CN106324523B - Lithium battery SOC estimation method based on discrete-time variable structure observer - Google Patents
Lithium battery SOC estimation method based on discrete-time variable structure observer Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 232
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 232
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000007599 discharging Methods 0.000 claims abstract description 45
- 238000007600 charging Methods 0.000 claims abstract description 31
- 238000002474 experimental method Methods 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims description 45
- 230000008569 process Effects 0.000 claims description 25
- 230000010287 polarization Effects 0.000 claims description 16
- 230000008859 change Effects 0.000 claims description 7
- 241000219312 Chenopodium Species 0.000 claims description 6
- 230000014509 gene expression Effects 0.000 claims description 5
- 238000010278 pulse charging Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 2
- 238000004519 manufacturing process Methods 0.000 abstract 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 8
- 229910001416 lithium ion Inorganic materials 0.000 description 8
- 238000001914 filtration Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 230000032683 aging Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003487 electrochemical reaction Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000003446 memory effect Effects 0.000 description 1
- BULVZWIRKLYCBC-UHFFFAOYSA-N phorate Chemical compound CCOP(=S)(OCC)SCSCC BULVZWIRKLYCBC-UHFFFAOYSA-N 0.000 description 1
- 230000004044 response 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]
- 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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Abstract
The lithium battery SOC estimation method based on discrete-time variable structure observer that the invention discloses a kind of.It is the following steps are included: carry out Fast Calibration experiment, acquisition SOC and open-circuit voltage OCV relation curve to lithium battery；Establish the lithium battery separate manufacturing firms model for SOC estimation；Pulsed discharge experiment is carried out to lithium battery, recognizes lithium battery model parameter；The end voltage and charging and discharging currents of lithium battery under operating condition are acquired in real time；Construct accurate estimation of the discrete-time variable structure observer realization to lithium battery SOC.The method of the present invention not only has preferable SOC estimation effect, while energy strict guarantee convergence, and shows stronger robustness to lithium battery modeling error, the perturbation of inner parameter and external disturbance.
Description
Technical Field
The invention belongs to the field of state of charge estimation of a lithium battery for a vehicle, and particularly relates to a lithium battery SOC estimation method based on a discrete variable structure observer.
Background
The battery management system BMS is an important component in an electric vehicle, and has basic functions such as battery state detection, battery state estimation, battery safety protection, and energy control management.
Battery SOC estimation is the core of the battery management system. The SOC is an important parameter for representing the residual capacity of the battery, an accurate SOC value is an important basis for controlling charging and discharging, balancing and formulating an energy management strategy of the battery, and the estimation precision directly influences the service life and the cost of the battery, so that the accurate estimation of the SOC is the key of the BMS.
The SOC is influenced by various factors such as battery temperature, charge and discharge multiplying power, self-discharge rate, service life and the like, can not be directly measured through a sensor, and can be obtained through modeling the battery and selecting an algorithm for indirect estimation by combining measured data such as charge and discharge current, terminal voltage, temperature and the like of the battery during working. When the battery of the electric automobile is used, the battery characteristics show high nonlinearity due to internal complex electrochemical reaction, so that the accurate estimation of the SOC of the battery has great difficulty.
Compared with the traditional electric automobile battery, the lithium battery has the advantages of high energy density, no memory effect, low environmental pollution, long cycle life, wide applicable temperature range and the like in performance, so the lithium battery has been developed into the most competitive power battery.
At present, common lithium battery SOC estimation algorithms include an open-circuit voltage method, an ampere-hour integral method, a kalman filter algorithm, a neural network algorithm and the like.
Chinese invention patent CN 103529398A in 2014 01 monthIn the "lithium ion battery SOC on-line estimation method based on extended Kalman filtering" disclosed in 22 days, a voltage-current relation of a first-order RC equivalent circuit and a voltage-current relation of a second-order RC equivalent circuit of a lithium ion battery to be measured are established; then, carrying out charge-discharge experiment on the tested lithium ion battery, and establishing a Kalman filtering initial value SOC of the tested lithium ion battery0A polynomial fitting function of; then obtaining the Kalman filtering initial value SOC of the lithium ion battery to be measured0And an initial error covariance P (0) of Kalman filtering; and then, carrying out battery SOC estimation based on the extended Kalman filtering to realize the online SOC estimation of the lithium ion battery. However, this method has disadvantages:
1) the algorithm requires that the noise is white noise and the statistical characteristics of the noise, such as the mean value, the variance and the like, are known, which is difficult to satisfy in practical application, not only because the statistical characteristics of the noise are difficult to obtain, but also the white noise exists only under ideal conditions;
2) the algorithm has higher requirement on the precision of a lithium ion battery performance model, and when the model precision is lower, a larger SOC estimation error can be caused;
3) after the nonlinear lithium ion battery model is linearized, if the deviation is large, the phenomenon of filtering divergence occurs, and the SOC estimation error is large.
