CN108037464A - A kind of method of the battery pack SOC estimations based on IMM-EKF - Google Patents

A kind of method of the battery pack SOC estimations based on IMM-EKF Download PDF

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CN108037464A
CN108037464A CN201711395497.1A CN201711395497A CN108037464A CN 108037464 A CN108037464 A CN 108037464A CN 201711395497 A CN201711395497 A CN 201711395497A CN 108037464 A CN108037464 A CN 108037464A
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soc
interaction models
moment
battery pack
model
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CN108037464B (en
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朱浩
陈华
邓元望
江银锋
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Hunan Hong Xun Yi Amperex Technology Ltd
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Hunan Hong Xun Yi Amperex Technology Ltd
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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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements

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Abstract

This application discloses a kind of method of the battery pack SOC estimations based on IMM EKF, including：Maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction models are established according to battery voltage；The SOC of maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction models is estimated respectively using IMM EKF filters；The information distribution factor of each model is calculated, and probability fusion is carried out to the SOC of each model according to each information distribution factor, obtains battery pack entirety SOC.Battery pack is divided into three models by this method, is then carried out probability fusion to the SOC of each model according to information distribution factor, is obtained the higher battery pack entirety SOC of precision, solve the problems, such as that single model estimation error is larger.The application additionally provides a kind of system of the battery pack SOC estimations based on IMM EKF at the same time, has above-mentioned beneficial effect.

Description

A kind of method of the battery pack SOC estimations based on IMM-EKF
Technical field
This application involves electric vehicle engineering field, the side of more particularly to a kind of battery pack SOC estimations based on IMM-EKF Method and system.
Background technology
Battery management system (Battery Management System, BMS) is the important component of electric automobile, The estimation of battery charge state (State of Charge, SOC) is the core of battery management system, and what SOC was represented is battery The ratio of the capacity of residual capacity and its fully charged state, precision directly influence the service life of battery, security performance, Weighing apparatus control and the customization of thermal management policy, therefore accurate SOC estimations are particularly important for BMS.
Due to the demand of voltage and energy during electric automobile during traveling, battery management system need to be by hundreds of single batteries Carry out serial or parallel connection.However, due to the difference of material in cell production process, and in charge and discharge process battery parameter change Change, can cause the battery cell in same battery pack there are a degree of inconsistency, and then cause the SOC of each battery cell Have differences so that the SOC of battery pack entirety is difficult to estimate.
In the prior art, the SOC estimations to battery pack entirety are usually replaced overall with minimum SOC or average SOC SOC, if using minimum SOC as entirety SOC, can cause battery pack integrally to overcharge, or the voltage of battery cell reaches charging section To when battery pack SOC be not 100%；If using average SOC as entirety SOC, with the use of battery pack, battery consistency is deteriorated, Also occur that battery cell overcharges or cross the problem of putting.
Another evaluation method is estimated whole group battery as a polymeric monomer battery in existing algorithm, but this The inside of " big battery " is increasingly complex, more increasingly difficult than single battery modeling, and the uniformity between whole group is than between monomer Uniformity is worse, estimates difficulty bigger.
Therefore, how to lift the estimation accuracy of battery pack entirety SOC is that those skilled in the art need to solve at present Technical problem.
The content of the invention
The purpose of the application is to provide a kind of method and system of the battery pack SOC estimations based on IMM-EKF, this method energy Enough lift the estimation accuracy of battery pack entirety SOC.
In order to solve the above technical problems, the application provides a kind of method of the battery pack SOC estimations based on IMM-EKF, should Method includes：
Maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage are established according to battery voltage Interaction models；
The maximum monomer voltage interaction models, the minimum monomer voltage are handed over respectively using IMM-EKF filters The SOC of mutual model and the average voltage interaction models is estimated that correspondence obtains SOCmax, SOCmin and SOCaverage；
Maximum monomer voltage interaction models, the minimum monomer voltage interaction models and the average voltage is calculated to hand over The respective information distribution factor of mutual model；
Probability is carried out to the SOCmax, the SOCmin and the SOCaverage according to each described information distribution factor Fusion, obtains battery pack entirety SOC.
Optionally, using IMM-EKF filters respectively to the maximum monomer voltage interaction models, the minimum monomer The SOC of voltage interaction models and the average voltage interaction models estimated, it is corresponding obtain SOCmax, SOCmin and SOCaverage, including：
To maximum monomer voltage interaction models, the minimum monomer voltage interaction models and the average voltage interaction Model carries out discretization, obtains the target state equation and observational equation based on the IMM-EKF filters：
The state equation of the battery pack model is：Xk+1,i=A × Xk,i+B×(Ik-Ik,drf)；
The observational equation of the battery pack model is：Uk,i=Uk,oc-V1k,i-V2k,i-ItrueR0,i；
Wherein, i max, min or average, corresponding to the maximum monomer voltage interaction models, the minimum monomer Voltage interaction models or the average voltage interaction models, Xk+1,iFor the battery state at corresponding model k+1 moment, Xk,iTo be right Answer the battery state at model k moment, IkFor the collection current value at k moment, Ik,drfFor the current drift amount at k moment, A and B are Systematic parameter；Uk,iFor the terminal voltage at corresponding model k moment, V1k,iFor the battery pack polarization capacity C at corresponding model k moment1Pole Change voltage, V2k,iFor the battery pack polarization capacity C at corresponding model k moment2Polarizing voltage, ItrueFor electric current actual value, and Itrue =Ik-Ik,drf, R0,iFor the battery pack internal resistance of corresponding model.
