CN114239463B - Battery cluster state of charge correction method based on big data - Google Patents

Battery cluster state of charge correction method based on big data Download PDF

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CN114239463B
CN114239463B CN202111544546.XA CN202111544546A CN114239463B CN 114239463 B CN114239463 B CN 114239463B CN 202111544546 A CN202111544546 A CN 202111544546A CN 114239463 B CN114239463 B CN 114239463B
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刘爱华
宋欣民
陈刚良
王荣强
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Hangzhou Kegong Electronic Technology Co ltd
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Abstract

The invention discloses a battery cluster state of charge correction method based on big data, and belongs to the technical field of SOC estimation. The method comprises the following steps: s1, cleaning and reconstructing battery operation data to extract data required by modeling, and then establishing a battery cluster equivalent circuit model; s2, identifying and obtaining optimal model parameters of the battery cluster equivalent circuit model by using a self-adaptive and acoustic search algorithm based on the battery operation data extracted in the step S1; and S3, correcting the SOC of the battery cluster by utilizing an unscented Kalman filtering algorithm according to the obtained optimal model parameters and the battery operation data extracted in the step S1. The method and the device construct a battery cluster equivalent circuit model based on big data, estimate the SOC of the battery cluster by combining a self-adaptive harmony search algorithm and an unscented Kalman filtering algorithm, improve the estimation precision of the SOC, do not need to be carried out in a battery off-line state in the estimation process, and are suitable for on-line prediction of the state of charge of the battery cluster.

Description

Battery cluster state of charge correction method based on big data
Technical Field
The invention relates to the technical field of battery state of charge estimation, in particular to a battery cluster state of charge correction method based on big data.
Background
The lithium ion battery system is used as an energy storage power supply and is a core component of the power energy storage system. The battery management system is necessary corollary equipment for ensuring the normal operation of the battery system. The battery cluster is formed by combining a plurality of unit batteries. Accurate estimation of the State of Charge (SOC) of a battery cluster is a core technology for battery cluster management. The SOC of the battery cluster cannot be directly measured, but can be estimated only by measuring other physical quantities, establishing a specific battery model and using an efficient algorithm.
At present, most common battery cluster SOC estimation methods, such as an open-circuit voltage method, an ampere-hour integral method, a neural network method, machine learning and the like, firstly establish a battery model, obtain battery model parameters through special working condition tests in an off-line state, and then apply an efficient algorithm to carry out precision verification. On one hand, the methods need a large amount of off-line test data for modeling or training, and on the other hand, the estimation accuracy of the SOC is reduced along with the change of the aging model parameters of the battery, so that the estimation of the SOC of the battery cluster of the energy storage system in the service period is not facilitated.
Disclosure of Invention
The invention provides a battery cluster state of charge correction method based on big data, aiming at improving the battery cluster state of charge estimation precision.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method for correcting the state of charge of the battery cluster based on the big data comprises the following steps:
s1, cleaning and reconstructing battery operation data to extract data required by modeling, and then establishing a battery cluster equivalent circuit model;
s2, identifying and obtaining optimal model parameters of the battery cluster equivalent circuit model by using a self-adaptive harmony search algorithm based on the battery operation data extracted in the step S1;
and S3, correcting the SOC of the battery cluster by utilizing an unscented Kalman filtering algorithm according to the obtained optimal model parameters and the battery operation data extracted in the step S1.
Preferably, in step S1, the method for cleaning the data at the k +1 th time in the battery operation data is expressed by the following formulas (1) to (3):
Figure BDA0003415358960000021
Figure BDA0003415358960000022
Figure BDA0003415358960000023
in formulas (1) to (3), Uk+1、UkBattery cluster voltage data, U, collected for adjacent collection timeskVoltage data, U, of said battery cluster acquired at time kk+1Representing voltage data, U, of said battery cluster acquired at a time k +1 adjacent to the time kk+2Voltage data representing the battery cluster at a time k +2 adjacent to the time k + 1;
Ik+1、Ikcurrent data of the battery cluster acquired for adjacent acquisition times, IkCurrent data of the battery cluster acquired at time k, Ik+1Representing current data of said battery cluster acquired at a time k +1 adjacent to the time k, Ik+2Current data representing the battery cluster at a time k +2 adjacent to the time k + 1;
Tk+1、Tktemperature data, T, of the battery cluster acquired for adjacent acquisition timeskRepresenting the temperature data, T, of the battery cluster acquired at time kk+1Representing temperature data of the battery cluster acquired at a time k +1 adjacent to the time k;
SOCk+1、SOCkstate of charge data, SOC, of the battery cluster calculated for adjacent timeskState of charge data, SOC, of said battery cluster representing a calculation at time kk+1Representing the state of charge data of the battery cluster calculated at a time k +1 adjacent to the time k;
ξrthe threshold value of the direct current internal resistance is expressed, and the direct current internal resistance is obtained by calculation according to the voltage and current data of the battery cluster to be cleaned;
ξTa threshold value representing temperature data for washing the battery cluster;
ξsa threshold value representing state of charge data for cleaning the battery cluster;
n represents the number of data sample records.
Preferably xir∈[0,0.002]；ξT∈[3,5]；ξs∈[0.05,0.11]；20℃≤Tk≤40℃。
Preferably, reconstructing the battery operation data is extracting SOC-U of the battery clusterocNumber of curvesAccording to the method, SOC-U of the battery cluster is extractedocThe method steps of the curve data include:
s11, searching a 0 value acquisition moment corresponding to current data with a current value of 0 in the current time sequence data as an open circuit moment of the battery cluster;
s12, determining the current sign of the battery cluster at the previous acquisition time of the 0 value acquisition time, and respectively marking the battery cluster in a discharge state or a charge state before an open circuit when the current sign is positive or negative;
s13, determining whether the current value of the battery cluster is always kept to be 0 within a duration time after the 0 value acquisition moment,
if yes, extracting the voltage U (t) and the state of charge SOC (t) of the battery cluster collected in the duration from the battery operation data;
if not, not extracting the voltage U (t) and the state of charge SOC (t) of the battery cluster collected in the duration;
s14, judging whether the quantity of the SOC (t) value extracted in the step S13 in each interval [0,0.1], [0.9,1], [0.1,0.9] exceeds a preset quantity threshold value or not,
if yes, finishing the extraction of the voltage data and the state of charge data to obtain an SOC (t) -U (t) array, and then turning to the step S15;
if not, returning to the step S11;
s15, performing polynomial fitting on a plurality of SOC (t) -U (t) arrays in the charging state or the discharging state of the battery to respectively obtain a charging open-circuit curve SOC of the batteryc～Uoc,cAnd discharge open circuit curve SOCd～Uoc,dComplete SOC and UocThe data of (3) are extracted.