The invention patent CN 105548898A discloses a lithium battery SOC estimation method for offline data segmented correction in 2016, 05, 04, firstly, a battery equivalent circuit model is established; obtaining an SOC-OCV curve; performing offline parameter identification on the equivalent circuit model by using a terminal voltage response curve when the battery discharge is finished; then calculating the SOH of the battery; calculating the current value of the SOC in real time by using an ampere-hour integration method; correcting the SOC value by using the health state of the battery; and finally, utilizing off-line data to eliminate the accumulated error in the ampere-hour integration method in a segmented manner. The defects of the method are as follows:
1) the parameters of the lithium battery equivalent circuit model are identified by an off-line identification method, and in the working process of the lithium battery, the parameters in the lithium battery model can change due to the influence of factors such as temperature, aging and service life, so that a large SOC estimation error can be caused.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a lithium battery SOC estimation method based on a discrete variable structure observer. The method has a good SOC estimation effect, has strong robustness on lithium battery modeling errors and internal parameter changes caused by factors such as temperature, aging and service life, can strictly ensure the convergence of the algorithm, and does not generate estimation divergence.
In order to solve the problems in the prior art, the invention provides a lithium battery SOC estimation method based on a discrete variable structure observer, which comprises the steps of collecting terminal voltage and charging and discharging current of a lithium battery under working conditions, and mainly comprises the following steps:
step 1, performing a rapid calibration experiment on a lithium battery to obtain a relation curve between SOC and open-circuit voltage OCV;
step 1.1, discharging a lithium battery with a charge cut-off voltage of 4.2V, a discharge cut-off voltage of 3V and a rated capacity of 5Ah at a constant current of 0.2 coulomb at room temperature until the voltage of the lithium battery is below 3V, standing for 2-3 hours, and waiting for an experiment;
step 1.2, carrying out constant current pulse charging on the lithium battery which is stood according to the step 1.1 by using 0.2 coulomb current, after 10 percent of rated capacity of the lithium battery is charged each time, breaking the circuit of the lithium battery and standing for 5 minutes, and measuring the terminal voltage U of the lithium battery in each standing time period in the charging process in real timec，OCVAnd finding out the minimum value U of the terminal voltage of the lithium battery in each standing time period in the charging processc，OCV，minUntil the lithium battery is full;
step 1.3, performing constant current pulse discharge on the lithium battery fully charged according to the step 1.2 by using 0.2 coulomb current, and after discharging 10 percent of the rated capacity of the lithium battery each time, disconnecting the lithium battery and standing for 5 minutesAnd measuring the terminal voltage U of the lithium battery in each standing time period in the discharging process in real timed，OCVAnd finding out the maximum value U of the terminal voltage of the lithium battery in each standing time period in the discharging processd，OCV，maxUntil the lithium battery is empty;
step 1.4, obtaining the minimum value U of the terminal voltage of the lithium battery in each standing time period in the charging process obtained in the step 1.2c，OCV，minThe maximum value U of the terminal voltage of the lithium battery in the standing time period which is equal to the SOC in the charging process in the discharging process obtained in the step 1.3d，OCV，maxAdding and taking an average value as the open-circuit voltage OCV which is calibrated quickly to obtain 10 open-circuit voltage OCVs in total;
step 1.5, according to the 10 open-circuit voltages OCV obtained in the step 1.4, performing multi-section type straight line fitting on the obtained experimental data in the whole SOC variation range, namely 0-100%, and obtaining a relation curve of the lithium battery SOC and the open-circuit voltages OCV;
in the multi-segment straight line fitting, the length delta SOC of each segment is 10%, and the expressions of the fitted SOC and the open-circuit voltage OCV in each segment are as follows:
OCVi＝ki*SOCi+di i＝1，2，3....10 (1)
wherein the OCViOpen circuit voltage OCV, SOC for ith lithium batteryiIs the SOC, k of the ith lithium batteryiSlope of the SOC versus open circuit Voltage OCV line fitted to section i, diThe intercept of the SOC and the open-circuit voltage OCV straight line fitted in the ith section;
step 2, establishing a lithium battery discrete state space model for SOC estimation according to the relation curve of the lithium battery SOC and the open-circuit voltage OCV obtained in the step 1 and by combining a lithium battery Thevenin equivalent circuit and a ampere-hour integral formula;
discrete equation of state:
discrete observation equation:
