CN112433161A - Automatic battery parameter identification method - Google Patents

Automatic battery parameter identification method Download PDF

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CN112433161A
CN112433161A CN202011086192.4A CN202011086192A CN112433161A CN 112433161 A CN112433161 A CN 112433161A CN 202011086192 A CN202011086192 A CN 202011086192A CN 112433161 A CN112433161 A CN 112433161A
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recursive
circuit voltage
recursion
open
parameters
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CN112433161B (en
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何博
宋爱
夏雨雨
刘兆斌
崔桐
冉小龙
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Gree Electric Appliances Inc of Zhuhai
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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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • 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
    • 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

Abstract

The invention relates to a method for automatically identifying battery parameters, which comprises the following steps: establishing a second-order RC equivalent circuit model, and performing discrete and differential transformation to obtain an electric expression of the circuit model; performing Laplace transformation on the electrical expression of the circuit model, converting the electrical expression into a z-transformation form, performing linear transformation, and converting the electrical expression into a time domain discrete equation; converting into a vector form based on a time domain discrete equation; establishing a recursive least square method with a forgetting factor lambda, and calculating a vector to be identified by recursive iteration based on the recursive least square method; after the recursion times d are set to reach the recursion times d, a set of model parameters are obtained by applying a recursion least square method, the SOC of the battery is calculated by applying the model parameters and combining with the extended Kalman filtering, and an open-circuit voltage OCV can be obtained through the calculated SOC due to the fact that a certain linear relation exists between the SOC and the open-circuit voltage OCV; meterCalculating open circuit voltage OCV and open circuit voltage
Figure 100004_DEST_PATH_IMAGE001
Difference between (A) and (B)
Figure 520035DEST_PATH_IMAGE002
) An absolute value of if
Figure 786937DEST_PATH_IMAGE002
If the absolute value of (A) exceeds a set range, the parameter is suppressed.

