CN110095723A - A kind of Li-ion battery model parameter and SOC online joint estimation method - Google Patents
A kind of Li-ion battery model parameter and SOC online joint estimation method Download PDFInfo
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- G—PHYSICS
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- 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 electric automobile power battery management domains, are related to the online joint estimation method of a kind of battery model parameter and SOC.This method mainly comprises the steps that acquisition experimental data first, establishes battery model；Secondly SOC is estimated parallel using two SOC computing modules, one uses Extended Kalman filter (EKF) algorithm, mutation disturbance is added first with EKF algorithm, after a period of time for another, and Unscented kalman filtering (UKF) algorithm is recycled to carry out SOC estimation.Then the estimation result of two modules is weighted and averaged, obtains current SOC estimated result.On-line identification is finally carried out to battery model parameter using forgetting factor least squares algorithm (FFRLS), to merge EKF, UKF and FFRLS algorithm, the real-time update of implementation model parameter and the On-line Estimation of SOC, the influence of model error is effectively eliminated, SOC estimation precision and algorithm stability are improved.
Description
Technical field
The invention belongs to batteries of electric automobile management domain, it is related to a kind of battery model parameter and SOC online joint estimation
Method.
Background technique
In recent years, new-energy automobile continues to develop, and the power battery as its core component has become the heat of various countries' research
Point.In the management link of power battery, the state-of-charge (SOC) of battery is reflect battery remaining power and acting ability one
Item important indicator, SOC estimation is then that battery management system develops most crucial technology, accurately estimates SOC for the peace of battery
Full reliability improves energy content of battery utilization rate, prolongs the service life with important theory significance and application value.
Internal state of the SOC as power battery, can not directly measure, can only be by cell voltage, electric current, internal resistance etc.
Parameter detecting is estimated.Currently, typical power battery SOC estimation method mainly has: current integration method, open circuit voltage method, mind
Through network technique, Kalman filtering method etc..Wherein, current integration method is realized simple, but the accumulated error in integral process can not disappear
It removes, is affected to estimation precision；Open circuit voltage method need battery standing for a period of time could Estimation and Measurement, be not suitable for online
Real-time estimation；Neural network needs mass data to be trained, and is difficult to realize using complexity；The core of Kalman filtering method is thought
Want to make the state of dynamical system the optimal estimation in lowest mean square meaning, error correcting capabilities are stronger, but estimated accuracy
It is higher to the accuracy dependence of battery model, and battery is a complicated nonlinear system, battery model parameter in use process
Real-time change, model is uncertain to cause Kalman filtering precision low.
Summary of the invention
The purpose of the present invention is in view of the above shortcomings of the prior art, provide a kind of Li-ion battery model parameter with
SOC online joint estimation method solves the problems, such as that SOC On-line Estimation is difficult to eliminate battery model error.
The purpose of the present invention can be achieved through the following technical solutions: a kind of Li-ion battery model parameter and SOC are online
Combined estimation method, for the on-line identification of battery model parameter and the real-time estimation of SOC, comprising steps of
S1: electric discharge is carried out to battery and stands experiment, obtains the relational expression of open-circuit voltage (OCV) and SOC；According to voltage, electricity
The basic experiments off-line data such as stream, temperature picks out the initial value of model parameter and establishes battery 1-RC equivalent-circuit model, setting
The matching factor initial value A of state space equation0、B0、C0、D0；
S2:SOC computing module 1, with the state-of-charge of Extended Kalman filter (EKF) algorithm estimation present battery
SOC1；
S3: judge whether the time is more than (the mutation interval) setting value T, if being not above time T, first SOC is calculated
Module continues to calculate, if it exceeds time T, then while SOC computing module 1 continues to calculate, into next step；
S4: as time t=T, according to the 1 calculated current SOC value SOC of institute of SOC computing moduleT, it is mutated by being added
The mode of disturbance obtains a new SOC value SOCm, and by SOCmInitial value as second SOC computing module.Wherein, institute
Mutation method are as follows:
Wherein R is the random number of (0,1) Normal Distribution N, " | | " indicate to take absolute value.
