CN107402353B - Method and system for carrying out filtering estimation on state of charge of lithium ion battery - Google Patents

Method and system for carrying out filtering estimation on state of charge of lithium ion battery Download PDF

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CN107402353B
CN107402353B CN201710524338.0A CN201710524338A CN107402353B CN 107402353 B CN107402353 B CN 107402353B CN 201710524338 A CN201710524338 A CN 201710524338A CN 107402353 B CN107402353 B CN 107402353B
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state
charge
lithium ion
ion battery
value
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CN107402353A (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
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    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
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Abstract

The invention discloses a method for carrying out filtering estimation on the state of charge of a lithium ion battery, which comprises the following steps: acquiring parameter data of the surface of a lithium ion battery according to a preset time threshold, and establishing an equivalent circuit model of the lithium ion battery; acquiring a mathematical model of the lithium ion battery according to the equivalent circuit model, acquiring parameters of the equivalent circuit model by using a deviation compensation recursive least square method, and acquiring the open-circuit voltage of the lithium ion battery through experiments; acquiring a state space model according to the model in the lithium ion battery noise environment and the mathematical model; estimating the state of charge of the lithium ion battery by using a Kalman filter recursion algorithm to obtain a first state of charge estimation value and a Kalman filtering precision value; by means of H∞A filter recursion algorithm obtains a second state of charge estimated value; and respectively calculating the weights of the first state of charge estimation value and the second state of charge estimation value, and calculating the state of charge estimation value of the lithium ion battery according to the weights.

Description

Method and system for carrying out filtering estimation on state of charge of lithium ion battery
Technical Field
The present invention relates to the field of metering electric vehicles, and more particularly, to a method and system for filter estimation of state of charge of a lithium ion battery.
Background
The battery management system is an important component of the electric vehicle, and in order to ensure that the battery can always keep good performance under various working conditions of the electric vehicle, the battery management system is required to monitor the working state of the battery in real time and acquire information on the aspects of battery performance, safety and the like. The State of Charge (SOC) of a battery is one of the most important parameters in a battery management system, and the size of the SOC directly reflects the State of the battery, and since the parameter cannot be directly measured and can only be indirectly estimated through parameters such as external voltage, current, internal resistance, temperature and the like of the battery, the accurate SOC estimation method is a hotspot of current research. The accurate estimation of the SOC can not only predict the driving range of the electric vehicle, limit the charging and discharging current, prevent overcharge or overdischarge and prolong the service life of the battery, but also improve the safety of the power battery and further improve the performance of the whole vehicle, so that the accurate and reliable acquisition of the state of charge of the battery has very important significance.
A common battery state of charge calculation method includes: ampere-hour integration method, open-circuit voltage method and Kalman filtering method based on model. However, due to measurement accuracy, error accumulation, algorithm instability and performance variation of the battery itself in a complex environment, the SOC estimation algorithm is difficult to achieve a satisfactory result.
Therefore, a method for estimating the state of charge of a lithium ion battery is needed to solve the problems that the measurement accuracy, the error accumulation, the algorithm instability and the SOC estimation algorithm are difficult to achieve satisfactory results due to the performance change of the battery under the complex environment.
Disclosure of Invention
The invention provides a method and a system for carrying out filtering estimation on the state of charge of a lithium ion battery, which aim to solve the problem of calculating the SOC estimation value of the lithium ion battery.
In order to solve the above problem, according to an aspect of the present invention, there is provided a method for filter estimation of a state of charge of a lithium ion battery, the method including:
acquiring parameter data of the surface of a lithium ion battery according to a preset time threshold, and establishing an equivalent circuit model of the lithium ion battery; wherein the parameter data comprises: terminal voltage, charge and discharge current and temperature;
acquiring a mathematical model of the lithium ion battery according to the equivalent circuit model, acquiring parameters of the equivalent circuit model by using a deviation compensation recursive least square method, and acquiring the open-circuit voltage of the lithium ion battery through experiments;
acquiring a state space model of the lithium ion battery according to the model in the noise environment of the lithium ion battery and the mathematical model;
estimating the state of charge of the lithium ion battery corresponding to the state space model by using a Kalman filter recursion algorithm to obtain a first state of charge estimation value and a Kalman filtering precision value;
by means of H∞Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value;
and respectively calculating the weights of the first state of charge estimation value and the second state of charge estimation value according to the precision value of the Kalman filtering, and calculating the state of charge estimation value of the lithium ion battery according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value and the second state of charge estimation value and the weight occupied by the second state of charge estimation value.
Preferably, wherein the equivalent circuit model is:
Figure BDA0001338227080000021
wherein Uoc is the open circuit voltage of the battery and represents the terminal voltage value of the battery in electrochemical balance; ut is the terminal voltage of the battery, which represents the value between the positive and negative electrodes of the battery at any charging and discharging time; up is polarization voltage, R0 is ohm internal resistance, Rp is polarization internal resistance, and Cp is polarization capacitance.
