CN108732509A - A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application - Google Patents
A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application Download PDFInfo
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Abstract
A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application, be related to lithium ion battery health management arts, in order to solve the problems, such as that estimated accuracy is low existing for existing lithium ion battery SOC methods of estimation, stability is poor and to the completeness of training dataset it is more demanding.Establish battery equivalent circuit model；Identification of Model Parameters is carried out, and considers the variation relation between model parameter and SOC, establishes model parameter interpolation table；Model parameter meets when applying current excitation signal to circuit model voltage responsive error in allowed limits；Establish the state space equation of state-of-charge estimating system；Using the state space equation of foundation, the On-line Estimation of current time battery charge state SOC is carried out based on UPF algorithms；According to the state-of-charge SOC estimation at current time, model parameter is updated in conjunction with the model parameter interpolation table of foundation, and by newer model parameter in the estimation of subsequent time state-of-charge.Suitable for On-line Estimation battery charge state.
Description
Technical field
The present invention relates to lithium ion battery health management arts, and in particular to a kind of lithium based on no mark particle filter algorithm
Ion battery state-of-charge On-line Estimation method.
Background technology
Lithium ion battery is high with monomer output voltage, have extended cycle life, self-discharge rate is low, energy density is big, without environment
The advantages that pollution, is widely used in the fields such as consumer electronics, electric vehicle, communication energy storage base station, and be gradually extended to aviation,
The military fields such as space flight, navigation.Especially in Aerospace Satellite application aspect, lithium ion battery can substantially reduce load weight and
Volume, more traditional Ni-MH battery and Ni-Cr battery have huge advantage, have become third generation satellite energy-storage battery.As
The sole energy source that satellite is run in the ground shadow phase, the safe and reliable operation of lithium ion battery are to ensure the spacecrafts such as satellite
Premise in orbit.Therefore, the lithium ion battery management for space-oriented application has become the hot spot of research.
State-of-charge (State of Charge, SOC) estimation is one of the core content of lithium ion battery health control,
Its online real-time estimation it is anticipated that system run time, and rational battery charging and discharging strategy is formulated, for safeguards system
Safe operation be of great significance.And SOC estimates for space application, it can be to lithium ion battery or spacecraft power supply subsystem
The energy of system distributes and optimum management, provides important reference.But the strong nonlinearity feature that has of lithium ion battery itself to
The accurate estimation of SOC brings huge challenge, and therefore, exploitation adapts in the SOC methods of estimation of complicated battery system be to work as
One technological difficulties of preceding battery management.
Currently, the method for estimation of lithium ion battery SOC mainly have current integration method, open circuit voltage method, Kalman filtering method,
The methods of extended Kalman filter (Extended Kalman Filtering, EKF) and neural network.Current integration method is real
It is now simple and most to used method of estimation under the not high occasion of required precision at present, but it estimates in long-term
There are cumulative errors during meter, and initial SOC not can determine that；Open circuit voltage method needs prolonged battery standing, is mostly
For test application under laboratory condition, it is unable to real-time online estimation SOC；Kalman filtering algorithm is only applicable to linear system,
For battery, this nonlinear system filtering accuracy is not high, or even diverging；Expanded Kalman filtration algorithm has nonlinear system
There is certain adaptability, but this strongly non-linear system still has certain defect to battery, there are stability declines, very
To the problem of diverging；The On-line Estimation of SOC may be implemented in neural network, but needs a large amount of test data set as branch
Support, i.e., it is more demanding to the completeness of training dataset.At the same time, particle filter serial algorithm is such as calculated without mark particle filter
Method (Unscented Particle Filter, UPF) has good adaptability to the complication system of nonlinear and non-Gaussian,
Battery status is estimated and life prediction field has broad application prospects.
Invention content
It is low, stable that the purpose of the present invention is to solve estimated accuracies existing for existing lithium ion battery SOC methods of estimation
Property the difference and problem more demanding to the completeness of training dataset, to provide a kind of lithium ion battery of space-oriented application
The On-line Estimation method of state-of-charge.
