CN115494398A - Battery SOC estimation method and system based on fusion filtering strategy - Google Patents

Battery SOC estimation method and system based on fusion filtering strategy Download PDF

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CN115494398A
CN115494398A CN202211156569.8A CN202211156569A CN115494398A CN 115494398 A CN115494398 A CN 115494398A CN 202211156569 A CN202211156569 A CN 202211156569A CN 115494398 A CN115494398 A CN 115494398A
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soc
battery
time
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ocv
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朱瑞
翟仑
王双岭
田锡园
贾伟宽
康伟翔
田善君
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Shandong Normal University
Yantai Dongfang Wisdom Electric Co Ltd
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Yantai Dongfang Wisdom Electric Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC

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Abstract

The invention provides a battery SOC estimation method and a system based on a fusion filtering strategy, which comprises the following steps: obtaining open-circuit voltages OCV at different SOC positions based on a test, and determining a mapping relation between the OCV and the SOC; initializing RC parameters and SOC of the battery equivalent model; determining open-circuit voltage OCV according to the OCV-SOC mapping relation and SOC, solving a discrete domain regression equation of a battery equivalent model by using an optimization algorithm, and identifying RC parameters at the current moment on line; and estimating the current SOC of the battery by utilizing a fusion filtering strategy of a Kalman filter and a smooth variable structure filter based on the RC parameters at the current moment and the updated state space expression of the battery system. The fusion filtering strategy is used for estimating the SOC of the battery, the estimation accuracy and robustness can be considered, and the reliability of the SOC estimation is improved.

Description

Battery SOC estimation method and system based on fusion filtering strategy
Technical Field
The invention belongs to the technical field of battery SOC estimation, and particularly relates to a battery SOC estimation method and system based on a fusion filtering strategy.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The firm support and the vigorous development of new energy automobile industry represented by electric automobiles are key ways for solving the problems of energy crisis and environmental pollution, and become the general consensus of governments of all countries in the world. Compared with other batteries, the lithium ion battery has the advantages of high energy density, long cycle life, no memory effect and the like, and thus becomes the first choice in the fields of electric automobiles and energy storage. The State of Charge (SOC) is one of the key states in the battery, and reflects the level of the remaining capacity, and the accurate estimation of the SOC can not only prevent the occurrence of dangerous accidents such as overcharge, overdischarge, fire, explosion and the like, but also prolong the battery life and improve the utilization efficiency and level of the battery. However, SOC is an implicit state quantity that cannot be directly measured, and usually needs to be estimated and predicted by a battery model (an equivalent circuit model, an electrochemical model, etc.) and other methods. Among them, kalman Filter methods represented by Extended Kalman Filters (EKFs) and Unscented Kalman filters (okf) are widely used in Battery Management Systems (BMS) because of their advantages of on-line, closed-loop, self-correction, high estimation accuracy, and the like. However, the premise of obtaining higher estimation accuracy by the method is that a more accurate battery model is needed, otherwise, the estimation accuracy is difficult to guarantee and even diverges in severe cases, and the optimality of the estimation effect is at the expense of robustness and stability. On the contrary, although a Smooth Variable Structure Filter (SVSF) developed from a Variable Structure theory and a sliding mode concept has strong robustness and stability, an ideal SOC estimation result cannot be obtained when the model matching degree is high.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a battery SOC estimation method and system based on a fusion filtering strategy, organically integrates the advantages of two methods, and provides a battery SOC estimation method which has both accuracy and robustness. The method has the advantages of simple realization, small calculation amount, small occupied memory space and the like while giving consideration to both accuracy and robustness.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions: a battery SOC estimation method based on a fusion filtering strategy is characterized by comprising the following steps:
obtaining open-circuit voltages OCV at different SOC positions based on a test, and determining a mapping relation between the OCV and the SOC;
initializing RC parameters and SOC of the battery equivalent model;
determining open-circuit voltage OCV according to the OCV-SOC mapping relation and SOC, solving a discrete domain regression equation of a battery equivalent model by using an optimization algorithm, and identifying RC parameters at the current moment on line;
and estimating the current SOC of the battery by utilizing a fusion filtering strategy of a Kalman filter and a smooth variable structure filter based on the RC parameters at the current moment and the updated state space expression of the battery system.
