased or decreased as appropriate, for example, in some jurisdictions, the computer readable medium does not include an electrical carrier signal or a telecommunications signal. For convenience of description, the foregoing disclosure shows only those parts relevant to the embodiments of the present invention, and specific technical details are not disclosed, but reference is made to the method parts of the embodiments of the present invention. The computer readable storage medium is non-transitory and can be stored in a storage device formed by various electronic devices, and can implement the execution procedure described in the method according to the embodiment of the present invention.
Example 1
After 8 60Ah lithium iron phosphate batteries with the same model are regulated to the same charge state, a battery pack is formed by serial connection, 116 parameters are obtained according to the charge-discharge and impedance test process in the data source step, and 100 times of total cycles are carried out to obtain 100 groups of capacity data, so that the effectiveness of the method is verified.
By pearson correlation analysis, wherein the real part Z at 200Hz of the alternating current impedance data 200 Real part Z at 12Hz 12 Real part Z at 0.15Hz 0.15 Charging a second voltage difference V c2 Discharging a third pressure difference V d3 Charging a first temperature difference T c2 Discharge a first temperature difference T d1 The correlation degree is more than 0.8, so that the 7 parameters are taken as the evaluation parameters of the dispersion degree of the energy storage battery and are marked as Z 200 ,Z 12 ,Z 0.15 ,V c2 ,V d3 ,Tc 2 ,T d1 ]。
And taking the data of the 100 th cycle to perform validity verification of the method. Get the corresponding [ Z ] of 8 batteries 200 ,Z 12 ,Z 0.15 ,V c2 ,V d3 ,Tc 2 ,T d1 ]Dispersion evaluation was performed. The capacities of the 8 batteries are 55.1Ah, 58.3Ah, 59.3Ah, 58.8Ah, 51.4Ah, 59.1Ah, 59.8Ah and 58.4Ah respectively; the capacity dispersion coefficient was 0.0497.
The 7 parameters were normalized to calculate a dispersion coefficient epsilon of 0.0481 with an error of 3.22% compared to the capacity dispersion coefficient.
According to the energy storage battery dispersion evaluation method, the covered evaluation parameters are multiple in types, the energy storage battery dispersion can be evaluated from various angles, and the reliability of the energy storage battery dispersion evaluation is improved; according to the invention, the Pearson correlation coefficient is adopted to further screen the evaluation parameters, so that the evaluation parameters with low correlation degree are reduced, and the confidence coefficient of the dispersion evaluation of the energy storage battery is improved.
It will be appreciated by those skilled in the art that 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 solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (15)
1. The method for evaluating the dispersion degree of the energy storage battery is characterized by comprising the following steps of:
acquiring the discharge capacity of each energy storage battery and each energy storage battery parameter, wherein the energy storage battery parameters comprise alternating current impedance data, voltage parameters in a charging process, voltage parameters in a discharging process, temperature parameters in the charging process and temperature parameters in the discharging process;
calculating the Pearson correlation coefficient of each energy storage battery parameter and the discharge capacity of each energy storage battery;
taking the energy storage battery parameter with the Pearson correlation coefficient larger than or equal to the set value as the parameter for evaluating the dispersion degree of the energy storage battery; and evaluating the dispersion degree of the energy storage battery according to the parameter of the dispersion degree evaluation of the energy storage battery.
2. The method of claim 1, wherein the ac impedance data includes real, imaginary, modulus and phase angles of impedance at different frequency points.
3. The energy storage battery dispersion evaluation method according to claim 1, wherein the alternating current impedance data is obtained by:
and (3) carrying out charge and discharge test on the battery module by using set constant power, wherein the charge cut-off condition is that any battery voltage in the battery module reaches the set charge voltage, and the discharge cut-off condition is that any battery voltage in the battery module reaches the set discharge voltage, and testing the alternating current impedance of all the single batteries in the battery module when each cycle is carried out, so as to obtain the real part, the imaginary part, the module value and the phase angle of the impedance at a plurality of frequency points in the alternating current impedance test.
