CN111123127A - Function fitting and electric quantity prediction method for lithium battery charging electric quantity - Google Patents
Function fitting and electric quantity prediction method for lithium battery charging electric quantity Download PDFInfo
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- CN111123127A CN111123127A CN201911219362.9A CN201911219362A CN111123127A CN 111123127 A CN111123127 A CN 111123127A CN 201911219362 A CN201911219362 A CN 201911219362A CN 111123127 A CN111123127 A CN 111123127A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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Abstract
The invention discloses a function fitting and electric quantity prediction method for lithium battery charging electric quantity. The method comprises the following steps: a. data acquisition: charging the lithium battery after complete discharge, and collecting data of a constant voltage charging stage in the charging process; b. constructing a total charging capacity function model of the lithium battery according to the acquired data; c. and predicting the total electric quantity of the lithium batteries of the same type by using a charging total electric quantity function model. The invention has the characteristics of reducing the time for battery production detection and improving the production efficiency.
Description
Technical Field
The invention relates to a lithium battery electric quantity measurement and prediction method, in particular to a function fitting and electric quantity prediction method for lithium battery charging electric quantity.
Background
With the development of new energy technology, the consumption of new energy automobiles and lithium batteries in daily life is rapidly increased, the production capacity of the lithium batteries is continuously improved, and China also becomes a large country for producing the lithium batteries.
In the production process of the battery, the original detection of the electric quantity of the battery is to evaluate the electric quantity of the lithium battery through the processes of full charging and full discharging and to grade. The method has the advantages that the battery charging and discharging detection time is long, and the production line efficiency cannot be obviously improved. Therefore, a method for accurately and quickly evaluating the electric quantity of the lithium battery is found, and the method has important significance for improving the efficiency of a battery production line. However, there is no threshold evaluation method for predicting the total battery capacity.
Disclosure of Invention
The invention aims to provide a function fitting and electric quantity prediction method for lithium battery charging electric quantity. The invention has the characteristics of reducing the time for battery production detection and improving the production efficiency.
The technical scheme of the invention is as follows: a function fitting and electric quantity predicting method for lithium battery charging electric quantity comprises the following steps:
a. data acquisition: charging the lithium battery after complete discharge, and collecting data of a constant voltage charging stage in the charging process;
b. constructing a total charging capacity function model of the lithium battery according to the acquired data;
c. and predicting the total electric quantity of the lithium batteries of the same type by using a charging total electric quantity function model.
In the step a of the method for function fitting of lithium battery charging capacity and capacity prediction, the time cut-off point of lithium battery charging is a time point when the current per minute reduction value in the constant voltage charging stage is less than 5 mA.
In step b of the method for function fitting of the charging capacity and capacity prediction of the lithium battery, the charging total capacity function model is an approximation function of a limit value about the total capacity in the charging process.
In step b of the foregoing method for function fitting of lithium battery charging capacity and capacity prediction, the charging total capacity function model has convergence and minimum variance.
In step b of the method for function fitting of lithium battery charging capacity and capacity prediction, the total charging capacity function model is:
Qt＝Q1+b1(Xtime+b2)/(Xtime+b3)；
wherein Q istCumulative total charge, in mAH; q1The total charged electric quantity at the switching point from the constant current charging stage to the constant voltage charging stage; b1、b2、b3Fitting regression calculation is carried out on the dimensionless parameters by a least square function; xtimeTo be the time-of-charge coordinate,unit: and second.
In step b of the foregoing method for function fitting of lithium battery charging capacity and predicting capacity, the step b1、b2、b3Is a value that ensures that the calculation does not overflow.
In the step b of the method for function fitting of lithium battery charging capacity and predicting capacity, data in 65% of the time period of the whole constant voltage charging stage in the collected data is used for constructing a charging total capacity function model.
