CN107831448B - A kind of state-of-charge estimation method of parallel connection type battery system - Google Patents
A kind of state-of-charge estimation method of parallel connection type battery system Download PDFInfo
- Publication number
- CN107831448B CN107831448B CN201711082843.0A CN201711082843A CN107831448B CN 107831448 B CN107831448 B CN 107831448B CN 201711082843 A CN201711082843 A CN 201711082843A CN 107831448 B CN107831448 B CN 107831448B
- Authority
- CN
- China
- Prior art keywords
- state
- battery
- battery system
- soc
- noise
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000001914 filtration Methods 0.000 claims abstract description 21
- 238000012937 correction Methods 0.000 claims abstract description 11
- 238000005259 measurement Methods 0.000 claims description 30
- 230000005611 electricity Effects 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000013461 design Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 2
- 239000002253 acid Substances 0.000 claims description 2
- 230000008901 benefit Effects 0.000 claims description 2
- 230000008878 coupling Effects 0.000 claims description 2
- 238000010168 coupling process Methods 0.000 claims description 2
- 238000005859 coupling reaction Methods 0.000 claims description 2
- 229910001416 lithium ion Inorganic materials 0.000 claims description 2
- 101100190464 Caenorhabditis elegans pid-2 gene Proteins 0.000 claims 1
- 238000001514 detection method Methods 0.000 claims 1
- 239000003990 capacitor Substances 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000001052 transient effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 240000002853 Nelumbo nucifera Species 0.000 description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 2
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003487 electrochemical reaction Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000004218 nerve net Anatomy 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
Classifications
-
- 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
-
- 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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
Abstract
The invention discloses a kind of state-of-charge estimation methods of parallel connection type battery system, the method is as follows: establishing parallel connection type battery system model according to battery cell equivalent-circuit model and parallel circuit characteristic, the input of each branch current of the battery system that will test and the battery system current of battery system model output as parameter correction device, obtains state-of-charge offset Δ SOCb through parameter correction device；Simultaneously, system noise estimated value, the cell system space state equation obtained by battery system model, in conjunction with battery system end voltage prediction value and battery system terminal voltage detecting value are obtained using noise estimator, Unscented kalman filtering method is recycled, battery system state-of-charge estimated value SOCb is obtained；Finally, Δ SOCb and SOCb superposition are obtained the SOCr after modifying just, and then SOCr is recycled to update battery system model, and the battery system state estimation of subsequent time is obtained, it is so cyclically updated, obtains the state-of-charge estimated value of accurate parallel connection type battery system.
Description
Technical field
The invention belongs to MW grades of battery energy storage system designs and control technology fields in smart grid, are related to a kind of parallel connection type
The state-of-charge estimation method of battery system.
Background technique
With fast developments such as wind-powered electricity generation, photovoltaic power generation and new-energy automobiles, battery cell and its battery system obtain considerable
Development.To meet large-scale wind-powered electricity generation, photovoltaic power generation accesses power grid 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 electrochemical reaction process of complexity, contained electricity are difficult to directly obtain by measuring, and usually use battery charge state
(State of Charge, SOC) characterizes the number of battery capacity.
