CN113821914B - Low-cost prediction method for cycle life of lithium ion battery - Google Patents

Low-cost prediction method for cycle life of lithium ion battery Download PDF

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CN113821914B
CN113821914B CN202110999355.6A CN202110999355A CN113821914B CN 113821914 B CN113821914 B CN 113821914B CN 202110999355 A CN202110999355 A CN 202110999355A CN 113821914 B CN113821914 B CN 113821914B
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lithium ion
ion battery
cycle life
cycle
prediction method
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毛昭勇
陈佩雨
田文龙
卢丞一
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Northwestern Polytechnical University
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Abstract

To solve the problem of using the existingThe invention provides a low-cost prediction method for the cycle life of a lithium ion battery. The invention discovers the average ohmic internal resistance IR and the discharge capacity Q of the lithium ion battery monomer in the N (N is more than or equal to 100) th cycle process by screening characteristic parameters d And a minimum temperature T min The three characteristic parameters are subjected to dimension reduction and fusion to obtain a new characteristic
Figure DDA0003235127880000011
And the cycle life Y of the lithium ion battery (i) And a good linear relation is shown, so that a lithium ion battery cycle life prediction model is obtained by specially processing the three characteristic parameters and the cycle life. When the prediction model obtained by the invention is used for predicting the cycle life of the lithium ion battery to be tested, the cycle life of the lithium ion battery to be tested can be predicted more accurately only by adopting test data in the Nth (N is more than or equal to 100) cycle process of the lithium ion battery to be tested, so that the prediction cost is greatly reduced.

