CN110554320A - SOC estimation method of lithium ion battery - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 64
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 title claims abstract description 26
- 229910001416 lithium ion Inorganic materials 0.000 title claims abstract description 26
- 230000010287 polarization Effects 0.000 claims abstract description 36
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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 invention discloses a method for estimating the SOC of a lithium ion battery, which comprises the following steps of 1: establishing a Thevenin battery model; 2: identifying an open-circuit voltage curve of the Thevenin battery model, and determining a relation curve of open-circuit voltage and state of charge in the Thevenin battery model according to the identification result of the open-circuit voltage curve; 3: obtaining the ohmic internal resistance and polarization internal resistance of the battery model; 4: establishing a battery state space model for reflecting the relation among voltage, current, charge state and battery internal resistance in the lithium ion battery according to the battery ohmic internal resistance and the battery polarization internal resistance of the battery model and the relation curve between open-circuit voltage and charge state in the Thevenin battery model; 5: and substituting the battery state space model into a Kalman filtering equation to carry out iterative calculation to obtain the state of charge of the battery. The invention can accurately estimate the SOC.
Description
Technical Field
The invention relates to the technical field of battery management systems of electric vehicles, in particular to a method for estimating the SOC of a lithium ion battery.
Background
Under different working environments, the lithium ion battery is influenced by changes of the internal working environment and the external use environment, and the specific characteristics of the battery can be greatly changed. Therefore, in practical applications, the SOC of the battery is affected by various factors and exhibits a nonlinear characteristic, so that it is necessary to overcome these problems in order to realize a good SOC estimation algorithm.
At present, the SOC estimation methods which are applied more frequently comprise a discharge experiment method, an ampere-hour integral method, an open-circuit voltage method, a resistance measurement method, a Kalman filtering method, a neural network method and the like.
The discharge experiment method and the open-circuit voltage method both need to estimate the SOC in a non-working state, and cannot realize online prediction;
the ampere-hour integration method is influenced by current detection errors and errors of an initial value of the SOC, the accumulated errors are larger and larger, the long-term accumulated errors cause the estimated value to deviate from the true value seriously, and the requirement of accuracy cannot be met;
the difficulty of measuring the internal resistance of the battery by a resistance measuring method is high, the measurement is more difficult under the condition of severe working environment, and the requirement of feasibility is not met;
the neural network method has high precision and strong convergence, but needs a large amount of training data resources, is greatly influenced by sample data and the training method, namely the calculated amount is large, so far, the method still stays in a simulation stage, and real-time estimation is difficult to realize;
The Kalman filtering algorithm can well make up the defects of SOC initial errors and accumulated errors in an ampere-hour integration method, can keep good precision in the estimation process, can effectively inhibit noise, does not need to be particularly precise in the initial value, and can also have good convergence and keep good precision through iterative processing of the algorithm. This is the mainstream algorithm for SOC estimation at present.
On the other hand, however, the kalman filter method also has its inherent drawbacks:
1. the dependence of the algorithm on the model is large, and when the model is not accurate, the estimation error of the SOC becomes large;
2. The Kalman filtering method is only suitable for a linear system, and for a highly nonlinear lithium ion battery, if the traditional Kalman filtering method is adopted, a linearization error is necessarily introduced.
disclosure of Invention
The invention aims to provide a method for estimating the SOC of a lithium ion battery, which comprises the steps of firstly establishing an accurate Thevenin battery model to obtain a space state equation, determining an initial value of the SOC by using an open-circuit voltage method, calculating the accumulation of the SOC by using an ampere-hour integration method, firstly carrying out linear processing on a nonlinear system by using an expanded Kalman filtering algorithm, and finally reducing the initial value, the measurement error and other interference factors to the minimum by using a classical Kalman filtering algorithm so as to reduce the external influence to the maximum extent. In addition, the battery model realizes the online identification of the battery core parameters in the whole life cycle so as to adapt to the lithium ion batteries of various different materials.