Currently used SOC estimation method mainly has: ampere-hour method, impedance method, neural network, obscures open circuit voltage method
Logical approach, Extended Kalman filter method (EKF) and standard Unscented kalman filtering method (UKF) etc..Current methods shortcoming:
(1) for ampere-hour method because there are error accumulation, knowing the disadvantages of specific SOC initial value, precision is not high；(2) open circuit voltage method is uncomfortable
It should be in On-line Estimation and time-consuming；(3) impedance method haves the shortcomings that algorithm comparison is complicated, practical operation is inconvenient；(4) nerve net
Network and fuzzy logic method need to obtain a large amount of experimental data, these data are difficult to obtain in battery actual moving process,
Available accuracy is not also high；(5) Extended Kalman filter method (EKF) be because that need to calculate Jacobian matrix, ignore the disadvantages of higher order term,
Estimated accuracy is not also high, the lithium battery lotus based on finite difference spreading kalman algorithm as disclosed in document (CN103116136A)
Electricity condition estimation method；(6) standard Unscented kalman filtering method (UKF) has small etc. without calculating Jacobian matrix, calculation amount
Advantage, but in actual application, (such as system noise measures the statistical information in standard Unscented kalman filtering method (UKF)
Noise etc.) it is not constant or even unknown or indefinite, lead to 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 statistics information, document
(CN106443496A) a kind of battery charge state estimation method of modified noise estimator is disclosed, this method passes through improvement
Type noise estimator can get system noise estimated information, improve battery charge state estimated accuracy to a certain extent, but
Still in following two major defect: first is that this method improve SOC precision precondition be to require battery model accurate, i.e., if
If battery model inaccuracy, SOC estimated accuracy will be also limited, however its precision of general battery model used by this method
It is inherently not high, battery SOC estimated accuracy will be caused limited；Second is that the modified noise estimator itself that this method uses is counted
It is more complicated, it is specifically shown in the claim 2 of document (CN106443496A), its will be caused computationally intensive and be not suitable for online
Estimation.To further increase parallel connection type battery system SOC estimated accuracy, the invention discloses a kind of lotuses of parallel connection type battery system
Electricity condition estimation method improves SOC estimated accuracy by two approach: first is that being further improved document (CN106443496A)
Disclosed noise estimator is allowed to algorithm and is more simply suitable for On-line Estimation；Second is that will be by being estimated based on improved noise
Device battery system SOC obtainedbClosed-loop control is constituted with by obtaining SOC offset based on parameter correction device, it is accurate to obtain
Battery system SOCr, and then battery system model is updated, to improve battery system model accuracy, and further increase battery system
Unite SOCbEstimated accuracy.
Summary 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-precision, small calculation amount, obtain to On-line Estimation parallel connection type battery system state-of-charge estimated value.
Object of the present invention is to be achieved through the following technical solutions:
The present invention provides a kind of battery charge state estimation method with modified noise estimator, and the method 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 detects N branch current I in battery system1~IN, the electricity predicted by battery system model
Cell system exports electric current, will obtain battery system state-of-charge offset Δ through parameter correction device (2) both as input quantity
SOCb；Third step obtains system noise estimated value (4) using noise estimator (3), by battery system models coupling state-of-charge
Definition, establishes battery space state equation (5)；4th step replaces Unscented kalman filtering method with system noise estimated value (4)
Noise statistics information in UKF (6), with battery charge state, the 2 RC parallel circuits in battery space state equation (5)
State variable of the voltage as Unscented kalman filtering method UKF (6) is held, 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 end voltage prediction value and battery system terminal voltage detecting value as no 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 estimated value SOCbWith state-of-charge offset Δ SOCbSuperposition obtains the battery system state-of-charge after modifying 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 estimated value 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 N number of battery cell by being formed in parallel, and N is the natural number greater than 1, such as Fig. 2
It is shown.The parallel connection type battery system equivalent-circuit model (1) be second order equivalent-circuit model, model main circuit by 2 RC simultaneously
Join circuit, controlled voltage source U0(SOC) and internal resistance of cell RbDeng composition, circuit diagram is as indicated at 3.It is obtained by Kirchhoff's law KVC
Battery model expression formula are as follows: U (t)=Ub0[SOC(t)]-Ib(t)Zb(t).It is determined using parallel circuit working characteristics and screening method
The determination of the basic model of each battery cell performance parameter and battery system performance parameter is as follows: battery system opens in basic model
Wire-end voltage calculates as follows: Ub0(SOC)=U0(SOC), wherein U0It (SOC) is battery cell open terminal voltage；In basic model
The impedance computation of battery system is as follows:
Wherein, RbIt (t) is battery system internal resistance, Rbs(t)、Rbl(t) and Cbs(t)、CblIt (t) is respectively description battery system transient response
Resistance, the capacitor of characteristic.R in basic modelbs(t)、Rbl(t) and Cbs(t)、Cbl(t) calculating difference is as follows:Cbs(t)=NCs(t)、Cbl(t)=NCl(t),
Wherein, R (t) is battery cell internal resistance, Rs(t)、Rl(t) and Cs(t)、ClIt (t) is respectively description battery cell transient response characteristic
Resistance, capacitor, the above performance parameter is related to SOC.SOC's is defined as:Wherein, SOC0For battery cell SOC initial value, generally 0~1 constant；
QuIt (t) is the unavailable capacity of battery cell, Q0For battery cell rated capacity.U0(SOC)、Rs(t)、Rl(t) and Cs(t)、Cl(t)
Calculating difference it 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.