A second aspect of the present invention provides a battery SOC estimation system based on a fusion filtering strategy, including:
a mapping relation determination module: obtaining open-circuit voltages OCV at different SOC positions based on a test, and determining a mapping relation between the OCV and the SOC;
an initialization module: initializing RC parameters and SOC of the battery equivalent model;
the RC parameter identification module: determining open-circuit voltage OCV according to the OCV-SOC mapping relation and SOC, solving a discrete domain regression equation of a battery equivalent model by using an optimization algorithm, and identifying RC parameters at the current moment on line;
an SOC estimation module: and based on the RC parameters at the current moment and the updated state space expression of the battery system, estimating the current SOC of the battery by using a fusion filtering strategy of a Kalman filter and a smooth variable structure filter.
A third aspect of the invention provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the above-described method.
A fourth aspect of the invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, which when executed by the processor, perform the steps of the above method.
The above one or more technical solutions have the following beneficial effects:
the invention fully researches the working principles of different methods, organically integrates the advantages of the two methods, and provides the battery SOC estimation method which gives consideration to both accuracy and robustness. The method has the advantages of simplicity in implementation, small calculated amount, small occupied memory space and the like while considering both accuracy and robustness, so that the method is expected to be widely concerned and applied by the industry and the academia of BMS and related fields, and has wide application prospect.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a circuit diagram of Thevenin model according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of an actual trajectory and an estimated trajectory of an SVSF according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating an implementation principle of EKF and SVSF fusion in an embodiment of the present invention.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all 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.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment discloses a battery SOC estimation method based on a fusion filtering strategy, which comprises the following steps:
step 1: obtaining Open Circuit Voltages (OCV) at different SOCs based on a test, and determining an OCV-SOC mapping relation;
and 2, step: initializing RC parameters and SOC of the battery equivalent model;
and step 3: determining open-circuit voltage OCV according to the OCV-SOC mapping relation and SOC, solving a discrete domain regression equation of a battery equivalent model by using an optimization algorithm, and identifying RC parameters at the current moment on line;
and 4, step 4: and (3) estimating the current SOC of the battery by utilizing a fusion filtering strategy of a Kalman filter and a smooth variable structure filter based on the RC parameters at the current moment and the updated state space expression of the battery system, wherein the estimation process is continuously and circularly carried out, and after the step is finished, the step 3 is continuously carried out.
Specifically, as shown in FIG. 1, study was conducted using Thevenin model, let E L (s)＝U t (s)-U oc (s), the transfer function of which can be expressed as:
Figure BDA0003859015100000041
by using a bilinear transformation method, the transfer function of the system is transformed from an s domain to a z domain, and the discrete transfer function is obtained as follows:
Figure BDA0003859015100000042
in the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000043
T s representing the sampling time.
Writing equation (2) as a regression form as follows:
Figure BDA0003859015100000044
wherein the subscript k denotes the time k,
Figure BDA0003859015100000045
θ k ＝[b 1 ,b 2 ,b 3 ] T 。
solving theta by using least square method or group intelligent optimization algorithms such as particle swarm optimization, genetic algorithm and the like k The solution of (1). Further, the RC parameters in Thevenin model can be solved as:
Figure BDA0003859015100000051
in the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000052
and
Figure BDA0003859015100000053
each represents R o 、R p 、C p And b 1 、b 2 、b 3 An estimate of (d).
State space expressions for the battery system are listed. The state space expression of Thevenin model shown in fig. 1 can be written as
Figure BDA0003859015100000054
In the formula of U p,k Represents the time k, the resistance R p And a capacitor C p A polarization voltage across; i is L,k The current flowing through the battery at the moment k is represented, and the discharge is defined as positive, and the charge is defined as negative; s k Representing the SOC of the battery at the k moment; η represents the coulombic efficiency; c N Represents the available capacity of the battery; u shape t Represents a terminal voltage of the battery; x is a radical of a fluorine atom k Is a state vector; y is k Is the output vector; u. of k Is an input vector; g (x) k+1 ,u k+1 ) A non-linear measurement function; a. The k And B k A system matrix and an input matrix which are state space expressions respectively; u shape oc Representing the open circuit voltage of the battery, as a function of SOC, can be written as:
Figure BDA0003859015100000055
in the formula (d) i Representing polynomial coefficients; m represents the polynomial order, and the specific numerical value depends on the open circuit voltage curves of different types of batteries under different health states.