In order to achieve the purpose, the invention provides a method for estimating the SOC of a lithium ion battery, which is characterized by comprising the following steps:
Step 1: establishing a Thevenin battery model;
step 2: identifying an open-circuit voltage curve of the Thevenin battery model, and determining a relation curve of open-circuit voltage and state of charge in the Thevenin battery model according to the identification result of the open-circuit voltage curve;
And step 3: obtaining a zero-input response characteristic curve of the Thevenin battery model at the moment of battery pulse discharge, and identifying RC parameters of the battery model for the zero-input response characteristic curve so as to obtain the ohmic internal resistance and the polarization internal resistance of the battery model;
And 4, step 4: establishing a space state equation for reflecting the relationship among the voltage, the current, the charge state and the internal resistance of the battery in the lithium ion battery according to the battery ohmic internal resistance and the battery polarization internal resistance of the battery model identified in the step and a relation curve between the open-circuit voltage and the charge state;
and 5: and substituting the space state equation into a Kalman filtering equation to carry out iterative calculation to obtain the state of charge of the battery.
Compared with the prior art, the invention has the following remarkable effects:
Firstly, the technique selects a second-order Thevenin equivalent circuit model, and compared with other equivalent circuit models, the technique has the following advantages: 1) the external characteristics (load voltage, current, environment temperature and the like) of the battery expressed in the actual use process can be better described, the ohmic internal resistance of the battery is simulated by using the series resistor, and the abrupt change characteristic of the voltage is represented; simulating the polarization internal resistance by using a first RC circuit, and simulating the concentration polarization internal resistance by using a second RC circuit to represent the gradual change characteristic of voltage; 2) the parameters of the model can be obtained and calibrated by curve fitting through a least square method; 3) the order of the model expression is not high, and meanwhile, the precision is good, so that the realization is facilitated. The SOC calculation error caused by model inaccuracy is solved by the point;
secondly, parameter identification is carried out in the whole life cycle of the product, and errors caused by parameter changes after the service life of the battery is attenuated can be corrected in time through online identification and self-learning of the parameters;
And finally, performing linearization processing on a system space state equation through Taylor expansion, and converting a nonlinear system into a linear system, so that the system can perform iterative computation through Kalman filtering, an error is computed for an optimal value obtained by each computation, and the linearization error brought by the traditional Kalman filtering algorithm is avoided by ensuring effective convergence. Compared with a conventional estimation method, the method has strong inhibition on external interference and can quickly correct the deviation;
in conclusion, the technology can well make up the defects of the conventional algorithm and control the estimation precision within 3 percent by combining the advantages; the estimation parameters can be adjusted in a self-adaptive manner, and the method is suitable for various power battery cells;
drawings
FIG. 1 is an equivalent circuit battery model of the second order;
FIG. 2 is a plot of the open circuit voltage versus the scatter in SOC of a battery;
FIG. 3 is a pulse discharge diagram of a battery;
in fig. 2, AB is a discharge instantaneous voltage sudden-drop section, BC is a voltage gradual-change section in the discharge process, CD is a discharge end voltage sudden-change back-rise section, and DE is a discharge end voltage gradual-change back-rise section. The abscissa of fig. 3 represents time and the ordinate codes voltage.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
The invention discloses a method for estimating the SOC of a lithium ion battery, which comprises the following steps:
Step 1: establishing a Thevenin battery model;
Step 2: identifying an open-circuit voltage curve of the Thevenin battery model, and determining a relation curve of open-circuit voltage and state of charge in the Thevenin battery model according to the identification result of the open-circuit voltage curve;
and step 3: obtaining a zero-input response characteristic curve of the Thevenin battery model at the moment of battery pulse discharge (a large amount of data are obtained by charging and discharging the battery and then curve fitting is carried out to obtain RC parameters), and carrying out RC parameter identification on the zero-input response characteristic curve of the battery model so as to obtain the ohmic internal resistance and polarization internal resistance of the battery model;
and 4, step 4: establishing a space state equation for reflecting the relationship among the voltage, the current, the charge state and the internal resistance of the battery in the lithium ion battery according to the battery ohmic internal resistance and the battery polarization internal resistance of the battery model identified in the step and a relation curve between the open-circuit voltage and the charge state;
And 5: and substituting the space state equation into a Kalman filtering equation to carry out iterative calculation to obtain the state of charge of the battery.
The battery model describes characteristics of an actual battery, namely response characteristics and internal characteristics, comprehensively by applying mathematical principles as much as possible, wherein the response characteristics are relations between battery current and voltage, and the internal characteristics are relations between various variables inside the battery, such as temperature, SOC and internal resistance of the battery. A suitable model should be able to better describe the characteristics exhibited by the battery under the influence of changes in the internal operating environment and the external environment. Therefore, when using the model, the complexity of the model, the order of the model, etc. become the indexes that must be measured, and it is determined whether the result that we want can be obtained in real time with sufficient accuracy, such as the accurate value of the SOC, the voltage and current values in the circuit, etc.