Preferably, the mathematical model of the lithium ion battery is:
Figure BDA0001338227080000022
Figure BDA0001338227080000031
wherein Urc is the difference between the open circuit voltage and the terminal voltage; z (k) is an observation matrix, and is the internal voltage drop of the battery at the moment k; h (k) is a data matrix which is a data information input matrix during parameter identification;
Figure BDA0001338227080000032
is an identification coefficient; a is1，b1，b2The element value corresponding to the identification coefficient.
Preferably, wherein the state space model is:
Figure BDA0001338227080000033
g(X)＝Uoc(S)-Up，
X＝[S Up]T，
wherein, X is a state variable, u is the input of the model, and u is selected as I; s is the estimated value of the state of charge of the battery; y is an observed variable of the model, and the selected Y is Ut; ts is a parameter measurement period, taupKc is the charge-discharge coefficient of the lithium ion battery.
Preferably, the estimating the state of charge of the lithium ion battery corresponding to the state space model by using a kalman filter recursion algorithm to obtain a first state of charge estimated value and an accuracy value of kalman filtering includes:
initializing and setting system parameters of Kalman filtering estimation, wherein the system parameters comprise: the system state variable, the error covariance of the system, the mean of the system noise, the covariance of the system noise, the mean of the parametric noise and the covariance of the parametric noise;
updating the numerical value of the system parameter, and estimating the state of charge of the lithium ion battery at the current moment by using a Kalman filter recursive algorithm according to the state of charge estimated value at the previous moment to obtain a first state of charge estimated value;
and calculating Kalman filtering gain, acquiring and updating the numerical value of the estimated system parameter, and estimating the state of charge of the lithium ion battery at the next moment.
Preferably, wherein said utilization H∞Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value, wherein the method comprises the following steps:
H∞initializing and setting system parameters of filtering, wherein the system parameters comprise: system state variables, error covariance of the system, state quantity matrix and covariance matrix;
according to the identification result of the system parameter, using H∞Estimating the state of charge of the lithium ion battery at the current moment by using a filter recursion algorithm to obtain a second state of charge estimation value;
and calculating and updating the numerical value of the current system parameter for estimating the state of charge of the lithium ion battery at the next moment.
Preferably, the calculating the weights of the first state of charge estimation value and the second state of charge estimation value according to the precision value of the kalman filter, and calculating the state of charge estimation value of the lithium ion battery according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value, the second state of charge estimation value and the weight occupied by the second state of charge estimation value includes:
Figure BDA0001338227080000041
Figure BDA0001338227080000042
wherein d isk+1A weight of the first state of charge estimate;
Figure BDA0001338227080000043
is the precision value of Kalman filtering; j. the design is a square2Minimum value of threshold value for precision judgment, J∞A and b are weighting coefficients for the maximum value of the precision judgment threshold;
Figure BDA0001338227080000044
and the estimated value of the state of charge of the lithium ion battery at the current moment is obtained.
According to another aspect of the invention, there is provided a system for filter estimation of state of charge of a lithium ion battery, the system comprising: an equivalent circuit model establishing unit, a mathematical model establishing unit, a state space model establishing unit, a first state of charge estimation value calculating unit, a second state of charge estimation value calculating unit and a state of charge estimation value determining unit,
the equivalent circuit model establishing unit is used for acquiring parameter data of the surface of the lithium ion battery according to a preset time threshold and establishing an equivalent circuit model of the lithium ion battery; wherein the parameter data comprises: terminal voltage, charge and discharge current and temperature;
the mathematical model establishing unit is used for acquiring a mathematical model of the lithium ion battery according to the equivalent circuit model, acquiring parameters of the equivalent circuit model by using a deviation compensation recursive least square method, and acquiring the open-circuit voltage of the lithium ion battery through experiments;
the state space model establishing unit is used for acquiring a state space model of the lithium ion battery according to the model in the noise environment of the lithium ion battery and the mathematical model;
the first state of charge estimation value calculation unit is used for estimating the state of charge of the lithium ion battery corresponding to the state space model by using a Kalman filter recursion algorithm to obtain a first state of charge estimation value and a Kalman filtering precision value;
the second state of charge estimation value calculation unit is used for utilizing H∞Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value;
and the state of charge estimation value determining unit is used for respectively calculating the weights of the first state of charge estimation value and the second state of charge estimation value according to the precision value of the Kalman filtering, and calculating the state of charge estimation value of the lithium ion battery according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value, the second state of charge estimation value and the weight occupied by the second state of charge estimation value.
Preferably, wherein the equivalent circuit model is:
Figure BDA0001338227080000051
wherein Uoc is the open circuit voltage of the battery and represents the terminal voltage value of the battery in electrochemical balance; ut is the terminal voltage of the battery, which represents the value between the positive and negative electrodes of the battery at any charging and discharging time; up is polarization voltage, R0 is ohm internal resistance, Rp is polarization internal resistance, and Cp is polarization capacitance.
