CN107942261B - Method and system for estimating state of charge of battery - Google Patents
Method and system for estimating state of charge of battery Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention discloses a method and a system for estimating the state of charge of a battery. The estimation method comprises the following steps: s1Acquiring charge and discharge data of a battery to be tested, and calculating a characteristic variable of the battery to be tested according to the charge and discharge data; s2Inputting the characteristic variables of the battery to be tested into a relevant model to obtain the maximum available capacity of the battery to be tested; the correlation model represents the quantitative relation between the characteristic variables and the maximum available capacity; s3And acquiring the residual capacity of the battery to be tested, and estimating the state of charge of the battery to be tested according to the residual capacity and the maximum available capacity. According to the invention, the current maximum available capacity of the battery is estimated on line through the correlation model, the SOC calculation of the full life cycle of the battery is realized on the basis, and the problem that the SOC estimation error becomes larger along with the attenuation of the battery capacity is solved. The algorithm of the invention is simple and practical, has low complexity and is suitable for the embedded BMS system.
Description
Technical Field
The invention relates to the technical field of batteries, in particular to a battery state of charge estimation method and system suitable for an embedded BMS (battery management system).
Background
The battery nuclear power State (SOC) is the most important parameter in a battery management system, and the accuracy of SOC estimation has important significance on the stable operation and safety of the system. The SOC is defined as follows: SOC is the remaining capacity/maximum available capacity, and the battery factory capacity is generally set as the maximum available capacity, and SOC is estimated. At the time of delivery, the maximum available capacity is equal to the delivery capacity, and the maximum available capacity is smaller than the delivery capacity along with the use of the battery.
In the prior art, the influence of the cycle life on the capacity is rarely considered in the SOC estimation method, the quantitative relation between the battery capacity and the maximum capacity cannot be determined, the capacity of the lithium battery is attenuated along with the increase of the use times, the ratio of the residual capacity to the factory-leaving capacity of the battery is simply used as the estimated value of the SOC, the method has high accuracy when the battery leaves a factory, and the error of the estimated value of the SOC is larger and larger along with the gradual attenuation of the cycle use capacity of the battery. In the prior art, an SOC estimation method for battery aging is available, but the method requires a large amount of data to be calculated, is complex, and is not suitable for an embedded BMS system due to limited calculation and storage capabilities of the embedded BMS system.
Disclosure of Invention
The invention aims to overcome the defects that the SOC estimation method in the prior art cannot accurately estimate the SOC of a battery due to the fact that the influence of the cycle life on the capacity is not considered, or the method is complex and the calculation amount is large, and provides the estimation method and the estimation system of the SOC of the battery, which have high accuracy and low algorithm complexity and are suitable for an embedded BMS system.
The invention solves the technical problems through the following technical scheme:
a method of estimating state of charge of a battery, the method comprising the steps of:
S1acquiring charge and discharge data of a battery to be tested, and calculating a characteristic variable of the battery to be tested according to the charge and discharge data;
S2inputting the characteristic variables of the battery to be tested into a relevant model to obtain the maximum available capacity of the battery to be tested;
the correlation model represents the quantitative relation between the characteristic variables and the maximum available capacity;
S3and acquiring the residual capacity of the battery to be tested, and estimating the state of charge of the battery to be tested according to the residual capacity and the maximum available capacity.
Preferably, step S2Before, the estimation method further comprises the step of establishing a correlation model;
the step of establishing the correlation model specifically includes:
S11obtaining the service life of a plurality of batteries with the same type as the battery to be testedAttenuation data;
S12constructing a characteristic variable of the battery according to the life attenuation data and calculating a capacity data sequence;
S13and fitting the capacity data sequence and the characteristic variables based on a least square method to construct a correlation model.
Preferably, step S12Before, still include:
and smoothly denoising the life attenuation data based on a digital filtering method.