In the above technical solution, the davinan battery model is expressed as:
wherein, UocIndicating the opening of the batteryLine voltage, R0Represents the ohmic internal resistance, R, of the battery1indicating the internal resistance of the cell polarization, C1Representing the polarization capacitance, R, of the cell2indicating the concentration polarization internal resistance of the cell, C2representing the concentration polarization capacitance, U, of the celllrepresenting battery terminal voltage, I representing battery load current, U1and U2Respectively representing the voltages of two RC circuits connected in series in the davinan cell model,andrespectively represent U1and U2The vector of (2).
In step 2 of the above technical scheme, the method for determining the relation curve between the open-circuit voltage and the state of charge in the Thevenin battery model comprises the following steps:
Discharging the battery to be empty at constant current by using different discharge multiplying factors (for example, the discharge multiplying factors of 1C and 2C are selected) to obtain two different load voltage curves, obtaining a calculation formula of the battery terminal voltage by using a Thevenin battery model, wherein the calculation formula is shown as a formula 1.2 and a formula 1.3, and simply processing the two formulas to obtain a formula 1.4 and a formula 1.5, namely determining a relation curve between open-circuit voltage and a charge state in the Thevenin battery model;
Ulm＝Uoc-(∑iRi)*Im 1.2
Uln＝Uoc-(∑iRi)*In 1.3
in the formula ImBattery current for constant current discharge at 1C, InThe battery current is the battery current for constant current discharge at 2C; u shapelmThe terminal voltage of the battery at which constant current discharge is performed at 1C,Ulnterminal voltage of the battery which is subjected to constant current discharge at 2C; riis R0，R1，R2Internal resistance of cell polarization R1And cell concentration polarization internal resistance R2respectively carrying out discharge tests on the battery at the current of 1C and 2C, wherein C is the charge-discharge multiplying power, obtaining the response curve of the battery, recording the load voltage value and the SOC value, and I is the value when the battery capacity is xAhm＝xA，Intaking the obtained voltage value and current value into formula 1.5, respectively, 2xA, a curve of the open-circuit voltage versus the state of charge in the davinan battery model can be obtained, as shown by the scatter in fig. 2.
according to the curve obtained by the method, the required parameter value can be obtained according to the principle of least square curve fitting, and further the relational expression of the open-circuit voltage and the SOC can be obtained. From the curve shape of fig. 2-2 and known empirical equations, the open circuit voltage and SOC function can be given as equation 1.6:
in this formula, the open circuit voltage UocAs equation 1.5 is found above, the value of SOC can be obtained during discharging, and the unknown parameters in the equation can be identified by the lsqcurvefit function, assuming the identification result is shown in the following table:
Table 1: OCV curve parameter table
| a0 | a1 | a2 | a3 | a4 | a5 |
| 1.1863 | 0.0018 | 0.3268 | 22.1000 | 10.2900 | 232.0000 |
in step 3 of the above technical scheme, when identifying the RC parameters of the battery model, a pulse discharge test needs to be performed on the lithium ion battery to obtain a pulse discharge response curve, a discharge instantaneous voltage dip section in the pulse discharge response curve, a jump drop occurs from no-load standing to loading of the voltage at the discharge end of the battery, and the battery polarization capacitor C can be known from the battery model1and battery concentration polarization capacitance C2the voltage at two ends cannot be suddenly changed, and the voltage drop is caused by the ohmic internal resistance R of the battery0Causing;
In the discharge process of the pulse discharge response curve, the voltage gradient section slowly reduces along with the increase of the discharge time in an exponential change trend until the last moment of discharge, and the voltage reaches a minimum value, which is a result influenced by two RC inertia links;
the lithium ion battery is changed from loading discharge to no-load standing, and the voltage response also undergoes the processes of jump-type rising and exponential rising;
The voltage change of the voltage gradient section and the voltage change of the discharge ending voltage gradient rise section in the discharge process in the response curve of the impulse discharge is due to the action of an RC (resistance capacitance) loop of the Thevenin battery model, the voltage finally tends to be stable along with the release of the electric quantity of the RC loop, and the discharge ending voltage gradient rise section accords with the zero input response characteristic of the RC circuit; the voltage gradient section and the discharge ending voltage gradient rise section in the discharge process in the response curve of charging and discharging reflect the gradient characteristics in the charge and discharge process, the discharge instant voltage sudden drop section and the discharge ending voltage sudden rise section in the response curve of charging and discharging reflect the sudden change characteristics of the charge and discharge instant voltage, and the ohmic internal resistance and the polarization internal resistance of the battery model are worked out through the exponential fitting of the voltage gradient section and the discharge ending voltage gradient rise section in the discharge process in the response curve of charging and discharging.