wherein, Vt(k) The terminal voltage at the moment k of the lithium battery, SOC (k) is the SOC and V at the moment k of the lithium battery1(k) Is the polarization voltage, V, of the lithium battery at time kt(k +1) is the terminal voltage of the lithium battery at the moment (k +1), SOC (k +1) is the SOC at the moment (k +1), V1(k +1) is the polarization voltage of the lithium battery at the moment (k +1), T is the sampling time, Is(k) The current value flowing through the lithium battery at the moment k, gamma is a disturbance input matrix, ξ (k) is a bounded scalar disturbance input, y (k) is an output quantity of the lithium battery at the moment k, and a1＝1/R1C1，a11＝kia1，a2＝1/R0CN，a22＝kia2，b1＝ki/CN+1/C1+R0/R1C1，b2＝1/C1，R1Is the polarization resistance of a lithium battery, C1Is the polarization capacitance, R, of a lithium battery0Is the ohmic internal resistance of the lithium battery, CNThe nominal capacity of the lithium battery;
step 3, performing a pulse discharge experiment on the lithium battery, and identifying the discrete state space model parameters of the lithium battery in the step 2;
step 3.1, discharging the lithium battery with the capacity of 5Ah for 5 minutes by using the current I, stopping discharging, standing for 10 minutes, discharging for 5 minutes by using the same current I, taking the 20 minutes as a pulse discharge period, and recording the terminal voltage U of the lithium battery in the pulse discharge period by using the charging and discharging equipmentbattery(ii) a change;
step 3.2, according to the terminal voltage U in one pulse discharge period of the lithium battery recorded in the step 3.1batteryChanging, analyzing the characteristic of the terminal voltage change curve, and identifying parameters in the lithium battery model, wherein the parameters comprise ohmic internal resistance R0Polarization resistance R1Polarization capacitance C1；
Step 4, acquiring terminal voltage V of the lithium battery under the working condition in real time through a voltage sensor and a current sensor respectivelyt(k) And charging and discharging current Is(k)；
Step 5, designing a discrete variable structure observer based on the lithium battery discrete state space model established in the step 2, and acquiring the voltage V of the lithium battery terminal acquired in the step 4t(k) And charging and discharging current Is(k) As signal input, estimating the SOC of the lithium battery in real time;
the equation of the discrete variable structure observer is as follows:
wherein x (k) is a state variable of the lithium battery at the time k, is an estimated value of x (k), x (k +1) is a state variable of the lithium battery (k +1) at the moment,is an estimate of x (k +1),is an estimate of y (k), λ is the positive feedback input matrix, v (k) is the extrinsic positive feedback compensation signal, h is the dispersionA gain matrix of the variable structure observer, G is a system matrix of the discrete variable structure observer,h is an input matrix of the discrete variable structure observer,c is the output matrix of the discrete variable structure observer, and C is [ 100 ]]And C lambda is not equal to 0.
Preferably, the thevenin equivalent circuit model in step 2 is:
Vt＝V1+IsR0+OCV (6)
wherein, VtIs terminal voltage of lithium battery, V1Is the polarization voltage of the lithium battery,is a V1Differential of (1)sThe OCV is an open circuit voltage of the lithium battery for an instantaneous current flowing through the lithium battery.
Preferably, the ampere-hour integral in step 2 is represented by:
therein, SOC0Is the initial value of the SOC of the lithium batterytIs the SOC instantaneous value of the lithium battery, CNIs the nominal capacity of the lithium battery, IsFor instantaneous current through a lithium battery, t0Is the initial time of the charging and discharging process, t1Is the end time of the charging and discharging process.
Preferably, the bounded scalar disturbance input ξ (k) in step 2 satisfies the following expression:
|ξ(k)|≤ξ0||e(k)|| (9)
wherein, ξ0The upper limit parameter is positive and is 0.05-0.15, and e (k) is the state error of lithium battery at time k, namely e (k) ═ x (k +1) -x (k).
Preferably, the voltage sensor and the current sensor in step 4 are a hall voltage sensor and a hall current sensor, respectively.
Preferably, the gain matrix h of the discrete variable structure observer in step 5 is determined according to the following formula:
wherein, CTIs the transpose matrix of the output matrix C of the discrete variable structure observer, and P is the discrete Chenopodium equationSolution of positive definite symmetric matrix, G in Chenopodium discredite Kati equationTThe invention relates to a transpose matrix of a system matrix G of a discrete variable structure observer, wherein Q is an arbitrary semi-positive definite symmetric matrix, Q is set as E, E is a 3 multiplied by 3 identity matrix, α is a positive real number, and the value range is 1-5.
Preferably, the positive feedback input matrix λ in step 5 is determined according to the following equation:
λ＝vH (11)
wherein v is a positive real number, and is 0.2-0.8.
Preferably, the extrinsic positive feedback compensation signal v (k) in step 5 is determined according to the following formula:
wherein e (k) is a state error of the lithium battery at the time k, i.e., e (k) ═ x (k +1) -x (k), and w (k) is a positive value function and satisfies | w (k) | < 1, and v (k) is a positive value functioneq(k) For equivalent external positive feedback compensation signal, by the formula veq(k)＝(Cλ)-1(CGe (k) + C γ ξ (k)) determination, where the bounded scalar disturbance input ξ (k) satisfies | ξ (k) | ≦ ξ (k) |0×||e(k)||，ξ0The upper limit parameter is positive number, and is 0.05-0.15.