Preferably, the feature variables include: isobaric drop discharge ampere-hour and/or isobaric drop charge ampere-hour;
the constant pressure drop discharge ampere-hour represents the ampere-hour number corresponding to the battery after the battery is discharged according to the constant pressure drop in each charge-discharge period;
and the constant-pressure-drop charging ampere-hour represents the corresponding ampere-hour number of the battery after the battery is charged according to the constant pressure drop in each charging and discharging period.
Preferably, step S3The step of obtaining the remaining capacity of the battery to be tested specifically includes:
calculating the initial value of the capacity of the battery to be tested according to the SOC-OCV curve;
calculating the capacity change value of the battery to be tested based on an ampere-hour integral method;
and calculating the residual capacity according to the initial capacity value and the capacity change value.
Preferably, the charge and discharge data and the lifetime decay data include the following parameters:
voltage, current and temperature of the battery during charging and discharging.
The present invention also provides a system for estimating a state of charge of a battery, the system comprising:
the data acquisition module is used for acquiring charge and discharge data of the battery to be detected and calculating a characteristic variable of the battery to be detected according to the charge and discharge data;
the calculation module is used for inputting the characteristic variables of the battery to be tested into a relevant model so as to obtain the maximum available capacity of the battery to be tested;
the correlation model represents the quantitative relation between the characteristic variables and the maximum available capacity;
the calculation module is further used for obtaining the residual capacity of the battery to be tested and estimating the state of charge of the battery to be tested according to the residual capacity and the maximum available capacity.
Preferably, the estimation system further comprises: a model building module;
the model building module specifically comprises:
the data acquisition unit is used for acquiring life attenuation data of a plurality of batteries with the same types as the batteries to be tested;
the computing unit is used for constructing a characteristic variable of the battery according to the life attenuation data and computing a capacity data sequence;
and the model construction unit is used for fitting the capacity data sequence and the characteristic variable based on a least square method so as to construct a relevant model.
Preferably, the model building module further comprises:
and the data processing unit is used for smoothly denoising the life attenuation data based on a digital filtering method.
Preferably, the feature variables include: isobaric drop discharge ampere-hour and/or isobaric drop charge ampere-hour;
the constant pressure drop discharge ampere-hour represents the ampere-hour number corresponding to the battery after the battery is discharged according to the constant pressure drop in each charge-discharge period;
and the constant-pressure-drop charging ampere-hour represents the corresponding ampere-hour number of the battery after the battery is charged according to the constant pressure drop in each charging and discharging period.
Preferably, the calculation module is specifically configured to calculate a capacity initial value of the battery to be measured according to an SOC-OCV curve, calculate a capacity variation value of the battery to be measured based on an ampere-hour integration method, and calculate the remaining capacity according to the capacity initial value and the capacity variation value.
Preferably, the charge and discharge data and the lifetime decay data include the following parameters:
voltage, current and temperature of the battery during charging and discharging.
The positive progress effects of the invention are as follows: according to the invention, the current maximum available capacity of the battery is estimated on line through the correlation model, the SOC calculation of the full life cycle of the battery is realized on the basis, and the problem that the SOC estimation error becomes larger along with the attenuation of the battery capacity is solved. The algorithm of the invention is simple and practical, has low complexity and is suitable for the embedded BMS system.
Drawings
Fig. 1 is a flowchart of a method for estimating a state of charge of a battery according to embodiment 1 of the present invention.
Fig. 2 is a graph comparing cell cycle number to change in effective capacity and isobaric drop ampere-hour.
Fig. 3 is a schematic diagram showing the results of fitting a capacity data sequence and characteristic variables in the method for estimating the state of charge of a battery according to embodiment 1 of the present invention.
Fig. 4 is a block diagram illustrating a method for estimating a state of charge of a battery according to embodiment 2 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, the method for estimating the state of charge of the battery according to the present embodiment includes the following steps:
Wherein the charge and discharge data comprises the following parameters: voltage, current and temperature of the battery during charging and discharging. The parameters are associated with battery degradation, are easy to measure, and can be obtained by monitoring the battery to be measured in real time through a BMS (battery management system).