The embodiment provides a fusion filtering strategy for estimating the battery SOC, which can give consideration to both the estimation accuracy and robustness and improve the reliability of SOC estimation, the proposed strategy can realize the fusion of a Kalman filter method (including but not limited to EKF and UKF) and an SVSF method, and the fusion of EKF and SVSF is taken as an example for analysis, and the specific explanation is as follows:
for a system with an inaccurate model, the EKF method generally has a large estimation error and even diverges due to a high dependency on the model, whereas the SVSF may gradually converge the estimated system state to the vicinity of the actual system trajectory (within the subspace) by using discontinuous gain switching, as shown in fig. 2, thereby ensuring the robustness and stability of the estimation. Meanwhile, frequent switching of discontinuous gain usually causes large jitter, and the problem can be solved by introducing a time-varying smooth boundary layer and correcting the gain.
The specific calculation process for estimating the SOC of the battery by the SVSF method is
And a time updating stage:
1. updating a prior state
Figure BDA0003859015100000061
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000062
is state x at time k k Is determined from the estimated value of the prior,
Figure BDA0003859015100000063
is (k-1) time state x k-1 A posteriori estimate of (d).
2. Updating a priori error covariance matrix
Figure BDA0003859015100000064
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000068
for the value of the prior error covariance matrix at time k, Q k-1 T represents the transpose operation for the value of the process noise covariance matrix at time (k-1).
3. Updating a priori measurement errors
Figure BDA0003859015100000065
In the formula, e k|k-1 Is the state at the time of (k-1)
Figure BDA0003859015100000066
The predicted k-time prior measures the value of the error.
A gain calculation stage:
4. computing a time-varying smoothed boundary layer (optimal smoothed boundary layer) matrix
Figure BDA0003859015100000067
Wherein gamma is memory rate or convergence rate, and is between 0 and 1; r k Measuring the value of the noise covariance matrix for the time k; | e k|k-1 I represents a pair e k|k-1 Taking an absolute value; s k Is the value of the innovation covariance matrix at the moment k; psi k The value of the smoothed boundary layer matrix at time k.
5. Calculating the gain according to the formula
Figure BDA0003859015100000071
In the formula, K k Is the gain at the time of k, and,
Figure BDA0003859015100000072
is C k The pseudo-inverse of (a) is,
Figure BDA0003859015100000073
representing the Hadamard product, diag (A) representing the diagonal matrix formed by the elements contained in the vector A, the saturation function sat (e) k|k-1 ,ψ k ) Is calculated by the formula
Figure BDA0003859015100000074
And a measurement updating stage:
6. updating a posterior state
Figure BDA0003859015100000075
7. Updating a posteriori error covariance matrix
Figure BDA0003859015100000076
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000077
is the value of the posterior error covariance matrix at the time k, and I is the identity matrix.
8. Updating posterior measurement error
Figure BDA0003859015100000078
In the formula, e k|k Is the value of the posterior measurement error at time k.
The process of estimating the SOC of the battery by the EKF method is basically the same as that of the SVSF method, the only difference is that the gain calculation mode is different, and the gain calculation formula of the EKF is as follows
Figure BDA0003859015100000079
And the gain calculation formula of SVSF is
Figure BDA00038590151000000710
Other procedures are not described in detail herein. Further, as can be seen from the formula (10), the time-varying smoothed boundary layer ψ k Is e k|k-1 、e k-1|k-1 、
Figure BDA00038590151000000712
And S k Due to e k|k-1 And e k-1|k-1 The level of modeling uncertainty is determined,
Figure BDA00038590151000000711
and S k Closely related to process noise and measurement noise, respectively, the time-varying smoothed boundary layer thus effectively indicates the level of modeling uncertainty and noise disturbance from which the level of current model uncertainty can be determined and further enables the fusion of EKF and SVSF.
FIG. 3 summarizes the implementation of EKF and SVSF fusion as follows: defining a threshold imposed on a time-varying smoothed Boundary layer as a Constant Smoothing Boundary layer (Constant Smoothing Boundary La)yer，ψ con ) When psi k ≥ψ con When the model uncertainty is large, updating the posterior state by using the gain of the SVSF so as to ensure the stability and robustness of estimation; when psi k ＜ψ con Then, the gain of EKF is used to update the posterior state to ensure the estimation accuracy.
