CN107831448A - A kind of state-of-charge method of estimation of parallel connection type battery system - Google Patents
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Abstract
The invention discloses a kind of state-of-charge method of estimation of parallel connection type battery system, methods described are as follows：Parallel connection type battery system model is established according to battery cell equivalent-circuit model and parallel circuit characteristic, using each branch current of the battery system detected and the battery system current of battery system model output as the input of parameter correction device, state-of-charge offset Δ SOCb is obtained through parameter correction device；Simultaneously, system noise estimate, the cell system space state equation obtained by battery system model, with reference to battery system terminal voltage predicted value and battery system terminal voltage detecting value are obtained using noise estimator, Unscented kalman filtering method is recycled, obtains battery system state-of-charge estimate SOCb；Finally, by Δ SOCb and SOCb superpositions, the SOCr after changing just is obtained, and then recycle SOCr renewal battery system models, and the battery system state estimation of subsequent time is obtained, so it is cyclically updated, obtains the state-of-charge estimate of accurate parallel connection type battery system.
Description
Technical field
The invention belongs to the design of MW levels battery energy storage system and control technology field in intelligent grid, it is related to a kind of parallel connection type
The state-of-charge method of estimation of battery system.
Background technology
With fast developments such as wind-powered electricity generation, photovoltaic generation and new-energy automobiles, battery cell and its battery system obtain considerable
Development.To meet that large-scale wind-powered electricity generation, photovoltaic generation access power network and powerful new-energy automobile needs, the electricity of battery system
Pressure and current class are also increasing, and the battery cell for forming battery system is also more and more.However, battery charge and discharge process is
A kind of complicated electrochemical reaction process, its contained electricity are difficult to directly obtain by measuring, and generally use battery charge state
(State of Charge, SOC) characterizes the number of battery electric quantity.
Currently used SOC methods of estimation mainly have：It is ampere-hour method, open circuit voltage method, impedance method, neural network, fuzzy
Logical approach, extended Kalman filter (EKF) and standard Unscented kalman filtering method (UKF) etc..Current methods weak point：
(1) for ampere-hour method because error accumulation being present, knowing the shortcomings of clear and definite SOC initial values, its precision is not high；(2) open circuit voltage method is uncomfortable
In On-line Estimation and it should take；(3) there is the shortcomings that complicated algorithm comparison, practical operation inconvenience in impedance method；(4) nerve net
Network needs to obtain substantial amounts of experimental data with fuzzy logic method, and these data are difficult to obtain in battery actual moving process, its
Available accuracy is not also high；(5) extended Kalman filter (EKF) be because that need to calculate Jacobian matrix, ignore the shortcomings of higher order term, its
Estimated accuracy is not also high, the lithium battery lotus based on finite difference spreading kalman algorithm as disclosed in document (CN103116136A)
Electricity condition method of estimation；(6) standard Unscented kalman filtering method (UKF) has that need not to calculate Jacobian matrix, amount of calculation small etc.
Advantage, but in actual application, the statistical information in standard Unscented kalman filtering method (UKF) (such as system noise, measures
Noise etc.) it is not constant, or even unknown or indefinite, cause that its estimated accuracy is high, poor robustness, such as document
(CN103675706A) a kind of power battery electric charge quantity estimation method disclosed in.To obtain noise statisticses information, document
(CN106443496A) a kind of battery charge state method of estimation of modified noise estimator is disclosed, this method passes through improvement
Type noise estimator can obtain system noise estimated information, improve battery charge state estimated accuracy to a certain extent, but
Still in following two major defects：When this method improve SOC precision precondition be that requirement battery model is accurate, i.e., if
If battery model inaccuracy, its SOC estimated accuracy also will be limited, but its precision of general battery model used by this method
It is inherently not high, battery SOC estimated accuracy will be caused to be limited；Second, the modified noise estimator that this method uses is counted in itself
It is more complicated, document (CN106443496A) claim 2 is specifically shown in, its will be caused computationally intensive and be not suitable for online
Estimation.Further to improve parallel connection type battery system SOC estimated accuracies, the invention discloses a kind of lotus of parallel connection type battery system
Electricity condition method of estimation, SOC estimated accuracies are improved by two approach：First, further improve document (CN106443496A)
Disclosed noise estimator, it is allowed to algorithm and is more simply suitable for On-line Estimation；Second, by by being estimated based on the noise after improvement
The battery system SOC that device is obtainedbIt is accurate to obtain with forming closed-loop control by obtaining SOC offsets based on parameter correction device
Battery system SOCr, and then battery system model is updated, to improve battery system model accuracy, and further improve battery system
Unite SOCbEstimated accuracy.