Preferably, in step S2, the method for identifying the optimal model parameter of the battery cluster equivalent circuit model by using the adaptive sum-of-noise search algorithm includes:
s21, setting the number ND of model parameters to be optimized, the volume HMS of a harmony memory library, the thinking probability HMCR of the harmony melody, the fine tuning probability PAR of the pitch, the fine tuning bandwidth BW of the pitch, the frequency LP of the maximum updating harmony memory library and the maximum creation frequency Maxp, and setting the current creation frequency p =0;
s22, randomly generating HMS harmony melody X in the search space of the model parameters to be optimizediWherein harmony melody subscript i =1, 2., HMS; harmony melody XiND model parameters to be optimized of the battery cluster equivalent circuit model are stored, and then the generated HMS harmony and melody components and the sound memory base { X }1,X2,...,Xi,...,XHMSIn which X isiRepresenting the ith harmony melody in the harmony memory library;
s23, calculating each harmony melody X in the harmony memory bank by using the cleaned battery operation dataiAn adaptation value of;
s24, selecting the harmony melody with the minimum adaptation value from the harmony memory library as the most beautiful harmony melody XbestThen, the harmony melody with the maximum adaptive value is selected as the worst harmony melody Xworst；
S25, judging whether the frequency of searching the optimal solution of the target function f is more than or equal to Maxp,
if yes, outputting the beautiful harmony melody XbestCorresponding optimal model parameters;
if not, the step S36 is executed;
s26, generating a new harmony Xnew，XnewThe generation method comprises the following steps: in the [0,1 ]]A random number r1 is generated and compared with the HMCR, whether r1 is less than the HMCR or not is judged,
if yes, randomly selecting a harmony variable X from the harmony memory libraryrand1Then in [0,1]Generating a random number r2, judging whether r2 is smaller than PAR, if yes, according to fine tuning bandwidth BW, adjusting harmony variable Xrand1After fine adjustment, a new harmony variable is obtained as the harmony XnewIf not, the harmony variable X is usedrand1As the harmony Xnew；
If not, randomly generating a harmony variable X from the search space of the model parameters to be optimizedrand2As the harmony Xnew；
S27, updating the harmony memory bank, wherein the updating method comprises the following steps: calculating the harmony XnewAdapted value of f (X)new) Whether or not it is less than f (X)worst)，
If yes, the harmony X is usednewReplacing the worst and melody X in the harmony memoryworst；
If not, not comparing the worst harmony melody X in the harmony memory baseworstReplacement is carried out;
and S28, repeating the steps S23-S27 until the authoring time p reaches the set maximum authoring time Maxp.
Preferably, in step S23, the harmony melody X is calculated by the following formula (4)iAdapted value of (a):
f＝A·|uk-U(k)|+B·[uk-U(k)2formula (4)
F in the formula (4) represents an objective function for solving the adaptive value;
A. b is a weight coefficient, a + B =1;
ukpredicting terminal voltage of the battery cluster equivalent circuit model;
u (k) is the measured terminal voltage of the equivalent circuit model of the battery cluster and is derived from the current I at the moment kkThe corresponding voltage.
Preferably, in step S26, Xrand3Calculated by equation (5):
Xrand3＝Xrand3+/-r 3 BW equation (5)
Wherein r3 is [0,1]A random number in between, if r3 > 0.5, then Xrand3＝Xrand3+r3·BW；
If r3 is less than or equal to 0.5, then Xrand3＝Xrand3-r3·BW。
Preferably, in step S3, the method for correcting the SOC of the battery cluster by using the unscented kalman filter algorithm includes:
s31, using the ohm voltage drop u of the equivalent circuit model of the battery clusterk 0A first RC circuit voltage uk 1A second RC circuitVoltage uk 2And k is applied to the capacitor CbVoltage across
Figure BDA0003415358960000041
As state variables, 4-dimensional random column vectors x are constructedk＝(uk 0，uk 1，uk 2，SOCk)TThe formula of each state component is shown in formula (6), and a model state equation expressed as formula (7) and a measurement equation expressed as formula (8) are further established:
Figure BDA0003415358960000042
Figure BDA0003415358960000051
Figure BDA0003415358960000052
in formulas (6) to (8), wherein
ikRepresenting a loop current of the battery cluster at time k;
R0expressing the ohm internal resistance in the identified battery cluster equivalent circuit model;
R1representing the resistance in the first RC circuit in the identified battery cluster equivalent circuit model;
C1representing the identified sum resistance R of the first RC circuit1A capacitor connected in parallel;
R2representing the resistance in a second RC circuit in the identified battery cluster equivalent circuit model;
C2representing the identified neutral resistance R of the second RC circuit2A capacitor connected in parallel;
Cbidentifying a capacitor connected in series with an ideal voltage source in the battery cluster equivalent circuit model;
Tsa large data sampling interval;
xk+1represents the state quantity at the time of k + 1;
xkrepresents a state quantity at time k; w is akRepresenting process excitation noise of the battery cluster equivalent circuit model at the moment k;
OCV(SOCk) The open-circuit voltage of the battery at the time k, the SOC of the battery cluster according to the open-circuit state, and the open-circuit charging curve SOCc～Uoc,cOr discharge open circuit curve SOCd～Uoc,dObtaining;
ukrepresenting the voltage at two ends of the battery cluster equivalent circuit model at the moment k;
vkrepresenting the measurement noise;
s32, assume xkObey Gaussian distribution
Figure BDA0003415358960000053
Strategy for selecting symmetric sampling to construct k moment state quantity xkSigma sample point set { χi,k}i＝0,1,…,n,n+1,…,2nThe calculation manner of each point is expressed by the following formula (9):
Figure BDA0003415358960000054
in the formula (9), χ0,kRepresents the set of Sigma sample points as { χ0,kThe state quantity of the system at the time k;
χi,krepresents a set of Sigma sample points as { χi,kThe state quantity of the system at the time k;
Figure BDA0003415358960000055
set of representations { xk-average value of };
n represents xkN =4;
lambda represents a scaling parameter for measuring the distribution of the Sigma sampling points and is used for adjusting the Sigma sampling points and x0The calculation formula of (2) is formula (10)。
λ＝α2(n + l) -n equation (10)
Wherein alpha is called a scale factor, and alpha is more than or equal to 0 and less than or equal to 1; l is the secondary scale factor, l =3-n.