Compared with the prior art, the invention has the following beneficial effects:
1. the requirement on the precision of a lithium battery performance model is not high, and modeling errors can be effectively compensated.
2. The problem of divergence of a discrete variable structure observer caused by improper linearization of a lithium battery model in an extended Kalman filtering algorithm does not exist, and the convergence of the algorithm can be strictly ensured.
3. When the off-line identification method is adopted to identify the equivalent circuit model parameters of the lithium battery, when the internal parameters of the lithium battery are changed due to factors such as temperature, aging and service life, the SOC can still be accurately estimated, and the robustness is high.
Drawings
FIG. 1 is a schematic flow chart of a lithium battery SOC estimation method of the present invention.
Fig. 2 is a diagram illustrating a relationship between the SOC and the OCV of the lithium battery.
Fig. 3 is a thevenin equivalent circuit of a lithium battery.
Fig. 4 is a voltage variation curve of lithium battery terminal in pulse discharge experiment.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and examples, but the embodiments of the present invention are not limited thereto.
Fig. 1 is a schematic flow chart of a lithium battery SOC estimation method of the present invention, and it can be seen from the schematic chart that the lithium battery SOC estimation method based on a discrete variable structure observer provided by the present invention includes the following steps:
step 1, performing a rapid calibration experiment on a lithium battery to obtain a relation curve between SOC and open-circuit voltage OCV;
step 1.1, discharging 0.2 coulomb constant current of a lithium battery with the charge cut-off voltage of 4.2V, the discharge cut-off voltage of 3V and the rated capacity of 5Ah at room temperature until the voltage of the lithium battery is below 3V, standing for 2-3 hours, and waiting for experiment use;
step 1.2, carrying out constant current pulse charging on the lithium battery which is stood according to the step 1.1 by using 0.2 coulomb current, after 10 percent of rated capacity of the lithium battery is charged each time, breaking the circuit of the lithium battery and standing for 5 minutes, and measuring the terminal voltage U of the lithium battery in each standing time period in the charging process in real timec，OCVAnd finding out the minimum value U of the terminal voltage of the lithium battery in each standing time period in the charging processc，OCV，minUntil the lithium battery is full;
step 1.3, performing constant current pulse discharge on the lithium battery fully charged according to the step 1.2 by using 0.2 coulomb current, breaking the lithium battery and standing for 5 minutes after discharging 10 percent of the rated capacity of the lithium battery every time, and measuring the terminal voltage U of the lithium battery in each standing time period in the discharging process in real timed，OCVAnd finding out the maximum value U of the terminal voltage of the lithium battery in each standing time period in the discharging processd，OCV，maxUntil the lithium battery is empty;
step 1.4, obtaining the minimum value U of the terminal voltage of the lithium battery in each standing time period in the charging process obtained in the step 1.2c，OCV，minThe maximum value U of the terminal voltage of the lithium battery in the standing time period which is equal to the SOC in the charging process in the discharging process obtained in the step 1.3d，OCV，maxAdding and averaging to obtain 10 switches as the open-circuit voltage OCV for quick calibrationA road voltage OCV;
step 1.5, according to the 10 open-circuit voltages OCV obtained in the step 1.4, performing multi-section type straight line fitting on the obtained experimental data in the whole SOC variation range, namely 0-100%, and obtaining a relation curve of the lithium battery SOC and the open-circuit voltages OCV;
in the multi-segment straight line fitting, the length delta SOC of each segment is 10%, and the expressions of the fitted SOC and the open-circuit voltage OCV in each segment are as follows:
OCVi＝ki*SOCi+di i＝1，2，3....10 (1)
wherein the OCViOpen circuit voltage OCV, SOC for ith lithium batteryiIs the SOC, k of the ith lithium batteryiSlope of the SOC versus open circuit Voltage OCV line fitted to section i, diThe intercept of the fitted SOC and open circuit voltage OCV straight line for the i-th segment.
In this embodiment, fig. 2 shows a relationship curve of the fitted SOC and OCV of the lithium battery, and specific parameters thereof can be seen in table 1.
Table 1: lithium battery SOC and open-circuit voltage OCV curve parameter table
| i | 1 | 2 | 3 | 4 | 5 |
| SOCi | 0％-10％ | 10％-20％ | 20％-30％ | 30％-40％ | 40％-50％ |
| ki | 6.933 | 0.400 | 0.178 | 0.200 | 0.011 |
| di | 3.0100 | 3.6633 | 3.7077 | 3.7011 | 3.7767 |
| i | 6 | 7 | 8 | 9 | 10 |
| SOCi | 50％-60％ | 60％-70％ | 70％-80％ | 80％-90％ | 90％-100％ |
| ki | 0.025 | 0.025 | 0.011 | 0.025 | 3.092 |
| di | 3.7697 | 3.7697 | 3.7795 | 3.7683 | 1.008 |
And 2, establishing a lithium battery discrete state space model for SOC estimation according to the relation curve of the lithium battery SOC and the open-circuit voltage OCV obtained in the step 1 and by combining a lithium battery Thevenin equivalent circuit and a ampere-hour integral formula, wherein the process is as follows.