Preferably, the mathematical model of the lithium ion battery is:
Figure BDA0001338227080000052
Figure BDA0001338227080000053
wherein Urc is the difference between the open circuit voltage and the terminal voltage; z (k) is an observation matrix, and is the internal voltage drop of the battery at the moment k; h (k) is a data matrix which is a data information input matrix during parameter identification;
Figure BDA0001338227080000054
is an identification coefficient; a is1，b1，b2The element value corresponding to the identification coefficient.
Preferably, wherein the state space model is:
Figure BDA0001338227080000061
g(X)＝Uoc(S)-Up，
X＝[S Up]T，
wherein, X is a state variable, u is the input of the model, and u is selected as I; s is the estimated value of the state of charge of the battery; y is an observed variable of the model, and the selected Y is Ut; ts is a parameter measurement period, taupKc is the charge-discharge coefficient of the lithium ion battery.
Preferably, the first state of charge estimation value calculation unit is specifically configured to:
initializing and setting system parameters of Kalman filtering estimation, wherein the system parameters comprise: the system state variable, the error covariance of the system, the mean of the system noise, the covariance of the system noise, the mean of the parametric noise and the covariance of the parametric noise;
updating the numerical value of the system parameter, and estimating the state of charge of the lithium ion battery at the current moment by using a Kalman filter recursive algorithm according to the state of charge estimated value at the previous moment to obtain a first state of charge estimated value;
and calculating Kalman filtering gain, acquiring and updating the numerical value of the estimated system parameter, and estimating the state of charge of the lithium ion battery at the next moment.
Preferably, the second state of charge estimation value calculation unit is specifically configured to:
H∞initializing and setting system parameters of filtering, wherein the system parameters comprise: system state variables, error covariance of the system, state quantity matrix and covariance matrix;
according to the identification of system parametersAs a result, H is utilized∞Estimating the state of charge of the lithium ion battery at the current moment by using a filter recursion algorithm to obtain a second state of charge estimation value;
and calculating and updating the numerical value of the current system parameter for estimating the state of charge of the lithium ion battery at the next moment.
Preferably, the state of charge estimation value determination unit includes:
Figure BDA0001338227080000071
Figure BDA0001338227080000072
wherein d isk+1A weight of the first state of charge estimate;
Figure BDA0001338227080000073
is the precision value of Kalman filtering; j. the design is a square2Minimum value of threshold value for precision judgment, J∞A and b are weighting coefficients for the maximum value of the precision judgment threshold;
Figure BDA0001338227080000074
and the estimated value of the state of charge of the lithium ion battery at the current moment is obtained.
The invention has the beneficial effects that:
the invention provides a method and a system for carrying out filtering estimation on the state of charge of a lithium ion battery, which combines Kalman filtering and H∞Filtering is combined, and Kalman filtering and H are respectively determined according to the precision of the Kalman filtering∞The filtered weight value is used for calculating the SOC estimated value of the lithium ion battery, so that the algorithm and the stability are sufficient; the method integrates the advantages of fast convergence of Kalman filtering initial values, good optimal estimation performance and H∞The filtering has the advantage of high robustness, and the defects of the filtering and the robustness are overcome; meanwhile, the redundancy of the SOC estimation algorithm is increased, so that the precision, the adaptability and the robustness of the estimation algorithm are improved compared with those of a single algorithm.
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A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
fig. 1 is a flow chart of a method 100 of filter estimation of state of charge of a lithium ion battery according to an embodiment of the invention;
fig. 2 is a circuit diagram of an equivalent circuit model of a lithium ion battery according to an embodiment of the present invention; and
fig. 3 is a schematic structural diagram of a system 300 for filtering and estimating a state of charge of a lithium ion battery according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
The invention provides a method and a system for carrying out filtering estimation on the state of charge of a lithium ion battery, which aim to solve the problem of calculating the SOC estimation value of the lithium ion battery. Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a Kalman filter recursion algorithm to obtain a first state of charge estimation value and a Kalman filtering precision value; by means of H∞Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain second state of chargeA state estimate; sum Kalman filtering and H∞Filtering is combined, and Kalman filtering and H are respectively determined according to the precision of the Kalman filtering∞And the filtered weight value is used for calculating the SOC estimated value of the lithium ion battery. The invention integrates the advantages of fast convergence of Kalman filtering initial values, good optimal estimation performance and H∞The method has the advantages of high filtering robustness, and meanwhile, the redundancy of the SOC estimation algorithm is increased, so that the precision, the adaptability and the robustness of the estimation algorithm are improved relative to a single algorithm, the estimation instability caused by sample data and model deviation can be inhibited, and the estimation performance can be good under a severe environment.