Pχ,kRepresenting a set of state quantities { χ }kCovariance of, initial value Pχ,k＝Px；
S33, utilizing the Sigma sampling point pair k +1 moment state quantity chi obtained in the step S32i,kSum error covariance matrix Pχ,kA prediction is made, the prediction process being expressed by the following equation (11):
Figure BDA0003415358960000061
in the formula (11), the first and second groups,
Figure BDA0003415358960000062
a state variable representing the predicted time k + 1;
Figure BDA0003415358960000063
an estimated value representing a system state prediction at time k;
e represents a matrix of the states of the system,
Figure BDA0003415358960000064
χi,krepresenting the system state at the time k;
f denotes the input matrix of the system,
Figure BDA0003415358960000065
okrepresenting the input quantity of the system at the time k;
Figure BDA0003415358960000066
representing a predicted error variance matrix;
Qk+1representing system process noise at time k +1Variance of the sound;
Figure BDA0003415358960000067
calculated by the following equation (12):
Figure BDA0003415358960000068
wherein, in chikObey a gaussian distribution, β =2;
Figure BDA0003415358960000071
represents the Sigma sample Point χ0,kIn calculating the mean value
Figure BDA0003415358960000072
A weight of time;
Figure BDA0003415358960000073
represents the Sigma sample Point χ0,kIn calculating covariance PxA weight of time;
Figure BDA0003415358960000074
represents the Sigma sample point χi,kIn calculating the mean value
Figure BDA0003415358960000075
A weight of time;
Figure BDA0003415358960000076
represents the Sigma sample point χi,kIn calculating covariance PxWeight of time.
S34, calculating the estimation value of the observed quantity at the k +1 moment by the following formula (13)
Figure BDA0003415358960000077
Figure BDA0003415358960000078
In the formula (13), the first and second groups of the compound,
Figure BDA0003415358960000079
representing the observed value at time k, H is a non-linear observation matrix,
Figure BDA00034153589600000710
s35, calculating a variance matrix P of the observed quantity at the k +1 moment by the following formula (14)yy：
Figure BDA00034153589600000711
In the formula (12), Rk+1Representing an observation noise variance matrix;
s36, calculating covariance P of state quantity and observed quantity at the time k +1 by the following formula (15)χy；
Figure BDA00034153589600000712
S37, calculating a Kalman filter gain K by the following formula (16)k+1：
Figure BDA00034153589600000713
S38, updating the state quantity and error covariance matrix by the following equation (17):
Figure BDA00034153589600000714
in the formula (17), Kk+1 TRepresents Kk+1The transposed matrix of (2);
s39, repeating the steps S31-S38 until the observed quantityThe error reaches the set value and then follows the state quantity xk+1The SOC of the battery cluster at the time k +1 is separated to correct the SOC of the battery cluster calculated by the BMS at that time.
According to the method, the battery cluster equivalent circuit model is established based on big data, the optimal model parameters of the established model are identified by using a self-adaptive and acoustic search algorithm, and then the battery cluster SOC is corrected by using an unscented Kalman filtering algorithm, so that the estimation precision of the battery cluster SOC is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a diagram illustrating implementation steps of a big-data-based battery cluster state of charge correction method according to an embodiment of the present invention;
FIG. 2 shows a reconstructed battery cluster SOC-UocA diagram of method steps for data;
FIG. 3 is a diagram of the method steps for identifying optimal model parameters for a battery cluster equivalent circuit model using an adaptive harmonic search algorithm;
FIG. 4 is a diagram of the method steps for correcting the SOC of a battery cluster using a Kalman filtering algorithm;
FIG. 5 is a schematic diagram of a constructed battery cluster equivalent circuit model;
FIG. 6 is a schematic diagram of the adaptive harmony search algorithm identifying model parameters;
FIG. 7 is a graph of state of charge SOC versus open circuit voltage OCV of a battery cluster;
fig. 8 is a schematic diagram illustrating the accuracy comparison between the battery pack state of charge correction method and the battery pack state of charge estimated by ampere-hour integration method according to the present invention.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not indicated or implied that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limitations of the present patent, and the specific meanings of the terms may be understood by those skilled in the art according to specific situations.
In the description of the present invention, unless otherwise explicitly specified or limited, the term "connected" or the like, if appearing to indicate a connection relationship between components, is to be understood broadly, for example, as being either fixedly connected, detachably connected, or integrated; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or may be connected through one or more other components or may be in an interactive relationship with one another. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The lithium ion battery operation data collected by the battery management system can be uploaded to a data management platform for data summarization, the data volume is large, the data types are multiple, the value density is low, and the data has the characteristics of randomness, time variation and the like and has typical big data characteristics. The more battery operating data, the more battery cluster state of charge information is contained. At present, a few big data analysis methods for energy storage batteries exist, and data resources are difficult to effectively utilize. Historical data and real-time data such as voltage, current, temperature and alarm state of the battery comprise various data of the battery operation cycle, and information required by battery model parameter identification and battery SOC estimation is contained, and the data are effectively utilized to improve the accuracy of the SOC estimation of the battery cluster.
Therefore, the embodiment of the invention provides a battery cluster state of charge correction method based on big data. The idea of solving the technical problem of low estimation accuracy of the existing state of charge by using the big data-based battery cluster state of charge correction method provided by the embodiment is as follows:
firstly, data cleaning and reconstruction are carried out on battery operation data, then a battery cluster equivalent circuit model is established, optimal model parameters of the battery cluster equivalent circuit model are identified by applying a self-adaptive harmony search algorithm, and finally the state of charge of the battery cluster is estimated by using a Kalman filtering algorithm to correct the SOC of the battery cluster.
The big data-based battery cluster state of charge correction method is shown in fig. 1, and specifically comprises the following steps:
step S1, cleaning and reconstructing battery operation data to extract data required by modeling, and then establishing a battery cluster equivalent circuit model; the data cleaning method adopted by the invention is expressed by the following formulas (1) to (3):
Figure BDA0003415358960000091
Figure BDA0003415358960000092
Figure BDA0003415358960000093
in formulas (1) to (3), Uk+1、UkFor adjacent collectionTime-collected battery cluster voltage data, UkVoltage data, U, representing the battery cluster collected at time kk+1Voltage data, U, representing a battery cluster acquired at a time k +1 adjacent to the time kk+2Voltage data indicating a battery cluster at a time k +2 adjacent to the time k + 1;
Ik+1、Ikcurrent data of the battery cluster acquired for adjacent acquisition times, IkRepresenting current data of the battery cluster acquired at time k, Ik+1Current data, I, representing the battery cluster collected at a time k +1 adjacent to the time kk+2Current data indicating a battery cluster at a time k +2 adjacent to the time k + 1;
Tk+1、Tktemperature data, T, of the battery cluster acquired for adjacent acquisition timeskRepresenting the temperature data, T, of the battery cluster acquired at time kk+1Representing temperature data of the battery cluster acquired at a k +1 moment adjacent to the k moment;
SOCk+1、SOCkstate of charge data, SOC, of a battery cluster calculated for adjacent timeskState of charge data, SOC, of a battery cluster representing a calculation at time kk+1Representing the state of charge data of the battery cluster calculated at a time k +1 adjacent to the time k;
ξrthe threshold value of the direct current internal resistance is shown, the direct current internal resistance is obtained by calculation according to the voltage and current data of the battery cluster to be cleaned,
Figure BDA0003415358960000101
ξTa threshold value representing temperature data of the cleaning battery cluster;
ξsa threshold value representing state of charge data of the cleaning battery cluster;
n represents the number of data sample records.
