CN108414947A - A kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales - Google Patents
A kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales Download PDFInfo
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- 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
Abstract
A kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales, is related to lithium ion battery management domain, in order to solve the problems, such as that existing method can not for a long time be estimated suitable for SOC of the battery under SOH degenerative conditions.Establish battery equivalent circuit model；Identification of Model Parameters is carried out, the model parameter interpolation table under the conditions of different SOH is established, a SOH condition corresponds to a group model parameter interpolation table；Each group model parameter interpolation table meets when applying current excitation signal to circuit model respectively voltage responsive error in allowed limits；Establish Multiple Time Scales state space equation；Using the Multiple Time Scales state space equation of foundation, the estimation of the SOH under the estimation and macro-scale of the SOC under micro-scale is carried out based on UPF algorithms, battery capacity and model parameter interpolation table according to the update of the degenerate case of SOH for SOC estimations.Suitable for estimating SOC.
Description
Technical field
The present invention relates to lithium ion battery management domains, and in particular to a kind of lithium ion battery shape based on Multiple Time Scales
State combined estimation method.
Background technology
Lithium ion battery is high with monomer output voltage, has extended cycle life, self-discharge rate is low, and energy density is big, no environment
The advantages that pollution, is widely used in consumer electronics, electric vehicle, the fields such as communication energy storage base station, and is gradually extended to aviation,
Space flight, the military fields such as 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 management,
Line real-time estimation it is anticipated that system run time, and rational battery charging and discharging strategy is formulated, for the peace of safeguards system
Row for the national games is of great significance.And SOC estimations are for space application, it can be to lithium ion battery or spacecraft power supply subsystem
The energy distributes and optimum management, provides important reference.Estimation of the strong nonlinearity feature that lithium ion battery itself has to SOC
Increase difficulty.At the same time, lithium ion battery is under the health status (State of Health, SOH) of the process of long-time service
Drop can lead to the change of battery capacity parameters and model parameter (both following to be referred to as " systematic parameter "), these will give SOC
It is long-term estimation bring challenges.
Modelling is a kind of typical SOC methods of estimation, needs the equivalent model for establishing description battery behavior, such as equivalent
Circuit model or electrochemical model, and combine certain nonlinear filtering algorithm, such as Extended Kalman filter, Unscented kalman
Filtering, particle filter, the methods of no mark particle filter (Unscented Particle Filtering, UPF) carry out SOC's
Filtering estimation.Such method using initial stage there is good SOC to estimate performance in battery, but as battery had been used for a long time
" aging " in journey, i.e. SOH degenerate, the characterization ability of the battery equivalent model and corresponding model parameter interpolation table established originally
Decline, while the capacity parameter of battery will also change, these can lead to the reduction of SOC estimation performances, or even diverging.
That is such method still not can effectively solve the problem that the long-term estimation problems of SOC of the battery under SOH degenerative conditions.Therefore, it opens
It sends out a kind of and adapting to the technological difficulties that the long-term methods of estimation of SOC under SOH degenerative conditions are present battery management.
Invention content
The purpose of the present invention is to solve existing methods can not be suitable for SOC long of the battery under SOH degenerative conditions
The problem of phase is estimated, to provide a kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales.
A kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales of the present invention, this method
Including：
Step 1: establishing battery equivalent circuit model；
Step 2: carrying out identification of Model Parameters, the model parameter interpolation table under the conditions of different SOH, a SOH condition are established
A corresponding group model parameter interpolation table；
Step 3: for each group model parameter interpolation table of step 2 identification, applies electric current to circuit model respectively and swash
Signal is encouraged, whether in allowed limits to judge voltage responsive error, if it is judged that being yes, then carries out step 4, it is no
Then return to step two；
Step 4: establishing Multiple Time Scales state space equation, that is, establish the state of the SOC estimating systems under micro-scale
The state space equation of SOH estimating systems under space equation and macro-scale；
Step 5: the Multiple Time Scales state space equation established using step 4, micro-scale is carried out based on UPF algorithms
Under SOC estimation and the SOH under macro-scale estimation, according to the degenerate case of SOH update for SOC estimation battery hold
Amount and model parameter interpolation table.
The present invention method for change over time faster battery status amount SOC using micro-scale estimation, and for
Change slowly battery status amount SOH then to be estimated under macro-scale, fully be examined in the estimation procedure of battery SOC
The change for having considered SOH, to improve the long-term estimation performance of SOC.
Description of the drawings
Fig. 1 is the principle of the present invention block diagram；
Fig. 2 is the schematic diagram of battery equivalent circuit model；
Fig. 3 is the flow of the SOH estimations under estimation and macro-scale based on the SOC under UPF algorithms progress micro-scale
Figure；
Fig. 4 is operating current curve under RW operating modes；
Fig. 5 is load voltage curve under RW operating modes；
Fig. 6 is the RW operating mode SOC estimated result figures for not considering SOH degenerate cases；
(a) it is SOC estimation curve, is (b) evaluated error curve；
Fig. 7 is the RW operating mode SOC estimated result figures that method using the present invention obtains；
(a) it is SOC estimation curve, is (b) evaluated error curve；
Fig. 8 is the front and back model parameter interpolation table comparison of update；
(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.
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 space lithium ion battery state joint method of estimation based on Multiple Time Scales, this method include：
Step 1: establishing battery equivalent circuit model；
Battery equivalent circuit model described in step 1 is First-order Rc Circuit model, including model parameter have ohmic 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 branches,
One terminating load of the branch, the branch the other end series connection ohmic internal resistance after connect power supply high potential end, the low electricity of power supply
The connection load of gesture end.
Step 2: carrying out identification of Model Parameters, identification of Model Parameters simulated environment is built in Matlab/Simulink,
The model parameter interpolation table under the conditions of different SOH is established, a SOH condition corresponds to a group model parameter interpolation table.
Test data for multigroup identification of Model Parameters is the HPPC floor datas under the conditions of different SOH, between SOH degenerates
It is divided into 0.05, acquiescence battery failure threshold value is SOH=0.8.The excitation for making identification environment with HPPC operating mode electric currents respectively inputs, electricity
Press the response output as identification environment.The mould under the conditions of different SOH is established according to multigroup identification of Model Parameters result of acquisition
Shape parameter interpolation table.
Step 3: the assessment of multigroup model parameter：For each group model parameter interpolation table of step 2 identification, give respectively
Circuit model applies current excitation signal, whether in allowed limits voltage responsive error is judged, if it is judged that being
It is then to carry out step 4, otherwise return to step two；
Voltage responsive error is the residual error responded between voltage and reference voltage (actual value).
Step 4: establishing Multiple Time Scales state space equation, that is, establish the state of the SOC estimating systems under micro-scale
The state space equation of SOH estimating systems under space equation and macro-scale；
(1) state space equation of SOC estimating systems is：
xk=Ak-1xk-1+Bk-1Uk-1+wk-1
yk=Ckxk-DkUk+f(SOCk)+vk
Wherein, k is the time scale under micro-scale, xkFor the system state amount at k moment, xk=[Up,k,SOCk]T, Up,k
For the terminal voltage of k moment parallel connection RC branches, Uk-1For the system control amount at k-1 moment, wk-1Systematic procedure for the k-1 moment is made an uproar
Sound, Ak-1And Bk-1The respectively corresponding state of the system state amount at k-1 moment and system control amount shifts transformation matrix；ykFor k
The systematic perspective at 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.
(2) state space equation of SOH estimating systems is：
θl=θl-1+ξl-1
Wherein, l is the time scale under macro-scale, the system state amount θ at l momentl=[al,bl,cl,dl]T, al, bl,
clAnd dlThe respectively two fingers number degradation model parameter at l moment, ξl-1For the systematic procedure noise at l-1 moment；θl,1, θl,2, θl,3With
θl,4Respectively first dimension of system state amount, second dimension, third dimension and four dimensions, the l moment it is online
Health factor HIlFor the observed quantity of system, μlTo measure noise, cycle is the current charge and discharge cycles periodicity of battery, g
(SOHl) mapping relations function between SOH and online HI.
Step 5: the Multiple Time Scales state joint estimation based on UPF algorithms：Multiple Time Scales state joint is estimated
There are the On-line Estimation of the SOC under micro-scale and the SOH under macro-scale to estimate two parts.It is faster for changing over time
Battery status amount SOC estimated using micro-scale, and for changing slowly battery status amount SOH then under macro-scale
Estimated.When the time scale k under micro-scale reaches a charge and discharge cycles period, meets change of scale condition, cut
It shifts under macro-scale l, carries out a SOH estimation.Whenever completing the SOH estimations under a macro-scale, need microcosmic
Current SOH degenerate cases are judged in SOC estimations, to decide whether to update battery capacity parameters and model parameter interpolation table.In this way,
The change of SOH is fully considered in the estimation procedure of battery SOC, and according to the update of the degenerate case of SOH for SOC estimations
Battery capacity parameters and model parameter interpolation table, to promote the long-term estimation performance of SOC.
The estimation procedure of SOC under micro-scale is as follows：
I. it initializes：UPF algorithm number of particles N of the setting for the SOC estimations under micro-scale, quantity of state initial value and
Noise variance etc., and initialize particle distribution and its covariance matrix.
Ii. judge whether the time scale k under micro-scale reaches a charge and discharge cycles period, if it is carry out
SOH estimates, otherwise according to the SOH estimated results under macro-scale, current SOH degenerate cases is judged, to decide whether more
New system parameter, if desired, then execute iii, otherwise execute iv.
Iii. battery capacity parameters and model parameter interpolation table are updated according to the SOH at current time.
Iv. particle suggestion is distributed
1) the sigma points distribution of each particle is calculated：
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.
2) 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.
3) update is measured：
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.
V. 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.
Vi. 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.
Vii.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.
Viii. model parameter interpolation calculation：According to the estimated value SOC at k momentk, and binding model parameter difference table updates
Parameter R0, Rp, CpAnd Em。
Above-mentioned ii~viii is repeated, until reaching battery life cycle management, exits estimation cycle.
The estimation procedure of SOH under macro-scale is as follows：
1. initialization：UPF algorithm number of particles M of the setting for the SOH estimations under macro-scale, quantity of state initial value and
Noise variance etc., and initialize particle distribution and its covariance matrix.
2. particle suggestion is distributed
1) the sigma points distribution of each particle is calculated：
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.ξ and Q is respectively that state is made an uproar
Sound matrix and variance, μ and R are to measure noise matrix and variance.
2) time updates：
Wherein,For a step updated value of system state amount,For l-1 moment quantity of state estimated values,For l-
1 moment quantity of state process noise；For quantity of state one-step prediction value,WithFor weights constant；For covariance one
Walk predicted value；For a step updated value of observed quantity,WithThe first of quantity of state is indicated respectively
A dimension, second dimension, third dimension and four dimensions, g () indicate the mapping relations between online HI and SOH,
Cycle is the current charge and discharge cycles periodicity of battery；For the one-step prediction value of observed quantity.
3) update is measured：
Wherein,To measure variance matrix；Covariance matrix between quantity of state and measurement；KlIt is filtered for Kalman
Wave gain；For the system state estimation value at l moment；Newer covariance matrix.
3. weight computing and normalization：
Wherein, qjFor the weights of each particle,For normalized particle weights, HIlIt is measured for the systematic perspective at l moment,WithFirst dimension of quantity of state, second dimension, third dimension and the 4th dimension are indicated respectively
Degree.
4. particle resampling：
Wherein,WithFor original particle and its covariance matrix,WithResampling generate particle and
Its covariance matrix, rl jIt is the random number between 0~1.
5.SOH estimates：
Wherein, SOHlFor the SOH estimated results at l moment,For the corresponding SOH estimated values of each particle.
Experimental verification：
Random operating mode (Random Walk, the RW) test data set for choosing NASA PCoE batteries sample B 09 makees more times
The experimental verification of nanoscale regime combined estimation method.Battery operating current and voltage change under the operating mode have very strong random
Property, it is a kind of sufficiently complex battery operation operating mode, corresponding operating current and load voltage difference are as shown in Figure 4 and Figure 5.
In experiment, not considering battery first, there are SOH degenerate cases, that is, assume that battery system parameter (capacity and model parameter interpolation table) is equal
It does not change, that is to say, that battery capacity is still the rated capacity (2.0977Ah) of new battery, and model parameter table takes SOH=
Interpolation table when 100%, corresponding SOC estimated results are as shown in Figure 6.
It, can by macro-scale estimation according to the space lithium ion battery state joint method of estimation based on Multiple Time Scales
To provide SOH estimated values at this time as 96.81%.According to current SOH estimated results, update used in microcosmic SOC estimations
Model parameter interpolation table, and it is 2.0308Ah to reset battery capacity, then SOC estimated results at this time are as shown in fig. 7, respectively refer to
Mark comparison is as shown in table 1.In experiment, the model parameter interpolation table at corresponding interpolation table update current time when with SOH=95%,
The front and back interpolation table comparison of update is as shown in Figure 8.
1 SOC of table estimates performance comparison
It can be obtained by experimental result：For the RW operating modes in this experiment, if not considering battery SOH degenerate cases, adopt
Systematic parameter when with new battery, SOC estimate that worst error is 0.2728, mean error 0.0909, and root-mean-square error is
0.1149.According to the space lithium ion battery state joint method of estimation proposed by the present invention based on Multiple Time Scales, that is, exist
The degeneration of battery SOH is considered in SOC estimations for a long time, and battery capacity parameters and model parameter are carried out according to the estimated result of SOH
The update of interpolation table, then SOC estimation worst errors at this time are 0.1114, and root-mean-square error 0.0543, mean error also drops
To 0.0463, performance is estimated for a long time to greatly improve SOC.In addition, being also not difficult to find out method proposed by the present invention multiple
Still there is good estimated accuracy and stability, averaged power spectrum error is within 5% under miscellaneous RW operating modes.
Claims (5)
1. a kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales, 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, the model parameter interpolation table under the conditions of different SOH is established, a SOH condition corresponds to
One group model parameter interpolation table；
Step 3: for each group model parameter interpolation table of step 2 identification, apply current excitation letter to circuit model respectively
Number, judge that whether in allowed limits voltage responsive error, if it is judged that being yes, then carries out step 4, otherwise return
Return step 2；
Step 4: the state space equation and the SOH estimating systems under macro-scale of SOC estimating systems under establishing micro-scale
State space equation；
Step 5: the Multiple Time Scales state space equation established using step 4, is carried out based on UPF algorithms under micro-scale
The estimation of the estimation and the SOH under macro-scale of SOC decides whether electricity of the update for SOC estimations according to the degenerate case of SOH
Tankage parameter and model parameter interpolation table.
2. a kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales 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 space lithium ion battery state joint method of estimation based on Multiple Time Scales according to claim 1,
It is characterized in that, the state space equation for the SOC estimating systems that step 4 is established is：
xk=Ak-1xk-1+Bk-1Uk-1+wk-1
yk=Ckxk-DkUk+f(SOCk)+vk
Wherein, k is the time scale under micro-scale, xkFor the system state amount at k moment, Uk-1It is controlled for the system at k-1 moment
Amount, wk-1For the systematic procedure noise at k-1 moment, Ak-1And Bk-1The respectively system state amount and system control amount pair at k-1 moment
The state transfer transformation matrix answered；ykIt is measured for the systematic perspective at k moment, Em=f (SOCk), EmValue is according to model parameter interpolation table
It checks in, SOCkFor the SOC at k moment, vkFor the measurement noise at k moment, CkFor the k moment the corresponding quantity of state of system state amount and
Transformation matrix between observed quantity, DkFor the transformation matrix between the corresponding quantity of state of system control amount at k moment and observed quantity.
4. a kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales according to claim 1,
It is characterized in that, the state space equation for the SOH estimating systems that step 4 is established is：
θl=θl-1+ξl-1
Wherein, l is the time scale under macro-scale, the system state amount θ at l momentl=[al,bl,cl,dl]T, al, bl, cl and
dlThe respectively two fingers number degradation model parameter at l moment, ξl-1For the systematic procedure noise at l-1 moment；θl,1, θl,2, θl,3And θl,4
Respectively first dimension of system state amount, second dimension, third dimension and four dimensions, the online of l moment are good for
Kang Yinzi HIlFor the observed quantity of system, μlTo measure noise, cycle is the current charge and discharge cycles periodicity of battery, g (SOHl)
Mapping relations function between SOH and online HI.
5. a kind of space lithium ion battery state joint method of estimation based on Multiple Time Scales according to claim 1,
It is characterized in that, in step 5, when SOC On-line Estimations, when the time scale k under micro-scale reaches a charge and discharge cycles week
When the phase, meet change of scale condition, carries out a SOH estimation.
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