CN117031283A - Method for evaluating SOC of reconfigurable lithium battery energy storage system - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000004146 energy storage Methods 0.000 title claims abstract description 27
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 24
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 24
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- 239000011159 matrix material Substances 0.000 claims description 18
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- 238000013178 mathematical model Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 5
- 230000007547 defect Effects 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 3
- 230000033228 biological regulation Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000029087 digestion Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
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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]
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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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
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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
Abstract
The invention discloses a method for evaluating a reconfigurable lithium battery energy storage system SOC, and relates to the technical field of electric power energy storage. A method of evaluating the SOC of a reconfigurable lithium battery energy storage system, having the steps of: step 1, designing a novel reconfigurable battery network based on switch bypass; step 2, selecting a second-order RC circuit model as an equivalent model of the lithium battery; step 3, establishing an OCV-SOC relation curve of the battery; step 4, carrying out parameter identification on the second-order RC equivalent circuit model; step 5, establishing a state equation and an observation equation according to the equivalent model and the ampere-hour integral formula; and 6, finishing accurate estimation of the SOC of the lithium battery by an EKF method. The technology for estimating the SOC by the EKF method can effectively solve the defects, and the initial error of the SOC can be effectively corrected in the linearization, initialization, prediction and correction stages of the EKF method so as to accurately estimate the state of charge of the battery, so that the estimation accuracy is high and the robustness is good.
Description
Technical Field
The invention discloses a method for evaluating the SOC of a reconfigurable lithium battery energy storage system, which relates to the technical field of electric power energy storage, realizes accurate estimation of the state of charge of each battery pack in a reconfigurable network, and is beneficial to safe and reliable operation of the reconfigurable lithium battery energy storage system.
Background
Along with the improvement of the demand of the power system on the regulation capability and the continuous increase of the development and the digestion scale of new energy sources, the novel energy storage construction period is short, the site selection is simple and flexible, the regulation capability is strong, the matching property with the development and the digestion of the new energy sources is better, the advantages are gradually obvious, and the large-scale application of the advanced energy storage technology is accelerated. The battery energy storage system is one of the most widely applied energy storage technologies at present due to the advantages of flexible installation, short construction period and the like, but in order to meet higher load demand voltage and capacity, a large number of single batteries are connected in series and parallel to form a whole, and the individual differences of the single batteries can cause phenomena of overcharging, overdischarging and the like of the batteries in the running process. And along with the increase of charge and discharge times, the inconsistency of each single battery is more prominent, so that the capacity and the cycle life of the whole battery energy storage system are greatly reduced. In addition, the flexibility of the energy storage system is greatly limited by the fixed serial-parallel connection mode of the traditional battery energy storage system, and the Battery Management System (BMS) is difficult to accurately manage and control the single battery, so that the combustion explosion accident caused by the thermal runaway of the single battery frequently occurs, and therefore, a new scheme needs to be designed to meet the actual application requirements.
Disclosure of Invention
The invention provides a method for evaluating the SOC of a reconfigurable lithium battery energy storage system, which has good use and popularization effects.
A method of evaluating the SOC of a reconfigurable lithium battery energy storage system, having the steps of:
step 1, designing a novel reconfigurable battery network based on switch bypass;
step 2, selecting a second-order RC circuit model as an equivalent model of the lithium battery;
step 3, establishing an OCV-SOC relation curve of the battery;
step 4, carrying out parameter identification on the second-order RC equivalent circuit model;
step 5, establishing a state equation and an observation equation according to the equivalent model and the ampere-hour integral formula;
and 6, finishing accurate estimation of the SOC of the lithium battery by an EKF method.
The specific process of the step 1 is as follows:
and 1.1, each battery pack is formed by connecting a plurality of single batteries in parallel, and a single battery pack is used as a minimum control unit to construct a reconstruction network, wherein the network topology is N parallel M strings. Each battery pack is connected with a switch S ij Series connection of N parallel battery packs and a bypass switch S i M such parallel networks are connected in series to form an integral switch bypass topology;
and 1.2, switching off a high-frequency power electronic switch of the battery pack to be estimated, and measuring the open-circuit voltage of the battery pack. The battery pack is formed by connecting a plurality of single batteries in parallel, and the open-circuit voltage of the battery pack is the open-circuit voltage of each single battery, so that the SOC of the battery pack can be obtained by combining the OCV-SOC curves of the single batteries, and then the accurate value of the SOC of the battery pack is obtained by correcting by an EKF method.
