CN109633479A - Lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering - Google Patents
Lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- 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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Abstract
The invention belongs to the battery management system fields of electric car, disclose a kind of lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering, battery performance parameter information is obtained, Order RC lithium battery equivalent-circuit model is established, and constructs the equation of description lithium battery dynamic characteristic；It recognizes equivalent circuit model parameter and obtains function of the OCV about SOC；Establish built-in capacitor product Kalman Filter observer；The data such as real-time voltage and the electric current of lithium battery are acquired, estimate SOC.The present invention solves the problems, such as that spherical surface volume point existing for traditional volume Kalman filtering algorithm may be complicated beyond integral domain, calculating process, have the characteristics that precision is high, convergence is good, it is a kind of new practice of New Algorithm in lithium battery SOC estimation field, is suitable for the real-time SOC estimation of dynamic lithium battery management system platform.
Description
Technical field
The invention belongs to the battery management system fields of electric car, more particularly to one kind to be based on built-in capacitor G-card Germania
The lithium battery SOC estimation on line method of filtering.
Background technique
Due to the fast development of electric car (EV), the lithium battery as energy resource system is widely used in electric car row
Industry.The performance of battery will affect the safety of electric car, reliability and efficiency, thus battery management system (BMS) is monitoring
Operation and offer lithium battery state aspect play a crucial role.State-of-charge (SOC) be most critical in BMS parameter it
One, accurate SOC estimation can optimize the management strategies such as the balance of the energy and battery, reliable, safety, be embodied in:
(1) lithium battery is protected.For the lithium battery of cycle charge-discharge, lithium battery will be generated permanently with over-discharge by overcharging
The destruction of property, greatly shortens the service life of lithium battery.Accurate SOC estimation, entire car controller (VCU) will control lithium battery
SOC prevents from overcharging and over-discharge situation, extension lithium battery service life in zone of reasonableness.
(2) electric car performance is promoted.The SOC estimated value of preparation can make electric car selection accurately control strategy, fill
The performance of lithium battery is waved in distribution, and then promotes vehicle performance.
The method that the estimation method of usual SOC can be classified as model and non-model.Method based on non-model is usually
Open-loop algorithm generally includes current integration method, open-circuit voltage (OCV) method and intelligent algorithm etc..
Current integration method height relies on the precision of sensor and the accuracy of initial SOC；Open circuit voltage method is that one kind can be with
The straightforward procedure of SOC is determined by OCV-SOC relation curve, but the OCV that this method takes a long time to obtain lithium battery is gone
It calculates, and when battery is in running order and is not suitable for；In addition, with the development of technology, some intelligent algorithms
Applied in lithium battery SOC estimation, performance depends on a large amount of training data, but these data cannot represent lithium battery work
Full terms, in addition to this, the calculation amount of this kind of algorithm is also very big.
Method based on model, due to the complexity of electrochemistry mould electricity, is based on usually based on a kind of lithium battery model
The Kalman filter (KF) of equivalent-circuit model (ECM), Extended Kalman filter (EKF), Unscented kalman filtering (UKF) and
Volume Kalman filtering (CKF) has been widely used in the estimation of SOC, and such method is easier to reality compared to artificial intelligence scheduling algorithm
It is existing.There has been proposed with.
But the SOC estimation based on EKF, the lithium battery system of nonlinearity is linearized using first order Taylor formula
Process greatly reduce the precision of estimation, and cause filtering unstability.Although UKF overcomes EKF local linearization and draws
The error risen, and the inconvenience for solving Jacobian matrix is avoided, but central point weight of the UKF at Unscented transform (UT) may
It is negative, causes UKF or UT to convert numerical value unstable.CKF is a kind of nonlinear filter proposed in recent years, compared to EKF and
UKF has better numerical stability and higher filtering accuracy.But there is also spherical surface volume points may exceed integral by CKF
Region, calculating process complexity problem.
In conclusion the SOC estimation on line method based on Kalman filtering the problem of be:
SOC estimation precision is not high, and convergence rate is unhappy, and filter stability is poor；
The process of linearisation reduces the estimation precision of EKF；
Central point weight when Unscented transform (UT) may be negative, and cause UKF filtering unstable；
CKF volume point may be plural number, cause CKF filter unstable, reduce filter accuracies etc..