Fig. 1 is a flow chart of a method 100 for filter estimation of state of charge of a lithium ion battery according to an embodiment of the present invention. As shown in fig. 1, the method 100 is used for filter estimation of the state of charge of a lithium ion battery. The method 100 for performing filtering estimation on the state of charge of the lithium ion battery starts from step 101, and in step 101, parameter data of the surface of the lithium ion battery is collected according to a preset time threshold, and an equivalent circuit model of the lithium ion battery is established; wherein the parameter data comprises: terminal voltage, charge and discharge current, and temperature. Fig. 2 is a circuit diagram of an equivalent circuit model of a lithium ion battery according to an embodiment of the present invention. As shown in fig. 2, in the embodiment of the present invention, the equivalent circuit model is established as follows:
Figure BDA0001338227080000091
wherein Uoc is the open circuit voltage of the battery and represents the terminal voltage value of the battery in electrochemical balance; ut is the terminal voltage of the battery, which represents the value between the positive and negative electrodes of the battery at any charging and discharging time; up is polarization voltage, R0 is ohm internal resistance, Rp is polarization internal resistance, and Cp is polarization capacitance. In an embodiment of the invention, the equivalent circuit is a Thevenin model, and a mathematical expression of the model is obtained by using kirchhoff's law.
Preferably, in step 103, a mathematical model of the lithium ion battery is obtained according to the equivalent circuit model, parameters of the equivalent circuit model are obtained by using a deviation compensation recursive least square method, and an open-circuit voltage of the lithium ion battery is obtained through experiments. Preferably, the mathematical model of the lithium ion battery is:
Figure BDA0001338227080000092
Figure BDA0001338227080000093
wherein Urc is the difference between the open circuit voltage and the terminal voltage; z (k) is an observation matrix, and is the internal voltage drop of the battery at the moment k; h (k) is a data matrix which is a data information input matrix during parameter identification;
Figure BDA0001338227080000094
is an identification coefficient; a is1，b1，b2The element value corresponding to the identification coefficient. In the embodiment of the invention, the equivalent circuit model of the lithium ion battery is discretized, and the parameters of the battery are obtained by using a deviation compensation recursive least square method. Wherein, the open circuit voltage curve of the lithium ion battery is obtained through experiments. Is provided with a UrcFor the difference between the open-circuit voltage and the terminal voltage, the equivalent circuit of the lithium ion battery is modeled into a standard form of a least square method, and a result mathematical model can be obtained as follows:
Figure BDA0001338227080000095
Figure BDA0001338227080000096
wherein z (k) is an observation matrix, which is the internal voltage drop of the cell at the time k; h (k) is a data matrix, which is a data information input matrix during parameter identification;
Figure BDA0001338227080000097
for identifying the coefficients, based on the identification coefficients andthe relation of the parameters of the equivalent circuit model can obtain the parameter values of the equivalent circuit model. Wherein, the parameter values of the equivalent circuit model include: ohmic internal resistance R0, polarization internal resistance Rp and polarization capacitance Cp. a is1，b1And b2Is the value of an element identifying a coefficient. a is1，b1And b2The value of (A) has a certain functional relationship with the ohmic internal resistance R0, the polarization internal resistance Rp and the polarization capacitance Cp.
Preferably, a state space model of the lithium ion battery is obtained in step 104 according to the model in the noise environment of the lithium ion battery and the mathematical model. Preferably, wherein the state space model is:
Figure BDA0001338227080000101
g(X)＝Uoc(S)-Up，
X＝[S Up]T，
wherein, X is a state variable, u is the input of the model, and u is selected as I; s is the estimated value of the state of charge of the battery; y is an observed variable of the model, and the selected Y is Ut; ts is a parameter measurement period, taupKc is the charge-discharge coefficient of the lithium ion battery. In the embodiment of the invention, a power battery model under a noise environment is obtained by combining noise information according to a mathematical model of the lithium ion battery, and meanwhile, a relation between open-circuit voltage and battery charge state is obtained according to a static test of the power battery, an ampere-hour integration method is introduced into a state equation and discretized to obtain a state space model of the linear discrete lithium ion battery.
Preferably, in step 104, a kalman filter recursion algorithm is used to estimate the state of charge of the lithium ion battery corresponding to the state space model, and a first state of charge estimation value and an accuracy value of kalman filtering are obtained. Preferably, the estimating the state of charge of the lithium ion battery corresponding to the state space model by using a kalman filter recursion algorithm to obtain a first state of charge estimated value and an accuracy value of kalman filtering includes: kalman filterInitializing and setting system parameters of wave estimation, wherein the system parameters comprise: the system state variable, the error covariance of the system, the mean of the system noise, the covariance of the system noise, the mean of the parametric noise and the covariance of the parametric noise; updating the numerical value of the system parameter, and estimating the state of charge of the lithium ion battery at the current moment by using a Kalman filter recursive algorithm according to the state of charge estimated value at the previous moment to obtain a first state of charge estimated value; and calculating Kalman filtering gain, acquiring and updating the numerical value of the estimated system parameter, and estimating the state of charge of the lithium ion battery at the next moment. In the embodiment of the invention, SOC of the lithium ion battery of the established model is estimated, and real-time recursive estimation is carried out by using a recursive algorithm of an adaptive extended Kalman filter. The SOC estimation result is finally obtained as
Figure BDA0001338227080000111
Firstly, setting initialization parameter Xu，Pu，
Figure BDA0001338227080000119
Wherein x isoThe initial value of the system state, i.e. the S value in the corresponding state space model, is the initial value of S, and the initial S value is used when the SOC value is calculated for the first time. P0The initial value of the error covariance of the system,
Figure BDA0001338227080000114
is the mean and covariance of the system noise,
Figure BDA0001338227080000115
mean and covariance of the parametric noise; then, updating the state value by one step, and calculating to obtain a first state of charge estimation value; and finally, calculating Kalman filtering gain according to a filtering algorithm, and obtaining an estimated posterior value and an error covariance value. And circulating the processes to obtain real-time update of the SOC value and the error covariance, wherein the obtained SOC value is the S value in the corresponding equivalent circuit model. SOC estimation value at next timeUpdated System State X at computation time0The S value is an SOC estimation value at the current time.