Description

Low-cost prediction method for cycle life of lithium ion battery
Technical Field
The invention relates to a method for testing the electrical condition of a battery, in particular to a low-cost prediction method for the cycle life of a lithium ion battery.
Background
The thermal power system is used as a traditional power system of an underwater vehicle, and cannot meet the requirement of large navigation depth due to the fact that noise is large, concealment is poor, and the thermal power system is influenced by back pressure. In recent years, under the era wave of actively advocating new energy in China, lithium ion batteries with excellent performance stand out and become research hotspots in various industries. For the underwater field, the electrodynamic system belongs to a closed system, does not discharge any outside, has no trail, and is not influenced by back pressure, so that the electrodynamic system becomes a favored object for researchers in the industry.
The lithium ion battery has the characteristics of high energy density and high power density. In order to maximize the efficiency of the battery while extending the life of the battery as much as possible, the battery is provided with a Battery Management System (BMS) under normal use conditions. Accurate estimation of battery state-of-health (SOH) is a key technology in BMS and is a reliable guarantee for safe operation and working efficiency of battery systems, including battery cycle life parameters.
The cycle life of the battery can be obtained through a test mode, but the lithium ion battery has long service life, and the lithium ion battery has long service life increasingly along with the increasing maturity of the research and development technology of the lithium ion battery in recent years, and the cycle frequency is even as high as ten thousand times, so that when the cycle life of the battery is obtained by adopting the existing cycle life test, long time is consumed, the cost is huge, and the prediction difficulty is also higher.
Disclosure of Invention
In order to solve the technical problems of long time consumption and high cost of obtaining the cycle life of a battery by utilizing the conventional cycle life test, the invention provides a low-cost prediction method of the cycle life of a lithium ion battery.
In order to solve the technical problems, the invention adopts the technical scheme that:
a low-cost prediction method for the cycle life of a lithium ion battery is characterized by comprising the following steps:
step 1: selecting m lithium ion battery monomers belonging to the same system, and respectively carrying out a cycle life test; m is more than or equal to 60;
step 2: recording the test parameter X of each lithium ion battery monomer in the Nth cycle process (i) And cycle life Y (i) ；X (i) ＝(IR (i) ，Q d (i) ，T min (i) ) (ii) a i is the serial number of the lithium ion battery monomer; IR (i) 、Q d (i) And T min (i) Respectively the average ohmic internal resistance, the discharge capacity and the minimum temperature of the ith lithium ion battery monomer in the Nth cycle process; n is more than or equal to 100;
and step 3: for the X (i) Performing dimensionality reduction treatment on the X (i) The three characteristic information in the system are fused into one characteristic
Figure BDA0003235127860000022
Cycle life Y of the lithium ion battery cell (i) Performing outlier processing;
and 4, step 4: to pairStep 3 obtaining by dimensionality reduction
Figure BDA0003235127860000023
Fitting the cycle life of the battery after the outlier treatment to obtain a lithium ion battery cycle life prediction model;
and 5: inputting the parameters (IR, Q) measured by the Nth cycle of the lithium ion battery to be measured into the lithium ion battery cycle life prediction model d ，T min And obtaining the cycle life of the lithium ion battery to be tested.
Further, N in step 1 is 100.
Further, step 2 uses PCA (Principal Component Analysis) method to analyze the X (i) And (4) performing dimension reduction treatment, wherein the objective function is as follows:
Figure BDA0003235127860000021
in the formula (I), the compound is shown in the specification,
w represents a direction vector, w ═ w 1 ,w 2 ,w 3 )；
m represents the number of the lithium ion battery monomers selected in the step 1;
i represents the number of the lithium ion battery monomer;
Figure BDA0003235127860000031
representing the features after the mean has been zeroed,
Figure BDA0003235127860000032
further, the outlier processing in step 3 is performed by using a boxplot analysis method, and the boundary values are defined as follows:
IQR＝Q 3 -Q 1
l u ＝Q 3 +1.5IQR
l l ＝Q 1 -1.5IQR
wherein, the first and the second end of the pipe are connected with each other,
Q 3 shows the cycle life Y of the lithium ion battery monomer obtained in the step 2 (i) The middle upper quarter value of (a);
Q 1 the cycle life Y of the lithium ion battery monomer obtained in the step 2 is shown (i) Middle and lower quarter values of (1);
IQR represents the cycle life Y of the lithium ion battery monomer obtained in the step 2 (i) The quartile range of (d);
l u is an upper limit value for screening outliers;
l l is the lower limit used to screen out outliers.
Further, in step 4, a least square method is adopted for fitting, and an objective function is as follows:
Figure BDA0003235127860000033
Figure BDA0003235127860000034
wherein, the first and the second end of the pipe are connected with each other,
a and b are linear regression equation coefficients;
n is the number of the monomers of the lithium ion battery selected in the step 1 after the outlier screening operation;
Figure BDA0003235127860000037
is X (i) Obtaining the characteristics after dimensionality reduction;
Y (i) the cycle life of the ith lithium ion battery cell is defined as the cycle life of the ith lithium ion battery cell;
Figure BDA0003235127860000035
is the mean value of the characteristics obtained by dimension reduction of all lithium ion battery monomers,
Figure BDA0003235127860000036
Figure BDA0003235127860000041
is the average value of the cycle life of all lithium ion battery monomers obtained after the treatment of the outlier,
Figure BDA0003235127860000042
compared with the prior art, the invention has the following beneficial effects:
1. the invention analyzes the cycle life test data of at least 60 groups of lithium ion battery monomers, and discovers the average ohmic internal resistance IR and the discharge capacity Q of the lithium ion battery monomers in the N (N is more than or equal to 100) th cycle process by screening characteristic parameters d And a minimum temperature T min The three characteristic parameters are subjected to dimension reduction and fusion to obtain a new characteristic
Figure BDA0003235127860000043
And the cycle life Y of the lithium ion battery (i) And a good linear relation is shown (and currently, a person skilled in the art generally thinks that the battery capacity attenuation and the cycle number are in a nonlinear relation), so that a lithium ion battery cycle life prediction model is obtained by specially processing the three characteristic parameters and the cycle life. When the prediction model obtained by the invention is used for predicting the cycle life of the lithium ion battery to be tested, the cycle life of the lithium ion battery to be tested can be predicted more accurately only by adopting test data in the Nth (N is more than or equal to 100) cycle process of the lithium ion battery to be tested, so that the prediction cost is greatly reduced.
