CN117517964A - Battery state of charge estimation method, device, electronic equipment and storage medium - Google Patents

Battery state of charge estimation method, device, electronic equipment and storage medium Download PDF

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CN117517964A
CN117517964A CN202311369358.7A CN202311369358A CN117517964A CN 117517964 A CN117517964 A CN 117517964A CN 202311369358 A CN202311369358 A CN 202311369358A CN 117517964 A CN117517964 A CN 117517964A
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battery
state
estimation
fractional
charge
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陈霞
曾宇楷
杨丘帆
林钰钧
文劲宇
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Huazhong University of Science and Technology
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Huazhong University of Science and Technology
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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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  • Tests Of Electric Status Of Batteries (AREA)

Abstract

The invention discloses a battery state of charge estimation method, a device, electronic equipment and a storage medium, which belong to the technical field of battery state estimation, wherein the battery state of charge estimation method comprises the following steps: fractional differential operator s in continuous state space equation for fractional equivalent circuit of battery r Discretizing to obtain a battery discrete state space equation, wherein r is the order of a fractional order differential operator, and the fractional order differential operator s r Discretizing by combining bilinear transformation and Muir recursion formula; the simplified discrete state space equation is obtained, the requirements of SOC estimation on hardware storage space and computing capacity are greatly reduced, meanwhile, the computing speed is remarkably improved, and experimental results show that the simplified discrete equation still has good estimation precision.

Description

Battery state of charge estimation method, device, electronic equipment and storage medium
Technical Field
The invention belongs to the technical field of battery state estimation, and in particular relates to a battery state of charge estimation method, a device, electronic equipment and a storage medium.
Background
In recent years, the application value of electrochemical batteries is more and more prominent, such as new energy automobile power batteries, power grid energy storage equipment, uninterruptible power supply UPS and the like. In the use process of various electrochemical batteries, the state of charge (SOC) is the most important state information, and an exact SOC value is required in a battery management system to ensure the normal use and operation safety of the batteries. However, the SOC of a battery is often difficult to measure directly and can only be estimated by an algorithm.
Among the SOC estimation algorithms of the battery, a kalman filter algorithm based on an equivalent circuit model is widely used. The equivalent circuit model method describes electrochemical phenomena such as polarization effect, diffusion effect and the like in the battery through resistance and capacitance elements. In the existing various equivalent circuit models, the fractional equivalent circuit better fits the impedance spectrum of the battery by introducing a constant phase angle element, and the description of the dynamics such as charge transfer reaction, double-layer effect, solid-phase diffusion and the like in the battery is more accurate, so that the SOC estimation accuracy is higher. However, the current Kalman filtering algorithm based on the fractional order model needs a large amount of historical data in each calculation process, and has high requirements on the storage capacity and calculation speed of a hardware memory. When the singlechip is used for battery management, the SOC estimation occupies a large amount of storage space and calculation power, and the conventional singlechip is difficult to be qualified.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a battery state of charge estimation method, a device, electronic equipment and a storage medium, which aim to simplify SOC estimation calculation while ensuring estimation precision and reduce the requirements of hardware on storage space, calculation capacity and the like.
To achieve the above object, according to one aspect of the present invention, there is provided a battery state of charge estimation method including:
determining a continuous state space equation of a fractional equivalent circuit of a battery, the continuous state space equation including a fractional differential equation for which a fractional differential operator s r Discretizing to obtain a battery discrete state space equation, wherein r isThe order of the fractional differential operator, wherein the fractional differential operator s r Discretization intoA 9 (z -1 R) and A 9 (z -1 -r) each represent a 9 th order polynomial obtained by a Muir recurrence formula, T being the sampling period;
based on a battery discrete state space equation, estimating the state of charge of the battery by using a Kalman filtering algorithm.