Has the advantages that: compared with the prior art, the method constructs a curve function (namely a charging total electric quantity function model) capable of fitting the electric quantity value accurately according to the electric quantity detection data in the actual lithium battery charging process, and the function model has better convergence and lower data variance and can be applied to the estimation of the electric quantity of the lithium battery charging more accurately. Specifically, on the basis of analyzing the electric quantity data of the lithium battery, the curve fitting regression of the least square method is applied, the effective analysis and function construction are carried out on the electric quantity data of the lithium battery, and for lithium battery manufacturers, due to the fact that the function model of the charging electric quantity is constructed, accurate prediction and classification of the electric quantity of the battery can be achieved through short-time charging electric quantity detection, the time of battery production detection is greatly shortened, the production efficiency is effectively improved, and the method has good market application prospects.
In order to prove the beneficial effects of the invention, the following experiments are applied:
experiment one: constructing a charging total electric quantity function model
Experimental methods
Step 1: the charging data of the lithium battery sample 1 at the constant-voltage charging stage of the constant-current constant-voltage charging after the complete discharging is collected, and a plurality of groups of data are obtained for modeling analysis. The data of the collected part is shown in table 1.
TABLE 1
Step 2: the data of table 1 were analyzed to yield:
① the total charge of lithium battery is close to full charge, the current is reduced under the condition of constant voltage charging, i.e. the charge has a limit value under the condition of saturation, the function design for the total charge of the process should be a limit value approximation function, see fig. 1.
② the voltage is raised to the voltage threshold of the charging mode switch when the lithium battery is charged with constant current, the time for reaching the constant voltage charging is different from the total charged electricity quantity because of the internal material mechanism of each battery.
And step 3: based on the above analysis, a total charge capacity function model is constructed as follows:
Qt＝Q1+b1(Xtime+b2)/(Xtime+b3)；
wherein Q istCumulative total charge, in mAH; q1Substituting the charged total electric quantity at the switching point from the constant-current charging stage to the constant-voltage charging stage when each lithium battery formula returns; b1、b2、b3Fitting regression calculation is carried out on the dimensionless parameters by a least square function; xtimeCharge time coordinate, unit: and second.
Based on the constructed function model, a calculation program is compiled in the environment of R data analysis software, and related parameters are calculated, wherein the main calculation program is as follows:
① first regression calculation
setwd("D:/temp/prog")
Dcell＝read.table("Data-cell04.txt",header＝TRUE)
attach(Dcell)
fit＝nls(YeQ～(2100+b1*(Xtime+b2)/(Xtime+b3)),start＝list(b1＝100,b2＝10,b3＝100))
summary(fit)
plot(Xtime,YeQ)；
detach(Dcell)
The procedure is described as follows:
data-cell04.txt is a charging Data variable file with a field name;
dcell is a charging data file multidimensional array;
fitting regression calculations were performed in R using nls least squares functions.
In the program variable, due to b1、b2、b3Unknown, is a parameter needing regression, so a preliminary preset value is given for calculation: b1＝100,b2＝10,b3＝100。
② after the first regression, b will be obtained1＝421,b2＝18,b3Substituting 948 into the program, a second regression was performed as follows: (Note: the other procedures are the same)
fit＝nls(YeQ～(2100+b1*(Xtime+b2)/(Xtime+b3)),start＝list(b1＝421,b2＝18,b3＝948))
③ the resulting function model is checked in a data sheet.
State space regression function for lithium battery sample 1:
Qt＝2100+421(Xtime+18)/(Xtime+948)
④, obtaining the final optimization regression function model (total charge capacity function model) after a plurality of iterations.
Qt＝2100+472(Xtime+3.7)/(Xtime+1152.75).
And 4, step 4: optimization of function model and data regression accuracy:
①, the fitted function is tested, the model is proved to be convergent and have small variance, the R language calculated result:
② the period variation ratio is calculated by data, the model precision is within 0.2, see table 2, regression error of lithium battery sample 1.
TABLE 2
Experiment two: lithium battery capacity prediction by using total charge quantity function model
The test method comprises the following steps:
step 1: and collecting the charging data of the lithium battery sample 2 in the constant-voltage charging stage of the constant-current constant-voltage charging after the complete discharging to obtain multiple groups of data for modeling analysis. The data of the collected part is shown in table 3.
TABLE 3
Step 2: constructing a charging total electric quantity function model according to a method of experiment one:
Qt＝Q1+b1(Xtime+b2)/(Xtime+b3)；
and step 3: the model of step 2 is trained using data from the entire constant voltage charging phase of lithium battery sample 2 over a 65% time period, and the specific model training data is shown in table 4.
