CN110554326A - energy storage battery SOC estimation method based on multi-rate strong tracking expansion - Google Patents
energy storage battery SOC estimation method based on multi-rate strong tracking expansion Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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Abstract
The invention discloses an energy storage battery SOC estimation method based on multi-rate strong tracking expansion, which comprises the steps of firstly determining an equivalent circuit model, carrying out cyclic intermittent charging and discharging experiments with different charging and discharging multiplying powers on a power battery, obtaining a battery SOC estimation value at each moment through obtained battery model parameters and a state of charge value obtained by multi-rate strong tracking expansion Kalman filter estimation, and then repeatedly carrying out staggered iteration. The invention has the beneficial effects that: the multi-rate strong tracking extended Kalman filter method can improve the gain margin of the system, so that the system has the advantages of uniform stability, easiness in decentralized control and the like, and the multi-rate strong tracking extended Kalman filter not only has the advantage of a strong tracking extended Kalman filter algorithm, but also improves the performance of a single-rate sampling system.
Description
Technical Field
The invention relates to an estimation method of an energy storage battery SOC, in particular to an estimation method of an energy storage battery SOC based on a multi-rate strong tracking extended Kalman filtering method, and belongs to the technical field of multi-rate sampling control systems.
background
along with the dynamic change of monitored objects becoming more and more complex and the requirement on the monitoring system being continuously improved, the single-rate sampling monitoring control system cannot meet the requirement of the more complex monitoring system due to the single sampling frequency, and cannot analyze the state of the current system through signals of different sampling rates, so the gain margin of the system still needs to be improved, and in addition, the existing single sampling system does not have uniform stability, so that the distributed control cannot be carried out, and further, the system performance of the single-rate sampling is poor.
Disclosure of Invention
The invention aims to solve the problems and provide an energy storage battery SOC estimation method based on multi-rate strong tracking expansion.
the invention realizes the purpose through the following technical scheme: an energy storage battery SOC estimation method based on multi-rate strong tracking expansion comprises the following steps:
Step 1, establishing an equivalent circuit model, and building a second-order RC circuit model according to the Thevenin equivalent circuit;
step 2, carrying out cyclic intermittent charge and discharge experiments with different charge and discharge multiplying powers on the power battery;
And 3, establishing a lithium iron phosphate battery dynamic model, and estimating a prediction curve and a reference curve of the standard SOC by a multi-rate strong tracking Kalman filter method.
as a still further scheme of the invention: in the step 1, a second-order RC circuit model, U, is set upocis the terminal voltage of the battery, UL is the open circuit voltage of the battery, R0Is the ohmic internal resistance, R, of the cell1,C1Is electrochemical polarization resistance and capacitance, R2,C2is a concentration polarization resistor and capacitor, in the circuit, R0the abrupt change characteristic of the terminal voltage can be reflected, and the gradient characteristic of the terminal voltage, U, can be reflected by the second-order RC parallel networkocthe relationship between voltage and SOC may be reflected.
as a still further scheme of the invention: in the step 2, the pulse type charge-discharge test experiment comprises the following steps:
Fully filling the lithium iron phosphate battery until the SOC is 1, and standing for 8 hours;
The discharge experiment was performed at a 0.3C discharge rate: the lithium iron phosphate battery discharges 15 seconds, stands for 5 minutes, discharges 20 minutes, stands for 1 hour, and takes the process as a cycle until the battery SOC is equal to 0, so that the lithium iron phosphate battery discharges 10% of SOC in each discharge cycle. Throughout the discharge process, cell voltage data per second is accurately collected for analysis.