In the present embodiment, the threshold ξ set for eliminating the abnormal valuer∈[0,0.002]；ξT∈[3,5]；ξs∈[0.05,0.11]；20℃≤Tk≤40℃。
Data ofIs reconstructed into SOC-UocAnd (5) data reconstruction. Because the energy storage power station continuously operates according to specific working conditions, the traditional off-line pulse test method is not suitable for obtaining the SOC-U of the battery clusterocData, so the invention is based on the SOC-U of big dataocThe data reconstruction method solves the problem that the SOC-U of the battery cluster of the energy storage power station cannot be obtained by off-line testingocA problem with the data.
The invention reconstructs the battery operation data to extract the SOC-U of the battery clusterocCurve data, extracting SOC-U of battery clusterocAs shown in fig. 2, the data method specifically includes:
s11, searching a 0 value acquisition moment corresponding to current data with a current value of 0 in the current time sequence data as an open circuit moment of the battery cluster; the current time sequence data are battery cluster current data arranged according to the acquisition time sequence, and if the element value of an element in the current time sequence data is '0', the battery cluster is in an open circuit state at the moment. The sign of the current of the battery cluster has positive and negative, wherein the positive indicates that the battery cluster is in a discharging state, and the negative indicates that the battery cluster is in a charging state;
step S12, determining the current sign of the battery cluster at the previous collection time of the value 0, and respectively marking the battery cluster in a discharge state or a charge state before an open circuit when the current sign is positive or negative;
step S13, determining whether the current value of the battery cluster is always kept to be '0' within a continuous time (preferably more than 20 minutes) after the 0 value acquisition time, wherein the fact that the current value is always kept to be '0' indicates that the open circuit state of the circuit is stable,
if yes, extracting the voltage U (t) and the state of charge SOC (t) of the battery cluster acquired in the duration from the battery operation data;
if not, not extracting the voltage U (t) and the state of charge SOC (t) of the battery cluster collected in the duration;
step S14, judging whether the quantity of the SOC (t) value extracted in step S13 in each interval [0,0.1], [0.9,1], [0.1,0.9] exceeds a preset quantity threshold value,
if so, finishing the extraction of the voltage data and the charge state data to obtain an SOC (t) -U (t) array, and then turning to the step S15;
if not, returning to the step S11;
the invention is in [0,0.1]]、[0.9,1]Each of the two SOC intervals obtains at least 3U (t), namely at [0,0.1]]、[0.9,1]Each of the two SOC intervals obtains at least 3 SOC (t) -U (t) arrays with corresponding relations; in the [0.1,0.9]This SOC interval takes at least 8U (t), i.e., at [0.1, 0.9%]At least 8 SOC (t) -U (t) arrays with corresponding relations are obtained in the SOC interval, and the data are used as fitting points of subsequent SOC (t) -U (t) curves to depict SOC-U of the battery cluster in a charging state or a discharging stateocIn order to ensure that the fitting effect is improved and the data reconstruction effect is improved so as to improve the SOC estimation accuracy of the subsequent steps S2-S3, at least 14 data are obtained in three SOC intervals as curve fitting points;
step S15, 6-degree polynomial fitting is carried out on a plurality of SOC (t) -U (t) arrays in the charging state or the discharging state of the battery to obtain a charging open-circuit curve SOC of the batteryc～UcAnd discharge open circuit curve SOCd～UdComplete the pairing of SOC and UocAnd (4) reconstructing the data. 6 th order polynomial fitting SOC-UocThe curve is obtained by the existing curve fitting method, so the fitting process is not specifically described.
And after the cleaning of the battery operation data and the data reconstruction are completed, entering a battery cluster equivalent circuit model construction link. As shown in fig. 5, the equivalent circuit model of the battery cluster constructed in this embodiment is composed of an ideal voltage source UocVariable capacitor CbResistance R0A first RC circuit (formed by a resistor R connected in parallel)1And a capacitor C1Composed of a parallel resistor R) and a second RC circuit2And a capacitor C2Composition) of Uoc、Cb、R0The first RC circuit and the second RC circuit respectively refer to the open-circuit voltage of the battery cluster, the capacity of the battery cluster, the ohmic internal resistance, the first polarization circuit and the second polarization circuit. U shapeocThe value of (D) is derived from the SOC obtained by fitting in step S15c(t)～Uc(t) or SOCd(t)～Ud(t) curveAnd (4) data.
In FIG. 5, UTRepresenting the battery cluster voltage. The battery cluster equivalent circuit model is mathematically described according to kirchhoff's law, and the time domain response expression of the RC parallel loop is expressed by the following formulas (4) - (5):
Figure BDA0003415358960000111
Figure BDA0003415358960000112
in the formulas (4) to (5),
U1,k+1represents the voltage of the first RC circuit at time k + 1;
R1、C1respectively represent the resistance R1Resistance value and capacitance C1The capacity value of (c);
Ikthe loop current of the battery cluster equivalent circuit model at the last acquisition time of the k +1 time, namely the k time, is represented;
Tsrepresenting two data sampling time intervals in the historical data;
U2,k+1represents the voltage of the second RC circuit at time k + 1;
R2、C2respectively represent the resistance R2Resistance value and capacitance C2The capacity value of (c);
U1,k、U2,krespectively, the voltages at time k of the first RC circuit and the second RC circuit.
According to the formulas (4) to (5), the state space equation and the measurement equation of the battery cluster equivalent circuit model are expressed by the following formulas (6) and (7), respectively:
Figure BDA0003415358960000121
Ut,k＝OCV(SOCk)-Ub,k-IkR0-U1,k-U2,kformula (7)
In equations (6) to (7), Ub,k+1Indicating that k +1 is applied to the capacitor CbThe voltage across;
Ub,kindicating that k is applied to the capacitor CbThe voltage across;
ηtdenotes the coulombic efficiency, η in this exampletThe value is 1;
Ut,krepresenting the voltage of the battery cluster at the current k moment;
OCV(SOCk) Representing reconstructed SOCc(t)～Uc(t) or SOCd(t)～UdIn the graph (t), the open circuit voltage corresponds to the battery pack SOC at the time k.
