CN111308379B - Battery health state estimation method based on local constant voltage charging data - Google Patents

Battery health state estimation method based on local constant voltage charging data Download PDF

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CN111308379B
CN111308379B CN202010173602.2A CN202010173602A CN111308379B CN 111308379 B CN111308379 B CN 111308379B CN 202010173602 A CN202010173602 A CN 202010173602A CN 111308379 B CN111308379 B CN 111308379B
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CN111308379A (en
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魏中宝
阮浩凯
何洪文
周稼铭
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Beijing Institute of Technology BIT
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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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • 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

Abstract

The invention discloses a battery health state estimation method based on local constant voltage charging data, which comprises the following steps: s1, carrying out a cyclic charge-discharge experiment on a battery under different temperature and charge multiplying power conditions, and measuring the current and terminal voltage of the battery in real time; s2, fitting a linear relation between the CV (constant voltage) charging capacity and the CCCV (constant current first and then constant voltage) charging capacity under the conditions of different temperatures and CC (constant current) charging multiplying power, and establishing a parameter mapping database of a linear model; s3, establishing a CV stage charging current prediction model, identifying model parameters by using local CV charging data, and predicting the whole CV charging data; and S4, selecting a corresponding linear model from the parameter mapping database according to the temperature and the CC charging multiplying power in the actual charging process, and calculating the SOH (state of health) of the battery according to the estimated CV charging capacity. The method has low calculation cost, can overcome the limitation that the traditional method needs complete charging data, and can estimate the SOH of the battery with high precision only through local CV charging data.

Description

Battery health state estimation method based on local constant voltage charging data
Technical Field
The present invention relates to state of health estimation of lithium ion batteries, and more particularly, to a battery state of health (SOH) estimation method based on local Constant Voltage (CV) charging data.
Background
Lithium ion batteries have the advantages of high energy density, long service life and the like, and are now the main energy storage tools of electric automobiles. However, as the number of cycles increases, the internal physical and chemical processes of the lithium ion battery change, and the capacity and performance of the lithium ion battery are continuously attenuated along with various side reactions, and even become invalid in severe cases, so that safety accidents are caused. The performance attenuation of the lithium ion battery system seriously influences the endurance mileage of the electric automobile, and greatly hinders the further popularization and promotion of the electric automobile. Therefore, accurately detecting the SOH of the battery is very important to the reliability of the electric vehicle.
Currently, methods for estimating SOH of a lithium ion battery are mainly classified into three types. The first is electrochemical measurement, which is mainly to measure impedance information of a battery through Electrochemical Impedance Spectroscopy (EIS) and map the impedance information to SOH; the method can describe the impedance more accurately, but the measuring process is complicated, a professional measuring instrument is needed, and the method is difficult to be applied practically. The second method is an estimation method based on a model, an electric model of the lithium ion battery is established by relying on theoretical supports such as Butler-Volmer law, kirchhoff law and the like, and parameters such as a plurality of states, capacity and the like are estimated by adopting filter algorithms such as extended Kalman filtering and the like; the method has the advantages of high estimation precision and strong robustness, has good universal applicability to different batteries, but has high requirements on model precision and high calculation cost, and has challenges in online application. The third method is an estimation method based on data driving, and utilizes battery voltage and current information which is convenient to measure to extract characteristic parameters with high correlation with battery aging so as to estimate the SOH of the battery; the method does not need a complex model, and reduces the calculation cost while ensuring the estimation precision. However, such methods require charging or discharging data in the full state of charge (SOC) range, and most of the practical applications of batteries can only obtain fragment data, so the environmental adaptability of such methods needs to be enhanced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a battery health state estimation method based on local constant voltage charging data. Calibrating a linear relation between the CV charging capacity and the CCCV charging capacity, predicting current change characteristics of the residual section CV charging by adopting a battery second-order RC equivalent circuit model according to local CV charging data of the battery aiming at a typical scene that the CV charging stage is incomplete, further extracting a health factor and estimating the SOH of the battery. The method provided by the invention has low calculation complexity, overcomes the limitation that the traditional method needs complete charging data, and can realize high-precision estimation of the SOH of the battery only according to the charging data of the local CV.