Parameter correction device (2) design is as follows: SOC corrector is made of N number of PID regulator and a weighter,
2 inputs of each PID regulator are respectively that i-th of branched battery string exports electric current IiTotal stream I is exported with battery modelb1/
N；By the SOC offset Δ SOC for obtaining N number of branched battery after each PID regulatori, i.e.,In formula, kPFor proportionality constant, kIFor integral constant, kDFor derivative constant, s is integral
The factor, i are the natural number greater than 1；Battery system SOC offset Δ SOC is obtained after weighted device againb, i.e.,In formula, kiFor weighting system
Number.
The foundation of the battery space state equation (5) is as follows: 1), with battery SOCbAnd the end voltage U of 2 RCbs(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 electricity
Road model foundation battery space state equation is
In formula, Ubs、UblFor 2 RC parallel circuit end voltages, τ1、τ2For time constant, ωkFor system
Noise, Δ t are the sampling period, and k is the natural number greater than 1；2), according to Kirchhoff's second law, in conjunction with battery equivalent circuit
Model can obtain battery output measurement equation are as follows: [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 greater than 1.
The key step of the Unscented kalman filtering method UKF (6) are as follows: 1) initialize x mean value E () varianceIt unites 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、ωcRespectively indicate the weight of variance and mean value, operatorFor symmetrical matrix
Cholesky is decomposed, and 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 mean value；The mean square error time is updated toQkFor state equation noise variance；System exports the time more
Newly it isIn formula, gk-1() is measurement equation, rkFor measurement equation noise mean value；4) it counts
Calculate gain matrix:In formula, Py,kFor auto-covariance, Pxy,k
For from cross covariance, RkFor state equation noise variance；5) measurement updaue of state estimation and mean square error: state estimation measurement
It is updated toMean square error measurement updaue is
The modified noise estimator (3) are as follows:Formula
In, diag () is diagonal matrix, ykActual measured value is exported for system,Respectively indicate the shape at k moment
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
Sound estimate of variance, the i.e. noise estimation value (4) at k moment.
It finally will be by the resulting offset Δ SOC of parameter correction device (2)bIt is exported with battery system equivalent-circuit model (1)
SOCbAfter addition, the input quantity SOC new as battery system equivalent-circuit modelr, so that battery system performance parameter is updated,
And then battery system equivalent-circuit model is updated, it so recycles, obtains the state-of-charge estimation of accurately parallel connection type battery system
Value.
The battery types are one of lithium ion battery or lead-acid battery.
Detailed description of the invention
Fig. 1 is a kind of state-of-charge estimation method 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.
