CN107402353A - A kind of state-of-charge to lithium ion battery is filtered the method and system of estimation - Google Patents

A kind of state-of-charge to lithium ion battery is filtered the method and system of estimation Download PDF

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CN107402353A
CN107402353A CN201710524338.0A CN201710524338A CN107402353A CN 107402353 A CN107402353 A CN 107402353A CN 201710524338 A CN201710524338 A CN 201710524338A CN 107402353 A CN107402353 A CN 107402353A
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msub
mrow
state
charge
lithium ion
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CN107402353B (en
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于海波
李贺龙
徐英辉
林繁涛
王春雨
刘佳
王兴媛
陈伟
李立
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Electric Power Research Institute of State Grid Chongqing Electric Power Co Ltd
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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Electric Power Research Institute of State Grid Chongqing Electric Power Co Ltd
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Abstract

The invention discloses a kind of method that state-of-charge to lithium ion battery is filtered estimation, including：The supplemental characteristic of lithium ion battery surface is gathered according to preset time threshold, and establishes the equivalent-circuit model of the lithium ion battery；The mathematical modeling of lithium ion battery is obtained according to the equivalent-circuit model, the parameter of the equivalent-circuit model is obtained using deviation compensation least square method of recursion, and the open-circuit voltage of lithium ion battery is obtained by testing；According to the model under lithium ion battery noise circumstance and the mathematical modeling, state-space model is obtained；The state-of-charge of lithium ion battery is estimated using Kalman filter recursive algorithm, obtains the accuracy value of the first state-of-charge estimate and Kalman filtering；Utilize H∞Wave filter recursive algorithm obtains the second state-of-charge estimate；The weight of the first state-of-charge estimate and the second state-of-charge estimate is calculated respectively, and according to the state-of-charge estimate of the weight calculation lithium ion battery.

Description

A kind of state-of-charge to lithium ion battery is filtered the method and system of estimation
Technical field
The present invention relates to metering electric automobile field, and more particularly, to a kind of charged shape to lithium ion battery State is filtered the method and system of estimation.
Background technology
Battery management system is the important component of electric car, in order to ensure battery begins under the various operating modes of electric automobile Good performance can be kept eventually, it is necessary to which battery management system monitors the working condition of battery, acquisition battery performance and safety in real time Etc. information.The state-of-charge (State of Charge, SOC) of battery is most important parameter in battery management system One of, its size directly reflects the state of battery, because the parameter is unable to direct measurement, can only by battery cell post voltage, Estimation obtains the parameters such as electric current, internal resistance, temperature indirectly, therefore SOC Accurate Estimation Method is the focus studied instantly.To SOC Accurate estimation can not only predict the continual mileage of electric car, limit charging and discharging currents, prevent from overcharging or overdischarge extends electricity Pond service life, and the security that electrokinetic cell uses can be improved, the performance of vehicle is further improved, thus it is accurate and reliable Ground, which obtains battery charge state, highly important meaning.
Common battery charge state computational methods include：Current integration method, open circuit voltage method and the karr based on model Graceful filter method etc..But due to measurement accuracy, the accumulation of error, algorithm unstability, and the battery itself under complex environment Performance change cause SOC algorithm for estimating to be extremely difficult to satisfied result.
Therefore, it is necessary to a kind of method of estimation of the state-of-charge of lithium ion battery, to solve measurement accuracy, the accumulation of error, Algorithm unstability, and the performance change of battery itself causes SOC algorithm for estimating to be extremely difficult to be satisfied with complex environment Result the problem of.
The content of the invention
The invention provides the method and system that a kind of state-of-charge to lithium ion battery is filtered estimation, to solve The computational problem of the state-of-charge SOC estimation of lithium ion battery.
In order to solve the above problems, according to one of present invention invention, there is provided a kind of charged shape to lithium ion battery The method that state is filtered estimation, methods described include：
According to preset time threshold gather lithium ion battery surface supplemental characteristic, and establish the lithium ion battery etc. Imitate circuit model；Wherein, the supplemental characteristic includes：Terminal voltage, charging and discharging currents and temperature；
The mathematical modeling of lithium ion battery is obtained according to the equivalent-circuit model, utilizes deviation compensation recursive least-squares Method obtains the parameter of the equivalent-circuit model, and the open-circuit voltage of lithium ion battery is obtained by testing；
According to the model under lithium ion battery noise circumstance and the mathematical modeling, the state space of lithium ion battery is obtained Model；
State-of-charge using Kalman filter recursive algorithm to lithium ion battery corresponding to the state-space model Estimated, obtain the accuracy value of the first state-of-charge estimate and Kalman filtering；
Utilize H∞Wave filter recursive algorithm is carried out to the state-of-charge of lithium ion battery corresponding to the state-space model Estimation, obtain the second state-of-charge estimate；
The first state-of-charge estimate and the second charged shape are calculated according to the accuracy value of the Kalman filtering respectively The weight of state estimate, and according to the first state-of-charge estimate and its shared weight and the second state-of-charge estimate And its state-of-charge estimate of shared weight calculation lithium ion battery.
Preferably, wherein the equivalent-circuit model is：
Wherein, Uoc is the open-circuit voltage of battery, represents terminal voltage value when battery is in electrochemical equilibrium；Ut is battery Terminal voltage, represent value of the battery between the battery plus-negative plate at any discharge and recharge moment；Up is polarizing voltage, and R0 is in ohm Resistance, the polarization resistance that Rp is, Cp is polarization capacity.