In one embodiment, the battery discrete state space equation is:
constructing an estimation result covariance matrix equation:
where x (k) is the battery state including the battery state of charge at sampling time k, I B (k) For the battery current at sampling instant k, ω k System process noise for sampling time k, A i 、B j All are the coefficients integrated after discretization; p (k) is covariance matrix of battery state estimation result at sampling time k, and Q is system process noise omega k Is a covariance matrix of (a);
the estimating the state of charge of the battery by using a Kalman filtering algorithm comprises:
correction value of battery state at sampling time k-9 to k-1To->Substituting the discrete state space equation of the battery to obtain an estimated value of the battery state at the sampling time k +.>
Correction value of covariance matrix of estimation result of sampling time k-9 to k-1To->Substituting the estimation result covariance matrix equation to obtain an estimation value of the estimation result covariance matrix at the sampling time k
Estimation based on battery stateEstimating battery terminal voltage y (k) and observing matrix estimation value
Calculating Kalman gain at sampling time k:
wherein R is the variance of the terminal voltage measurement error;
estimation of battery statePerforming correction to obtain a correction value of the battery stateWherein V (k) is the terminal voltage obtained by measuring the sampling time k;
for the estimation value of the observation matrixCorrecting to obtain observation matrix correction value +.>
Estimation value of covariance matrix of estimation resultCorrection is carried out to obtain a correction value +.>Wherein I is an identity matrix.
In one embodiment, when k < 9, the parameters substituted into the battery discrete state space equation and the estimation result covariance matrix equation are parameters starting from time 0.
In one embodiment, the fractional equivalent circuit of the battery includes an open circuit voltage V OC Resistance R 0 Resistance R 1 Constant phase angle element cpe and constant phase angle element W, wherein constant phase angle element cpe and resistor R 1 Parallel connection to form parallel connection structure, self-open circuit voltage V OC The positive electrode of (a) is serially connected with a resistor R in turn 0 The parallel structure and the constant phase angle element W.
In one embodiment, the continuous state space equation is:
in the formula, SOC is the charge state of the battery, V cpe 、V W For the voltages of the constant phase angle element cpe and the constant phase angle element W, the battery state includes the battery state of charge SOC and the voltage V cpe 、V W ；Q n For battery capacity, C cpe And C W Capacitance values of the constant phase angle element cpe and the constant phase angle element W, respectively, α cpe 、α W The orders, I, of the constant phase angle element cpe and the constant phase angle element W, respectively B For outflow ofCurrent level, ω of the battery k Is a system process noise.
In one embodiment, the method further comprises determining parameter values of each circuit element in the fractional equivalent circuit by a parameter identification method.
In one embodiment, the battery is a lead carbon battery or a lithium ion battery or a nickel hydrogen battery.
According to another aspect of the present invention, there is provided a battery state of charge estimation apparatus including:
a battery discrete state space equation construction unit for determining a continuous state space equation of a fractional equivalent circuit of a battery, the continuous state space equation including a fractional differential equation for which a fractional differential operator s r Performing discretization to obtain a battery discrete state space equation, wherein r is the order of a fractional order differential operator, and the fractional order differential operator s r Discretization intoA 9 (z -1 R) and A 9 (z -1 -r) each represent a 9 th order polynomial obtained by a Muir recurrence formula, T being the sampling period;
and the estimation unit is used for estimating the state of charge of the battery by using a Kalman filtering algorithm based on the discrete state space equation of the battery.
According to another aspect of the invention there is provided an electronic device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
According to another aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method described above.
In general, the above technical solutions conceived by the present invention, compared with the prior art, enable the following beneficial effects to be obtained:
the invention is realized by the method of the inventionThe invention firstly proposes that when constructing a battery discrete state space equation, the method adopts a double linear transformation (Tustin transformation) and a Muir recursion method in the mathematical field to approximate discretization, and for fractional differential operator s r The approximate discretization is completed by combining bilinear transformation and Muir recursion, so that the discretized state space equation is simplified. After simplification, the discrete state equation is only related to the data of the past 9 sampling moments, a large amount of historical data is not required to be stored when the battery SOC is estimated by Kalman filtering, ten thousands of iterative calculations are not required in the calculation process, the requirements of SOC estimation on the hardware storage space and the calculation capability are greatly reduced, meanwhile, the calculation speed is remarkably improved, and the experimental result shows that the simplified discrete equation still has good estimation precision.
Furthermore, the invention provides an optimized Kalman filtering algorithm aiming at a battery discrete state space equation, and the estimation accuracy can be improved by correcting an estimation result.
Furthermore, the invention provides a method for identifying parameters, which can rapidly and accurately determine the parameter values of all circuit elements in the fractional equivalent circuit.
