CN117110925B - A method and system for evaluating the health status of a battery pack - Google Patents
A method and system for evaluating the health status of a battery pack Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 50
- 230000003862 health status Effects 0.000 title claims description 10
- 230000036541 health Effects 0.000 claims abstract description 188
- 238000011156 evaluation Methods 0.000 claims abstract description 55
- 238000000605 extraction Methods 0.000 claims abstract description 44
- 238000012216 screening Methods 0.000 claims abstract description 21
- 238000013210 evaluation model Methods 0.000 claims abstract description 9
- 238000007599 discharging Methods 0.000 claims description 23
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- 230000015556 catabolic process Effects 0.000 claims description 11
- 238000006731 degradation reaction Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 9
- 239000000178 monomer Substances 0.000 claims description 9
- 238000010187 selection method Methods 0.000 claims description 9
- 238000004806 packaging method and process Methods 0.000 claims description 8
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- 238000013075 data extraction Methods 0.000 claims description 6
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- 230000010354 integration Effects 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 12
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 6
- 229910001416 lithium ion Inorganic materials 0.000 description 6
- 238000012545 processing Methods 0.000 description 5
- 238000012549 training Methods 0.000 description 4
- 230000032683 aging Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
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- 230000018109 developmental process Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- KJONHKAYOJNZEC-UHFFFAOYSA-N nitrazepam Chemical compound C12=CC([N+](=O)[O-])=CC=C2NC(=O)CN=C1C1=CC=CC=C1 KJONHKAYOJNZEC-UHFFFAOYSA-N 0.000 description 1
- 238000011160 research 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]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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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]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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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]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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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
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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Abstract
The invention discloses a health state evaluation method and system of a battery pack, wherein the method comprises the steps of acquiring charge and discharge operation data in a first SOC interval from acquired historical charge and discharge operation data through a preset SOC interval threshold value, acquiring a health index corresponding to the battery pack to be evaluated from the charge and discharge operation data through a preset health index extraction formula, carrying out feature screening on the health index to obtain a health index feature set corresponding to the battery pack to be evaluated, inputting the health index feature set into a preset battery pack health state evaluation model, carrying out health state evaluation on the battery pack to be evaluated to obtain an evaluation result corresponding to the battery pack to be evaluated, and improving the efficiency and accuracy of the battery pack health state evaluation.
Description
Technical Field
The present invention relates to the field of energy storage battery evaluation technologies, and in particular, to a method and a system for evaluating a health status of a battery pack.
Background
Along with the high-speed development of the lithium ion battery industry in China, the lithium ion battery is widely applied to the fields of electric automobiles, energy storage power stations and the like. The accurate assessment of the battery health status is an important guarantee of safe and stable operation of the system, and the inaccurate assessment of the health status can influence the service performance of the battery and even cause safety problems such as thermal runaway and the like. In practical application, a large number of battery cells are generally connected in series-parallel to form a battery pack so as to meet the requirements of a battery system for high capacity and high power. Therefore, the development of the health state evaluation of the lithium ion battery pack is more in line with the actual requirements.
However, the initial performance of the battery cells is different before grouping, and meanwhile, the internal performance variation degree of each battery cell is different due to the temperature, voltage and mechanical stress difference of each battery cell in the cyclic aging process of the battery pack, so that the inconsistency among the cells is further enlarged. Therefore, the deterioration of the performance of the battery pack is not only caused by the aging of the internal cells thereof, but also by the variation in the inconsistency between the cells.
In addition, compared with the single battery, the working process of the battery pack is more complex, and the number and the variety of the parameters which can be monitored are reduced. At present, the problems of extracting characteristics based on a battery wide SOC interval or a complete charge-discharge curve which is difficult to acquire in an actual scene, modeling through large sample data and the like still exist in the research on lithium ion battery pack health state estimation.
Disclosure of Invention
The invention discloses a method and a system for evaluating the health state of a battery pack, which improve the accuracy and the efficiency of the health state evaluation of the battery pack.
In order to achieve the above object, the present invention discloses a method for evaluating the health status of a battery pack, comprising:
Acquiring historical charge and discharge operation data of a battery pack to be evaluated, and acquiring charge and discharge operation data in a first SOC interval from the historical charge and discharge operation data through a preset SOC interval threshold value, wherein the charge and discharge operation data comprises charge data of the battery pack in the first SOC interval and battery pack operation data of a battery pack charge and discharge system switching moment point in the first SOC interval;
acquiring a health index corresponding to the battery pack to be evaluated through a preset health index extraction formula according to the charge and discharge operation data in the first SOC interval;
Performing feature screening on the health index through a preset feature selection method to obtain a health index feature set corresponding to the battery pack to be evaluated;
And inputting the health index feature set into a preset battery pack health state evaluation model, and evaluating the health state of the battery pack to be evaluated to obtain an evaluation result corresponding to the battery pack to be evaluated.
The invention discloses a health state evaluation method of a battery pack, which comprises the steps of firstly acquiring historical charge and discharge operation data of the battery pack to be evaluated, screening and obtaining the charge and discharge operation data in a partial SOC interval from the historical charge and discharge operation data, on one hand, reducing data processing amount, improving evaluation efficiency, on the other hand, carrying out health state evaluation based on stable charge and discharge operation data, ensuring the accuracy of an evaluation result, carrying out health index extraction on the screened charge and discharge operation data through a preset health index extraction formula after the deleted historical operation data is obtained, so that the evaluation efficiency is further improved according to the extracted health index, then carrying out feature screening on the health index, so as to solve model overfitting caused by health index redundancy, improving the evaluation accuracy, and finally carrying out health state evaluation on an input feature set by utilizing a preset evaluation model, thereby obtaining the health state evaluation result of the battery pack.
