CN111198327A - Self-detection method for short circuit fault in single battery - Google Patents

Self-detection method for short circuit fault in single battery Download PDF

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CN111198327A
CN111198327A CN202010110503.XA CN202010110503A CN111198327A CN 111198327 A CN111198327 A CN 111198327A CN 202010110503 A CN202010110503 A CN 202010110503A CN 111198327 A CN111198327 A CN 111198327A
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CN111198327B (en
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魏中宝
胡鉴
何洪文
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Beijing Institute of Technology BIT
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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
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • 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]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables

Abstract

The invention discloses a self-detection method for short circuit faults in a single battery, which comprises the following steps: s1, establishing a first-order equivalent circuit model of a battery, establishing a functional relation between a state of charge (SOC) and an open-circuit voltage (OCV), and identifying model parameters; s2, measuring the load current and terminal voltage of the battery in real time; s3, calculating the ampere-hour increment of adjacent moments, estimating the SOC of the battery on line by adopting a closed-loop observer, and calculating the internal short-circuit current according to the obtained ampere-hour increment and the SOC variation value; s4, filtering the estimated internal short-circuit current, and identifying the internal short-circuit resistance of the battery on line by adopting a recursive least square method with a self-adaptive forgetting factor according to the filtered internal short-circuit current and the load voltage; and comparing and judging the short circuit state in the battery according to a preset internal short circuit resistance threshold value. The invention can realize the on-line identification of the short-circuit resistance in the single battery only by measuring the terminal voltage and the load current of the single battery without other single information in the battery pack.

Description

Self-detection method for short circuit fault in single battery
Technical Field
The invention relates to the detection of short-circuit faults in batteries, in particular to a self-detection method for the short-circuit faults in single batteries.
Background
The lithium ion battery is the most commonly used power source of the electric automobile at present, and has the advantages of high specific energy and specific power, long cycle life and the like, but in recent years, the lithium ion battery thermal runaway accident gradually draws attention of consumers to the safety of the lithium ion battery. Short circuit fault in the battery is one of the leading causes of thermal runaway accidents of the battery, so detection of short circuit fault in the battery is particularly important for improving safety and reliability of the lithium ion battery.
The existing method for detecting the short circuit in the lithium ion battery based on the abnormal heat release of the battery is suitable for the application scene with serious short circuit in the lithium ion battery, and the internal short circuit resistance is larger and the heat release is small when the internal short circuit fault of the battery occurs in the early stage, so the method has the challenge on the detection of the internal short circuit fault in the early stage. The internal short circuit detection method based on the electrical model can identify early slight internal short circuit, and typically adopts a battery pack average equivalent model combined with a monomer difference model; however, the method needs to acquire information and estimate the state of charge (SOC) of a plurality of battery cells, and has high requirement on the accuracy of a cell difference model.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a self-detection method for short circuit faults in a single battery. The short-circuit current in the battery cell is calculated through the ampere-hour accumulation characteristic and a closed-loop SOC estimator with error feedback correction, the short-circuit resistance in the battery cell is identified on line by adopting a recursive least square method with a self-adaptive forgetting factor, and then the short-circuit fault state in the battery cell is judged according to the size of the short-circuit resistance. The method provided by the invention can realize the diagnosis of the internal short circuit of the battery monomer only according to the load current and the terminal voltage measured value of a specific single battery.
The purpose of the invention is realized by the following technical scheme: a self-detection method for short circuit fault in a single battery comprises the following steps:
s1, establishing a first-order equivalent circuit model of the lithium ion battery. Performing an intermittent discharge-standing experiment, fitting and determining a function relation expression of the SOC and the Open Circuit Voltage (OCV), and then identifying other model parameters off line;
s2, measuring the voltage and the load current of the lithium ion battery in real time by adopting a voltage sensor and a current sensor;
s3, calculating the ampere-hour increment of adjacent moments by using the measured values of the load current and the terminal voltage, estimating the SOC of the battery on line by using a closed-loop observer, and calculating the internal short-circuit current according to the obtained ampere-hour increment and the SOC change value;
and S4, filtering the internal short-circuit current, identifying the internal short-circuit resistance of the battery on line by using the filtered internal short-circuit current and terminal voltage measured values and adopting a recursive least square method with a self-adaptive forgetting factor, and judging the internal short-circuit state by comparing the identification result of the internal short-circuit resistance with a set internal segment resistance threshold value.