Step 2.1, establishing a first-order equivalent circuit model based on the lithium battery Thevenin equivalent circuit;
FIG. 3 is a Thevenin equivalent circuit diagram of a lithium battery, wherein OCV is the open-circuit voltage of the lithium battery, R0Is the ohmic internal resistance, R, of the lithium battery1Is the polarization resistance of a lithium battery, C1Is the polarization capacitance of the lithium battery.
Vt＝V1+IsR0+OCV (2)
Wherein, VtIs terminal voltage of lithium battery, V1Is the polarization voltage of the lithium battery,is a V1Differential of (1)sIs the instantaneous current flowing through the lithium battery.
2.2, establishing a lithium battery continuous state space model by combining an ampere-hour integral formula according to the lithium battery first-order equivalent circuit model established in the step 2.1;
the ampere-hour integral formula is:
therein, SOC0Is the initial value of the SOC of the lithium batterytIs the SOC instantaneous value of the lithium battery, CNIs the nominal capacity of the lithium battery, IsFor instantaneous current through a lithium battery, t0Is the initial time of the charging and discharging process, t1Is the end time of the charging and discharging process.
Simultaneous equations (1), (2), and (4) yield:
wherein,is the differential of the lithium battery SOC.
Simultaneous equations (1), (2), and (3) yield:
wherein,for terminal voltage V of lithium batterytDifferentiation of (2).
Selecting the state variable of the lithium battery asThe input quantity is current I and the output quantity is terminal voltage VtIs provided with diEstablishing a continuous state space model of the lithium battery according to the formulas (3), (5) and (6) as 0:
equation of continuous state:
continuous observation equation:
wherein,is a VtThe differential of (a) is determined,is the differential of the SOC of the battery,is a V1Y is the output of the lithium battery, i.e. the terminal voltage Vt，a1＝1/R1C1，a11＝kia1，a2＝1/R0CN，a22＝kia2，b1＝ki/CN+1/C1+R0/R1C1，b2＝1/C1。
Step 2.3, discretizing the lithium battery continuous state space model established in the step 2.2 to obtain a lithium battery discrete state space model;
discrete equation of state:
discrete observation equation:
wherein, Vt(k) The terminal voltage at the moment k of the lithium battery, SOC (k) is the SOC and V at the moment k of the lithium battery1(k) Is the polarization voltage, V, of the lithium battery at time kt(k +1) is the terminal voltage of the lithium battery at the moment (k +1), SOC (k +1) is the SOC at the moment (k +1), V1(k +1) is the polarization voltage of the lithium battery at the moment (k +1), y (k) is the output quantity of the lithium battery at the moment k, Is(k) The current value flowing through the lithium battery at time k is T, which is the sampling time, and the value T is 0.001s in this example.
Step 2.4, adding an uncertainty gamma ξ (k) on the basis of the lithium battery discrete state space model established in the step 2.3, wherein the uncertainty gamma ξ (k) is used for compensating nonlinearity, external disturbance and modeling errors;
modified discrete equation of state:
discrete observation equation:
wherein let the state at time kGamma is the disturbance input matrix, in this exampleξ (k) is a bounded scalar disturbance input and satisfies | ξ (k) | ≦ ξ0| e (k) |, e (k) is the state error of the lithium battery at the time k, i.e. e (k) ═ x (k +1) -x (k), ξ0For the upper limit parameter, in this embodiment, the value ξ is taken0＝0.1。
Step 3, performing a pulse discharge experiment on the lithium battery, and identifying the lithium battery model parameters in the step 2, wherein the parameters comprise ohmic internal resistance R0Polarization resistance R1Polarization capacitance C1The process is as follows,
step 3.1, discharging the lithium battery with the capacity of 5Ah for 5 minutes at 1C current, stopping discharging, standing for 10 minutes, discharging for 5 minutes at 1C current, taking the 20 minutes as a pulse discharge period, and recording the terminal voltage U of the lithium battery in the pulse discharge period by the charging and discharging equipmentbattery(ii) a change;
step 3.2, according to the terminal voltage U in one pulse discharge period of the lithium battery recorded in the step 3.1batteryAnd (3) changing, analyzing the characteristic of the terminal voltage change curve, and identifying the lithium battery model parameters in the step (2).