In the above technical solution, the calculation process of the ohmic internal resistance of the battery is that the instantaneous change value of the battery voltage is set as U0The value is represented by R0caused by, then there is R0＝U0I, where I is the discharge current,
Whereby R can be obtained0A value;
The calculation method of the polarization resistance capacitance and the concentration polarization resistance capacitance is as follows:
Zero input response U of known first order RC parallel circuitjthe analytical formula of (t) is shown below:
In the formula of Uj(0)＝Rj*i0at the initial value, the voltage across the battery is expressed by the equation 1.8:
wherein i0Is a discharge current; u shapelis the load voltage of the battery; u shapeoc(SOC) is an expression representing the open-circuit voltage of the battery by SOC; rjIs the resistance value of the jth parallel circuit; cjFor a second-order equivalent battery model, j takes a value of 1, 2, t is a time constant, e is a natural constant, and n represents the order of the model;
The results of curve fitting by the least square method using the equations 1.7 and 1.8 as the actual curves are shown in the following table to obtain the ohmic internal resistance and polarization internal resistance of the battery model
Table 2: RC parameter table
| R0(Ω) | R1(Ω) | R2(Ω) | C1(F) | C2(F) |
| 0.0051 | 0.001 | 0.053 | 2000 | 200 |
In the above technical solution, the battery state space model is:
Where η is conversion efficiency, (coulombic efficiency) η ═ 1, f (x)1) Is UocExpression of (SOC), QNIs total capacity of cell, x1state of charge, x2＝U1，x3＝U2，R1Indicating the internal resistance of the cell polarization, C1Representing the polarization capacitance, R, of the cell2Indicating the concentration polarization internal resistance of the cell, C2Representing the concentration polarization capacitance, R, of the cell0Expressing the ohmic internal resistance, U, of the cell1And U2respectively representing the voltages of two RC circuits connected in series in the davinan cell model,AndRespectively represent x1、x2and x3And u is the battery charging and discharging current.
In the technical scheme, the state noise and the process noise added by the formula 1.9 are input into a Kalman filtering equation;
Equation 1.9 is redefined as a general form of a nonlinear system, with the state variable x ═ x1x2x3]t, wherein x1＝SOC，x2＝U1，x3＝U2(ii) a w (k) and v (k) are respectively the process noise and the measurement noise of the state space, the nonlinear model of the lithium ion battery can be written as formula 2.0 and formula 2.1:
x (k) ═ f [ x (k-1), u (k-1) ] + w (k-1) formula 2.0
y (k) ═ g [ x (k), u (k) ] + v (k) formula 2.1
U (k) represents a battery current, k represents a current time, k-1 represents a previous time, and y (k) represents a battery terminal voltage Ulf and g are respectively functional expressions;
the terminal voltage of the battery after standing does not change any more, so the terminal voltage of the battery can be considered as an open-circuit voltage, the voltage is measured to be 1.19V, the value is substituted into a function expression of the relation between the OCV and the SOC of the target battery, the unique real number root of the SOC is 88.4 by solving an equation, and the value is determined as the initial value of the SOC of the battery, namely the SOC of the battery is 88.4% when the experiment begins; in the model, the sampling time T ═ ls, the state noise and the process noise w (k), and v (k) are all selected to be uncorrelated white gaussian noise with mean value 0 and variance 1, so the initial value of the system state is expressed by equations 2.2 and 2.3:
P[w(k)]＝P[v(k)]＝1，QN6.5A 3600s 23400C formula 2.2
Wherein,To representAn initial value of (d);
when the SOC of the battery is estimated by adopting the extended Kalman filtering algorithm, firstly, the predicted value of the electronic SOC is surrounded by the formula 1.9Taylor expansion is carried out, more than two-order items are omitted, and the A (k), B (k) and C (k) after model linearization processing can be obtained,As follows:
T1 represents the time taken, SOC represents the state of charge of the lithium ion battery,Represents a pair of U1the deviation is calculated and the deviation is calculated,Represents a pair of U2Calculating a deviation derivative;
Finally, the discrete model after battery linearization can be obtained as shown in the formulas 2.6 and 2.7
x(k)＝A(k-1)*x(k-1)+B(k-1)*u(k-1)+w(k-1) 2.6
D(k)＝-R0；
According to the following steps of an extended Kalman filter algorithm, namely, the step 2.8 to the step 2.13, the estimation of the SOC can be realized;
the specific steps of the extended kalman filtering are shown as follows:
initialization: at the time k-1
A vector of initial values at time x at k-1;
xk-1: k-1 the initial value of time x;
x is the SOC value that needs to be estimated
Pk-1Error covariance of the extended Kalman filtering system at time k-1;
E represents expectation, T represents a transposed matrix;