Preferably, H is utilized in step 105∞And estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value. Preferably, wherein said utilization H∞Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value, wherein the method comprises the following steps: h∞Initializing and setting system parameters of filtering, wherein the system parameters comprise: system state variables, error covariance of the system, state quantity matrix and covariance matrix; according to the identification result of the system parameter, using H∞Estimating the state of charge of the lithium ion battery at the current moment by using a filter recursion algorithm to obtain a second state of charge estimation value; and calculating and updating the numerical value of the current system parameter for estimating the state of charge of the lithium ion battery at the next moment. First initialize X0，P0，θ，Lk，S0，Q′k，
Figure BDA0001338227080000116
Setting a suitable initial value, wherein X0Is an initial value of the system state, P0The initial value of the error covariance of the system, L is a state quantity matrix, and can be taken as Lk-[1 0]，Q′k，
Figure BDA0001338227080000117
Is a covariance matrix, theta is a set, small number that determines the accuracy of the algorithm, SkAnd theta is proportional; then calculate a particular matrix Q'k，
Figure BDA0001338227080000118
According to the parameter identification result, assigning the coefficient of the state equation at the current moment, and calculating an error covariance matrix and a gain matrix; and finally, updating the state of the SOC estimation value of the estimation value and updating the error covariance matrix.
Preferably, in step 106, the weights of the first state of charge estimation value and the second state of charge estimation value are respectively calculated according to the precision value of the kalman filter, and the state of charge estimation value of the lithium ion battery is calculated according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value, and the second state of charge estimation value and the weight occupied by the second state of charge estimation value. Preferably, the calculating the weights of the first state of charge estimation value and the second state of charge estimation value according to the precision value of the kalman filter, and calculating the state of charge estimation value of the lithium ion battery according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value, the second state of charge estimation value and the weight occupied by the second state of charge estimation value includes:
Figure BDA0001338227080000121
Figure BDA0001338227080000122
wherein d isk+1A weight of the first state of charge estimate;
Figure BDA0001338227080000123
is the precision value of Kalman filtering; j. the design is a square2Minimum value of threshold value for precision judgment, J∞A and b are weighting coefficients for the maximum value of the precision judgment threshold;
Figure BDA0001338227080000124
and the estimated value of the state of charge of the lithium ion battery at the current moment is obtained.
In an embodiment of the present invention, a supremum boundary J that can evaluate the accuracy of filtering is defined2And infimum J∞. When the filter can be used well and the precision meets the requirement, the filter can be always used
Figure BDA0001338227080000125
If the filter diverges or has poor accuracy, there is always
Figure BDA0001338227080000126
For other cases, the filtering effect can be considered as general. The weighting coefficient calculation formula of the hybrid filtering is as follows:
Figure BDA0001338227080000127
wherein, the estimated value of Kalman filtering at the current moment is
Figure BDA0001338227080000128
dk+1A weighting coefficient for hybrid filtering, the magnitude of which is determined according to the estimation accuracy of kalman filtering; a and b are weighting coefficients whose magnitude determines the speed of weight change, and the values of a and b are determined empirically. After the weighting coefficient of the current time is obtained, the SOC estimation value of the lithium ion battery can be calculated according to the following formula:
Figure BDA0001338227080000131
performing update operation according to the above method to obtain
Figure BDA0001338227080000132
The estimated value of the state of charge SOC at the current moment of the lithium ion battery is obtained.
Fig. 3 is a schematic structural diagram of a system 300 for filtering and estimating a state of charge of a lithium ion battery according to an embodiment of the present invention. As shown in fig. 3, the system 300 for filtering and estimating the state of charge of the lithium ion battery includes: a system for filter estimation of state of charge of a lithium ion battery, the system 300 comprising: an equivalent circuit model establishing unit 301, a mathematical model establishing unit 302, a state space model establishing unit 303, a first state of charge estimation value calculating unit 304, a second state of charge estimation value calculating unit 305, and a state of charge estimation value determining unit 306.