Drawings
FIG. 1 is a flowchart illustrating steps of a method for estimating a battery state of charge according to an embodiment;
fig. 2 is a schematic diagram of a fractional equivalent circuit of a lithium ion battery according to an embodiment;
FIG. 3 is a flowchart illustrating steps for estimating a battery state of charge using a Kalman filtering algorithm according to an embodiment;
FIG. 4 is a schematic diagram showing a variation of terminal voltage of a lithium ion battery according to an embodiment;
FIG. 5 is a schematic diagram illustrating the variation of the output current of a lithium ion battery according to an embodiment;
fig. 6 is a schematic diagram showing a comparison between an estimated value and a reference value of an SOC obtained by the estimation method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Example 1
Fig. 1 is a flowchart showing the steps of a battery state of charge estimation method, which mainly includes two steps:
step S1: determining a continuous state space equation of a fractional equivalent circuit of the battery, wherein the continuous state space equation comprises a fractional differential equation, and a fractional differential operator s of the fractional differential equation r Performing discretization to obtain a battery discrete state space equation, wherein r is the order of a fractional order differential operator, and the fractional order differential operator s r Discretization intoA 9 (z -1 R) and A 9 (z -1 R) are all represented by 9 th order polynomials obtained by Muir recurrence formula, T being the sampling period.
Firstly, the battery type needs to be clarified and a fractional equivalent circuit of the battery is established, and a continuous state space equation of the battery is determined based on the established fractional equivalent circuit, wherein the continuous state space equation can involve a fractional differential operator s r The invention aims at providing a fractional differential operator s in the method r An approximate discretization process is performed to convert the continuous state space equation into a simple discrete state space equation and to ensure the estimation accuracy.
Different battery types will correspond to different fractional equivalent circuits, as long as the fractional differential operator s is involved in the continuous state space equation r The discretization can be performed by the method of the invention. For example, electrochemical cells such as lead carbon cells, lithium ion cells, or nickel hydrogen cells can all be used for state estimation using this approach.
Taking lithium ion battery as an example, its fractional equivalent circuit is shown in FIG. 2 and comprises an open circuit voltage V OC Resistance R 0 Resistance R 1 Constant phase angle element cpe describing polarization effect inside a battery and constant phase angle element W describing diffusion effect inside a battery, wherein the constant phase angle element cpe and the resistor R 1 Parallel connection to form parallel connection structure, self-open circuit voltage V OC The positive electrode of (a) is serially connected with a resistor R in turn 0 A parallel structure and a constant phase angle element W. Wherein the open circuit voltage V OC The relationship between the two is determined experimentally, for example, by fitting a curve to obtain the open circuit voltage V OC Relation V with battery state of charge SOC OC =f (SOC). Resistor R 0 Resistance R 1 Capacitor C of two normal phase angle elements cpe And C W Order alpha of two constant phase angle elements cpe 、α W The parameter values of the above circuit elements in the fractional equivalent circuit can be determined experimentally, for example, by a parameter identification method. Specifically, the impedance spectrum of the battery may be measured for parameter identification. For example, parameters of each element in the fractional equivalent circuit of the lithium ion battery obtained by fitting impedance spectrum identification are shown in the following table.
Constructing a continuous state space equation corresponding to the fractional equivalent circuit according to the identified parameters, for example, aiming at the fractional equivalent circuit in fig. 2, the established continuous state space equation is:
wherein s is a differential operator, SOC is a battery state of charge to be estimated, V cpe 、V W The voltages of two constant phase angle elements in the equivalent circuit of the battery are estimated along with the SOC of the battery, Q n For battery capacity, I B To flow out of the battery current level omega k Is a system process noise.
For the continuous state space equation, the invention uses the fractional differential operator And performing approximate discretization processing. The specific process is as follows:
first, tustin transformation is carried out on the differential operator s, namely
Where T is the sampling period. For two fractional differential operators, there are:
the two fractional differential operators can be approximately unfolded into a polynomial expression about z through a Muir recursive algorithm, so that differential differentiation is easy to carry out, and a discrete state equation is obtained. The specific recurrence algorithm is as follows:
wherein A is 0 (z -1 ,r)＝1，A n (z -1 ,r)＝A n-1 (z -1 ,r)-c n z n A n-1 (z,r)，From analysis, it was found that n=9, so in the invention, n=9 is taken, and according to the above formula, the method can obtain by iterative calculation:
will A 9 (z -1 R in r) is replaced by-r, A can be obtained 9 (z -1 ,-r)。
Thus, s can be obtained r Is a similar discretization of (a):
wherein l i Is polynomial A 9 (z -1 R) coefficient related to r, k j Is polynomial A 9 (z -1 -r) the coefficient related to r.