The purpose of the invention is realized by the following technical scheme: a battery state of health estimation method based on local constant voltage charging data includes the following steps:
s1, carrying out a cyclic charge-discharge experiment on a battery under different temperature and charge multiplying power conditions, and measuring the current and terminal voltage of the battery in real time by adopting a current sensor and a voltage sensor;
s2, extracting the CV charging capacity by adopting an ampere-hour integration method, fitting a linear relation between the CV charging capacity and the CCCV charging capacity under different temperatures and CC charging multiplying power conditions, and establishing a parameter mapping database of a linear model;
wherein CV represents constant voltage, CC represents constant current, CCCV represents constant current first and then constant voltage;
s3, establishing a second-order RC equivalent circuit model, establishing a CV stage charging current prediction model on the basis of the second-order RC equivalent circuit model, identifying model parameters by using local CV charging data, and predicting the whole CV charging data;
and S4, according to the temperature and the CC charging rate in the actual charging process, selecting a corresponding linear model from the parameter mapping database in the step S2, and calculating the SOH of the battery according to the estimated CV charging capacity, wherein the SOH represents the state of health.
The invention has the beneficial effects that: the invention provides a battery SOH estimation method based on local CV charging dataCV-QCCCVIs calculated from the linear relationship ofCCCVAnd then estimating the SOH of the battery. The method of the invention has three advantages: first, the health factor Q can be directly extractedCVThe conversion of a complex capacity increment curve, a differential voltage curve and the like is not needed, so that the calculation cost can be reduced; second, Q in the method of the present inventionCV-QCCCVHas good linear relation, does not need complex machine learning algorithm, and can realize the SOH of the battery only through a simple linear modelHigh-precision estimation; and thirdly, the method only needs partial data of the CV charging stage, and has better practical application prospect.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 shows example QCVAnd QCCCVThe fitting curve of (1);
FIG. 3 is a schematic circuit diagram of a second-order RC equivalent circuit model in the embodiment;
FIG. 4 is a predicted curve of CV of a battery according to a second-order RC equivalent circuit model in an embodiment;
FIG. 5 shows the prediction error of the CV prediction curves in the examples.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a method for estimating SOH of a battery based on local CV charge data includes the steps of:
s1, carrying out a cyclic charge-discharge experiment on a battery under different temperature and charge multiplying power conditions, and measuring the current and terminal voltage of the battery in real time by adopting a current sensor and a voltage sensor;
the step S1 includes the following sub-steps:
s101, charging the battery to the state of charge (SOC) of 100% by adopting a constant-current-constant-voltage charging method, and monitoring the current I of the battery in real time in the charging processLAnd terminal voltage UlAnd storing the voltage U at the CC stagelThe current I at the CV stage is up to the preset cut-off valueLReaching a preset cut-off value;
s102, performing a discharge experiment by adopting a CC discharge method until the terminal voltage is reduced to a lower limit cut-off voltage, wherein the CC discharge multiplying power and the CC charge multiplying power do not need to be kept consistent;
s103, repeating S101-S102 until the rated capacity of the battery is reduced to the end-of-life standard, and the end voltage U at the CV stage in different cycleslNeed to be kept consistent, integrate the current and terminal voltage information collected during the process, establish a specific temperature and CC charge rateA battery aging database;
s104, repeating S101-S103 at different temperatures and different CC charging multiplying factors to obtain battery aging databases at different temperatures and different CC charging multiplying factor variables.
In the embodiment of the present application, after step S103 or S104 is finished, data preprocessing for missing value padding and error value deletion needs to be performed on the battery aging database. The error value comprises a value with overlarge deviation compared with the previous and next data (the deviation exceeds a preset threshold value compared with the previous and next data), and can be directly deleted during preprocessing; the missing value includes the condition that the current value and the voltage value at a certain moment are vacant, and the preprocessing mode includes but is not limited to padding by the average value of the previous data and the next data.