Preferably, the equivalent circuit model establishing unit 301 is configured to acquire parameter data of a lithium ion battery surface according to a preset time threshold, and establish an equivalent circuit model of the lithium ion battery; wherein the parameter data comprises: terminal voltage, charge and discharge current, and temperature. Preferably, wherein the equivalent circuit model is:
Figure BDA0001338227080000133
wherein Uoc is the open circuit voltage of the battery and represents the terminal voltage value of the battery in electrochemical balance; ut is the terminal voltage of the battery, which represents the value between the positive and negative electrodes of the battery at any charging and discharging time; up is polarization voltage, R0 is ohm internal resistance, Rp is polarization internal resistance, and Cp is polarization capacitance.
Preferably, the mathematical model establishing unit 302 is configured to obtain a mathematical model of the lithium ion battery according to the equivalent circuit model, obtain parameters of the equivalent circuit model by using a deviation compensation recursive least square method, and obtain an open-circuit voltage of the lithium ion battery through an experiment. Preferably, the mathematical model of the lithium ion battery is:
Figure BDA0001338227080000134
Figure BDA0001338227080000135
wherein Urc is the difference between the open circuit voltage and the terminal voltage; z (k) is an observation matrix, and is the internal voltage drop of the battery at the moment k; h (k) is a data matrix which is a data information input matrix during parameter identification;
Figure BDA0001338227080000141
is an identification coefficient; a is1，b1，b2The element value corresponding to the identification coefficient.
Preferably, the state space model establishing unit 303 is configured to obtain a state space model of the lithium ion battery according to the model in the lithium ion battery noise environment and the mathematical model. Preferably, wherein the state space model is:
Figure BDA0001338227080000142
g(X)＝Uoc(S)-Up，
X＝[S Up]T，
wherein, X is a state variable, u is the input of the model, and u is selected as I; s is the estimated value of the state of charge of the battery; y is an observed variable of the model, and the selected Y is Ut; ts is a parameter measurement period, taupKc is the charge-discharge coefficient of the lithium ion battery.
Preferably, the first state of charge estimation value calculation unit 304 is configured to estimate the state of charge of the lithium ion battery corresponding to the state space model by using a kalman filter recursive algorithm, and obtain a first state of charge estimation value and an accuracy value of kalman filtering. Preferably, the first state of charge estimation value calculation unit 304 is specifically configured to:
initializing and setting system parameters of Kalman filtering estimation, wherein the system parameters comprise: the system state variable, the error covariance of the system, the mean of the system noise, the covariance of the system noise, the mean of the parametric noise and the covariance of the parametric noise;
updating the numerical value of the system parameter, and estimating the state of charge of the lithium ion battery at the current moment by using a Kalman filter recursive algorithm according to the state of charge estimated value at the previous moment to obtain a first state of charge estimated value;
and calculating Kalman filtering gain, acquiring and updating the numerical value of the estimated system parameter, and estimating the state of charge of the lithium ion battery at the next moment.
Preferably, the second state of charge estimation value calculation unit 305 is configured to utilize H∞And estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value. Preferably, wherein said second state of chargeThe estimated value calculating unit 305 is specifically configured to:
H∞initializing and setting system parameters of filtering, wherein the system parameters comprise: system state variables, error covariance of the system, state quantity matrix and covariance matrix;
according to the identification result of the system parameter, using H∞Estimating the state of charge of the lithium ion battery at the current moment by using a filter recursion algorithm to obtain a second state of charge estimation value;
and calculating and updating the numerical value of the current system parameter for estimating the state of charge of the lithium ion battery at the next moment.
Preferably, the state of charge estimation value determining unit 306 is configured to calculate weights of the first state of charge estimation value and the second state of charge estimation value according to the precision value of the kalman filter, and calculate the state of charge estimation value of the lithium ion battery according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value, the second state of charge estimation value and the weight occupied by the second state of charge estimation value. Preferably, the state of charge estimation value determination unit includes:
Figure BDA0001338227080000151
Figure BDA0001338227080000152
wherein d isk+1A weight of the first state of charge estimate;
Figure BDA0001338227080000153
is the precision value of Kalman filtering; j. the design is a square2Minimum value of threshold value for precision judgment, J∞A and b are weighting coefficients for the maximum value of the precision judgment threshold;
Figure BDA0001338227080000154
and the estimated value of the state of charge of the lithium ion battery at the current moment is obtained.