Preferably, wherein the mathematical modeling of the lithium ion battery is：
Wherein, Urc is the difference of open-circuit voltage and terminal voltage；Z (k) is observing matrix, is inside battery of the battery at the k moment Pressure drop；H (k) is data matrix, data message input matrix when being parameter identification；To recognize coefficient；a1, b1, b2, for identification Element value corresponding to coefficient.
Preferably, wherein the state-space model is：
G (X)=Uoc(S)-Up,
X=[S Up]T,
Wherein, X is state variable, and u is the input of model, selectes u=I；S is the state-of-charge estimate of battery；Y is mould The observational variable of type, select Y=Ut；Ts is the parameter measurement cycle, τpFor the time constant of resistance-capacitance network, Kc is lithium ion battery Charge and discharge electrostrictive coefficient.
Preferably, wherein described utilize Kalman filter recursive algorithm to lithium ion corresponding to the state-space model The state-of-charge of battery is estimated, obtains the accuracy value of the first state-of-charge estimate and Kalman filtering, including：
The systematic parameter Initialize installation of Kalman Filter Estimation, wherein the systematic parameter includes：System state variables, The error covariance of system, the average of system noise, the covariance of system noise, the association of the average of parametric noise and parametric noise Variance；
The numerical value of systematic parameter is updated, and Kalman filtering is utilized according to the state-of-charge estimate of previous moment Device recursive algorithm is estimated the state-of-charge at lithium ion battery current time, obtains the first state-of-charge estimate；
Calculate Kalman filtering gain, the numerical value of the systematic parameter after acquisition and more new estimation, for subsequent time The state-of-charge of lithium ion battery is estimated.
Preferably, wherein described utilize H∞Wave filter recursive algorithm is to lithium ion battery corresponding to the state-space model State-of-charge estimated, obtain the second state-of-charge estimate, including：
H∞The systematic parameter Initialize installation of filtering, wherein the systematic parameter includes：The mistake of system state variables, system Poor covariance, state moment matrix and covariance matrix；
According to the identification result of systematic parameter, H is utilized∞Wave filter recursive algorithm is charged to lithium ion battery current time State is estimated, obtains the second state-of-charge estimate；
Calculate and update the numerical value of current systematic parameter, the state-of-charge for the lithium ion battery to subsequent time enters Row estimation.
Preferably, wherein the accuracy value according to the Kalman filtering calculates the first state-of-charge estimation respectively The weight of value and the second state-of-charge estimate, and according to the first state-of-charge estimate and its shared weight and second The state-of-charge estimate of state-of-charge estimate and its shared weight calculation lithium ion battery, including：
Wherein, dk+1The weight of first state-of-charge estimate；For the accuracy value of Kalman filtering；J2Judge for precision The minimum value of threshold value, J∞For the maximum of precision judgment threshold, a and b are weight coefficient；For current time lithium ion battery State-of-charge estimate.
According to another aspect of the present invention, the invention provides a kind of state-of-charge to lithium ion battery to be filtered The system of estimation, the system include：Equivalent-circuit model is established unit, Mathematical Models unit, state-space model and built Vertical unit, the first state-of-charge estimate computing unit, the second state-of-charge estimate computing unit and state-of-charge estimate Determining unit,
The equivalent-circuit model establishes unit, for gathering the parameter of lithium ion battery surface according to preset time threshold Data, and establish the equivalent-circuit model of the lithium ion battery；Wherein, the supplemental characteristic includes：Terminal voltage, discharge and recharge electricity Stream and temperature；
The Mathematical Models unit, for obtaining the mathematical modulo of lithium ion battery according to the equivalent-circuit model Type, the parameter of the equivalent-circuit model is obtained using deviation compensation least square method of recursion, and lithium ion is obtained by testing The open-circuit voltage of battery；
The state-space model establishes unit, for according to the model under lithium ion battery noise circumstance and the mathematics Model, obtain the state-space model of lithium ion battery；
The first state-of-charge estimate computing unit, for utilizing Kalman filter recursive algorithm to the state The state-of-charge of lithium ion battery corresponding to spatial model is estimated, obtains the first state-of-charge estimate and Kalman's filter The accuracy value of ripple；
The second state-of-charge estimate computing unit, for utilizing H∞Wave filter recursive algorithm is to the state space The state-of-charge of lithium ion battery corresponding to model is estimated, obtains the second state-of-charge estimate；
The state-of-charge estimate determining unit, described in being calculated respectively according to the accuracy value of the Kalman filtering The weight of first state-of-charge estimate and the second state-of-charge estimate, and according to the first state-of-charge estimate and its Shared weight and the second state-of-charge estimate and its state-of-charge estimate of shared weight calculation lithium ion battery.
Preferably, wherein the equivalent-circuit model is：
Wherein, Uoc is the open-circuit voltage of battery, represents terminal voltage value when battery is in electrochemical equilibrium；Ut is battery Terminal voltage, represent value of the battery between the battery plus-negative plate at any discharge and recharge moment；Up is polarizing voltage, and R0 is in ohm Resistance, the polarization resistance that Rp is, Cp is polarization capacity.
Preferably, wherein the mathematical modeling of the lithium ion battery is：
Wherein, Urc is the difference of open-circuit voltage and terminal voltage；Z (k) is observing matrix, is inside battery of the battery at the k moment Pressure drop；H (k) is data matrix, data message input matrix when being parameter identification；To recognize coefficient；a1, b1, b2, for identification Element value corresponding to coefficient.
Preferably, wherein the state-space model is：
G (X)=Uoc(S)-Up,
X=[S Up]T,
Wherein, X is state variable, and u is the input of model, selectes u=I；S is the state-of-charge estimate of battery；Y is mould The observational variable of type, select Y=Ut；Ts is the parameter measurement cycle, τpFor the time constant of resistance-capacitance network, Kc is lithium ion battery Charge and discharge electrostrictive coefficient.