The characteristic variables include: lowering ampere time by constant pressure. The isobaric pressure drop ampere hour comprises the following steps: constant voltage drop discharge ampere-hour and/or constant voltage drop charge ampere-hour. The constant voltage drop discharge ampere-hour represents the ampere-hour number corresponding to the battery after discharging according to the constant voltage drop in each charging and discharging period, namely the discharge ampere-hour corresponding to the voltage drop from one high voltage to another low voltage in each discharging period. And the constant pressure drop charging ampere-hour represents the corresponding ampere-hour number of the battery after the battery is charged according to the constant pressure drop in each charging and discharging period. For the lithium ion battery with cyclic charge and discharge, referring to fig. 2, in the graph, L1 represents a change rule curve of equal-voltage-drop charge ampere-hour along with the cycle number, L2 represents a change rule curve of equal-voltage-drop discharge ampere-hour along with the cycle number, and L3 represents a change rule curve of effective capacity along with the cycle number. In the discharge cycle, the time required for the battery voltage to drop from one higher voltage to another lower voltage shows a decreasing trend along with the increasing number of charging and discharging times, namely, the time has a certain correlation with the actual capacity of the lithium ion battery and is easy to construct through direct monitoring. Therefore, in the present embodiment, the equal-pressure-drop discharge ampere-hour and/or the equal-pressure-drop charge ampere-hour are selected as the characteristic variables, wherein the equal-pressure-drop discharge ampere-hour is also used as the optimal selection for calculating the maximum available capacity of the battery. It should be noted that, before calculating the characteristic variables of the battery to be measured, charge and discharge data preprocessing may be performed, and abnormal data may be removed in a digital filtering manner, so as to ensure the accuracy of the calculation.
And 102, inputting the characteristic variables of the battery to be tested into the relevant model to obtain the maximum available capacity of the battery to be tested.
Wherein the correlation model characterizes a quantitative relationship of the characteristic variables to the maximum available capacity. The input parameters of the correlation model are characteristic variables, and the output parameters are maximum available capacity.
In this embodiment, before step 102, the estimation method further includes a step of establishing a correlation model.
The step of establishing the correlation model specifically comprises the following steps:
step 100-1, life attenuation data of a plurality of batteries with the same types as the batteries to be tested are obtained.
And step 100-2, constructing characteristic variables of the battery according to the life decay data and calculating a capacity data sequence.
Wherein the lifetime decay data comprises the following parameters: voltage, current and temperature of the battery during charging and discharging.
Before the step 100-2, the method further comprises the following steps: and smoothly denoising the life attenuation data based on a digital filtering method. So as to screen out abnormal values and problematic data and improve the accuracy of the relevant model.
And step 100-3, fitting the capacity data sequence and the characteristic variables based on a least square method to construct a correlation model.
The step 100-3 specifically comprises: and performing first-order polynomial fitting or second-order polynomial fitting on the characteristic variable and the battery capacity by using a least square method.
The obtained fitting polynomials are respectively:
a first order polynomial fitting formula:
CAP＝1.0245C+65.7621；
CAP is the effective capacity, C is the characteristic variable (or health factor);
mean value of error E x0, 0.8216 as the standard deviation σ of error.
The graph of the fitting effect of the first order polynomial is shown as L4 in fig. 3. The original data in fig. 3 is also the numerical value of the isobaric drop discharge ampere-hour.
Quadratic polynomial fitting formula:
CAP＝0.0129C2+0.5955C+69.0271；
CAP is the effective capacity, C is the characteristic variable;
mean value of error E x0, 0.7954 as the standard deviation σ of error.
The fitting effect graph of the quadratic polynomial is shown as L5 in fig. 3.