FIG. 4 shows the terminal voltage U of the lithium battery obtained in step 3.1 in a pulse discharge cyclebatterySchematic diagram of variation curve, (U)2-U1) Ohmic internal resistance R when the pulse discharge ending moment t is 300s0The resulting pressure drop; (U)3-U2) In the standing stage t is equal to R in 300 s-900 s1，C1The varying voltage across the loop; in the standing stage, the voltage U of the lithium battery terminalbatteryRise to (U) over a time of 3 tau3-U2) Of 95%, measuring a time 3 τ, wherein the time constant τ ═ C1R1Ohmic internal resistance R of the lithium battery0Polarization resistance R1And a polarization capacitor C1Calculated according to the following formula:
step 4, respectively using a Hall voltage sensor and a Hall current sensor to acquire the terminal voltage V of the lithium battery under the working condition in real timet(k) And charging and discharging current Is(k)。
Step 5, designing a discrete variable structure observer based on the lithium battery discrete state space model established in the step 2, and acquiring the voltage V of the lithium battery terminal acquired in the step 4t(k) And charging and discharging current Is(k) As signal input, estimating the SOC of the lithium battery in real time, wherein the process is as follows:
step 5.1, designing a discrete variable structure observer based on the lithium battery discrete state space model established in the step 2;
wherein,is an estimate of x (k),is an estimate of x (k +1),is an estimated value of y (k), h is a gain matrix of the stractural observer, λ is a positive feedback input matrix, v (k) is an extrinsic positive feedback compensation signal, G is a system matrix of the stractural energy observer,h is an input matrix of the discrete variable structure observer,c is the output matrix of the discrete variable structure observer, and C is [ 100 ]]。
Step 5.2, the voltage V of the lithium battery terminal collected in the step 4 is processedt(k) And the charge and discharge current I (k) is used as signal input, and the SOC of the lithium battery is estimated in real time by using the designed discrete variable structure observer.
In this embodiment, the gain matrix h of the discrete variable structure observer is determined according to the following formula:
wherein, CTIs the transpose matrix of the output matrix C of the discrete variable structure observer, and P is the discrete Chenopodium equationSolution of positive definite symmetric matrix, G in Chenopodium discredite Kati equationTThe invention is a transposed matrix of a system matrix G of a discrete variable structure observer, Q is an arbitrary semi-positive definite symmetric matrix, Q is set as E, E is a unit matrix of 3 multiplied by 3, α is a positive real number, and the value of the example is α as 1.
In this embodiment, the positive feedback input matrix λ is determined according to the following equation:
λ＝vH (19)
wherein v is a positive real number, and the value v in this case is 0.5.
In this embodiment, the extrinsic positive feedback compensation signal v (k) is determined according to the following equation:
wherein e (k) is a state error of the lithium battery at the time k, i.e., e (k) ═ x (k +1) -x (k), w (k) is a positive value function and satisfies | w (k) | < 1, and Veq(k) For equivalent external positive feedback compensation signal, by the formula veq(k)＝(Cλ)-1(CGe (k) + C γ ξ (k)) determining that the bounded scalar disturbance input ξ (k) satisfies | ξ (k) | ≦ 0.1 × | | e (k) | |.
Claims (9)
1. A lithium battery SOC estimation method based on a discrete variable structure observer comprises the steps of collecting terminal voltage and charging and discharging current of a lithium battery under working conditions, and is characterized by mainly comprising the following steps:
step 1, performing a rapid calibration experiment on a lithium battery to obtain a relation curve between the SOC of the lithium battery and the open-circuit voltage OCV;
step 1.1, discharging a lithium battery with a charge cut-off voltage of 4.2V, a discharge cut-off voltage of 3V and a rated capacity of 5Ah at a constant current of 0.2 coulomb at room temperature until the voltage of the lithium battery is below 3V, standing for 2-3 hours, and waiting for an experiment;
step 1.2, carrying out constant current pulse charging on the lithium battery which is stood according to the step 1.1 by using 0.2 coulomb current, after 10 percent of rated capacity of the lithium battery is charged each time, breaking the circuit of the lithium battery and standing for 5 minutes, and measuring the terminal voltage U of the lithium battery in each standing time period in the charging process in real timec，OCVAnd finding out the minimum value U of the terminal voltage of the lithium battery in each standing time period in the charging processc，OCV，minUntil the lithium battery is full;
step 1.3, performing constant current pulse discharge on the lithium battery fully charged according to the step 1.2 by using 0.2 coulomb current, breaking the lithium battery and standing for 5 minutes after discharging 10 percent of the rated capacity of the lithium battery every time, and measuring the terminal voltage U of the lithium battery in each standing time period in the discharging process in real timed，OCVAnd finding out the maximum value U of the terminal voltage of the lithium battery in each standing time period in the discharging processd，OCV，maxUntil the lithium battery is empty;