The invention has the beneficial effects that: the invention provides a self-detection method for short circuit faults in single batteries, which is used for carrying out on-line identification on short circuit resistance in a battery only according to load current and terminal voltage measurement values of a certain specific single battery so as to realize on-line self-detection of the short circuit faults in the single batteries. The method has wide detection range and can realize early diagnosis of the short circuit in the battery monomer. Compared with the existing method, the method can effectively solve the problems that the internal short-circuit fault generates less heat in the early stage and is difficult to detect through abnormal heat generation; and the method does not depend on other monomers in the battery pack, and solves the problems of high uncertainty and calculation complexity and the like caused by a complex system and a complex model.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic circuit diagram of a first-order RC equivalent circuit model in the embodiment.
FIG. 3 is a graph of load current and terminal voltage under FUDS operating conditions in the example.
FIG. 4 is a graph showing the estimation result of the internal short circuit resistance under FUDS operating condition in the embodiment.
FIG. 5 is an error curve of the estimation of the internal short circuit resistance under FUDS operating condition in the embodiment.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a method for self-detecting a short circuit fault in a single battery includes the following steps:
s1, establishing a first-order equivalent circuit model of the lithium ion battery, performing an intermittent discharge-standing experiment, fitting and determining a function relation expression of SOC and OCV, and then identifying other model parameters in an off-line manner;
s101, establishing a battery model, specifically, adopting a first-order equivalent circuit model in the embodiment, wherein a circuit schematic diagram is shown in FIG. 2.
The state space equation of the model is:
Figure BDA0002389811170000021
Ul(t)＝Uoc(t)+IL(t)R0+Up(t)
Figure BDA0002389811170000022
wherein t is time, ILIs the load current (during discharge I)LIs negative, when charging ILIs a positive number), corresponding toL(t) is the load current at time t, UpTo polarize the voltage, UlIs terminal voltage, UocIs OCV, z is SOC, Q0Is the rated capacity, R, of the battery0、RpAnd CpModel parameters to be identified, specifically: r0Is ohmic internal resistance, RpIs a polarization resistance, CpIs a polarization capacitor;
s102, charging the lithium ion battery until the SOC reaches 100%, performing an intermittent discharge-standing experiment, and fitting to determine that the SOC-OCV function relation expression is as follows:
Figure BDA0002389811170000023
wherein n ispTo fit the polynomial order, ciAre fitting parameters.
S103, testing Hybrid Pulse Power Characteristic (HPPC) of the battery, identifying parameters of the battery model offline according to test data, and identifying ohmic internal resistance R of the battery model offline by adopting a batch least square method0Internal polarization resistance RpAnd a polarization capacitor Cp。
Specifically, during HPPC testing, the I of the cell is measured and recordedL、UOC、Ul。
Discretizing an equation representing the battery voltage characteristics in the S101 system state equation into:
Figure BDA0002389811170000031
Ut(k)＝UOC(k)+IL(k)R0+Up(k)
in the formula, delta t is the measurement sampling time interval, and the two formulas are combined to eliminate UpThe following can be obtained:
Ut(k)＝UOC(k)-α1UOC(k-1)+α1Ut(k-1)+α2IL(k)-α3IL(k-1)
in the formula
Figure BDA0002389811170000032
α2＝R0、
Figure BDA0002389811170000033
The formula is arranged as follows:
E(k)＝α1E(k-1)+α2IL(k)-α3IL(k-1)
wherein E (k) ═ Ul(k)-UOC(k) A regression equation is established according to the above equation:
E(k)＝τT(k)χ(k)
wherein τ (k) ═ α1α2α3]T、χ(k)＝[E(k-1)IL(k)-IL(k-1)]T。
According to IL、UOC、UlThe regression equation is solved by using a batch least square method to obtain a target vector tau (k) ═ α1α2α3]TAfter the optimal solution of (1), R0、Rp、CpThe solution can be solved by:
Figure BDA0002389811170000034
s2, measuring load current I of battery in real timeL(k) And terminal voltage Ul(k)。
S3, calculating the ampere-hour increment of adjacent moments by using the load current and terminal voltage measured values, estimating the SOC of the battery on line by using a closed-loop observer, and calculating the internal short-circuit current according to the obtained ampere-hour increment and the SOC change value;
s301, calculating the ampere-hour increment from the moment k-1 to the moment k by using the load current value at the moment k-1:
Qe(k)＝IL(k-1)Δt
wherein Qe(k) Is the ampere-hour increment from time k-1 to time k.
S302, estimating the SOC of the battery in real time by adopting a common closed-loop observer:
discretizing the continuous state space equation of the system in S101 as:
Figure BDA0002389811170000041
Ul(k)＝Up(k)+R0IL(k)+f(z(k))
the coefficient matrix of the discrete state space equation of the system is as follows:
Figure BDA0002389811170000042
and based on the discrete state space equation with the structure, a state observer based on error feedback correction is adopted to estimate the SOC in real time. Specifically, the present embodiment adopts the extended kalman filter to estimate the SOC in real time. To introduce the extended kalman filter, the kalman filter is first introduced. In a real dynamic system, not all state quantities are measurable, such as SOC, so a state observer needs to be used to estimate the state quantities of the system, and kalman filtering is essentially a state observer, and the algorithm flow of the kalman filtering is as follows:
discretizing the state space equation of the linear system as:
xk+1＝Akxk+Bkuk+wk
yk＝Ckxk+Dkuk+vk
wherein xkIs the state vector at time k, ykIs the system output at time k, ukFor the input of the system at time k, Ak、Bk、Ck、DkIs a matrix of coefficients, wkRandom process noise reflects some unmeasured interference inputs that affect the system state; u. ofkFor sensor noise, the system output y is reflectedkThe measurement error of (2).
Initialization:
Figure BDA0002389811170000043
iterative calculations are performed at each predetermined calculation point:
and performing state estimation according to a state equation:
Figure BDA0002389811170000044
error covariance estimation:
Figure BDA0002389811170000045
calculating a Kalman gain matrix:
Figure BDA0002389811170000046
according to the observed value to the stateAnd (3) feedback correction:
Figure BDA0002389811170000047
error covariance observation feedback correction:
Figure BDA0002389811170000051
wherein L iskIs the kalman gain; i is an identity matrix; q and R are covariance matrixes of input and output measurement noises respectively, and the calculation formula is as follows: q ═ E [ wkwk T]，R＝E[vkvk T]And P is the state estimation error covariance matrix, which indicates the uncertainty of the state estimate. In the kalman filter algorithm, the state is updated twice in each sampling interval. The first update is based on an initial estimate of the equation of state
Figure BDA0002389811170000052
And
Figure BDA0002389811170000053
to indicate. Second feedback update based on measured values, updated state for
Figure BDA0002389811170000054
And
Figure BDA0002389811170000055
to indicate.
For a typical nonlinear system such as a battery, firstly, a state space equation of the nonlinear system is linearized, and is expanded according to an order taylor formula, and then, a kalman filtering method is adopted to perform state estimation on the linearized dynamic system, the method is called as extended kalman filtering, and an algorithm flow of the extended kalman filtering is as follows:
setting a discrete state space equation of a nonlinear system:
xk+1＝f(xk,uk)+wk
yk＝g(xk,uk)+vk
defining:
Figure BDA0002389811170000056
initialization:
Figure BDA0002389811170000057
iterative calculations are performed at each predetermined calculation point:
estimating the state according to the state equation:
Figure BDA0002389811170000058
error covariance estimation:
Figure BDA0002389811170000059
calculating a Kalman gain matrix:
Figure BDA00023898111700000510
and (3) correcting state feedback according to the observed value:
Figure BDA00023898111700000511
error covariance observation feedback correction:
Figure BDA00023898111700000512
according to the state space equation established in S302, the system state, input and output are respectively defined as:
Figure BDA00023898111700000513
u(k)＝IL(k)，y(k)＝Ul(k)，
correspondingly, the correlation matrix in the extended kalman filter algorithm is:
Figure BDA0002389811170000061
estimating the battery SOC in real time according to the algorithm flow of the extended Kalman filtering, and recording the optimal estimation value of the battery SOC at the moment k as Ze(k)。
S304, according to the ampere-hour increment from the k-1 moment to the k moment and the SOC difference value between the k moment and the k-1 moment, the calculation formula of the short-circuit current in the k moment is as follows:
Figure BDA0002389811170000062
s4, filtering the internal short-circuit current, and estimating the short-circuit resistance in the battery in real time by using the filtered internal short-circuit current and terminal voltage measured values and adopting a recursive least square method with a self-adaptive forgetting factor; and comparing and judging the short circuit state in the battery according to a preset internal short circuit resistance threshold value.
S401, the internal short circuit current estimated in step S304 contains high frequency noise, and in order to obtain good linearity, the internal short circuit current data is first filtered (such as mean filtering, median filtering, gaussian filtering, etc.). Specifically, the present embodiment employs mean filtering, and the calculation formula is:
Figure BDA0002389811170000063
wherein If(k) The current is the filtered internal short circuit current at the moment k, and n is the number of sampling points.
S402, establishing a regression equation according to the functional relation among the filtered internal short circuit current, the filtered internal short circuit resistance and the filtered load voltage:
Ul(k)＝If(k)·θ(k)
in the formula, theta (k) is the internal short circuit resistance to be identified at the moment k.
Referring to the regression description method, the recursive least square algorithm with the adaptive forgetting factor has the following flow:
firstly, algorithm parameters are initialized according to existing information and experience, specifically, the following initialization parameters are adopted in the embodiment: theta (0) is 0, and theta (0),
Figure BDA0002389811170000064
λ(1)＝0.995；λmin＝0.997；
iterative calculations are performed at each predetermined calculation point:
Figure BDA0002389811170000065
Figure BDA0002389811170000066
θ(k)＝θ(k-1)+α(k)[Ul(k)-θ(k-1)If(k)]
ε(k)＝Ul(k)-θ(k)If(k)
Figure BDA0002389811170000071
Figure BDA0002389811170000072
the method is characterized in that the method is a covariance matrix, α (k) is a gain, epsilon (k) is an estimation error, lambda (k) is an adaptive forgetting factor, the forgetting factor is set to increase the weight of new data and reduce the influence of old data, the forgetting factor is too large, the influence of the old data is too large, the tracking capability of the parameter identification process is not strong, the forgetting factor is too small, the weight of the new data is too large, and the identification result is unstable once the internal short-circuit current is changed violently.
And estimating the internal short circuit resistance of the battery in real time according to the steps, comparing and judging the internal short circuit state of the single battery at the current moment according to the preset internal short circuit resistance threshold and the calculated real-time internal short circuit resistance.
In the embodiment of the patent, a Samsung 18650 lithium ion battery with a nominal capacity of 2.2Ah is taken as an experimental object, the step 1 of the method is carried out at room temperature, an equivalent circuit model is built, a functional relation between SOC and OCV is established, and model parameters are identified. Then, a 25-ohm resistor is connected in parallel with the output end of the battery to simulate and trigger the internal short circuit fault of the battery, the internal short circuit fault of the battery is estimated in real time at room temperature by adopting the U.S. federal city operating conditions (FUDS), the average value of the estimation result of the internal short circuit fault is 22.93 ohm, and the root mean square error is 2.29 ohm. The measurement curves of the battery load current and the terminal voltage under the FUDS working condition are shown in the attached figure 3; the result of the estimation of the internal short-circuit resistance is shown in fig. 4, and the error of the estimation of the internal short-circuit resistance is shown in fig. 5. As shown in fig. 4 and 5, the method of the present invention can realize online identification of the short-circuit resistance in the battery, and has the advantages of high convergence speed and high accuracy.
In summary, the invention only uses the measurement values of the load current and the terminal voltage of the battery, calculates the short circuit current in the battery cell through the ampere-hour accumulation characteristic and the closed-loop SOC estimator with error feedback correction, adopts the recursive least square method with the self-adaptive forgetting factor to identify the short circuit resistance in the battery on line, and further judges the short circuit fault state in the battery cell according to the size of the short circuit resistance. Compared with a method for detecting the short circuit in the lithium ion battery based on abnormal heat release, the method can effectively solve the problems that the internal short circuit fault generates less heat in the early stage and is difficult to detect through abnormal heat generation; compared with the traditional internal short circuit detection method based on the electrical model, the method provided by the invention does not depend on other monomers in the battery pack, and solves the problems of high uncertainty and calculation complexity and the like caused by a complex system and a complex model.
The foregoing is a preferred embodiment of the present invention, it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as excluding other embodiments, and is capable of other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. A self-detection method for short circuit fault in a single battery is characterized in that: the method comprises the following steps:
s1, establishing a first-order equivalent circuit model of the lithium ion battery, performing an intermittent discharge-standing experiment, fitting and determining a functional relation between a state of charge (SOC) and an open-circuit voltage (OCV), and then identifying other model parameters off line;
s2, measuring the voltage and the load current of the lithium ion battery in real time by adopting a voltage sensor and a current sensor;
s3, calculating the ampere-hour increment of adjacent moments by using the load current and terminal voltage measured values, estimating the SOC of the battery on line by using a closed-loop observer, and calculating the internal short-circuit current according to the obtained ampere-hour increment and the SOC change value;
s4, filtering the internal short-circuit current, and estimating the short-circuit resistance in the battery in real time by using the filtered internal short-circuit current and terminal voltage measured values and adopting a recursive least square method with a self-adaptive forgetting factor; and comparing and judging the short circuit state in the battery according to a preset internal short circuit resistance threshold value.
2. The self-detection method for the short-circuit fault in the single battery according to claim 1, characterized in that: the step S1 includes the following sub-steps:
s101, establishing a first-order equivalent circuit model of the battery;
the state space equation of the model is:
Figure FDA0002389811160000011
Ul(t)＝Uoc(t)+IL(t)R0+Up(t)
Figure FDA0002389811160000012
wherein t is time, ILFor load current, corresponding toL(t) is the load current at time t, UpTo polarize the voltage, UlIs terminal voltage, where UocZ (t) is OCV, SOC at time t, Q0Is the rated capacity, R, of the battery0、RpAnd CpIs the model parameter to be identified, specifically: r0Is ohmic internal resistance, RpIs a polarization resistance, CpIs a polarization capacitor;
s102, carrying out intermittent discharge-standing experiments, and fitting to determine an SOC-OCV relational expression as follows:
Figure FDA0002389811160000013
wherein n ispTo fit the polynomial order, ciIs a fitting coefficient;
s103, carrying out hybrid power pulse capability characteristic test on the battery, and identifying the ohmic internal resistance R of the battery model in an off-line manner according to the data obtained by the test0Internal polarization resistance RpAnd a polarization capacitor Cp。
3. The self-detection method for the short-circuit fault in the single battery according to claim 2, characterized in that: the offline identification in step S103 adopts a common optimization algorithm, which includes one of a batch least square method, a genetic algorithm, or a particle swarm algorithm.
4. The self-detection method for the short-circuit fault in the single battery according to claim 1, characterized in that: the step S3 includes the following sub-steps:
s301, calculating the ampere-hour increment from the moment k-1 to the moment k by using the load current value at the moment k-1:
Qe(k)＝IL(k-1)Δt
wherein Q ise(k) Is the ampere-hour increment from the moment k-1 to the moment k, and △ t is the sampling time interval of the load current;
s302, estimating the SOC of the battery in real time by adopting a common closed-loop observer, wherein the SOC estimation result at the k moment is counted as Ze(k)；
S303, according to the ampere-hour increment from the moment k-1 to the moment k and the SOC difference between the moment k and the moment k-1, the calculation formula of the short-circuit current in the battery at the moment k is as follows:
Figure FDA0002389811160000021
5. the self-detection method for the short-circuit fault in the single battery according to claim 1, characterized in that: the closed-loop state observer in step S3 includes one of a lunberger observer, an extended kalman filter, an infinite kalman filter, a particle filter, or a synovial observer.
6. The self-detection method for the short-circuit fault in the single battery according to claim 1, characterized in that: the method for filtering the internal short-circuit current in step S4 includes a mean filtering method, a median filtering method or a gaussian filtering method; the filtered internal short-circuit current at the time k is recorded as If(k)。
7. The self-detection method for the short-circuit fault in the single battery according to claim 1, characterized in that: the method comprises the following steps of adopting a recursive least square method with an adaptive forgetting factor to estimate the short circuit resistance in the battery in real time in step S4:
establishing a regression equation:
Ul(k)＝If(k)·θ(k)；
in the formula, θ (k) is an internal short circuit resistance to be identified at time k.
According to the regression description method, the recursive least square algorithm flow with the self-adaptive forgetting factor is as follows:
initializing algorithm parameters according to the existing information and experience, wherein the parameters needing to be initialized specifically comprise: theta, theta,
Figure FDA0002389811160000024
λ、λmin；
Iterative calculations are performed at each predetermined calculation point:
Figure FDA0002389811160000022
Figure FDA0002389811160000023
θ(k)＝θ(k-1)+α(k)[Ul(k)-θ(k-1)If(k)]
ε(k)＝Ul(k)-θ(k)If(k)
Figure FDA0002389811160000031
wherein
Figure FDA0002389811160000032
For covariance matrix, α (k) is the gain, ε (k) is the estimation error, and λ (k) is the adaptive forgetting factor.
Estimating the short-circuit resistance in the battery in real time according to the steps; and comparing and judging the internal short circuit state of the single battery at the current moment according to the preset internal short circuit resistance threshold and the calculated real-time internal short circuit resistance, so as to realize the detection of the internal short circuit fault of the battery.
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