Specific embodiment
Below 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, it provides 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, main including the following steps:
1, parallel connection type battery system equivalent-circuit model is determined
The parallel connection type battery system is by 3 battery cells by being formed in parallel, parallel connection type battery system equivalent circuit
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.Battery model expression formula is obtained by Kirchhoff's law KVC are as follows: 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 determination of battery system performance parameter is as follows: the open terminal voltage of battery system calculates as follows in basic model: Ub0
(SOC)=U0(SOC), wherein U0It (SOC) is battery cell open terminal voltage；The impedance computation of battery system is such as in basic model
Under:Wherein, RbIt (t) is battery system
Internal resistance, Rbs(t)、Rbl(t) and Cbs(t)、CblIt (t) is respectively resistance, the capacitor for describing battery system transient response characteristic.Substantially
R in modelbs(t)、Rbl(t) and Cbs(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
(t) and Cs(t)、ClIt (t) is respectively resistance, the capacitor for describing battery cell transient response characteristic, the above performance parameter is and SOC
It is related.SOC's is defined as:Wherein, SOC0For battery cell SOC initial value,
Generally 0~1 constant；QuIt (t) is the unavailable capacity of battery cell, Q0For battery cell rated capacity.U0(SOC)、Rs(t)、
Rl(t) and Cs(t)、Cl(t) calculating difference is as follows: Wherein, a0~a5Value difference
It is -0.915,40.867,3.632,0.537,0.499,0.522, b0~b5Value is respectively 0.1463,30.27,0.1037,
0.0584,0.1747,0.1288, c0~c2Value is respectively 0.1063,62.49,0.0437, d0~d2Value respectively -200,
138,300, e0~e2Value is respectively 0.0712,61.4,0.0288, f0~f2Value is respectively 3083,180,5088.
2, design parameter corrector
Parameter correction device (2) design is as follows: SOC corrector is made of 3 PID regulators and a weighter,
2 inputs of each PID regulator are respectively that i-th of branched battery string exports electric current IiTotal stream I is exported with battery modelb1/
3；By the SOC offset Δ SOC for obtaining 3 branched batteries after each PID regulatori, i.e.,In formula, kPFor proportionality constant, kIFor integral constant, kDFor derivative constant, s is integral
The factor, i are the natural number greater than 1；Battery system SOC offset Δ SOC is obtained after weighted device againb, i.e.,In formula, kiFor weighting coefficient.
At the k moment, it is Δ SOC that parameter correction device, which can obtain battery system SOC offset,b,k
3, battery space state equation is established
1), with battery SOCbAnd the end voltage U of 2 RCbs(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 end voltages, τ1、τ2For time constant, ωkFor system
Noise, Δ t are the sampling period, and k is the natural number greater than 1.
2) battery output measurement equation, can be obtained in conjunction with battery equivalent circuit model according to Kirchhoff's second law are as follows:
[Ub,k]=Ub0,k-RkIb,k-Ubs,k-Ubl,k+υk=gk(xk)+υk=yk, in formula, υkFor system measurements noise, k is oneself greater than 1
So number.
4, the noise estimation value (4) at k moment is obtained using noise estimator (3)
The system noise estimated information of last moment is combined to obtain the noise estimation value (4) at k moment using noise estimator,
I.e.
5, 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.,
Using the end voltage of battery charge state SOC, 2 RC parallel circuits in battery space state equation (5) as nothing
The state variable of mark Kalman filtering method UKF (6), i.e.,
Distinguished with the input state space equation of cell system space state equation (5), output voltage state space equation
As the nonlinear state Equation f () and measurement equation g () of Unscented kalman filtering method UKF (6), i.e.,
gk(xk)=U0,k-RkIb,k-Ubs,k-Ubl,k。
6, battery SOC estimation is carried out using Unscented kalman filtering method UKF (6).
1) init state variable x mean value 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 value is that 2, h value is 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 output
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 being estimatedFirst element output, that is, export the k moment battery system state-of-charge
SOCb,kEstimated value.
7, by the k moment by the resulting offset Δ SOC of parameter correction device (2)b,kWith k moment battery system equivalent circuit
The SOC of model (1) outputb,kAfter addition, the input quantity SOC new as k moment battery system equivalent-circuit modelr,k, thus 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 recycle, obtain the state-of-charge estimated value of accurately parallel connection type battery system.
