CN111257753B - Battery system fault diagnosis method - Google Patents
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- CN111257753B CN111257753B CN202010162086.3A CN202010162086A CN111257753B CN 111257753 B CN111257753 B CN 111257753B CN 202010162086 A CN202010162086 A CN 202010162086A CN 111257753 B CN111257753 B CN 111257753B
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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
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
Abstract
A battery system fault diagnosis method, comprising: s1, establishing a battery equivalent circuit model in a healthy state, and identifying parameters; s2, respectively obtaining the frequency response of each single battery and the equivalent circuit model; s3, diagnosing whether each battery monomer in the actual battery system has a fault or not through the battery equivalent circuit model, the frequency response value of each battery monomer and a dynamic threshold value; and S4, establishing a fault model through the equivalent circuit model, and judging the fault type by utilizing a multi-fault model technology. The invention solves the problems of narrow application range and inaccurate judgment in the traditional battery system fault diagnosis method.
Description
Technical Field
The invention belongs to the technical field of batteries, and particularly relates to a battery system fault diagnosis method.
Background
The electric new energy electric automobile has the advantages of low noise and almost zero emission, and is an important way for solving the problems of energy shortage and environmental pollution. The power battery is an important component of the electric automobile, and determines the performance of the electric automobile to a great extent. The lithium ion battery has the characteristics of high voltage, high energy, no memory and the like, and the battery pack formed by the lithium ion battery is widely applied to systems of electric vehicles, energy storage and the like. Because vehicle accidents are frequently caused by the safety problem of the battery, and the problem of frequent occurrence of the safety accidents of the lithium ion battery is solved, more and more attention is paid to battery fault diagnosis in the development process of the electric automobile.
The existing battery fault diagnosis method has certain defects, for example, in the model-based fault diagnosis method, the fault diagnosis technology applying the observer is suitable for a battery system with little or no measurement noise, and the application range is narrow. In addition, most of the current battery fault diagnoses evaluate residual errors by using a fixed threshold value, and influence of some uncertain factors such as influence of a detection circuit on a battery exists.
Disclosure of Invention
The invention aims to provide a battery system fault diagnosis method, which solves the problems of narrow application range and inaccurate judgment in the prior art.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention provides a battery system fault diagnosis method, which comprises the following steps:
s1: establishing a battery equivalent circuit model in a healthy state, and identifying parameters;
s2: respectively obtaining the equivalent circuit model after the parameter identification and the frequency response of each battery monomer in the battery system;
s3: whether each battery monomer in the actual battery system breaks down or not is diagnosed through the equivalent circuit model, the frequency response value of each battery monomer and the dynamic threshold value;
s4: and establishing a fault model through the equivalent circuit model, and judging the fault type by utilizing a multi-fault model technology.
In one embodiment of the invention, the battery system comprises a plurality of batteries.
In one embodiment of the invention, the equivalent circuit model comprises capacitance caused by charge diffusion, capacitance caused by charge transfer, internal resistance representing the ohm of the battery, resistance caused by charge diffusion and resistance caused by charge transfer.
In one embodiment of the present invention, step S2 includes the following steps:
s21: exciting the equivalent circuit model and an actual battery system by using current;
s22: acquiring the terminal voltage of a battery equivalent circuit model and the terminal voltage of each single battery of an actual battery system;
s23: and performing Fourier transform on the obtained terminal voltage to obtain an equivalent circuit model and a frequency response value of each single battery of the actual battery system.
In one embodiment of the invention, the current is a sinusoidal current.
In one embodiment of the present invention, step S3 includes the following steps:
s31: establishing an amplitude square coherent function for the equivalent circuit model and the frequency response value of each single battery of the actual battery system;
s32: acquiring a dynamic threshold;
s33: and if the square coherence function value of the amplitude is less than or equal to the dynamic threshold value, the corresponding single battery in the battery system breaks down.
In one embodiment of the present invention, step S4 includes the following steps:
s41: establishing a multi-fault model according to the equivalent circuit model;
s42: the fault type is determined by a multiple fault model technique.
In one embodiment of the invention, the fault model is obtained by performing parameter identification under the condition that the battery has a fault by using an equivalent circuit model.
In one embodiment of the invention, the fault types include overcharge, overdischarge, and a battery internal short circuit.
In one embodiment of the invention, the fault model is obtained by using the equivalent circuit model to perform parameter identification under the condition that the battery has a fault.
The invention provides a battery system fault diagnosis system, comprising:
at least one battery;
the battery management system is connected with the battery and used for carrying out fault diagnosis on the battery;
the battery management system establishes a battery equivalent circuit model and carries out parameter identification;
after the battery management system identifies parameters, acquiring frequency response values of the equivalent circuit model and each single battery of an actual battery system;
the battery management system judges whether the actual battery system fails according to the equivalent circuit model and the frequency response value of each single battery of the actual battery system;
and the battery management system establishes a multi-fault model by using the equivalent battery circuit model so as to judge the fault type.