S2, extracting the CV charging capacity by adopting an ampere-hour integration method, fitting a linear relation between the CV charging capacity and the CCCV charging capacity under different temperatures and CC charging multiplying power conditions, and establishing a parameter mapping database of a linear model;
wherein CV represents constant voltage, CC represents constant current, CCCV represents constant current first and then constant voltage;
s201, selecting CV stage charging data of a plurality of cycles under the same temperature and CC charging rate from a battery aging database, and extracting CV charging capacity Q of each selected cycle by adopting an ampere-hour integration methodCVAs a health factor;
s202, calculating the CCCV charging capacity Q of each extracted cycle by adopting an ampere-hour integration methodCCCV；
The calculation method of the charge capacity described in steps S201 and S202 is as follows:
Figure BDA0002410058780000031
Figure BDA0002410058780000032
wherein m is the initial moment of CV charging, n is the moment when the CV charging current reaches the preset cut-off value, IL(k) For the current value at the k-th time, Δ t load current sampleA time interval.
S203, fitting Q off line by adopting a least square methodCV-QCCCVThe formula is as follows:
QCCCV＝aQCV+b
wherein a and b are fitting parameters obtained by least square fitting. The fitting method comprises the following specific steps:
according to QCVThe measured values of (A) were as follows: qCCCV＝aQCV+ b calculates the corresponding QCCCVIs marked as
Figure BDA0002410058780000033
Calculating the measured value Q directly obtained from the experimental dataCCCVAnd a calculated value obtained by the calculation
Figure BDA0002410058780000034
The square sum R of the dispersion of (a), the formula is as follows:
Figure BDA0002410058780000041
substituting the linear equation to be fitted into the formula:
Figure BDA0002410058780000042
wherein Q isCCCViAnd QCViRespectively represent the ith QCCCVAnd QCVAnd (4) data. Then, the fitting parameters a and b are respectively subjected to partial derivation:
Figure BDA0002410058780000043
Figure BDA0002410058780000044
will measure the value QCCCVAnd QCVCalculated by substituting the formula when
Figure BDA0002410058780000045
And when the sum is 0, the values of a and b are obtained. In this embodiment, QCV-QCCCVThe linear function of (a) is:
QCCCV＝-3.5853QCV+3.5344
the fitted curve is shown in FIG. 2;
s204, repeating S201-S203, obtaining the linear relation of the CV charging capacity and the CCCV charging capacity under different temperature and CC charging multiplying factor variables, namely a plurality of groups of [ a, b ] parameter sets under different temperature and CC charging multiplying factors, and forming a parameter mapping database of a linear model.