From the above analysis, the specific process of estimating the battery SOC by fusing the EKF and the SVSF can be summarized as follows:
and a time updating stage:
1. updating a prior state
Figure BDA0003859015100000081
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000082
is state x at time k k Is determined from the estimated value of the prior,
Figure BDA0003859015100000083
is (k-1) time state x k-1 A posteriori estimate of (d).
2. Updating a priori error covariance matrix
Figure BDA0003859015100000084
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000085
is the value of the prior error covariance matrix at time k, Q k-1 T represents the transpose operation for the value of the process noise covariance matrix at time (k-1).
3. Updating a priori measurement errors
Figure BDA0003859015100000086
In the formula, e k|k-1 Is the state at the time of (k-1)
Figure BDA0003859015100000087
The predicted k-time prior measures the value of the error.
A gain calculation stage:
4. computing a time-varying smoothed boundary layer (optimal smoothed boundary layer) matrix
Figure BDA0003859015100000091
Wherein gamma is memory rate or convergence rate, and is between 0 and 1; r is k Measuring the value of the noise covariance matrix for the time k; | e k|k-1 I represents a pair e k|k-1 Taking an absolute value; s k Is the value of the innovation covariance matrix at the moment k; psi k The value of the smoothed boundary layer matrix at time k.
5. Setting a constant smooth boundary layer psi con According to psi k And psi con Different gain calculation methods are selected for the relationship (2). The specific calculation formula is
Figure BDA0003859015100000092
In the formula, K k Is the gain at the time of k, and,
Figure BDA0003859015100000093
is C k The pseudo-inverse of (a) is,
Figure BDA0003859015100000094
representing the Hadamard product, diag (A) representing the diagonal matrix formed by the elements contained in the vector A, the saturation function sat (e) k|k-1 ,ψ k ) Is calculated by the formula
Figure BDA0003859015100000095
And a measurement updating stage:
6. updating a posterior state
Figure BDA0003859015100000099
7. Updating a posteriori error covariance matrix
Figure BDA0003859015100000096
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000097
is the value of the posterior error covariance matrix at the time k, and I is the identity matrix.
8. Updating posterior measurement error
Figure BDA0003859015100000098
In the formula, e k|k Is the value of the posterior measurement error at time k.
Example two
The embodiment provides a process for estimating battery SOC through fusion of UKF and SVSF, which specifically comprises the following steps:
1. generating a vector of states
Figure BDA0003859015100000101
2n +1 sample points and corresponding weight coefficients
Figure BDA0003859015100000102
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000103
is a state vector x k-1 The ith sigma sample point of (a) is,
Figure BDA0003859015100000104
and
Figure BDA0003859015100000105
respectively representing the state vector x at time (k-1) k-1 The a posteriori estimate of (c) and the error covariance matrix,
Figure BDA0003859015100000106
indicates the (k-1) time
Figure BDA0003859015100000107
Decomposing the ith column of the square root matrix by lower triangle, λ controlling the division of the sigma sample point near the mean, n being the state vector x k-1 Dimension (d) of (a).
Corresponding to the sample point
Figure BDA0003859015100000108
Mean weight coefficient of
Figure BDA0003859015100000109
Sum covariance matrix weight coefficients
Figure BDA00038590151000001010
Can be respectively defined as:
Figure BDA00038590151000001011
wherein λ =3 α 2 -n; alpha value range is 10 -4 To 1; beta is consideration x k-1 The parameter of the prior information has an optimal value of 2 for gaussian distribution.
2. By means of state transfer functions
Figure BDA00038590151000001012
Calculating a one-step model prediction value at the k moment, and further calculating the mean value of the state vectors
Figure BDA00038590151000001013
Sum covariance
Figure BDA00038590151000001014
Can be expressed as:
Figure BDA00038590151000001015
in the formula (I), the compound is shown in the specification,
Figure BDA00038590151000001016
for the value of the prior error covariance matrix at time k, Q k-1 Is the value of the (k-1) time instance process noise covariance matrix.