the value of the initial value refers to formula 2.3;
and a prediction part:
The state prediction equation:
uk-1the battery current value at the time k-1,Representing the predicted SOC value at the time k;
State covariance prediction equation:
Qk-1: the state noise of the extended kalman filter system,Representing the error covariance of the extended Kalman filter system at the moment k;
correction section
the feedback gain equation:
Rk-1: process noise for extended Kalman Filter systems
Kk: kalman gain for extended Kalman filtering system
c (k) is a vectorThe size of (d);
The filter equation:
optimum value of SOC at time k
Error covariance matrix update equation:
Pkand expressing the error value of the extended Kalman filter system at the K moment.
As can be seen from the above description, kalman filtering is performed in a recursion sequence of "prediction-actual measurement-correction", and after a finite number of iterations, the estimated value x (k) gradually converges to the true value x (k), thereby completing an efficient and accurate filtering process.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.
Claims (6)
1. A method for estimating the SOC of a lithium ion battery is characterized by comprising the following steps:
step 1: establishing a Thevenin battery model;
Step 2: identifying an open-circuit voltage curve of the Thevenin battery model, and determining a relation curve of open-circuit voltage and state of charge in the Thevenin battery model according to the identification result of the open-circuit voltage curve;
And step 3: obtaining a zero-input response characteristic curve of the Thevenin battery model at the moment of battery pulse discharge, and identifying RC parameters of the battery model for the zero-input response characteristic curve so as to obtain the ohmic internal resistance and the polarization internal resistance of the battery model;
and 4, step 4: establishing a space state equation for reflecting the relationship among the voltage, the current, the charge state and the internal resistance of the battery in the lithium ion battery according to the battery ohmic internal resistance and the battery polarization internal resistance of the battery model identified in the step and a relation curve between the open-circuit voltage and the charge state;
And 5: and substituting the space state equation into a Kalman filtering equation to carry out iterative calculation to obtain the state of charge of the battery.
2. The method of estimating SOC of a lithium ion battery according to claim 1, characterized in that: the Thevenin cell model is represented as:
wherein, UocRepresents the open circuit voltage, R, of the battery0represents the ohmic internal resistance, R, of the battery1Indicating the internal resistance of the cell polarization, C1Representing the polarization capacitance, R, of the cell2Indicating the concentration polarization internal resistance of the cell, C2representing the concentration polarization capacitance, U, of the celllRepresenting battery terminal voltage, I representing battery load current, U1and U2Respectively representing the voltages of two RC circuits connected in series in the davinan cell model,andrespectively represent U1And U2The vector of (2).
3. the method of estimating SOC of a lithium ion battery according to claim 1, characterized in that: in the step 2, the method for determining the relation curve of the open-circuit voltage and the state of charge in the Thevenin battery model comprises the following steps:
Discharging the battery to be empty at constant current by using different discharge multiplying powers to obtain two different load voltage curves, obtaining a calculation formula of the battery terminal voltage by using a Thevenin battery model, wherein the calculation formula is shown as a formula 1.2 and a formula 1.3, and simply processing the two formulas to obtain a formula 1.4 and a formula 1.5, namely determining a relation curve of open-circuit voltage and charge state in the Thevenin battery model;
Ulm＝Uoc-(∑iRi)*Im 1.2
Uln＝Uoc-(∑iRi)*In 1.３
in the formula Imbattery current for constant current discharge at 1C, Inthe battery current is the battery current for constant current discharge at 2C; u shapelmTerminal voltage of battery, U, for constant current discharge at 1ClnTerminal voltage of the battery which is subjected to constant current discharge at 2C; riIs R0，R1，R2Internal resistance of cell polarization R1And cell concentration polarization internal resistance R2respectively carrying out discharge tests on the battery at the current of 1C and 2C, wherein C is the charge-discharge multiplying power, obtaining the response curve of the battery, recording the load voltage value and the SOC value, and I is the value when the battery capacity is xAhm＝xA，InThe obtained voltage value and current value are taken into formula (1.5) 2xA, respectively, and a relationship curve between the open-circuit voltage and the state of charge in the thevenin cell model can be obtained.