A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application of the present invention, this method
Including：
Step 1: establishing battery equivalent circuit model；
Step 2: carrying out identification of Model Parameters, and consider the variation relation between model parameter and SOC, establishes model parameter
Interpolation table；
Step 3: the model parameter based on step 2 identification, applies current excitation signal to circuit model, judge that voltage is rung
It whether in allowed limits to answer error, if it is judged that being yes, then carries out step 4, otherwise return to step two；
Step 4: establishing the state space equation of state-of-charge estimating system；
Step 5: the state space equation of the foundation using step 4, current time battery charge is carried out based on UPF algorithms
The On-line Estimation of state SOC；
Step 6：According to the state-of-charge SOC estimation at current time, in conjunction with the model parameter interpolation established in step 2
Table updates model parameter, and by newer model parameter in the estimation of subsequent time state-of-charge.
Preferably, the battery equivalent circuit model described in step 1 be First-order Rc Circuit model, including model parameter
There is ohmic internal resistance R0, polarization resistance Rp, polarization capacity CpWith electromotive force of source Em；Polarization capacity is in parallel with polarization resistance, is formed simultaneously
Join RC branches, a terminating load of the branch, the branch the other end series connection ohmic internal resistance after connect power supply high potential end, electricity
The low potential end connection load in source.
Preferably, battery coulombic efficiency in charge and discharge process is considered in the state space equation that step 4 is established
The variation of parameter, and filtering update in real time is carried out using coulombic efficiency as a dimension of quantity of state, state space equation
Expression formula is：
xk=Ak-1xk-1+Bk-1Uk-1+wk-1
yk=Ckxk-DkUk+f(SOCk)+vk
Wherein, xkFor the system state amount at k moment, Uk-1For the system control amount at k-1 moment, wk-1What it is for the k-1 moment is
System process noise, Ak-1And Bk-1The respectively corresponding state transfer transformation square of the system state amount at k-1 moment and system control amount
Battle array；ykIt is measured for the systematic perspective at k moment, Em=f (SOCk), EmValue is checked according to model parameter interpolation table, SOCkFor the k moment
SOC, vkFor the measurement noise at k moment, CkFor the transformation matrix between the corresponding quantity of state of system state amount at k moment and observed quantity,
DkFor the transformation matrix between the corresponding quantity of state of system control amount at k moment and observed quantity.
Preferably,
yk=[Ut,k], Ck=[0-1 0], Dk=[R0], vk=[vk]
Wherein, Up,kFor the terminal voltage of k moment parallel connection RC branches, TsFor sampling time interval, τ=Rp*CpFor time constant,
CNFor battery maximum capacity, ηkFor the coulombic efficiency at battery k moment, Ut,kFor the load terminal voltage at k moment, IL,kFor bearing for k moment
Carry end electric current, wk-1,1、wk-1,2And wk-1,3Quantity of state η, U are indicated respectivelypProcess noise corresponding with SOC.
The On-line Estimation method of the charge states of lithium ion battery of a kind of space-oriented application of the present invention, in the electricity of foundation
Consider that SOC changes the influence to model parameter in the equivalent model of pond, and realizes the On-line Estimation of SOC based on UPF algorithms.The present invention
Method can be adapted for the SOC On-line Estimations under multiple battery operating mode and a variety of uncertain environments, and it is high with estimated accuracy,
The good advantage of stability.
Description of the drawings
Fig. 1 is a kind of On-line Estimation method flow of the charge states of lithium ion battery of space-oriented application of the present invention
Figure；
Fig. 2 is battery equivalent circuit model schematic diagram；
Fig. 3 is the HPPC operating mode map of current for identification of Model Parameters；
Fig. 4 is the HPPC operating mode voltage patterns for identification of Model Parameters；
Fig. 5 is the variation relation curve recognized between obtained model parameter and SOC；
(a) it is R0Variation relation curve between SOC, (b) is RpVariation relation curve between SOC, (c) is CpWith SOC
Between variation relation curve, (d) be EmVariation relation curve between SOC；
Fig. 6 is the CCD operating mode estimated result figures under SOC initial value condition of uncertainty；
(a) it is SOC estimation curve, is (b) evaluated error curve；
Fig. 7 is the HPPC operating mode estimated result figures under SOC initial value condition of uncertainty；
(a) it is SOC estimation curve, is (b) evaluated error curve；
Fig. 8 is that there are the CCD operating mode estimated result figures under current measurement noise conditions；
(a) it is SOC estimation curve, is (b) evaluated error curve；
Fig. 9 is that there are the HPPC operating mode estimated result figures under current measurement noise conditions；
(a) it is SOC estimation curve, is (b) evaluated error curve；
Figure 10 is the estimated result comparison diagram of two different methods under CCD operating modes；
(a) it is SOC estimation curve, is (b) evaluated error curve；
Figure 11 is the estimated result comparison diagram of two different methods under HPPC operating modes；
(a) it is SOC estimation curve, is (b) evaluated error curve.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art obtained under the premise of not making creative work it is all its
His embodiment, shall fall within the protection scope of the present invention.
