CN112379277A - Lithium ion battery capacity prediction method - Google Patents
Lithium ion battery capacity prediction method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 46
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 24
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 24
- 238000007599 discharging Methods 0.000 claims abstract description 32
- 238000012417 linear regression Methods 0.000 claims description 22
- 238000004904 shortening Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000002360 preparation method Methods 0.000 abstract description 2
- 230000007306 turnover Effects 0.000 abstract description 2
- 230000000052 comparative effect Effects 0.000 description 6
- 238000005265 energy consumption Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 229910052744 lithium 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
- 238000004891 communication Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000028161 membrane depolarization Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010287 polarization Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000010998 test method 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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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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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Abstract
The invention relates to a lithium ion battery preparation technology and discloses a lithium ion battery capacity prediction method which comprises the steps of taking A electric cores with capacities to be predicted to perform complete charging and discharging, deriving discharge capacities and calculating a discharge capacity mean value Cm; fully charging each battery cell with the capacity to be predicted, and then charging and discharging according to the following procedures: carrying out constant current discharge on a battery cell with a capacity to be predicted, and setting the discharge cutoff capacity of the battery cell to be C'; secondly, continuing to discharge with small current till the discharge cut-off voltage V1, and counting the total discharge capacity C1 of the first two steps; thirdly, charging the battery cell with the capacity to be predicted to a cut-off voltage V2 by using a small current, counting the charging capacity C2, and counting the reverse drop voltage OCV after standing for a certain time; and inputting the C1, the C2 and the OCV into a capacity prediction formula to obtain a predicted capacity value of the battery cell. The invention can obtain the capacity value of the complete discharge of the battery cell without the complete discharge, and the average prediction error is within 0.8 percent, thereby shortening the turnover time of the battery cell, improving the productivity, reducing the cost and improving the efficiency.
Description
Technical Field
The invention relates to a lithium ion battery preparation technology, in particular to a lithium ion battery capacity prediction method.
Background
The lithium ion battery has the advantages of high energy density, long cycle life, environmental friendliness and the like, is widely applied to the fields of mobile electronic equipment, electric automobiles, aerospace and the like, and with the arrival of the 5G era, the demand of the lithium ion battery in the fields of energy storage and communication is rapidly increased. However, with the gradual cancellation of the national relevant lithium battery subsidy policy, the lithium battery industry has a strong competition in recent years, so it is very important how to reduce cost and improve efficiency to occupy a place in the market.
The capacity of a lithium ion battery is one of the important technical indexes for evaluating the quality of the battery. It is often necessary to collect actual capacity data of the battery through complete charging and discharging during the production process. The capacity is also a more important selection index in the grouping process, and the selection of the electric cores with more consistent capacity is beneficial to the performance exertion of the battery pack, so that the capacity of the battery needs to be detected and screened in the production process of the lithium ion battery. At present, the capacity detection of the lithium ion battery is usually carried out according to standard low-current charging and discharging, longer time needs to be consumed, and complete charging and discharging causes larger energy consumption, and a large amount of test equipment needs to be equipped, so that the capacity requirement can be met only by occupying larger plant area. In addition, the battery core is generally in an electrified state, the battery core can be discharged after power is supplemented after the battery core is subjected to complete charge-discharge detection capacity, the discharging time of the whole battery core can be prolonged, and the energy consumption is increased. Therefore, if the battery cell can be discharged to the shipment SOC state and the actual capacity of the battery cell can be predicted, the production process of the battery cell is greatly shortened, the capacity is improved, and the energy consumption is saved. Therefore, the adoption of an efficient and reasonable prediction method to replace the traditional test method is an urgent need of the industry.
Disclosure of Invention
Aiming at the defects of time consumption and energy consumption in capacity detection in the prior art, the invention provides a time-saving and energy-saving lithium ion battery capacity prediction method capable of rapidly predicting the lithium ion battery capacity.
In order to solve the technical problem, the invention is solved by the following technical scheme:
the method for predicting the capacity of the lithium ion battery comprises the following steps:
s1, taking A battery cells with capacities to be predicted to perform complete charging and discharging, deriving discharge capacities and calculating the mean value Cm of the discharge capacities of the A battery cells; a is more than or equal to 50;
s2, fully charging each battery cell with the capacity to be predicted, and then charging and discharging according to the following procedures: firstly, performing constant current discharge on a battery cell with capacity to be predicted, setting the discharge cut-off capacity of the battery cell to be C', continuously discharging the battery cell with the capacity to be predicted to a discharge cut-off voltage V1, counting firstly, two steps of total discharge capacity C1, secondly, charging the battery cell with the capacity to be predicted to a cut-off voltage V2, counting the charge capacity C2, and standing for at least 10min, and then, counting an inverse drop voltage OCV;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula to obtain the predicted capacity value of the battery cell with the capacity to be predicted.
Further, in S3, the predicted capacity formula is (C1-C2) × 0.4/(3.5655-OCV), where Cy is the predicted capacity value of the cell whose capacity is to be predicted.
Further, a linear regression equation of SOC-OCV curve with SOC in the range of 10% to 30% is fitted to obtain both, where SOC is (OCV-3.1655)/0.4, and from SOC + DOD is 1, a predicted capacity formula obtained by Cy being (C1-C2)/DOD is obtained, where DOD is (3.5655-OCV)/0.4.
Further, in S2, the charge cut-off voltage V2 is set within the OCV range corresponding to the SOC being in the (100-X)% interval in the SOC-OCV curve, where X is 70-90.
Further, in S2, C ═ X% Cm, and X is 70 to 90. The value of X is 70-90%, namely the discharge cut-off capacity C' is set as the corresponding capacity when the discharge depth DOD is in the range of 70-90%, namely the residual capacity SOC is 30% -10%, and the SOC-OCV curve shows that the two have obvious linear relation in the range, and the range is selected to be beneficial to saving time (the delivery capacity of the battery cell is in the range, so that recharging is not needed during delivery) on one hand and to be beneficial to evaluating the actual discharge depth DOD according to the voltage on the other hand.
Further, in S1, the battery cell with the capacity to be predicted is charged and discharged at a rate of 0.2C to 1C. And the battery cell is less polarized when charged and discharged in the multiplying power range, and the result is more accurate.
Further, in S2, the battery cell is fully charged at a rate of 0.2C to 1C. Charging under this multiplying power scope, electric core polarization is less, and the result is more accurate.
Further, in step S2, the battery cell is discharged at a current of 0.015-0.05C, and in step S2, the battery cell is charged at a current of 0.015-0.05C. Carry out charge-discharge under this multiplying power scope and be favorable to electric core depolarization, make the electric core state more unanimous.
Further, the discharge cut-off voltage V1 was 3.18-3.26V. This range is a voltage range corresponding to the discharge cut-off voltage V1 in the SOC range of 10% to 30%, and contributes to shortening the discharge time.
The key point of the above capacity prediction process lies in the selection of the discharge SOC, and it can be seen from the SOC-OCV curve (as shown in fig. 1) that SOC and OCV have obvious linear relations when SOC is in the interval of 10% -30% and 55% -65%, and a linear regression equation of the SOC and the OCV can be obtained by fitting the linear relation when SOC is in the interval of 10% -30%. And obtaining the corresponding depth of discharge DOD according to a linear regression equation between the SOC and the OCV and a formula of SOC + DOD being 1, and obtaining the predicted capacity value of the battery cell with the capacity to be predicted by substituting Cy being (C1-C2)/DOD and the OCV value obtained in the predicting method step at the end.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that:
according to the method, after incomplete discharge, the battery cell is charged to a specified SOC interval, namely the SOC value is in a range of 10-30%, an OCV value and an incomplete discharge capacity value C3 of the battery cell at the moment are collected, C3 is C1-C2, a linear regression equation of the OCV value and the incomplete discharge capacity value is obtained by fitting an SOC-OCV curve of which the SOC is in a range of 10% -30%, the linear regression equation is SOC-3.1655/0.4, therefore, the OCV value is substituted into the linear regression equation to obtain a more accurate SOC value of the battery cell at the moment, then a DOD value (conversion formula DOD-3.5655-OCV)/0.4) is obtained by changing the OCV value into 1, and the capacity of the battery cell with the predicted capacity at the moment can be calculated by dividing the incomplete discharge capacity value C3 by the DOD value at the moment, namely the predicted capacity value. The method can obtain the capacity of complete discharge without complete low-current discharge, and can save the charging energy after the battery cell is delivered, thereby shortening the turnover time of the battery cell and improving the productivity.
According to the capacity prediction step, the collected C1, C2 and OCV values are substituted into a capacity prediction formula to calculate the predicted capacity, so that the capacity grading and shipment processes of the battery cell can be shortened, the production efficiency is improved, and the energy consumption is reduced; the electric core does not need to be supplied with electricity again when being delivered, the electric core is more convenient to maintain, the production efficiency can be improved, and the predicted capacity obtained by the method is verified to have the average deviation with the actual capacity within 0.8 percent and is smaller than the standard of 1 percent C of the group content difference required by the group matching standard. The method can be suitable for the capacity prediction of lithium iron phosphate batteries with different types and capacities.
The invention overcomes the problems of long capacity test period, large energy consumption, large occupation of a large number of plants and personnel and high production cost in the existing industrial application. On the other hand, the method overcomes the defects that the prior capacity prediction is not suitable for industrial application due to high and deep theory, complex formula and the like.
Drawings
FIG. 1 is a lithium iron phosphate battery SOC-OCV curve;
FIG. 2 is a graph showing the comparison between the predicted capacity and the actual capacity and the deviation in the embodiment 1 of the present invention;
FIG. 3 is a graph of the predicted capacity versus actual capacity and deviation for comparative example 1 of the present invention;
FIG. 4 is a graph of the predicted capacity versus actual capacity and deviation for comparative example 2 of the present invention;
fig. 5 is a graph of the predicted deviation of comparative example 3 of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
The invention provides a method for predicting the capacity of a lithium ion battery, which comprises the following steps:
s1, taking A battery cells with the capacity to be predicted to be completely charged and discharged at a multiplying power of 0.2C-1C, deriving the discharge capacity and calculating the mean value Cm of the discharge capacity of the A battery cells; a is more than or equal to 50. And C is the rated capacity of the battery core.
And S2, fully charging each battery cell with the capacity to be predicted, wherein the charging rate is the same as that of the battery cell in the S1, and then charging and discharging according to the following process: performing constant current discharge on a battery cell with a capacity to be predicted, wherein the constant current discharge multiplying power is the same as the charge-discharge multiplying power of S1, the discharge cutoff capacity of the battery cell is set to be C', C ═ X% Cm, and X is 70-90; continuously discharging the battery cell with the capacity to be predicted to a discharge cut-off voltage V1 at a small current of 0.015-0.05C, counting the total discharge capacity C1 of the two steps, then charging the battery cell with the capacity to be predicted to a charge cut-off voltage V2 at a small current of 0.015-0.05C, counting the charge capacity C2, and counting the reverse drop voltage OCV after standing for at least 10 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula, namely Cy (C1-C2)/DOD (C1-C2) 0.4/(3.5655-OCV), so that the predicted capacity value Cy of the battery cell with the capacity to be predicted can be obtained. OCV is in units of V, and C1 and C2 are in units of Ah.
And obtaining a linear regression equation of the SOC and the OCV by fitting an SOC-OCV curve of which the SOC is in a range of 10% -30%, wherein the linear regression equation is that SOC is (OCV-3.1655)/0.4, obtaining the depth of discharge DOD of the battery cell is (3.5655-OCV)/0.4 according to the SOC + DOD is 1, and obtaining a predicted capacity formula according to the Cy is (C1-C2)/DOD.
The discharge cut-off voltage V1 is 3.18-3.26V. At S2, the charge cut-off voltage V2 is set within the OCV range corresponding to the SOC at the (100-X)% section in the SOC-OCV curve, where X is 70-90.
Example 1
The method for predicting the capacity of the lithium ion battery comprises the following steps:
s1, taking 200 electric cores with capacities to be predicted, and completely charging and discharging at a multiplying power of 0.2C, deriving discharge capacities and calculating a discharge capacity average value Cm;
s2, fully charging each battery cell with the capacity to be predicted at a multiplying power of 0.2C, and then charging and discharging according to the following procedures: carrying out constant current discharge on a battery cell with a capacity to be predicted at a multiplying power of 0.2C, and setting the discharge cutoff capacity of the battery cell as C ', wherein C' is 70% Cm; continuously discharging the battery cell with the capacity to be predicted by using a small current of 0.05C until the discharge cut-off voltage V1 is 3.26V, counting the total discharge capacity C1 of the two steps, then charging the battery cell with the capacity to be predicted by using a small current of 0.05C until the charge cut-off voltage V2 is 3.2855V, counting the charge capacity C2, and counting the reverse drop voltage OCV after standing for 15 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula Cy (C1-C2) × 0.4/(3.5655-OCV), and obtaining the predicted capacity value Cy of the battery cell with the capacity to be predicted. OCV is in units of V, and C1 and C2 are in units of Ah.
And obtaining a linear regression equation of the SOC and the OCV by fitting an SOC-OCV curve of which the SOC is in a range of 10% -30%, wherein the linear regression equation is that SOC is (OCV-3.1655)/0.4, obtaining the depth of discharge DOD of the battery cell is (3.5655-OCV)/0.4 according to the SOC + DOD is 1, and obtaining a predicted capacity formula according to the Cy is (C1-C2)/DOD.
The average prediction error of the obtained predicted capacity value is within 0.7%, and the specific numerical value is shown in fig. 2.
Example 2
The method for predicting the capacity of the lithium ion battery comprises the following steps:
s1, taking 100 electric cores with capacity to be predicted, and completely charging and discharging at a multiplying power of 0.5C, deriving discharge capacity, and calculating a discharge capacity average value Cm of the electric cores A;
s2, fully charging each battery cell with the capacity to be predicted at a multiplying power of 0.5C, and then charging and discharging according to the following procedures: carrying out constant current discharge on a battery cell with a capacity to be predicted at a multiplying power of 0.5C, and setting the discharge cutoff capacity of the battery cell as C ', wherein C' is 70% Cm; continuously discharging the battery cell with the capacity to be predicted by using a small current of 0.015 until the discharge cut-off voltage V1 is 3.24V, counting the total discharge capacity C1 of the two steps, then charging the battery cell with the capacity to be predicted by using a small current of 0.03C until the charge cut-off voltage V2 is 3.2855V, counting the charge capacity C2, and counting the reverse drop voltage OCV after standing for 25 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula Cy (C1-C2) × 0.4/(3.5655-OCV), and obtaining the predicted capacity value Cy of the battery cell with the capacity to be predicted. OCV is in units of V, and C1 and C2 are in units of Ah.
And obtaining a linear regression equation of the SOC and the OCV by fitting an SOC-OCV curve of which the SOC is in a range of 10% -30%, wherein the linear regression equation is that SOC is (OCV-3.1655)/0.4, obtaining the depth of discharge DOD of the battery cell is (3.5655-OCV)/0.4 according to the SOC + DOD is 1, and obtaining a predicted capacity formula according to the Cy is (C1-C2)/DOD.
The average prediction error of the obtained predicted capacity value is within 0.65 percent.
Example 3
The method for predicting the capacity of the lithium ion battery comprises the following steps:
s1, taking 150 electric cores with capacity to be predicted, and completely charging and discharging at a multiplying power of 0.2C, deriving discharge capacity, and calculating a discharge capacity average value Cm of the electric cores A;
s2, fully charging each battery cell with the capacity to be predicted at a multiplying power of 0.2C, and then charging and discharging according to the following procedures: carrying out constant current discharge on a battery cell with a capacity to be predicted at a multiplying power of 0.2C, and setting the discharge cutoff capacity of the battery cell as C ', wherein C' is 71% Cm; continuously discharging the battery cell with the capacity to be predicted by using a small current of 0.05C until the discharge cut-off voltage V1 is 3.26V, counting the total discharge capacity C1 of the two steps, then charging the battery cell with the capacity to be predicted by using a small current of 0.05C until the charge cut-off voltage V2 is 3.2815V, counting the charge capacity C2, and counting the reverse drop voltage OCV after standing for 30 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula Cy (C1-C2) × 0.4/(3.5655-OCV), and obtaining the predicted capacity value Cy of the battery cell with the capacity to be predicted. OCV is in units of V, and C1 and C2 are in units of Ah.
And obtaining a linear regression equation of the SOC and the OCV by fitting an SOC-OCV curve of which the SOC is in a range of 10% -30%, wherein the linear regression equation is that SOC is (OCV-3.1655)/0.4, obtaining the depth of discharge DOD of the battery cell is (3.5655-OCV)/0.4 according to the SOC + DOD is 1, and obtaining a predicted capacity formula according to the Cy is (C1-C2)/DOD.
The average prediction error of the obtained predicted capacity value is within 0.6 percent.
Example 4
The method for predicting the capacity of the lithium ion battery comprises the following steps:
s1, taking 200 electric cores with capacities to be predicted, and completely charging and discharging at the rate of 1C, deriving discharge capacities and calculating the mean value Cm of the discharge capacities of the A electric cores;
s2, fully charging each battery cell with the capacity to be predicted at a multiplying power of 1C, and then charging and discharging according to the following procedures: carrying out constant current discharge on a battery cell with a capacity to be predicted at a multiplying power of 1C, and setting the discharge cutoff capacity of the battery cell as C ', wherein C' is 70% Cm; continuously discharging the battery cell with the capacity to be predicted by using a small current of 0.05C until the discharge cut-off voltage V1 is 3.25V, counting the total discharge capacity C1 of the two steps, then charging the battery cell with the capacity to be predicted by using a small current of 0.05C until the charge cut-off voltage V2 is 3.2855V, counting the charge capacity C2, and counting the reverse drop voltage OCV after standing for 45 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula Cy (C1-C2) × 0.4/(3.5655-OCV), and obtaining the predicted capacity value Cy of the battery cell with the capacity to be predicted. OCV is in units of V, and C1 and C2 are in units of Ah.
And obtaining a linear regression equation of the SOC and the OCV by fitting an SOC-OCV curve of which the SOC is in a range of 10% -30%, wherein the linear regression equation is that SOC is (OCV-3.1655)/0.4, obtaining the depth of discharge DOD of the battery cell is (3.5655-OCV)/0.4 according to the SOC + DOD is 1, and obtaining a predicted capacity formula according to the Cy is (C1-C2)/DOD.
The average prediction error of the obtained predicted capacity value is within 0.6 percent.
Example 5
The method for predicting the capacity of the lithium ion battery comprises the following steps:
s1, taking 200 electric cores with capacities to be predicted, and completely charging and discharging at a multiplying power of 0.2C, deriving discharge capacities and calculating a discharge capacity average value Cm of the A electric cores;
s2, fully charging each battery cell with the capacity to be predicted at a multiplying power of 0.2C, and then charging and discharging according to the following procedures: carrying out constant current discharge on a battery cell with a capacity to be predicted at a multiplying power of 0.2C, and setting the discharge cutoff capacity of the battery cell as C ', wherein C' is 90% Cm; continuously discharging the battery cell with the capacity to be predicted by using a small current of 0.05C until the discharge cut-off voltage V1 is 3.18V, counting the total discharge capacity C1 of the two steps, then charging the battery cell with the capacity to be predicted by using a small current of 0.05C until the charge cut-off voltage V2 is 3.2055V, counting the charge capacity C2, and counting the reverse drop voltage OCV after standing for 15 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula Cy (C1-C2) × 0.4/(3.5655-OCV), and obtaining the predicted capacity value Cy of the battery cell with the capacity to be predicted. OCV is in units of V, and C1 and C2 are in units of Ah.
And obtaining a linear regression equation of the SOC and the OCV by fitting an SOC-OCV curve of which the SOC is in a range of 10% -30%, wherein the linear regression equation is that SOC is (OCV-3.1655)/0.4, obtaining the depth of discharge DOD of the battery cell is (3.5655-OCV)/0.4 according to the SOC + DOD is 1, and obtaining a predicted capacity formula according to the Cy is (C1-C2)/DOD.
The average prediction error of the obtained predicted capacity value is within 0.75 percent.
Example 6
The method for predicting the capacity of the lithium ion battery comprises the following steps:
s1, taking 200 electric cores with capacities to be predicted, and completely charging and discharging at a multiplying power of 0.2C, deriving discharge capacities and calculating a discharge capacity average value Cm;
s2, fully charging each battery cell with the capacity to be predicted at a multiplying power of 0.2C, and then charging and discharging according to the following procedures: carrying out constant current discharge on a battery cell with a capacity to be predicted at a multiplying power of 0.2C, and setting the discharge cutoff capacity of the battery cell as C ', wherein C' is 60% Cm; continuously discharging the battery cell with the capacity to be predicted by using a small current of 0.05C until the discharge cut-off voltage V1 is 3.288V, counting the total discharge capacity C1 of the two steps, then charging the battery cell with the capacity to be predicted by using a small current of 0.05C until the charge cut-off voltage V2 is 3.298V, counting the charge capacity C2, and counting the reverse drop voltage OCV after standing for 15 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula Cy (C1-C2) × 0.34/(3.4366-OCV), and obtaining the predicted capacity value Cy of the battery cell with the capacity to be predicted.