Solve the meaning of above-mentioned technical problem:
In conclusion precision is high in order to obtain, stability is good, fast convergence rate SOC estimation on line method, the present invention
One kind rolling over lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering.Make it that there is estimation Robust Performance, essence
The strong feature of height, strong robustness, anti-white noise ability is spent, and is easily achieved.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of, and the lithium based on built-in capacitor G-card Kalman Filtering is electric
Pond SOC estimation on line method.This method can obtain the filtering of higher precision more more stable than traditional CKF.
The technical scheme adopted by the invention is that a kind of lithium battery SOC based on built-in capacitor G-card Kalman Filtering is online
Evaluation method the following steps are included:
Step 1: obtaining lithium battery performance parameter information, initial SOC value is determined；
Step 2: establishing Order RC lithium battery equivalent-circuit model, and state equation and the output of lithium battery is calculated
Equation:
Establish lithium battery Order RC equivalent-circuit model, including concatenated resistance R0、R1、R2, open-circuit voltage Uoc, capacitor C1
With resistance R1Parallel connection, capacitor C2With resistance R2It is in parallel
Step 3: the function U of the parameter and acquisition OCV of identification equivalent-circuit model about SOCoc；
The calibration curve for obtaining battery OCV-SOC is fitted acquisition using quintic algebra curve, and quintic algebra curve is public
Formula are as follows:
Uoc=a0+a1*SOC+a2*SOC2+a3*SOC3+a4*SOC4+a5*SOC5
Wherein, ai(i=0,1 ..., 5) be multinomial coefficient, SOC be lithium battery remaining capacity state.
Step 4: using lithium battery SOC and second order polarizing voltage as the state equation of system, with equivalent-circuit model terminal
Voltage equation establishes built-in capacitor G-card Germania observer as measurement equation.Usual Kalman Filter observer includes as follows
State equation and measurement equation:
Wherein, xkState vector, ykTo measure vector, ukFor input variable, ωkBe mean value be 0, variance be Q process make an uproar
Sound, υkBe mean value be 0, variance be R measurement noise.In conjunction with lithium battery dynamic characteristic equation, built-in capacitor G-card Germania is established
The state equation of observer are as follows:
Measure equation are as follows:
Ut,k=Uoc,k+I0,kR0+U1,k+U2,k
Wherein, Q is lithium battery rated capacity.
Further, the estimation process of the built-in capacitor G-card Germania observer includes:
1) initial value for setting state equation, sets process noise Q0, measurement noise R0With the covariance value of state error
P0；
2) it calculates and propagates built-in capacitor plot point；
Further, compared to traditional volume point, the built-in capacitor plot point ξi, calculation expression are as follows:
Wherein, δ is free parameter, and suitable value will improve the estimation precision of built-in capacitor G-card Kalman Filtering；
3) time updates and obtains current time state vector and state error covariance；
Wherein, state vector calculation expression are as follows:
xi,k|k-1=f (χi,k-1|k-1,uk-1)
Compared in traditional volume Kalman filtering, weighted value ωi, calculation expression are as follows:
Wherein, δ is free parameter, and which determine embedded volume criterion form and the precision of filter.
4) it according to the state covariance in step 3), recalculates and propagates built-in capacitor plot point, expression formula is；
5) the built-in capacitor plot point updated according to step 4) obtains measurement equation vector and measurement error covariance；
6) posteriori error covariance and current time built-in capacitor product kalman gain are obtained；
7) prior state vector is corrected using gain matrix, updates state value and the error association side at current time
Difference；
8) using the state vector and error covariance updated, step 1) is repeated to 7), subsequent time is estimated.
Another object of the present invention is to provide the lithium batteries based on built-in capacitor G-card Kalman Filtering described in a kind of realize
The lithium battery SOC estimation control system based on built-in capacitor G-card Kalman Filtering of SOC estimation on line method.