Let r equal to the order alpha of the two constant phase angle elements, respectively cpe And alpha W An approximate discretized version of the two fractional differential operators is obtained. Substituting the discretized fractional differential operator into a continuous state equation, and carrying out equation integration to obtain the discretized state equation.
For example, a fractional order differential operatorSubstituted into the continuous state equation above, there are:
two fractional order equations are divided by V on both sides cpe (k) And V is equal to W (k) I.e. the coefficients of the above-mentioned square matrix:
the state space equation of the approximate discretization of the fractional equivalent circuit can be obtained:
let battery state x (k) = [ SOC (k), V at sampling time k cpe (k),V W (k)] T The discrete state space equation above may be expressed in the form:
A i 、B j all are the coefficients integrated after discretization.
Thereby realizing discretization of the state space equation. From the discretized state space equation, the state space equation obtained by approximate discretization is only related to the historical data of the past 9 sampling moments, so that the hardware storage space occupied by the SOC estimation algorithm can be greatly reduced, and the calculation speed can be improved.
Step S2: based on a battery discrete state space equation, estimating the state of charge of the battery by using a Kalman filtering algorithm.
First, after determining the discrete state space equation again, the estimation result covariance matrix equation can be constructed:
p (k) is covariance matrix of battery state estimation result at sampling time k, and Q is system process noise omega k Is a covariance matrix of (a).
As shown in fig. 3, the process of estimating the state of charge of the battery using the kalman filter algorithm includes the steps of:
step S21: correction value of battery state at sampling time k-9 to k-1To->Substituting the discrete state space equation of the battery to obtain the estimated value of the battery state at the sampling time k>
The calculation formula is that
Specifically, when estimating the battery state at the current sampling time k, the battery state correction value of the previous 9 times and the battery current of the previous 9 times and the current sampling time k need to be obtained and substituted into an equation, so as to obtain a preliminary estimated value of the battery state at the current sampling time k, and the value needs to be corrected through a subsequent step.
Step S22: correction value of covariance matrix of estimation result of sampling time k-9 to k-1To the point ofSubstituting the estimation result covariance matrix equation to obtain the estimation value of the estimation result covariance matrix at the sampling time k
The calculation formula is that
Specifically, when estimating the estimation result covariance matrix of the current sampling time k, a correction value of the estimation result covariance matrix of the first 9 times needs to be obtained and substituted into an equation to obtain a preliminary estimation value of the estimation result covariance matrix of the current sampling time k, and the preliminary estimation value needs to be corrected through a subsequent step.
When k is smaller than 9, various parameters substituted into the battery discrete state space equation and the estimation result covariance matrix equation are parameters starting from the time 0. In other words, when the number of data obtained at the previous time is less than 9, all the existing data may be substituted. Wherein the parameter at time 0 is an initialized parameter, and initial values of the parameters in the algorithm, including measurement error v, are set during initialization k Variance R of process noise omega k Covariance matrix Q, state initial value of (a)And initial value of covariance matrix of estimation result +.>For example, the measurement error v is set k Variance r=100, process noise ω k Covariance matrix q=diag (0.01,0.01,0.01). State initial value x (0) = [0.8,0,0 ]] T Initial value of covariance matrix of estimation result
Step S23: estimation based on battery stateEstimating battery terminal voltage y (k) and observing matrix estimation value
The calculation formula is that
Specifically, the terminal voltage of the battery at the current sampling time is related to the battery state at the current time, and the relation between the terminal voltage and the battery state can be determined by combining a specific equivalent model. For example, for an equivalent circuit model of a lithium ion battery, the relationship between the terminal voltage y (k) and the battery state x (k) can be expressed as:
y(k)＝V OC (k)+[0,-1,-1]x(k)-R 0 I B (k)+v(k)
wherein V is OC (k) For the open-circuit voltage at the current sampling time, the open-circuit voltage V can be used as the reference to the current charge state of the battery OC Relation V with battery state of charge SOC OC Direct calculation of =f (SOC) to obtain the current open circuit voltage V OC (k)。R 0 Is a known parameter, I B (k) The battery current measured at the current sampling time, v (k), is the measurement error of the terminal voltage, which cannot be obtained, and is ignored when estimating the terminal voltage y (k).