Preferably, wherein the first state-of-charge estimate computing unit, is specifically used for：
The systematic parameter Initialize installation of Kalman Filter Estimation, wherein the systematic parameter includes：System state variables, The error covariance of system, the average of system noise, the covariance of system noise, the association of the average of parametric noise and parametric noise Variance；
The numerical value of systematic parameter is updated, and Kalman filtering is utilized according to the state-of-charge estimate of previous moment Device recursive algorithm is estimated the state-of-charge at lithium ion battery current time, obtains the first state-of-charge estimate；
Calculate Kalman filtering gain, the numerical value of the systematic parameter after acquisition and more new estimation, for subsequent time The state-of-charge of lithium ion battery is estimated.
Preferably, wherein the second state-of-charge estimate computing unit, is specifically used for：
H∞The systematic parameter Initialize installation of filtering, wherein the systematic parameter includes：The mistake of system state variables, system Poor covariance, state moment matrix and covariance matrix；
According to the identification result of systematic parameter, H is utilized∞Wave filter recursive algorithm is charged to lithium ion battery current time State is estimated, obtains the second state-of-charge estimate；
Calculate and update the numerical value of current systematic parameter, the state-of-charge for the lithium ion battery to subsequent time enters Row estimation.
Preferably, wherein the state-of-charge estimate determining unit, including：
Wherein, dk+1The weight of first state-of-charge estimate；For the accuracy value of Kalman filtering；J2Judge for precision The minimum value of threshold value, J∞For the maximum of precision judgment threshold, a and b are weight coefficient；For current time lithium ion battery State-of-charge estimate.
The beneficial effects of the present invention are：
The present invention has supplied a kind of state-of-charge to lithium ion battery to be filtered the method and system of estimation, by Kalman Filtering and H∞Filtering is combined, and Kalman filtering and H are determined respectively according to the precision of Kalman filtering∞The weights of filtering, with Calculate the SOC estimation of lithium ion battery so that there is enough algorithm and stability；Combine the convergence of Kalman filtering initial value Hurry up, optimal estimation performance it is good, and H∞The advantages of filtering high robust, the shortcomings that overcoming both；Meanwhile add SOC estimations The redundancy of algorithm so that precision, adaptability and the robustness of algorithm for estimating increase relative to single algorithm.
Brief description of the drawings
By reference to the following drawings, the illustrative embodiments of the present invention can be more fully understood by：
Fig. 1 is the method 100 that estimation is filtered according to the state-of-charge to lithium ion battery of embodiment of the present invention Flow chart；
Fig. 2 is the circuit diagram according to the equivalent-circuit model of the lithium ion battery of embodiment of the present invention；And
Fig. 3 is the system 300 that estimation is filtered according to the state-of-charge to lithium ion battery of embodiment of the present invention Structural representation.
Embodiment
The illustrative embodiments of the present invention are introduced with reference now to accompanying drawing, however, the present invention can use many different shapes Formula is implemented, and is not limited to embodiment described herein, there is provided these embodiments are to disclose at large and fully The present invention, and fully pass on the scope of the present invention to person of ordinary skill in the field.Show for what is be illustrated in the accompanying drawings Term in example property embodiment is not limitation of the invention.In the accompanying drawings, identical cells/elements are attached using identical Icon is remembered.
Unless otherwise indicated, term (including scientific and technical terminology) used herein has to person of ordinary skill in the field It is common to understand implication.Further it will be understood that the term limited with usually used dictionary, be appreciated that and its The linguistic context of association area has consistent implication, and is not construed as Utopian or overly formal meaning.
The present invention has supplied a kind of state-of-charge to lithium ion battery to be filtered the method and system of estimation, to solve lithium The computational problem of the state-of-charge SOC estimation of ion battery.By using Kalman filter recursive algorithm to the state The state-of-charge of lithium ion battery corresponding to spatial model is estimated, obtains the first state-of-charge estimate and Kalman's filter The accuracy value of ripple；Utilize H∞Wave filter recursive algorithm enters to the state-of-charge of lithium ion battery corresponding to the state-space model Row estimation, obtains the second state-of-charge estimate；By Kalman filtering and H∞Filtering is combined, according to the essence of Kalman filtering Degree determines Kalman filtering and H respectively∞The weights of filtering, to calculate the SOC estimation of lithium ion battery.The present invention combines card The convergence of Kalman Filtering initial value is fast, optimal estimation performance is good, and H∞The advantages of filtering high robust, while add SOC estimations and calculate The redundancy of method so that precision, adaptability and the robustness of algorithm for estimating increase relative to single algorithm, can suppress to adopt Sample data and model estimate unstability caused by deviateing, and still have been able to estimate performance well under rugged environment.
Fig. 1 is the method 100 that estimation is filtered according to the state-of-charge to lithium ion battery of embodiment of the present invention Flow chart.As shown in figure 1, methods described 100 is used to be filtered estimation to the state-of-charge of lithium ion battery.It is described to lithium The state-of-charge of ion battery is filtered the method 100 of estimation since step 101 place, in step 101 according to preset time The supplemental characteristic of threshold acquisition lithium ion battery surface, and establish the equivalent-circuit model of the lithium ion battery；Wherein, it is described Supplemental characteristic includes：Terminal voltage, charging and discharging currents and temperature.Fig. 2 be according to the lithium ion battery of embodiment of the present invention etc. Imitate the circuit diagram of circuit model.As shown in Fig. 2 in embodiments of the present invention, the equivalent-circuit model of foundation is：
Wherein, Uoc is the open-circuit voltage of battery, represents terminal voltage value when battery is in electrochemical equilibrium；Ut is battery Terminal voltage, represent value of the battery between the battery plus-negative plate at any discharge and recharge moment；Up is polarizing voltage, and R0 is in ohm Resistance, the polarization resistance that Rp is, Cp is polarization capacity.In embodiments of the present invention, equivalent circuit is Thevenin models, and The mathematic(al) representation of model is obtained using Kirchhoff's law.