As a preferred example, the acquiring the historical charge and discharge operation data of the battery pack to be evaluated, and acquiring the charge and discharge operation data in the first SOC interval from the historical charge and discharge operation data through a preset SOC interval threshold value includes:
Acquiring the discharge cut-off voltage and the charge cut-off voltage of the battery pack to be evaluated, and acquiring historical charge and discharge operation data of the battery pack to be evaluated according to the discharge cut-off voltage and the charge cut-off voltage;
extracting charging data of the battery pack in the first SOC interval, which is positioned in the SOC interval threshold, from historical charging and discharging operation data corresponding to the battery pack to be evaluated according to a preset SOC interval threshold;
And according to the SOC interval threshold value, acquiring the switching time of the current value in the historical charge-discharge operation data, acquiring the historical charge-discharge operation data corresponding to the switching time, and acquiring the battery pack operation data of the battery pack charge-discharge system switching time point in the first SOC interval.
According to the invention, the charge cut-off voltage and the discharge cut-off voltage of the battery pack are taken as the circulating nodes, the historical charge and discharge operation data of the battery pack in the complete operation process are obtained, and part of operation data is extracted from the historical charge and discharge operation data through the preset SOC interval threshold value, so that on one hand, the stable operation data is utilized for evaluation, the evaluation accuracy is improved, on the other hand, the processing capacity of the data is reduced, and the evaluation efficiency is improved.
As a preferred example, the obtaining, according to the charge and discharge operation data in the first SOC interval, the health indicator corresponding to the battery pack to be evaluated according to a preset health indicator extraction formula includes:
according to the charge and discharge operation data in the first SOC interval, a first health index representing the overall degradation trend of the battery pack to be evaluated is obtained through a preset first health index extraction formula;
And obtaining a second health index representing the inconsistency difference among the single batteries through a preset second health index extraction formula according to charge and discharge operation data of each single battery in the battery pack to be evaluated, wherein the charge and discharge operation data correspond to each single battery in the first SOC interval.
According to the invention, the health indexes representing the overall performance decline trend of the battery pack and the inconsistent differences among the monomers in the battery pack are respectively extracted by utilizing the preset different health index extraction formulas, so that the battery pack is evaluated through the overall and monomer differences, the evaluation comprehensiveness is improved, and the accuracy of the evaluation result is further improved.
As a preferred example, obtaining the first health index representing the overall degradation trend of the battery pack to be evaluated through a preset first health index extraction formula includes:
Obtaining the average voltage of the battery pack to be evaluated in the first SOC interval, wherein the calculation formula of the average voltage is as follows:
Wherein U pack is the battery voltage, I is the battery charging current, Is the average voltage of the battery pack in a part of the SOC interval in the t 1-t2 charging process;
Acquiring a capacity standard deviation stdQ pack and a capacity differential standard deviation stddQ pack of the battery pack to be evaluated in the first SOC interval, wherein the acquisition process is as follows:
Acquiring a voltage range [ V pack_min,Vpack_max ] corresponding to the battery pack to be evaluated in the first SOC interval, and segmenting the voltage range at equal intervals according to V interval:
Vpack_segment＝[Vpack_min,Vpack_min+Vinterval,Vpack_min+Vinterval*2,...,Vpack_max]
Calculating the capacity sequence of the battery pack to be evaluated in the voltage range by an ampere-hour integration method:
Qpack_segment(i)＝[Q2-Q1,Q3-Q2,...,Qn-Qn-1],(i=1,2,...,N)
Wherein, 1,2, & N corresponds to a corresponding voltage point within the battery voltage interval range, 1,2, & N corresponds to the number of cycles of the battery, and Q pack_segment(i) is the capacity sequence of the battery at the ith cycle;
And subtracting the capacity sequence of the battery pack to be evaluated under different cycles from the capacity sequence under the first cycle to obtain a capacity difference sequence:
Qpack_segment_difference(i)＝Qpack_segment(i)-Qpack_segment(1),(i=1,2,...,N)
Wherein, Q pack_segment_difference(i) is the capacity difference sequence of the battery pack under the ith cycle;
Respectively solving a standard deviation stdQ pack of the battery capacity sequence Q pack_segment to be evaluated and a standard deviation stddQ pack of the battery capacity difference sequence Q pack_segment_difference to be evaluated under different cycles;
acquiring a battery voltage U pack_changepoint at a charging system switching moment in the charging process of the battery to be evaluated;
Acquiring a battery voltage U pack_rest of the battery to be evaluated after the battery to be evaluated is charged and stands for a certain time;
and when the battery pack to be evaluated starts to discharge, acquiring a battery pack pressure drop U packdrop_5s in a certain period of time of instantaneous discharge.
According to the charge and discharge operation data in the first SOC interval, the invention extracts the health index representing the overall performance decline trend of the battery pack, so that the data for health state assessment is provided from the overall aspect of the battery pack, and the accuracy of health state assessment is improved.
As a preferred example, obtaining the second health index representing the inconsistency difference between the unit cells through the preset second health index extraction formula includes:
extracting the same characteristics as those of the battery pack to be evaluated from each single battery in the battery pack to be evaluated StdQ cell(i)、stddQcell(i) and point feature U cell(i)_changepoint、Ucell(i)_rest、Ucell(i)drop_5s;
calculating the polar difference, standard deviation and information entropy among the characteristics of each single battery to represent the inconsistent differences among the single voltage, the capacity and the internal resistance, wherein the expressions are as follows:
ran=xmax-xmin
Wherein i=1, 2,., n, n are the number of single batteries in the battery pack, x max is the maximum value of the single characteristic quantity in the battery pack, x min is the minimum value of the single characteristic quantity in the battery pack;
wherein n is the number of single batteries connected in series in the battery pack, x i is the characteristic quantity of the ith single battery; the average value of all serial monomer characteristic quantities in the battery pack is obtained;
Wherein n is the number of single batteries connected in series in the battery pack, and p (i) is the characteristic quantity corresponding to the ith single battery.