The specific process of the step 2 is as follows:
in step 2, the second-order RC equivalent circuit model comprises a battery open circuit voltage U oc Equivalent internal resistance R 0 Polarization resistance R 1 、R 2 And polarization capacitor C 1 、C 2 Polarization resistance R 1 And polarization capacitor C 1 Parallel connection, terminal voltage U 1 Polarization resistance R 2 And polarization capacitor C 2 Parallel connection, terminal voltage U 2 Equivalent internal resistance R 0 Terminal voltage U 1 And U 2 The RC equivalent circuit of (2) is connected with the open circuit voltage U of the battery in the form of a series circuit oc Is connected in series with the open circuit voltage U of the battery and the positive output end of the RC equivalent circuit oc The battery terminal voltage between the negative electrode output terminals is U, and the mathematical expression of the second-order RC equivalent circuit is as follows:
the specific process of the step 3 is as follows:
step 3.1, fully charging the battery at 25 ℃, standing for 60min, and measuring the open-circuit voltage of the battery when the SOC=1;
step 3.2, performing constant-current discharge on the battery for 12min at a discharge rate of 0.5C, standing for 60min, and measuring the open-circuit voltage of the battery at the moment of SOC=0.9;
step 3.3, repeating step 3.1, and measuring the open circuit voltage of the battery when soc=0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, respectively.
And 3.4, after the test is finished, fully charging the battery.
The specific process of the step 4 is as follows:
step 4.1, intercepting an OCV-SOC relation curve of which the SOC is changed from 1 to 0.9, and fitting the curve to the curve by using a curve fitting tool boxFitting and recording the identified model parameters R 0 、R 1 、R 2 、C 1 、C 2 。
Step 4.2, repeating step 4.1, fitting the OCV-SOC curve using a curve fitting tool box at soc=0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, respectively, and recording the identified model parameters R 0 、R 1 、R 2 、C 1 、C 2 。
The specific process of the step 5 is as follows:
step 5.1, using the SOC of the battery and the voltage U across the polarized capacitor 1 、U 2 As a state variable of the system, the charge and discharge current I is an input quantity, the terminal voltage U is an output quantity, and a state equation of the battery is obtained according to the established battery equivalent model and an ampere-hour integral formula, wherein the state equation is as follows:
the output equation is:
U(t)＝U oc (t)-U 1 (t)-U 2 (t)-R 0 I(t)
step 5.2, discretizing a state equation and an output equation of the battery respectively to obtain:
U(k)＝U oc (k)-U 1 (k)-U 2 (k)-R 0 I(k)
wherein C is N Δt is the sampling time interval, which is the actual capacity of the battery.
The specific process of the step 6 is as follows:
step 6.1, a state space model of the nonlinear discrete system is as follows:
wherein x is k Is the state variable at time k; u (u) k Is the control input quantity of the system; y is k Is the output value at time k; f (x) k ,u k ) Is a state transfer function; g (x) k ,u k ) Is a measurement function; w (w) k And v k The process noise and the measurement noise are zero in mean value respectively and are independent of each other, namely w k ～N(0,Q k )，v k ～N(0,R k ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein Q is k Covariance matrix of process noise; r is R k Is the covariance matrix of the observed noise.