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the present invention can phase
Mutually combination.
The invention will be further described in the following with reference to the drawings and specific embodiments, but not as limiting to the invention.
A kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application, this method include：
Step 1: establishing battery equivalent circuit model；The parameter that the equivalent-circuit model includes has ohmic internal resistance R0, polarization
Resistance Rp, polarization capacity CpWith electromotive force Em；
Step 2: building identification of Model Parameters environment under matlab/simulink softwares, identification of Model Parameters is carried out,
And consider the variation relation between model parameter and SOC, establish model parameter interpolation table；
Make the excitation of identification environment, response output of the HPPC operating modes voltage as identification environment with HPPC operating mode electric currents.
Step 3: circuit model is assessed, i.e., the circuit model parameters recognized based on step 2 give circuit model to apply electric current
Whether in allowed limits pumping signal judges voltage responsive error, if it is judged that being yes, then carries out step 4, no
Then return to step two；
Give model application identical current excitation signal, response voltage and the reference voltage (real voltage) of comparison model,
Voltage responsive error is no more than 22mV.Therefore, the circuit model and its parameter identification result established have battery good
Characterization ability can be applied in subsequent SOC estimations.
Step 4: considering the change of battery coulombic efficiency parameter in charge and discharge process in the state space equation established
Change, and coulombic efficiency is subjected to filtering update, the expression formula of state space equation in real time as a dimension of quantity of state and is：
xk=Ak-1xk-1+Bk-1Uk-1+wk-1 (1)
yk=Ckxk-DkUk+f(SOCk)+vk (2)
Wherein, xkFor the system state amount at k moment, Uk-1For the system control amount at k-1 moment, wk-1What it is for the k-1 moment is
System process noise, Ak-1And Bk-1The respectively corresponding state transfer transformation square of the system state amount at k-1 moment and system control amount
Battle array；ykIt is measured for the systematic perspective at k moment, Em=f (SOCk), EmValue is checked according to model parameter interpolation table, SOCkFor the k moment
SOC, vkFor the measurement noise at k moment, CkFor the transformation matrix between the corresponding quantity of state of system state amount at k moment and observed quantity,
DkFor the transformation matrix between the corresponding quantity of state of system control amount at k moment and observed quantity.
Due to considering the variation of battery coulombic efficiency parameter in charge and discharge process, and using coulombic efficiency as quantity of state
A dimension carry out filtering update in real time, therefore the effective estimation performance for improving SOC.
Detailed derivation is as follows：
If assuming, the terminal voltage of parallel connection RC branches is Up, load terminal voltage and electric current are respectively UtAnd IL, then single order RC batteries
The circuit equation that model includes has：
Ut=Em-Up-ILR0 (3)
Equation is discrete to be turned to：
Ut,k=Em,k-IL,kR0-Up,k (5)
Wherein, TsFor sampling time interval, τ=Rp*CpFor time and constant.
Meanwhile the discrete recurrence equation that state-of-charge SOC can be obtained by current integration method is：
Wherein, CNFor battery maximum capacity, it is battery coulombic efficiency usually to take the rated capacity of battery, η.
Choose battery coulombic efficiency η, state-of-charge SOC and polarizing voltage UpIt, can on-line checking as the quantity of state of system
Load voltage UtFor the observed quantity of system, then corresponding state space equation is：
Wherein, wk-1,1、wk-1,2And wk-1,3Quantity of state η, U are indicated respectivelypWith the process noise of SOC；vkTo measure noise.
If note：
yk=[Ut,k], Ck=[0-1 0], Dk=[R0], vk=[vk]
Then above-mentioned state space equation is represented by：
xk=Ak-1xk-1+Bk-1Uk-1+wk-1 (10)
yk=Ckxk-DkUk+f(SOCk)+vk (11)
Wherein, Em=f (SOCk), EmValue can be checked according to the model parameter interpolation table of foundation.
