CN110286325B - Local sensitivity analysis method of lithium ion battery - Google Patents

Local sensitivity analysis method of lithium ion battery Download PDF

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CN110286325B
CN110286325B CN201910688791.4A CN201910688791A CN110286325B CN 110286325 B CN110286325 B CN 110286325B CN 201910688791 A CN201910688791 A CN 201910688791A CN 110286325 B CN110286325 B CN 110286325B
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value
equivalent circuit
charge
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CN110286325A (en
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胡广地
周鹏凯
郭峰
赛景辉
胡坚耀
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Sichuan Jiaya Automobile Technology Co ltd
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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
    • G01R31/005—Testing of electric installations on transport means
    • G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
    • 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 discloses a local sensitivity analysis method of a lithium ion battery, which comprises the following steps: s1, establishing a circuit equation of an equivalent circuit model in the target lithium ion battery state of charge estimation according to kirchhoff' S law; s2, obtaining parameter values of circuit elements in the equivalent circuit model through an HPPC (HPPC) experiment; s3, obtaining the local sensitivity of the circuit element parameters in the equivalent circuit model to the equivalent circuit model accuracy and the local sensitivity of the circuit element parameters to the state of charge estimation accuracy through a control variable method. The invention provides a uniform, clear, definite and quantitative local sensitivity analysis method for measuring the influence of the change of parameters of different circuit elements in an equivalent circuit model on the model accuracy and the SOC estimation accuracy.

Description

Local sensitivity analysis method of lithium ion battery
Technical Field
The invention relates to the field of battery analysis, in particular to a local sensitivity analysis method of a lithium ion battery.
Background
Since the 21 st century, people expect automobiles to be cleaner and more environmentally friendly, and electric automobiles receive wide attention. Compared with the traditional fuel oil automobile, the electric automobile has no pollution, low noise and excellent acceleration performance, and is favored by governments and consumers in various countries. The battery is a core part of the electric automobile. A battery management system is essential to the battery. The most important function of a Battery Management System (BMS) is to perform estimation of a State of Charge (SOC) of a battery.
In various lithium ion battery SOC estimation algorithms, an equivalent circuit model is the basis of estimation. Common equivalent circuit models include Rint model, Thevenin model, second-order RC model, PNGV model, etc. The biggest challenge faced in the equivalent circuit model-based SOC estimation method is that circuit element parameters have different values in different situations, such as different discharge rates, battery aging degrees, different ambient temperatures, and the like.
In order to quantitatively analyze the influence of the circuit element parameter variation of the equivalent circuit model on the model accuracy and the SOC estimation accuracy, a uniform, clear and quantitative sensitivity analysis method is needed.
Disclosure of Invention
Aiming at the defects in the prior art, the method for analyzing the local sensitivity of the lithium ion battery provided by the invention provides a quantitative method for analyzing the local sensitivity.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
provided is a local sensitivity analysis method of a lithium ion battery, which comprises the following steps:
s1, establishing a circuit equation of an equivalent circuit model in the target lithium ion battery state of charge estimation according to kirchhoff' S law;
s2, obtaining parameter values of circuit elements in the equivalent circuit model through an HPPC (HPPC) experiment;
s3, obtaining the local sensitivity of the circuit element parameters in the equivalent circuit model to the equivalent circuit model accuracy and the local sensitivity of the circuit element parameters to the state of charge estimation accuracy through a control variable method.