The battery fault diagnosis method comprises the steps of establishing a fault model and confirming a dynamic threshold value, so that the battery fault diagnosis is rapid and accurate, and the use safety performance of the battery is greatly improved.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic illustration of an embodiment of the present invention;
FIG. 2 is a flow chart of an implementation of the present invention;
FIG. 3 is a second order RC equivalent circuit model of the battery;
FIG. 4 is a lithium ion OCV-SOC diagram;
FIG. 5 generation of multi-model residuals and probability estimation;
fig. 6 is a schematic diagram of a battery system fault diagnostic system.
Component numbers:
100-battery system fault diagnosis system, 200-battery management device, 201-current sensor, 202-voltage sensor, 203-upper computer, 300-battery system, 400-equivalent circuit model, 401-open circuit voltage, 402-resistance caused by charge diffusion, 403-resistance caused by charge transfer, 404-battery ohmic internal resistance, 405-capacitance caused by charge diffusion, 406-capacitance caused by charge transfer, 407-current, 500-SOC-OCV curve, 600-multiple fault model technology.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a battery system fault diagnosis method which comprises the steps of identifying parameters of a battery equivalent circuit model under different fault conditions, obtaining fault types of batteries and conducting troubleshooting and repairing, and therefore the efficiency of the whole battery system is improved. The method comprises the steps of establishing a fault model and determining a dynamic threshold value, so that the fault diagnosis is fast and accurate and the use safety performance of the battery is improved integrally.
Referring to fig. 1-2, in the present embodiment, the method for diagnosing a battery system at least includes the following steps: establishing a battery equivalent circuit model 400 in a healthy state, and performing parameter identification (step S1); after parameter identification, respectively acquiring the equivalent circuit model after parameter identification and the frequency response value of each battery cell in the battery system (step S2); diagnosing whether each battery cell in the actual battery system has a fault or not through the equivalent circuit model, the frequency response value of each battery cell and a dynamic threshold (step S3); a fault model 500 is established by the equivalent circuit model 400, and the fault type is determined by the multiple fault model technique 600 (step S4).
Referring to fig. 1-3, in the present embodiment, a battery equivalent circuit model 400 under a healthy state is first established, and parameter identification is performed, and further, a capacitor 405 caused by charge diffusion, a capacitor 406 caused by charge transfer, a battery ohmic internal resistance 404, a resistor 402 caused by charge diffusion, and a resistor 403 caused by charge transfer are obtained through the parameter identification. Secondly, respectively acquiring the equivalent circuit model after parameter identification and the frequency response value of each battery cell in the battery system after parameter identification, wherein the step specifically comprises the following steps: exciting the equivalent circuit model 400 and the actual battery system with a current, wherein the current used may be a sinusoidal current; acquiring the terminal voltage of the equivalent circuit model and the terminal voltage of each single battery of the actual battery system; and performing Fourier transform on the terminal voltage to obtain an equivalent circuit model and a frequency response value of each single battery of the actual battery system. And diagnosing whether each battery cell in the actual battery system has a fault or not through the equivalent circuit model, the frequency response value of each battery cell and a dynamic threshold, and further, the method specifically comprises the following steps: establishing an amplitude square coherence function through the equivalent circuit model and the frequency response value of each single battery of the actual battery system; acquiring a dynamic threshold; and if the square coherence function value of the amplitude is less than or equal to the dynamic threshold value, the corresponding battery monomer in the battery system breaks down.
Referring to fig. 3 and 4, in the present embodiment, the equivalent circuit model 400 established in step S1 may establish a second-order RC equivalent circuit model in the present embodiment, as shown in fig. 3, wherein Vocv shown in fig. 3 is an open circuit voltage 401, V0 is a battery terminal voltage, C1 represents a capacitor 405 caused by charge diffusion, C2 represents a capacitor 406 caused by charge transfer, R0 represents a battery ohmic internal resistance 404, R1 represents a resistor 402 caused by charge diffusion, R2 represents a resistor 403 caused by charge transfer, and i represents a charging or discharging current 407. Wherein, the open-circuit voltage Vocv401 and the State of charge (SOC) of the battery have a nonlinear system, which can be expressed as follows:
V ocv ＝a 1 SOC 8 +a 2 SOC 7 +a 3 SOC 6 +a 4 SOC 5
+a 5 SOC 4 +a 6 SOC 3 +a 7 SOC 2 +a 8 SOC
+a 9
in this embodiment, an 8-step method may be used, and in other embodiments, a 7-step method or a 9-step method may be used, before the overall battery system fault diagnosis is started, a complete charge-discharge process may be set to obtain experimental data, and then a parameter a in a nonlinear relationship between an open-circuit voltage and a battery state of charge may be fitted in software, for example, MATLAB 1 ,a 2 ,…a 9 。
Wherein, in particular, the voltage v1 and the voltage v2 are represented as:
specifically, the terminal voltage of the equivalent battery circuit model can be obtained from the following formula:
v 0 ＝iR 0 +v 1 +v 2 +V ocv
specifically, the current i, the voltage v1 and the voltage v2 of the equivalent circuit model 400 are positive values during charging, and the current i, the voltage v1 and the voltage v2 of the equivalent battery circuit model are negative values during discharging.