S3, establishing a battery second-order RC equivalent circuit model, establishing a CV stage charging current prediction model on the basis of the battery second-order RC equivalent circuit model, identifying model parameters by using local CV charging data, and predicting the whole CV charging data through a battery model;
s301, acquiring current and terminal voltage data in the CCCV charging process in real time by adopting a current and voltage sensor under an actual charging working condition; defining the time when the terminal voltage reaches the upper limit cut-off voltage and the current starts to drop as the algorithm starting time;
s302, establishing a second-order RC equivalent circuit model of the battery, wherein a circuit schematic diagram of the second-order RC equivalent circuit model is shown in FIG. 3;
the circuit equation of the second-order RC equivalent circuit model of the battery is as follows:
Figure BDA0002410058780000046
Figure BDA0002410058780000047
UOC+R0IL+Up1+Up2＝Ul
wherein, Up1、Up2To polarize the voltage, UlIs terminal voltage, UocBeing batteriesOpen Circuit Voltage (OCV), ILIs a current, Rp1、Rp2、Cp1、Cp2And R0Model parameters to be identified, specifically: rp1、Rp2Is a polarization resistance, Cp1、Cp2Is a polarization capacitance, R0Ohmic internal resistance;
s303, establishing a prediction equation of the CV charging curve based on a battery second-order RC equivalent circuit model:
firstly, performing laplace transform on an equation of a second-order RC equivalent circuit model:
Figure BDA0002410058780000051
Figure BDA0002410058780000052
Figure BDA0002410058780000053
where s is the complex variable of the laplace transform. After a relational expression of the difference value of the current, the terminal voltage and the open circuit voltage is established, inverse Laplace transform is carried out on the equation:
Figure BDA0002410058780000054
wherein a ═ R0Rp1Rp2Cp1Cp2，b＝R0Rp1Cp1+R0Rp2Cp2+Cp1Rp1Rp2+Cp2Rp1Rp2，c＝R0+Rp2+Rp1. The calculated CV current is expressed as:
Figure BDA0002410058780000055
wherein, ILThe current value at time t (positive value during charging and negative value during discharging) is shown as (t). Open circuit voltage U of battery due to CV charging stageocIs generally close to the battery terminal voltage UlResult in (U)l-Uoc) The value of (c) is small. For simple calculation, in the CV stage charging current prediction model, a/c (U) is ignoredl-Uoc) To IL(t), i.e., establishing a CV-stage charging current prediction model as follows:
Figure BDA0002410058780000056
wherein, IL1、IL2、τeq,1、τeq,2In order to predict the parameters to be identified in the equation, the identification method adopts a Levenberg-Marquardt (LM) algorithm, and comprises the following steps:
setting the calculated value of current calculated according to the prediction equation as
Figure BDA0002410058780000057
Calculating the measured value I directly obtained from the experimental dataLAnd the calculated value
Figure BDA0002410058780000058
The square sum R of the dispersion of (a), the formula is as follows:
Figure BDA0002410058780000059
Figure BDA00024100587800000510
calculate the Jacobian matrix J (x):
Figure BDA0002410058780000061
wherein x isiRepresenting the parameters of the ith row in vector x. Subsequently, the gradient of R is calculated as:
g＝R′(x)＝J(x)Tf(x)
then, solving an iteration step h, wherein the formula is as follows:
Figure BDA0002410058780000062
wherein, Jk＝J(xk)，fk＝f(xk)，xkFor the value of x after iterating k times, I is an identity matrix, u is a positive number, and the function of shortening the iteration step length is realized in the formula, and the determination method is as follows:
Figure BDA0002410058780000063
Figure BDA0002410058780000064
where ρ is a gain ratio, and when ρ is greater than 0, the formula is as follows:
Figure BDA0002410058780000065
when ρ is equal to or less than 0, the formula is as follows:
uk+1＝uk×vk
vk+1＝2vk
in this embodiment, the initial value of v is 2, and the initial value of u is calculated as follows:
A0＝J(x0)TJ(x0)
u0＝τ×max{aii}
wherein, aiiIs a matrix A0The elements on the diagonal. Repeating the iterative process:
xk+1＝xk-h
when the iteration process meets one of the following conditions, exiting the iteration;
g≤ε1
‖h‖≤ε2(‖x‖+ε2)
wherein | represents the matrix norm, ε1、ε2Any small value can be selected as the termination condition for the preset parameter, and the value range can be 10-8～10-12The concrete adjustment can be made according to the actual situation; when the iteration is judged to meet the termination condition, I at the momentL1、IL2、τeq,1、τeq,2The value of (a) is the identification result, and the parameter identification result in this embodiment is: i isL1＝0.9545、IL2＝0.5455、τeq,1＝769.23、τeq,2＝2564.1；
S304, if the CV charging is not cut off in advance, namely the charging is finished until the current is reduced to the CV cut-off current, recording the complete CV charging capacity according to an ampere-hour integration method; if the CV charging is cut off in advance, that is, the current does not decrease to the CV cut-off current at the end of CV, the change of the future current is calculated according to the charging current prediction model in S303 until the CV cut-off current is reached, and the complete CV charging capacity is calculated according to an ampere-hour integration method.