After the cleaning and reconstruction of the battery operation data in step S1 and the construction of the battery cluster equivalent circuit model are completed, as shown in fig. 1, the method for correcting the state of charge of the battery cluster based on the big data provided in this embodiment is shifted to:
s2, identifying and obtaining optimal model parameters of the battery cluster equivalent circuit model by using a self-adaptive and acoustic search algorithm based on the battery operation data extracted in the step S1;
the method for identifying the optimal model parameters of the battery cluster equivalent circuit model by using the adaptive harmony search algorithm is shown in fig. 3 and 6, and specifically comprises the following steps:
step S21, setting the number ND of model parameters to be optimized (in the invention, the model parameter to be optimized is C)b、R0、R1、C1、R2、C2Therefore ND = 6) and harmony memory bank capacity HMS, harmony melody thinking probability HMCR, tonality trimming probability PAR, tonality trimming bandwidth BW, maximum update and vocal memory bank number LP, maximum authoring number Maxp, and setting current authoring number p =0, wherein HMCR, PAR, BW are all dynamically adjusted according to iterative search, and HMCR, PAR are adaptive adjustments, and the range is dynamically adjusted by updating the history of harmony. The values of HMCR and PAR are respectively subject to normal distribution, and the mean value HMCR of HMCRm∈[0.9,1]Standard deviation of HMCRσ=0.01; mean PAR of PARm∈[0,1]Standard deviation of PAR of the sameσ=0.05. Setting initial value HMCRm=0.98, initial value PARm=0.9, then the search is started from the HMCR and PAR generated from the normal distribution. In an iterative process, harmony X is recordednewSuccessful replacement of worst-case sum melody X in HM (sum tone memory library)worstThe corresponding HMCR and PAR values. After a specified number of iterations LP, the HMCR is regenerated by averaging all HMCR and PAR values recorded during this periodmAnd PARm. With new mean and fixed standard deviation (HMCR)σ＝0.01、PARσ= 0.05), new HMCR and PAR are generated and used for subsequent iterations.
In this embodiment, BW ∈ [0,0.5], and the method of dynamic adjustment thereof is expressed by the following formula (8):
Figure BDA0003415358960000131
in formula (8), BW (t) denotes BW at time t;
BWmax、BWminrespectively representing an upper limit value and a lower limit value of BW;
p is the number of creations, p =1,2, \ 8230;, maxp.
Step S22, generating HMS harmony melody X randomly in the search space of the model parameters to be optimizediWherein harmony melody subscript i =1, 2., HMS; harmony melody XiND model parameters to be optimized of the battery cluster equivalent circuit model are stored, and then the generated HMS harmony and melody components and the sound memory base { X }1,X2,...,Xi,...,XHMSIn which X isiRepresents the ith harmony melody in the harmony memory bank,
ith harmony melody X created at the p-th timei,pIs expressed by the following formula (9)
Xi,p＝(Cbi,p、R0i,p、R1i,p、C1i,p、R2i,p、C2i,p) Formula (9)
Cbi,p、R0i,p、R1i,p、C1i,p、R2i,p、C2i,pRespectively representing the ith harmony melody X of the p-th creationi,pC of (A)b、R0、R1、C1、R2、C2Parameter values for the 6 model parameters;
step S23, calculating each harmony melody X in the harmony memory bank by using the cleaned battery operation dataiAn adaptation value of;
the invention calculates the sum melody X by the following formula (10)iAdapted value of (a):
f＝A·|uk-U(k)|+B·[uk-U(k)2formula (10)
F in the formula (4) represents an objective function for solving the adaptive value; A. b is a weight coefficient, a + B =1; u. ukPredicting terminal voltage for the battery cluster equivalent circuit model; u (k) is the measured terminal voltage of the equivalent circuit model of the battery cluster and is derived from the current I at the moment kkThe corresponding voltage. Recording f (X) corresponding to each harmony melody X, the form of the harmony memory base is expressed by the following formula (11):
Figure BDA0003415358960000132
step S24, selecting harmony melody with minimum adaptive value from harmony memory base as the most beautiful harmony melody XbestThen, the harmony melody with the largest adaptation value is selected as the worst harmony melody Xworst；
Step S25, judging whether the times of searching the optimal solution of the target function f is more than or equal to Maxp,
if yes, outputting the most beautiful harmony melody XbestCorresponding optimal model parameters;
if not, the step S36 is executed;
step S26, generating a new harmony Xnew，XnewThe generation method comprises the following steps: in the [0, 1]]A random number r1 is generated and compared with the HMCR, whether r1 is less than the HMCR or not is judged,
if yes, randomly selecting a harmony variable X from the harmony memory libraryrand1Then in [0, 1]]Generating a random number r2, judging whether r2 is smaller than PAR, if so, according to the fine tuning bandwidth BW pair harmony variable Xrand1After fine tuning, a new harmony variable X is obtainedrand3＝Xrand3+/-r 3. BW where r3 is [0,1]If r3 is more than 0.5, taking a plus sign; if r3 is less than or equal to 0.5, taking a minus sign; fine tuned Xrand3As harmony XnewIf not, harmony variable X is generatedrand1As harmony Xnew；
If not, randomly generating a harmony variable X from the search space of the model parameter to be optimizedrand2As harmony Xnew；
Step S27, updating the harmony memory base, wherein the updating method comprises the following steps: calculating harmony XnewAdapted value of f (X)new) Whether or not less than f (X)worst)，
If yes, the harmony X is usednewReplacement of worst-case sum melody X in harmony memoryworst；
If not, the worst harmony melody X in the harmony memory base is not matchedworstReplacement is carried out;
and S28, repeating the steps S23-S27 until the authoring time p reaches the set maximum authoring time Maxp.
After the optimal model parameters of the battery cluster equivalent circuit model are identified, please continue to refer to fig. 1, the method for correcting the state of charge of the battery cluster based on the big data provided by the invention is shifted to:
and S3, correcting the SOC of the battery cluster by utilizing an unscented Kalman filtering algorithm according to the obtained optimal model parameters and the battery operation data extracted in the step S1.
As shown in fig. 4, the correction method specifically includes:
s31, using the ohm voltage drop u of the equivalent circuit model of the battery clusterk 0First RC circuit voltage uk 1A second RC circuit voltage uk 2And k is applied to the capacitor CbVoltage across
Figure BDA0003415358960000141
As state variables, 4-dimensional random column vectors x are formedk＝(uk 0，uk 1，uk 2，SOCk)TThe formula for each state component is shown in equation (12), and further a model state equation expressed as equation (13) and a measurement equation expressed as equation (14) are established:
Figure BDA0003415358960000151
Figure BDA0003415358960000152
Figure BDA0003415358960000153
in formulas (12) to (14), wherein
ikRepresenting a loop current of the battery cluster at time k;
R0expressing the ohm internal resistance in the identified battery cluster equivalent circuit model;
R1representing the identified resistance in the first RC circuit in the battery cluster equivalent circuit model;
C1representing the identified sum resistance R of the first RC circuit1A capacitor connected in parallel;
R2representing the resistance in a second RC circuit in the identified battery cluster equivalent circuit model;
C2representing the identified neutral resistance R of the second RC circuit2A capacitor connected in parallel;
Cbidentifying a capacitor connected in series with an ideal voltage source in the battery cluster equivalent circuit model;
Tsa large data sampling interval;
xk+1represents the state quantity at the time k + 1;
xkrepresents a state quantity at time k;
wkrepresenting process excitation noise of the battery cluster equivalent circuit model at the moment k;
OCV(SOCk) The open-circuit voltage of the battery at the time k, the SOC of the battery cluster according to the open-circuit state, and the charging open-circuit curve SOCc～Uoc,cOr discharge open circuit curve SOCd～Uoc,dObtaining;
ukrepresenting the voltage at two ends of the battery cluster equivalent circuit model at the moment k;
vkrepresenting the measurement noise;
s32, assume xkObeying a Gaussian distribution
Figure BDA0003415358960000154
Strategy for selecting symmetric sampling to construct k moment state quantity chikSigma sample point set { χi,k}i＝0,1,…,n,n+1,…,2nThe calculation manner of each point is expressed by the following formula (15):
Figure BDA0003415358960000161
in the formula (15), χ0,kRepresents the set of Sigma sample points as { χ0,kThe state quantity of the system at the time k;
χi,krepresents the set of Sigma sample points as { χi,kThe state quantity of the system at the time k;
Figure BDA0003415358960000162
representation set { xk-average value of };
n represents xkN =4;
lambda represents a conversion parameter measuring the distribution of Sigma sample points for adjusting the Sigma sample points to x0The calculation formula (2) is formula (16).