In the pulse type charge and discharge test experiment, the existence of the ohmic resistor 0R enables the battery voltage to jump instantly at the beginning and the end of discharge.
as a still further scheme of the invention: in the step 3, establishing a lithium iron phosphate battery dynamic model needs to pass
terminal voltage response expression:
Initial voltage of RC parallel circuit:
The battery is kept still for a time after discharging, the pulse discharging voltage response of the battery is zero input voltage response, and the terminal voltage response expression is as follows:
y＝a-be-ct-de-ft
Calculating battery polarization parameter R1，C1，R2，C2。
setting the sampling period of current and voltage:
mTU＝T0,nTI＝T0,n＝Nm
wherein, TI，TURespectively a current sampling period and a voltage sampling period, T0is the system frame period, N, m, N are integers. The above formula shows that the voltage sampling period is m times the frame period and the current sampling period is n times the frame period. In a voltage sampling period, the current is sampled for N times;
in the k-th frame period [ kT ]0,(k+1)T0) The current and voltage are sampled as follows:
The voltage of the single battery is sampled m times in one frame period:
kT0+t1,kT0+t2,……,kT0+tm
wherein 0 ═ t1<t2<….<tm<T0and tm+1＝T0。
the current of the single battery is sampled n times in a period of one frame:
In the interval [ kT0,(k+1)T0) In, i is e [1, m ∈]The current is sampled N times within this interval:
kT0+ti 1,kT0+ti 2,……,kT0+ti N
wherein, 0<ti 1<ti 2<….<ti N<ti+1
Then, input variables and process noise are determined, which can be considered as spread vectors, and the spread matrix is as follows:
The spreading matrix for the current coefficients and the process noise is as follows:
Defining a lifted discrete state space model:
the prediction updating equation of the algorithm of the multi-rate strong tracking extended Kalman filter is as follows:
Measurement update equation:
wherein the content of the first and second substances,P0which is representative of the initial input signal, is,Representing the output signal, k ═ 1,2, …, Nt,Λkthe representation is a plurality of fading factor matrices,Is kronecker product, Ntrepresenting the total time step. Qkand RkThe covariance matrix, which represents the noise, is assumed to be constant.
the invention has the beneficial effects that: the method for estimating the SOC of the energy storage battery based on the multi-rate strong tracking extended Kalman filter method is reasonable in design, the multi-rate strong tracking extended Kalman filter can analyze the state of a current system through signals of different sampling rates, and compared with a single-rate controller, the method has the advantages that the gain margin of the system is improved, the system has unified stability, the distributed control is easy to realize, and the like.
Drawings
FIG. 1 is a schematic flow chart of the algorithm of the present invention;
FIG. 2 is a schematic diagram of a second-order RC circuit model built according to the present invention;
FIG. 3 is a schematic diagram of the instant jump structure of the ohmic resistor of the present invention.
Detailed Description
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1 to 3, a method for estimating the SOC of an energy storage battery based on multi-rate strong tracking expansion includes the following steps:
step 1, establishing an equivalent circuit model, and building a second-order RC circuit model according to the Thevenin equivalent circuit;
step 2, carrying out cyclic intermittent charge and discharge experiments with different charge and discharge multiplying powers on the power battery;
And 3, establishing a lithium iron phosphate battery dynamic model, and estimating a prediction curve and a reference curve of the standard SOC by a multi-rate strong tracking Kalman filter method.
further, in the embodiment of the present invention, in step 1, a second-order RC circuit model, U, is builtocIs the terminal voltage of the battery, UL is the open circuit voltage of the battery, R0is the ohmic internal resistance, R, of the cell1,C1is electrochemical polarization resistance and capacitance, R2,C2Is a concentration polarization resistor and capacitor, in the circuit, R0the abrupt change characteristic of the terminal voltage can be reflected, and the gradient characteristic of the terminal voltage, U, can be reflected by the second-order RC parallel networkocthe relationship between voltage and SOC may be reflected.
Further, in the embodiment of the present invention, in the step 2, the pulse type charge and discharge test experiment includes the following steps:
1. Fully filling the lithium iron phosphate battery until the SOC is 1, and standing for 8 hours;
2. The discharge experiment was performed at a 0.3C discharge rate: the lithium iron phosphate battery discharges 15 seconds, stands for 5 minutes, discharges 20 minutes, stands for 1 hour, and takes the process as a cycle until the battery SOC is equal to 0, so that the lithium iron phosphate battery discharges 10% of SOC in each discharge cycle. Throughout the discharge process, cell voltage data per second is accurately collected for analysis.
in the pulse type charge and discharge test experiment, the existence of the ohmic resistor 0R enables the battery voltage to jump instantly at the beginning and the end of discharge.