S4, according to the temperature and the CC charging multiplying power in the actual charging process, selecting a corresponding linear model from the parameter mapping database in the S2, and calculating the SOH of the battery according to the estimated CV charging capacity, wherein the specific implementation scheme is as follows:
selecting a corresponding linear model in the parameter mapping database in S204 according to the actual temperature and the CC charging multiplying power; q acquired in S304CVSubstitution into a Linear model to calculate QCCCVAnd further calculating the SOH of the battery at the current moment, wherein the formula is as follows:
QCCCV＝-3.5853QCV+3.5344
Figure BDA0002410058780000071
where S is the estimation of battery SOH, QratedIs the nominal capacity of the battery.
And estimating the health state of the battery in real time according to the steps, comparing the health state of the battery with the calculated health state of the battery according to a preset service life ending standard, and judging whether the current health state of the battery meets the safety standard or not.
In the embodiment of the patent, 18650 lithium ion batteries with nominal capacity of 2Ah are taken as experimental objects, the method S1 is carried out at different temperatures, and battery aging databases with different temperatures and CC discharge rates are established. Next, Q is calculated by the ampere-hour integration methodCVAnd QCCCVFitting Q by least squaresCVAnd QCCCVThe linear relationship of (a) to (b) constitutes a parameter mapping database. Aiming at a typical scene that CV charging is incomplete, a second-order RC equivalent circuit model is built, and on the basis, a CV stage charging current prediction model is established. And then, estimating the SOH of the battery to be tested. The upper limit cut-off voltage of the terminal voltage of the battery to be tested is 4.2A, CV, the lower limit cut-off current of the current is 0.05A, the constant current of the discharge experiment is 2A, and the lower limit cut-off voltage is 2.5V. When the current data at the CV stage is complete, the ampere-hour integration method can be directly adopted to calculate QCV(ii) a And when the current of the battery to be measured in the CV stage does not fall to the CV lower limit cut-off current, fitting local CV current data by using the established charging current prediction model, and predicting the residual CV current until the CV cut-off current is reached. The CV prediction curve of the charging current prediction model in the embodiment is shown in fig. 4, and the prediction error is shown in fig. 5. Finally, the complete CV capacity was calculated to be 0.5507Ah according to the ampere-hour integration method. Selecting corresponding linear model, and calculating to obtain QCCCVIt was 1.56 Ah. Finally pass through QCCCVThe ratio of the calibrated capacity determines the SOH of the cell to be 78%. And Q obtained by actual measurementCCCV1.6109Ah, the estimated value differs from 0.0509Ah with a relative error of 3.1%. The actual battery SOH was 80.55%, and the relative error of the estimate was within 3%. Therefore, the method can estimate the health state of the battery based on the local CV charging data, and has low calculation cost and small data requirement under the condition of meeting the precision requirement.
In summary, the invention provides a battery SOH estimation method based on local CV charging data, and the high-precision estimation of the health state of a battery can be realized only according to the local CV charging data through a model prediction method, so that the limitation that the traditional method needs complete charging data is overcome. The method only involves a voltage sensor and a current sensor, and does not need complex experimental equipment. Q in the processCVAnd QCCCVThe calculation cost is low, and the conversion of a complex capacity increment curve, a differential voltage curve and the like is not needed. Q of the processCVAnd QCCCVThe method has good linear relation, does not need a complex machine learning algorithm, and can realize the high-precision estimation of the SOH of the battery only through a simple linear model.