4. The method for evaluating the dispersion of an energy storage battery according to claim 1, wherein the voltage parameter of the charging process is obtained by: selecting the voltage difference of each battery in each cycle from the beginning of charging to each set time period, and recording the time period which is less than or exceeds the set time period at the end of charging as the set time period to obtain the voltage parameter of the charging process;
the voltage parameter of the discharge process is obtained by the following process: selecting the voltage difference of each battery in each cycle from the beginning of discharge to each set time period, and recording the time period which is less than or exceeds the set time period at the end of discharge as the set time period to obtain the voltage parameter of the discharge process;
the temperature parameter of the charging process is obtained by the following process: selecting the temperature difference of each battery in each cycle from the beginning of charging to each set time period, and recording the time period which is less than or exceeds the set time period at the end of charging as the set time period to obtain the temperature parameter of the charging process;
the temperature parameter of the discharge process is obtained by the following process: and selecting the temperature difference of each battery from the beginning of discharge to each set time period, and recording the time period which is less than or exceeds the set time period at the end of discharge as the set time period to obtain the temperature parameter of the discharge process.
5. The method of evaluating the dispersion of an energy storage battery according to claim 1, wherein the pearson correlation coefficient is calculated by the following formula:
wherein r is xy For the pearson correlation coefficient, n is the number of data samples, x i For the evaluation parameters to be screened out,for the average value of the evaluation parameters to be screened, y i For the discharge capacity per cycle, +.>I is the average value of discharge capacity, and is the number of data samples.
6. The method of evaluating the dispersion of an energy storage battery according to claim 1, wherein the set value is 0.8.
7. The method for evaluating the dispersion of the energy storage battery according to claim 1, wherein the step of evaluating the dispersion of the energy storage battery based on the parameter of the dispersion evaluation of the energy storage battery comprises the steps of: and calculating a dispersion coefficient according to the parameters of the dispersion evaluation of the energy storage battery, wherein if the dispersion coefficient is 0, the energy storage battery is not dispersed, and if epsilon is 1, the energy storage battery is completely dispersed.
8. The method of claim 7, wherein the dispersion coefficient epsilon is calculated by the following formula:
wherein X is i And X is an energy storage battery parameter with the Pearson correlation coefficient larger than or equal to a set value for the dispersion evaluation parameter.
9. An energy storage battery dispersion evaluation system, comprising:
the energy storage battery parameter acquisition module is used for acquiring the discharge capacity of each energy storage battery and each energy storage battery parameter, wherein the energy storage battery parameters comprise alternating current impedance data, the voltage parameter in the charging process, the voltage parameter in the discharging process, the temperature parameter in the charging process and the temperature parameter in the discharging process;
the pearson correlation coefficient calculation module is used for calculating the pearson correlation coefficient of each energy storage battery parameter and the discharge capacity of each energy storage battery;
the energy storage battery dispersion evaluation module is used for taking the energy storage battery parameter with the Pearson correlation coefficient larger than or equal to the set value as the parameter for evaluating the dispersion of the energy storage battery; and evaluating the dispersion degree of the energy storage battery according to the parameter of the dispersion degree evaluation of the energy storage battery.
10. The energy storage cell dispersion evaluation system of claim 9, wherein the ac impedance data includes real, imaginary, modulus and phase angle of impedance at different frequency points.
11. The energy storage cell dispersion evaluation system of claim 9, wherein the ac impedance data is obtained by:
and (3) carrying out charge and discharge test on the battery module by using set constant power, wherein the charge cut-off condition is that any battery voltage in the battery module reaches the set charge voltage, and the discharge cut-off condition is that any battery voltage in the battery module reaches the set discharge voltage, and testing the alternating current impedance of all the single batteries in the battery module when each cycle is carried out, so as to obtain the real part, the imaginary part, the module value and the phase angle of the impedance at a plurality of frequency points in the alternating current impedance test.
12. The energy storage cell dispersion evaluation system of claim 9, wherein the pearson correlation coefficient is calculated by:
wherein r is xy For the pearson correlation coefficient, n is the number of data samples, x i For the evaluation parameters to be screened out,for the average value of the evaluation parameters to be screened, y i For the discharge capacity per cycle, +.>I is the average value of discharge capacity, and is the number of data samples.
13. The energy storage cell dispersion evaluation system of claim 12, wherein the dispersion coefficient epsilon is calculated by:
wherein X is i Is discreteAnd the degree evaluation parameter X is an energy storage battery parameter with the Pearson correlation coefficient larger than or equal to a set value.
14. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for evaluating the dispersion of the energy storage battery according to any one of claims 1 to 8 when executing the computer program.
15. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the energy storage battery dispersion evaluation method according to any one of claims 1 to 8.
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