Optionally, the battery state X at the corresponding model k+1 momentk+1,iSpecially
The battery state X at the corresponding model k momentk,iSpecially
It is described electric to the maximum monomer voltage interaction models, the minimum monomer respectively using IMM-EKF filters Pressure interaction models and the SOC of the average voltage interaction models estimated, it is corresponding obtain SOCmax, SOCmin and SOCaverage, including：
According to formula S OCk,i=Xk,i×[1 0 0 0]TSOC is calculatedk,max、SOCk,minAnd SOCk,average；
Wherein, SOCk+1,iFor the SOC value at corresponding model k+1 moment, including SOCk+1,max、SOCk+1,minWith SOCk+1,average, SOCk,iFor the SOC value at corresponding model k moment, including SOCk,max、SOCk,minAnd SOCk,average, Ik+1,drfFor The current drift amount at k+1 moment, V1k+1,iFor the battery pack polarization capacity C at corresponding model k+1 moment1Polarizing voltage, V2k+1,i For the battery pack polarization capacity C at corresponding model k+1 moment2Polarizing voltage.
Optionally, the XK, iCalculating process include：
According to formulaCalculate the kalman gain matrix at corresponding model k moment Kk,i；
According to formula Kk,i=[KSOC,i KV1,i KV2,i KI]TBy the kalman gain value K corresponding to V1 and V2V1And KV2Multiply With corresponding rejection coefficient k1 and k2, and by Kk,iIt is updated to
Kk,i=[KSOC,i k1×KV1,i k2×KV2,i KI]T；
According to formulaThe battery state X at the corresponding model k moment is calculatedk,i；
Wherein, P- k,iFor the error covariance estimation of corresponding model, h (Xk,Itrue) be the battery pack model observation side Journey, Ck,iFor the observational equation h (X of the battery pack modelk,Itrue) partial derivative, KSOC,iIt is corresponding for corresponding model SOC value Kalman gain value, KV1,iFor V1Corresponding kalman gain value, KV2,iFor V2Corresponding kalman gain value, KIFor current drift Value IdrfCorresponding kalman gain value, k1 and k2 are rejection coefficient,For the state estimation at corresponding model k moment, Kk,iFor The kalman gain matrix at corresponding model k moment, ek,iFor the new breath at corresponding model k moment.
Optionally, it is described according to formulaThe battery pack shape at the corresponding model k moment is calculated State Xk,i, including：
According to formula ek,i=U(t,k)i-h(Xk,Itrue) calculate the new breath e of the battery pack modelk,i；
Judge | ek,i| whether less than M；
If so, then make Kk,i=0；
If it is not, then according to formula Ek,i=ek,i×ek-1,iCalculate Ek,iValue, and judge Ek,iWhether 0 is more than；
If Ek,iMore than 0, then by Kk,iIt is updated to Kk,i×Kstrength；
If Ek,iNo more than 0, then by Kk,iIt is updated to Kk,i×Kcontrol；
Wherein, P-k,iFor the error covariance estimation of corresponding model, h (Xk,Itrue) be the battery pack model observation side Journey, Ck,iFor the observational equation h (X of the battery pack modelk,Itrue) first derivative, U(t,k)iGathered for the corresponding model k moment The magnitude of voltage arrived, KstrengthFor gain suppression coefficient, KcontrolFor rejection coefficient.
Optionally, the systematic parameter A is specially
The systematic parameterBSpecially
Wherein, t is the sampling time, C1,iAnd C2,iFor the polarization capacity of corresponding model battery pack, R1,iAnd R2,iFor corresponding mould The polarization resistance of type battery pack, η are coulombic efficiency, and Ca releases capacity for battery pack is maximum.
Optionally, the R1,i、C1,i、R2,i、C2,i、R0,iCalculating process includes：
Battery parameter appraising model is established, and trigger condition is set；
According to the acquisition got the R1,i、C1,i、R2,i、C2,i、R0,iInitial value determine battery parameter estimation Model parameter in model；
When the trigger condition is triggered, the model parameter in the battery parameter appraising model is updated；
The R is calculated using the battery parameter appraising model after renewal1,i、C1,i、R2,i、C2,i、R0,iCurrency.
Optionally, it is described to calculate the maximum monomer voltage interaction models, the minimum monomer voltage interaction models and institute The respective information distribution factor of average voltage interaction models is stated, including：
Judge whether the terminal voltage at the maximum monomer voltage interaction models k moment is less than maximum charge blanking voltage, and Whether the terminal voltage at the minimum monomer voltage interaction models k moment is more than discharge cut-off voltage；
If so, then according to formulaCalculate The information distribution factor ω at the average voltage interaction models k momentk,average, and according to formulaCalculate the information distribution at the maximum monomer voltage interaction models k moment because Sub- ωk,maxAnd the information distribution factor ω at the minimum monomer voltage interaction models k momentk,min；
Wherein, SOCk,midFor SOCk,maxWith SOCk,minAverage value, SOCk-1,packIt is overall for the battery pack at k-1 moment SOC。
Optionally, according to each described information distribution factor to the SOCmax, the SOCmin and the SOCaverage Probability fusion is carried out, obtains battery pack entirety SOC, including：
According to formulaObtain the electricity Total state estimation X at pond group k momentk,j, and total covariance P at the battery pack k momentk,j；
According to formula S OCk,pack=Xk,j×[1 0 0 0]TCalculate the battery pack entirety SOC at k momentk,pack。
The application also provides a kind of system of the battery pack SOC estimations based on IMM-EKF, which includes：
Modeling module, for establishing maximum monomer voltage interaction models, minimum monomer voltage interaction according to battery voltage Model and average voltage interaction models；
Estimation block, for using IMM-EKF filters respectively to the maximum monomer voltage interaction models, it is described most The SOC of minor comonomer voltage interaction models and the average voltage interaction models estimated, it is corresponding obtain SOCmax, SOCmin and SOCaverage；
Computing module, for calculate the maximum monomer voltage interaction models, the minimum monomer voltage interaction models and The respective information distribution factor of average voltage interaction models；
Fusion Module, for according to each described information distribution factor to the SOCmax, the SOCmin and described SOCaverage carries out probability fusion, obtains battery pack entirety SOC.