When the data in the 100 th cycle process is adopted to establish the prediction model, the prediction precision can meet the actual requirement, and the modeling time is shortest.
2. The prediction model obtained by the method is simple and visual, and meanwhile, the prediction precision can basically meet the actual requirements, so that the method has great significance for the continuous and reliable development of the lithium ion battery.
3. The invention adopts PCA to X (i) Dimension reduction processing is carried out, so that the projection of the sample on the vector can be ensured to retain original sample information as much as possible, and the test is removedNoise generated in the process; in addition, modeling is carried out after a plurality of characteristics are fused, so that the model is more visual and convenient to show.
4. Before fitting, the invention adopts a boxplot analysis method to determine the cycle life Y (i) And processing is carried out, outliers in the cycle life data caused by sensor noise are screened out, and the prediction precision is improved.
5. The invention adopts least square to fit, the model is simple and easy to understand, and the calculated amount is small.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a diagram of an example of a prediction model obtained by an embodiment of the present invention.
Detailed Description
The invention will be explained in more detail below with reference to the drawings.
As shown in fig. 1, the method for predicting the cycle life of a lithium ion battery with low cost provided by the embodiment of the present invention includes the following specific steps:
step 1: selecting m lithium ion battery monomers belonging to the same system, and respectively carrying out a cycle life test on the lithium ion battery monomers; m is more than or equal to 60 (m is 124 in the embodiment; the system is distinguished by positive electrode materials, and the same positive electrode material is the battery of the same system, such as a lithium iron phosphate battery taking lithium iron phosphate as the positive electrode material and a lithium nickel cobalt manganese oxide battery taking lithium nickel cobalt manganese oxide as the positive electrode material.
Step 2: for each lithium ion battery cell selected in step 1, the test parameter X during the N (N in this embodiment is 100) th cycle is determined (i) And cycle life Y (i) Storing the data into a database; x (i) ＝(IR (i) ，Q d (i) ，T min (i) ) (ii) a Wherein, IR (i) The average ohmic internal resistance of the ith lithium ion battery monomer in the Nth cycle process; q d (i) The discharge capacity of the ith lithium ion battery monomer in the Nth cycle process is obtained; t is min (i) The minimum temperature of the ith lithium ion battery monomer in the Nth cycle process; cycle life Y of battery (i) Refers to the number of cycles of a lithium ion battery when the battery capacity decays to 80% of the rated capacity.
And step 3: data processing:
for X obtained in step 1 (i) Performing dimensionality reduction treatment on the X (i) Three characteristic information IR in (i) ，Q d (i) And T min (i) Are combined into one characteristic
Figure BDA0003235127860000051
For eliminating noise existing during data acquisition, the cycle life Y of a lithium ion battery monomer (i) And (5) carrying out outlier treatment.
In this example, a method of PCA (Principal Component Analysis) is preferable for X (i) And (5) performing dimensionality reduction treatment. PCA is an optimization algorithm for data dimension reduction, and the objective is to find a unit vector in the feature space of a sample, so that the projection of the sample on the vector can retain the original sample information as much as possible, and the objective function is as follows:
Figure BDA0003235127860000061
in the formula (I), the compound is shown in the specification,
w represents a direction vector, X in the present invention (i) There are three pieces of feature information, so w is a three-dimensional vector, i.e., w ═ w 1 ,w 2 ,w 3 )；
m represents the total amount of the samples, and the number of the lithium ion battery monomers selected in the step 1 is the number;
i represents the ith sample, and is the number of the lithium ion battery monomer in the invention;
Figure BDA0003235127860000062
the feature after mean zeroing is represented, the dimension is mx 3, and the calculation mode is as follows:
Figure BDA0003235127860000063
Figure BDA0003235127860000064
representing the feature mean vector, and calculating the way as follows:
Figure BDA0003235127860000065
therefore, the method comprises the following steps:
Figure BDA0003235127860000066
the preferred boxplot analysis method for cycle life Y in this example (i) Outlier processing was performed, with the boundary values defined as follows:
IQR＝Q 3 -Q 1
l u ＝Q 3 +1.5IQR
l l ＝Q 1 -1.5IQR
wherein, the first and the second end of the pipe are connected with each other,
Q 3 the cycle life Y of the lithium ion battery monomer obtained in the step 2 is shown (i) The middle upper quarter value of (1);
Q 1 the cycle life Y of the lithium ion battery monomer obtained in the step 2 is shown (i) Middle and lower quarter values of (1);
IQR represents the cycle life Y of the lithium ion battery cell obtained in the step 2 (i) The quartile range of (d);
l u is the upper limit of boxplot, the upper limit used to screen out outliers;
l l is the lower limit of boxplot, the lower limit used to screen out outliers.
And 4, step 4: obtained by reducing the dimension in the step 3
Figure BDA0003235127860000079
And fitting the cycle life of the lithium ion battery after the outlier treatment to obtain a lithium ion battery cycle life prediction model.
The least square method is preferably used for fitting in the embodiment, and the least square method is a simple linear regression algorithm, and the objective function of the least square method is as follows:
Figure BDA0003235127860000071
Figure BDA0003235127860000072
Figure BDA0003235127860000073
wherein a and b represent linear regression equation coefficients;
n is the number of the monomers of the lithium ion battery selected in the step 1 after the outlier screening operation;
Figure BDA0003235127860000074
represents X (i) Reducing the dimension of the feature;
Y (i) the cycle life of the ith lithium ion battery cell is shown;
Figure BDA0003235127860000075
representing the mean value of the characteristics obtained after dimension reduction of all lithium ion battery monomers, and the calculation mode is as follows:
Figure BDA0003235127860000076
Figure BDA0003235127860000077
the mean value of the cycle life of all lithium ion battery monomers obtained after the outlier treatment is represented, and the calculation mode is as follows:
Figure BDA0003235127860000078
the prediction model obtained in this embodiment is shown in fig. 2, and it can be seen from the figure that the new feature X is subjected to the dimensionality reduction by the PCA algorithm re And the method has a better linear relation with the cycle life, and the model is simple and visual and is convenient to apply.