According to the invention, on the basis of obtaining the first health index, the health index representing the inconsistent difference among the single cells is obtained by obtaining the charge and discharge data representing the corresponding parts of the single cells in the battery pack, so that the health state of the battery pack is estimated comprehensively from the whole battery pack and the single cells, and the accuracy of estimation is improved.
As a preferred example, the feature screening of the health index by a preset feature selection method to obtain a feature set of the health index corresponding to the battery pack to be evaluated includes:
calculating a pearson correlation coefficient between the health index and the battery capacity of the battery to be evaluated, and filtering the health index according to the pearson correlation coefficient to obtain an initial health index feature set;
And performing forward sequence search on the initial health index feature set by using a preset packaging method, and adding new features to the initial health index feature set to obtain the health index feature set.
According to the invention, firstly, the pearson correlation coefficient of each health index and the capacity of the battery pack is calculated, the health indexes are screened according to the pearson correlation coefficient, the data processing amount is reduced, the evaluation efficiency is improved, and meanwhile, the health indexes are searched by using a packaging method, so that the optimal health index feature set is obtained, and the evaluation accuracy is improved.
As a preferred example, the pearson correlation coefficient between the calculated health index and the battery capacity of the battery under evaluation comprises:
calculating the pearson correlation coefficient by using a preset calculation formula, wherein the calculation formula is as follows:
Wherein x i and y i are true values of the health index and the battery capacity, AndThe closer the absolute value of the obtained result is to 1 for the corresponding mean value of the two, the higher the correlation is.
The invention provides a Pelson coefficient calculation formula for calculating the correlation between each health index and the capacity of a battery pack, so that the health index with higher correlation can be extracted for state evaluation, and the accuracy and the collection efficiency of evaluation are improved.
On the other hand, the invention discloses a health state evaluation system of a battery pack, which comprises a data acquisition module, an index extraction module, a characteristic screening module and a state evaluation module;
The data acquisition module is used for acquiring historical charge and discharge operation data of the battery pack to be evaluated, and acquiring charge and discharge operation data in a first SOC section from the historical charge and discharge operation data through a preset SOC section threshold value, wherein the charge and discharge operation data comprise charge data of the battery pack in the first SOC section and battery pack operation data at the moment of switching of charge and discharge modes of the battery pack in the first SOC section;
The index extraction module is used for obtaining a health index corresponding to the battery pack to be evaluated through a preset health index extraction formula according to the charge and discharge operation data in the first SOC interval;
the feature screening module is used for carrying out feature screening on the health indexes through a preset feature selection method to obtain a health index feature set corresponding to the battery pack to be evaluated;
The state evaluation module is used for inputting the health index feature set into a preset battery pack health state evaluation model, and performing health state evaluation on the battery pack to be evaluated to obtain an evaluation result corresponding to the battery pack to be evaluated.
According to the health state evaluation system of the battery pack, historical charge and discharge operation data of the battery pack to be evaluated are firstly obtained, the charge and discharge operation data in a partial SOC interval are obtained through screening from the historical charge and discharge operation data, on one hand, the data processing amount is reduced, the evaluation efficiency is improved, on the other hand, the health state evaluation is carried out based on the stable charge and discharge operation data, the accuracy of an evaluation result can be ensured, after the deleted historical operation data is obtained, health index extraction is carried out on the screened charge and discharge operation data through a preset health index extraction formula, so that the evaluation efficiency is further improved according to the extracted health index, then feature screening is carried out on the health index, the model overfitting caused by health index redundancy is solved, the evaluation accuracy is improved, and finally, the health state evaluation is carried out on an input feature set by utilizing a preset evaluation model, so that the health state evaluation result of the battery pack is obtained.
As a preferable example, the data acquisition module includes a history data unit and a data extraction unit;
The historical data unit is used for acquiring the discharge cut-off voltage and the charge cut-off voltage of the battery pack to be evaluated, and acquiring historical charge and discharge operation data of the battery pack to be evaluated according to the discharge cut-off voltage and the charge cut-off voltage;
The data extraction unit is used for extracting charging data of the battery pack in the first SOC interval, which is positioned in the SOC interval threshold, in the historical charging and discharging operation data corresponding to the battery pack to be evaluated according to a preset SOC interval threshold, acquiring switching time of a current value in the historical charging and discharging operation data according to the SOC interval threshold, acquiring historical charging and discharging operation data corresponding to the switching time, and acquiring battery pack operation data of the battery pack charging and discharging system switching time point in the first SOC interval.
According to the invention, the charge cut-off voltage and the discharge cut-off voltage of the battery pack are taken as the circulating nodes, the historical charge and discharge operation data of the battery pack in the complete operation process are obtained, and part of operation data is extracted from the historical charge and discharge operation data through the preset SOC interval threshold value, so that on one hand, the stable operation data is utilized for evaluation, the evaluation accuracy is improved, on the other hand, the processing capacity of the data is reduced, and the evaluation efficiency is improved.
As a preferable example, the index extraction module includes a first index unit and a second index unit;
The first index unit is used for obtaining a first health index representing the overall degradation trend of the battery pack to be evaluated through a preset first health index extraction formula according to the charge and discharge operation data in the first SOC interval;
The second index unit is used for obtaining a second health index representing the inconsistency difference among the single batteries according to charge and discharge operation data of each single battery in the battery pack to be evaluated in the first SOC interval through a preset second health index extraction formula.