In conclusion advantages of the present invention and good effect are as follows:
The beneficial effects of the present invention are: the present invention is a kind of lithium battery based on built-in capacitor product Kalman filtering algorithm
SOC estimation on line method obtains battery performance parameter information, the SOC value of initial electrochemical cell；Establish the equivalent electricity of Order RC lithium battery
Road model, and construct the equation of description lithium battery dynamic characteristic；It recognizes equivalent circuit model parameter and obtains OCV about SOC
Function Uoc；Using lithium battery SOC and second order polarizing voltage as the state equation of system, lithium battery terminal voltage is as system
Equation is measured, built-in capacitor product Kalman Filter observer is established；The data such as real-time voltage and the electric current of lithium battery are acquired, are estimated
Calculate SOC.
This method is to obtain a tradition CKF more preferably filter using embedded volume criterion and gamma function property,
Solving spherical surface volume point existing for traditional volume Kalman filtering algorithm may ask beyond integral domain, calculating process are complicated
Topic, can restrain more quickly and obtain accurate SOC estimation, simultaneously, it is easy to accomplish.
Detailed description of the invention
Fig. 1 is the estimation on line side lithium battery SOC provided in an embodiment of the present invention based on built-in capacitor G-card Kalman Filtering
Method flow chart.
Fig. 2 is the estimation on line side lithium battery SOC provided in an embodiment of the present invention based on built-in capacitor G-card Kalman Filtering
The lithium battery equivalent-circuit model schematic diagram of method, the open-circuit voltage U including batteryOC(SOC), internal resistance of cell R0, in Order RC ring
Polarization resistance R1、R2With polarization capacity C1、C2。
Fig. 3 is the estimation on line side lithium battery SOC provided in an embodiment of the present invention based on built-in capacitor G-card Kalman Filtering
The SOC estimation error comparison diagram of one specific embodiment of method.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
SOC estimation precision is not high, and convergence rate is unhappy, and filter stability is poor；
The process of linearisation reduces the estimation precision of EKF；
Central point weight when Unscented transform (UT) may be negative, and cause UKF filtering unstable；
CKF volume point may be plural number, cause CKF filter unstable, reduce filter accuracies etc.
To solve the above problems, below with reference to technical solution, the present invention is described in detail.
As shown in Figure 1, the lithium battery SOC provided in an embodiment of the present invention based on built-in capacitor product Kalman filtering algorithm exists
Line estimation method the following steps are included:
Step 1: obtaining lithium battery performance parameter information, initial SOC value is determined；
Step 2: establishing Order RC lithium battery equivalent-circuit model, and state equation and the output side of battery is calculated
Journey:
Step 3: the function U of the parameter and acquisition OCV of identification equivalent-circuit model about SOCoc；
Step 4: using lithium battery SOC and second order polarizing voltage as the state equation of system, with equivalent-circuit model terminal
Voltage equation establishes built-in capacitor G-card Germania observer as measurement equation.
Step 5: the data such as real-time voltage, electric current of acquisition lithium battery, estimation on line SOC.
As the preferred embodiment of the present invention, step 1 includes:
By collected voltage data for the first time, function U is utilizedocThe SOC value of acquisition is estimated first as lithium battery SOC
Initial value.
It is a kind of lithium battery SOC based on built-in capacitor G-card Kalman Filtering of the present invention with reference to Fig. 2 in the embodiment of the present invention
The equivalent-circuit model figure of the specific embodiment of estimation on line method.
As the preferred embodiment of the present invention, in step 2, lithium battery equivalent-circuit model is established, voltage source, ohm are passed through
Ammeter, polarization resistance and polarization capacity built-up circuit modeling lithium battery dynamic characteristic, including concatenated Ohmic resistance R0, pole
Change resistance R1And R2, open-circuit voltage Uoc, with polarization resistance R1Polarization capacity C in parallel1, with polarization resistance R2Polarization electricity in parallel
Hold C2.Then lithium battery dynamic characteristic equation is obtained according to Kirchhoff's theorem:
Wherein, Δ t is sampling time interval, I0To flow through Ohmic resistance R0Electric current.
As the preferred embodiment of the present invention, step 3 the following steps are included:
The lithium battery model parameter on-line identification method is used to be recognized based on the least-squares algorithm with forgetting factor
Method.