The foregoing is a preferred embodiment of the present invention, it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as excluding other embodiments, and is capable of other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (6)

1. A battery health state estimation method based on local constant voltage charging data is characterized in that: the method comprises the following steps:
s1, performing a constant-current-constant-voltage cyclic charge-discharge experiment on a battery under different temperature and CC charge multiplying power conditions, and measuring the current and terminal voltage of the battery in real time by adopting a current sensor and a voltage sensor;
s2, extracting the CV charging capacity by adopting an ampere-hour integration method, fitting a linear relation between the CV charging capacity and the CCCV charging capacity under different temperatures and CC charging multiplying power conditions, and establishing a parameter mapping database of a linear model;
wherein CV represents constant voltage, CC represents constant current, CCCV represents constant current first and then constant voltage;
s3, establishing a second-order RC equivalent circuit model, establishing a CV stage charging current prediction model on the basis of the second-order RC equivalent circuit model, identifying model parameters by using local CV charging data, and predicting the whole CV charging data;
and S4, selecting a corresponding linear model from the parameter mapping database in the step S2 according to the temperature and the CC charging rate in the actual charging process, estimating the CV charging capacity according to the whole CV charging data obtained by predicting in the step S3, and calculating the SOH of the battery according to the estimated CV charging capacity, wherein the SOH represents the state of health.
2. The method of claim 1, wherein the method comprises: the step S1 includes the following sub-steps:
s101, charging the battery to the state of charge (SOC) of 100% by adopting a constant-current-constant-voltage charging method, and monitoring the current I of the battery in real time in the charging processLAnd terminal voltage UlAnd storing the voltage U at the CC stagelThe current I at the CV stage is up to the preset cut-off valueLReaching a preset cut-off value;
s102, performing a discharge experiment by adopting a CC discharge method until the terminal voltage is reduced to a lower limit cut-off voltage, wherein the CC discharge multiplying power and the CC charge multiplying power do not need to be kept consistent;
s103, repeating S101-S102 until the rated capacity of the battery is reduced to the end-of-life standard, and the end voltage U at the CV stage in different cycleslIntegrating the current and terminal voltage information collected in the process and establishing a battery aging database at a specific temperature and a CC charging rate;
s104, repeating S101-S103 at different temperatures and different CC charging multiplying factors to obtain battery aging databases at different temperatures and different CC charging multiplying factor variables.
3. The method of claim 2, wherein the method comprises: the step S1 further includes: and after the step S103 or S104 is finished, performing data preprocessing of missing value padding and error value deletion on the battery aging database.
4. The method of claim 2, wherein the method comprises: the step S2 includes the following substeps:
s201, selecting CV stage charging data of a plurality of cycles under the same temperature and CC charging rate from a battery aging database, and extracting CV charging capacity Q of each selected cycle by adopting an ampere-hour integration methodCVAs a health factor;
s202, calculating the CCCV charging capacity Q of each selected cycle by adopting an ampere-hour integration methodCCCV；
S203, fitting Q off line by adopting a least square methodCV-QCCCVThe formula is as follows:
QCCCV＝aQCV+b
wherein, a and b are fitting parameters obtained by least square fitting, and the fitting process is as follows:
according to QCVThe measured values of (A) were as follows: qCCCV＝aQCV+ b calculates the corresponding QCCCVIs marked as
Figure FDA0002771296360000021
Calculating the measured value Q directly obtained from the experimental dataCCCVAnd a calculated value obtained by the calculation
Figure FDA0002771296360000022
The square sum R of the dispersion of (a), the formula is as follows:
Figure FDA0002771296360000023
substituting the linear equation to be fitted into the formula:
Figure FDA0002771296360000024
wherein Q isCCCViAnd QCViRespectively represent the ith QCCCVAnd QCVData; then, the fitting parameters a and b are respectively subjected to partial derivation:
Figure FDA0002771296360000025
Figure FDA0002771296360000026
will measure the value QCCCVAnd QCVCalculated by substituting the formula when
Figure FDA0002771296360000027
When the sum is 0, the values of a and b are obtained;
s204, repeating S201-S203, obtaining the linear relation of the CV charging capacity and the CCCV charging capacity under different temperature and CC charging multiplying factor variables, namely a plurality of groups of [ a, b ] parameter sets under different temperature and CC charging multiplying factors, and forming a parameter mapping database of a linear model.