According to the invention, the health indexes representing the overall performance decline trend of the battery pack and the inconsistent differences among the monomers in the battery pack are respectively extracted by utilizing the preset different health index extraction formulas, so that the battery pack is evaluated through the overall and monomer differences, the evaluation comprehensiveness is improved, and the accuracy of the evaluation result is further improved.
Drawings
Fig. 1 is a schematic flow chart of a method for evaluating a health status of a battery pack according to an embodiment of the present invention;
Fig. 2 is a schematic structural diagram of a system for evaluating a health status of a battery pack according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing a trend of capacity degradation of a battery pack according to an embodiment of the present invention;
FIG. 4 is a graph showing a trend of average voltages of a battery pack based on cycle numbers according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a standard deviation trend of a battery capacity sequence based on cycle number according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a standard deviation trend of a battery capacity difference sequence based on cycle number according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a voltage trend of a battery pack at a variable current moment based on cycle number according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a voltage trend of a battery pack after full power standing based on cycle number according to an embodiment of the present invention;
FIG. 9 is a graph showing the voltage drop trend of the battery pack within 5s of instantaneous discharge based on the number of cycles according to the embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
Examples
The embodiment of the invention provides a method for evaluating the health state of a battery pack, referring to fig. 1, mainly comprising steps 101 to 104, wherein the steps are as follows:
step 101, acquiring historical charge and discharge operation data of a battery pack to be evaluated, and acquiring charge and discharge operation data in a first SOC interval from the historical charge and discharge operation data through a preset SOC interval threshold, wherein the charge and discharge operation data comprise charge data of the battery pack in the first SOC interval and battery pack operation data at a charge and discharge system switching moment of the battery pack in the first SOC interval.
In the embodiment, the method mainly comprises the steps of obtaining a discharge cut-off voltage and a charge cut-off voltage of a battery pack to be evaluated, obtaining historical charge and discharge operation data of the battery pack to be evaluated according to the discharge cut-off voltage and the charge cut-off voltage, extracting charging data of the battery pack in a first SOC interval, which is located in the SOC interval threshold, in the historical charge and discharge operation data corresponding to the battery pack to be evaluated according to a preset SOC interval threshold, obtaining switching time of current values in the historical charge and discharge operation data according to the SOC interval threshold, and obtaining historical charge and discharge operation data corresponding to the switching time to obtain battery pack operation data of a battery pack charge and discharge system switching time point in the first SOC interval.
In this embodiment, the battery pack to be evaluated is set to be charged from a discharge cutoff voltage (a battery pack minimum voltage, if 28V) to a charge cutoff voltage (a battery pack maximum voltage, if 34V), and then the battery pack is discharged from the charge cutoff voltage (the battery pack maximum voltage, if 28V) to the discharge cutoff voltage (the battery pack minimum voltage, if 34V) as a complete charge-discharge process, so as to obtain charge-discharge operation data of the battery pack to be evaluated in the complete charge-discharge process, and meanwhile, battery pack charge data in a partial SOC interval is extracted from the complete battery pack charge-discharge data, taking the battery pack voltage as a condition, according to a certain correspondence between the battery pack SOC and the battery pack voltage. (for example, in the whole charging process of the battery pack, the voltage of the battery pack is increased from 28V to 34V, only data such as the voltage, the current and the operation time of the battery pack corresponding to a 30V-32V interval (corresponding to a 30% SOC-70% SOC of the SOC interval) are extracted from the battery pack, namely the charging data of the battery pack in a partial SOC interval), the charging and discharging operation data at the switching moment of the charging and discharging system can be obtained by taking the current as a judging condition, the current in the charging and discharging process of the battery pack to be evaluated is not unchanged, the current values in different stages are different (for example, the charging of the first stage 100A, the charging of the second stage 50A, the charging of the third stage 25A and the discharging of the fourth stage 200A), and the switching moment (for example, the current is switched from 100A to 50A, and the current is switched from 25A to 200A) at the switching moment of the different stages is the charging and discharging system operation data of the battery pack.
Step 102, according to the charge and discharge operation data in the first SOC interval, obtaining a health index corresponding to the battery pack to be evaluated through a preset health index extraction formula.
In the embodiment, the method mainly comprises the steps of obtaining a first health index representing the overall decline trend of the battery pack to be evaluated according to charge and discharge operation data in the first SOC interval through a preset first health index extraction formula, and obtaining a second health index representing the inconsistency difference among the single batteries according to charge and discharge operation data, corresponding to each single battery in the battery pack to be evaluated, in the first SOC interval through a preset second health index extraction formula.
Further, in this embodiment, the obtaining, by a preset first health index extraction formula, a first health index that characterizes the overall degradation trend of the battery pack to be evaluated includes:
Obtaining the average voltage of the battery pack to be evaluated in the first SOC interval, wherein the calculation formula of the average voltage is as follows:
Wherein U pack is the battery voltage, I is the battery charging current, Is the average voltage of the battery pack in a part of the SOC interval in the t 1-t2 charging process;
Acquiring a capacity standard deviation stdQ pack and a capacity differential standard deviation stddQ pack of the battery pack to be evaluated in the first SOC interval, wherein the acquisition process is as follows:
Acquiring a voltage range [ V pack_min,Vpack_max ] corresponding to the battery pack to be evaluated in the first SOC interval, and segmenting the voltage range at equal intervals according to V interval:
Vpack_segment＝[Vpack_min,Vpack_min+Vinterval,Vpack_min+Vinterval*2,...,Vpack_max]
Calculating the capacity sequence of the battery pack to be evaluated in the voltage range by an ampere-hour integration method:
Qpack_segment(i)＝[Q2-Q1,Q3-Q2,...,Qn-Qn-1],(i=1,2,...,N)
Wherein, 1,2, & N corresponds to a corresponding voltage point within the battery voltage interval range, 1,2, & N corresponds to the number of cycles of the battery, and Q pack_segment(i) is the capacity sequence of the battery at the ith cycle;
And subtracting the capacity sequence of the battery pack to be evaluated under different cycles from the capacity sequence under the first cycle to obtain a capacity difference sequence:
Qpack_segment_difference(i)＝Qpack_segment(i)-Qpack_segment(1),(i=1,2,...,N)
Wherein, Q pack_segment_difference(i) is the capacity difference sequence of the battery pack under the ith cycle;
Respectively solving a standard deviation stdQ pack of the battery capacity sequence Q pack_segment to be evaluated and a standard deviation stddQ pack of the battery capacity difference sequence Q pack_segment_difference to be evaluated under different cycles;
acquiring a battery voltage U pack_changepoint at a charging system switching moment in the charging process of the battery to be evaluated;
Acquiring a battery voltage U pack_rest of the battery to be evaluated after the battery to be evaluated is charged and stands for a certain time;
and when the battery pack to be evaluated starts to discharge, acquiring a battery pack pressure drop U packdrop_5s in a certain period of time of instantaneous discharge.
Meanwhile, the obtaining, by a preset second health index extraction formula, a second health index representing the inconsistency difference between the single batteries includes:
extracting the same characteristics as those of the battery pack to be evaluated from each single battery in the battery pack to be evaluated StdQ cell(i)、stddQcell(i) and point feature U cell(i)_changepoint、Ucell(i)_rest、Ucell(i)drop_5s;
calculating the polar difference, standard deviation and information entropy among the characteristics of each single battery to represent the inconsistent differences among the single voltage, the capacity and the internal resistance, wherein the expressions are as follows:
ran=xmax-xmin
Wherein i=1, 2,., n, n are the number of single batteries in the battery pack, x max is the maximum value of the single characteristic quantity in the battery pack, x min is the minimum value of the single characteristic quantity in the battery pack;
wherein n is the number of single batteries connected in series in the battery pack, x i is the characteristic quantity of the ith single battery; the average value of all serial monomer characteristic quantities in the battery pack is obtained;
Wherein n is the number of single batteries connected in series in the battery pack, and p (i) is the characteristic quantity corresponding to the ith single battery.
In this embodiment, the health index is extracted based on the battery pack operation data of 30% -70% soc interval and the charge-discharge system switching time point obtained by the above steps, in the example, as shown in fig. 3, as the battery pack cycle number increases, it can be known from fig. 3, the battery pack capacity gradually decreases, then the health index representing the overall performance degradation trend of the battery pack is extracted based on the external characteristics of the battery pack partial charge-discharge curve and the preset first health index extraction formula, the trend of the health index evolving along with the aging of the battery pack can refer to fig. 4 to 9, and the health index representing the health index is obtained according to the corresponding characteristic values of the battery pack in the battery pack, the preset entropy and the preset standard deviation formula, wherein the health index respectively includes the battery pack voltage average trend diagram based on the cycle number in fig. 4, the battery pack capacity sequence standard deviation trend diagram based on the cycle number in fig. 5, the battery pack capacity differential sequence standard deviation trend diagram based on the cycle number in fig. 6, the battery pack voltage trend diagram based on the cycle number at the variable current time of the cycle number in fig. 7, the battery voltage trend diagram based on the cycle number in the transient discharge 5s in fig. 8 after full-charge-discharge of the battery pack voltage trend diagram based on the cycle number, and the health index extraction formula based on the preset entropy and the preset second health index;
And 103, screening the characteristics of the health indexes by a preset characteristic selection method to obtain a health index characteristic set corresponding to the battery pack to be evaluated.
In the embodiment, the method mainly comprises the steps of calculating a pearson correlation coefficient between the health index and the battery capacity of the battery to be evaluated, filtering the health index according to the pearson correlation coefficient to obtain an initial health index feature set, and carrying out forward sequence search on the initial health index feature set by using a preset packaging method to add new features to the initial health index feature set to obtain the health index feature set.
Further, in this embodiment, the calculating the pearson correlation coefficient between the health indicator and the battery capacity of the battery to be evaluated includes:
calculating the pearson correlation coefficient by using a preset calculation formula, wherein the calculation formula is as follows:
Wherein x i and y i are true values of the health index and the battery capacity, AndThe closer the absolute value of the obtained result is to 1 for the corresponding mean value of the two, the higher the correlation is.
In this embodiment, based on the health indexes obtained in the above table, a filtering method is adopted to calculate pearson correlation coefficients of each health index and the capacity of the battery pack in the above table, the health indexes are screened according to the pearson correlation coefficients, preferably, irrelevant health indexes with pearson correlation coefficients smaller than 0.2 between the capacities of the battery pack are filtered, meanwhile, features with pearson correlation coefficients larger than 0.9 between the capacities of the battery pack are selected from the health indexes representing the overall performance degradation trend of the battery pack and are respectively used as initial feature subsets of the packaging method, a forward sequence search is adopted by the packaging method, new features are continuously added into the initial feature subsets, and an optimal feature subset with good improvement on the model effect is selected, wherein the obtained optimal feature subset is shown in the following table;
preferably, after the optimal feature subset is obtained, a preset root mean square error expression may be used to evaluate the advantages and disadvantages of the optimal feature subset, where the root mean square error expression is:
where y i is the battery capacity truth value and y' i is the battery capacity estimate.
In this embodiment, when the difference between the root mean square errors corresponding to the optimal feature subsets of the previous round of searching according to the subsequent round of searching of the packaging method is less than one third of the minimum root mean square error in the previous round of searching, the searching is stopped, and the optimal feature subset under the current condition is selected through analysis and comparison between the feature subsets which are different from the obtained N initial features and are obtained from the two aspects of the number of features contained in the feature subset and the corresponding minimum root mean square error.
Step 104, inputting the health index feature set into a preset battery pack health state evaluation model, and evaluating the health state of the battery pack to be evaluated to obtain an evaluation result corresponding to the battery pack to be evaluated.
In this embodiment, a machine learning algorithm combined with support vector regression is used to construct the battery health state estimation model, and the battery health state estimation model is used to perform health state estimation on the battery to be estimated according to the obtained optimal feature subset, after the battery health state estimation model is obtained, the optimal feature subset obtained by screening under a preset training set is used as a model input to realize good estimation of the health state of the lithium ion battery in the whole life cycle, the estimation results of the test set under different training sets are shown in fig. 5, and the maximum error and the root mean square error are shown in the following table:
| Training set proportion | Maximum relative error of test set | Root mean square error of test set |
| 20% | 1.60% | 0.77Ah |
| 50% | 1.63% | 0.68Ah |
| 80% | 1.41% | 0.67Ah |
On the other hand, the invention also discloses a system for evaluating the health status of the battery pack, and the specific structural composition of the system can refer to fig. 2, and mainly comprises a data acquisition module 201, an index extraction module 202, a feature screening module 203 and a status evaluation module 204.
The data acquisition module 201 is configured to acquire historical charge and discharge operation data of a battery pack to be evaluated, and acquire charge and discharge operation data in a first SOC interval from the historical charge and discharge operation data through a preset SOC interval threshold, where the charge and discharge operation data includes charge data of the battery pack in the first SOC interval and battery pack operation data at a time point of switching a charge and discharge system of the battery pack in the first SOC interval.
The index extraction module 202 is configured to obtain, according to the charge and discharge operation data in the first SOC interval, a health index corresponding to the battery pack to be evaluated according to a preset health index extraction formula.
The feature screening module 203 is configured to perform feature screening on the health indicator by using a preset feature selection method, so as to obtain a feature set of the health indicator corresponding to the battery pack to be evaluated.
The state evaluation module 204 is configured to input the health index feature set into a preset battery pack health state evaluation model, perform health state evaluation on the battery pack to be evaluated, and obtain an evaluation result corresponding to the battery pack to be evaluated.
In this embodiment, the data acquisition module 201 includes a history data unit and a data extraction unit.
The historical data unit is used for acquiring the discharge cut-off voltage and the charge cut-off voltage of the battery pack to be evaluated, and acquiring historical charge and discharge operation data of the battery pack to be evaluated according to the discharge cut-off voltage and the charge cut-off voltage.
The data extraction unit is used for extracting charging data of the battery pack in the first SOC interval, which is positioned in the SOC interval threshold, in the historical charging and discharging operation data corresponding to the battery pack to be evaluated according to a preset SOC interval threshold, acquiring switching time of a current value in the historical charging and discharging operation data according to the SOC interval threshold, acquiring historical charging and discharging operation data corresponding to the switching time, and acquiring battery pack operation data of the battery pack charging and discharging system switching time point in the first SOC interval.
In this embodiment, the index extraction module 202 includes a first index unit and a second index unit.
The first index unit is used for obtaining a first health index representing the overall degradation trend of the battery pack to be evaluated through a preset first health index extraction formula according to the charge and discharge operation data in the first SOC interval.
The second index unit is used for obtaining a second health index representing the inconsistency difference among the single batteries according to charge and discharge operation data of each single battery in the battery pack to be evaluated in the first SOC interval through a preset second health index extraction formula.
According to the method and the system for evaluating the health state of the battery pack, provided by the embodiment of the invention, the health indexes representing the overall performance decline trend of the battery pack and the inconsistent differences among the monomers in the battery pack are extracted through partial charge and discharge data, and the stable screening of the optimal feature subset is realized through the feature selection method combining the filtering method and the packaging method, so that the model for evaluating the health state of the battery pack is established by combining the support vector regression algorithm, and the accurate evaluation of the health state of the lithium ion battery pack in the whole life cycle is realized. The method can solve the problem of model overfitting caused by less training data and redundancy among health indexes to a certain extent, and has strong adaptability to battery packs with different running conditions and different grouping modes.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (6)
1. A method of evaluating a state of health of a battery pack, comprising:
Acquiring historical charge and discharge operation data of a battery pack to be evaluated, and acquiring charge and discharge operation data in a first SOC interval from the historical charge and discharge operation data through a preset SOC interval threshold value, wherein the charge and discharge operation data comprises charge data of the battery pack in the first SOC interval and battery pack operation data of a battery pack charge and discharge system switching moment point in the first SOC interval;
Obtaining a health index corresponding to the battery pack to be evaluated through a preset health index extraction formula according to the charge and discharge operation data in the first SOC interval, wherein a first health index representing the overall decline trend of the battery pack to be evaluated is obtained through the preset first health index extraction formula according to the charge and discharge operation data in the first SOC interval;
The obtaining a first health index representing the overall degradation trend of the battery pack to be evaluated through a preset first health index extraction formula comprises obtaining an average voltage of the battery pack to be evaluated in the first SOC interval, wherein the average voltage is calculated according to the following formula:
wherein U pack is the battery voltage, I pacK is the battery charging current, Is the average voltage of the battery pack in a part of the SOC interval in the t 1-t2 charging process;
Acquiring a capacity standard deviation stdQ pack and a capacity differential standard deviation stddQ pack of the battery pack to be evaluated in the first SOC interval, wherein the acquisition process is as follows:
Acquiring a voltage range [ V pack_min,Vpack_max ] corresponding to the battery pack to be evaluated in the first SOC interval, and segmenting the voltage range at equal intervals according to V interval:
Vpack_segment＝[Vpack_min,Vpack_min+Vinterval,Vpack_min+Vinterval*2,...,Vpack_max]
Calculating the capacity sequence of the battery pack to be evaluated in the voltage range by an ampere-hour integration method:
Qpack_segment(i)＝[Q2-Q1,Q3-Q2,...,Qn-Qn-1],(i=1,2,...,N)
Wherein, 1,2, & N corresponds to a corresponding voltage point within the battery voltage interval range, 1,2, & N corresponds to the number of cycles of the battery, and Q pack_segment(i) is the capacity sequence of the battery at the ith cycle;
And subtracting the capacity sequence of the battery pack to be evaluated under different cycles from the capacity sequence under the first cycle to obtain a capacity difference sequence:
Qpack_segment_difference(i)＝Qpack_segment(i)-Qpack_segment(1),(i=1,2,...,N)
Wherein, Q pack_segment_difference(i) is the capacity difference sequence of the battery pack under the ith cycle;
Respectively solving a standard deviation stdQ pack of the battery capacity sequence Q pack_segment to be evaluated and a standard deviation stddQ pack of the battery capacity difference sequence Q pack_segment_difference to be evaluated under different cycles;
acquiring a battery voltage U pack_changepoint at a charging system switching moment in the charging process of the battery to be evaluated;
Acquiring a battery voltage U pack_rest of the battery to be evaluated after the battery to be evaluated is charged and stands for a certain time;
acquiring a battery pack pressure drop U packdrop_5s in a certain period of instant discharging when the battery pack to be evaluated starts discharging;
The obtaining a second health index representing the inconsistency difference between the single batteries through a preset second health index extraction formula comprises extracting the same characteristics of each single battery in the battery pack to be evaluated as those of the battery pack to be evaluated StdQ cell(i)、stddQcell(i) and point feature U cell(i)_changepoint、Ucell(i)_rest、Ucell(i)drop_5s;
calculating the polar difference, standard deviation and information entropy among the characteristics of each single battery to represent the inconsistent differences among the single voltage, the capacity and the internal resistance, wherein the expressions are as follows:
ran=xmax-xmin
Wherein i=1, 2,., n, n are the number of single batteries in the battery pack, x max is the maximum value of the single characteristic quantity in the battery pack, x min is the minimum value of the single characteristic quantity in the battery pack;
wherein n is the number of single batteries connected in series in the battery pack, x i is the characteristic quantity of the ith single battery; the average value of all serial monomer characteristic quantities in the battery pack is obtained;
wherein n is the number of single batteries connected in series in the battery pack, p (i) is the characteristic quantity corresponding to the ith single battery;
Performing feature screening on the health index through a preset feature selection method to obtain a health index feature set corresponding to the battery pack to be evaluated;
And inputting the health index feature set into a preset battery pack health state evaluation model, and evaluating the health state of the battery pack to be evaluated to obtain an evaluation result corresponding to the battery pack to be evaluated.
2. The method for evaluating the health status of a battery pack according to claim 1, wherein the acquiring historical charge and discharge operation data of the battery pack to be evaluated and acquiring charge and discharge operation data in a first SOC interval from the historical charge and discharge operation data through a preset SOC interval threshold value comprises:
Acquiring the discharge cut-off voltage and the charge cut-off voltage of the battery pack to be evaluated, and acquiring historical charge and discharge operation data of the battery pack to be evaluated according to the discharge cut-off voltage and the charge cut-off voltage;
extracting charging data of the battery pack in the first SOC interval, which is positioned in the SOC interval threshold, from historical charging and discharging operation data corresponding to the battery pack to be evaluated according to a preset SOC interval threshold;
And according to the SOC interval threshold value, acquiring the switching time of the current value in the historical charge-discharge operation data, acquiring the historical charge-discharge operation data corresponding to the switching time, and acquiring the battery pack operation data of the battery pack charge-discharge system switching time point in the first SOC interval.
3. The method for evaluating the health status of a battery pack according to claim 1, wherein the feature screening of the health index by a preset feature selection method to obtain a feature set of the health index corresponding to the battery pack to be evaluated comprises:
calculating a pearson correlation coefficient between the health index and the battery capacity of the battery to be evaluated, and filtering the health index according to the pearson correlation coefficient to obtain an initial health index feature set;
And performing forward sequence search on the initial health index feature set by using a preset packaging method, and adding new features to the initial health index feature set to obtain the health index feature set.
4. A method of evaluating the state of health of a battery as defined in claim 3, wherein said calculating a pearson correlation between said health indicator and the battery capacity of said battery to be evaluated comprises:
calculating the pearson correlation coefficient by using a preset calculation formula, wherein the calculation formula is as follows:
Wherein x i and y i are true values of the health index and the battery capacity, AndThe closer the absolute value of the obtained result is to 1 for the corresponding mean value of the two, the higher the correlation is.