Preferably, the mathematical modeling of lithium ion battery is obtained according to the equivalent-circuit model in step 103, utilizes deviation The parameter that least square method of recursion obtains the equivalent-circuit model is compensated, and the open circuit electricity of lithium ion battery is obtained by testing Pressure.Preferably, wherein the mathematical modeling of the lithium ion battery is：
Wherein, Urc is the difference of open-circuit voltage and terminal voltage；Z (k) is observing matrix, is inside battery of the battery at the k moment Pressure drop；H (k) is data matrix, data message input matrix when being parameter identification；To recognize coefficient；a1, b1, b2, for identification Element value corresponding to coefficient.In embodiments of the present invention, the equivalent-circuit model of lithium ion battery is carried out at discretization Reason, the parameter of battery is obtained using deviation compensation least square method of recursion.Wherein, it is also necessary to obtain lithium ion battery by testing Open circuit voltage curve.If UrcFor open-circuit voltage and the difference of terminal voltage, the equivalent-circuit model of lithium ion battery is melted into minimum The canonical form of square law, can obtain result mathematical modeling is：
Wherein, z (k) is observing matrix, is inside battery pressure drop of the battery at the k moment；H (k) is data matrix, is parameter Data message input matrix during identification；, can according to the relation of identification coefficient and the parameter of equivalent-circuit model to recognize coefficient To obtain the parameter value of equivalent-circuit model.Wherein, the parameter value of equivalent-circuit model includes：Ohmic internal resistance R0, polarization resistance Rp and polarization capacity Cp.a1, b1And b2To recognize the element value of coefficient.a1, b1And b2Value and ohmic internal resistance R0, polarization resistance Rp Certain functional relation be present with polarization capacity Cp.
Preferably, in step 104 according to the model under lithium ion battery noise circumstance and the mathematical modeling, obtain lithium from The state-space model of sub- battery.Preferably, wherein the state-space model is：
G (X)=Uoc(S)-Up,
X=[S Up]T,
Wherein, X is state variable, and u is the input of model, selectes u=I；S is the state-of-charge estimate of battery；Y is mould The observational variable of type, select Y=Ut；Ts is the parameter measurement cycle, τpFor the time constant of resistance-capacitance network, Kc is lithium ion battery Charge and discharge electrostrictive coefficient.In embodiments of the present invention, according to the mathematical modeling of lithium ion battery, made an uproar with reference to noise information Electrokinetic cell model under acoustic environment, meanwhile, test to obtain open-circuit voltage and battery charge state according to the standing of electrokinetic cell Between relation, current integration method is introduced into state equation and its discretization is obtained to the shape of the lithium ion battery of linear discrete State space model.
Preferably, step 104 using Kalman filter recursive algorithm to lithium corresponding to the state-space model from The state-of-charge of sub- battery is estimated, obtains the accuracy value of the first state-of-charge estimate and Kalman filtering.Preferably, The wherein described state-of-charge using Kalman filter recursive algorithm to lithium ion battery corresponding to the state-space model Estimated, obtain the accuracy value of the first state-of-charge estimate and Kalman filtering, including：Kalman Filter Estimation is Parameter initialization of uniting is set, wherein the systematic parameter includes：System state variables, the error covariance of system, system noise Average, the covariance of system noise, the covariance of the average of parametric noise and parametric noise；The numerical value of systematic parameter is carried out When updating, and utilizing Kalman filter recursive algorithm current to lithium ion battery according to the state-of-charge estimate of previous moment The state-of-charge at quarter is estimated, obtains the first state-of-charge estimate；Calculate Kalman filtering gain, acquisition and more new estimation The numerical value of systematic parameter afterwards, the state-of-charge for the lithium ion battery to subsequent time are estimated.In the reality of the present invention Apply in mode, SOC estimation is carried out to the lithium ion battery of established model, with adaptive extended kalman filtering device recursion Algorithm carries out real-time recursion estimation.The SOC estimated results finally obtained areInitiation parameter X is set firstu, Pu,Wherein, xoFor system mode initial value, i.e., corresponding to S values in state-space model, S values are with one Initial value, when first time calculating SOC value, what is used is exactly initial S values.P0The initial value of the error covariance of system,For the average and covariance of system noise,For the average and covariance of parametric noise；Then to above-mentioned state The step of value one updates, and the first state-of-charge estimate is calculated；Finally according to filtering algorithm, Kalman filtering gain is calculated, And obtain estimating posterior value and error covariance value.Circulate said process, you can obtain real-time SOC value and error covariance Update, the S values in the i.e. corresponding equivalent-circuit model of obtained SOC value.When the SOC estimation for carrying out subsequent time calculates, System mode X after renewal0Value be S values be current time SOC estimation.