In this embodiment, a plurality of initial characteristic variables, such as isobaric drop discharge ampere-hour, isobaric drop discharge voltage difference, isobaric drop charge ampere-hour, and isobaric time charge voltage difference, may be selected before the correlation model is established; the equal ampere-hour discharge voltage difference represents the voltage difference corresponding to the battery after the battery is discharged according to the equal ampere-hour in each charge-discharge period; and the equal ampere-hour charging voltage difference represents the voltage difference corresponding to the battery after the battery is charged according to the equal ampere-hour in each charging and discharging period. And analyzing the correlation between the characteristic variables and the capacity data sequence of the battery, judging whether the characteristic variables and the capacity data sequence have a linear relation, and specifically calculating a correlation coefficient between the capacity data sequence and the characteristic variables by a Pearson correlation analysis method to verify the correlation. The Pearson correlation coefficient r of the ampere-hour health factor is 0.9694 due to constant pressure drop; it can be seen that the isobaric drop-off charge ampere hour health factor has a strong correlation with capacity, and therefore isobaric drop-off charge ampere hour is taken as a characteristic variable for characterizing the maximum available capacity. The obtained correlation model can accurately represent the quantitative relation between the characteristic variable and the maximum available capacity.
And 103, acquiring the residual capacity of the battery to be tested, and estimating the state of charge of the battery to be tested according to the residual capacity and the maximum available capacity.
In step 103, the step of obtaining the remaining capacity of the battery to be tested specifically includes:
and 103-1, calculating an initial value of the capacity of the battery to be measured according to the SOC-OCV curve.
And 103-2, calculating the capacity change value of the battery to be measured based on an ampere-hour integration method.
And step 103-3, calculating the residual capacity according to the initial capacity value and the capacity change value.
Specifically, in step 103-3, the remaining capacity Q' is calculated according to the following formula:
Q’＝(Q0+Q1)；
further, in step 103, the state of charge SOC may be calculated according to the following formula:
SOC＝(Q0+Q1)/Qmax；
wherein Q is0Is an initial value of capacity, Q1To a value of capacity change, QmaxIs the maximum available capacity.
The accuracy of calculating the state of charge SOC of the battery using the estimation method of the present embodiment is explained below by a specific example:
selecting a battery with the factory capacity of 100AH as a battery to be measured, and after the battery to be measured runs for a period of time, estimating the currently available maximum capacity of the battery to be measured to be 90 percent of the factory capacity, namely Q, according to the voltage and current data obtained by measurement and the fitting formula obtained in the step 100-3max90 AH. Calculating an initial capacity value Q according to the SOC-OCV curve0At 40AH, the capacity was calculated by the ampere-hour integration methodValue of variation Q120AH, SOC of the battery to be tested is (Q)0+Q1)/Qmax＝(40+20)/90＝66.7％。
On the other hand, if the SOC of the battery is calculated according to the conventional method, since the influence of the cycle life on the capacity is not considered, the SOC is (Q)0+Q1)/QFactory value60.0% in (40+ 20)/100. It can be seen that the SOC of the conventional method generates a large estimation error when the battery is aged, and the estimation error is proportional to the aging degree of the battery.
In the embodiment, the current maximum available capacity of the battery is estimated on line through the correlation model, SOC calculation of the full life cycle of the battery is realized on the basis, and the problem that SOC estimation errors become larger along with the attenuation of the battery capacity is solved. And the algorithm is simple and practical, has low complexity and is suitable for an embedded BMS system.
Example 2
As shown in fig. 4, the system for estimating the state of charge of the battery of the present embodiment includes: the device comprises a data acquisition module 1, a calculation module 2 and a model building module 3.
The data acquisition module 1 is used for acquiring charge and discharge data of the battery to be detected and calculating characteristic variables of the battery to be detected according to the charge and discharge data. Wherein the charge and discharge data comprises the following parameters: voltage, current and temperature of the battery during charging and discharging.