Further, the specific method of step S3 includes the following sub-steps:
s3-1, taking the parameter values of the circuit elements obtained in the step S2 as reference values, and randomly selecting one operation condition of the battery; wherein the reference value of the l-th circuit element is
Figure BDA0002147221810000023
1,2,3, ·, L; l is the total number of circuit elements;
s3-2, respectively according to the formula
Figure BDA0002147221810000021
Figure BDA0002147221810000022
Obtaining the root mean square error E of the equivalent circuit model terminal voltage under the reference valueU,baseAnd root mean square error of state of charge estimate ESOC,base(ii) a Wherein m is the duration of the entire battery operating state; u shapek,ModelThe terminal voltage estimated value is calculated by the equivalent circuit model at the moment k; u shapek.RefThe real value of the terminal voltage is actually measured at the moment k; SOCk,EstThe estimated value of the state of charge is calculated by the equivalent circuit model at the moment k; SOCk.RefTrue value for state of charge;
s3-3, generating n numbers of at [0,1 ] by adopting a linear congruence method]Random number m with uniform distribution1,m2,m3,...,mj,...,mn，j＝1,2...,n；
S3-4, using 0.5-1.5 times of reference value as parameter value variation interval, obtaining circuit element X according to generated random numberlN values are uniformly distributed in the parameter value change interval;
s3-5, selecting circuit elements X one by onelObtaining the circuit element X by the same method as the step S3-2lTaking the jth value
Figure BDA0002147221810000036
Root mean square error E of terminal voltage of time-equivalent circuit modelU,l,jAnd root mean square error of state of charge estimate ESOC,l,j；
S3-6, according to the formula
Figure BDA0002147221810000031
Obtaining a circuit element XlLocal sensitivity to equivalent circuit model accuracy
Figure BDA0002147221810000034
S3-7, according to the formula
Figure BDA0002147221810000032
Obtaining a circuit element XlLocal sensitivity to state of charge estimation accuracy
Figure BDA0002147221810000035
Further, the specific method of calculating the estimated value of the state of charge from the equivalent circuit model in step S3-2 includes the following sub-steps:
s3-2-1, converting the circuit equation into a state space equation of a nonlinear system and an output equation of the state space equation:
xk＝f(xk-1,uk-1)+wk-1
yk＝g(xk,uk)+vk
wherein xkThe state of the nonlinear system at the moment k; x is the number ofk-1The state of the nonlinear system at the moment k-1; u. ofk-1Is the output of the nonlinear system at the moment k-1; w is ak-1White Gaussian noise at the time of k-1; f (-) is a state equation of the nonlinear system and is a nonlinear function; v. ofkIs Gaussian white noise, vkAnd wkAre independent of each other; g (-) is the output equation of the nonlinear system, and is a nonlinear function; u. ofkIs the output of the nonlinear system at time k; y iskThe state of the output equation of the state space equation of the nonlinear system at the moment k;
s3-2-2 according to the formula
Figure BDA0002147221810000033
Figure BDA0002147221810000041
Respectively estimating system state estimation values of nonlinear system at initial moment
Figure BDA0002147221810000042
Sum error covariance estimation
Figure BDA0002147221810000043
Carrying out initialization; wherein E [. C]To count the expected operators; x is the number of0Is the initial state of the nonlinear system; (.)TIs the transposition of the matrix;
s3-2-3, according to the formula
Figure BDA0002147221810000044
Figure BDA0002147221810000045
Figure BDA0002147221810000046
Obtaining system state predicted value at k moment
Figure BDA0002147221810000047
Sum error covariance prediction
Figure BDA0002147221810000048
Wherein
Figure BDA0002147221810000049
The estimated value of the system state at the k-1 moment is obtained;
Figure BDA00021472218100000410
the error covariance estimation value at the k-1 moment;
Figure BDA00021472218100000411
is Ak-1Transpose of (A)k-1Is an intermediate parameter; q is wkCorresponding covariance matrix, wkWhite gaussian noise at time k;
s3-2-4, according to the formula
Figure BDA00021472218100000412
Figure BDA00021472218100000413
Obtaining a Kalman gain Kk(ii) a Wherein C iskAs an intermediate parameter, Ck TIs CkTransposing; r is vkThe covariance matrix of (a);
s3-2-5, according to the formula
Figure BDA00021472218100000414
Figure BDA00021472218100000415
Obtaining the system state estimation value of the nonlinear system at the k moment after correction
Figure BDA00021472218100000416
Sum error covariance estimation
Figure BDA00021472218100000417
Estimating the system state
Figure BDA00021472218100000418
An estimated value of the state of charge obtained as the state of charge; where I is the identity matrix.