step 1.4, obtaining the minimum value U of the terminal voltage of the lithium battery in each standing time period in the charging process obtained in the step 1.2c，OCV，minThe maximum value U of the terminal voltage of the lithium battery in the standing time period which is equal to the SOC in the charging process in the discharging process obtained in the step 1.3d，OCV，maxAdding and taking an average value as the open-circuit voltage OCV which is calibrated quickly to obtain 10 open-circuit voltage OCVs in total;
step 1.5, according to the 10 open-circuit voltages OCV obtained in the step 1.4, performing multi-section type straight line fitting on the obtained experimental data in the whole lithium battery SOC variation range, namely 0-100%, and obtaining a relation curve of the lithium battery SOC and the open-circuit voltages OCV;
in the multi-section straight line fitting, the length delta SOC of each section is 10%, and the expressions of the lithium battery SOC and the open-circuit voltage OCV fitted in each section are as follows:
OCVi＝ki*SOCi+di，i＝1，2，3....10 (1)
wherein the OCViOpen circuit voltage OCV, SOC for ith lithium batteryiIs the SOC, k of the ith lithium batteryiSlope of SOC versus open circuit Voltage OCV line fitted for segment i，diThe intercept of the SOC and the open-circuit voltage OCV straight line fitted in the ith section;
step 2, establishing a lithium battery discrete state space model for estimating the SOC of the lithium battery according to the relation curve of the SOC of the lithium battery and the open-circuit voltage OCV obtained in the step 1 and by combining a Thevenin equivalent circuit of the lithium battery and a ampere-hour integral formula;
discrete equation of state:
discrete observation equation:
wherein, Vt(k) The terminal voltage at the moment k of the lithium battery, SOC (k) is the SOC and V at the moment k of the lithium battery1(k) The polarization voltage of the lithium battery at the k moment; vt(k +1) is the terminal voltage of the lithium battery at the moment (k +1), SOC (k +1) is the SOC at the moment (k +1), V1(k +1) is the polarization voltage of the lithium battery (k +1) at the moment; t is the sampling time, Is(k) The current value flowing through the lithium battery at the moment k, gamma is a disturbance input matrix, ξ (k) is a bounded scalar disturbance input, y (k) is an output quantity of the lithium battery at the moment k, and a1＝1/R1C1，a11＝kia1，a2＝1/R0CN，a22＝kia2，b1＝ki/CN+1/C1+R0/R1C1，b2＝1/C1，R1Is the polarization resistance of a lithium battery, C1Is the polarization capacitance, R, of a lithium battery0Is the ohmic internal resistance of the lithium battery, CNThe nominal capacity of the lithium battery;
step 3, performing a pulse discharge experiment on the lithium battery, and identifying the discrete state space model parameters of the lithium battery in the step 2;
step 3.1, firstly, the lithium battery with the capacity of 5Ah is charged with currentI discharging for 5 minutes, stopping discharging, standing for 10 minutes, discharging for 5 minutes at the same current I, taking the 20 minutes as a pulse discharging period, and recording the terminal voltage U of the lithium battery in the pulse discharging period by the charging and discharging equipmentbattery(ii) a change;
step 3.2, according to the terminal voltage U in one pulse discharge period of the lithium battery recorded in the step 3.1batteryAnd changing, analyzing the characteristic of the terminal voltage change curve, and identifying parameters in the discrete state space model of the lithium battery, wherein the parameters comprise ohmic internal resistance R0Polarization resistance R1Polarization capacitance C1；
Step 4, acquiring terminal voltage V of the lithium battery under the working condition in real time through a voltage sensor and a current sensor respectivelyt(k) And charging and discharging current Is(k)；
Step 5, designing a discrete variable structure observer based on the lithium battery discrete state space model established in the step 2, and acquiring the voltage V of the lithium battery terminal acquired in the step 4t(k) And charging and discharging current Is(k) As signal input, estimating the SOC of the lithium battery in real time;
the equation of the discrete variable structure observer is as follows:
wherein x (k) is a state variable of the lithium battery at the time k, is an estimated value of x (k), x (k +1) is a state variable of the lithium battery (k +1) at the moment,is an estimate of x (k +1),is an estimate of y (k), λ is a positive feedback input matrix, v (k) is an extrinsic positive feedback compensation signal, h is a gain matrix of the stractural observer, G is a system matrix of the stractural observer,h is an input matrix of the discrete variable structure observer,c is the output matrix of the discrete variable structure observer, and C is [ 100 ]]And C lambda is not equal to 0.
2. The lithium battery SOC estimation method based on the discrete variable structure observer according to claim 1, wherein the Thevenin equivalent circuit model in the step 2 is:
Vt＝V1+IsR0+OCV (6)
wherein, VtIs terminal voltage of lithium battery, V1Is the polarization voltage of the lithium battery,is a V1Differential of (1)sThe OCV is an open circuit voltage of the lithium battery for an instantaneous current flowing through the lithium battery.
3. The lithium battery SOC estimation method based on the discrete variable structure observer according to claim 1, wherein the ampere-hour integral formula in the step 2 is:
therein, SOC0Is the initial value of the SOC of the lithium batterytIs the SOC instantaneous value of the lithium battery, CNIs the nominal capacity of the lithium battery, IsFor instantaneous current through a lithium battery, t0Is the initial time of the charging and discharging process, t1Is the end time of the charging and discharging process.