S5:SOC computing module 2 starts the state-of-charge for estimating present battery with Unscented kalman filtering (UKF) algorithm
SOC2, while SOC computing module 1 continues with Extended Kalman filter (EKF) algorithm and estimates current SOC value；
S6: when judging SOC computing module 2 with Unscented kalman filtering algorithm estimation SOC, the predicted voltage and reality of battery
Whether the difference DELTA U for surveying voltage is more than given threshold UthIf being not above threshold value Uth, SOC computing module 2 continues to calculate, such as
Fruit has been more than Uth, then enter in next step；
S7: SOC computing module 1 at this time and the estimation result of computing module 2 are weighted and averaged, obtained revised
SOC value: SOCw=W1*SOC1+W2*SOC2, wherein W1、W2For corresponding weight；
S8: judge revised SOC value SOCwWhether effectively, if effectively, the current SOC of battery is set as revised
SOC value SOCwIf in vain, the current SOC of battery to be set as to the estimated result SOC of SOC computing module 11；
S9: according to the current SOC value of the calculated battery of previous step and open-circuit voltage and SOC relationship, forgetting factor is utilized
Least square method (FFRLS) on-line identification model parameter R0、R1、C1And update the A in system state equationk、Bk、Ck、Dk。
In the step S7, the determination method of weight when correcting SOC value are as follows: W1=D2/(D1+D2), W2=D1/(D1+D2),
D1=(ZEKF-U)2, D2=(ZUKF-U)2.Wherein, ZEKFIndicate that current time SOC computing module 1 uses Extended Kalman filter
(EKF) algorithm predicted voltage value, ZUKFIndicate that current time SOC computing module 2 is pre- using Unscented kalman filtering (UKF) algorithm
Voltage value is surveyed, U indicates current time battery measurement voltage, D1With D2Respectively indicate ZEKFAnd ZUKFWith the degrees of offset of U.
In the step S8, the method for revised SOC value validity is judged are as follows: more revised SOC value and open circuit
The SOC value that voltage method calculates is repaired if revised SOC value differs within the specified scope with the SOC value that open circuit voltage method calculates
Effectively, otherwise in vain SOC value after just is.
The beneficial effects of the present invention are:
1. be added mutation disturbance by way of, quickly off-limits SOC value can be handled, avoid due to
Filtering divergence phenomenon caused by data exception；Meanwhile by the difference of decision algorithm predicted voltage and measurement voltage whether be more than
Threshold value, improves the stability of algorithm at a possibility that further decreasing filtering divergence.
2. battery model carries out on-line identification using forgetting factor least squares algorithm, the precision of model is effectively guaranteed,
Influence of the model parameter variation to algorithm estimation precision is avoided, the robustness of algorithm is improved.
3. the battery model parameter that the present invention uses and SOC online joint estimation algorithm can be avoided Unscented kalman filtering
The high computation complexity of algorithm and the low convergence rate of expanded Kalman filtration algorithm can possess high-precision, low meter simultaneously
Calculate complexity and rapid convergence speed.
Detailed description of the invention
Fig. 1: Li-ion battery model parameter and SOC Combined estimator algorithm flow chart
Fig. 2: lithium ion battery 1-RC equivalent-circuit model figure
Specific embodiment
In the following, being further described in conjunction with attached drawing to a specific embodiment of the invention.
The present invention provides a kind of Li-ion battery model parameters to combine On-line Estimation method, this method flow chart with SOC
As shown in Figure 1, including step S1-S9, each implementation steps are described in detail below:
S1: electric discharge is carried out to battery and stands experiment, obtains the relational expression of open-circuit voltage (OCV) and SOC；According to voltage, electricity
The basic experiments off-line data such as stream, temperature picks out the initial value of model parameter and establishes battery 1-RC equivalent-circuit model, setting
The matching factor initial value A of state space equation0、B0、C0、D0；
Based on electrochemical principle, the 1-RC battery equivalent circuit model of foundation is as shown in Fig. 2, by ideal voltage source UOC, Europe
Nurse internal resistance R0Reflect that the RC network of battery polarization characteristic is constituted with one.Electric current I is input quantity (charging is positive), and end voltage U is
Output quantity, system discretization state equation are as follows:
Output equation are as follows:
U (k)=Uoc(k)+U1(k)+R0I(k)；
Wherein Δ t is sampling interval, QvFor battery actual capacity, τ=R1C1, w and v respectively indicate process noise and measurement
Noise；Choose state variable x=[SOC, U1]T, the matching factor of corresponding states equation are as follows:
S2:SOC computing module 1, with the state-of-charge of Extended Kalman filter (EKF) algorithm estimation present battery
SOC1；Wherein EKF algorithm key step is as follows:
S201: algorithm initialization
Set original state x0, original state error covariance P0, process noise covariance Q and measurement noise covariance R；
S202: time update is carried out to the k moment by the state and its error covariance of k-1
S203: Kalman filtering gain calculates
S204: update is measured to state and its error covariance with the measured value at k moment
In formulaIndicate the error between battery terminal voltage measured value and predicted value；
S205: the optimal estimation value of output k moment state
S3: judge whether the time is more than setting value T, if being not above time T, first SOC computing module continues to count
It calculates, if it exceeds time T, then while SOC computing module 1 continues to calculate, into next step；Wherein, T=6 τ is and system
The related parameter of structure.