And (3) expanding the nonlinear equation at a state estimation point by first-order Taylor to obtain:
the state variables of the battery SOC estimation system are: x is x k ＝[SOC(k),U 1 (k),U 2 (k)] T The input quantity of the system is as follows: u (u) k =i (k). After a state space model equation of a system is linearized by a first-order Taylor formula, the linearized matrix parameters are obtained as follows:
D k ＝R 0
step 6.2, initializing and circularly recursively calculating:
(1) An initialization stage:
(2) Prediction stage:
state variable predicted value:
covariance matrix of state variable prediction error:
(3) And (3) correction:
gain matrix expression:
state variable correction:
covariance matrix of state variable correction errors:
P k ＝(I-K k C k )P k|k-1 。
the invention has the following advantages:
1. novel reconfigurable battery network structure based on switch bypass type: in the structure, the battery packs are used as the minimum control unit, each battery pack is formed by connecting a plurality of single batteries in parallel, the current flowing through the battery packs is the sum of the currents flowing through the single batteries, the terminal voltage of the battery packs is equal to the terminal voltage of each single battery, and the battery pack SOC estimation process is simpler and more convenient than a serial-parallel structure; meanwhile, the reconstruction path of the reconstruction network is formed by connecting a plurality of battery packs in series and pressurizing the battery packs in series, so that the network can meet the current, voltage and power requirements of operation on the premise of ensuring flexibility and safety, and the economy and BMS computational complexity of the network are optimized;
the ekf method estimates the SOC of the battery pack: and (3) disconnecting a control switch of the battery pack, measuring the voltage of the battery pack, and combining an OCV-SOC curve to finish accurate estimation of the SOC of the battery pack by using linearization, initialization, prediction and correction stages of an EKF method.
The novel reconfigurable battery network based on the switch bypass technology can effectively solve the problems, each battery module is controlled by a high-frequency power electronic switch, the real-time state of each battery is monitored according to the actual running condition as a constraint condition, an optimal reconfiguration path is made, and the reconfiguration of the battery network is realized through the on-off of the high-frequency power electronic switch. Therefore, compared with the traditional battery network, the reconfigurable battery network has higher flexibility, and a proper battery can be selected to be constructed into an energy storage network for working according to actual operation requirements and battery module states; secondly, the safety of the reconfigurable battery network is higher, when the current, the voltage and the temperature of the battery module are abnormal, the BMS rapidly isolates the fault battery, and workers timely replace the fault battery, so that the probability of dangerous accidents is reduced, the faults are prevented from being further upgraded, and the safety of the whole energy storage system is endangered.
The state of charge of the battery can represent the residual capacity of the battery and is a key index for judging whether the battery energy storage system can safely and reliably operate. In addition, accurate estimation of the SOC of each battery module in the energy storage system is a basis for realizing an optimal reconstruction path of the reconfigurable battery network, so the SOC estimation method is particularly important. The estimation method of the SOC of each battery module in the current reconfigurable battery network mainly comprises an ampere-hour integration method, an open-circuit voltage method, a machine learning method and a Kalman filtering method. The ampere-hour integration method needs to provide an initial value to estimate the SOC, and meanwhile, the problem of accumulated error exists and is easy to be influenced by noise; the open-circuit voltage method is to estimate the SOC through the OCV-SOC curve of the battery, and the battery to be measured needs to be fully stood during estimation, so that the estimation time is longer and is easy to be influenced by noise; when estimating the SOC by the machine learning method, a large amount of data is needed to be relied on, and the influence of data errors is larger; the overall effect of estimating SOC by the Kalman Filter (KF) and OCV-SOC mapping method is good, but the battery system is a nonlinear system, and the estimation error is relatively large for the nonlinear section.
The technology for estimating the SOC by the EKF method can effectively solve the defects, and the initial error of the SOC can be effectively corrected in the linearization, initialization, prediction and correction stages of the EKF method so as to accurately estimate the state of charge of the battery, so that the estimation accuracy is high and the robustness is good.
Drawings
FIG. 1 is a flow chart of the technical scheme of the invention;
FIG. 2 is a novel reconfigurable battery network architecture based on switch bypass;
FIG. 3 is a schematic diagram of an energy storage network reconfiguration process;
FIG. 4 is a flow chart of an OCV-SOC curve fitting scheme
FIG. 5 is a schematic diagram of a second order RC equivalent circuit;
FIG. 6 is a plot of the lithium battery SOC from 1 to 0.9 terminal voltage response;
FIG. 7 is a flowchart of an EKF-based SOC estimation scheme;
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings 1-7, in conjunction with the detailed description.