The calibration curve for obtaining battery OCV-SOC, is fitted acquisition, quintic algebra curve formula using quintic algebra curve
Are as follows:
Uoc=a0+a1*SOC+a2*SOC2+a3*SOC3+a4*SOC4+a5*SOC5
Wherein, UocFor open-circuit voltage, ai(i=0,1 ..., 5) be multinomial coefficient, SOC be lithium battery remaining capacity shape
State.
As the preferred embodiment of the present invention, built-in capacitor product Kalman Filter observer is established in step 4, was derived
Journey are as follows:
According to embedded volume criterion, three rank volume Kalmans statement are as follows:
Wherein, 0, I respectively indicates zero and one density matrix, and δ is free parameter (value in the present embodiment),
Decide embedded volume criterion form, built-in capacitor plot point ξ in above formulaiAnd weights omegaiCalculation expression is as follows:
Built-in capacitor plot point and weight are placed under the frame of traditional Kalman filtering, the filter of built-in capacitor G-card Germania can be obtained
Wave.Usual Kalman Filter observer includes following state equation and measurement equation:
Wherein, xkState vector, ykTo measure vector, ukFor input variable, ωkBe mean value be 0, variance be Q process make an uproar
Sound, υkBe mean value be 0, variance be R measurement noise.
As the preferred embodiment of the present invention, in conjunction with the lithium battery dynamic characteristic equation established in step 2, with lithium battery
The state equation of SOC and second order polarizing voltage as system is built using equivalent-circuit model terminal voltage equation as measurement equation
The state equation of vertical built-in capacitor G-card Germania observer are as follows:
Measure equation are as follows:
Ut,k=Uoc,k+I0,kR0+U1,k+U2,k
Wherein, Q is lithium battery rated capacity.
In embodiments of the present invention, carrying out estimation to the SOC of battery based on built-in capacitor product kalman filter method includes
Following steps:
1) initial value for setting state equation, sets process noise Q0, measurement noise R0With the covariance value of state error
P0；
2) it calculates and propagates built-in capacitor plot point, expression formula are as follows:
The built-in capacitor plot point ξi, it is characterised in that:
Wherein, δ is free parameter, and suitable value will improve the estimation precision of built-in capacitor G-card Kalman Filtering；
Sk-1|k-1It is the Choleski decomposition of state error covariance, Sk-1|k-1=chol (Pk-1)。
3) time updates and obtains current time state vector and state error covariance, which is characterized in that current time
Quantity of state expression formula are as follows:
xi,k|k-1=f (χi,k-1|k-1,uk-1)
State error covariance expression formula are as follows:
The calculation expression, which is characterized in that the weighted value ωi, calculation expression are as follows:
4) it according to the state covariance in step 3), recalculates and propagates built-in capacitor plot point, expression formula are as follows:
Sk|k-1It is the Choleski decomposition of state error covariance, Sk|k-1=chol (Pk)
5) the built-in capacitor plot point updated according to step 4) obtains measurement equation vector and measurement error covariance, wherein
Measure equation vector calculation expression are as follows:
Measurement error covariance calculation expression are as follows:
6) posteriori error covariance and current time built-in capacitor product kalman gain are obtained, wherein association, posteriori error association
Variance calculation expression are as follows:
Gain calculation expression are as follows:
7) prior state vector is corrected using gain matrix, updates state value and the error association side at current time
Difference；Wherein, state vector more new-standard cement are as follows:
Error covariance more new-standard cement are as follows:
8) using the state vector and error covariance updated, step 1) is repeated to 7), subsequent time is estimated.