The content of the invention
It is an object of the present invention in view of the above-mentioned problems, propose a kind of state-of-charge estimation side of parallel connection type battery system
Method, to realize high accuracy, small amount of calculation, obtain to On-line Estimation parallel connection type battery system state-of-charge estimate.
The present invention seeks to be achieved through the following technical solutions：
The present invention provides a kind of battery charge state method of estimation with modified noise estimator, and methods described is as follows：
The first step determines that parallel connection type battery system is equivalent by battery cell model, parallel circuit characteristic and battery system state-of-charge SOC
Circuit model (1)；Second step, detect N bar branch currents I in battery system1~IN, the electricity predicted by battery system model
Cell system output current, battery system state-of-charge offset Δ will be obtained through parameter correction device (2) both as input quantity
SOCb；3rd step, system noise estimate (4) is obtained using noise estimator (3), by battery system models coupling state-of-charge
Definition, establishes battery space state equation (5)；4th step, Unscented kalman filtering method is replaced with system noise estimate (4)
Noise statisticses information in UKF (6), with the battery charge state in battery space state equation (5), 2 RC parallel circuits
State variable of the terminal voltage as Unscented kalman filtering method UKF (6), it is empty with the input state of battery space state equation (5)
Between equation, output voltage state space equation respectively as Unscented kalman filtering method UKF (6) nonlinear state Equation f
() and measurement equation g (), using battery system terminal voltage predicted value and battery system terminal voltage detecting value as without mark karr
The input quantity of graceful filter method UKF (6) filtering gain；5th step, the battery system that Unscented kalman filtering method UKF (6) is exported
State-of-charge estimate SOCbWith state-of-charge offset Δ SOCbSuperposition, obtain the battery system state-of-charge after changing just
SOCr；6th step, utilizes SOCrBattery system model is updated, and obtains the battery system state estimation of subsequent time, is so followed
Ring updates, and obtains the state-of-charge estimate of accurate parallel connection type battery system.Fig. 1 is that parallel connection type battery system state-of-charge is estimated
Meter method structure chart.
The parallel connection type battery system is by being formed in parallel by N number of battery cell, and N is natural number more than 1, such as Fig. 2
It is shown.The parallel connection type battery system equivalent-circuit model (1) is second order equivalent-circuit model, model main circuit by 2 RC simultaneously
Join circuit, controlled voltage source U0And internal resistance of cell R (SOC)bDeng composition, its circuit diagram is as indicated at 3.Obtained by Kirchhoff's law KVC
Battery model expression formula is：U (t)=Ub0[SOC(t)]-Ib(t)Zb(t).Determined using parallel circuit working characteristics and screening method
The basic model of each battery cell performance parameter and battery system performance parameter is defined below：Battery system opens in basic model
Wire-end voltage is calculated as follows：Ub0(SOC)=U0(SOC), wherein, U0(SOC) it is battery cell open terminal voltage；In basic model
The impedance computation of battery system is as follows：
Wherein, Rb(t) it is battery system internal resistance, Rbs(t)、RblAnd C (t)bs(t)、Cbl(t) it is respectively description battery system transient response
Resistance, the electric capacity of characteristic.R in basic modelbs(t)、RblAnd C (t)bs(t)、Cbl(t) calculating difference is as follows：Cbs(t)=NCs(t)、Cbl(t)=NCl(t), its
In, R (t) is battery cell internal resistance, Rs(t)、RlAnd C (t)s(t)、Cl(t) it is respectively description battery cell transient response characteristic
Resistance, electric capacity, above performance parameter are related to SOC.SOC definition is：
Wherein, SOC0For battery cell SOC initial values, generally 0~1 constant；Qu(t) it is the unavailable capacity of battery cell, Q0For electricity
Pond monomer rated capacity.U0(SOC)、Rs(t)、RlAnd C (t)s(t)、Cl(t) calculating difference is as follows： Wherein, a0~a5、c0~c2、d0
~d2、e0~e2、f0~f2、b0~b5It is model coefficient, can be obtained by battery measurement data through fitting.