Further, the specific method of step S3-4 is:
taking 0.5-1.5 times of the reference value as the variation interval of the parameter value, and linearly mapping the variation interval to the interval [0,1]In, make the circuit element XlIs in one-to-one correspondence with the n uniformly distributed random numbers generated in step S3-3, to obtain circuit element XlN values are uniformly distributed in the parameter value change interval; wherein the circuit element XlThe jth value of
Figure BDA0002147221810000051
The invention has the beneficial effects that: the invention provides a uniform, clear, definite and quantitative local sensitivity analysis method for measuring the influence of the change of parameters of different circuit elements in an equivalent circuit model on the model accuracy and the SOC estimation accuracy. The method can analyze the circuit element parameters of various equivalent circuit models, such as a Rint model, a wear Viron model, a second-order RC model and the like. After the sensitivity ranking is obtained, the method is used for the SOC estimation algorithm of parameter online estimation, only parameters with high sensitivity can be estimated, the operation amount is reduced, and balance is obtained between the SOC estimation accuracy and the operation overhead.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a second order RC equivalent circuit model diagram.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the method for analyzing local sensitivity of a lithium ion battery includes the following steps:
s1, establishing a circuit equation of an equivalent circuit model in the target lithium ion battery state of charge estimation according to kirchhoff' S law;
s2, obtaining parameter values of circuit elements in the equivalent circuit model through an HPPC (HPPC) experiment;
s3, obtaining the local sensitivity of the circuit element parameters in the equivalent circuit model to the equivalent circuit model accuracy and the local sensitivity of the circuit element parameters to the state of charge estimation accuracy through a control variable method.
The specific method of step S3 includes the following substeps:
s3-1, taking the parameter values of the circuit elements obtained in the step S2 as reference values, and randomly selecting one operation condition of the battery; wherein the reference value of the l-th circuit element is
Figure BDA0002147221810000061
1,2,3, ·, L; l is the total number of circuit elements;
s3-2, respectively according to the formula
Figure BDA0002147221810000062
Figure BDA0002147221810000063
Obtaining the root mean square error E of the equivalent circuit model terminal voltage under the reference valueU,baseAnd root mean square error of state of charge estimate ESOC,base(ii) a Wherein m is the duration of the entire battery operating state; u shapek,ModelThe terminal voltage estimated value is calculated by the equivalent circuit model at the moment k; u shapek.RefThe real value of the terminal voltage is actually measured at the moment k; SOCk,EstThe estimated value of the state of charge is calculated by the equivalent circuit model at the moment k; SOCk.RefTrue value for state of charge;
s3-3, generating n numbers of at [0,1 ] by adopting a linear congruence method]With internal uniform distributionRandom number m1,m2,m3,...,mj,...,mn，j＝1,2...,n；
S3-4, using 0.5-1.5 times of reference value as parameter value variation interval, obtaining circuit element X according to generated random numberlN values are uniformly distributed in the parameter value change interval;
s3-5, selecting circuit elements X one by onelObtaining the circuit element X by the same method as the step S3-2lTaking the jth value
Figure BDA0002147221810000071
Root mean square error E of terminal voltage of time-equivalent circuit modelU,l,jAnd root mean square error of state of charge estimate ESOC,l,j；
S3-6, according to the formula
Figure BDA0002147221810000072
Obtaining a circuit element XlLocal sensitivity to equivalent circuit model accuracy
Figure BDA0002147221810000073
S3-7, according to the formula
Figure BDA0002147221810000074
Obtaining a circuit element XlLocal sensitivity to state of charge estimation accuracy