For example, for lithium ion batteries, the open circuit voltage V OC The relationship with the battery state of charge SOC can be expressed as:
V OC (SOC)＝3.421+0.08786SOC 2 -0.1737SOC+0.08428lnSOC-0.00622ln(1-SOC)
after the relation between the terminal voltage y (k) and the battery state x (k) is determined, the y (k) can be derived to obtain a derived expression, namely an equation of the observation matrix. For example, with respect to the above-described relational expression of the terminal voltage y (k) and the battery state x (k), the equation of the observation matrix obtained after derivation is:
in this step, the estimated value of the battery state at the current sampling time is calculatedSubstituting the estimated value into the observation matrix to obtain the estimated value +.>
Step S24: kalman gain K for calculating sampling time K k 。
Wherein R is the variance of the terminal voltage measurement error v (k), and is a parameter set in advance.
Step S25: estimation of battery stateCorrection is performed to obtain a correction value of the battery state +.>
The calculation formula is thatWhere V (k) is the terminal voltage measured at sampling time k.
Step S26: for the estimation value of the observation matrixCorrecting to obtain observation matrix correction value +.>
The calculation formula is as follows:
in this step, after the correction value of the battery state is obtained, the correction value is substituted into the equation of the observation matrix again, and the correction value of the observation matrix can be obtained.
Step S27: estimation value of covariance matrix of estimation resultCorrection is carried out to obtain a correction value +.>
The calculation formula is thatWherein I is an identity matrix.
In the step, after obtaining the correction value of the observation matrix, the estimation result covariance matrix is corrected to obtain the correction value of the estimation result covariance matrix.
It should be noted that the above step sequence is only illustrative, but not limited thereto, and the execution sequence of the steps is not limited as long as each step can be ensured to be executed smoothly.
The corrected value is the estimation result output at the current moment, and the state of charge of the battery at the current moment can be extracted from the estimated result. All correction parameters can be used for battery state estimation at the next moment, and at each moment, the steps S21-S27 are executed, so that the battery estimation at the corresponding moment can be completed, and the real-time estimation of the battery state can be realized.
In order to verify the effect of the estimation method provided by the invention, a discharge experiment is carried out on the lithium ion battery according to the standard of the urban cycle condition UDDS, and the terminal voltage and the output current of the lithium ion battery at each sampling time are measured. As shown in fig. 4 and 5. According to the voltage and current data measured in real time, the real-time estimation of the SOC of the lithium ion battery can be performed through the state equation and the fractional order Kalman filtering algorithm established above. The reference value curve and the estimated value curve of the SOC are compared, and as shown in fig. 6, the reference value curve and the estimated value curve almost always coincide. Calculating Root Mean Square Error (RMSE) of the SOC estimation value to obtain
The estimation result shows that the SOC estimation method provided by the invention has good estimation precision, and meanwhile, because the simplified discrete state equation is only related to the state quantity of the first 9 sampling moments, compared with the existing fractional order Kalman filtering algorithm, the method has lower requirements on the storage space and the computing capacity of hardware, and the computing speed is obviously improved.
Example 2
The invention also refers to a battery state of charge estimation device comprising:
a battery discrete state space equation construction unit for determining a continuous state space equation of a fractional equivalent circuit of a battery, the continuous state space equation including a fractional differential equation for which a fractional differential operator s r Performing discretization to obtain a battery discrete state space equation, wherein r is the order of a fractional order differential operator, and the fractional order differential operator s r Discretization intoA 9 (z -1 R) and A 9 (z -1 -r) each represent a 9 th order polynomial obtained by a Muir recurrence formula, T being the sampling period;
and the estimation unit is used for estimating the state of charge of the battery by using a Kalman filtering algorithm based on the discrete state space equation of the battery.
The above battery state-of-charge estimation device may be used to perform the battery state-of-charge estimation method in embodiment 1, and each unit in the device may be used to perform the corresponding steps in the estimation method, and the description thereof may be referred to in the foregoing, and will not be repeated here.
Example 3
The invention also relates to an electronic device comprising a memory storing a computer program and a processor implementing the steps of the above method when the processor executes the computer program.
The electronic device can be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital SignalProcessor, DSP), application specific integrated circuits (Application Specific IntegratedCircuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The memory may be used to store computer programs and/or modules, and the processor may be used to perform various functions of the electronic device by executing or executing the computer programs and/or modules stored in the memory, and invoking data stored in the memory.
Example 4
The invention also relates to a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, implements the steps of the above method.
In particular, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid state storage device.