Described parameter correction device (2) design is as follows：SOC adjusters are made up of N number of PID regulator and a weighter,
2 inputs of each PID regulator are respectively i-th of branched battery string output current IiTotal stream I is exported with battery modelb
1/N；By the SOC offset Δs SOC that N number of branched battery is obtained after each PID regulatori, i.e.,In formula, kPFor proportionality constant, kIFor integral constant, kDFor derivative constant, s is integration
The factor, i are the natural number more than 1；Battery system SOC offset Δs SOC is obtained after weighted device againb, i.e.,In formula, kiIt is for weighting
Number.
The foundation of the battery space state equation (5) is as follows：1), with battery SOCbAnd 2 RC terminal voltage Ubs(t)、
Ubl(t) it is used as system state variables xk, with Ub、IbRespectively as system measurements variable ykAnd system input variable, according to equivalent electric
Road model establishes battery space state equation
In formula, Ubs、UblFor 2 RC parallel circuit terminal voltages, τ1、τ2For time constant, ωkFor system
Noise, Δ t are the sampling period, and k is the natural number more than 1；2), according to Kirchhoff's second law, with reference to battery equivalent circuit
Model, can obtain battery output measurement equation is：[Ub,k]=Ub0,k-RkIb,k-Ubs,k-Ubl,k+υk=gk(xk)+υk=yk, in formula,
υkFor system measurements noise, k is the natural number more than 1.
Unscented kalman filtering method UKF (6) mainly comprise the following steps：1) x averages E () variance is initializedUnited with noise
Count information：2) sampled point x is calculatedi,k
With respective weights ωi：
In formula, λ=α2(n+h)-n, n are the dimension of state variable；ωm、ωcThe weight of variance and average, operator are represented respectivelyTo be right
The Cholesky of battle array is claimed to decompose, α, β, h are constant；3) time of state estimation and mean square error updates：The state estimation time
It is updated toIn formula, qkFor state equation noise average；The mean square error time
It is updated toQkFor state equation noise variance；System exports
Time is updated toIn formula, gk-1() is measurement equation, rkIt is equal for measurement equation noise
Value；4) gain matrix is calculated：In formula, Py,kFor self tuning side
Difference, Pxy,kFor from cross covariance, RkFor state equation noise variance；5) measurement updaue of state estimation and mean square error：State is estimated
Meter measurement is updated toMean square error measurement updaue is
Described modified noise estimator (3) is：Formula
In, diag () is diagonal matrix, ykActual measured value is exported for system,The shape at k moment is represented respectively
State equation noise Estimation of Mean value, state equation Noise Variance Estimation value, measurement equation noise Estimation of Mean value, measurement equation are made an uproar
The noise estimation value (4) of sound estimate of variance, i.e. k moment.
Finally will be as the offset Δ SOC obtained by parameter correction device (2)bExported with battery system equivalent-circuit model (1)
SOCbAfter addition, the input quantity SOC new as battery system equivalent-circuit modelr, so as to update battery system performance parameter,
And then battery system equivalent-circuit model is updated, so circulation, obtain the state-of-charge estimation of accurately parallel connection type battery system
Value.
Described battery types are one kind in lithium ion battery or lead-acid battery.
Brief description of the drawings
Fig. 1 is a kind of state-of-charge method of estimation structure chart of parallel connection type battery system；
Fig. 2 is the parallel connection type battery system structure figure containing N number of battery cell；
Fig. 3 is the battery equivalent circuit model figure containing 2 RC parallel circuits.
Embodiment
With reference to specific example, the present invention is described in further detail, it is described for explanation of the invention without
It is to limit.