Preferably, H is utilized in step 105∞Wave filter recursive algorithm is to lithium-ion electric corresponding to the state-space model The state-of-charge in pond is estimated, obtains the second state-of-charge estimate.Preferably, wherein described utilize H∞Wave filter recursion is calculated Method is estimated the state-of-charge of lithium ion battery corresponding to the state-space model, obtains the estimation of the second state-of-charge Value, including：H∞The systematic parameter Initialize installation of filtering, wherein the systematic parameter includes：The mistake of system state variables, system Poor covariance, state moment matrix and covariance matrix；According to the identification result of systematic parameter, H is utilized∞Wave filter recursive algorithm pair The state-of-charge at lithium ion battery current time is estimated, obtains the second state-of-charge estimate；Calculate and update current The numerical value of systematic parameter, the state-of-charge for the lithium ion battery to subsequent time are estimated.X is initialized first0, P0, θ, Lk, S0, Q 'k,Suitable initial value is set, wherein, X0For system mode initial value, P0The error covariance of system it is initial Value, L is state moment matrix, can take Lk- [1 0], Q 'k,It is covariance matrix, θ is the number of a value very little of setting, it Determine the precision of algorithm, SkValue with θ is proportional；Then particular matrix Q ' is calculatedk,According to parameter identification result, The coefficient of the state equation at assignment current time, calculation error covariance matrix and gain matrix；Estimate SOC is finally carried out to estimate The state renewal of evaluation, and the renewal of error co-variance matrix.
Preferably, first state-of-charge is calculated respectively according to the accuracy value of the Kalman filtering in step 106 to estimate The weight of evaluation and the second state-of-charge estimate, and according to the first state-of-charge estimate and its shared weight and The state-of-charge estimate of two state-of-charge estimates and its shared weight calculation lithium ion battery.Preferably, it is wherein described The first state-of-charge estimate and the second state-of-charge estimate are calculated according to the accuracy value of the Kalman filtering respectively Weight, and according to the first state-of-charge estimate and its shared weight and the second state-of-charge estimate and its shared Weight calculation lithium ion battery state-of-charge estimate, including：
Wherein, dk+1The weight of first state-of-charge estimate；For the accuracy value of Kalman filtering；J2Judge for precision The minimum value of threshold value, J∞For the maximum of precision judgment threshold, a and b are weight coefficient；For current time lithium ion battery State-of-charge estimate.
In embodiments of the present invention, definition can evaluate the supremum J of the precision of filtering2With with infimum J∞.When Wave filter can be good use, precision meets when requiring, then all the timeIf when filter divergence or very poor precision, Have all the timePair with other situations, then it is considered that filtering effect it is general.The weight coefficient of mixed filtering calculates public Formula is：
Wherein, the estimate of the Kalman filtering at current time isdk+1For the weight coefficient of mixed filtering, the value Size determines according to the estimated accuracy of Kalman filtering；A and b is weight coefficient, and their size determines weights change Speed, a and b value are empirically determined.After the weight coefficient at current time is obtained, it can be calculated according to equation below The SOC estimation of lithium ion battery：
Operation is updated according to above-mentioned method, is obtainedThe lotus at the current time of as described lithium ion battery Electricity condition SOC estimate.
Fig. 3 is the system 300 that estimation is filtered according to the state-of-charge to lithium ion battery of embodiment of the present invention Structural representation.As shown in figure 3, the system 300 that the state-of-charge to lithium ion battery is filtered estimation includes：One The system that kind is filtered estimation to the state-of-charge of lithium ion battery, the system 300 include：Equivalent-circuit model is established single Member 301, Mathematical Models unit 302, state-space model establish unit 303, the first state-of-charge estimate computing unit 304th, the second state-of-charge estimate computing unit 305 and state-of-charge estimate determining unit 306.
Preferably, the equivalent-circuit model establishes unit 301, for gathering lithium ion battery according to preset time threshold The supplemental characteristic on surface, and establish the equivalent-circuit model of the lithium ion battery；Wherein, the supplemental characteristic includes：End electricity Pressure, charging and discharging currents and temperature.Preferably, wherein the equivalent-circuit model is：
Wherein, Uoc is the open-circuit voltage of battery, represents terminal voltage value when battery is in electrochemical equilibrium；Ut is battery Terminal voltage, represent value of the battery between the battery plus-negative plate at any discharge and recharge moment；Up is polarizing voltage, and R0 is in ohm Resistance, the polarization resistance that Rp is, Cp is polarization capacity.
Preferably, the Mathematical Models unit 302, for obtaining lithium ion battery according to the equivalent-circuit model Mathematical modeling, the parameter of the equivalent-circuit model is obtained using deviation compensation least square method of recursion, and obtain by experiment Take the open-circuit voltage of lithium ion battery.Preferably, wherein the mathematical modeling of the lithium ion battery is：
Wherein, Urc is the difference of open-circuit voltage and terminal voltage；Z (k) is observing matrix, is inside battery of the battery at the k moment Pressure drop；H (k) is data matrix, data message input matrix when being parameter identification；To recognize coefficient；a1, b1, b2, for identification Element value corresponding to coefficient.
Preferably, the state-space model establishes unit 303, for according to the model under lithium ion battery noise circumstance With the mathematical modeling, the state-space model of lithium ion battery is obtained.Preferably, wherein the state-space model is：
G (X)=Uoc(S)-Up,
X=[S Up]T,
Wherein, X is state variable, and u is the input of model, selectes u=I；S is the state-of-charge estimate of battery；Y is mould The observational variable of type, select Y=Ut；Ts is the parameter measurement cycle, τpFor the time constant of resistance-capacitance network, Kc is lithium ion battery Charge and discharge electrostrictive coefficient.
Preferably, the first state-of-charge estimate computing unit 304, for utilizing Kalman filter recursive algorithm The state-of-charge of lithium ion battery corresponding to the state-space model is estimated, obtain the first state-of-charge estimate with And the accuracy value of Kalman filtering.Preferably, wherein the first state-of-charge estimate computing unit 304, is specifically used for：
The systematic parameter Initialize installation of Kalman Filter Estimation, wherein the systematic parameter includes：System state variables, The error covariance of system, the average of system noise, the covariance of system noise, the association of the average of parametric noise and parametric noise Variance；
The numerical value of systematic parameter is updated, and Kalman filtering is utilized according to the state-of-charge estimate of previous moment Device recursive algorithm is estimated the state-of-charge at lithium ion battery current time, obtains the first state-of-charge estimate；
Calculate Kalman filtering gain, the numerical value of the systematic parameter after acquisition and more new estimation, for subsequent time The state-of-charge of lithium ion battery is estimated.