Description

Automatic battery parameter identification method
Technical Field
The invention relates to the technical field of battery detection, in particular to a battery parameter automatic identification method.
Background
The battery state estimation is the key point of a battery management system, accurate state estimation cannot be separated from parameter identification of a battery, currently, in the field, the parameter identification of the battery is an identified battery model parameter, the battery model parameter has important significance for the battery state estimation, and a battery parameter identification method comprises off-line identification and on-line identification. The offline identification is generally obtained by utilizing the HPPC (pulse discharge) data fitting of the battery, the offline identification is simple and easy to realize in engineering, but the parameters obtained by the offline identification are a set of fixed values, and the offline identification ignores the influence of the parameters on the factors such as the service life of the battery, the discharge multiplying power and the like. The online identification is that parameter values are continuously updated along with the iteration times according to real-time data (including current and voltage values and the like) of external tests, which cannot be realized by offline identification, and because model parameters can change along with factors such as battery life, discharge multiplying power and the like, the online identification just can continuously update parameters, so that the online identification has a real-time parameter correction function, but the engineering realization of the online identification is difficult, and the calculation amount is large.
Chinese patent 201710421602.8 (a least square method lithium battery model parameter identification method with forgetting factor) discloses the technical scheme as follows: on the basis of a second-order RC model, a Laplace equation of an equivalent circuit model is subjected to bilinear transformation to obtain a difference equation of input and output of a model system, and model parameters are obtained by recursive least squares with forgetting factors on the basis. But the technical scheme does not relate to the rationality elimination of the sampling data.
Chinese patent 201910256426.6 (an online parameter identification and backtracking method for power battery systems) discloses the following technical scheme: based on the first-order RC, the method mainly utilizes the terminal voltage error to carry out recursive backtracking on the basis of the conventional recursive least square with forgetting factors, and can inhibit the abnormal jitter of the conventional recursive least square parameters. But the technical scheme does not relate to the rationality elimination of the sampling data.
The technical scheme of online identification disclosed in the prior art has the problem that abnormal divergent sample data cannot be eliminated, and because a sampling sensor connected with a battery has an error, parameters are diverged in the identification process due to some sampling data.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides an automatic battery parameter identification method which is based on recursive least squares and can realize online identification of battery parameters. The automatic battery parameter identification method provided by the invention can reasonably eliminate the sampling data and promote the identified parameters not to diverge any more, and can be applied to battery state estimation (SOC) (the premise of battery state estimation is that the parameters need to be identified).
In order to achieve the purpose, the invention adopts the following technical scheme: a method for automatically identifying battery parameters comprises the following steps:
step 1, establishing a second-order RC equivalent circuit model, and performing discrete and differential transformation to obtain an electric expression of the circuit model;
step 2, performing Laplace transformation on the electrical expression of the circuit model obtained in the step 1, converting the electrical expression into a z-transformation form and introducing the z-transformation form
Figure 100002_DEST_PATH_IMAGE001
Performing linear transformation, and converting into a time domain discrete equation;
step 3, converting the time domain discrete equation obtained in the step 2 into a vector form;
step 4, establishing a recursive least square method with a forgetting factor lambda, and calculating a vector to be identified through recursive iteration based on the recursive least square method;
step 5, setting recursion times d (d)<1000) Obtaining a group of model parameters by applying the recursive least square method obtained in the step 4 after the recursive number d is reached, wherein the model parameters comprise open-circuit voltage
Figure 888787DEST_PATH_IMAGE002
The SOC (state of charge) of the battery is calculated by combining model parameters with the extended Kalman filter, and an open-circuit voltage OCV can be obtained through the calculated SOC due to the fact that a certain linear relation exists between the SOC and the open-circuit voltage OCV;
step 6, calculating the open circuit voltage OCV and the open circuit voltage obtained in the step 5
Figure 61143DEST_PATH_IMAGE002
Difference between (A) and (B)
Figure 100002_DEST_PATH_IMAGE003
) And judging the absolute value of
Figure 950601DEST_PATH_IMAGE003
If the absolute value of the absolute value exceeds the set range, jumping to a step 7 if the absolute value does not exceed the set range, otherwise, switching to a step 8;
step 7, not performing parameter suppression, continuing to perform recursion based on the recursive least square method obtained in the step 4, and skipping to the step 5;
step 8, carrying out parameter suppression until
Figure 406859DEST_PATH_IMAGE003
The absolute value of (2) does not exceed the set range, and the step (5) is skipped.
In step 1, the electrical expression of the circuit model is specifically as follows:
Figure 254730DEST_PATH_IMAGE004
（1）
Figure 100002_DEST_PATH_IMAGE005
（2）
Figure 648802DEST_PATH_IMAGE006
（3）。
in step 2, equations (1), (2) and (3) are laplace transformed to obtain:
Figure 607531DEST_PATH_IMAGE007
（4）。
in step 2, the equation (4) is converted to the z-transformed form and introduced
Figure 403448DEST_PATH_IMAGE008
Performing a linear transformation to obtain:
Figure DEST_PATH_IMAGE009
（5）。
in step 2, the equation (5) is converted into a time domain discrete equation to obtain:
Figure 156641DEST_PATH_IMAGE010
（6）。
in step 3, equation (6) is converted into vector form:
Figure 552856DEST_PATH_IMAGE011
（7）
in step 3, an initial value is determined
Figure 315275DEST_PATH_IMAGE012
I.e., a zero vector of 6 elements,
Figure 231279DEST_PATH_IMAGE013