Since the 15s pulse discharge time is short, the change in SOC before and after discharge is small, and the SOC is approximately assumed to be constant, and the voltage difference between the discharge start time and the discharge end time is similar.
further, in the embodiment of the present invention, in step 3, establishing a dynamic model of a lithium iron phosphate battery needs to pass
Terminal voltage response expression:
Initial voltage of RC parallel circuit:
the battery is kept still for a time after discharging, the pulse discharging voltage response of the battery is zero input voltage response, and the terminal voltage response expression is as follows:
y＝a-be-ct-de-ft
Calculating battery polarization parameter R1，C1，R2，C2。
Setting the sampling period of current and voltage:
mTU＝T0,nTI＝T0,n＝Nm
wherein, TI，TURespectively a current sampling period and a voltage sampling period, T0Is the system frame period, N, m, N are integers. The above formula shows that the voltage sampling period is m times the frame period and the current sampling period is n times the frame period. In a voltage sampling period, the current is sampled for N times;
in the k-th frame period [ kT ]0,(k+1)T0) The current and voltage are sampled as follows:
the voltage of the single battery is sampled m times in one frame period:
kT0+t1,kT0+t2,……,kT0+tm
Wherein 0 ═ t1<t2<….<tm<T0and tm+1＝T0。
the current of the single battery is sampled n times in a period of one frame:
In the interval [ kT0,(k+1)T0) In, i is e [1, m ∈]the current is sampled N times within this interval:
kT0+ti 1,kT0+ti 2,……,kT0+ti N
Wherein, 0<ti 1<ti 2<….<ti N<ti+1
Then, input variables and process noise are determined, which can be considered as spread vectors, and the spread matrix is as follows:
The spreading matrix for the current coefficients and the process noise is as follows:
Defining a lifted discrete state space model:
the prediction updating equation of the algorithm of the multi-rate strong tracking extended Kalman filter is as follows:
measurement update equation:
Wherein the content of the first and second substances,P0which is representative of the initial input signal, is,Representing the output signal, k ═ 1,2, …, Nt,Λkthe representation is a plurality of fading factor matrices,is kronecker product, NtRepresenting the total time step. Qkand Rkthe covariance matrix, which represents the noise, is assumed to be constant.
Example one
And (3) carrying out a multi-rate strong tracking extended Kalman filtering test by using 12 lithium iron phosphate battery packs, wherein the rated voltage of each battery is 3.3V, the capacity is 50Ah, and 2kWh of energy is output in total. And (3) expressing the dynamic characteristics of the battery by adopting a battery pulse discharge test so as to obtain parameters of a battery model.
after the cutoff voltage was charged at the constant current charging value of 1/3C, the constant current charging was continued to the cutoff voltage at 0.2C, and finally the battery was charged to the cutoff voltage at the constant current of 0.1C, at which time the battery was fully charged, i.e., SOC was 100%. And then, selecting sampling points of the SOC, wherein two points of 5% and 95% are cancelled, and 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% and 100% are finally selected as sampling points of the battery, because the reduction effect of the electric quantity discharged by pulse discharge on the SOC is increased when the SOC is too small, so that the deviation of an actual experimental value from a true value is large. Finally, the battery pack was periodically discharged at a discharge current of 1/2C. The change in the terminal voltage of the battery during the entire discharge process is recorded.