Figure BDA0002147221810000075
The specific method of the estimated value of the state of charge calculated by the equivalent circuit model in step S3-2 includes the following substeps:
s3-2-1, converting the circuit equation into a state space equation of a nonlinear system and an output equation of the state space equation:
xk＝f(xk-1,uk-1)+wk-1
yk＝g(xk,uk)+vk
wherein xkThe state of the nonlinear system at the moment k; x is the number ofk-1The state of the nonlinear system at the moment k-1; u. ofk-1Is the output of the nonlinear system at the moment k-1; w is ak-1White Gaussian noise at the time of k-1; f (-) is a state equation of the nonlinear system and is a nonlinear function; v. ofkIs Gaussian white noise, vkAnd wkAre independent of each other; g (-) is the output equation of the nonlinear system, and is a nonlinear function; u. ofkIs the output of the nonlinear system at time k; y iskThe state of the output equation of the state space equation of the nonlinear system at the moment k;
s3-2-2 according to the formula
Figure BDA0002147221810000076
Figure BDA0002147221810000081
Respectively estimating system state estimation values of nonlinear system at initial moment
Figure BDA0002147221810000082
Sum error covariance estimate P0 +Carrying out initialization; wherein E [. C]To count the expected operators; x is the number of0Is the initial state of the nonlinear system; (.)TIs the transposition of the matrix;
s3-2-3, according to the formula
Figure BDA0002147221810000083
Figure BDA0002147221810000084
Figure BDA0002147221810000085
Obtaining system state predicted value at k moment
Figure BDA0002147221810000086
Sum error covariance prediction
Figure BDA0002147221810000087
Wherein
Figure BDA0002147221810000088
The estimated value of the system state at the k-1 moment is obtained;
Figure BDA0002147221810000089
the error covariance estimation value at the k-1 moment;
Figure BDA00021472218100000810
is Ak-1Transpose of (A)k-1Is an intermediate parameter; q is wkCorresponding covariance matrix, wkWhite gaussian noise at time k;
s3-2-4, according to the formula
Figure BDA00021472218100000811
Figure BDA00021472218100000812
Obtaining a Kalman gain Kk(ii) a Wherein C iskAs an intermediate parameter, Ck TIs CkTransposing; r is vkThe covariance matrix of (a);
s3-2-5, according to the formula
Figure BDA00021472218100000813
Figure BDA00021472218100000814
Obtaining the system state estimation value of the nonlinear system at the k moment after correction
Figure BDA00021472218100000815
Sum error covariance estimation
Figure BDA00021472218100000816
Estimating the system state
Figure BDA00021472218100000817
An estimated value of the state of charge obtained as the state of charge; where I is the identity matrix.
Figure BDA00021472218100000818
The obtained value can be used for calculating a value corresponding to the k +1 moment, and the corrected system state estimated value of the nonlinear system at each moment can be obtained by repeating the steps from S3-2-2 to S3-2-5, so that the estimated value of the state of charge at each moment can be obtained.
The specific method of step S3-4 is as follows: taking 0.5-1.5 times of the reference value as the variation interval of the parameter value, and linearly mapping the variation interval to the interval [0,1]In, make the circuit element XlIs in one-to-one correspondence with the n uniformly distributed random numbers generated in step S3-3, to obtain circuit element XlN values are uniformly distributed in the parameter value change interval; wherein the circuit element XlThe jth value of
Figure BDA0002147221810000091
In the specific implementation process, the flow of the HPPC experiment is as follows:
a) after the fully charged battery is placed still for 1 hour, discharging at the rate of 1C for 6 minutes until the SOC is 90 percent;
b) standing for 1 hour;
c) discharging at 3C multiplying power for 10 seconds, standing for 40 seconds, charging at 2.25C multiplying power for 10 seconds, and standing for 40 seconds;
d) discharging at 1C rate for 352.5 seconds, so that the total discharge amount of the steps C and d is 10% of the battery capacity;
e) and c, circulating the steps b to d for 9 times.