5. The method of claim 4, wherein the method comprises: the step S3 includes the following substeps:
s301, acquiring current and terminal voltage data in the CCCV charging process in real time by adopting a current and voltage sensor under an actual charging working condition; defining the time when the terminal voltage reaches the upper limit cut-off voltage and the current starts to drop as the algorithm starting time;
s302, establishing a second-order RC equivalent circuit model of the battery;
the circuit equation of the second-order RC equivalent circuit model of the battery is as follows:
Figure FDA0002771296360000028
Figure FDA0002771296360000031
UOC+R0IL+Up1+Up2＝Ul
wherein, Up1、Up2To polarize the voltage, UlIs terminal voltage, UocIs the open circuit voltage of the battery, ILIs a current, Rp1、Rp2、Cp1、Cp2And R0Model parameters to be identified, specifically: rp1、Rp2Is a polarization resistance, Cp1、Cp2Is a polarization capacitance, R0Ohmic internal resistance;
s303, establishing a prediction equation of the CV charging curve based on a battery second-order RC equivalent circuit model:
Figure FDA0002771296360000032
wherein t is time, IL1、IL2、τeq,1、τeq,2In order to predict the parameters to be identified in the equation, the identification method adopts a Levenberg-Marquardt algorithm, and comprises the following steps:
setting the calculated value of current calculated according to the prediction equation as
Figure FDA0002771296360000033
Calculating the measured value I directly obtained from the experimental dataLAnd the calculated value
Figure FDA0002771296360000034
The square sum R of the dispersion of (a), the formula is as follows:
Figure FDA0002771296360000035
wherein x is a parameter vector to be identified; to minimize the sum of squares of the dispersion, find the best functional match of the data, the Levenberg-Marquardt algorithm parameter fitting process is as follows:
first, the gradient g of r (x) is calculated:
g＝R′(x)＝J(x)Tf(x)
where J (x) is the Jacobian matrix of R (x), and then solving for an iteration step h:
Figure FDA0002771296360000036
wherein, Jk＝J(xk)，fk＝f(xk)，xkFor the value of x after k iterations, I is the identity matrix and u is a positive number, which acts to shorten the iteration step in the formula, followed by xkAnd (3) updating the numerical value:
xk+1＝xk-h
repeating the iteration process, and when one of the following conditions is met in the iteration process, achieving the required iteration precision and exiting the iteration;
g≤ε1
‖h‖≤ε2(‖x‖+ε2)
wherein | represents the matrix norm, ε1、ε2Is a preset parameter as a termination condition; when the iteration is judged to meet the termination condition, I at the momentL1、IL2、τeq,1、τeq,2The value of (b) is the identification result;
s304, if the CV charging is not cut off in advance, namely the charging is finished until the current is reduced to the CV cut-off current, recording the complete CV charging capacity according to an ampere-hour integration method; if the CV charging is cut off in advance, namely the current is not reduced to the CV cut-off current at the end of CV, the change of the future current is calculated according to the prediction equation of the CV charging curve in the S303 until the CV cut-off current is reached, and then the complete CV charging capacity is calculated according to an ampere-hour integration method.
6. The method of claim 5, wherein the method comprises: in step S4, estimating the SOH of the battery according to the estimated CV charging capacity, specifically including:
selecting a corresponding linear model in the parameter mapping database in S204 according to the actual temperature and the CC charging multiplying power; predicting the whole VC charging data by the prediction equation of the step S303, and estimating the CV charging capacity Q according to the step S304CVQ obtained in S304CVSubstitution into a Linear model to calculate QCCCVAnd further calculating the SOH of the battery at the current moment, wherein the formula is as follows:
Figure FDA0002771296360000041
where S is the estimation of battery SOH, QratedIs the nominal capacity of the battery;
and estimating the health state of the battery in real time according to the steps, comparing the health state of the battery with the calculated health state of the battery according to a preset service life ending standard, and judging whether the current health state of the battery meets the safety standard or not.
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