According to the terminal voltage response expression:
The initial voltage of the RC parallel circuit is:
After the battery is discharged and the battery is kept still, the pulse discharge voltage response of the battery is zero input voltage response, and the terminal voltage response expression is as follows:
y＝a-be-ct-de-ft
r can be obtained according to the expression1，C1，R2，C2The values of (A) are shown in the following table:
| SOC/％ | R1/mΩ | C1/F | R2/Mω | C2/F |
| 90 | 47.5 | 523 | 23.5 | 1200.1 |
| 80 | 40.4 | 492.4 | 22.2 | 1475.3 |
| 70 | 36.6 | 502.5 | 17.5 | 1182.5 |
| 60 | 37.4 | 497.5 | 18.1 | 997.5 |
| 50 | 37.1 | 478.9 | 17.9 | 934.5 |
| 40 | 35.2 | 460.5 | 16.5 | 1029.3 |
| 30 | 40.5 | 482.5 | 22.8 | 1132.8 |
| 20 | 62.6 | 366.2 | 30.4 | 1765.5 |
| 10 | 92.3 | 341.1 | 42.1 | 1841.6 |
| 0 | 155.3 | 321.8 | 70 | 1940.2 |
The initialization is carried out on the corresponding parameters, and the method comprises two parts: firstly, initializing second-order RC model parameters of the lithium battery based on a traditional KF algorithm, and initializing a covariance matrix: p is 0.005, the state variable is initialized: θ (0) ([ 0,0,0 ]]T(ii) a Then initializing the SOC of the lithium battery based on the multi-rate strong tracking extended Kalman filter, measuring a voltage value of the battery which is kept still for a long time before working by a battery management system, and reversely solving the SOC value at the current moment according to the relation between the OCV and the SOC: that is, the SOC is initialized to SOC (0) and the state is initialized to X0＝[SOC(0),0,0]TWith state variables initialized to x0＝E[X0]the covariance matrix is initialized to 00000E [ (X)0-x0)(X0-x0)T](ii) a Collecting battery current voltage data at corresponding k moments, namely I (k), vo (k), by a battery management system; then obtaining the open-circuit voltage value Voc (k) at the current moment according to a relational expression of OCV and SOC by the value of SOC (k), and calculating the value of y (k) at the moment of k according to the following formula:
y＝a-be-ct-d-ft
The coefficient matrix ht (k) is then obtained, and the corresponding voltage-current data is substituted into the iterative formula of the kalman filter:
Recursion to obtain the identification parameter theta (k) of the lithium battery
prediction update equation of algorithm using multi-rate strong tracking extended Kalman filter
And a measurement update equation:
obtaining an estimated value SOC (k +1) of the SOC of the lithium battery at the k +1 moment based on the voltage and current data measured on line
the obtained SOC (k +1) estimation value is substituted into the following equation:
y＝a-be-ct-de-ft
And obtaining the open-circuit voltage value Uoc (k +1) at the moment of k +1, and repeating the steps until the cycle is ended.
Wherein k is 1,2,3 … N.
And obtaining the SOC estimated value of the battery at each moment by the obtained battery model parameters and the state of charge value estimated by the multi-rate strong tracking extended Kalman filter and then repeatedly interleaving and iterating.
The working principle is as follows: when the method for estimating the SOC of the energy storage battery based on the multi-rate strong tracking extended Kalman filtering method is used, firstly, an equivalent circuit model is determined, a cyclic intermittent charging and discharging experiment with different charging and discharging multiplying powers is carried out on the power battery, the SOC estimation value of the battery at each moment can be obtained through the obtained battery model parameters and the state of charge value estimated by the multi-rate strong tracking extended Kalman filter, and then repeated staggered iteration is carried out, so that the method has the advantages of improving the gain margin of a system, enabling the system to have uniform stability, realizing easy distributed control and the like. The multi-rate strong tracking extended Kalman filter not only has the advantages of the algorithm of the strong tracking extended Kalman filter, but also improves the system performance of single-rate sampling.
it will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (4)
1. an energy storage battery SOC estimation method based on multi-rate strong tracking expansion is characterized in that: the method comprises the following steps:
Step 1, establishing an equivalent circuit model, and building a second-order RC circuit model according to the Thevenin equivalent circuit;
Step 2, carrying out cyclic intermittent charge and discharge experiments with different charge and discharge multiplying powers on the power battery;
and 3, establishing a lithium iron phosphate battery dynamic model, and estimating a prediction curve and a reference curve of the standard SOC by a multi-rate strong tracking Kalman filter method.