In the SOC estimation by combining an extended Kalman filtering algorithm with an equivalent circuit model, the input of a system is current i, the output of the system is the terminal voltage U of the equivalent circuit model, the state of the system is SOC, and if an RC (resistance capacitance) link exists in the equivalent circuit model, the state quantity is added with the terminal voltages at two ends of the RC link.
When the sensitivity of the equivalent circuit model circuit element parameters to the equivalent circuit model accuracy is analyzed, the input parameters changed in the analysis are the parameter values of the equivalent circuit model circuit element, and the output is the root mean square error E of the equivalent circuit model terminal voltageU(ii) a When the sensitivity of the equivalent circuit model circuit element parameters to the SOC estimation accuracy is analyzed, the input parameters changed in the analysis are the parameter values of the equivalent circuit model circuit element, and the output is the root mean square error E of the SOCSOC。
In one embodiment of the present invention, as illustrated in FIG. 2, 5 circuit elements R of the second order RC equivalent circuit model0、R1、R2、C1、C2For example, a circuit equation for establishing an equivalent circuit model in lithium ion battery SOC estimation based on kirchhoff's law is as follows:
Figure BDA0002147221810000101
UOC＝f(SOC)
5 circuit element parameters R can be obtained through the steps of the method0、R1、R2、C1、C2Accuracy of model pairs EUAnd SOC estimation accuracy ESOCSensitivity of (2)
Figure BDA0002147221810000102
And
Figure BDA0002147221810000103
and a sensitivity ranking of 5 circuit element parameters.
In conclusion, the invention provides a uniform, clear, definite and quantitative local sensitivity analysis method for measuring the influence of the change of parameters of different circuit elements in the equivalent circuit model on the model accuracy and the SOC estimation accuracy. The method can analyze the circuit element parameters of various equivalent circuit models, such as a Rint model, a wear Viron model, a second-order RC model and the like. After the sensitivity ranking is obtained, the method is used for the SOC estimation algorithm of parameter online estimation, only parameters with high sensitivity can be estimated, the operation amount is reduced, and balance is obtained between the SOC estimation accuracy and the operation overhead.

Claims (3)

1. A local sensitivity analysis method of a lithium ion battery is characterized by comprising the following steps:
s1, establishing a circuit equation of an equivalent circuit model in the target lithium ion battery state of charge estimation according to kirchhoff' S law;
s2, obtaining parameter values of circuit elements in the equivalent circuit model through an HPPC (HPPC) experiment;
s3, obtaining the local sensitivity of the circuit element parameters in the equivalent circuit model to the equivalent circuit model accuracy and the local sensitivity of the circuit element parameters to the state of charge estimation accuracy by a control variable method;
the specific method of step S3 includes the following substeps:
s3-1, taking the parameter values of the circuit elements obtained in the step S2 as reference values, and randomly selecting one operation condition of the battery; wherein the reference value of the l-th circuit element is
Figure FDA0002946538220000011
L is the total number of circuit elements;
s3-2, respectively according to the formula
Figure FDA0002946538220000012
Figure FDA0002946538220000013
Obtaining the root mean square error E of the equivalent circuit model terminal voltage under the reference valueU，baseAnd root mean square error of state of charge estimate ESOC，base(ii) a Wherein m is the duration of the entire battery operating state; u shapek，ModelThe terminal voltage estimated value is calculated by the equivalent circuit model at the moment k; u shapek.RefThe real value of the terminal voltage is actually measured at the moment k; SOCk，EstThe estimated value of the state of charge is calculated by the equivalent circuit model at the moment k; SOCk.RefTrue value for state of charge;
s3-3, generating n numbers of at [0,1 ] by adopting a linear congruence method]Random number m with uniform distribution1，m2，m3，...，mj，...，mn，j＝1，2…，n；