3. From the equation of measurement
Figure BDA0003859015100000111
Propagating sample points
Figure BDA0003859015100000112
Calculating a priori measurement error e k|k-1 Measuring the covariance matrix
Figure BDA0003859015100000113
And cross covariance matrix of states and measurements
Figure BDA0003859015100000114
Can be expressed as:
Figure BDA0003859015100000115
in the formula, R k The values of the noise covariance matrix are measured for time k.
4. Computing a time-varying smoothed boundary layer (optimal smoothed boundary layer) matrix
Figure BDA0003859015100000116
Wherein gamma is memory rate or convergence rate, and is between 0 and 1; r k Measuring the value of the noise covariance matrix for the time k; | e k|k-1 I represents a pair e k|k-1 Taking an absolute value; s u,k Is the value of the innovation covariance matrix at the moment k; psi u,k The value of the smoothed boundary layer matrix at time k.
5. Setting a constant smooth boundary layer psi con According to psi u,k And psi con Different gain calculation methods are selected for the relationship (2). The specific calculation formula is
Figure BDA0003859015100000117
In the formula, K k Is the gain at the time of k, and,
Figure BDA0003859015100000118
is C u,k The pseudo-inverse of (a) is,
Figure BDA0003859015100000119
representing the Hadamard product, diag (A) representing the diagonal matrix formed by the elements contained in the vector A, the saturation function sat (e) k|k-1 ,ψ u,k ) Is calculated by the formula
Figure BDA00038590151000001110
6. Updating a posterior state
Figure BDA0003859015100000121
Covariance of posterior error
Figure BDA0003859015100000122
And a posteriori measurement error e k|k
Figure BDA0003859015100000123
In the formula (I), the compound is shown in the specification,
Figure BDA0003859015100000124
is the value of the posterior error covariance matrix at time k, I is the identity matrix,e k|k is the value of the posterior measurement error at time k.
EXAMPLE III
The present embodiment aims to provide a battery SOC estimation system based on a fusion filtering strategy, which includes:
a mapping relation determination module: obtaining open-circuit voltages OCV at different SOC positions based on a test, and determining the OCV-SOC mapping relation;
an initialization module: initializing RC parameters and SOC of the battery equivalent model;
the RC parameter identification module: determining open-circuit voltage OCV according to the OCV-SOC mapping relation and SOC, solving a discrete domain regression equation of a battery equivalent model by using an optimization algorithm, and identifying RC parameters at the current moment on line;
an SOC estimation module: and estimating the current SOC of the battery by utilizing a fusion filtering strategy of a Kalman filter and a smooth variable structure filter based on the RC parameters at the current moment and the updated state space expression of the battery system.
Example four
It is an object of this embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.
EXAMPLE five
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The steps involved in the devices of the third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
It will be understood by those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computer device, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by the computing device, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A battery SOC estimation method based on a fusion filtering strategy is characterized by comprising the following steps:
obtaining open-circuit voltages OCV at different SOC positions based on a test, and determining a mapping relation between the OCV and the SOC;
initializing RC parameters and SOC of the battery equivalent model;
determining open-circuit voltage OCV according to the OCV-SOC mapping relation and SOC, solving a discrete domain regression equation of a battery equivalent model by using an optimization algorithm, and identifying RC parameters at the current moment on line;
and based on the RC parameters at the current moment and the updated state space expression of the battery system, estimating the current SOC of the battery by using a fusion filtering strategy of a Kalman filter and a smooth variable structure filter.
2. The method of claim 1, wherein the discrete domain regression equation of the battery equivalent model is solved by one of least square method, particle swarm optimization and genetic algorithm.
3. The method for estimating the SOC of the battery based on the fusion filtering strategy according to claim 1, wherein the fusion of the kalman filter and the smooth variable structure filter is specifically:
and judging the uncertainty of the model through the time-varying smoothing layer, if the value of the time-varying smoothing boundary layer matrix at the moment k is greater than a threshold value applied to the time-varying smoothing boundary layer, updating the posterior state by adopting the gain of the smoothing variable structure filter, and otherwise updating the posterior state by adopting the gain of the Kalman filter.
4. The method of claim 1, wherein the fusion of the kalman filter and the smoothed-structure filter comprises a fusion of an extended kalman filter and a smoothed-structure filter, and a fusion of an unscented kalman filter and a smoothed filter.