Preferably, the second state-of-charge estimate computing unit 305, for utilizing H∞Wave filter recursive algorithm is to institute The state-of-charge for stating lithium ion battery corresponding to state-space model is estimated, obtains the second state-of-charge estimate.It is preferred that Ground, wherein the second state-of-charge estimate computing unit 305, is specifically used for：
H∞The systematic parameter Initialize installation of filtering, wherein the systematic parameter includes：The mistake of system state variables, system Poor covariance, state moment matrix and covariance matrix；
According to the identification result of systematic parameter, H is utilized∞Wave filter recursive algorithm is charged to lithium ion battery current time State is estimated, obtains the second state-of-charge estimate；
Calculate and update the numerical value of current systematic parameter, the state-of-charge for the lithium ion battery to subsequent time enters Row estimation.
Preferably, the state-of-charge estimate determining unit 306, for according to the accuracy value of the Kalman filtering point The weight of the first state-of-charge estimate and the second state-of-charge estimate is not calculated, and according to first state-of-charge Estimate and its shared weight and the charged shape of the second state-of-charge estimate and its shared weight calculation lithium ion battery State estimate.Preferably, wherein the state-of-charge estimate determining unit, including：
Wherein, dk+1The weight of first state-of-charge estimate；For the accuracy value of Kalman filtering；J2Judge for precision The minimum value of threshold value, J∞For the maximum of precision judgment threshold, a and b are weight coefficient；For current time lithium ion battery State-of-charge estimate.
The system 300 and this hair that are used to be filtered the state-of-charge of lithium ion battery estimation of embodiments of the invention The system 100 for being used to be filtered the state-of-charge of lithium ion battery estimation of another bright embodiment is corresponding, herein Repeat no more.
The present invention is described by reference to a small amount of embodiment.However, it is known in those skilled in the art, as What subsidiary Patent right requirement was limited, except the present invention other embodiments disclosed above equally fall the present invention's In the range of.
Normally, all terms used in the claims are all solved according to them in the usual implication of technical field Release, unless clearly being defined in addition wherein.All references " one/described/be somebody's turn to do [device, component etc.] " are all opened ground At least one example being construed in described device, component etc., unless otherwise expressly specified.Any method disclosed herein Step need not all be run with disclosed accurately order, unless explicitly stated otherwise.

Claims (14)

1. a kind of method that state-of-charge to lithium ion battery is filtered estimation, it is characterised in that methods described includes：
The supplemental characteristic of lithium ion battery surface is gathered according to preset time threshold, and establishes the equivalent electric of the lithium ion battery Road model；Wherein, the supplemental characteristic includes：Terminal voltage, charging and discharging currents and temperature；
The mathematical modeling of lithium ion battery is obtained according to the equivalent-circuit model, is obtained using deviation compensation least square method of recursion The parameter of the equivalent-circuit model is taken, and the open-circuit voltage of lithium ion battery is obtained by testing；
According to the model under lithium ion battery noise circumstance and the mathematical modeling, the state space mould of lithium ion battery is obtained Type；
The state-of-charge of lithium ion battery corresponding to the state-space model is carried out using Kalman filter recursive algorithm Estimation, obtain the accuracy value of the first state-of-charge estimate and Kalman filtering；
Utilize H∞Wave filter recursive algorithm estimated the state-of-charge of lithium ion battery corresponding to the state-space model, Obtain the second state-of-charge estimate；
The first state-of-charge estimate is calculated according to the accuracy value of the Kalman filtering respectively and the second state-of-charge is estimated The weight of evaluation, and according to the first state-of-charge estimate and its shared weight and the second state-of-charge estimate and its The state-of-charge estimate of shared weight calculation lithium ion battery.
2. according to the method for claim 1, it is characterised in that the equivalent-circuit model is：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>U</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>U</mi> <mrow> <mi>o</mi> <mi>c</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>IR</mi> <mi>o</mi> </msub> <mo>-</mo> <msub> <mi>U</mi> <mi>p</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mover> <mi>U</mi> <mo>&amp;CenterDot;</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <mi>I</mi> <mo>/</mo> <msub> <mi>C</mi> <mi>p</mi> </msub> <mo>-</mo> <msub> <mi>U</mi> <mi>p</mi> </msub> <mo>/</mo> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>p</mi> </msub> <msub> <mi>R</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
Wherein, Uoc is the open-circuit voltage of battery, represents terminal voltage value when battery is in electrochemical equilibrium；Ut is the end of battery Voltage, represent value of the battery between the battery plus-negative plate at any discharge and recharge moment；Up is polarizing voltage, and R0 is ohmic internal resistance, Rp For polarization resistance, Cp is polarization capacity.
3. according to the method for claim 1, it is characterised in that the mathematical modeling of the lithium ion battery is：
<mrow> <mi>z</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>h</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&amp;times;</mo> <mover> <mi>&amp;theta;</mi> <mo>^</mo> </mover> <mo>,</mo> </mrow>
Z (k)=Urc(k), h (k)=[- Urc(k-1)I(k)I(k-1)],
Wherein, Urc is the difference of open-circuit voltage and terminal voltage；Z (k) is observing matrix, is inside battery pressure of the battery at the k moment Drop；H (k) is data matrix, data message input matrix when being parameter identification；To recognize coefficient；a1, b1, b2, it is for identification Element value corresponding to number.