S3-4, using 0.5-1.5 times of reference value as parameter value variation interval, obtaining circuit element X according to generated random numberlN values are uniformly distributed in the parameter value change interval;
s3-5, selecting circuit elements X one by onelObtaining the circuit element X by the same method as the step S3-2lTaking the jth value
Figure FDA0002946538220000021
Root mean square error E of terminal voltage of time-equivalent circuit modelU，l，jAnd root mean square error of state of charge estimate ESOC，l，j；
S3-6, according to the formula
Figure FDA0002946538220000022
Obtaining a circuit element XlEquivalent circuit modelLocal sensitivity of accuracy
Figure FDA0002946538220000023
S3-7, according to the formula
Figure FDA0002946538220000024
Obtaining a circuit element XlLocal sensitivity to state of charge estimation accuracy
Figure FDA0002946538220000025
2. The local sensitivity analysis method of the lithium ion battery according to claim 1, wherein the specific method of the estimated value of the state of charge calculated by the equivalent circuit model in the step S3-2 comprises the following sub-steps:
s3-2-1, converting the circuit equation into a state space equation of a nonlinear system and an output equation of the state space equation:
xk＝f(xk-1，uk-1)+wk-1
yk＝g(xk，uk)+vk
wherein xkThe state of the nonlinear system at the moment k; x is the number ofk-1The state of the nonlinear system at the moment k-1; u. ofk-1Is the output of the nonlinear system at the moment k-1; w is ak-1White Gaussian noise at the time of k-1; f (-) is a state equation of the nonlinear system and is a nonlinear function; v. ofkIs Gaussian white noise, vkAnd wkAre independent of each other; g (-) is the output equation of the nonlinear system, and is a nonlinear function; u. ofkIs the output of the nonlinear system at time k; y iskThe state of the output equation of the state space equation of the nonlinear system at the moment k;
s3-2-2 according to the formula
Figure FDA0002946538220000031
Figure FDA0002946538220000032
Respectively estimating system state estimation values of nonlinear system at initial moment
Figure FDA0002946538220000033
Sum error covariance estimation
Figure FDA0002946538220000034
Carrying out initialization; wherein E [. C]To count the expected operators; x is the number of0Is the initial state of the nonlinear system; (.)TIs the transposition of the matrix;
s3-2-3, according to the formula
Figure FDA0002946538220000035
Figure FDA0002946538220000036
Figure FDA0002946538220000037
Obtaining system state predicted value at k moment
Figure FDA0002946538220000038
Sum error covariance prediction
Figure FDA0002946538220000039
Wherein
Figure FDA00029465382200000310
The estimated value of the system state at the k-1 moment is obtained;
Figure FDA00029465382200000311
the error covariance estimation value at the k-1 moment;
Figure FDA00029465382200000312
is Ak-1Transpose of (A)k-1Is an intermediate parameter; q is wkCorresponding covariance matrix, wkWhite gaussian noise at time k;
s3-2-4, according to the formula
Figure FDA00029465382200000313
Figure FDA00029465382200000314
Obtaining a Kalman gain Kk(ii) a Wherein C iskAs an intermediate parameter, Ck TIs CkTransposing; r is vkThe covariance matrix of (a);
s3-2-5, according to the formula
Figure FDA0002946538220000041
Figure FDA0002946538220000042
Obtaining the system state estimation value of the nonlinear system at the k moment after correction
Figure FDA0002946538220000043
Sum error variance prevention estimate
Figure FDA0002946538220000044
Estimating the system state
Figure FDA0002946538220000045
An estimated value of the state of charge obtained as the state of charge; where I is the identity matrix.
3. The local sensitivity analysis method of the lithium ion battery according to claim 1, wherein the specific method of step S3-4 is as follows:
taking 0.5-1.5 times of the reference value as the variation interval of the parameter value, and linearly mapping the variation interval to the interval [0,1]In, make the circuit element XlIs in one-to-one correspondence with the n uniformly distributed random numbers generated in step S3-3, to obtain circuit element XlN values are uniformly distributed in the parameter value change interval; wherein the circuit element XlThe jth value of
Figure FDA0002946538220000046
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