Finally it should be noted that only illustrating technical solution of the present invention rather than its limitations in conjunction with 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 a specific embodiment of the invention
Change or equivalent replacement, but these modifications or change are being applied among pending claims.
Claims (4)
1. a kind of state-of-charge estimation method 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 branch current I1~IN in detection battery system, and predicted by battery system equivalent-circuit model
Battery system export electric current, using two kinds of electric currents be used as input quantity, through parameter correction device obtain battery system state-of-charge benefit
Repay value Δ SOCb；
Step (3): obtaining system noise estimated value using noise estimator, defined by battery system models coupling state-of-charge,
Establish battery space state equation；
Step (4): replacing the noise statistics information in Unscented kalman filtering method UKF with system noise estimated value, with battery sky
Between battery charge state in state equation, 2 RC parallel circuits state of the end voltage as Unscented kalman filtering method UKF
Variable, using the input state space equation of battery space state equation, output voltage state space equation as no mark card
The nonlinear state Equation f () and measurement equation g () of Kalman Filtering method UKF, by battery system end voltage prediction value and electricity
Input quantity of the cell system terminal voltage detecting value as Unscented kalman filtering method UKF filtering gain；
Step (5): the battery system state-of-charge estimated value SOC that Unscented kalman filtering method UKF is exportedbIt is mended with state-of-charge
Repay value Δ SOCbSuperposition obtains the battery system state-of-charge SOC after modifying justr；
Step (6): SOC is utilizedrBattery 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 estimated value of accurate parallel connection type battery system；
The parameter correction device design is as follows: SOC corrector is made of N number of PID regulator and a weighter, each PID
2 inputs of adjuster are respectively that i-th of branched battery string exports electric current IiTotal current I is exported with battery modelb1/N；Pass through
The SOC offset Δ SOC of N number of branched battery is obtained after each PID regulatori, i.e.,Formula
In, kPFor proportionality constant, kIFor integral constant, kDFor derivative constant, s is integrating factor, and i is the natural number greater than 1；Again through adding
Battery system SOC offset Δ SOC is obtained after power deviceb, i.e.,In formula, kiFor weighting system
Number；
The foundation of the battery space state equation is as follows:
1), with battery SOCbAnd the end voltage of 2 RC 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 end voltages, τ1、τ2For time constant, ωkFor system
Noise, Δ t are the sampling period, and k is the natural number greater than 1；
2) battery output measurement equation, is obtained are as follows: [U in conjunction with battery equivalent circuit model according to Kirchhoff's second lawb,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 greater than 1.
2. a kind of state-of-charge estimation method of parallel connection type battery system according to claim 1, which is characterized in that described
Unscented kalman filtering method UKF includes the following steps:
1) x, mean value E, variance are initializedWith noise statistics 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、ωcRespectively indicate variance
And the weight of mean value, operatorIt is decomposed 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 mean value；The mean square error time is updated toQkFor state equation noise variance；System exports the time more
Newly it isIn formula, gk-1() is measurement equation, rkFor measurement equation noise mean 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 estimation measurement updaue is
Mean square error measurement updaue is
3. a kind of state-of-charge estimation method of parallel connection type battery system according to claim 2, which is characterized in that described
Noise estimator are as follows:
In formula, diag () is diagonal matrix,Respectively indicate the state equation noise Estimation of Mean value at k moment, state equation Noise Variance Estimation value,
Measurement equation noise Estimation of Mean value, measurement equation Noise Variance Estimation value, i.e. the system noise estimated value at k moment.