The method of a kind of battery pack SOC estimations based on IMM-EKF provided herein, by according to battery voltage Establish maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction models；It is more using interactive mode Model extension Kalman filtering (interactive multiple model extended kalman filter, IMM-EKF) Program respectively to the SOC of maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction models into Row estimation, correspondence obtain SOCmax, SOCmin and SOCaverage；Calculate maximum monomer voltage interaction models, minimum monomer electricity Press interaction models and the respective information distribution factor of average voltage interaction models；According to each information distribution factor to SOCmax, SOCmin and SOCaverage carries out probability fusion, obtains battery pack entirety SOC.
In the prior art, the SOC estimations to battery pack entirety usually replace entirety SOC with minimum SOC or average SOC (SOC only being estimated according to Vmin in such as patent CN105445665A, the SOC of estimation is less than real battery pack SOC), if with most Small SOC is entirety SOC, then battery pack can be caused integrally to overcharge, or battery cell voltage reach charging by when battery pack SOC is not 100%；If using average SOC as entirety SOC, with the use of battery pack, battery consistency is deteriorated, and electricity also occurs Pond monomer overcharges or crosses the problem of putting.
Another evaluation method is to be estimated whole group battery as a polymeric monomer battery in existing algorithm, but this The inside of " big battery " is increasingly complex, more increasingly difficult than single battery modeling, and the uniformity between whole group is than between monomer Uniformity is worse, estimates difficulty bigger.
And the application is divided into three models by the maximum voltage, minimum voltage and average voltage of battery in battery pack monomer, And using IMM-EKF filters respectively to maximum monomer voltage interaction models, minimum monomer voltage interaction models and average electricity The SOC of pressure interaction models is estimated, then is calculated maximum monomer voltage interaction models, minimum monomer voltage interaction models and be averaged The respective information distribution factor of voltage interaction models, the feelings for overcharging and crossing and put are considered by the calculating of information distribution factor at the same time Condition, finally carries out probability fusion to the SOC of each model according to each information distribution factor, it is overall to obtain the higher battery pack of precision SOC, solves the problems, such as that single model estimation error is larger.The application additionally provides a kind of battery pack based on IMM-EKF at the same time The system of SOC estimations, has above-mentioned beneficial effect, details are not described herein.
Brief description of the drawings
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, below will be to embodiment or existing There is attached drawing needed in technology description to be briefly described, it should be apparent that, drawings in the following description are only this The embodiment of application, for those of ordinary skill in the art, without creative efforts, can also basis The attached drawing of offer obtains other attached drawings.
The flow for the method that a kind of battery pack SOC based on IMM-EKF that Fig. 1 is provided by the embodiment of the present application is estimated Figure；
Fig. 2 is a kind of structure diagram of Order RC battery equivalent circuit model；
The flow for the method that another battery pack SOC based on IMM-EKF that Fig. 3 is provided by the embodiment of the present application is estimated Figure；
One kind of S203 is real in the method that another battery pack SOC based on IMM-EKF that Fig. 4 is provided by Fig. 3 is estimated The flow chart of border manifestation mode；
Flows of the Fig. 5 by battery pack SOC of another that the embodiment of the present application provides based on the IMM-EKF methods estimated Figure；
The structure for the system that a kind of battery pack SOC based on IMM-EKF that Fig. 6 is provided by the embodiment of the present application is estimated is shown It is intended to.
Embodiment
The core of the application is to provide a kind of method and system of the battery pack SOC estimations based on IMM-EKF, this method energy Enough lift the estimation accuracy of battery pack entirety SOC.
To make the purpose, technical scheme and advantage of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application In attached drawing, the technical solution in the embodiment of the present application is clearly and completely described, it is clear that described embodiment is Some embodiments of the present application, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art All other embodiments obtained without making creative work, shall fall in the protection scope of this application.
Please refer to Fig.1 and a kind of battery pack SOC based on IMM-EKF that Fig. 2, Fig. 1 are provided by the embodiment of the present application estimates The flow chart of the method for calculation；Fig. 2 is a kind of structure diagram of Order RC battery equivalent circuit model.
It specifically includes following steps：
S101：Maximum monomer voltage interaction models, minimum monomer voltage interaction models peace are established according to battery voltage Equal voltage interaction models；
Based on the SOC estimations to battery pack entirety in the prior art usually with minimum SOC or average SOC come instead of overall SOC, causes battery pack integrally to overcharge or cross the problems such as putting, and this application provides a kind of battery pack SOC estimations based on IMM-EKF Method, the higher battery pack entirety SOC of precision can be obtained；
Maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage are established according to battery voltage Interaction models, are specifically as follows：
The maximum voltage V of each single battery in battery pack is got by BMS voltage monitoring modulesmaxAnd minimum voltage Vmin；
According to formulaCalculate average voltageVaverage；
According to Vmax、VminAnd VaverageValue establish maximum monomer voltage interaction models, minimum monomer voltage interaction models With average voltage interaction models；Wherein, VtotalFor assembled battery total voltage, n is the number of single battery in battery pack；
Optionally, maximum monomer voltage interaction can be established according to Order RC battery equivalent circuit model as shown in Figure 2 Model, minimum monomer voltage interaction models and average voltage interaction models, Order RC battery equivalent circuit model mentioned herein Specially
Wherein, UOCFor battery open circuit voltage, there are non-linear relation, R with SOC1And R2For battery polarization internal resistance, C1And C2For Battery polarization capacitance, R0For battery ohmic internal resistance, V1And V2Correspond to C1And C2The polarizing voltage at both ends, U are battery terminal voltage；
It should be noted that being directed to same battery pack, step S101 is only performed once.