4. according to the method for claim 1, it is characterised in that the state-space model is：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <msub> <mi>T</mi> <mi>s</mi> </msub> <msub> <mi>&amp;tau;</mi> <mi>p</mi> </msub> </mfrac> </mrow> </msup> </mtd> </mtr> </mtable> </mfenced> <msub> <mi>X</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <mo>-</mo> <msub> <mi>K</mi> <mi>c</mi> </msub> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>R</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <msub> <mi>T</mi> <mi>s</mi> </msub> <msub> <mi>&amp;tau;</mi> <mi>p</mi> </msub> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <msub> <mi>u</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>Y</mi> <mo>=</mo> <mi>g</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>(</mo> <mi>k</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>R</mi> <mn>0</mn> </msub> <msub> <mi>u</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
G (X)=Uoc(S)-Up,
X=[S Up]T,
Wherein, X is state variable, and u is the input of model, selectes u=I；S is the state-of-charge estimate of battery；Y is model Observational variable, select Y=Ut；Ts is the parameter measurement cycle, τpFor the time constant of resistance-capacitance network, Kc is filling for lithium ion battery Discharge coefficient.
5. according to the method for claim 1, it is characterised in that described to utilize Kalman filter recursive algorithm to the shape The state-of-charge of lithium ion battery is estimated corresponding to state space model, obtains the first state-of-charge estimate and Kalman The accuracy value of filtering, including：
The systematic parameter Initialize installation of Kalman Filter Estimation, wherein the systematic parameter includes：System state variables, system Error covariance, the average of system noise, the covariance of system noise, the association side of the average of parametric noise and parametric noise Difference；
The numerical value of systematic parameter is updated, and passed according to the state-of-charge estimate of previous moment using Kalman filter Predication method is estimated the state-of-charge at lithium ion battery current time, obtains the first state-of-charge estimate；
Calculate Kalman filtering gain, obtain and more new estimation after systematic parameter numerical value, for the lithium to subsequent time from The state-of-charge of sub- battery is estimated.
6. according to the method for claim 1, it is characterised in that described to utilize H∞Wave filter recursive algorithm is empty to the state Between the state-of-charge of lithium ion battery corresponding to model estimated, obtain the second state-of-charge estimate, including：
H∞The systematic parameter Initialize installation of filtering, wherein the systematic parameter includes：The error association of system state variables, system Variance, state moment matrix and covariance matrix；
According to the identification result of systematic parameter, H is utilized∞State-of-charge of the wave filter recursive algorithm to lithium ion battery current time Estimated, obtain the second state-of-charge estimate；
Calculate and update the numerical value of current systematic parameter, the state-of-charge for the lithium ion battery to subsequent time is estimated Meter.
7. according to the method for claim 1, it is characterised in that the accuracy value according to the Kalman filtering is counted respectively The weight of the first state-of-charge estimate and the second state-of-charge estimate is calculated, and is estimated according to first state-of-charge Value and its shared weight and the state-of-charge of the second state-of-charge estimate and its shared weight calculation lithium ion battery are estimated Evaluation, including：
<mrow> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>J</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>be</mi> <mrow> <mo>-</mo> <mfrac> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mi>a</mi> </mfrac> </mrow> </msup> <mo>,</mo> <msub> <mi>J</mi> <mn>2</mn> </msub> <mo>&lt;</mo> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>J</mi> <mi>&amp;infin;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>&gt;</mo> <msub> <mi>J</mi> <mi>&amp;infin;</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
<mrow> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mn>2</mn> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mi>&amp;infin;</mi> </msub> <mo>,</mo> </mrow>
Wherein, dk+1The weight of first state-of-charge estimate；For the accuracy value of Kalman filtering；J2For precision judgment threshold Minimum value, J∞For the maximum of precision judgment threshold, a and b are weight coefficient；For the lotus of current time lithium ion battery Electricity condition estimate.
8. the system that a kind of state-of-charge to lithium ion battery is filtered estimation, it is characterised in that the system includes：Deng Effect circuit model establishes unit, Mathematical Models unit, state-space model and establishes unit, the first state-of-charge estimate meter Unit, the second state-of-charge estimate computing unit and state-of-charge estimate determining unit are calculated,
The equivalent-circuit model establishes unit, for gathering the parameter number of lithium ion battery surface according to preset time threshold According to, and establish the equivalent-circuit model of the lithium ion battery；Wherein, the supplemental characteristic includes：Terminal voltage, charging and discharging currents And temperature；
The Mathematical Models unit, for obtaining the mathematical modeling of lithium ion battery, profit according to the equivalent-circuit model The parameter of the equivalent-circuit model is obtained with deviation compensation least square method of recursion, and lithium ion battery is obtained by testing Open-circuit voltage；
The state-space model establishes unit, for according to the model under lithium ion battery noise circumstance and the mathematical modulo Type, obtain the state-space model of lithium ion battery；
The first state-of-charge estimate computing unit, for utilizing Kalman filter recursive algorithm to the state space The state-of-charge of lithium ion battery corresponding to model estimated, obtains the first state-of-charge estimate and Kalman filtering Accuracy value；
The second state-of-charge estimate computing unit, for utilizing H∞Wave filter recursive algorithm is to the state-space model The state-of-charge of corresponding lithium ion battery is estimated, obtains the second state-of-charge estimate；
The state-of-charge estimate determining unit, for calculating described first respectively according to the accuracy value of the Kalman filtering The weight of state-of-charge estimate and the second state-of-charge estimate, and according to the first state-of-charge estimate and its shared Weight and the second state-of-charge estimate and its state-of-charge estimate of shared weight calculation lithium ion battery.