The characteristic variables include: constant voltage drop discharge ampere-hour and/or constant voltage drop charge ampere-hour. And the constant voltage drop discharge ampere-hour represents the ampere-hour number corresponding to the battery after the battery is discharged according to the constant voltage drop in each charge-discharge period. And the constant pressure drop charging ampere-hour represents the corresponding ampere-hour number of the battery after the battery is charged according to the constant pressure drop in each charging and discharging period. In the embodiment, equal-pressure-drop discharge ampere-hour and/or equal-pressure-drop charge ampere-hour are selected as characteristic variables, wherein equal-pressure-drop discharge ampere-hour is also taken as an optimal selection, and the maximum available capacity of the battery is calculated by using the optimal selection.
The model building module 3 is used for building a relevant module. The correlation model represents the quantitative relation between the characteristic variable and the maximum available capacity, the input parameter of the correlation model is the characteristic variable, and the output parameter is the maximum available capacity.
The model building module 3 specifically includes: a data acquisition unit 31, a calculation unit 32 and a model construction unit 33. The data acquisition unit is used for acquiring the service life attenuation data of a plurality of batteries with the same type as the battery to be tested. Wherein the charge and discharge data and the lifetime decay data comprise the following parameters: voltage, current and temperature of the battery during charging and discharging. The calculating unit is used for constructing characteristic variables of the battery according to the life attenuation data and calculating a capacity data sequence. The model building unit is used for fitting the capacity data sequence and the characteristic variables based on a least square method so as to build a relevant model. Specifically, the model construction unit performs first-order polynomial fitting or second-order polynomial fitting on the characteristic variable and the battery capacity by using a least square method to construct the relevant model.
In this embodiment, the model building module further includes: a data processing unit 34. The data processing unit is used for smoothly denoising the life attenuation data based on a digital filtering method. Therefore, the calculation unit constructs the characteristic variable of the battery according to the life attenuation data subjected to smooth denoising and calculates a capacity data sequence, so that the accuracy of the related model is improved.
The correlation model can be stored in the BMS after being built, and can be called at any time when the calculation module estimates the current nuclear power state of the battery so as to calculate the current maximum available capacity of the battery.
The calculation module 2 is used for inputting the characteristic variables of the battery to be tested into the relevant model so as to obtain the maximum available capacity Q of the battery to be testedmaxAnd acquiring the residual capacity of the battery to be tested, and estimating the state of charge of the battery to be tested according to the residual capacity and the maximum available capacity.
Specifically, the calculation module calculates an initial capacity value Q of the battery to be measured according to the SOC-OCV curve0Calculating the capacity variation value Q of the battery to be measured based on the ampere-hour integral method1And calculating the residual capacity according to the initial capacity value and the capacity change value, and further calculating the SOC, wherein the specific formula is as follows:
SOC＝(Q0+Q1)/Qmax。
in the embodiment, the current maximum available capacity of the battery is estimated on line through the correlation model, SOC calculation of the full life cycle of the battery is realized on the basis, and the problem that SOC estimation errors become larger along with the attenuation of the battery capacity is solved. And the algorithm is simple and practical, has low complexity and is suitable for an embedded BMS system.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A method of estimating state of charge of a battery, the method comprising the steps of:
S1acquiring charge and discharge data of a battery to be tested, and calculating a characteristic variable of the battery to be tested according to the charge and discharge data;
the characteristic variables include: isobaric drop discharge ampere-hour and/or isobaric drop charge ampere-hour;
the constant pressure drop discharge ampere-hour represents the ampere-hour number corresponding to the battery after the battery is discharged according to the constant pressure drop in each charge-discharge period;
the isobaric pressure drop charging ampere-hour represents the corresponding ampere-hour number of the battery after the battery is charged according to the isobaric pressure drop in each charging and discharging period;
S2inputting the characteristic variables of the battery to be tested into a relevant model to obtain the maximum available capacity of the battery to be tested;
the correlation model represents the quantitative relation between the characteristic variables and the maximum available capacity;
S3acquiring the residual capacity of the battery to be tested, and estimating the state of charge of the battery to be tested according to the residual capacity and the maximum available capacity.