S102：Using IMM-EKF filters respectively to maximum monomer voltage interaction models, minimum monomer voltage interaction mould The SOC of type and average voltage interaction models is estimated that correspondence obtains SOCmax, SOCmin and SOCaverage；
Optionally, it is contemplated that coloured noise --- the current drift amount that Hall sensor drift is brought, can be in S101 The maximum monomer voltage interaction models of foundation, minimum monomer voltage interaction models and average voltage interaction models carry out discretization, Obtain the target state equation and observational equation based on IMM-EKF filters：
The state equation of the battery pack model is：Xk+1,i=A × Xk,i+B×(Ik-Ik,drf)；
The observational equation of the battery pack model is：Uk,i=Uk,oc-V1k,i-V2k,i-ItrueR0,i；
Wherein, i max, min or average, corresponding to the maximum monomer voltage interaction models, the minimum monomer Voltage interaction models or the average voltage interaction models, Xk+1,iFor the battery state at corresponding model k+1 moment, Xk,iTo be right Answer the battery state at model k moment, IkFor the collection current value at k moment, Ik,drfFor the current drift amount at k moment, A and B are Systematic parameter；Uk,iFor the terminal voltage at corresponding model k moment, V1k,iFor the battery pack polarization capacity C at corresponding model k moment1Pole Change voltage, V2k,iFor the battery pack polarization capacity C at corresponding model k moment2Polarizing voltage, ItrueFor electric current actual value, and Itrue =Ik-Ik,drf, current drift value is subtracted come calculating current actual value by gathering current value, effectively compensate for Kalman filtering To the short slab of coloured noise processing；R0,iFor the battery pack internal resistance of corresponding model；
Optionally, can be according to formulaThe battery state at corresponding model k moment is calculated Xk,i；
Wherein,For the state estimation at corresponding model k moment, Kk,iFor the kalman gain square at corresponding model k moment Battle array, ek,iFor the new breath at corresponding model k moment；
Optionally, the short slabs that handles coloured noise of IMM-EKF are considered, can be in the method for adoption status expansion by IdrfMake For state value, i.e. Xk+1,iIt is specifically as follows
Xk,iCorrespond to
On this basis, using IMM-EKF filters respectively to maximum monomer voltage interaction models, minimum monomer voltage The SOC of interaction models and average voltage interaction models is estimated that correspondence obtains
SOCmax, SOCmin and SOCaverage, are specifically as follows：
According to formula S OCk,i=Xk,i×[1 0 0 0]TSOC is calculatedk,max、SOCk,minAnd SOCk,average；
Wherein, SOCk+1,iFor the SOC value at corresponding model k+1 moment, including SOCk+1,max、SOCk+1,minWith SOCk+1,average, SOCk,iFor the SOC value at corresponding model k moment, including SOCk,max、SOCk,minAnd SOCk,average, Ik+1,drfFor The current drift amount at k+1 moment, V1k+1,iFor the battery pack polarization capacity C at corresponding model k+1 moment1Polarizing voltage, V2k+1,i For the battery pack polarization capacity C at corresponding model k+1 moment2Polarizing voltage.
S103：Calculate maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction models Respective information distribution factor；
It is mostly discrete disconnected that battery pack SOC estimations, which utilize weight coefficient during minimax SOC Weighted Fusions, in the prior art Point form, causes the SOC value of estimation is unsmooth to have saltus step；Meanwhile monomer is estimated and weighed by existing weights method estimation battery pack SOC Value fusion is divided into two independent parts so that the precision of battery pack entirety SOC (i.e. SOCpack) directly depends on the choosing of weights Select, but used weights do not obtain the proof of theory, based on this, the application is provided with a kind of letter of adaptive polo placement Distribution factor is ceased, can be distributed according to the battery pack entirety SOC at k-1 moment come the respective information of three models for determining the k moment The value of the factor；
Optionally, the calculating process of information distribution factor is specifically as follows：
Judge whether the terminal voltage at maximum monomer voltage interaction models k moment is less than maximum charge blanking voltage, and it is minimum Whether the terminal voltage at monomer voltage interaction models k moment is more than discharge cut-off voltage；
If so, then according to formulaCalculate The information distribution factor ω at average voltage interaction models k momentk,average, and according to formulaCalculate the information distribution factor at maximum monomer voltage interaction models k moment ωk,maxAnd the information distribution factor ω at minimum monomer voltage interaction models k momentk,min；
Wherein, SOCk,midFor SOCk,maxWith SOCk,minAverage value, SOCk-1,packIt is overall for the battery pack at k-1 moment SOC；
Before calculating the value of information distribution factor, it is necessary first to judge current battery whether in overcharging or The state of putting is crossed, that is, judges whether the terminal voltage at maximum monomer voltage interaction models k moment is less than maximum charge blanking voltage, and most Whether the terminal voltage at minor comonomer voltage interaction models k moment is more than discharge cut-off voltage；
Intermediate value SOCmid is calculated further according to SOCmax and SOCmin, and by comparing the big of SOCmid and SOCaverage It is small, it is more for SOCmax or SOCmin with entirety SOC deviations to determine, and then corresponding adjustment is made, such as：If SOCmid> SOCaverage then shows that maximum SOC deviation entirety SOC is more, then makes the information distribution factor of SOCaveragSo that the information distribution factor ω of SOCaveragk,averageIt is opposite to become higher, reach The effect for adjusting and balancing；Similarly, if SOCmid<SOCaverage then shows that minimum SOC deviation entirety SOC is more, then The distribution factor of SOCaverage is
Optionally, can also be to being accounted for when SOC is 100% and 0%, i.e.,：
When the terminal voltage at maximum monomer voltage model k moment is greater than or equal to maximum charge blanking voltage, ω is madek,max= 1, ωk,min=0, ωk,average=0；
When the terminal voltage at minimum monomer voltage model k moment is less than or equal to discharge cut-off voltage, ω is madek,max=0, ωk,min=0, ωk,average=1；
The battery pack entirety SOC being calculated using the information distribution factor of adaptive polo placement, can be simpler than existing The result that weights SOC is calculated is more smooth, and the SOC at both ends also more meets convention.