According to embodiments of the present invention, as shown in Figure 1, Figure 2 and Figure 3, there is provided a kind of with the charged of parallel connection type battery system
Method for estimating state, the flow chart of embodiment is as shown in figure 1, mainly include following steps：
1st, parallel connection type battery system equivalent-circuit model is determined
The parallel connection type battery system is the parallel connection type battery system equivalent circuit by being formed in parallel by 3 battery cells
Model (1) is second order equivalent-circuit model, and model main circuit is by 2 RC parallel circuits, controlled voltage source U0(SOC) and in battery
Hinder RbDeng composition, as shown in Figure 2.Its circuit diagram is as indicated at 3.Obtaining battery model expression formula by Kirchhoff's law KVC is：U(t)
=Ub0[SOC(t)]-Ib(t)Zb(t).Using parallel circuit working characteristics and screening method determine each battery cell performance parameter with
The basic model of battery system performance parameter is defined below：The open terminal voltage of battery system is calculated as follows in basic model：Ub0
(SOC)=U0(SOC), wherein, U0(SOC) it is battery cell open terminal voltage；The impedance computation of battery system is such as in basic model
Under：Wherein, Rb(t) it is battery system
Internal resistance, Rbs(t)、RblAnd C (t)bs(t)、Cbl(t) resistance, the electric capacity of battery system transient response characteristic are respectively described.Substantially
R in modelbs(t)、RblAnd C (t)bs(t)、Cbl(t) calculating difference is as follows：
Cbs(t)=3Cs(t)、Cbl(t)=3Cl(t), wherein, R (t) is battery cell internal resistance, Rs(t)、Rl
And C (t)s(t)、Cl(t) it is respectively the resistance, the electric capacity that describe battery cell transient response characteristic, above performance parameter is and SOC
It is related.SOC definition is：Wherein, SOC0It is initial for battery cell SOC
Value, generally 0~1 constant；Qu(t) it is the unavailable capacity of battery cell, Q0For battery cell volume
Constant volume.U0(SOC)、Rs(t)、RlAnd C (t)s(t)、Cl(t) calculating difference is as follows： Wherein, a0~a5Value is distinguished
For -0.915,40.867,3.632,0.537,0.499,0.522, b0~b5Value is respectively 0.1463,30.27,0.1037,
0.0584th, 0.1747,0.1288, c0~c2Value is respectively 0.1063,62.49,0.0437, d0~d2Value respectively -200,
138th, 300, e0~e2Value is respectively 0.0712,61.4,0.0288, f0~f2Value is respectively 3083,180,5088.
2nd, design parameter adjuster
Described parameter correction device (2) design is as follows：SOC adjusters are by 3 PID regulators and a weighter
Form, 2 inputs of each PID regulator are respectively i-th of branched battery string output current IiAnd battery model
The total stream I of outputb1/3；By the SOC offset Δs SOC that 3 branched batteries are obtained after each PID regulatori, i.e.,In formula, kPFor proportionality constant, kIFor integral constant, kDFor derivative constant, s is
Integrating factor, i are the natural number more than 1；Battery system SOC offset Δs SOC is obtained after weighted device againb, i.e.,In formula, kiFor weight coefficient.
At the k moment, it is Δ SOC that parameter correction device, which can obtain battery system SOC offsets,b,k
3rd, battery space state equation is established
1), with battery SOCbAnd 2 RC terminal voltage Ubs(t)、Ubl(t) it is used as system state variables xk, with Ub、IbRespectively
As system measurements variable ykAnd system input variable, establishing battery space state equation according to equivalent-circuit model is
In formula, Ubs、UblFor 2 RC parallel circuit terminal voltages, τ1、τ2For time constant, ωkFor system
Noise, Δ t are the sampling period, and k is the natural number more than 1.
2), according to Kirchhoff's second law, with reference to battery equivalent circuit model, can obtain battery output measurement equation is：
[Ub,k]=Ub0,k-RkIb,k-Ubs,k-Ubl,k+υk=gk(xk)+υk=yk, in formula, υkFor system measurements noise, k is oneself more than 1
So number.