2. The method of estimating state of charge of a battery of claim 1, wherein step S is2Previously, the estimation methodThe method also comprises the step of establishing a correlation model;
the step of establishing the correlation model specifically includes:
S11acquiring life attenuation data of a plurality of batteries with the same types as the batteries to be tested;
S12constructing characteristic variables of the battery according to the life attenuation data and calculating a capacity data sequence;
S13fitting the capacity data sequence and the characteristic variables based on a least squares method to construct a correlation model.
3. The method of estimating state of charge of a battery of claim 2, wherein step S is12Before, still include:
and smoothly denoising the life attenuation data based on a digital filtering method.
4. The method of estimating state of charge of a battery of claim 1, wherein step S is3The step of obtaining the remaining capacity of the battery to be tested specifically includes:
calculating the initial value of the capacity of the battery to be tested according to the SOC-OCV curve;
calculating the capacity change value of the battery to be tested based on an ampere-hour integral method;
and calculating the residual capacity according to the initial capacity value and the capacity change value.
5. The method of estimating state of charge of a battery of claim 2, wherein said charge-discharge data and said life decay data comprise the following parameters:
voltage, current and temperature of the battery during charging and discharging.
6. A battery state of charge estimation system, comprising:
the data acquisition module is used for acquiring charge and discharge data of the battery to be detected and calculating a characteristic variable of the battery to be detected according to the charge and discharge data;
the characteristic variables include: isobaric drop discharge ampere-hour and/or isobaric drop charge ampere-hour;
the constant pressure drop discharge ampere-hour represents the ampere-hour number corresponding to the battery after the battery is discharged according to the constant pressure drop in each charge-discharge period;
the isobaric pressure drop charging ampere-hour represents the corresponding ampere-hour number of the battery after the battery is charged according to the isobaric pressure drop in each charging and discharging period;
the calculation module is used for inputting the characteristic variables of the battery to be tested into a relevant model so as to obtain the maximum available capacity of the battery to be tested;
the correlation model represents the quantitative relation between the characteristic variables and the maximum available capacity;
the calculation module is further used for obtaining the residual capacity of the battery to be tested and estimating the state of charge of the battery to be tested according to the residual capacity and the maximum available capacity.
7. The battery state of charge estimation system of claim 6, further comprising: a model building module;
the model building module specifically comprises:
the data acquisition unit is used for acquiring life attenuation data of a plurality of batteries with the same types as the batteries to be tested;
the computing unit is used for constructing a characteristic variable of the battery according to the life attenuation data and computing a capacity data sequence;
and the model construction unit is used for fitting the capacity data sequence and the characteristic variable based on a least square method so as to construct a relevant model.
8. The battery state of charge estimation system of claim 7, wherein the model building module further comprises:
and the data processing unit is used for smoothly denoising the life attenuation data based on a digital filtering method.
9. The system according to claim 6, wherein the calculation module is specifically configured to calculate an initial value of the capacity of the battery to be tested according to an SOC-OCV curve, calculate a change value of the capacity of the battery to be tested based on an ampere-hour integration method, and calculate the remaining capacity according to the initial value of the capacity and the change value of the capacity.
10. The battery state of charge estimation system of claim 7, wherein the charge-discharge data and the life decay data comprise the following parameters:
voltage, current and temperature of the battery during charging and discharging.
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| CN115219906B (en) * | 2022-06-27 | 2024-11-05 | 武汉理工大学 | Multi-model fusion battery state of charge prediction method and system based on GA-PSO optimization |
| CN115219902B (en) * | 2022-07-06 | 2024-08-16 | 山东大学 | A method and system for quickly testing the life of a power battery |
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