λ＝α2(n + l) -n equation (16)
Wherein alpha is called a scale factor, and alpha is more than or equal to 0 and less than or equal to 1; l is a secondary scale factor, l =3-n.
Pχ,kRepresenting a set of state quantities { χ }kCovariance of, initial value Pχ,k＝Px；
S33, utilizing the Sigma sampling point pair k +1 moment state quantity chi obtained in the step S32i,kSum error covariance matrix Pχ,kA prediction is made, the prediction process being expressed by the following equation (17):
Figure BDA0003415358960000163
in the formula (11), the first and second groups,
Figure BDA0003415358960000164
a state variable representing the predicted time k + 1;
Figure BDA0003415358960000165
an estimated value representing a system state prediction at time k;
e represents a matrix of the states of the system,
Figure BDA0003415358960000166
χi,krepresenting the system state at the time k;
f denotes the input matrix of the system,
Figure BDA0003415358960000167
okrepresenting the input quantity of the system at the time k;
Figure BDA0003415358960000171
representing a predicted error variance matrix;
Qk+1representing the variance of the system process noise at time k + 1;
Figure BDA0003415358960000172
calculated by the following equation (18):
Figure BDA0003415358960000173
wherein, in chikObey a gaussian distribution, β =2;
Figure BDA0003415358960000174
represents the Sigma sample point χ0,kIn calculating the mean value
Figure BDA0003415358960000175
A weight of time;
Figure BDA0003415358960000176
represents the Sigma sample Point χ0,kCalculating the covariance PxA weight of time;
Figure BDA0003415358960000177
represents the Sigma sample Point χi,kIn calculating the mean value
Figure BDA0003415358960000178
A weight of time;
Figure BDA0003415358960000179
represents the Sigma sample Point χi,kIn calculating covariance PxWeight of time.
S34, calculating the estimation value of the observed quantity at the k +1 moment through the following formula (19)
Figure BDA00034153589600001710
Figure BDA00034153589600001711
In the formula (19), the first and second groups of the compound,
Figure BDA00034153589600001712
representing the observed value at time k, H is a non-linear observation matrix,
Figure BDA00034153589600001713
s35, calculating a variance matrix P of the observed quantity at the time k +1 by the following formula (20)yy：
Figure BDA00034153589600001714
In the formula (20), Rk+1Representing an observation noise variance matrix;
s36, calculating covariance P of state quantity and observed quantity at the time k +1 by the following formula (21)χy；
Figure BDA00034153589600001715
S37, calculating a Kalman filter gain K by the following formula (22)k+1：
Figure BDA00034153589600001716
S38, updating the state quantity and error covariance matrix by the following equation (23):
Figure BDA00034153589600001717
in the formula (23), Kk+1 TRepresents Kk+1The transposed matrix of (2);
s39, repeating the steps S31-S38 until the observed quantity error reaches a set value, and then, starting from the state quantity xk+1The SOC of the battery pack at the time k +1 is separated to determine the time BMS (battery management system)) And correcting the calculated SOC of the battery cluster.
The specific implementation of the method for correcting the state of charge of the battery cluster based on the big data provided by the embodiment is further described below by taking an energy storage system matched with a certain photovoltaic power station as an example:
assuming that the battery of the energy storage system is a lithium iron phosphate battery of 3.2V240Ah, the upper limit of the charging voltage of the battery is 3.65V, and the lower limit of the discharging voltage of the battery is 2.5V. Firstly, cleaning battery operation data of the lithium iron phosphate battery, wherein the battery operation data comprises battery voltage U (t), current I (t), SOC, battery temperature and the like. The cleaning method is not repeated as described above, and the cleaned abnormal data does not participate in parameter identification of the battery equivalent circuit model and calculation of the SOC of the battery cluster. Then extracting data and fitting SOCc(t)～Uc(t) Curve and SOCd(t)～Ud(t) curves, as in FIG. 7. The-polynomial (charge) "in fig. 7 corresponds to SOCc(t)～Uc(t) the curve, "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - (Y- -SOC (discharge) corresponds to SOCd(t)～Ud(t) curve.
Then, a battery cluster equivalent circuit model shown in fig. 5 is established, mathematical description is performed based on kirchhoff's law, and then a time domain response expression of the RC parallel circuit (the first RC circuit and the second RC circuit) is a voltage and current relational expression found through circuit analysis, so that a state space equation of the model, namely an observation equation, is obtained.
Then, model parameters are identified by using an adaptive harmony search algorithm to obtain optimal model parameters, for example, when setting ND =6, hms =100, lp =100, maxp =1800, bwmax＝0.02，BWmin=0.0005, a =0.63, b =0.37. When the fitness function f reaches the set error value 1e-5, the optimal model parameter value (C) can be obtained through model parameter identification of the adaptive harmony search algorithmb,R0,R1,C1,R2,C2)＝(238.6,0.5,0.6,7249,0.4,69754)。
Finally, the optimal model parameter values (C)b,R0,R1,C1,R2,C2) = (238.6, 0.5,0.6,7249,0.4, 69754) as input parameter,and establishing an unscented Kalman filtering algorithm SOC estimation model, and correcting the SOC of the battery cluster according to the steps S31-S38.
The accuracy comparison graph of the battery cluster SOC corrected by the battery cluster state-of-charge correction method based on big data and the battery cluster SOC estimated by the ampere-hour integration method is shown in figure 8, and the estimation error is within 2 percent and accords with the expectation.
In conclusion, the battery cluster equivalent circuit model is constructed based on big data, the SOC of the battery cluster is estimated by combining the adaptive harmony search algorithm and the unscented Kalman filtering algorithm, the estimation precision of the SOC is improved, the estimation process is not carried out in a battery off-line state, and the method is suitable for online prediction of the SOC of the battery cluster.