5. The method for estimating the SOC of the battery based on the fusion filtering strategy according to claim 4, wherein the fusion of the extended kalman filter and the smooth variable structure filter selects different gain calculation formulas as follows:
Figure FDA0003859015090000021
wherein, K k Is the gain at the time of k, and,
Figure FDA0003859015090000022
is C k The pseudo-inverse of (a) is,
Figure FDA0003859015090000029
representing the Hadamard product, diag (A) representing the diagonal matrix formed by the elements contained in vector A, sat (e) k|k-1 ,ψ k ) In order to be a saturated matrix of the light,
Figure FDA0003859015090000023
is k atPositioning a threshold imposed on a time-varying smoothed boundary layer to a constant smoothed boundary layer psi by carving the values of the prior error covariance matrix con ，ψ k Smoothing the value of the boundary layer matrix for time k, S k Is the value of the innovation covariance matrix at time k.
6. The method for estimating battery SOC based on fusion filtering strategy according to claim 4, wherein the fusion of unscented kalman filter and smoothing filter selects different gain calculation formulas as:
Figure FDA0003859015090000024
wherein, K k Is the gain at the time of k, and,
Figure FDA0003859015090000025
is C u,k The pseudo-inverse of (a) is,
Figure FDA0003859015090000026
representing the Hadamard product, diag (A) representing the diagonal matrix formed by the elements contained in vector A, sat (e) k|k-1 ,ψ k ) For the saturation matrix, the threshold applied on the time-varying smoothed boundary layer is positioned to be a constant smoothed boundary layer psi con ，ψ k The value of the boundary layer matrix is smoothed for time k,
Figure FDA0003859015090000027
for the cross-covariance matrix of the states and measurements,
Figure FDA0003859015090000028
is a measured covariance matrix.
7. A battery SOC estimation system based on a fusion filtering strategy is characterized by comprising:
a mapping relation determination module: obtaining open-circuit voltages OCV at different SOC positions based on a test, and determining a mapping relation between the OCV and the SOC;
an initialization module: initializing RC parameters and SOC of the battery equivalent model;
the RC parameter identification module: determining open-circuit voltage OCV according to the OCV-SOC mapping relation and SOC, solving a discrete domain regression equation of a battery equivalent model by using an optimization algorithm, and identifying RC parameters at the current moment on line;
an SOC estimation module: and estimating the current SOC of the battery by utilizing a fusion filtering strategy of a Kalman filter and a smooth variable structure filter based on the RC parameters at the current moment and the updated state space expression of the battery system.
8. The system according to claim 7, wherein the fusion of the kalman filter and the smooth variable structure filter is specifically:
and judging the uncertainty of the model through the time-varying smoothing layer, if the value of the time-varying smoothing boundary layer matrix at the moment k is greater than a threshold value applied to the time-varying smoothing boundary layer, updating the posterior state by adopting the gain of the smoothing structure filter, and otherwise, updating the posterior state by adopting the gain of the Kalman filter.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of a method for battery SOC estimation based on a fusion filtering strategy according to any one of claims 1 to 7.
10. A processing apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of a method for battery SOC estimation based on a fusion filtering strategy according to any of claims 1-7.
CN202211156569.8A 2022-09-22 2022-09-22 Battery SOC estimation method and system based on fusion filtering strategy Pending CN115494398A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116400228A (en) * 2023-06-09 2023-07-07 中国华能集团清洁能源技术研究院有限公司 Battery fault detection method and device based on hybrid filter
CN116973770A (en) * 2023-09-25 2023-10-31 东方电子股份有限公司 Battery SOC estimation method and system based on steady-state Kalman filter
CN117074966A (en) * 2023-10-19 2023-11-17 东方电子股份有限公司 Battery SOC estimation method and system based on attenuation memory Kalman filter

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116400228A (en) * 2023-06-09 2023-07-07 中国华能集团清洁能源技术研究院有限公司 Battery fault detection method and device based on hybrid filter
CN116973770A (en) * 2023-09-25 2023-10-31 东方电子股份有限公司 Battery SOC estimation method and system based on steady-state Kalman filter
CN116973770B (en) * 2023-09-25 2023-12-08 东方电子股份有限公司 Battery SOC estimation method and system based on steady-state Kalman filter
CN117074966A (en) * 2023-10-19 2023-11-17 东方电子股份有限公司 Battery SOC estimation method and system based on attenuation memory Kalman filter

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