。
in step 4, the recursive least square method with forgetting factor λ is specifically:
Figure 155373DEST_PATH_IMAGE014
（8）
Figure 789616DEST_PATH_IMAGE015
（9）
Figure 90147DEST_PATH_IMAGE016
（10）；
wherein, k represents the time of day,
Figure 391816DEST_PATH_IMAGE017
and theta represents an unknown parameter vector needing to be identified for the actual terminal voltage value at the moment k. And carrying out recursive iteration on the basis of the recursive least square method to calculate the vector to be identified.
In step 5, a set of model parameters is obtained by applying the recursive least square method obtained in step 4, and the specific steps are as follows:
is provided with
Figure 486811DEST_PATH_IMAGE018
（11）
Combining equation (5) can obtain:
Figure 608351DEST_PATH_IMAGE019
（12）
Figure 973559DEST_PATH_IMAGE020
（13）
Figure 598576DEST_PATH_IMAGE021
（14）
Figure 130051DEST_PATH_IMAGE022
（15）
Figure 473308DEST_PATH_IMAGE023
（16）
Figure 381221DEST_PATH_IMAGE024
（17）
the following model parameters can be obtained according to equations (12), (13), (14), (15), (16) and (17):
Figure 126323DEST_PATH_IMAGE025
。
in a step 6, the process is carried out,
Figure 828700DEST_PATH_IMAGE026
compare with e, which is 0.01.
In step 8, the specific steps for performing parameter suppression are as follows: first, the open-circuit voltage obtained by recursion of the first 20 recursions of the recursion times d is calculated
Figure 659252DEST_PATH_IMAGE002
Find out to make
Figure 370857DEST_PATH_IMAGE027
And recording the time when the minimum value is obtained, setting the time as dm, eliminating the sampling data between dm and d, and restarting recursion for dm times, so that the sampling data which causes the parameter dispersion is eliminated, and the parameter is restrained.
Because the existing current and voltage sampling sensor has errors, the battery parameters can be diverged in the online parameter identification iteration process by the data collected by the current and voltage sampling sensor connected with the battery, and when the subsequent time continues iteration, if the diverged parameters are used, the parameters obtained by the subsequent time iteration cannot be converged, so that the identification failure is caused. The automatic battery parameter identification method provided by the invention can be used for reasonably eliminating the sampled data and ensuring that the data is not scattered.
Compared with the prior art, the invention can achieve the following beneficial effects:
1. the automatic battery parameter identification method provided by the invention is characterized in that on the basis of a second-order RC model, variable forgetting factor recursion least square is used for identifying parameters, and on the basis, an identification result is restrained by combining an SOC-OCV curve so as to reduce parameter fluctuation and even dispersion;
2. the method of the invention can overcome the problem of parameter change caused by the factors of service life, temperature and the like of the battery, and constantly ensure the parameter update.
Drawings
FIG. 1 is a flow chart of a method for automatically identifying battery parameters.
FIG. 2 is a SOC-OCV graph.
FIG. 3 is a parameter identification diagram of convergence.
FIG. 4 is a discrete parameter identification diagram.
Detailed Description
To facilitate an understanding of the present invention for those skilled in the art, the present invention will be described in further detail below with reference to specific embodiments and accompanying drawings.
As shown in fig. 1, the present invention provides a method for automatically identifying battery parameters, comprising the following steps:
step 1, a current and voltage sampling sensor connected with a battery collects data, and the method comprises the following steps: u (K), U (K-2), I (K-1), I (K-2);
step 2, calculating a recursion matrix K (k) according to equations (8), (9) and (10);
step 3, calculating an error covariance matrix P (k) according to equations (8), (9) and (10);
step 4, updating recursion parameters, and obtaining model parameters according to equations (12), (13), (14), (15), (16) and (17), wherein the model parameters comprise
Figure 219733DEST_PATH_IMAGE025
；
Step 5, after a preset recursion number d (d <1000) is reached, calculating the SOC (state of charge) of the battery through extended Kalman filtering, and obtaining an open-circuit voltage OCV through the calculated SOC due to the fact that a certain linear relation exists between the SOC and the open-circuit voltage OCV;
step 6, calculating the open circuit voltage OCV and the open circuit voltage obtained in the step 5
Figure 93011DEST_PATH_IMAGE002
Difference between (A) and (B)
Figure 410860DEST_PATH_IMAGE027
) And judging the absolute value of
Figure 394996DEST_PATH_IMAGE027
If the absolute value of the absolute value exceeds the set range e, jumping to the step 7 if the absolute value does not exceed the set range e, otherwise, switching to the step 8;
step 7, not performing parameter suppression, continuing to perform recursion based on the recursive least square method obtained in the step 4, and skipping to the step 5;
step 8, performing parameter suppression, namely calculating the open-circuit voltage obtained by recursion of the previous 20 times of recursion times d
Figure 114690DEST_PATH_IMAGE002
Find out to make
Figure 158870DEST_PATH_IMAGE027
The time of minimum is recorded, the time is set as dm, the sampling data between dm and d is eliminated, and the recursion is restarted for dm times, so that the sampling data which causes the parameter dispersion is eliminated, and the parameter is suppressed until
Figure 964015DEST_PATH_IMAGE027
After the absolute value of (1) does not exceed the set range, skipping to step 5.
An SOC-OCV graph of the SOC and the open circuit voltage OCV obtained according to the above method for automatically identifying battery parameters is shown in fig. 2.
As shown in fig. 3, the converged parameter identification map obtained by the above method for automatically identifying battery parameters can determine that the data is more converged and stable, and it can be observed that most of the data after the divergence point is removed oscillates around 0, and even if there is a large oscillation (peak), the data can be converged quickly, which is a good result caused by the removal of the divergence point.
As shown in fig. 4, the discrete parameter identification map obtained by the existing on-line identification technical scheme can determine the identification result caused by the divergent sampling data, and it can be seen that the data oscillation is large, and at the end, the data directly becomes large (the peak is high), and the data directly diverges, directly resulting in the identification failure.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (9)

1. A method for automatically identifying battery parameters is characterized by comprising the following steps:
step 1, establishing a second-order RC equivalent circuit model, and performing discrete and differential transformation to obtain an electric expression of the circuit model;
step 2, performing Laplace transformation on the electrical expression of the circuit model obtained in the step 1, converting the electrical expression into a z-transformation form and introducing the z-transformation form
Figure DEST_PATH_IMAGE001
Performing linear transformation, and converting into a time domain discrete equation;
step 3, converting the time domain discrete equation obtained in the step 2 into a vector form;
step 4, establishing a recursive least square method with a forgetting factor lambda, and calculating a vector to be identified through recursive iteration based on the recursive least square method;
step 5, recursion times d are set, after the recursion times d are reached, a group of model parameters are obtained by applying the recursion least square method obtained in the step 4, and the model parameters comprise open-circuit voltage
Figure 350780DEST_PATH_IMAGE002
The SOC (state of charge) of the battery is calculated by combining model parameters with the extended Kalman filter, and an open-circuit voltage OCV can be obtained through the calculated SOC due to the fact that a certain linear relation exists between the SOC and the open-circuit voltage OCV;
step 6, calculating the difference between the open circuit voltage OCV and the open circuit voltage obtained in step 5 (
Figure DEST_PATH_IMAGE003
) And judging the absolute value of
Figure 393823DEST_PATH_IMAGE003
If the absolute value of the absolute value exceeds the set range, jumping to a step 7 if the absolute value does not exceed the set range, otherwise, switching to a step 8;
step 7, not performing parameter suppression, continuing to perform recursion based on the recursive least square method obtained in the step 4, and skipping to the step 5;
step 8, carrying out parameter suppression until
Figure 608903DEST_PATH_IMAGE003
The absolute value of (2) does not exceed the set range, and the step (5) is skipped.
2. The method according to claim 1, wherein in step 1, the electrical expression of the circuit model is as follows:
Figure 885033DEST_PATH_IMAGE004
。
3. the method according to claim 2, wherein in the step 2, the time domain discrete equation is:
Figure DEST_PATH_IMAGE005
（6）。
4. the method according to claim 3, wherein in the step 4, the recursive least square method is:
Figure 945393DEST_PATH_IMAGE006
。
5. the method according to claim 4, wherein the model parameters further comprise
Figure 374100DEST_PATH_IMAGE007
。
6. The method according to claim 5, wherein the model parameters satisfy:
Figure DEST_PATH_IMAGE008
。
7. the method according to claim 6, wherein the setting range is 0.01.
8. The method according to claim 7, wherein d is not greater than 1000.
9. The method for automatically identifying battery parameters according to claim 8, wherein in the step 8, the step of suppressing the parameters comprises: first, the open-circuit voltage obtained by recursion of the first 20 recursions of the recursion times d is calculated
Figure 228923DEST_PATH_IMAGE002
Find out to make
Figure 257928DEST_PATH_IMAGE003
And recording the time when the minimum value is obtained, setting the time as dm, eliminating the sampling data between dm and d, and restarting recurrence for dm times.
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