S104：Probability fusion is carried out to SOCmax, SOCmin and SOCaverage according to each information distribution factor, obtains electricity Pond group entirety SOC.
Optionally, can be according to formula To total state estimation X at battery pack k momentk,j, and total covariance P at battery pack k momentk,j；
According to formula S OCk,pack=Xk,j×[1 0 0 0]TCalculate the battery pack entirety SOC at k momentk,pack。
Based on above-mentioned technical proposal, the method for the battery pack SOC estimations provided by the embodiments of the present application based on IMM-EKF, By being divided into three models by the maximum voltage, minimum voltage and average voltage of battery in battery pack monomer, and utilize IMM-EKF Filter is respectively to maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction models SOC is estimated, then calculates maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction models Respective information distribution factor, the situation for overcharging and crossing and put, last basis are considered by the calculating of information distribution factor at the same time Each information distribution factor carries out probability fusion to the SOC of each model, obtains the higher battery pack entirety SOC of precision, solves list The problem of model estimation error is larger.
Existing battery pack SOC estimations do not account for difference (such as SOC value and the polarization electricity of the state value order of magnitude in state vector Press the quantity between Up values differential, the constant interval of SOC is between 0-1, and the value of V1 and V2 is in below 0.1V, and IdrfValue with More than 1A can be reached by the use of sensor), and easily there is overshoot using same error checking institute is stateful, quantity can be caused Overshoot occurs for the small quantity of state of level, causes algorithm to dissipate, based on this, on the basis of above-described embodiment, this application provides another A kind of method of the battery pack SOC estimations based on IMM-EKF, please refers to Fig.3, Fig. 3 is provided another by the embodiment of the present application The flow chart of the method for battery pack SOC estimation of the kind based on IMM-EKF；
It specifically includes following steps：
S201：According to formulaCalculate the kalman gain at corresponding model k moment Matrix Kk,i；
Wherein, P- k,i, can be according to formula for the error covariance estimation of corresponding model iCount Q Obtain, Ak,iFor systematic parameter mentioned above, Q is state-noise variance, and R is measurement noise variance, Ck,iFor battery pack mould Observational equation h (the X of typek,Itrue) partial derivative.
S202：According to formula Kk,i=[KSOC,i KV1,i KV2,i KI]TBy the kalman gain value K corresponding to V1 and V2V1And KV2It is multiplied by corresponding rejection coefficient k1 and k2, and by Kk,iIt is updated to
Kk,i=[KSOC,i k1×KV1,i k2×KV2,i KI]T；
Wherein, KSOC,iFor the corresponding kalman gain value of corresponding model SOC value, KV1,iFor V1Corresponding kalman gain Value, KV2,iFor V2Corresponding kalman gain value, KIFor current drift value IdrfCorresponding kalman gain value, k1 and k2 are suppression Coefficient；
Optionally, k1, k2 ∈ (0,1)；
Optionally, kalman gain can be integrally multiplied by rejection coefficient as iteration step length is smaller during real vehicle use 0.1, on the one hand it can be fluctuated with correction for reduction, on the other hand can reduce deflection difference prevents from calculating by error-detecting in controlled range Method dissipates.
S203：According to formulaThe battery state X at corresponding model k moment is calculatedk,i。
Wherein,For the state estimation at corresponding model k moment, Kk,iFor the kalman gain square at corresponding model k moment Battle array, ek,iFor the new breath at corresponding model k moment.
Based on above-mentioned technical proposal, in a kind of method of battery pack SOC estimations based on IMM-EKF provided herein The computational methods of the battery state at corresponding model k moment, according to the constant interval of each state value in state equation to karr When graceful gain size makees corresponding adjustment, the amplitude that can be adjusted according to needed for each parameter is reasonably each parameter setting Different rejection coefficient, to reach the effect for avoiding algorithm from dissipating.
The problem of existing battery pack SOC estimations do not account for algorithm diverging, can not take diverging process and effectively arrange Control is applied, such as：Q in traditional Kalman filtering algorithm, R are huge to the influence in iterative process, inappropriate Q, the direct shadows of R Arithmetic accuracy is rung, diverging is even resulted in, based on this, please refers to Fig.4, Fig. 4 is provided another based on IMM-EKF's by Fig. 3 The flow chart of a kind of practical manifestation mode of S203 in the method for battery pack SOC estimations.
It specifically includes following steps：
S301：According to formula ek,i=U(t,k)i-h(Xk,Itrue) calculate the new breath e of battery pack modelk,i；
The new breath e of battery pack model mentioned hereink,iFor collection voltages value and the difference of observation magnitude of voltage, it is possible to understand that For error；
Wherein, h (Xk,Itrue) be battery pack model observational equation, U(t,k)iThe voltage collected for the corresponding model k moment Value.
S302：Judge | ek,i| whether less than M；
If so, then enter step S303；If it is not, then enter step S304；
Wherein, M is the maximum deflection difference value of corresponding model accuracy.