It is to be understood that the above-described embodiments are merely preferred embodiments of the invention and that the technical principles herein may be applied. It will be understood by those skilled in the art that various modifications, equivalents, changes, and the like can be made to the present invention. However, such variations are within the scope of the invention as long as they do not depart from the spirit of the invention. In addition, certain terminology used in the description and claims of the present application is not limiting, but is used for convenience only.

Claims (6)

1. A battery cluster state of charge correction method based on big data is characterized by comprising the following steps:
s1, cleaning and reconstructing battery operation data to extract data required by modeling, and then establishing a battery cluster equivalent circuit model;
s2, identifying and obtaining the optimal model parameters of the battery cluster equivalent circuit model by using a self-adaptive harmony search algorithm based on the battery operation data extracted in the step S1;
s3, correcting the SOC of the battery cluster by utilizing an unscented Kalman filtering algorithm according to the obtained optimal model parameters and the battery operation data extracted in the step S1;
in step S1, the method for cleaning the data at the k +1 th time in the battery operation data is expressed by the following formulas (1) to (3):
Figure FDA0003798565070000011
Figure FDA0003798565070000012
Figure FDA0003798565070000013
in formulas (1) to (3), Uk+1、UkBattery cluster voltage data, U, collected for adjacent collection timeskVoltage data, U, of said battery cluster acquired at time kk+1Representing voltage data, U, of the battery cluster acquired at a time k +1 adjacent to the time kk+2Voltage data representing the battery cluster at a time k +2 adjacent to the time k + 1;
Ik+1、Ikcurrent data of the battery cluster acquired for adjacent acquisition times, IkRepresenting the current data of said battery cluster acquired at time k, Ik+1Representing current data of said battery cluster acquired at a time k +1 adjacent to the time k, Ik+2Current data representing the battery cluster at a time k +2 adjacent to the time k + 1;
Tk+1、Tktemperature data, T, of the battery cluster acquired for adjacent acquisition timeskRepresenting the temperature data, T, of the battery cluster acquired at time kk+1Representing temperature data of the battery cluster acquired at a time k +1 adjacent to the time k;
SOCk+1、SOCkstate of charge data, SOC, of the battery cluster calculated for adjacent timeskState of charge data, SOC, of said battery cluster representing a calculation at time kk+1Representing state of charge data of the battery cluster calculated at a time k +1 adjacent to the time k;
ξra threshold value representing the DC internal resistance according to the voltage of the battery cluster to be cleanedCalculating current data;
ξTa threshold value representing temperature data for washing the battery cluster;
ξsa threshold value representing state of charge data for cleaning the battery cluster;
n represents the number of data sample records;
the method for identifying the optimal model parameters of the battery cluster equivalent circuit model by using the self-adaptive and acoustic search algorithm comprises the following steps:
s21, setting the number ND of model parameters to be optimized, the volume HMS of a harmony memory library, the thinking probability HMCR of the harmony melody, the fine tuning probability PAR of the pitch, the fine tuning bandwidth BW of the pitch, the frequency LP of the maximum updating harmony memory library and the maximum creation frequency Maxp, and setting the current creation frequency p =0;
s22, randomly generating HMS harmony melodies X in the search space of the model parameters to be optimizediWherein harmony melody subscript i =1, 2., HMS; harmony melody XiND model parameters to be optimized of the battery cluster equivalent circuit model are stored, and then the generated HMS harmony melody components and the sound memory base { X }1，X2，...，Xi，...，XHMSIn which X isiRepresenting the ith harmony melody in the harmony memory library;
s23, calculating each harmony melody X in the harmony memory bank by using the cleaned battery operation dataiAn adaptation value of;
s24, selecting the harmony melody with the minimum adaptive value from the harmony memory library as the most beautiful harmony melody XbestThen, the harmony melody with the largest adaptation value is selected as the worst harmony melody Xworst；
S25, judging whether the frequency of searching the optimal solution of the target function f is more than or equal to Maxp,
if yes, outputting the beautiful harmony melody XbestCorresponding optimal model parameters;
if not, the step S36 is executed;
s26, generating a new harmony Xnew，XnewThe generation method comprises the following steps: in the [0, 1]]A random number r1 is generated and compared with the HMCR, whether r1 is less than the HMCR or not is judged,
if yes, randomly selecting a harmony variable X from the harmony memory libraryrand1Then in [0,1]Generating a random number r2, and judging whether r2 is smaller than PAR, if yes, according to fine tuning bandwidth BW, making harmony variable Xrand1After fine adjustment, a new harmony variable is obtained as the harmony XnewIf not, the harmony variable X is usedrand1As the harmony Xnew；
If not, randomly generating a harmony variable X from the search space of the model parameters to be optimizedrand2As the harmony Xnew；
S27, updating the harmony memory bank, wherein the updating method comprises the following steps: calculating the harmony XnewAdapted value of f (X)new) Whether or not less than f (X)worst)，
If yes, the harmony X is usednewReplacing the worst and melody X in the harmony memoryworst；
If not, not comparing the worst harmony melody X in the harmony memory baseworstReplacement is carried out;
and S28, repeating the steps S23-S27 until the authoring time p reaches the set maximum authoring time Maxp.
2. The big-data-based battery cluster state-of-charge correction method according to claim 1, wherein ξr∈[0，0.002]；ξT∈[3，5]；ξs∈[0.05，0.11]；20℃≤Tk≤40℃。
3. The big-data-based battery cluster state-of-charge correction method according to claim 1, wherein reconstructing the battery operation data is to extract SOC-U of the battery clusterocCurve data, extracting SOC-U of the battery clusterocThe method steps of the curve data include:
s11, searching a 0 value acquisition moment corresponding to current data with a current value of 0 in the current time sequence data as an open circuit moment of the battery cluster;
s12, determining a current sign of the battery cluster at a previous acquisition time of the 0 value acquisition time, and respectively marking that the battery cluster is in a discharge state or a charge state before an open circuit when the current sign is positive or negative;
s13, determining whether the current value of the battery cluster is always kept to be 0 within a duration time after the 0 value acquisition time,
if yes, extracting the voltage U (t) and the state of charge SOC (t) of the battery cluster collected in the duration from the battery operation data;
if not, not extracting the voltage U (t) and the state of charge SOC (t) of the battery cluster acquired in the duration;
s14, judging whether the quantity of the SOC (t) value extracted in the step S13 in each interval [0,0.1], [0.9,1], [0.1,0.9] exceeds a preset quantity threshold value or not,
if so, finishing the extraction of the voltage data and the charge state data to obtain an SOC (t) -U (t) array, and then turning to the step S15;
if not, returning to the step S11;
s15, performing polynomial fitting on a plurality of SOC (t) -U (t) arrays in the charging state or the discharging state of the battery to respectively obtain a charging open-circuit curve SOC of the batteryc～Uoc，cAnd discharge open circuit curve SOCd～Uoc，dComplete SOC and UocThe data of (2) is extracted.