And obtaining a linear regression equation of the SOC and the OCV by fitting an SOC-OCV curve of which the SOC is in a range of 55-65%, wherein the linear regression equation is that SOC is (OCV-3.0966)/0.34, obtaining the depth of discharge DOD of the battery cell is (3.4366-OCV)/0.34 according to the SOC + DOD is 1, and obtaining a predicted capacity formula according to the Cy is (C1-C2)/DOD.
The average prediction error of the obtained predicted capacity value is within 0.7 percent.
Comparative example 1
Prediction was performed according to the procedure of example 1 except that the total charge and discharge capacities C1 and C2 were collected, and the predicted capacity value was directly obtained according to the predicted capacity formula of example 1, with the discharge SOC of 70% as the conversion coefficient.
As shown in FIG. 3, the prediction error of the obtained predicted capacity value is mostly between 2-4.5%.
Comparative example 2
Prediction was performed by following the procedure of example 1 except that the charge cut-off voltage V2 of step 3 in S2 was adjusted to 3.68V.
As shown in FIG. 4, the absolute value of the prediction error of the obtained predicted capacity value is mostly between 0 and 3%.
Comparative example 3
The prediction was performed in the procedure of example 1, except that the OCV corresponding to the discharge cutoff capacity C' in S0 being adjusted to the SOC of 70% was used as the voltage cutoff condition.
As shown in fig. 5, the prediction errors of the obtained predicted capacity values are dispersed and large.
In summary, the above-mentioned embodiments are only preferred embodiments of the present invention, and all equivalent changes and modifications made in the claims of the present invention should be covered by the claims of the present invention.
Claims (10)
1. The method for predicting the capacity of the lithium ion battery is characterized by comprising the following steps of:
s1, taking A battery cells with capacities to be predicted to perform complete charging and discharging, deriving discharge capacities and calculating the mean value Cm of the discharge capacities of the A battery cells;
s2, fully charging each battery cell with the capacity to be predicted, and then charging and discharging according to the following procedures: firstly, performing constant current discharge on a battery cell with capacity to be predicted, setting the discharge cut-off capacity of the battery cell to be C', continuously discharging the battery cell with the capacity to be predicted to a discharge cut-off voltage V1, counting firstly, two steps of total discharge capacity C1, secondly, charging the battery cell with the capacity to be predicted to a charge cut-off voltage V2, counting the charge capacity C2, and counting an inverse drop voltage OCV after standing for at least 10 min;
and S3, inputting the three values of C1, C2 and OCV into a capacity prediction formula to obtain the predicted capacity value of the battery cell with the capacity to be predicted.
2. The method of claim 1, wherein in S3, the predicted capacity formula is Cy ═ (C1-C2) × 0.4/(3.5655-OCV), where Cy is the predicted capacity value of the cell whose capacity is to be predicted.
3. The method of claim 1, wherein the linear regression equation of the SOC-OCV curve with SOC in the range of 10% to 30% is obtained by fitting the SOC-OCV curve, and the linear regression equation is SOC ═ c
(OCV-3.1655)/0.4, and the SOC + DOD is 1, and the depth of discharge DOD of the cell is (3.5655-OCV)/0.4, and the predicted capacity formula is obtained from Cy (C1-C2)/DOD.
4. The method of claim 1, wherein in S2, the charge cut-off voltage V2 is set within an OCV range corresponding to an SOC in an interval of (100-X)% in an SOC-OCV curve, where X is 70-90.
5. The method of claim 1, wherein in S2, C' ═ X% Cm, and X is 70 to 90.
6. The method for predicting the capacity of the lithium ion battery according to claim 1, wherein in S1, the battery cell with the capacity to be predicted is charged and discharged at a rate of 0.2C to 1C.
7. The method for predicting the capacity of the lithium ion battery according to claim 1, wherein in S2, the battery cell is fully charged at a rate of 0.2C to 1C.
8. The method of claim 1, wherein in step S2, the battery cell is discharged at a current of 0.015-0.05C.
9. The method of claim 1, wherein in step three of S2, the electric core is charged with a current of 0.015-0.05C.
10. The method according to claim 1, wherein the discharge cut-off voltage V1 is 3.18 to 3.26V in S2.
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