4. The method for estimating the SOC of the lithium battery based on the discrete variable structure observer according to claim 1, wherein the bounded scalar disturbance input ξ (k) in the step 2 satisfies the following expression:
|ξ(k)|≤ξ0||e(k)|| (9)
wherein, ξ0The upper limit parameter is positive and is 0.05-0.15, and e (k) is the state error of lithium battery at time k, namely e (k) ═ x (k +1) -x (k).
5. The lithium battery SOC estimation method based on the discrete variable structure observer according to claim 1, wherein the voltage sensor and the current sensor in step 4 are a Hall voltage sensor and a Hall current sensor, respectively.
6. The method for estimating the SOC of the lithium battery based on the discrete variable structure observer according to claim 1, wherein the gain matrix h of the discrete variable structure observer in the step 5 is determined according to the following formula:
wherein, CTIs the transpose matrix of the output matrix C of the discrete variable structure observer, and P is the discrete Chenopodium equationSolution of positive definite symmetric matrix, G in Chenopodium discredite Kati equationTThe method is a transposed matrix of a system matrix G of the discrete variable structure observer, Q is an arbitrary semi-positive definite symmetric matrix, α is a positive real number, and the value range is 1-5.
7. The lithium battery SOC estimation method based on the discrete variable structure observer according to claim 1, wherein the positive feedback input matrix λ in step 5 is determined according to the following formula:
λ＝vH (11)
wherein v is a positive real number, and is 0.2-0.8.
8. The lithium battery SOC estimation method based on the discrete variable structure observer according to claim 1, wherein the extrinsic positive feedback compensation signal v (k) in the step 5 is determined according to the following formula:
wherein e (k) is a state error of the lithium battery at the time k, i.e., e (k) ═ x (k +1) -x (k), and w (k) is a positive value function and satisfies | w (k) | < 1, and v (k) is a positive value functioneq(k) For equivalent external positive feedback compensation signal, by the formula veq(k)＝(Cλ)-1(CGe (k) + C γ ξ (k)) determination, where the bounded scalar disturbance input ξ (k) satisfies | ξ (k) | ≦ ξ (k) |0×||e(k)||，ξ0The upper limit parameter is positive number, and is 0.05-0.15.
9. The method for estimating the SOC of the lithium battery based on the discrete variable structure observer according to claim 6, wherein Q is E, and E is a 3 × 3 identity matrix.
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| CN110907835B (en) * | 2019-12-10 | 2020-12-11 | 北京理工大学 | Battery model parameter identification and SOC estimation method with noise immunity characteristic |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102645637A (en) * | 2012-04-25 | 2012-08-22 | 中航锂电（洛阳）有限公司 | Method for estimating SOC (state of charge) of equalized batteries |
| CN103439668A (en) * | 2013-09-05 | 2013-12-11 | 桂林电子科技大学 | Charge state evaluation method and system of power lithium ion battery |
| CN103941196A (en) * | 2014-05-07 | 2014-07-23 | 吉林大学 | Lithium ion battery state-of-charge estimation method |
| CN104535932A (en) * | 2014-12-20 | 2015-04-22 | 吉林大学 | Lithium ion battery charge state estimating method |
| CN104678316A (en) * | 2015-02-28 | 2015-06-03 | 北京交通大学 | Method and device for estimating charge state of lithium ion battery |
| EP2963434A1 (en) * | 2014-06-30 | 2016-01-06 | Foundation Of Soongsil University-Industry Cooperation | Battery state estimation method and system using dual extended kalman filter, and recording medium for performing the method |
| CN105717460A (en) * | 2016-02-19 | 2016-06-29 | 深圳大学 | Power battery SOC estimation method and system based on nonlinear observer |
-
2016
- 2016-09-26 CN CN201610850573.2A patent/CN106324523B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102645637A (en) * | 2012-04-25 | 2012-08-22 | 中航锂电（洛阳）有限公司 | Method for estimating SOC (state of charge) of equalized batteries |
| CN103439668A (en) * | 2013-09-05 | 2013-12-11 | 桂林电子科技大学 | Charge state evaluation method and system of power lithium ion battery |
| CN103941196A (en) * | 2014-05-07 | 2014-07-23 | 吉林大学 | Lithium ion battery state-of-charge estimation method |
| EP2963434A1 (en) * | 2014-06-30 | 2016-01-06 | Foundation Of Soongsil University-Industry Cooperation | Battery state estimation method and system using dual extended kalman filter, and recording medium for performing the method |
| CN104535932A (en) * | 2014-12-20 | 2015-04-22 | 吉林大学 | Lithium ion battery charge state estimating method |
| CN104678316A (en) * | 2015-02-28 | 2015-06-03 | 北京交通大学 | Method and device for estimating charge state of lithium ion battery |
| CN105717460A (en) * | 2016-02-19 | 2016-06-29 | 深圳大学 | Power battery SOC estimation method and system based on nonlinear observer |
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