As the preferred embodiment of the present invention, Fig. 3 is a kind of lithium battery based on built-in capacitor G-card Kalman Filtering of the present invention
The lithium battery SOC estimation error comparison diagram of the specific embodiment of SOC estimation on line method, including carried out with EKF, UKF, CKF
The comparing result of line estimation.From the results, it was seen that the present invention provides a kind of precision is high, convergence is fast is suitable for battery management
The dynamic lithium battery SOC estimation on line algorithm of system platform can be real-time by acquiring lithium battery voltage, current information in real time
It estimates lithium battery SOC, is a kind of new practice using New Algorithm.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (7)
1. a kind of lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering, which is characterized in that described to be based on
The lithium battery SOC estimation on line method of built-in capacitor G-card Kalman Filtering includes:
Step 1 obtains lithium battery performance parameter information, determines initial SOC value；
Step 2 establishes Order RC lithium battery equivalent-circuit model, and the state equation and output equation of lithium battery is calculated,
Establish lithium battery Order RC equivalent-circuit model, including concatenated resistance R0、R1、R2, open-circuit voltage Uoc, capacitor C1With resistance R1
Parallel connection, capacitor C2With resistance R2It is in parallel；
Step 3 recognizes the parameter of equivalent-circuit model and obtains function U of the OCV about SOCoc；
Step 4, using lithium battery SOC and second order polarizing voltage as the state equation of system, with equivalent-circuit model terminal voltage
Equation establishes built-in capacitor G-card Germania observer as measurement equation.
2. the lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering as described in claim 1, feature
It is, in step 3, obtains the calibration curve of battery OCV-SOC, be fitted acquisition, quintic algebra curve using quintic algebra curve
Formula are as follows:
Uoc=a0+a1*SOC+a2*SOC2+a3*SOC3+a4*SOC4+a5*SOC5
Wherein, ai(i=0,1 ..., 5) be multinomial coefficient, SOC be lithium battery remaining capacity state.
3. the lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering as described in claim 1, feature
It is, in step 4, in conjunction with lithium battery dynamic characteristic equation, establishes the state equation of built-in capacitor G-card Germania observer are as follows:
Measure equation are as follows:
Ut,k=Uoc,k+I0,kR0+U1,k+U2,k
Wherein, Q is lithium battery rated capacity.
4. the lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering as described in claim 1, feature
It is, the lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering further comprises:
1) initial value of state equation, setting process noise, the covariance value for measuring noise and state error are set；
2) it calculates and propagates built-in capacitor plot point；
3) time updates and obtains current time state vector and state error covariance；
4) it according to the state covariance in step 3), recalculates and propagates built-in capacitor plot point；
5) the built-in capacitor plot point updated according to step 4) obtains measurement equation vector and measurement error covariance；
6) posteriori error covariance and current time built-in capacitor product kalman gain are obtained；
7) prior state vector is corrected using gain matrix, updates the state value and error covariance at current time；
8) using the state vector and error covariance updated, step 1) is repeated to 7), subsequent time is estimated.
5. the lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering as claimed in claim 4, feature
It is, in step 2), built-in capacitor plot point is ξi, calculation expression are as follows:
Wherein, δ is free parameter.
6. the lithium battery SOC estimation on line method based on built-in capacitor G-card Kalman Filtering as claimed in claim 4, feature
It is, in step 3), state vector calculation expression are as follows:
xi,k|k-1=f (χi,k-1|k-1,uk-1)
7. a kind of lithium battery SOC estimation on line method realized described in claim 1 based on built-in capacitor G-card Kalman Filtering
Lithium battery SOC estimation control system based on built-in capacitor G-card Kalman Filtering.
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| CN110196393A (en) * | 2019-05-31 | 2019-09-03 | 中国矿业大学 | A kind of lithium battery charge state, the joint On-line Estimation method of energy state and power rating |
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| CN110824363A (en) * | 2019-10-21 | 2020-02-21 | 江苏大学 | Lithium battery SOC and SOE joint estimation method based on improved CKF |
| CN110824363B (en) * | 2019-10-21 | 2022-01-11 | 江苏大学 | Lithium battery SOC and SOE joint estimation method based on improved CKF |
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| CN113125962A (en) * | 2021-04-21 | 2021-07-16 | 东北大学 | Lithium titanate battery state estimation method under temperature and time variation |
| CN115327385A (en) * | 2022-07-29 | 2022-11-11 | 武汉理工大学 | Power battery SOC value estimation method and system |
| CN115902667A (en) * | 2023-02-15 | 2023-04-04 | 广东电网有限责任公司东莞供电局 | Lithium battery SOC estimation method based on weight and volume point self-adaption |
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