Step 5: using the state space equation of step 4, carry out present battery state-of-charge SOC's based on UPF algorithms
On-line Estimation：
Step 5 one：Initialization
UPF algorithm number of particles N, quantity of state initial value and noise variance etc. are set, and initializes particle distribution and its association
Variance matrix.
Distribution p (x based on initial time (zero moment)0), randomly generate N number of particle { x0 (i)+And corresponding covariance square
Battle array { P0 (i)+, i=1,2 ..., N, and original model parameter R is provided according to initial SOC0, Rp, CpAnd Em；
Step 5 two：Calculate each particle sigma points distribution
Wherein,For the sigma dot matrixs of generation,For the augmented matrix of system state amount and noise,For shape
The augmentation covariance matrix of state amount and noise,L is the dimension of quantity of state, and λ is constant.W and Q is respectively that state is made an uproar
Sound matrix and variance, v and R are to measure noise matrix and variance.
Step 5 three：Time updates
Wherein,For a step updated value of system state amount,For k-1 moment quantity of state estimated values,For k-1
Moment quantity of state process noise, Ak-1And Bk-1Transformation matrix, U are shifted for statek-1For system control amount；For one step of quantity of state
Predicted value,WithFor weights constant；For covariance one-step prediction value；For a step updated value of observed quantity, Ck
And DkTransformation matrix between quantity of state and observed quantity, f () indicate SOC and electromotive force EmBetween functional relation；For observation
The one-step prediction value of amount.
Step 5 four：Measure update
Wherein,To measure variance matrix；Covariance matrix between quantity of state and measurement；KkIt is filtered for Kalman
Wave gain；For the system state estimation value at k moment；Newer covariance matrix.
Step 5 five：Weight computing and normalization
Wherein, qiFor the weights of each particle,For normalized particle weights, ykIt is measured for the systematic perspective at k moment.
Step 5 six：Particle resampling
Wherein,WithFor original particle and its covariance matrix,WithResampling generate particle and
Its covariance matrix,It is the random number between 0~1.
Step 5 seven：SOC estimates
Wherein, SOCkFor the SOC estimated results at k moment,For the corresponding SOC estimation of each particle,For shape
Second dimension values of state amount.
Step 6: the estimated result SOC at the k moment obtained according to step 5 sevenk, binding model parameter interpolation table update mould
Shape parameter R0, Rp, CpAnd Em, and in the SOC of subsequent time estimations；
Step 5 two is repeated to step 6, carries out the estimation of battery charge state SOC, until estimation cycle terminates.
Experimental verification：
Method using the present invention is made under two kinds of different battery operating modes (CCD and HPPC), two kinds of condition of uncertainty (at the beginning of SOC
Initial value is uncertain, and there are current measurement noises) SOC estimation experiments, and compared with EKF methods.
(1) SOC initial values are uncertain
Estimated result when Fig. 6 is CCD operating modes, estimated result when Fig. 7 is HPPC operating modes；It is obtained according to experimental result
Detailed estimation performance when to initial SOC0=0.85 is as shown in table 1.
Estimated result when 1 initial SOC0=0.85 of table
Experiment obtains, under the conditions of SOC initial values are uncertain, maximum estimated error under two kinds of operating modes 5% with
Interior, averaged power spectrum error is less than 2%, and convergence (error the is less than 1%) response time is no more than 120s, that is, the method for estimation used
Still there is good estimation performance and convergence under the conditions of SOC initial values are uncertain.
(2) there are current measurement noises
Estimated result when Fig. 8 is CCD operating modes, estimated result when Fig. 9 is HPPC operating modes；It is deposited according to experimental result
SOC estimations detailed performance under the conditions of current noise is as shown in table 2.
SOC estimated results under 2 current noise of table
It can obtain, in the case that current detecting is interfered there are certain noise, the maximum estimated error of two kinds of operating modes is equal
No more than 5%, mean error still has good estimation performance at this time within 2%.
(3) method compares
Figure 10 is the estimated result comparison diagram of two different methods under CCD operating modes, and Figure 11 is that two kinds not under HPPC operating modes
The estimated result comparison diagram of same method, detailed estimation performance comparison are shown in Table 3 and table 4.