Step 1, as shown in fig. 2 and 3, the specific process is as follows based on the estimation thinking of the reconfigurable battery network design and the battery pack SOC of the switch bypass type:
and 1.1, each battery pack is formed by connecting a plurality of single batteries in parallel, and a single battery pack is used as a minimum control unit to construct a reconstruction network, wherein the network topology is N parallel M strings. Each battery pack is connected with a switch S ij Series connection of N parallel battery packs and a bypass switch S i M such parallel networks are connected in series to form an integral switch bypass topology;
and 1.2, switching off a high-frequency power electronic switch of the battery pack to be estimated, and measuring the open-circuit voltage of the battery pack. The battery pack is formed by connecting a plurality of single batteries in parallel, and the open-circuit voltage of the battery pack is the open-circuit voltage of each single battery, so that the SOC of the battery pack can be obtained by combining the OCV-SOC curves of the single batteries, and then the accurate value of the SOC of the battery pack is obtained by correcting by an EKF method. Meanwhile, the reconstruction path of the reconstruction network is formed by connecting a plurality of battery packs in series and pressurizing the battery packs in series, so that the network can meet the current, voltage and power requirements of operation on the premise of ensuring flexibility and safety.
Step 2, as shown in fig. 5, the lithium battery equivalent circuit model and the parameter identification thereof comprise the following specific processes:
the second-order RC equivalent circuit model comprises a battery open circuit voltage U oc Equivalent internal resistance R 0 Polarization resistance R 1 、R 2 And polarization capacitor C 1 、C 2 Polarization resistance R 1 And polarization capacitor C 1 Parallel connection, terminal voltage U 1 Polarization resistance R 2 And polarization capacitor C 2 Parallel connection, terminal voltage U 2 Equivalent internal resistance R 0 Terminal voltage U 1 And U 2 The RC equivalent circuit of (2) is connected with the open circuit voltage U of the battery in the form of a series circuit oc Is connected in series with the open circuit voltage U of the battery and the positive output end of the RC equivalent circuit oc The battery terminal voltage between the negative electrode output terminals is U, and the mathematical expression of the second-order RC equivalent circuit is as follows:
step 3, as shown in fig. 4, the reference relationship between the open-circuit voltage and the state of charge of the battery is tested, and the specific process is as follows:
step 3.1, fully charging the battery at 25 ℃, standing for 60min, and measuring the open-circuit voltage of the battery when the SOC=1;
step 3.2, performing constant-current discharge on the battery for 12min at a discharge rate of 0.5C, standing for 60min, and measuring the open-circuit voltage of the battery at the moment of SOC=0.9;
step 3.3, repeating step 3.1, and measuring the open circuit voltage of the battery when soc=0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, respectively.
And 3.4, after the test is finished, fully charging the battery.
And 4, the specific process of identifying the parameters of the second-order RC equivalent circuit model is as follows:
step 4.1, as shown in FIG. 6, intercepting an OCV-SOC relation curve of SOC changing from 1 to 0.9, wherein the sudden rise of BC segment voltage is expressed as ohmic internal resistance R of the battery in the discharging process 0 ：
R 0 ＝U CB /I
Recording model parameters R 0 ；
The voltage of the CD section slowly rises, and the working voltage of the battery at any time of the CD section is as follows:
fitting by a fitting tool box user-defined second-order exponential function, wherein a fitting formula is as follows:
f(x)＝A-Be -ax -Ce -bx
the fitting formula and the battery working voltage formula at any moment are correspondingly equal to obtain model parameters as follows:
recording the identified model parameters R 1 、R 2 、C 1 、C 2 。
Step 4.2, repeating step 4.1, and recording model parameters R at soc=0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, respectively 0 、R 1 、R 2 、C 1 、C 2 。
Step 5, establishing a state equation and an output equation of the battery, wherein the specific process is as follows:
step 5.1, using the SOC of the battery and the voltage U across the polarized capacitor 1 、U 2 As a state variable of the system, the charge and discharge current I is an input quantity, the terminal voltage U is an output quantity, and a state equation of the battery is obtained according to the established battery equivalent model and an ampere-hour integral formula, wherein the state equation is as follows:
the output equation is:
U(t)＝U oc (t)-U 1 (t)-U 2 (t)-R 0 I(t)
step 5.2, discretizing a state equation and an output equation of the battery respectively to obtain:
U(k)＝U oc (k)-U 1 (k)-U 2 (k)-R 0 I(k)
wherein C is N Δt is the sampling time interval, which is the actual capacity of the battery.