4. a kind of state-of-charge estimation method of parallel connection type battery system according to claim 1, which is characterized in that described
Battery types are one of lithium ion battery or lead-acid battery.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711082843.0A CN107831448B (en) | 2017-11-07 | 2017-11-07 | A kind of state-of-charge estimation method of parallel connection type battery system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711082843.0A CN107831448B (en) | 2017-11-07 | 2017-11-07 | A kind of state-of-charge estimation method of parallel connection type battery system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107831448A CN107831448A (en) | 2018-03-23 |
| CN107831448B true CN107831448B (en) | 2019-11-26 |
Family
ID=61653813
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711082843.0A Active CN107831448B (en) | 2017-11-07 | 2017-11-07 | A kind of state-of-charge estimation method of parallel connection type battery system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107831448B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN111929585B (en) * | 2019-05-13 | 2023-08-04 | 丰翼科技(深圳)有限公司 | Battery charge state calculating device, method, server and medium |
| 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 |
| CN111239634B (en) * | 2020-03-20 | 2022-10-14 | 中创新航科技股份有限公司 | Method and device for detecting branch state of battery system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105116338B (en) * | 2015-07-15 | 2017-12-05 | 盐城工学院 | A kind of parallel connection type battery system modeling method based on SOC compensators |
| CN106597304A (en) * | 2016-12-08 | 2017-04-26 | 盐城工学院 | Battery system charge of state estimation method using improved noise estimator |
| CN106443496A (en) * | 2016-12-08 | 2017-02-22 | 盐城工学院 | Battery charge state estimation method with improved noise estimator |
| CN106772074A (en) * | 2016-12-08 | 2017-05-31 | 盐城工学院 | A kind of battery system state-of-charge method of estimation with noise estimator |
-
2017
- 2017-11-07 CN CN201711082843.0A patent/CN107831448B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN107831448A (en) | 2018-03-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107831448B (en) | A kind of state-of-charge estimation method of parallel connection type battery system | |
| CN105116343B (en) | The electrokinetic cell state of charge method of estimation and system of least square method supporting vector machine | |
| CN108508371A (en) | A kind of power battery SOC/SOH/SOP combined estimation methods based on equivalent-circuit model | |
| CN106324521B (en) | A kind of method of Combined estimator electrokinetic cell system parameter and state-of-charge | |
| CN104617623B (en) | A kind of electric automobile power battery group balance control method | |
| CN108594135A (en) | A kind of SOC estimation method for the control of lithium battery balance charge/discharge | |
| CN106405433A (en) | Extended Kalman particle filtering based SOC (State Of Charge) estimation method and system | |
| CN105445671A (en) | Lithium ion battery service life prediction method based on traceless particle filtering | |
| CN113049962B (en) | LSTM-based energy storage device operation situation deduction method | |
| CN104535932A (en) | Lithium ion battery charge state estimating method | |
| CN106093783A (en) | The battery SOC method of estimation that Kalman filtering merges with data-driven | |
| CN105203969A (en) | Modification-based state-of-charge estimation method for RC battery model | |
| CN109669132A (en) | A kind of battery charge state estimation method based on variation Bayesian filter | |
| CN105954679A (en) | Lithium battery charge state online estimating method | |
| CN108445421A (en) | A kind of health state of lithium ion battery On-line Estimation method of space-oriented application | |
| CN105182245A (en) | High-capacity battery system charge state estimation method based on unscented Kalman filter | |
| CN105353315A (en) | Estimation method of state of charge of battery system on the basis of Unscented Kalman Filter | |
| CN106443492A (en) | Method for estimating SOC (State Of Charge) of lithium battery of low-velocity electronic vehicle | |
| CN106093782A (en) | The least square method supporting vector machine SOC method of estimation of dynamic modeling | |
| CN109828215A (en) | A kind of method and system promoting battery cell SOC estimation precision | |
| CN105182246A (en) | Parallel battery system charge state estimation method based on unscented Kalman filter | |
| CN110095723A (en) | A kind of Li-ion battery model parameter and SOC online joint estimation method | |
| CN105116346A (en) | Series-connected battery system and method for estimating state of charge thereof | |
| CN112946481A (en) | Based on federation H∞Filtering sliding-mode observer lithium ion battery SOC estimation method and battery management system | |
| CN109901072A (en) | Retired battery parameter detection method based on historical data and laboratory test data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |