CN102289557A - Battery model parameter and residual battery capacity joint asynchronous online estimation method - Google Patents

Battery model parameter and residual battery capacity joint asynchronous online estimation method Download PDF

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CN102289557A
CN102289557A CN2011101274791A CN201110127479A CN102289557A CN 102289557 A CN102289557 A CN 102289557A CN 2011101274791 A CN2011101274791 A CN 2011101274791A CN 201110127479 A CN201110127479 A CN 201110127479A CN 102289557 A CN102289557 A CN 102289557A
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battery
estimation
model parameter
calculate
matrix
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CN102289557B (en
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何志伟
高明煜
曾毓
黄继业
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Hangzhou Dianzi University
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Hangzhou Dianzi University
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Abstract

The invention relates to a battery model parameter and residual battery capacity joint asynchronous online estimation method. The existing method generally supposes that the parameters such as internal resistance of the same type of batteries is basically unchanged, thus the influence of battery aging on the residual battery capacity estimation precision is difficult to overcome. The method provided by the invention measures the battery end voltage and battery supply current at the current moment, estimates the residual battery capacity at the moment by a sampling point Kalman filtering algorithm based on the estimation result of moment battery model parameter according to a reasonable battery model and based on proper initialization, and then finishes estimation on the battery model parameter by the sampling point Kalman filtering algorithm by use of the residual battery capacity estimated at the moment. Estimation of the residual battery capacity and battery model parameter is asynchronously and alternately finished in an online mode. The method provided by the invention can conveniently perform online estimation on the residual battery capacity, has high convergence rate and high estimation precision, and suffers little influence of the battery aging.

Description

A kind of battery model parameter and dump energy are united asynchronous On-line Estimation method
Technical field
The invention belongs to the battery technology field, be specifically related to a kind of battery model parameter and dump energy and unite asynchronous On-line Estimation method.
Background technology
Battery has obtained in fields such as communication, electric system, military equipments using widely as standby power supply.Compare with traditional fuel-engined vehicle, electric automobile can be realized zero-emission, is the main developing direction of following automobile therefore.Battery is directly as the active energy supply part in electric automobile, so the quality of its duty is directly connected to the driving safety and the operational reliability of whole automobile.Good for guaranteeing the battery performance in the electric automobile, prolong electric battery serviceable life, must be in time, exactly the running status of electrolytic cell, battery is carried out rational and effective management and control.
The accurate estimation of battery charge state (State of Charge is hereinafter to be referred as SOC) is a technology most crucial in the battery management system (bms).The SOC of battery can't directly record with a kind of sensor, and it must pass through the measurement to some other physical quantitys, and adopts certain mathematical model and algorithm to estimate to obtain.
Battery SOC method of estimation commonly used at present has open-circuit voltage method, ampere-hour method etc.The open-circuit voltage method carry out battery SOC when estimating battery must leave standstill the long period reaching steady state (SS), and only be applicable to that the SOC of electric automobile under dead ship condition estimates, can not satisfy online detection requirements.The ampere-hour method is vulnerable to the influence of current measurement precision, and under high temperature or the violent situation of current fluctuation, precision is very poor.On the other hand, existing method supposes all that generally parameters such as its internal resistance of battery of the same type are constant substantially, thereby same type cell is carried out all adopting when SOC estimates same group model parameter, this hypothesis is set up when battery does not take place to wear out often, but when cell degradation is serious, the internal resistances of cell etc. can have greatly changed, and carry out SOC based on original model parameter again this moment and estimate deviation largely will certainly take place.
Summary of the invention
Purpose of the present invention overcomes the deficiencies in the prior art exactly, propose a kind of battery model parameter and dump energy and unite asynchronous On-line Estimation method, when On-line Estimation goes out battery SOC, can unite asynchronous On-line Estimation to model parameter, thereby overcome because the battery parameter that cell degradation brings changes the influence of battery SOC being estimated accuracy.The inventive method goes for all batteries, and estimated accuracy is higher.
Battery model parameter of the present invention and dump energy are united asynchronous On-line Estimation method, and concrete steps are:
Step (1) is measured
Figure 2011101274791100002DEST_PATH_IMAGE001
Battery terminal voltage constantly
Figure 261102DEST_PATH_IMAGE002
With the powered battery electric current ,
Figure 193024DEST_PATH_IMAGE004
Step (2) is represented each state-of-charge dependence constantly of battery with state equation and observation equation:
State equation:
Figure 2011101274791100002DEST_PATH_IMAGE005
Observation equation:
Wherein Be the state-of-charge of battery, i.e. dump energy;
Figure 58922DEST_PATH_IMAGE008
Be the discharge scale-up factor of battery, reflection be the influence degrees of factors such as discharge rate, temperature to battery SOC, only consider the influence of discharge rate among the present invention;
Figure 2011101274791100002DEST_PATH_IMAGE009
Be that battery is in room temperature 25 Getable specified total electric weight under the condition, when discharging with the discharge rate of 1/30 times of rated current,
Figure 2011101274791100002DEST_PATH_IMAGE011
Be measuring intervals of TIME,
Figure 552537DEST_PATH_IMAGE012
For handling noise.
Figure 2011101274791100002DEST_PATH_IMAGE013
Being the parameter of battery observation model, is a column vector;
Figure 10063DEST_PATH_IMAGE014
Be the internal resistance of battery,
Figure 2011101274791100002DEST_PATH_IMAGE015
Be observation noise.
The discharge scale-up factor
Figure 201267DEST_PATH_IMAGE008
Definite method be:
(a) will be full of the battery of electricity fully with different discharge rates
Figure 836779DEST_PATH_IMAGE016
( ,
Figure 477713DEST_PATH_IMAGE018
Nominal discharge current for battery) constant-current discharge
Figure 2011101274791100002DEST_PATH_IMAGE019
Inferior, calculate the total electric weight of battery under the corresponding discharge rate
Figure 551980DEST_PATH_IMAGE020
,
Figure 2011101274791100002DEST_PATH_IMAGE021
(b) simulate according to least square method
Figure 648287DEST_PATH_IMAGE020
With
Figure 454701DEST_PATH_IMAGE016
Between quafric curve relation, promptly under minimum mean square error criterion, obtain simultaneously and satisfy
Figure 209030DEST_PATH_IMAGE022
,
Figure 2011101274791100002DEST_PATH_IMAGE023
Be optimal coefficient.
(c) at discharge current be
Figure 585522DEST_PATH_IMAGE003
The time, corresponding discharge scale-up factor
Figure 108908DEST_PATH_IMAGE008
For:
Figure 335490DEST_PATH_IMAGE024
Herein, because discharge scale-up factor and cell degradation etc. are irrelevant, therefore, optimal coefficient
Figure 577115DEST_PATH_IMAGE023
Battery for same type only need be determined once, can be used as the remaining capacity estimation that known constant is directly used in all batteries of the same type after determining.
Step (3) is carried out following initialization procedure:
(a) initialization of battery dump energy estimation:
Initial state
Figure 2011101274791100002DEST_PATH_IMAGE025
And variance
Figure 930867DEST_PATH_IMAGE026
Be respectively:
Figure 2011101274791100002DEST_PATH_IMAGE027
，
Handle noise
Figure 348652DEST_PATH_IMAGE012
Variance , observation noise Variance
Figure 969438DEST_PATH_IMAGE030
Be respectively:
Figure 2011101274791100002DEST_PATH_IMAGE031
,
Figure 575737DEST_PATH_IMAGE032
Scale parameter
Figure 2011101274791100002DEST_PATH_IMAGE033
For:
Figure 409701DEST_PATH_IMAGE034
State vector after the expansion
Figure 2011101274791100002DEST_PATH_IMAGE035
And covariance
Figure 173389DEST_PATH_IMAGE036
For:
，
Figure 118211DEST_PATH_IMAGE038
The average weighting coefficient
Figure 2011101274791100002DEST_PATH_IMAGE039
With the variance weighted coefficient
Figure 785209DEST_PATH_IMAGE040
Be respectively:
Figure 2011101274791100002DEST_PATH_IMAGE041
，
Figure 603124DEST_PATH_IMAGE042
， ，
Figure 103375DEST_PATH_IMAGE044
(b) initialization of battery model parameter estimation:
Choose the initial model parameter arbitrarily
Figure 2011101274791100002DEST_PATH_IMAGE045
Set
Figure 101156DEST_PATH_IMAGE046
Square root mean square deviation matrix be
Figure 2011101274791100002DEST_PATH_IMAGE047
,
Figure 370463DEST_PATH_IMAGE048
Wherein
Figure 2011101274791100002DEST_PATH_IMAGE049
For
Figure 296962DEST_PATH_IMAGE050
Unit matrix;
Choose proportionality constant
Figure 2011101274791100002DEST_PATH_IMAGE051
,
Figure 284510DEST_PATH_IMAGE052
Set variable
Figure 2011101274791100002DEST_PATH_IMAGE053
Set weighting coefficient
Figure 885649DEST_PATH_IMAGE054
,
Figure 2011101274791100002DEST_PATH_IMAGE055
Step (4) adopts the sampling point Kalman filtering algorithm recursion that circulates:
Constantly
Figure 25774DEST_PATH_IMAGE004
, according to the battery terminal voltage that records
Figure 44546DEST_PATH_IMAGE002
And the supply current of battery
Figure 519389DEST_PATH_IMAGE003
, follow these steps to the asynchronous estimation of associating that iteration is carried out battery model parameter and dump energy:
(a) the estimation flow process of battery dump energy
1. basis
Figure 547388DEST_PATH_IMAGE056
Extended mode vector constantly And covariance
Figure 40555DEST_PATH_IMAGE058
, calculate all sampled point sequences in this moment :
2. carrying out time domain according to state equation upgrades:
By the sampled point sequence
Figure 5417DEST_PATH_IMAGE059
, upgrade according to state equation calculating sampling point
Figure 2011101274791100002DEST_PATH_IMAGE061
:
Figure 633845DEST_PATH_IMAGE062
Sampled point is upgraded
Figure 261746DEST_PATH_IMAGE061
Be weighted, computing mode is estimated
Figure 2011101274791100002DEST_PATH_IMAGE063
:
Figure 950216DEST_PATH_IMAGE064
Computing mode is estimated Variance
Figure 2011101274791100002DEST_PATH_IMAGE065
:
Figure 254607DEST_PATH_IMAGE066
3. finish to measure according to observation equation and upgrade:
Upgrade by sampled point
Figure 473099DEST_PATH_IMAGE061
And
Figure 270153DEST_PATH_IMAGE056
Estimates of parameters constantly
Figure 2011101274791100002DEST_PATH_IMAGE067
, calculate measurement according to observation equation and upgrade
Figure 190574DEST_PATH_IMAGE068
:
Figure 2011101274791100002DEST_PATH_IMAGE069
Upgrade measuring
Figure 160804DEST_PATH_IMAGE068
Be weighted, calculate to measure and estimate
Figure 984534DEST_PATH_IMAGE070
:
Figure 2011101274791100002DEST_PATH_IMAGE071
Calculate to measure and estimate
Figure 14807DEST_PATH_IMAGE070
Variance
Figure 110939DEST_PATH_IMAGE072
:
Figure DEST_PATH_IMAGE073
Calculate
Figure 137058DEST_PATH_IMAGE061
With
Figure 64562DEST_PATH_IMAGE068
Cross covariance
Figure 203420DEST_PATH_IMAGE074
:
Figure DEST_PATH_IMAGE075
Calculate kalman gain
Figure 599897DEST_PATH_IMAGE076
:
Figure DEST_PATH_IMAGE077
Computing mode is upgraded
Figure 489093DEST_PATH_IMAGE078
:
Figure DEST_PATH_IMAGE079
Computing mode is upgraded
Figure 271105DEST_PATH_IMAGE078
Variance
Figure 580863DEST_PATH_IMAGE080
:
Figure DEST_PATH_IMAGE081
By above-mentioned flow process, resulting state updating value
Figure 464637DEST_PATH_IMAGE078
Be current time
Figure 580360DEST_PATH_IMAGE001
The estimated battery dump energy that obtains.
(b) the estimation flow process of battery model parameter:
1. the estimated value of computation model parameter :
Figure DEST_PATH_IMAGE083
The estimated value of the square root mean square deviation matrix of computation model parameter
Figure 215314DEST_PATH_IMAGE084
:
Figure DEST_PATH_IMAGE085
, wherein,
Figure 648701DEST_PATH_IMAGE086
,
Figure DEST_PATH_IMAGE087
The column vector that constitutes for the diagonal entry of corresponding matrix.
2. calculate
Figure 302536DEST_PATH_IMAGE082
The sampled point sequence :
Figure DEST_PATH_IMAGE089
Be 6 * 1 column vectors, Be 6 * 6 matrixes, so
Figure 462811DEST_PATH_IMAGE088
Be 6 * 13 matrixes.
3. measure by following various calculating and upgrade:
The observation sequence of calculating sampling point
Figure 277183DEST_PATH_IMAGE090
:
Figure DEST_PATH_IMAGE091
,
Figure 975012DEST_PATH_IMAGE090
Be 6 * 13 matrixes;
The calculating observation sequence
Figure 242045DEST_PATH_IMAGE090
Estimated value
Figure 440945DEST_PATH_IMAGE092
:
Figure DEST_PATH_IMAGE093
,
Figure 486655DEST_PATH_IMAGE094
For
Figure DEST_PATH_IMAGE095
Row;
The calculating observation sequence
Figure 47397DEST_PATH_IMAGE090
Square root mean square deviation matrix :
Figure DEST_PATH_IMAGE097
Calculate covariance matrix
Figure 681696DEST_PATH_IMAGE098
:
Calculate kalman gain
Figure 173857DEST_PATH_IMAGE100
:
Calculating parameter upgrades
Figure 228532DEST_PATH_IMAGE102
:
Figure DEST_PATH_IMAGE103
Calculate temporary variable
Figure 565972DEST_PATH_IMAGE104
:
Figure DEST_PATH_IMAGE105
The renewal of the square root mean square deviation matrix of computation model parameter
Figure 512239DEST_PATH_IMAGE106
:
Figure DEST_PATH_IMAGE107
Wherein
Figure 722771DEST_PATH_IMAGE108
The ORTHOGONAL TRIANGULAR DECOMPOSITION of matrix is asked in expression, and returns the upper triangular matrix that obtains;
Figure DEST_PATH_IMAGE109
Be the transpose of a matrix operation;
Figure 514010DEST_PATH_IMAGE110
Matrix is asked in expression
Figure DEST_PATH_IMAGE111
Cholesky decompose.
By above-mentioned flow process, resulting
Figure 904409DEST_PATH_IMAGE102
Be current time
Figure 402386DEST_PATH_IMAGE001
The estimated battery model parameter that obtains.
At each constantly, above-mentioned steps 4 (a), 4 (b) hocket, and therefore, the estimation of battery dump energy depends on the estimated result of a moment battery model parameter, on the other hand, the estimation of battery model parameter is then finished based on the estimated battery dump energy that obtains of current time.The whole circulation recursive process is online finishing, i.e. online asynchronous finish each estimation of battery dump energy constantly and the estimation of battery model parameter in the battery practical work process.
The present invention can carry out the Fast estimation of battery SOC easily, and can overcome the influence of cell degradation to model parameter.This method fast convergence rate, the estimated accuracy height, and be applicable to the Fast estimation of various battery SOCs.
According to a first aspect of the invention, disclose and a kind ofly be used for the battery model parameter and dump energy is united the measuring amount that asynchronous On-line Estimation method is relied on, be respectively the terminal voltage of battery and the supply current of battery.
According to a second aspect of the invention, a kind of state equation and observation equation that battery model parameter and dump energy are united asynchronous On-line Estimation method that be used for disclosed.
According to a third aspect of the invention we, disclose and a kind ofly be used for the battery model parameter and dump energy is united the initial value that asynchronous On-line Estimation method is relied on.Comprise the initialization value of battery dump energy estimation and the initial value of battery model parameter estimation etc.These initial values needn't be very accurate, in the successive iterations process of sampling point Kalman filtering their can be very rapid convergence near actual value.
According to a forth aspect of the invention, disclose a kind of application sample point Kalman filtering iteration and carried out the idiographic flow that battery model parameter and battery dump energy are united asynchronous On-line Estimation.The estimation of battery dump energy depends on the estimated result of a moment battery model parameter, and the estimation of battery model parameter is then finished based on the estimated battery dump energy that obtains of current time, estimates that flow processs replace asynchronous carrying out for two kinds.
Embodiment
Battery model parameter and dump energy are united asynchronous On-line Estimation method, and concrete steps are:
Step (1) is measured
Figure 236350DEST_PATH_IMAGE001
Battery terminal voltage constantly
Figure 186988DEST_PATH_IMAGE002
With the powered battery electric current
Figure 882543DEST_PATH_IMAGE003
,
Figure 235027DEST_PATH_IMAGE004
Step (2) is represented each state-of-charge dependence constantly of battery with state equation and observation equation:
State equation:
Figure 177575DEST_PATH_IMAGE005
Observation equation:
Figure 677827DEST_PATH_IMAGE006
Wherein Be the state-of-charge of battery, i.e. dump energy;
Figure 885528DEST_PATH_IMAGE008
Be the discharge scale-up factor of battery, reflection be the influence degrees of factors such as discharge rate, temperature to battery SOC, only consider the influence of discharge rate among the present invention;
Figure 998977DEST_PATH_IMAGE009
Be that battery is in room temperature 25
Figure 924208DEST_PATH_IMAGE010
Getable specified total electric weight under the condition, when discharging with the discharge rate of 1/30 times of rated current,
Figure 210833DEST_PATH_IMAGE011
Be measuring intervals of TIME, For handling noise.
Figure 369730DEST_PATH_IMAGE013
Being the parameter of battery observation model, is a column vector;
Figure 782256DEST_PATH_IMAGE014
Be the internal resistance of battery,
Figure 810255DEST_PATH_IMAGE015
Be observation noise.
The discharge scale-up factor
Figure 54155DEST_PATH_IMAGE008
Definite method be:
(a) will be full of the battery of electricity fully with different discharge rates
Figure 243828DEST_PATH_IMAGE016
(
Figure 143651DEST_PATH_IMAGE017
,
Figure 21346DEST_PATH_IMAGE018
Nominal discharge current for battery) constant-current discharge
Figure 323014DEST_PATH_IMAGE019
Inferior, calculate the total electric weight of battery under the corresponding discharge rate
Figure 745905DEST_PATH_IMAGE020
,
Figure 133024DEST_PATH_IMAGE021
(b) simulate according to least square method
Figure 502825DEST_PATH_IMAGE020
With
Figure 206470DEST_PATH_IMAGE016
Between quafric curve relation, promptly under minimum mean square error criterion, obtain simultaneously and satisfy
Figure 3525DEST_PATH_IMAGE022
,
Figure 674678DEST_PATH_IMAGE023
Be optimal coefficient.
(c) at discharge current be
Figure 848170DEST_PATH_IMAGE003
The time, corresponding discharge scale-up factor For:
Figure 141322DEST_PATH_IMAGE024
Herein, because discharge scale-up factor and cell degradation etc. are irrelevant, therefore, optimal coefficient
Figure 237454DEST_PATH_IMAGE023
Battery for same type only need be determined once, can be used as the remaining capacity estimation that known constant is directly used in all batteries of the same type after determining.
Step (3) is carried out following initialization procedure:
(a) initialization of battery dump energy estimation:
Initial state And variance
Figure 876563DEST_PATH_IMAGE026
Be respectively:
Figure 15420DEST_PATH_IMAGE027
，
Figure 411897DEST_PATH_IMAGE028
Handle noise
Figure 661613DEST_PATH_IMAGE012
Variance
Figure 646887DEST_PATH_IMAGE029
, observation noise
Figure 18962DEST_PATH_IMAGE015
Variance
Figure 89686DEST_PATH_IMAGE030
Be respectively:
Figure 454677DEST_PATH_IMAGE031
,
Figure 294457DEST_PATH_IMAGE032
Scale parameter For:
State vector after the expansion
Figure 986973DEST_PATH_IMAGE035
And covariance
Figure 494309DEST_PATH_IMAGE036
For:
Figure 145870DEST_PATH_IMAGE037
，
Figure 191186DEST_PATH_IMAGE038
The average weighting coefficient
Figure 648712DEST_PATH_IMAGE039
With the variance weighted coefficient
Figure 197505DEST_PATH_IMAGE040
Be respectively:
Figure 600061DEST_PATH_IMAGE041
，
Figure 867095DEST_PATH_IMAGE042
，
Figure 65995DEST_PATH_IMAGE043
，
Figure 531611DEST_PATH_IMAGE044
(b) initialization of battery model parameter estimation:
Choose the initial model parameter arbitrarily
Figure 790554DEST_PATH_IMAGE045
Set
Figure 357933DEST_PATH_IMAGE046
Square root mean square deviation matrix be
Figure 94945DEST_PATH_IMAGE047
,
Figure 618330DEST_PATH_IMAGE048
Wherein
Figure 110491DEST_PATH_IMAGE049
For
Figure 86537DEST_PATH_IMAGE050
Unit matrix;
Choose proportionality constant
Figure 938825DEST_PATH_IMAGE051
,
Figure 582296DEST_PATH_IMAGE052
Set variable
Figure 917462DEST_PATH_IMAGE053
Set weighting coefficient ,
Figure 787515DEST_PATH_IMAGE055
Step (4) adopts the sampling point Kalman filtering algorithm recursion that circulates:
Constantly
Figure 98542DEST_PATH_IMAGE004
, according to the battery terminal voltage that records
Figure 604609DEST_PATH_IMAGE002
And the supply current of battery
Figure 820827DEST_PATH_IMAGE003
, follow these steps to the asynchronous estimation of associating that iteration is carried out battery model parameter and dump energy:
(a) the estimation flow process of battery dump energy
1. basis
Figure 765649DEST_PATH_IMAGE056
Extended mode vector constantly
Figure 118133DEST_PATH_IMAGE057
And covariance
Figure 60681DEST_PATH_IMAGE058
, calculate all sampled point sequences in this moment
Figure 637349DEST_PATH_IMAGE059
:
Figure 57966DEST_PATH_IMAGE060
2. carrying out time domain according to state equation upgrades:
By the sampled point sequence , upgrade according to state equation calculating sampling point :
Figure 365954DEST_PATH_IMAGE062
Sampled point is upgraded
Figure 403311DEST_PATH_IMAGE061
Be weighted, computing mode is estimated
Figure 730387DEST_PATH_IMAGE063
:
Figure 77055DEST_PATH_IMAGE064
Computing mode is estimated
Figure 489581DEST_PATH_IMAGE063
Variance
Figure 252001DEST_PATH_IMAGE065
:
Figure 745168DEST_PATH_IMAGE066
3. finish to measure according to observation equation and upgrade:
Upgrade by sampled point
Figure 200420DEST_PATH_IMAGE061
And Estimates of parameters constantly
Figure 728671DEST_PATH_IMAGE067
, calculate measurement according to observation equation and upgrade :
Figure 203962DEST_PATH_IMAGE069
Upgrade measuring
Figure 591081DEST_PATH_IMAGE068
Be weighted, calculate to measure and estimate
Figure 757621DEST_PATH_IMAGE070
:
Figure 913795DEST_PATH_IMAGE071
Calculate to measure and estimate
Figure 710850DEST_PATH_IMAGE070
Variance
Figure 634200DEST_PATH_IMAGE072
:
Figure 807693DEST_PATH_IMAGE073
Calculate
Figure 880691DEST_PATH_IMAGE061
With
Figure 848647DEST_PATH_IMAGE068
Cross covariance :
Figure 203853DEST_PATH_IMAGE075
Calculate kalman gain :
Computing mode is upgraded
Figure 119222DEST_PATH_IMAGE078
:
Figure 368938DEST_PATH_IMAGE079
Computing mode is upgraded
Figure 665796DEST_PATH_IMAGE078
Variance
Figure 975555DEST_PATH_IMAGE080
:
Figure 108596DEST_PATH_IMAGE081
By above-mentioned flow process, resulting state updating value
Figure 162002DEST_PATH_IMAGE078
Be current time
Figure 1782DEST_PATH_IMAGE001
The estimated battery dump energy that obtains.
(b) the estimation flow process of battery model parameter:
1. the estimated value of computation model parameter :
The estimated value of the square root mean square deviation matrix of computation model parameter
Figure 445030DEST_PATH_IMAGE084
:
Figure 201634DEST_PATH_IMAGE085
, wherein,
Figure 167709DEST_PATH_IMAGE086
,
Figure 213025DEST_PATH_IMAGE087
The column vector that constitutes for the diagonal entry of corresponding matrix.
2. calculate
Figure 608235DEST_PATH_IMAGE082
The sampled point sequence
Figure 219345DEST_PATH_IMAGE088
:
Figure 307386DEST_PATH_IMAGE089
Figure 387469DEST_PATH_IMAGE082
Be 6 * 1 column vectors,
Figure 320790DEST_PATH_IMAGE084
Be 6 * 6 matrixes, so
Figure 989669DEST_PATH_IMAGE088
Be 6 * 13 matrixes.
3. measure by following various calculating and upgrade:
The observation sequence of calculating sampling point
Figure 310928DEST_PATH_IMAGE090
: ,
Figure 802270DEST_PATH_IMAGE090
Be 6 * 13 matrixes;
The calculating observation sequence
Figure 637239DEST_PATH_IMAGE090
Estimated value
Figure 801504DEST_PATH_IMAGE092
:
Figure 105447DEST_PATH_IMAGE093
, For
Figure 24041DEST_PATH_IMAGE090
Figure 437836DEST_PATH_IMAGE095
Row;
The calculating observation sequence
Figure 901179DEST_PATH_IMAGE090
Square root mean square deviation matrix
Figure 307889DEST_PATH_IMAGE096
:
Figure 805867DEST_PATH_IMAGE097
Calculate covariance matrix
Figure 311934DEST_PATH_IMAGE098
:
Calculate kalman gain :
Figure 139972DEST_PATH_IMAGE101
Calculating parameter upgrades
Figure 816941DEST_PATH_IMAGE102
:
Figure 254876DEST_PATH_IMAGE103
Calculate temporary variable
Figure 754122DEST_PATH_IMAGE104
:
Figure 961112DEST_PATH_IMAGE105
The renewal of the square root mean square deviation matrix of computation model parameter
Figure 136878DEST_PATH_IMAGE106
:
Figure 62109DEST_PATH_IMAGE107
Wherein
Figure 286417DEST_PATH_IMAGE108
The ORTHOGONAL TRIANGULAR DECOMPOSITION of matrix is asked in expression, and returns the upper triangular matrix that obtains;
Figure 925078DEST_PATH_IMAGE109
Be the transpose of a matrix operation;
Figure 209428DEST_PATH_IMAGE110
Matrix is asked in expression
Figure 418693DEST_PATH_IMAGE111
Cholesky decompose.
By above-mentioned flow process, resulting
Figure 446692DEST_PATH_IMAGE102
Be current time
Figure 628274DEST_PATH_IMAGE001
The estimated battery model parameter that obtains.
At each constantly, above-mentioned steps 4 (a), 4 (b) hocket, and therefore, the estimation of battery dump energy depends on the estimated result of a moment battery model parameter, on the other hand, the estimation of battery model parameter is then finished based on the estimated battery dump energy that obtains of current time.The whole circulation recursive process is online finishing, i.e. online asynchronous finish each estimation of battery dump energy constantly and the estimation of battery model parameter in the battery practical work process.

Claims (1)

1. battery model parameter and dump energy are united asynchronous On-line Estimation method, it is characterized in that the concrete steps of this method are:
Step (1) is measured
Figure 2011101274791100001DEST_PATH_IMAGE002
Battery terminal voltage constantly
Figure 2011101274791100001DEST_PATH_IMAGE004
With the powered battery electric current
Figure 2011101274791100001DEST_PATH_IMAGE006
,
Figure 2011101274791100001DEST_PATH_IMAGE008
Step (2) is represented each state-of-charge dependence constantly of battery with state equation and observation equation:
State equation:
Figure 2011101274791100001DEST_PATH_IMAGE010
Observation equation:
Figure DEST_PATH_IMAGE012
Wherein
Figure DEST_PATH_IMAGE014
Be the state-of-charge of battery, i.e. dump energy;
Figure DEST_PATH_IMAGE016
Be the discharge scale-up factor of battery, reflection be the influence degrees of factors such as discharge rate, temperature to battery SOC, only consider the influence of discharge rate among the present invention;
Figure DEST_PATH_IMAGE018
Be that battery is in room temperature 25 Getable specified total electric weight under the condition, when discharging with the discharge rate of 1/30 times of rated current,
Figure DEST_PATH_IMAGE022
Be measuring intervals of TIME, For handling noise;
Figure DEST_PATH_IMAGE026
Being the parameter of battery observation model, is a column vector;
Figure DEST_PATH_IMAGE028
Be the internal resistance of battery,
Figure DEST_PATH_IMAGE030
Be observation noise;
The discharge scale-up factor
Figure 130639DEST_PATH_IMAGE016
Definite method be:
(a) will be full of the battery of electricity fully with different discharge rates
Figure DEST_PATH_IMAGE032
Constant-current discharge
Figure DEST_PATH_IMAGE034
Inferior, calculate the total electric weight of battery under the corresponding discharge rate
Figure DEST_PATH_IMAGE036
,
Figure DEST_PATH_IMAGE038
,
Figure DEST_PATH_IMAGE040
,
Figure DEST_PATH_IMAGE042
,
Figure DEST_PATH_IMAGE044
Nominal discharge current for battery;
(b) simulate according to least square method With
Figure 887691DEST_PATH_IMAGE032
Between quafric curve relation, promptly under minimum mean square error criterion, obtain simultaneously and satisfy ,
Figure DEST_PATH_IMAGE048
Be optimal coefficient;
(c) at discharge current be
Figure 779993DEST_PATH_IMAGE006
The time, corresponding discharge scale-up factor
Figure 109343DEST_PATH_IMAGE016
For:
Herein, because discharge scale-up factor and cell degradation are irrelevant, so optimal coefficient
Figure 121293DEST_PATH_IMAGE048
Battery for same type only need be determined once, can be used as the remaining capacity estimation that known constant is directly used in all batteries of the same type after determining;
Step (3) is carried out following initialization procedure:
(a) initialization of battery dump energy estimation:
Initial state And variance
Figure DEST_PATH_IMAGE054
Be respectively:
Figure DEST_PATH_IMAGE056
，
Figure DEST_PATH_IMAGE058
Handle noise
Figure 462275DEST_PATH_IMAGE024
Variance
Figure DEST_PATH_IMAGE060
, observation noise
Figure 721218DEST_PATH_IMAGE030
Variance
Figure DEST_PATH_IMAGE062
Be respectively:
Figure DEST_PATH_IMAGE064
,
Figure DEST_PATH_IMAGE066
Scale parameter
Figure DEST_PATH_IMAGE068
For:
Figure DEST_PATH_IMAGE070
State vector after the expansion
Figure DEST_PATH_IMAGE072
And covariance
Figure DEST_PATH_IMAGE074
For:
Figure DEST_PATH_IMAGE076
，
Figure DEST_PATH_IMAGE078
The average weighting coefficient
Figure 2011101274791100001DEST_PATH_IMAGE080
With the variance weighted coefficient
Figure 2011101274791100001DEST_PATH_IMAGE082
Be respectively:
Figure 2011101274791100001DEST_PATH_IMAGE084
，
Figure 2011101274791100001DEST_PATH_IMAGE086
，
Figure 2011101274791100001DEST_PATH_IMAGE088
，
Figure 2011101274791100001DEST_PATH_IMAGE090
(b) initialization of battery model parameter estimation:
Choose the initial model parameter arbitrarily
Figure 2011101274791100001DEST_PATH_IMAGE092
Set
Figure 2011101274791100001DEST_PATH_IMAGE094
Square root mean square deviation matrix be
Figure 2011101274791100001DEST_PATH_IMAGE096
,
Figure 2011101274791100001DEST_PATH_IMAGE098
, wherein
Figure 2011101274791100001DEST_PATH_IMAGE100
For
Figure 2011101274791100001DEST_PATH_IMAGE102
Unit matrix;
Choose proportionality constant ,
Figure 2011101274791100001DEST_PATH_IMAGE106
Set variable
Figure 2011101274791100001DEST_PATH_IMAGE108
Set weighting coefficient
Figure 2011101274791100001DEST_PATH_IMAGE110
,
Step (4) adopts the sampling point Kalman filtering algorithm recursion that circulates:
Constantly
Figure 368492DEST_PATH_IMAGE008
, according to the battery terminal voltage that records And the supply current of battery
Figure 439009DEST_PATH_IMAGE006
, follow these steps to the asynchronous estimation of associating that iteration is carried out battery model parameter and dump energy:
(a) the estimation flow process of battery dump energy
1. basis
Figure 2011101274791100001DEST_PATH_IMAGE114
Extended mode vector constantly
Figure 2011101274791100001DEST_PATH_IMAGE116
And covariance
Figure 2011101274791100001DEST_PATH_IMAGE118
, calculate all sampled point sequences in this moment
Figure DEST_PATH_IMAGE120
:
Figure DEST_PATH_IMAGE122
2. carrying out time domain according to state equation upgrades:
By the sampled point sequence
Figure 619585DEST_PATH_IMAGE120
, upgrade according to state equation calculating sampling point :
Figure DEST_PATH_IMAGE126
Sampled point is upgraded
Figure 657949DEST_PATH_IMAGE124
Be weighted, computing mode is estimated
Figure DEST_PATH_IMAGE128
:
Figure DEST_PATH_IMAGE130
Computing mode is estimated
Figure 74018DEST_PATH_IMAGE128
Variance :
Figure DEST_PATH_IMAGE134
3. finish to measure according to observation equation and upgrade:
Upgrade by sampled point
Figure 533467DEST_PATH_IMAGE124
And Estimates of parameters constantly
Figure DEST_PATH_IMAGE136
, calculate measurement according to observation equation and upgrade
Figure DEST_PATH_IMAGE138
:
Figure DEST_PATH_IMAGE140
Upgrade measuring Be weighted, calculate to measure and estimate
Figure DEST_PATH_IMAGE142
:
Figure DEST_PATH_IMAGE144
Calculate to measure and estimate
Figure 489419DEST_PATH_IMAGE142
Variance
Figure DEST_PATH_IMAGE146
:
Figure DEST_PATH_IMAGE148
Calculate
Figure 862763DEST_PATH_IMAGE124
With
Figure 368831DEST_PATH_IMAGE138
Cross covariance
Figure DEST_PATH_IMAGE150
:
Figure DEST_PATH_IMAGE152
Calculate kalman gain
Figure DEST_PATH_IMAGE154
:
Figure DEST_PATH_IMAGE156
Computing mode is upgraded
Figure DEST_PATH_IMAGE158
:
Figure DEST_PATH_IMAGE160
Computing mode is upgraded
Figure 332851DEST_PATH_IMAGE158
Variance
Figure DEST_PATH_IMAGE162
:
Figure DEST_PATH_IMAGE164
By above-mentioned flow process, resulting state updating value
Figure 90723DEST_PATH_IMAGE158
Be current time
Figure 443206DEST_PATH_IMAGE002
The estimated battery dump energy that obtains;
(b) the estimation flow process of battery model parameter:
1. the estimated value of computation model parameter
Figure DEST_PATH_IMAGE166
:
The estimated value of the square root mean square deviation matrix of computation model parameter
Figure DEST_PATH_IMAGE170
:
Figure DEST_PATH_IMAGE172
, wherein,
Figure DEST_PATH_IMAGE174
,
Figure DEST_PATH_IMAGE176
The column vector that constitutes for the diagonal entry of corresponding matrix;
2. calculate
Figure 127698DEST_PATH_IMAGE166
The sampled point sequence :
Figure DEST_PATH_IMAGE180
；
Figure 440999DEST_PATH_IMAGE166
Be 6 * 1 column vectors,
Figure 861616DEST_PATH_IMAGE170
Be 6 * 6 matrixes, so
Figure 147235DEST_PATH_IMAGE178
Be 6 * 13 matrixes;
3. measure by following various calculating and upgrade:
The observation sequence of calculating sampling point
Figure DEST_PATH_IMAGE182
:
Figure DEST_PATH_IMAGE184
,
Figure 57422DEST_PATH_IMAGE182
Be 6 * 13 matrixes;
The calculating observation sequence
Figure 795702DEST_PATH_IMAGE182
Estimated value
Figure DEST_PATH_IMAGE186
:
Figure DEST_PATH_IMAGE188
,
Figure DEST_PATH_IMAGE190
For
Figure 892447DEST_PATH_IMAGE182
Figure DEST_PATH_IMAGE192
Row;
The calculating observation sequence
Figure 281840DEST_PATH_IMAGE182
Square root mean square deviation matrix
Figure DEST_PATH_IMAGE194
:
Calculate covariance matrix :
Figure DEST_PATH_IMAGE200
Calculate kalman gain
Figure DEST_PATH_IMAGE202
:
Figure DEST_PATH_IMAGE204
Calculating parameter upgrades :
Figure DEST_PATH_IMAGE208
Calculate temporary variable :
Figure DEST_PATH_IMAGE212
The renewal of the square root mean square deviation matrix of computation model parameter
Figure DEST_PATH_IMAGE214
:
Wherein
Figure DEST_PATH_IMAGE218
The ORTHOGONAL TRIANGULAR DECOMPOSITION of matrix is asked in expression, and returns the upper triangular matrix that obtains; Be the transpose of a matrix operation; Matrix is asked in expression
Figure DEST_PATH_IMAGE224
Cholesky decompose;
By above-mentioned flow process, resulting
Figure 945951DEST_PATH_IMAGE206
Be current time
Figure 358478DEST_PATH_IMAGE002
The estimated battery model parameter that obtains;
At each constantly, above-mentioned steps 4 (a), 4 (b) hocket, and therefore, the estimation of battery dump energy depends on the estimated result of a moment battery model parameter, on the other hand, the estimation of battery model parameter is then finished based on the estimated battery dump energy that obtains of current time; The whole circulation recursive process is online finishing, i.e. online asynchronous finish each estimation of battery dump energy constantly and the estimation of battery model parameter in the battery practical work process.
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CN102928783A (en) * 2012-07-19 2013-02-13 北京金山安全软件有限公司 Method and device for estimating available time of battery power and mobile equipment
CN102928783B (en) * 2012-07-19 2014-09-03 北京金山安全软件有限公司 Method and device for estimating available time of battery power and mobile equipment
CN103077291A (en) * 2013-01-25 2013-05-01 华北电力大学 Battery charge and discharge process digital simulation method capable of setting initial state of charge
CN103077291B (en) * 2013-01-25 2016-05-18 华北电力大学 The battery charge and discharge process digital simulation method of initial state-of-charge can be set
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CN107421543B (en) * 2017-06-22 2020-06-05 北京航空航天大学 Implicit function measurement model filtering method based on state dimension expansion
CN107421543A (en) * 2017-06-22 2017-12-01 北京航空航天大学 A kind of implicit function measurement model filtering method being augmented based on state
CN108008320A (en) * 2017-12-28 2018-05-08 上海交通大学 A kind of charge states of lithium ion battery and the adaptive combined method of estimation of model parameter
CN108008320B (en) * 2017-12-28 2020-03-17 上海交通大学 Lithium ion battery state of charge and model parameter self-adaptive joint estimation method
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