S4: as time t=T, according to the 1 calculated current SOC value SOC of institute of SOC computing moduleT, it is mutated by being added
The mode of disturbance obtains a new SOC value SOCm, and by SOCmInitial value as SOC computing module 2.Wherein, used
Mutation method are as follows:
Wherein R is the random number of (0,1) Normal Distribution N, " | | " indicate to take absolute value.
Since SOC is the variable between [O, 1], if the 0 < SOC of estimated value of T moment SOC computing module 1T< 1 is indicated
In the normal range, by this value directly as the state initial value of UKF algorithm；If SOCT>=1 or SOCT≤ 0, belong to beyond model
The exceptional value enclosed utilizes formula S OCm=| SOCT+R-max(SOCT, R) |, exceptional value is restored between [0,1] rapidly, is guaranteed
The correct operation of UKF algorithm, while a possibility that reduce two SOC computing module filtering divergences.
S5:SOC computing module 2 starts the state-of-charge for estimating present battery with Unscented kalman filtering (UKF) algorithm
SOC2, while SOC computing module 1 continues with Extended Kalman filter (EKF) algorithm and estimates current SOC value；Wherein, UKF is calculated
The key step of method are as follows:
S501: init state mean variable value x0With mean square error P0
Here state variable extends the state variable in step S1, i.e. x=[SOC, U1, w, v]T, extend shape
The dimension of state is n.
S502: sampled point x is obtainediAnd respective weights w.
In formula, λ=α2(n+ κ)-n, wm, wcIt is particle point mean value and the corresponding weight of variance respectively, α, β are controlled respectively
Particle point distribution distance and higher order term error size in sampled point.
S503: the time of state estimation and mean square error updates
The state estimation time updates:
In formula, f (*) indicate state equation nonlinear function；
The mean square error time updates:
System exports time update:
In formula, g (*) indicates the nonlinear function of measurement equation；
S504: filtering gain matrix are calculated
Lk=PXy, kPY, k -1
In formula,
S505: the measurement updaue of state estimation and mean square error
State estimation measurement updaue:
Mean square error measurement updaue:
Pk=Pk/k-1-LkPY, kLk T
S506: the optimal estimation value of output k moment state
S6: when judging SOC computing module 2 with Unscented kalman filtering algorithm estimation SOC, the predicted voltage and reality of battery
Whether the difference DELTA U for surveying voltage is more than given threshold UthIf being not above threshold value Uth, SOC computing module 2 continues to calculate, such as
Fruit has been more than Uth, then enter in next step；Wherein threshold value UthIt is set as 0.05, i.e. predicted voltage error is no more than 0.05V.
S7: SOC computing module 1 at this time and the estimation result of computing module 2 are weighted and averaged, obtained revised
SOC value: SOCw=W1*SOC1+W2*SOC2, wherein W1、W2For corresponding weight；The determination method of weight are as follows: W1=D2/(D1+
D2), W2=D1/(D1+D2), D1=(ZEKF-U)2, D2=(ZUKF-U)2, XEKFIndicate current time SOC computing module 1 using extension
Kalman filtering (EKF) algorithm predicted voltage value, ZUKFIndicate that current time SOC computing module 2 uses Unscented kalman filtering
(UKF) algorithm predicted voltage value, U indicate current time battery measurement voltage, D1With D2Respectively indicate ZEKFAnd ZUKFWith the offset of U
Degree.