2. the energy storage battery SOC estimation method based on the multi-rate strong tracking expansion is characterized in that: in the step 1, a second-order RC circuit model, U, is set upocIs the terminal voltage of the battery, UL is the open circuit voltage of the battery, R0Is the ohmic internal resistance, R, of the cell1,C1is electrochemical polarization resistance and capacitance, R2,C2Is a concentration polarization resistor and capacitor, in the circuit, R0The abrupt change characteristic of the terminal voltage can be reflected, and the gradient characteristic of the terminal voltage, U, can be reflected by the second-order RC parallel networkocThe relationship between voltage and SOC may be reflected.
3. The energy storage battery SOC estimation method based on the multi-rate strong tracking expansion is characterized in that: in the step 2, the pulse type charge-discharge test experiment comprises the following steps:
1) Fully filling the lithium iron phosphate battery until the SOC is 1, and standing for 8 hours;
2) The discharge experiment was performed at a 0.3C discharge rate: discharging for 15 seconds, standing for 5 minutes, discharging for 20 minutes, standing for 1 hour, taking the process as a cycle until the battery SOC is equal to 0, calculating that each discharging cycle, the lithium iron phosphate battery releases 10% of SOC, accurately collecting the voltage data of the battery per second for analysis in the whole discharging process,
Wherein, in the pulse type charge-discharge test experiment, the existence of the ohmic resistor 0R causes the battery voltage to jump instantly when the discharge starts and ends,
4. The energy storage battery SOC estimation method based on the multi-rate strong tracking expansion is characterized in that: in the step 3, establishing a lithium iron phosphate battery dynamic model needs to pass a terminal voltage response expression:
Initial voltage of RC parallel circuit:
The battery is kept still for a time after discharging, the pulse discharging voltage response of the battery is zero input voltage response, and the terminal voltage response expression is as follows:
y＝a-be-ct-de-ft
calculating battery polarization parameter R1，C1，R2，C2。
Setting the sampling period of current and voltage:
mTU＝T0,nTI＝T0,n＝Nm
wherein, TI，TURespectively a current sampling period and a voltage sampling period, T0Is the system frame period, N, m, N are integers. The above formula shows that the voltage sampling period is m times of the frame period, and the current sampling period is n times of the frame period;
In a voltage sampling period, the current is sampled for N times;
in the k-th frame period [ kT ]0,(k+1)T0) The current and voltage are sampled as follows:
The voltage of the single battery is sampled m times in one frame period:
kT0+t1,kT0+t2,……,kT0+tm
wherein 0 ═ t1<t2<….<tm<T0And tm+1＝T0，
The current of the single battery is sampled n times in a period of one frame:
In the interval [ kT0,(k+1)T0) In, i is e [1, m ∈]the current is sampled N times within this interval:
kT0+ti 1,kT0+ti 2,……,kT0+ti N
wherein, 0<ti 1<ti 2<….<ti N<ti+1
Then, input variables and process noise are determined, which can be considered as spread vectors, and the spread matrix is as follows:
The spreading matrix for the current coefficients and the process noise is as follows:
Defining a lifted discrete state space model:
the prediction updating equation of the algorithm of the multi-rate strong tracking extended Kalman filter is as follows:
measurement update equation:
wherein the content of the first and second substances,P0which is representative of the initial input signal, is,representing the output signal, k ═ 1,2, …, Nt,ΛkThe representation is a plurality of fading factor matrices,Is kronecker product, Ntrepresenting the total time step, QkAnd RkThe covariance matrix, which represents the noise, is assumed to be constant.
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| PB01 | Publication | ||
| PB01 | Publication | ||
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| CB02 | Change of applicant information |
Address after: Room 8050, 8 / F, 1033 Changning Road, Changning District, Shanghai 200050 Applicant after: Aopu (Shanghai) new energy Co., Ltd Address before: 201103 22 / F, building B, Dongyin center, No. 500, ruby Road, Changning District, Shanghai Applicant before: SHANGHAI YUYUAN POWER TECHNOLOGY Co.,Ltd. |
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| CB02 | Change of applicant information | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191210 |
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| RJ01 | Rejection of invention patent application after publication |