3 CCD operating modes of table estimate performance comparison
| Method | Worst error | Mean error | Root-mean-square error |
| EKF | 0.0497 | 0.0194 | 0.0225 |
| UPF | 0.0203 | 0.0082 | 0.0098 |
4 HPPC operating modes of table estimate performance comparison
| Method | Worst error | Mean error | Root-mean-square error |
| EKF | 0.0648 | 0.0150 | 0.0207 |
| UPF | 0.0131 | 0.0030 | 0.0038 |
It can be obtained from experimental result, under two different working conditions, UPF algorithms are in estimation worst error, averagely
It is superior to EKF algorithms on error and root-mean-square error these three evaluation indexes.In addition, UPF algorithms are in entire SOC change procedures
Can keep very high estimated accuracy and good stability, and EKF algorithms in battery discharge latter stage estimated result with larger
Deviation, or even have the tendency that " dissipating ", i.e., the stability of algorithm is being decreased obviously.Therefore, the On-line Estimation sides SOC proposed
Method has good estimated accuracy and stability.
Claims (4)
1. a kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application, which is characterized in that this method packet
It includes：
Step 1: establishing battery equivalent circuit model；
Step 2: carrying out identification of Model Parameters, and consider the variation relation between model parameter and SOC, establishes model parameter interpolation
Table；
Step 3: the model parameter based on step 2 identification, applies current excitation signal to circuit model, judge that voltage responsive misses
Whether in allowed limits difference, if it is judged that being yes, then carries out step 4, otherwise return to step two；
Step 4: establishing the state space equation of state-of-charge estimating system；
Step 5: the state space equation established using step 4, carries out current time state-of-charge SOC's based on UPF algorithms
On-line Estimation；
Step 6：According to the state-of-charge SOC estimation at current time, more in conjunction with the model parameter interpolation table established in step 2
New model parameter, and by newer model parameter in the estimation of subsequent time state-of-charge.
2. a kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application according to claim 1,
It is characterized in that, battery equivalent circuit model described in step 1 is First-order Rc Circuit model, including model parameter have ohm
Internal resistance R0, polarization resistance Rp, polarization capacity CpWith electromotive force of source Em；Polarization capacity is in parallel with polarization resistance, forms parallel connection RC branch
Road, a terminating load of the branch, the branch the other end series connection ohmic internal resistance after connect power supply high potential end, power supply it is low
The connection load of potential end.
3. a kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application according to claim 1,
It is characterized in that, considering battery coulombic efficiency parameter in charge and discharge process in the state space equation that step 4 is established
Variation, and carry out filtering update in real time, the expression formula of state space equation using coulombic efficiency as a dimension of quantity of state
For：
xk=Ak-1xk-1+Bk-1Uk-1+wk-1
yk=Ckxk-DkUk+f(SOCk)+vk
Wherein, xkFor the system state amount at k moment, Uk-1For the system control amount at k-1 moment, wk-1For the systematic procedure at k-1 moment
Noise, Ak-1And Bk-1The respectively corresponding state of the system state amount at k-1 moment and system control amount shifts transformation matrix；ykFor
The systematic perspective at k moment measures, Em=f (SOCk), EmValue is checked according to model parameter interpolation table, SOCkFor the SOC at k moment, vkFor k
The measurement noise at moment, CkFor the transformation matrix between the corresponding quantity of state of system state amount at k moment and observed quantity, DkFor the k moment
The corresponding quantity of state of system control amount and observed quantity between transformation matrix.
4. a kind of On-line Estimation method of the charge states of lithium ion battery of space-oriented application according to claim 3 its
It is characterized in that,
yk=[Ut,k], Ck=[0-1 0], Dk=[R0], vk=[vk]
Wherein, Up,kFor the terminal voltage of k moment parallel connection RC branches, TsFor sampling time interval, τ=Rp*CpFor time constant, CNFor
Battery maximum capacity, ηkFor the coulombic efficiency at battery k moment, Ut,kFor the load terminal voltage at k moment, IL,kFor the load at k moment
Electric current, wk-1,1、wk-1,2And wk-1,3Quantity of state η, U are indicated respectivelypProcess noise corresponding with SOC.
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