It will be readily appreciated by those skilled in the art that the foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (10)

1. A battery state of charge estimation method, comprising:
determining a continuous state space equation of a fractional equivalent circuit of a battery, the continuous state space equation including a fractional differential equation for which a fractional differential operator s r Performing discretization to obtain a battery discrete state space equation, wherein r is the order of a fractional order differential operator, and the fractional order differential operator s r Discretization intoA 9 (z -1 R) and A 9 (z -1 -r) each represent a 9 th order polynomial obtained by a Muir recurrence formula, T being the sampling period;
based on a battery discrete state space equation, estimating the state of charge of the battery by using a Kalman filtering algorithm.
2. The battery state of charge estimation method of claim 1, wherein the battery discrete state space equation is:
constructing an estimation result covariance matrix equation:
where x (k) is the battery state including the battery state of charge at sampling time k, I B (k) For the battery current at sampling instant k, ω k System process noise for sampling time k, A i 、B j All are the coefficients integrated after discretization; p (k) is covariance matrix of battery state estimation result at sampling time k, and Q is system process noise omega k Is a covariance matrix of (a);
the estimating the state of charge of the battery by using a Kalman filtering algorithm comprises:
correction value of battery state at sampling time k-9 to k-1To->Substituting the discrete state space equation of the battery to obtain an estimated value of the battery state at the sampling time k +.>
Correction value of covariance matrix of estimation result of sampling time k-9 to k-1To->Substituting the estimation result covariance matrix equation to obtain an estimation value of the estimation result covariance matrix at the sampling time k
Estimation based on battery stateEstimating battery terminal voltage y (k) and observing matrix estimation value
Calculating Kalman gain at sampling time k:
wherein R is the variance of the terminal voltage measurement error;
estimation of battery statePerforming correction to obtain a correction value of the battery stateWherein V (k) is the terminal voltage obtained by measuring the sampling time k;
for the estimation value of the observation matrixCorrecting to obtain observation matrix correction value +.>
Estimation value of covariance matrix of estimation resultCorrecting to obtain the corrected value of covariance matrix of estimation resultWherein I is an identity matrix.
3. The battery state of charge estimation method according to claim 2, wherein when k < 9, the various parameters substituted into the battery discrete state space equation and the estimation result covariance matrix equation are parameters starting from time 0.
4. The method of estimating a state of charge of a battery of claim 1, wherein the fractional equivalent circuit of the battery comprises an open circuit voltage V OC Resistance R 0 Resistance R 1 Constant phase angle element cpe and constant phase angle element W, wherein constant phase angle element cpe and resistor R 1 Parallel connection to form parallel connection structure, self-open circuit voltage V OC The positive electrode of (a) is serially connected with a resistor R in turn 0 The parallel structure and the constant phase angle element W.
5. The battery state of charge estimation method of claim 3, wherein the continuous state space equation is:
in the formula, SOC is the charge state of the battery, V cpe 、V W For the voltages of the constant phase angle element cpe and the constant phase angle element W, the battery state includes the battery state of charge SOC and the voltage V cpe 、V W ；Q n For battery capacity, C cpe And C W Capacitance values of the constant phase angle element cpe and the constant phase angle element W, respectively, α cpe 、α W The orders, I, of the constant phase angle element cpe and the constant phase angle element W, respectively B To flow out of the battery current level omega k Is a system process noise.
6. The battery state of charge estimation method of claim 1, further comprising determining parameter values for each circuit element in the fractional equivalent circuit by means of parameter identification.
7. The battery state of charge estimation method of claim 1, wherein the battery is a lead carbon battery or a lithium ion battery or a nickel hydrogen battery.
8. A battery state of charge estimation apparatus, comprising:
a battery discrete state space equation construction unit for determining a continuous state space equation of a fractional equivalent circuit of a battery, the continuous state space equation including a fractional differential equation for which a fractional differential operator s r Performing discretization to obtain a battery discrete state space equation, wherein r is the order of a fractional order differential operator, and the fractional order differential operator s r Discretization intoA 9 (z -1 R) and A 9 (z -1 -r) each represent a 9 th order polynomial obtained by a Muir recurrence formula, T being the sampling period;
and the estimation unit is used for estimating the state of charge of the battery by using a Kalman filtering algorithm based on the discrete state space equation of the battery.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
CN202311369358.7A 2023-10-20 2023-10-20 Battery state of charge estimation method, device, electronic equipment and storage medium Pending CN117517964A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118068198A (en) * 2024-04-18 2024-05-24 北京智芯微电子科技有限公司 Battery voltage sampling method and device based on multiple correction Kalman filtering

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