S303：Make Kk,i=0；
If | ek,i| then show to have reached the equilibrium state of correction less than M, then make kalman gain Kk,i=0, i.e., should be repeatedly Ride instead of walk and updated suddenly without parameter, prevent from introducing model error.
S304：According to formula Ek,i=ek,i×ek-1,iCalculate Ek,iValue；
If | ek,i| not less than M, then by analyzing Ek,iWith ek,iRelation adjust kalman gain Kk,i, to prevent from sending out Dissipate.
S305：Judge Ek,iWhether 0 is more than；
If so, then enter step S306；If it is not, then enter step S307.
S306：If Ek,iMore than 0, then by Kk,iIt is updated to Kk,i×Kstrength；
If Ek,iMore than 0, then show the trend for the direction change that last corrected strength is inadequate, and error is reduced to absolute value Slowly, stronger control action is at this moment needed, by Kk,iIt is updated to Kk,i×Kstrength；
Optionally, Kstrength∈(1,10)；
S307：If Ek,iNo more than 0, then by Kk,iIt is updated to Kk,i×Kcontrol。
If Ek,iNo more than 0, then show that last corrected strength is excessive, at this moment need weaker control action, by Kk,iMore New is Kk,i×Kcontrol；
Optionally, Kcontrol∈(0,1)；
Wherein, KstrengthFor gain suppression coefficient, KcontrolFor rejection coefficient.
Based on above-mentioned technical proposal, the present embodiment can effectively solve to filter by carrying out battery model new breath detection Wave-path sequence divergence problem, simultaneously as introduce error-detecting has carried out corresponding enhancing and inhibition to kalman gain, The situation that two parameters of process noise Q and measurement noise R bring algorithm diverging is slackened, so as to expand Kalman filtering journey As long as the value of the two parameters can be carried out iterating to calculate in the same order of magnitude in sequence, the numerous and diverse of adjusting parameter is avoided repeatedly Process.
Based on above-described embodiment, systematic parameter A is specifically as followsSystematic parameter B has Body can beWherein, t is the sampling time, C1,iAnd C2,iFor the polarization electricity of corresponding model battery pack Hold, R1,iAnd R2,iFor the polarization resistance of corresponding model battery pack, η is coulombic efficiency, and Ca releases capacity for battery pack is maximum, is based on This, refer to Fig. 5, the method that Fig. 5 is estimated by battery pack SOC of another that the embodiment of the present application provides based on IMM-EKF Flow chart.
It specifically includes following steps：
S401：Battery parameter appraising model is established, and trigger condition is set；
By Order RC battery equivalent circuit modelDiscretization obtains：
Uk=UOC,k-b1UOC,k-1-b2UOC,k-2+b1Uk-1+b2Uk-2+b3Ik+b4Ik-1+b5Ik-2；
Define battery parameter appraising model data matrix be：
Φ=[Uk-1-UOC,k-1 Uk-2-UOC,k-2 I Ik-1 Ik-2]T；
Define battery parameter appraising model parameter matrix be：θ=[b1 b2 b3 b4 b5]T；
Least square method of recursion is carried out for the parameter matrix：
Wherein, t is the sampling time, b1、b2、b3、b4、b5For the element in systematic parameter matrix；
It is mentioned herein setting trigger condition specifically can according in discharge and recharge history number h, temperature difference, SOC value extremely One item missing determines；
Optionally, which specifically could be provided as：
1) every 10 charge and discharge cycles start primary parameter estimation；
2) primary parameter estimation is started for every 5 DEG C；
3) primary parameter estimation is started per 10%SOC intervals.
S402：According to the acquisition R got1,i、C1,i、R2,i、C2,i、R0,iInitial value determine battery parameter estimate mould Model parameter in type；
Optionally, Initial R can be gone out by HPPC experimental calculations1,i、C1,i、R2,i、C2,i、R0,iSo that first subsystem starts Use.
S403：When trigger condition is triggered, the model parameter in battery parameter appraising model is updated；
Optionally, the model parameter in battery parameter appraising model is updated, is specially：
Gain K updates：Kk=Pk-1Φk T(ΦkPk-1Φk T+1)；
Parameter θ updates：θk=θk-1+Kk[Ut-Φkθk-1]；
Covariance P updates：Pk=[1-KkΦk]Pk-1；
Wherein：P0=1000 × eye (5), θ=[0.1 0.1 0.1 0.1 0.1]T；
S404：R is calculated using the battery parameter appraising model after renewal1,i、C1,i、R2,i、C2,i、R0,iCurrency.
Based on above-mentioned technical proposal, the side of another battery pack SOC estimation based on IMM-EKF provided herein Method, is carried out the renewal of battery parameter list with Model Fusion using multi-Scale Data driving, prevents aging and operating environment from bringing Parametric variations battery model precision, so as to introduce error into SOC；Meanwhile the renewal of this Multiple Time Scales can have The calculating duration reduced in driving conditions of effect, improves the real-time of SOC estimations.
Based on above-described embodiment, Fig. 6, the battery pack based on IMM-EKF that Fig. 6 is provided by the embodiment of the present application are referred to The structure chart of the system of SOC estimations.
The system can include：
Modeling module 100, for establishing maximum monomer voltage interaction models according to battery voltage, minimum monomer voltage is handed over Mutual model and average voltage interaction models；
Estimation block 200, for single to maximum monomer voltage interaction models, minimum respectively using IMM-EKF filters The SOC of bulk voltage interaction models and average voltage interaction models estimated, it is corresponding obtain SOCmax, SOCmin and SOCaverage；
Computing module 300, for calculating maximum monomer voltage interaction models, minimum monomer voltage interaction models and average electricity Press the respective information distribution factor of interaction models；
Fusion Module 400, for carrying out probability to SOCmax, SOCmin and SOCaverage according to each information distribution factor Fusion, obtains battery pack entirety SOC.