5. The system for evaluating the health state of the battery pack is characterized by comprising a data acquisition module, an index extraction module, a characteristic screening module and a state evaluation module;
The data acquisition module is used for acquiring historical charge and discharge operation data of the battery pack to be evaluated, and acquiring charge and discharge operation data in a first SOC section from the historical charge and discharge operation data through a preset SOC section threshold value, wherein the charge and discharge operation data comprise charge data of the battery pack in the first SOC section and battery pack operation data at the moment of switching of charge and discharge modes of the battery pack in the first SOC section;
The index extraction module is used for obtaining a health index corresponding to the battery pack to be evaluated through a preset health index extraction formula according to the charge and discharge operation data in the first SOC interval; the index extraction module comprises a first index unit and a second index unit; the first index unit is used for obtaining a first health index representing the overall degradation trend of the battery pack to be evaluated through a preset first health index extraction formula according to the charge and discharge operation data in the first SOC interval; the second index unit is used for obtaining a second health index representing the inconsistent difference among the single batteries according to charge and discharge operation data of each single battery in the battery pack to be evaluated in the first SOC interval through a preset second health index extraction formula;
The obtaining a first health index representing the overall degradation trend of the battery pack to be evaluated through a preset first health index extraction formula comprises obtaining an average voltage of the battery pack to be evaluated in the first SOC interval, wherein the average voltage is calculated according to the following formula:
wherein U pack is the battery voltage, I pacK is the battery charging current, Is the average voltage of the battery pack in a part of the SOC interval in the t 1-t2 charging process;
Acquiring a capacity standard deviation stdQ pack and a capacity differential standard deviation stddQ pack of the battery pack to be evaluated in the first SOC interval, wherein the acquisition process is as follows:
Acquiring a voltage range [ V pack_min,Vpack_max ] corresponding to the battery pack to be evaluated in the first SOC interval, and segmenting the voltage range at equal intervals according to V interval:
Vpack_segment＝[Vpack_min,Vpack_min+Vinterval,Vpack_min+Vinterval*2,...,Vpack_max]
Calculating the capacity sequence of the battery pack to be evaluated in the voltage range by an ampere-hour integration method:
Qpack_segment(i)＝[Q2-Q1,Q3-Q2,...,Qn-Qn-1],(i=1,2,...,N)
Wherein, 1,2, & N corresponds to a corresponding voltage point within the battery voltage interval range, 1,2, & N corresponds to the number of cycles of the battery, and Q pack_segment(i) is the capacity sequence of the battery at the ith cycle;
And subtracting the capacity sequence of the battery pack to be evaluated under different cycles from the capacity sequence under the first cycle to obtain a capacity difference sequence:
Qpack_segment_difference(i)＝Qpack_segment(i)-Qpack_segment(1),(i=1,2,...,N)
Wherein, Q pack_segment_difference(i) is the capacity difference sequence of the battery pack under the ith cycle;
Respectively solving a standard deviation stdQ pack of the battery capacity sequence Q pack_segment to be evaluated and a standard deviation stddQ pack of the battery capacity difference sequence Q pack_segment_difference to be evaluated under different cycles;
acquiring a battery voltage U pack_changepoint at a charging system switching moment in the charging process of the battery to be evaluated;
Acquiring a battery voltage U pack_rest of the battery to be evaluated after the battery to be evaluated is charged and stands for a certain time;
acquiring a battery pack pressure drop U packdrop_5s in a certain period of instant discharging when the battery pack to be evaluated starts discharging;
The obtaining a second health index representing the inconsistency difference between the single batteries through a preset second health index extraction formula comprises extracting the same characteristics of each single battery in the battery pack to be evaluated as those of the battery pack to be evaluated StdQ cell(i)、stddQcell(i) and point feature U cell(i)_changepoint、Ucell(i)_rest、Ucell(i)drop_5s;
calculating the polar difference, standard deviation and information entropy among the characteristics of each single battery to represent the inconsistent differences among the single voltage, the capacity and the internal resistance, wherein the expressions are as follows:
ran=xmax-xmin
Wherein i=1, 2,., n, n are the number of single batteries in the battery pack, x max is the maximum value of the single characteristic quantity in the battery pack, x min is the minimum value of the single characteristic quantity in the battery pack;
wherein n is the number of single batteries connected in series in the battery pack, x i is the characteristic quantity of the ith single battery; the average value of all serial monomer characteristic quantities in the battery pack is obtained;
wherein n is the number of single batteries connected in series in the battery pack, p (i) is the characteristic quantity corresponding to the ith single battery;
the feature screening module is used for carrying out feature screening on the health indexes through a preset feature selection method to obtain a health index feature set corresponding to the battery pack to be evaluated;
The state evaluation module is used for inputting the health index feature set into a preset battery pack health state evaluation model, and performing health state evaluation on the battery pack to be evaluated to obtain an evaluation result corresponding to the battery pack to be evaluated.
6. The battery pack health assessment system according to claim 5, wherein said data acquisition module comprises a history data unit and a data extraction unit;
The historical data unit is used for acquiring the discharge cut-off voltage and the charge cut-off voltage of the battery pack to be evaluated, and acquiring historical charge and discharge operation data of the battery pack to be evaluated according to the discharge cut-off voltage and the charge cut-off voltage;
The data extraction unit is used for extracting charging data of the battery pack in the first SOC interval, which is positioned in the SOC interval threshold, in the historical charging and discharging operation data corresponding to the battery pack to be evaluated according to a preset SOC interval threshold, acquiring switching time of a current value in the historical charging and discharging operation data according to the SOC interval threshold, acquiring historical charging and discharging operation data corresponding to the switching time, and acquiring battery pack operation data of the battery pack charging and discharging system switching time point in the first SOC interval.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115980612A (en) * | 2023-03-21 | 2023-04-18 | 中国人民解放军火箭军工程大学 | Satellite battery pack health state assessment method, system and equipment |
Non-Patent Citations (1)
| Title |
|---|
| 锂离子电池组多维度健康状态估计方法研究;侯林涛;硕士论文工程科技Ⅱ辑;20240322;全文 * |
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