Step 6, as shown in fig. 7, the specific process of estimating the SOC by the EKF method is as follows:
step 6.1, a state space model of the nonlinear discrete system is as follows:
wherein x is k Is the state variable at time k; u (u) k Is the control input quantity of the system; y is k Is the output value at time k; f (x) k ,u k ) Is a state transfer function; g (x) k ,u k ) Is a measurement function; w (w) k And v k The process noise and the measurement noise are zero in mean value respectively and are independent of each other, namely w k ～N(0,Q k )，v k ～N(0,R k ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein Q is k Covariance matrix of process noise; r is R k Is the covariance matrix of the observed noise.
And (3) expanding the nonlinear equation at a state estimation point by first-order Taylor to obtain:
the state variables of the battery SOC estimation system are: x is x k ＝[SOC(k),U 1 (k),U 2 (k)] T The input quantity of the system is as follows: u (u) k =i (k). After a state space model equation of a system is linearized by a first-order Taylor formula, the linearized matrix parameters are obtained as follows:
D k ＝R 0
step 6.2, initializing and circularly recursively calculating: (1) an initialization stage:
(2) Prediction stage:
state variable predicted value:
covariance matrix of state variable prediction error:
(3) And (3) correction:
gain matrix expression:
state variable correction:
covariance matrix of state variable correction errors:
P k ＝(I-K k C k )P k|k-1 。
examples
The invention is implemented by a lithium battery equivalent circuit model and an SOC estimation model, and comprises the following specific steps:
(1) OCV-SOC curve fitting: and (3) selecting a certain lithium battery to be tested, recording the corresponding open-circuit voltage values under different SOCs in the step (2) in the experimental step to obtain an OCV-SOC corresponding relation, and performing polynomial fitting on the SOC value and the open-circuit voltage value by using a fitting tool box to obtain an OCV-SOC graph and a fitting polynomial.
(2) And (3) parameter identification: in the experimental step, the voltage value in the step 2 is recorded, the time interval is recorded for 1s, and the response curve of the battery terminal voltage along with time is obtained. Model parameters R identified when soc=0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0 were recorded, respectively 0 、R 1 、R 2 、C 1 、C 2 。
(3) And (3) parameter verification: substituting the identified parameters into the lithium battery equivalent circuit model, and judging the accuracy of parameter identification through comparing the voltage response curve of the simulation model with the actual voltage curve.
(4) And (3) SOC estimation: substituting the identified parameters into an SOC estimation model, obtaining an SOC estimation value under the working conditions of constant current discharge and intermittent discharge, comparing the SOC estimation value with an actual value, analyzing steady-state errors between the SOC estimation value and the actual value, and verifying that the invention can correct the errors so as to accurately estimate the SOC.
Claims (7)
1. A method of evaluating the SOC of a reconfigurable lithium battery energy storage system, characterized by: comprises the following steps of;
step 1, designing a novel reconfigurable battery network based on switch bypass;
step 2, selecting a second-order RC circuit model as an equivalent model of the lithium battery;
step 3, establishing an OCV-SOC relation curve of the battery;
step 4, carrying out parameter identification on the second-order RC equivalent circuit model;
step 5, establishing a state equation and an observation equation according to the equivalent model and the ampere-hour integral formula;
and 6, finishing accurate estimation of the SOC of the lithium battery by an EKF method.
2. The method for estimating SOC of a reconfigurable lithium battery energy storage system according to claim 1, wherein the specific process of step 1 is:
step 1.1, each battery pack is formed by connecting a plurality of single batteries in parallel, a single battery pack is used as a minimum control unit to construct a reconstruction network, and the network topology is N-parallel M strings; each battery pack is connected with a switch S ij Series connection of N parallel battery packs and a bypass switch S i M such parallel networks are connected in series to form an integral switch bypass topology;
step 1.2, switching off a high-frequency power electronic switch of a battery pack to be estimated, and measuring the open-circuit voltage of the battery pack; the battery pack is formed by connecting a plurality of single batteries in parallel, and the open-circuit voltage of the battery pack is the open-circuit voltage of each single battery, so that the SOC of the battery pack can be obtained by combining the OCV-SOC curves of the single batteries, and then the accurate value of the SOC of the battery pack is obtained by correcting by an EKF method.