4th, the noise estimation value (4) at k moment is obtained using noise estimator (3)
The noise estimation value (4) at the system noise estimated information acquisition k moment of last moment is combined using noise estimator,
I.e.
5th, by the noise estimation value (4) at k momentRespectively as Unscented kalman filtering method UKF
(6) statistical information value (qk、Qk、rk、Rk), i.e.,
Nothing is used as using the battery charge state SOC in battery space state equation (5), 2 RC parallel circuits terminal voltage
Mark Kalman filtering method UKF (6) state variable, i.e.,
Distinguished with the input state space equation of cell system space state equation (5), output voltage state space equation
As Unscented kalman filtering method UKF (6) nonlinear state Equation f () and measurement equation g (), i.e.,
gk(xk)=U0,k-RkIb,k-Ubs,k-Ubl,k。
6th, battery SOC estimation is carried out using Unscented kalman filtering method UKF (6).
1) init state variable x average E () and noise information：
2) sampled point x is calculatedi,kWith respective weights ω： In formula, λ=α2(n+h)-n, n=3, α value are 1, β values are that 2, h values are 0；
3) time of state estimation and mean square error updates：The state estimation time is updated to
The mean square error time is updated toSystem exports
Time is updated to
4) gain matrix is calculated：
5) measurement updaue of state estimation and mean square error：State estimation measurement updaue isMean square error measurement updaue is
Meanwhile state variable is estimatedFirst element output, that is, export the k moment battery system state-of-charge
SOCb,kEstimate.
7th, by the k moment as the offset Δ SOC obtained by parameter correction device (2)b,kWith k moment battery system equivalent circuits
The SOC of model (1) outputb,kAfter addition, the input quantity SOC new as k moment battery system equivalent-circuit modelsr,k, so as to more
New battery system performance parameter, and then the battery system equivalent-circuit model at k+1 moment is obtained, export the battery system at k+1 moment
Unite state-of-charge SOCb,k+1, so circulate, obtain the state-of-charge estimate of accurately parallel connection type battery system.
Finally it should be noted that only illustrating technical scheme rather than its limitations with reference to above-described embodiment.Institute
The those of ordinary skill in category field is it is to be understood that those skilled in the art can repair to the embodiment of the present invention
Change or equivalent substitution, but these modifications or change are among pending claims are applied for.
Claims (6)
1. a kind of state-of-charge method of estimation of parallel connection type battery system, the described method comprises the following steps：
Step (1)：Parallel connection type battery system is determined by battery cell model, parallel circuit characteristic and battery system state-of-charge SOC
System equivalent-circuit model；
Step (2)：N bar branch current I1~IN in battery system are detected, and are predicted by battery system equivalent-circuit model
Battery system output current, using two kinds of electric currents as input quantity, the state-of-charge that battery system is obtained through parameter correction device is mended
Repay value Δ SOCb；
Step (3)：System noise estimate is obtained using noise estimator, is defined by battery system models coupling state-of-charge,
Establish battery space state equation；
Step (4)：The noise statisticses information in Unscented kalman filtering method UKF is replaced with system noise estimate, it is empty with battery
Between the state of battery charge state in state equation, the terminal voltage of 2 RC parallel circuits as Unscented kalman filtering method UKF
Variable, using the input state space equation of battery space state equation, output voltage state space equation as without mark card
Kalman Filtering method UKF nonlinear state Equation f () and measurement equation g (), by battery system terminal voltage predicted value and electricity
Input quantity of the cell system terminal voltage detecting value as Unscented kalman filtering method UKF filtering gains；
Step (5)：The battery system state-of-charge estimate SOC that Unscented kalman filtering method UKF is exportedbMended with state-of-charge
Repay value Δ SOCbSuperposition, obtain the battery system state-of-charge SOC after changing justr；
Step (6)：Utilize SOCrBattery system model is updated, and obtains the battery system state estimation of subsequent time, is so followed
Ring updates, and obtains the state-of-charge estimate of accurate parallel connection type battery system.