4. The big-data based battery cluster state of charge correction method according to claim 1, wherein in step S23, the sum melody X is calculated by the following formula (4)iAdapted value of (a):
f＝A·|uk-U(k)|+B·[uk-U(k)]2formula (4)
F in the formula (4) represents an objective function for solving the adaptive value;
A. b is a weight coefficient, a + B =1;
ukpredicting terminal voltage for the battery cluster equivalent circuit model;
u (k) is the measured terminal voltage of the equivalent circuit model of the battery cluster and is derived from the current I at the moment kkThe corresponding voltage.
5. The big-data-based battery cluster state of charge correction method according to claim 1, wherein in step S26, Xrand3Calculated by equation (5):
Xrand3＝Xrand3+/-r 3 BW equation (5)
Wherein r3 is [0,1]A random number in between, if r3 > 0.5, then Xrand3＝Xrand3+r3·BW；
If r3 is less than or equal to 0.5, then Xrand3＝Xrand3-r3·BW。
6. The big data based battery cluster state of charge correction method according to claim 1, wherein in step S3, the method for correcting the SOC of the battery cluster by using the unscented kalman filter algorithm comprises the steps of:
s31, using the ohm voltage drop u of the equivalent circuit model of the battery clusterk 0A first RC circuit voltage uk 1A second RC circuit voltage uk 2And k is applied to the capacitor CbVoltage across
Figure FDA0003798565070000041
As state variables, 4-dimensional random column vectors x are formedk＝(uk 0，uk 1，uk 2，SOCk)TThe formula for calculating each state component is shown in formula (6), and a model state equation expressed as formula (7) and a measurement equation expressed as formula (8) are further established:
Figure FDA0003798565070000042
Figure FDA0003798565070000043
Figure FDA0003798565070000044
in formulas (6) to (8), wherein
ikRepresenting a loop current of the battery cluster at time k;
R0expressing ohmic internal resistance in the battery cluster equivalent circuit model obtained through identification;
R1representing the resistance in the first RC circuit in the identified battery cluster equivalent circuit model;
C1representing the identified sum resistance R of the first RC circuit1A capacitor connected in parallel;
R2representing the resistance in a second RC circuit in the identified battery cluster equivalent circuit model;
C2representing the identified and the resistance R in the second RC circuit2A capacitor connected in parallel;
Cbidentifying a capacitor obtained and connected in series with an ideal voltage source in the battery cluster equivalent circuit model;
Tsa large data sampling interval;
xk+1represents the state quantity at the time k + 1;
xkrepresents a state quantity at time k; w is akRepresenting process excitation noise of the battery cluster equivalent circuit model at the moment k;
OCV(SOCk) The open-circuit voltage of the battery at the time k, the SOC of the battery cluster according to the open-circuit state, and the open-circuit charging curve SOCc～Uoc，cOr discharge open circuit curve SOCd～Uoc，dObtaining;
ukrepresenting the voltage at two ends of the battery cluster equivalent circuit model at the moment k;
vkrepresenting the measurement noise;
s32, assume xkObey Gaussian distribution
Figure FDA0003798565070000051
Strategy for selecting symmetric sampling to construct k moment state quantity xkSigma sample point set of (x:)i，k}i＝0，1，…，n，n+1，…，2nThe calculation manner of each point is expressed by the following formula (9):
Figure FDA0003798565070000052
in the formula (9), χ0，kRepresents the set of Sigma sample points as { χ0，kThe state quantity of the system at the time k;
χi，krepresents the set of Sigma sample points as { χi，kThe state quantity of the system at the time k;
Figure FDA0003798565070000053
representation set { xk-average value of };
n represents xkN =4;
lambda represents a conversion parameter measuring the distribution of Sigma sample points for adjusting the Sigma sample points to x0The calculation formula is formula (10);
λ＝α2(n + l) -n equation (10)
Wherein alpha is called a scale factor, and alpha is more than or equal to 0 and less than or equal to 1; l is the secondary scale factor, l =3-n.
Pχ，kRepresenting a set of state quantities χkCovariance of, initial value Pχ，k＝Px；
S33, utilizing the Sigma sampling point pair k +1 moment state quantity chi obtained in the step S32i，kSum error covariance matrix Pχ，kA prediction is made, the prediction process being expressed by the following equation (11):
Figure FDA0003798565070000054
in the formula (11), the first and second groups of the compound,
Figure FDA0003798565070000055
a state variable representing the predicted time k + 1;
Figure FDA0003798565070000056
an estimated value representing a system state prediction at time k;
e represents a matrix of the states of the system,
Figure FDA0003798565070000057
χi，krepresenting the system state at time k;
f denotes a system input matrix and F denotes a system input matrix,
Figure FDA0003798565070000061
okrepresenting the input quantity of the system at the time k;
Figure FDA0003798565070000062
representing a predicted error variance matrix;
Qk+1representing the variance of the system process noise at time k + 1;
Figure FDA0003798565070000063
calculated by the following equation (12):
Figure FDA0003798565070000064
wherein, in chikWhen obeying a gaussian distribution, β =2;
Figure FDA0003798565070000065
represents the Sigma sample point χ0，kIn calculating the mean value
Figure FDA0003798565070000066
A weight of time;
Figure FDA0003798565070000067
represents the Sigma sample point χ0，kIn calculating covariance PxA weight of time;
Figure FDA0003798565070000068
represents the Sigma sample point χi，kIn calculating the mean value
Figure FDA0003798565070000069
A weight of time;
Figure FDA00037985650700000610
represents the Sigma sample point χi，kIn calculating covariance PxA weight of time;
s34, calculating the estimation value of the observed quantity at the k +1 moment through the following formula (13)
Figure FDA00037985650700000611
Figure FDA00037985650700000612
In the formula (13), the first and second groups,
Figure FDA00037985650700000613
representing the observed value at time k, H is a non-linear observation matrix,
Figure FDA00037985650700000614
s35, calculating a variance matrix P of the observed quantity at the time k +1 by the following formula (14)yy：
Figure FDA00037985650700000615
In the formula (12), Rk+1Representing an observation noise variance matrix;
s36, calculating covariance P of state quantity and observed quantity at the moment k +1 by the following formula (15)χy；
Figure FDA00037985650700000616
S37, calculating a Kalman filter gain K by the following formula (16)k+1：
Figure FDA0003798565070000071
S38, updating the state quantity and error covariance matrix by the following equation (17):
Figure FDA0003798565070000072
in the formula (17), Kk+1 TRepresents Kk+1The transposed matrix of (2);
s39, repeating the steps S31-S38 until the observed quantity error reaches a set value, and then, starting from the state quantity xk+1The SOC of the battery cluster at the time k +1 is separated to correct the SOC of the battery cluster calculated by the BMS at that time.
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