4. the method of estimating SOC of a lithium ion battery according to claim 1, characterized in that: in the step 3, when the battery model RC parameter identification is carried out, the battery needs to be identifiedthe lithium ion battery is subjected to a pulse discharge test to obtain a pulse discharge response curve, a discharge instantaneous voltage sudden-drop section in the pulse discharge response curve, the battery is subjected to a jump-type drop from no-load standing to a loaded discharge end voltage, and a battery polarization capacitor C can be known through a battery model1and battery concentration polarization capacitance C2the voltage at two ends cannot be suddenly changed, and the voltage drop is caused by the ohmic internal resistance R of the battery0Causing;
In the discharge process of the pulse discharge response curve, the voltage gradient section slowly reduces along with the increase of the discharge time in an exponential change trend until the last moment of discharge, and the voltage reaches a minimum value, which is a result influenced by two RC inertia links;
the lithium ion battery is changed from loading discharge to no-load standing, and the voltage response also undergoes the processes of jump-type rising and exponential rising;
The voltage change of the voltage gradient section and the voltage change of the discharge ending voltage gradient rise section in the discharge process in the response curve of the impulse discharge is due to the action of an RC (resistance capacitance) loop of the Thevenin battery model, the voltage finally tends to be stable along with the release of the electric quantity of the RC loop, and the discharge ending voltage gradient rise section accords with the zero input response characteristic of the RC circuit; the voltage gradient section and the discharge ending voltage gradient rise section in the discharge process in the response curve of charging and discharging reflect the gradient characteristics in the charge and discharge process, the discharge instant voltage sudden drop section and the discharge ending voltage sudden rise section in the response curve of charging and discharging reflect the sudden change characteristics of the charge and discharge instant voltage, and the ohmic internal resistance and the polarization internal resistance of the battery model are worked out through the exponential fitting of the voltage gradient section and the discharge ending voltage gradient rise section in the discharge process in the response curve of charging and discharging.
5. The method of estimating SOC of a lithium ion battery according to claim 4, characterized in that: the ohmic internal resistance of the battery is calculated in such a way that the instantaneous change value of the battery voltage is set as U0The value is represented by R0Caused by, then there is R0＝U0Where I is the discharge current, whereby R can be obtained0A value;
the calculation method of the polarization resistance capacitance and the concentration polarization resistance capacitance is as follows:
zero input response U of known first order RC parallel circuitjThe analytical formula of (t) is shown below:
In the formula of Uj(0)＝Rj*i0at the initial value, the voltage across the battery is expressed by the equation 1.8:
wherein i0is a discharge current; u shapelIs the load voltage of the battery; u shapeoc(SOC) is an expression representing the open-circuit voltage of the battery by SOC; rjis the resistance value of the jth parallel circuit; cjIs the capacitance value of the jth parallel circuit, t is a time constant, e is a natural constant, and n represents the order of the model;
and (3) taking the formulas 1.7 and 1.8 as actual curves, and performing curve fitting by using a least square method to obtain the ohmic internal resistance and polarization internal resistance of the battery model.
6. the method of estimating SOC of a lithium ion battery according to claim 1, characterized in that: the battery state space model is as follows:
Where eta is conversion efficiency, f (x)1) Is UocExpression of (SOC), QNIs total capacity of cell, x1State of charge, x2＝U1，x3＝U2，R1Indicating the internal resistance of the cell polarization, C1representing the polarization capacitance, R, of the cell2Indicating the concentration polarization internal resistance of the cell, C2Representing the concentration polarization capacitance, R, of the cell0Expressing the ohmic internal resistance, U, of the cell1And U2respectively representing the voltages of two RC circuits connected in series in the davinan cell model,AndRespectively represent x1、x2and x3And u is the battery charging and discharging current.
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