2. the state-of-charge method of estimation of a kind of parallel connection type battery system according to claim 1, it is characterised in that described
Parameter correction device design it is as follows：SOC adjusters are made up of N number of PID regulator and a weighter, and the 2 of each PID regulator
Individual input is respectively i-th of branched battery string output current IiTotal stream I is exported with battery modelb1/N；Pass through each PID regulator
The SOC offset Δs SOC of N number of branched battery is obtained afterwardsi, i.e.,In formula, kPFor ratio
Constant, kIFor integral constant, kDFor derivative constant, s is integrating factor, and i is the natural number more than 1；Again electricity is obtained after weighted device
Cell system SOC offset Δs SOCb, i.e.,
In formula, kiFor weight coefficient.
3. the state-of-charge method of estimation of a kind of parallel connection type battery system according to claim 2, it is characterised in that described
The foundation of battery space state equation is as follows：
1), with battery SOCbAnd 2 RC terminal voltage is as system state variables xk, with Ub、IbRespectively as system measurements variable
ykAnd system input variable, establishing battery space state equation according to equivalent-circuit model is
In formula, Ubs、UblFor 2 RC parallel circuit terminal voltages, τ1、τ2For time constant, ωkFor system
Noise, Δ t are the sampling period, and k is the natural number more than 1；
2), according to Kirchhoff's second law, with reference to battery equivalent circuit model, can obtain battery output measurement equation is：[Ub,k]
=Ub0,k-RkIb,k-Ubs,k-Ubl,k+υk=gk(xk)+υk=yk, in formula, υkFor system measurements noise, k is the natural number more than 1.
4. the state-of-charge method of estimation of a kind of parallel connection type battery system according to claim 3, it is characterised in that described
Unscented kalman filtering method UKF comprises the following steps：
1) x, average E, variance are initializedWith noise statisticses information：
2) sampled point x is calculatedi,kWith respective weights ωi： In formula, λ=α2(n+h)-n, n are the dimension of state variable；ωm、ωcVariance is represented respectively
And the weight of average, operatorDecomposed for the Cholesky of symmetrical matrix, α, β, h are constant；
3) time of state estimation and mean square error updates：The state estimation time is updated toIn formula, qkFor state equation noise average；The mean square error time is updated toQkFor state equation noise variance；System output time is more
It is newlyIn formula, gk-1() is measurement equation, rkFor measurement equation noise average；
4) gain matrix is calculated：In formula, Py,kFor self tuning side
Difference, Pxy,kFor from cross covariance, RkFor state equation noise variance；
5) measurement updaue of state estimation and mean square error：State estimation measurement updaue is
Square error measure is updated to
5. the state-of-charge method of estimation of a kind of parallel connection type battery system according to claim 4, it is characterised in that described
Noise estimator be：
In formula, diag () is diagonal matrix,Represent respectively the state equation noise Estimation of Mean value at k moment, state equation Noise Variance Estimation value,
Measurement equation noise Estimation of Mean value, the system noise estimate of measurement equation Noise Variance Estimation value, i.e. k moment.
6. the state-of-charge method of estimation of a kind of parallel connection type battery system according to claim 1, it is characterised in that described
Battery types are one kind in lithium ion battery or lead-acid battery.
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Cited By (6)
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| CN108732509A (en) * | 2018-06-06 | 2018-11-02 | 哈尔滨工业大学 | A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application |
| CN110082683A (en) * | 2019-05-09 | 2019-08-02 | 合肥工业大学 | Inhibit the closed loop compensation method of ampere-hour integral SOC evaluated error |
| CN111929585A (en) * | 2019-05-13 | 2020-11-13 | 顺丰科技有限公司 | Battery state of charge calculation apparatus, battery state of charge calculation method, battery state of charge calculation server, and battery state of charge calculation medium |
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| CN110907834B (en) * | 2019-10-29 | 2021-09-07 | 盐城工学院 | Parallel battery system modeling method |
| CN110888063A (en) * | 2019-12-02 | 2020-03-17 | 上海国际港务（集团）股份有限公司 | SOC estimation method based on design of port machine tire crane lithium iron phosphate battery parallel system |
| CN111239634A (en) * | 2020-03-20 | 2020-06-05 | 中航锂电（洛阳）有限公司 | Method and device for detecting branch state of battery system |
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