Each part in system above can be applied in a following actual flow：
Modeling module according to battery voltage establish maximum monomer voltage interaction models, minimum monomer voltage interaction models and Average voltage interaction models；Estimation block is using IMM-EKF filters respectively to maximum monomer voltage interaction models, minimum list The SOC of bulk voltage interaction models and average voltage interaction models estimated, it is corresponding obtain SOCmax, SOCmin and SOCaverage；Computing module calculates maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage and hands over The respective information distribution factor of mutual model；Fusion Module according to each information distribution factor to SOCmax, SOCmin and SOCaverage carries out probability fusion, obtains battery pack entirety SOC.
Each embodiment is described by the way of progressive in specification, and what each embodiment stressed is and other realities Apply the difference of example, between each embodiment identical similar portion mutually referring to.For device disclosed in embodiment Speech, since it is corresponded to the methods disclosed in the examples, so description is fairly simple, related part is referring to method part illustration .
Professional further appreciates that, with reference to each exemplary unit of the embodiments described herein description And algorithm steps, can be realized with electronic hardware, computer software or the combination of the two, in order to clearly demonstrate hardware and The interchangeability of software, generally describes each exemplary composition and step according to function in the above description.These Function is performed with hardware or software mode actually, application-specific and design constraint depending on technical solution.Specialty Technical staff can realize described function to each specific application using distinct methods, but this realization should not Think to exceed scope of the present application.
The battery pack SOC provided herein based on the IMM-EKF method and system estimated have been carried out in detail above Introduce.Specific case used herein is set forth the principle and embodiment of the application, the explanation of above example It is only intended to help and understands the present processes and its core concept.It should be pointed out that the ordinary skill people for the art For member, on the premise of the application principle is not departed from, some improvement and modification can also be carried out to the application, these improve and Modification is also fallen into the application scope of the claims.
It should also be noted that, in the present specification, relational terms such as first and second and the like be used merely to by One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation Between there are any actual relationship or order.Moreover, term " comprising ", "comprising" or its any other variant meaning Covering non-exclusive inclusion, so that process, method, article or equipment including a series of elements not only include that A little key elements, but also including other elements that are not explicitly listed, or further include for this process, method, article or The intrinsic key element of equipment.In the absence of more restrictions, the key element limited by sentence "including a ...", is not arranged Except also there are other identical element in the process, method, article or apparatus that includes the element.

Claims (10)

  1. A kind of 1. method of the battery pack SOC estimations based on IMM-EKF, it is characterised in that including：
    Maximum monomer voltage interaction models, minimum monomer voltage interaction models and average voltage interaction are established according to battery voltage Model；
    Using IMM-EKF filters respectively to the maximum monomer voltage interaction models, the minimum monomer voltage interaction mould The SOC of type and the average voltage interaction models is estimated that correspondence obtains SOCmax, SOCmin and SOCaverage；
    Calculate the maximum monomer voltage interaction models, the minimum monomer voltage interaction models and average voltage interaction mould The respective information distribution factor of type；
    Probability is carried out according to each described information distribution factor to the SOCmax, the SOCmin and the SOCaverage to melt Close, obtain battery pack entirety SOC.
  2. 2. according to the method described in claim 1, it is characterized in that, using IMM-EKF filters respectively to described maximum single The SOC of bulk voltage interaction models, the minimum monomer voltage interaction models and the average voltage interaction models is estimated, right SOCmax, SOCmin and SOCaverage should be obtained, including：
    To maximum monomer voltage interaction models, the minimum monomer voltage interaction models and the average voltage interaction models Discretization is carried out, obtains the target state equation and observational equation based on the IMM-EKF filters：
    The state equation of the battery pack model is：Xk+1,i=A × Xk,i+B×(Ik-Ik,drf)；
    The observational equation of the battery pack model is：Uk,i=Uk,oc-V1k,i-V2k,i-ItrueR0,i；
    Wherein, i max, min or average, corresponding to the maximum monomer voltage interaction models, the minimum monomer voltage Interaction models or the average voltage interaction models, Xk+1,iFor the battery state at corresponding model k+1 moment, Xk,iFor corresponding mould The battery state at type k moment, IkFor the collection current value at k moment, Ik,drfFor the current drift amount at k moment, A and B are system Parameter；Uk,iFor the terminal voltage at corresponding model k moment, V1k,iFor the battery pack polarization capacity C at corresponding model k moment1Polarization electricity Pressure, V2k,iFor the battery pack polarization capacity C at corresponding model k moment2Polarizing voltage, ItrueFor electric current actual value, and Itrue= Ik-Ik,drf, R0,iFor the battery pack internal resistance of corresponding model.
  3. 3. according to the method described in claim 2, it is characterised in that it includes：
    The battery state X at the corresponding model k+1 momentk+1,iSpecially
    The battery state X at the corresponding model k momentk,iSpecially
    It is described that the maximum monomer voltage interaction models, the minimum monomer voltage are handed over respectively using IMM-EKF filters The SOC of mutual model and the average voltage interaction models is estimated that correspondence obtains SOCmax, SOCmin and SOCaverage, Including：
    According to formula S OCk,i=Xk,i×[1 0 0 0]TSOC is calculatedk,max、SOCk,minAnd SOCk,average；
    Wherein, SOCk+1,iFor the SOC value at corresponding model k+1 moment, including SOCk+1,max、SOCk+1,minAnd SOCk+1,average, SOCk,iFor the SOC value at corresponding model k moment, including SOCk,max、SOCk,minAnd SOCk,average, Ik+1,drfFor the electricity at k+1 moment Flow drift value, V1k+1,iFor the battery pack polarization capacity C at corresponding model k+1 moment1Polarizing voltage, V2k+1,iFor corresponding model k+ The battery pack polarization capacity C at 1 moment2Polarizing voltage.