9. system according to claim 8, it is characterised in that the equivalent-circuit model is：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>U</mi> <mi>t</mi> </msub> <mo>=</mo> <msub> <mi>U</mi> <mrow> <mi>o</mi> <mi>c</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>IR</mi> <mi>o</mi> </msub> <mo>-</mo> <msub> <mi>U</mi> <mi>p</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mover> <mi>U</mi> <mo>&amp;CenterDot;</mo> </mover> <mi>p</mi> </msub> <mo>=</mo> <mi>I</mi> <mo>/</mo> <msub> <mi>C</mi> <mi>p</mi> </msub> <mo>-</mo> <msub> <mi>U</mi> <mi>p</mi> </msub> <mo>/</mo> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mi>p</mi> </msub> <msub> <mi>R</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
Wherein, Uoc is the open-circuit voltage of battery, represents terminal voltage value when battery is in electrochemical equilibrium；Ut is the end of battery Voltage, represent value of the battery between the battery plus-negative plate at any discharge and recharge moment；Up is polarizing voltage, and R0 is ohmic internal resistance, Rp For polarization resistance, Cp is polarization capacity.
10. system according to claim 8, it is characterised in that the mathematical modeling of the lithium ion battery is：
<mrow> <mi>z</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>h</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>&amp;times;</mo> <mover> <mi>&amp;theta;</mi> <mo>^</mo> </mover> <mo>,</mo> </mrow>
Z (k)=Urc(k), h (k)=[- Urc(k-1)I(k)I(k-1)],
Wherein, Urc is the difference of open-circuit voltage and terminal voltage；Z (k) is observing matrix, is inside battery pressure of the battery at the k moment Drop；H (k) is data matrix, data message input matrix when being parameter identification；To recognize coefficient；a1, b1, b2, it is for identification Element value corresponding to number.
11. system according to claim 8, it is characterised in that the state-space model is：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>X</mi> <mi>k</mi> </msub> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <msub> <mi>T</mi> <mi>s</mi> </msub> <msub> <mi>&amp;tau;</mi> <mi>p</mi> </msub> </mfrac> </mrow> </msup> </mtd> </mtr> </mtable> </mfenced> <msub> <mi>X</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mrow> <mo>-</mo> <msub> <mi>K</mi> <mi>c</mi> </msub> <msub> <mi>T</mi> <mi>s</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>R</mi> <mi>p</mi> </msub> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <mfrac> <msub> <mi>T</mi> <mi>s</mi> </msub> <msub> <mi>&amp;tau;</mi> <mi>p</mi> </msub> </mfrac> </mrow> </msup> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> </mfenced> <msub> <mi>u</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>Y</mi> <mo>=</mo> <mi>g</mi> <mrow> <mo>(</mo> <mi>X</mi> <mo>(</mo> <mi>k</mi> <mo>)</mo> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>R</mi> <mn>0</mn> </msub> <msub> <mi>u</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
G (X)=Uoc(S)-Up,
X=[S Up]T,
Wherein, X is state variable, and u is the input of model, selectes u=I；S is the state-of-charge estimate of battery；Y is model Observational variable, select Y=Ut；Ts is the parameter measurement cycle, τpFor the time constant of resistance-capacitance network, Kc is filling for lithium ion battery Discharge coefficient.
12. system according to claim 8, it is characterised in that the first state-of-charge estimate computing unit, specifically For：
The systematic parameter Initialize installation of Kalman Filter Estimation, wherein the systematic parameter includes：System state variables, system Error covariance, the average of system noise, the covariance of system noise, the association side of the average of parametric noise and parametric noise Difference；
The numerical value of systematic parameter is updated, and passed according to the state-of-charge estimate of previous moment using Kalman filter Predication method is estimated the state-of-charge at lithium ion battery current time, obtains the first state-of-charge estimate；
Calculate Kalman filtering gain, obtain and more new estimation after systematic parameter numerical value, for the lithium to subsequent time from The state-of-charge of sub- battery is estimated.
13. system according to claim 8, it is characterised in that the second state-of-charge estimate computing unit, specifically For：
H∞The systematic parameter Initialize installation of filtering, wherein the systematic parameter includes：The error association of system state variables, system Variance, state moment matrix and covariance matrix；
According to the identification result of systematic parameter, H is utilized∞State-of-charge of the wave filter recursive algorithm to lithium ion battery current time Estimated, obtain the second state-of-charge estimate；
Calculate and update the numerical value of current systematic parameter, the state-of-charge for the lithium ion battery to subsequent time is estimated Meter.
14. system according to claim 8, it is characterised in that the state-of-charge estimate determining unit, including：
<mrow> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>J</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>be</mi> <mrow> <mo>-</mo> <mfrac> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mi>a</mi> </mfrac> </mrow> </msup> <mo>,</mo> <msub> <mi>J</mi> <mn>2</mn> </msub> <mo>&lt;</mo> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>J</mi> <mi>&amp;infin;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> <msub> <mover> <mi>J</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>&gt;</mo> <msub> <mi>J</mi> <mi>&amp;infin;</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
<mrow> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mn>2</mn> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>d</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <msub> <mover> <mi>x</mi> <mo>^</mo> </mover> <mi>&amp;infin;</mi> </msub> <mo>,</mo> </mrow>
Wherein, dk+1The weight of first state-of-charge estimate；For the accuracy value of Kalman filtering；J2For precision judgment threshold Minimum value, J∞For the maximum of precision judgment threshold, a and b are weight coefficient；For the lotus of current time lithium ion battery Electricity condition estimate.
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