TABLE 4
Finally, the function model of the total charging capacity of the lithium battery sample 2 is obtained through training (the function model curve is shown in figure 2):
Qt＝2178+358(Xtime-1.7)/(Xtime+1007).
and 4, step 4: the model of step 3 was used to predict and verify the remaining 35% of the time for electricity, see data in table 5.
TABLE 5
As can be seen from Table 5: the prediction precision is within 0.4%, so that the total electric quantity of the lithium battery can be accurately predicted.
Drawings
Fig. 1 is charge data of a lithium battery sample 1 with abscissa time;
fig. 2 is charge data of the lithium battery sample 2 with abscissa time.
Detailed Description
The invention is further illustrated by the following figures and examples, which are not to be construed as limiting the invention.
Example 1. A function fitting and electric quantity predicting method for lithium battery charging electric quantity comprises the following steps: the method comprises the following steps:
a. data acquisition: charging the lithium battery after complete discharge, and collecting data of a constant voltage charging stage in the charging process;
b. constructing a total charging capacity function model of the lithium battery according to the acquired data;
c. and predicting the total electric quantity of the lithium batteries of the same type by using a charging total electric quantity function model.
In the step a, the time cut-off point of the lithium battery charging is a time point when the current per minute decrease value in the constant voltage charging stage is less than 5 mA.
In the foregoing step b, the charging total electric quantity function model is an approximation function of a limit value related to the total electric quantity in the charging process.
In the foregoing step b, the charging total capacity function model has convergence and minimum variance.
In the foregoing step b, the total charging capacity function model is:
Qt＝Q1+b1(Xtime+b2)/(Xtime+b3)；
wherein Q istCumulative total charge in mAH mAh; q1The total charged electric quantity at the switching point from the constant current charging stage to the constant voltage charging stage; b1、b2、b3Fitting regression calculation is carried out on the dimensionless parameters by a least square function; xtimeCharge time coordinate, unit: and second. The charging total electric quantity function model can adopt an nls least square function to carry out fitting regression in R.
In the foregoing step b, b is1、b2、b3Are each a value that ensures that the calculation does not overflow, as used in the above experiment1、b2、b3Are 100,10,100, respectively. This value is used as the initial value for the regression operation.
In the step b, the data in the 65% time period of the whole constant voltage charging stage in the collected data are used to construct a charging total electric quantity function model.
Claims (7)
1. A function fitting and electric quantity predicting method for lithium battery charging electric quantity is characterized by comprising the following steps:
a. data acquisition: charging the lithium battery after complete discharge, and collecting data of a constant voltage charging stage in the charging process;
b. constructing a total charging capacity function model of the lithium battery according to the acquired data;
c. and predicting the total electric quantity of the lithium batteries of the same type by using a charging total electric quantity function model.
2. The method of claim 1, wherein the cut-off point of the lithium battery charging time in step a is a time point when the current per minute decrease value in the constant voltage charging stage is less than 5 mA.
3. The method of claim 1, wherein in step b, the function model of the total charge is an approximation function of a limit value of the total charge during the charging process.
4. The method of claim 3, wherein the model of the total charge capacity function has convergence and minimum variance in step b.
5. The method of claim 1, wherein in step b, the total charge capacity function model is:
Qt＝Q1+b1(Xtime+b2)/(Xtime+b3)；
wherein Q istCumulative total charge, in mAH; q1The total charged electric quantity at the switching point from the constant current charging stage to the constant voltage charging stage; b1、b2、b3Fitting regression calculation is carried out on the dimensionless parameters by a least square function; xtimeCharge time coordinate, unit: and second.
6. The method as claimed in claim 5, wherein in step b, b is a function of the charging capacity of the lithium battery and the capacity prediction is performed1、b2、b3Is a value that ensures that the calculation does not overflow.
7. The method as claimed in claim 5, wherein in the step b, the data collected during 65% of the whole constant voltage charging period is used to construct a charging total capacity function model.
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Application publication date: 20200508 |