S8: judge revised SOC value SOCwWhether effectively, if effectively, the current SOC of battery is set as SOCwIf
In vain, then the current SOC of battery is set as to the estimated result SOC of SOC computing module 11；Judge revised SOC value validity
Method are as follows: the SOC value that more revised SOC value and open circuit voltage method calculate, if revised SOC value and open-circuit voltage
Within the specified scope, effectively, otherwise in vain revised SOC value is to the SOC value difference that method calculates.
S9: according to the current SOC value of the calculated battery of previous step and open-circuit voltage and SOC relationship, forgetting factor is utilized
Least square method (FFRLS) on-line identification model parameter R0、R1、C1And update the A in system state equationk、Bk、Ck、Dk。
Battery equivalent circuit shown in Fig. 2 can indicate are as follows: U=[R1/(R1C1s+1)+R0]I+Uoc, utilize backward difference
Transformation can convert are as follows: y (k)=a1y(k-1)+b0I(k)+b1I (k-1), wherein output quantity y=U-UocIndicate battery terminal voltage
And the difference of open-circuit voltage, electric current I are input quantity, enable Φ (k)=[y (k-1), I (k), I(k-1)], then θ=[a1, b0, b1]TFor
Parameter to be identified.Then θ is picked out using FFRLS algorithm, updates model parameterWith
And state equation matching factor Ak、Bk、Ck、Dk.Wherein, the key step of FFRLS algorithm are as follows:
S901: least square covariance P is determined0With the initial value θ of parameter matrix0
S902: it calculates least square gain matrix K (k)
K (k)=P (k-1) Φ (k) [λ+ΦT(k)P(k-1)Φ(k)]-1
λ is least square weighted factor in formula, takes λ=0.98；
S903: calculating parameter estimated matrix
S904: covariance matrix update
P (k)=[I-K (k) ΦT(k)]P(k-1)
S905: output k moment state estimation
Specific embodiment of the present invention is presented above, but the present invention is not limited to described embodiment.
All within the spirits and principles of the present invention, in such a way that those skilled in the art are readily apparent that, carry out any modification, etc.
With replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (6)
1. a kind of Li-ion battery model parameter and SOC online joint estimation method, the on-line identification for battery model parameter
With the real-time estimation of SOC, comprising steps of
S1: battery carries out electric discharge and stands experiment, obtains the relational expression of open-circuit voltage (OCV) and SOC；According to voltage, electric current, temperature
Etc. basic experiments off-line data pick out the initial value of model parameter and establish battery 1-RC equivalent-circuit model, set state space
The matching factor initial value A of equation0、B0、C0、D0；
S2:SOC computing module 1 starts the state-of-charge for estimating current time battery with Extended Kalman filter (EKF) algorithm
SOC1；
S3: judging whether the time is more than (the mutation interval) setting value T, if being not above time T, first SOC computing module
Continue to calculate, if it exceeds time T, then while SOC computing module 1 continues to calculate, into next step；
S4: as time t=T, according to the 1 calculated current SOC value SOC of institute of SOC computing moduleT, by the way that mutation disturbance is added
Mode obtains a new SOC value SOCm, and by SOCmInitial value as SOC computing module 2；Wherein, mutation side used
Method are as follows:
R is the random number of (0,1) Normal Distribution N, " | | " indicate to take absolute value；
S5:SOC computing module 2 starts the state-of-charge SOC for estimating present battery with Unscented kalman filtering (UKF) algorithm2,
SOC computing module 1 continues with Extended Kalman filter (EKF) algorithm and estimates current SOC value simultaneously；
S6: when judging SOC computing module 2 with Unscented kalman filtering algorithm estimation SOC, the predicted voltage and actual measurement electricity of battery
Whether the difference DELTA U of pressure is more than given threshold UthIf being not above threshold value Uth, SOC computing module 2 continues to calculate, if super
U is crossedth, then enter in next step；
S7: SOC computing module 1 at this time and the estimation result of computing module 2 are weighted and averaged, revised SOC value is obtained:
SOCW=W1*SOC1+W2*SOC2, wherein W1、W2For corresponding weight；
S8: judge revised SOC value SOCwWhether effectively, if effectively, the current SOC of battery is set as revised SOC value
SOCwIf in vain, the current SOC of battery to be set as to the estimated result SOC of SOC computing module 11；
S9: according to the current SOC value of the calculated battery of previous step and open-circuit voltage and SOC relationship, forgetting factor minimum is utilized
Square law (FFRLS) on-line identification model parameter R0、R1、C1And update the A in system state equationk、Bk、Ck、Dk。
2. a kind of Li-ion battery model parameter according to claim 1 and SOC online joint estimation method, feature exist
In in the step S3, when with EKF algorithm estimation SOC, mutation interval T=6 τ is and system structure SOC computing module 1
Related parameter, system model parameter is real-time update, therefore T is also real-time update.