  4. 4. the according to the method described in claim 3, it is characterized in that, Xk,iCalculating process include：
    According to formulaCalculate the kalman gain matrix K at corresponding model k momentk,i；
    According to formula Kk,i=[KSOC,i KV1,i KV2,i KI]TBy the kalman gain value K corresponding to V1 and V2V1And KV2It is multiplied by phase The rejection coefficient k1 and k2 answered, and by Kk,iIt is updated to
    Kk,i=[KSOC,i k1×KV1,i k2×KV2,i KI]T；
    According to formulaThe battery state X at the corresponding model k moment is calculatedk,i；
    Wherein, P - k,iFor the error covariance estimation of corresponding model, h (Xk,Itrue) be the battery pack model observational equation, Ck,iFor the observational equation h (X of the battery pack modelk,Itrue) partial derivative, KSOC,iFor the corresponding karr of corresponding model SOC value Graceful yield value, KV1,iFor V1Corresponding kalman gain value, KV2,iFor V2Corresponding kalman gain value, KIFor current drift value IdrfCorresponding kalman gain value, k1 and k2 are rejection coefficient,For the state estimation at corresponding model k moment, Kk,iTo be right Answer the kalman gain matrix at model k moment, ek,iFor the new breath at corresponding model k moment.
  5. It is 5. according to the method described in claim 4, it is characterized in that, described according to formulaIt is calculated The battery state X at the corresponding model k momentk,i, including：
    According to formula ek,i=U(t,k)i-h(Xk,Itrue) calculate the new breath e of the battery pack modelk,i；
    Judge | ek,i| whether less than M；
    If so, then make Kk,i=0；
    If it is not, then according to formula Ek,i=ek,i×ek-1,iCalculate Ek,iValue, and judge Ek,iWhether 0 is more than；
    If Ek,iMore than 0, then by Kk,iIt is updated to Kk,i×Kstrength；
    If Ek,iNo more than 0, then by Kk,iIt is updated to Kk,i×Kcontrol；
    Wherein, P- k,iFor the error covariance estimation of corresponding model, h (Xk,Itrue) be the battery pack model observational equation, Ck,iFor the observational equation h (X of the battery pack modelk,Itrue) first derivative, U(t,k)iThe corresponding model k moment collects Magnitude of voltage, KstrengthFor gain suppression coefficient, KcontrolFor rejection coefficient.
  6. 6. according to claim 2-5 any one of them methods, it is characterised in that including：
    The systematic parameter A is specially
    The systematic parameter B is specially
    Wherein, t is the sampling time, C1,iAnd C2,iFor the polarization capacity of corresponding model battery pack, R1,iAnd R2,iFor corresponding model electricity The polarization resistance of pond group, η are coulombic efficiency, and Ca releases capacity for battery pack is maximum.
  7. 7. the according to the method described in claim 6, it is characterized in that, R1,i、C1,i、R2,i、C2,i、R0,iCalculating process includes：
    Battery parameter appraising model is established, and trigger condition is set；
    According to the acquisition got the R1,i、C1,i、R2,i、C2,i、R0,iInitial value determine the battery parameter appraising model In model parameter；
    When the trigger condition is triggered, the model parameter in the battery parameter appraising model is updated；
    The R is calculated using the battery parameter appraising model after renewal1,i、C1,i、R2,i、C2,i、R0,iCurrency.
  8. 8. according to the method described in claim 1, it is characterized in that, described calculate the maximum monomer voltage interaction models, institute Minimum monomer voltage interaction models and the respective information distribution factor of the average voltage interaction models are stated, including：
    Judge whether the terminal voltage at the maximum monomer voltage interaction models k moment is less than maximum charge blanking voltage, and it is described Whether the terminal voltage at minimum monomer voltage interaction models k moment is more than discharge cut-off voltage；
    If so, then according to formulaCalculate described flat The information distribution factor ω at equal voltage interaction models k momentk,average, and according to formulaCalculate the information distribution at the maximum monomer voltage interaction models k moment because Sub- ωk,maxAnd the information distribution factor ω at the minimum monomer voltage interaction models k momentk,min；
    Wherein, SOCk,midFor SOCk,maxWith SOCk,minAverage value, SOCk-1,packFor the battery pack entirety SOC at k-1 moment.
  9. 9. according to the method described in claim 8, it is characterized in that, according to each described information distribution factor to the SOCmax, The SOCmin and SOCaverage carries out probability fusion, obtains battery pack entirety SOC, including：
    According to formulaObtain the battery pack Total state estimation X at k momentk,j, and total covariance P at the battery pack k momentk,j；
    According to formula S OCk,pack=Xk,j×[1 0 0 0]TCalculate the battery pack entirety SOC at k momentk,pack。
  10. A kind of 10. system of the battery pack SOC estimations based on IMM-EKF, it is characterised in that including：
    Modeling module, for establishing maximum monomer voltage interaction models, minimum monomer voltage interaction models according to battery voltage With average voltage interaction models；
    Estimation block, for single to the maximum monomer voltage interaction models, the minimum respectively using IMM-EKF filters The SOC of bulk voltage interaction models and the average voltage interaction models estimated, it is corresponding obtain SOCmax, SOCmin and SOCaverage；
    Computing module, for calculating the maximum monomer voltage interaction models, minimum monomer voltage interaction models and described The respective information distribution factor of average voltage interaction models；
    Fusion Module, for according to each described information distribution factor to the SOCmax, the SOCmin and described SOCaverage carries out probability fusion, obtains battery pack entirety SOC.
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