The system 300 for performing filter estimation on the state of charge of the lithium ion battery according to the embodiment of the present invention corresponds to the system 100 for performing filter estimation on the state of charge of the lithium ion battery according to another embodiment of the present invention, and is not described herein again.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

Claims (12)

1. A method of filter estimation of state of charge of a lithium ion battery, the method comprising:
acquiring parameter data of the surface of a lithium ion battery according to a preset time threshold, and establishing an equivalent circuit model of the lithium ion battery; wherein the parameter data comprises: terminal voltage, charge and discharge current and temperature;
acquiring a mathematical model of the lithium ion battery according to the equivalent circuit model, acquiring parameters of the equivalent circuit model by using a deviation compensation recursive least square method, and acquiring the open-circuit voltage of the lithium ion battery through experiments;
acquiring a state space model of the lithium ion battery according to the model in the noise environment of the lithium ion battery and the mathematical model;
estimating the state of charge of the lithium ion battery corresponding to the state space model by using a Kalman filter recursion algorithm to obtain a first state of charge estimation value and a Kalman filtering precision value;
by means of H∞Filter recursion algorithm pairEstimating the state of charge of the lithium ion battery corresponding to the state space model to obtain a second state of charge estimation value;
respectively calculating the weights of the first state of charge estimation value and the second state of charge estimation value according to the precision value of the Kalman filtering, and calculating the state of charge estimation value of the lithium ion battery according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value and the second state of charge estimation value and the weight occupied by the second state of charge estimation value; the method specifically comprises the following steps:
Figure FDA0002459847110000011
Figure FDA0002459847110000012
wherein d isk+1A weight of the first state of charge estimate;
Figure FDA0002459847110000013
is the precision value of Kalman filtering; j. the design is a square2Minimum value of threshold value for precision judgment, J∞A and b are weighting coefficients for the maximum value of the precision judgment threshold;
Figure FDA0002459847110000014
the estimated value of the state of charge of the lithium ion battery at the current moment is obtained;
Figure FDA0002459847110000015
is a first state of charge estimate;
Figure FDA0002459847110000021
is a second state of charge estimate.
2. The method of claim 1, wherein the equivalent circuit model is:
Figure FDA0002459847110000022
wherein Uoc is the open circuit voltage of the battery and represents the terminal voltage value of the battery in electrochemical balance; ut is the terminal voltage of the battery, which represents the value between the positive and negative electrodes of the battery at any charging and discharging time; up is polarization voltage, R0 is ohm internal resistance, Rp is polarization internal resistance, and Cp is polarization capacitance.
3. The method of claim 1, wherein the mathematical model of the lithium ion battery is:
Figure FDA0002459847110000023
z(k)＝Urc(k),h(k)＝[-Urc(k-1) I(k) I(k-1)],
Figure FDA0002459847110000026
wherein Urc is the difference between the open circuit voltage and the terminal voltage; z (k) is an observation matrix, and is the internal voltage drop of the battery at the moment k; h (k) is a data matrix which is a data information input matrix during parameter identification;
Figure FDA0002459847110000024
is an identification coefficient; a is1，b1，b2The element value corresponding to the identification coefficient.
4. The method of claim 1, wherein the state space model is:
Figure FDA0002459847110000025
g(X)＝Uoc(S)-Up，
X＝[S Up]T，
wherein, X is a state variable, u is the input of the model, and u is selected as I; s is the state of charge of the batteryAn estimated value; y is an observed variable of the model, and the selected Y is Ut; ts is a parameter measurement period, taupKc is the charge-discharge coefficient of the lithium ion battery; r0Ohmic internal resistance; rp is the polarization internal resistance; uoc is the open circuit voltage of the cell, representing the terminal voltage value of the cell at electrochemical equilibrium; up is the polarization voltage.
5. The method of claim 1, wherein the estimating the state of charge of the lithium ion battery corresponding to the state space model by using a kalman filter recursion algorithm to obtain a first estimated state of charge value and an accuracy value of kalman filtering comprises:
initializing and setting system parameters of Kalman filtering estimation, wherein the system parameters comprise: the system state variable, the error covariance of the system, the mean of the system noise, the covariance of the system noise, the mean of the parametric noise and the covariance of the parametric noise;
updating the numerical value of the system parameter, and estimating the state of charge of the lithium ion battery at the current moment by using a Kalman filter recursive algorithm according to the state of charge estimated value at the previous moment to obtain a first state of charge estimated value;
and calculating Kalman filtering gain, acquiring and updating the numerical value of the estimated system parameter, and estimating the state of charge of the lithium ion battery at the next moment.
6. The method of claim 1, wherein the utilizing H∞Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value, wherein the method comprises the following steps:
H∞initializing and setting system parameters of filtering, wherein the system parameters comprise: system state variables, error covariance of the system, state quantity matrix and covariance matrix;
according to the identification result of the system parameter, using H∞Charge state of filter recursion algorithm on lithium ion battery at current momentEstimating the state to obtain a second state of charge estimation value;
and calculating and updating the numerical value of the current system parameter for estimating the state of charge of the lithium ion battery at the next moment.