Claims (5)

1. A low-cost prediction method for the cycle life of a lithium ion battery is characterized by comprising the following steps:
step 1: selecting m lithium ion battery monomers belonging to the same system, and respectively carrying out a cycle life test; m is more than or equal to 60;
step 2: recording the test parameter X of each lithium ion battery monomer in the Nth cycle process (i) And cycle life Y (i) ；X (i) ＝(IR (i) ，Q d (i) ，T min (i) ) (ii) a i is the serial number of the lithium ion battery monomer; IR (i) 、Q d (i) And T min (i) Respectively the average ohmic internal resistance, the discharge capacity and the minimum temperature of the ith lithium ion battery monomer in the Nth cycle process; n is more than or equal to 100;
and step 3: for the X (i) Performing dimensionality reduction treatment on the X (i) The three characteristic information in the system are fused into one characteristic
Figure FDA0003235127850000011
For the cycle life Y of the lithium ion battery monomer (i) Carrying out outlier treatment;
and 4, step 4: obtained by reducing the dimension in the step 3
Figure FDA0003235127850000012
Fitting the cycle life of the battery after the outlier treatment to obtain a lithium ion battery cycle life prediction model;
and 5: inputting lithium ions to be tested into the lithium ion battery cycle life prediction modelParameters measured in the Nth cycle of the battery { IR, Q } d ，T min And obtaining the cycle life of the lithium ion battery to be tested.
2. The lithium ion battery cycle life low-cost prediction method of claim 1, characterized in that: n in step 1 is 100.
3. The lithium ion battery cycle life low-cost prediction method of claim 1 or 2, characterized in that: step 2, performing Principal Component Analysis (PCA) on the X (i) And (4) performing dimension reduction treatment, wherein the objective function is as follows:
Figure FDA0003235127850000013
in the formula (I), the compound is shown in the specification,
w represents a direction vector, w ═ w 1 ，w 2 ，w 3 )；
m represents the number of the lithium ion battery monomers selected in the step 1;
i represents the number of the lithium ion battery cell;
Figure FDA0003235127850000021
representing the features after the mean has been zeroed,
Figure FDA0003235127850000022
4. the lithium ion battery cycle life low-cost prediction method of claim 1, characterized in that: in step 3, outlier processing adopts a boxplot analysis method, and the boundary values are defined as follows:
IQR＝Q 3 -Q 1
l u ＝Q 3 +1.5IQR
l l ＝Q 1 -1.5IQR
wherein the content of the first and second substances,
Q 3 shows the cycle life Y of the lithium ion battery monomer obtained in the step 2 (i) The middle upper quarter value of (1);
Q 1 shows the cycle life Y of the lithium ion battery monomer obtained in the step 2 (i) The middle lower quarter value of (a);
IQR represents the cycle life Y of the lithium ion battery cell obtained in the step 2 (i) The quartile range of (d);
l u is an upper limit value for screening outliers;
l l is the lower limit used to screen out outliers.
5. The lithium ion battery cycle life low-cost prediction method of claim 4, characterized in that: and 4, fitting by adopting a least square method, wherein the target function is as follows:
Figure FDA0003235127850000023
Figure FDA0003235127850000024
wherein the content of the first and second substances,
a and b are linear regression equation coefficients;
n is the number of the monomers of the lithium ion battery selected in the step 1 after the outlier screening operation;
Figure FDA0003235127850000031
is X (i) Obtaining the characteristics after dimensionality reduction;
Y (i) the cycle life of the ith lithium ion battery monomer is represented;
Figure FDA0003235127850000032
is the mean value of the characteristics obtained by dimension reduction of all lithium ion battery monomers,
Figure FDA0003235127850000033
Figure FDA0003235127850000034
is the average value of the cycle life of all lithium ion battery monomers obtained after the treatment of the outlier,
Figure FDA0003235127850000035
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106324524A (en) * 2016-10-11 2017-01-11 合肥国轩高科动力能源有限公司 Rapid prediction method of cycle life of lithium-ion battery
CN109856559A (en) * 2019-02-28 2019-06-07 武汉理工大学 A kind of prediction technique of lithium battery cycle life

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106324524A (en) * 2016-10-11 2017-01-11 合肥国轩高科动力能源有限公司 Rapid prediction method of cycle life of lithium-ion battery
CN109856559A (en) * 2019-02-28 2019-06-07 武汉理工大学 A kind of prediction technique of lithium battery cycle life

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于MIV的BP神经网络磷酸铁锂电池寿命预测;张金国等;《电源技术》;20160120(第01期);全文 *

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