3. a kind of Li-ion battery model parameter according to claim 1 and SOC online joint estimation method, feature exist
In in the step S4, the mode of mutation disturbance is added in the t=T moment are as follows:
R is the random number of (0,1) Normal Distribution N, " | | " indicate to take absolute value；Since SOC is the variable between [0,1],
If the 0 < SOC of estimated value of T moment SOC computing module 1T< 1, indicates in the normal range, this value is calculated directly as UKF
The state initial value of method, is effectively reduced the initial error of algorithm；If SOCT>=1 or SOCT≤ 0, belong to off-limits exception
Value, utilizes formula S OCm=| SOCT+R-max(SOCT, R) |, exceptional value is restored between [0,1] rapidly, guarantees UKF algorithm
Correct operation, while improving the stability of algorithm.
4. a kind of Li-ion battery model parameter according to claim 1 and SOC online joint estimation method, feature exist
In in the step S5, two SOC computing modules are run parallel, and first module is estimated with EKF algorithm always
It calculates, second module first uses EKF algorithm, after T after a period of time, mutation disturbance is added, then estimated with UKF algorithm
It calculates, then the calculated result of two modules is weighted and averaged, is effectively guaranteed the stabilization of SOC estimation precision and algorithm
Property；Weighting algorithm used in it are as follows: SOCW=W1*SOC1+W2*SOC2, wherein weight W1=D2/(D1+D2), W2=D1/(D1+
D2), D1=(ZEKF-U)2, D2=(ZUKF-U)2, ZEKFIndicate that current time SOC computing module 1 uses EKF algorithm predicted voltage value,
ZUKFIndicate that current time SOC computing module 2 uses UKF algorithm predicted voltage value, U indicates current time battery measurement voltage, D1
With D2Respectively indicate ZEKFAnd ZUKFWith the degrees of offset of U.
5. a kind of Li-ion battery model parameter according to claim 1 and SOC online joint estimation method, feature exist
In judging the method for revised SOC value validity are as follows: more revised SOC value and open circuit voltage method in the step S8
The SOC value of calculating, it is revised if revised SOC value differs within the specified scope with the SOC value that open circuit voltage method calculates
Effectively, otherwise in vain SOC value is；The method for obtaining current time SOC optimal estimation value are as follows: if repaired obtained in step S7
Positive value SOCwEffectively, current SOC is set as SOCwIf in vain, current SOC to be set as to the estimation of SOC computing module 1
As a result SOC1。
6. a kind of Li-ion battery model parameter according to claim 1 and SOC online joint estimation method, feature exist
In utilizing forgetting factor least squares algorithm (FFRLS) on-line identification model parameter R in the step S90、R1、C1And it updates and is
The matching factor A of system state equationk、Bk、Ck、Dk, then repeatedly step S1-S9, so that EKF, UKF and FFRLS algorithm are merged,
Realize the real-time update of battery model parameter and the On-line Estimation of SOC.
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| CN112630661B (en) * | 2020-12-28 | 2022-12-09 | 广州橙行智动汽车科技有限公司 | Battery state of charge (SOC) estimation method and device |
| CN113420444A (en) * | 2021-06-23 | 2021-09-21 | 南通大学 | Lithium ion battery SOC estimation method based on parameter online identification |
| CN118409215A (en) * | 2024-04-15 | 2024-07-30 | 昆明理工大学 | Lithium ion battery SOC estimation method based on multi-model fusion and on-line parameter identification |
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