3. The method for estimating SOC of a reconfigurable lithium battery energy storage system according to claim 1, wherein step 2 comprises the following specific procedures:
in step 2, the second-order RC equivalent circuit model comprises a battery open circuit voltage U oc Equivalent internal resistance R 0 Polarization resistance R 1 、R 2 And polarization capacitor C 1 、C 2 Polarization resistance R 1 And polarization capacitor C 1 Parallel, endThe voltage is U 1 Polarization resistance R 2 And polarization capacitor C 2 Parallel connection, terminal voltage U 2 Equivalent internal resistance R 0 Terminal voltage U 1 And U 2 The RC equivalent circuit of (2) is connected with the open circuit voltage U of the battery in the form of a series circuit oc Is connected in series with the open circuit voltage U of the battery and the positive output end of the RC equivalent circuit oc The battery terminal voltage between the negative electrode output terminals is U, and the mathematical expression of the second-order RC equivalent circuit is as follows:
4. the mathematical model of a reconfigurable lithium battery energy storage system of claim 2, wherein step 3 comprises the specific steps of:
step 3.1, fully charging the battery at 25 ℃, standing for 60min, and measuring the open-circuit voltage of the battery when the SOC=1;
step 3.2, performing constant-current discharge on the battery for 12min at a discharge rate of 0.5C, standing for 60min, and measuring the open-circuit voltage of the battery at the moment of SOC=0.9;
step 3.3, repeating step 1.2, and measuring the open circuit voltage of the battery when soc=0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, respectively;
and 3.4, after the test is finished, fully charging the battery.
5. The method for estimating SOC of a reconfigurable lithium battery energy storage system according to claim 1, wherein step 4 comprises the following specific procedures:
step 4.1, intercepting an OCV-SOC relation curve of which the SOC is changed from 1 to 0.9, fitting the curve by using a curve fitting tool box, and recording the identified model parameter R 0 、R 1 、R 2 、C 1 、C 2 ；
Step 4.2, repeating step 4.1, using a curve fitter when soc=0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.0, respectivelyThe tool box fits the OCV-SOC curve and records the identified model parameters R 0 、R 1 、R 2 、C 1 、C 2 。
6. The method of claim 1, wherein step 5 comprises the steps of:
step 5.1, using the SOC of the battery and the voltage U across the polarized capacitor 1 、U 2 As a state variable of the system, the charge and discharge current I is an input quantity, the terminal voltage U is an output quantity, and a state equation of the battery is obtained according to the established battery equivalent model and an ampere-hour integral formula, wherein the state equation is as follows:
the output equation is:
U(t)＝U oc (t)-U 1 (t)-U 2 (t)-R 0 I(t)
step 5.2, discretizing a state equation and an output equation of the battery respectively to obtain:
U(k)＝U oc (k)-U 1 (k)-U 2 (k)-R 0 I(k)
wherein C is N Δt is the sampling time interval, which is the actual capacity of the battery.
7. The method of claim 1, wherein step 6 comprises the steps of:
step 6.1, a state space model of the nonlinear discrete system is as follows:
wherein x is k Is the state variable at time k; u (u) k Is the control input quantity of the system; y is k Is the output value at time k; f (x) k ,u k ) Is a state transfer function; g (x) k ,u k ) Is a measurement function; w (w) k And v k The process noise and the measurement noise are zero in mean value respectively and are independent of each other, namely w k ～N(0,Q k )，v k ～N(0,R k ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein Q is k Covariance matrix of process noise; r is R k A covariance matrix for observing noise;
and (3) expanding the nonlinear equation at a state estimation point by first-order Taylor to obtain:
the state variables of the battery SOC estimation system are: x is x k ＝[SOC(k),U 1 (k),U 2 (k)] T The input quantity of the system is as follows: u (u) k =i (k); after a state space model equation of a system is linearized by a first-order Taylor formula, the linearized matrix parameters are obtained as follows:
D k ＝R 0 ；
step 6.2, initializing and circularly recursively calculating:
(1) An initialization stage:
(2) Prediction stage:
state variable predicted value:
covariance matrix of state variable prediction error:
(3) And (3) correction:
gain matrix expression:
state variable correction:
covariance matrix of state variable correction errors:
P k ＝(I-K k C k )P k|k-1 。
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