7. A system for filter estimation of state of charge of a lithium ion battery, the system comprising: an equivalent circuit model establishing unit, a mathematical model establishing unit, a state space model establishing unit, a first state of charge estimation value calculating unit, a second state of charge estimation value calculating unit and a state of charge estimation value determining unit,
the equivalent circuit model establishing unit is used for acquiring parameter data of the surface of the lithium ion battery according to a preset time threshold and establishing an equivalent circuit model of the lithium ion battery; wherein the parameter data comprises: terminal voltage, charge and discharge current and temperature;
the mathematical model establishing unit is used for acquiring a mathematical model of the lithium ion battery according to the equivalent circuit model, acquiring parameters of the equivalent circuit model by using a deviation compensation recursive least square method, and acquiring the open-circuit voltage of the lithium ion battery through experiments;
the state space model establishing unit is used for acquiring a state space model of the lithium ion battery according to the model in the noise environment of the lithium ion battery and the mathematical model;
the first state of charge estimation value calculation unit is used for estimating the state of charge of the lithium ion battery corresponding to the state space model by using a Kalman filter recursion algorithm to obtain a first state of charge estimation value and a Kalman filtering precision value;
the second state of charge estimation value calculation unit is used for utilizing H∞Estimating the state of charge of the lithium ion battery corresponding to the state space model by using a filter recursion algorithm to obtain a second state of charge estimation value;
the state of charge estimation value determining unit is used for respectively calculating the weights of the first state of charge estimation value and the second state of charge estimation value according to the precision value of the Kalman filtering, and calculating the state of charge estimation value of the lithium ion battery according to the first state of charge estimation value and the weight occupied by the first state of charge estimation value and the second state of charge estimation value and the weight occupied by the second state of charge estimation value; the method specifically comprises the following steps:
Figure FDA0002459847110000041
Figure FDA0002459847110000042
wherein d isk+1A weight of the first state of charge estimate;
Figure FDA0002459847110000043
is the precision value of Kalman filtering; j. the design is a square2Minimum value of threshold value for precision judgment, J∞A and b are weighting coefficients for the maximum value of the precision judgment threshold;
Figure FDA0002459847110000044
the estimated value of the state of charge of the lithium ion battery at the current moment is obtained;
Figure FDA0002459847110000045
is a first state of charge estimate;
Figure FDA0002459847110000046
is a second state of charge estimate.
8. The system of claim 7, wherein the equivalent circuit model is:
Figure FDA0002459847110000051
wherein Uoc is the open circuit voltage of the battery and represents the terminal voltage value of the battery in electrochemical balance; ut is the terminal voltage of the battery, which represents the value between the positive and negative electrodes of the battery at any charging and discharging time; up is polarization voltage, R0 is ohm internal resistance, Rp is polarization internal resistance, and Cp is polarization capacitance.
9. The system of claim 7, wherein the mathematical model of the lithium ion battery is:
Figure FDA0002459847110000052
z(k)＝Urc(k),h(k)＝[-Urc(k-1) I(k) I(k-1)],
Figure FDA0002459847110000053
wherein Urc is the difference between the open circuit voltage and the terminal voltage; z (k) is an observation matrix, and is the internal voltage drop of the battery at the moment k; h (k) is a data matrix which is a data information input matrix during parameter identification;
Figure FDA0002459847110000054
is an identification coefficient; a is1，b1，b2The element value corresponding to the identification coefficient.
10. The system of claim 7, wherein the state space model is:
Figure FDA0002459847110000055
g(X)＝Uoc(S)-Up，
X＝[S Up]T，
wherein, X is a state variable, u is the input of the model, and u is selected as I; s is the estimated value of the state of charge of the battery; y is an observed variable of the model, and the selected Y is Ut; ts is a parameter measurement period, taupKc is the charge-discharge coefficient of the lithium ion battery; r0Ohmic internal resistance; rp is the polarization internal resistance; uoc is electricityThe open circuit voltage of the cell, representing the terminal voltage value at which the cell is at electrochemical equilibrium; up is the polarization voltage.
11. The system of claim 7, wherein the first state of charge estimate calculation unit is specifically configured to:
initializing and setting system parameters of Kalman filtering estimation, wherein the system parameters comprise: the system state variable, the error covariance of the system, the mean of the system noise, the covariance of the system noise, the mean of the parametric noise and the covariance of the parametric noise;
updating the numerical value of the system parameter, and estimating the state of charge of the lithium ion battery at the current moment by using a Kalman filter recursive algorithm according to the state of charge estimated value at the previous moment to obtain a first state of charge estimated value;
and calculating Kalman filtering gain, acquiring and updating the numerical value of the estimated system parameter, and estimating the state of charge of the lithium ion battery at the next moment.
12. The system of claim 7, wherein the second state of charge estimate calculation unit is specifically configured to:
H∞initializing and setting system parameters of filtering, wherein the system parameters comprise: system state variables, error covariance of the system, state quantity matrix and covariance matrix;
according to the identification result of the system parameter, using H∞Estimating the state of charge of the lithium ion battery at the current moment by using a filter recursion algorithm to obtain a second state of charge estimation value;
and calculating and updating the numerical value of the current system parameter for estimating the state of charge of the lithium ion battery at the next moment.
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