The specific state of charge of the battery is a ratio of the remaining capacity of the battery to the rated capacity of the battery, and can be obtained by the following formula:
wherein SOC (0) in the above formula is an initial value of the battery state of charge, C n The rated capacity of the battery is expressed in ampere-hours, and η represents the coulombic efficiency. The magnitude of η is expressed as follows:
wherein, the pair type
And
the discretization can be performed using a zero order keeper to obtain
And
specifically, the equivalent circuit model 400 has a state variable x ═ SOC v 1 v 2 ] T The state can be represented by the formula x (k) ═ f (x) k-1 ,i k-1 )+ω k-1 The following formula z (k) h (x) is shown k ,i k )+v k Where z (k) represents the output, ω is input white gaussian noise with zero mean, and v is measurement noise with zero mean. From the above, it can be seen that the relationship between the state of charge and the terminal voltage in the equivalent battery circuit model 400 can be expressed as:
the relationship among the internal resistance 403, the battery state of charge, and the terminal voltage in the equivalent circuit model can be expressed as:
Referring to fig. 1-3, in the present embodiment, the step 2 specifically includes the following steps:
s21: exciting the equivalent circuit model 400 and the actual battery system with a current 404;
s22: acquiring the terminal voltage of the equivalent circuit model 400 and the terminal voltage of each single battery of the actual battery system;
s23: and converting the obtained terminal voltage to obtain the equivalent circuit model 400 and the frequency response value of each single battery of the actual battery system.
Specifically, in this embodiment, the equivalent circuit model 400 and the actual battery system may be excited by using a sinusoidal current, the terminal voltage of the actual equivalent circuit model may be obtained by using a kalman algorithm, the terminal voltage of each battery cell of the actual battery system may be obtained by measurement, and the obtained two terminal voltages may be subjected to fast fourier transform, so as to obtain the frequency response value of the equivalent circuit model
And the frequency response value of each single battery of the actual battery system
Referring to fig. 1-3, in the present embodiment, the step 3 specifically includes the following steps:
s31: establishing an amplitude square coherence function through the equivalent circuit model and the frequency response value of each single battery of the actual battery system;
s32: acquiring a dynamic threshold;
s33: and if the square coherence function value of the amplitude is less than or equal to the dynamic threshold value, the corresponding single battery in the battery system breaks down.
Specifically, the actual battery system circuit may be evaluated using an amplitude coherence square function and a dynamic threshold, where the equivalent circuit model and the amplitude coherence square spectrum of each battery cell of the actual battery may be defined as:
wherein, the first and the second end of the pipe are connected with each other,
is a square-of-magnitude coherent spectrum,
is that
Due to the normalization term in the denominator, the coherence spectrum is less than or equal to 1, expressed as
In the embodiment, under the condition of noise, the detection is improved in order to reduce the false alarm rateProbability, setting an estimation threshold value, recording test statistics test, and then:
wherein, 0<th<1 is a threshold value. Let us mean the test statistic test in unit time T and variance σ 2 According to the Chebyshev inequality, for any real number epsilon > 0, the following can be obtained:
converting the above equation can result in:
in this embodiment, the Chebyshev inequality can be used to ensure the distribution of the test statistics, and in any data set, the ratio of the data with the difference of the average value of the test statistics and the test statistics exceeding the standard deviation multiplied by epsilon is at most equal to
The statistical range of the test is as follows:
th∈[μ i -εσ i ,μ i +εσ i ]
wherein, mu i Is the dynamic mean value, σ i Is the dynamic standard deviation, and i represents the ith data. Since the test statistic is less than the threshold value in the present embodiment, it is considered that a failure has occurred, and therefore, the lower limit μ thereof is taken i -εσ i . Test statistical mean μ i Sum standard deviation σ i The updating is continuously carried out by the following two formulas:
where j +1 represents the total amount of data in the dynamic data subset and i must be greater than j. Thus, a dynamic threshold th can be obtained, which is expressed as follows:
th＝μ i -εσ i
where j and ε are parameters that are set as needed and determine how smooth the threshold curve (SOC-OCV)500 is, when j is very large, the mean of the test statistics in the dynamic subset is close to the mean of the samples, and the resulting threshold curve (SOC-OCV)500 is very smooth, i.e., the resulting dynamic thresholds are very close. The epsilon value determines the size of the allowed data deviation from the average value, and the larger the epsilon value is, the larger the probability of missing report of the system is; the smaller the value of ε, the greater the probability of false positives. Therefore, the values of j and epsilon need to be controlled to achieve the balance between the false alarm rate and the false alarm rate.
Referring to fig. 1-3, in the present embodiment, the step 4 specifically includes the following steps:
s41: establishing a fault model according to the equivalent circuit model 400, identifying R in different states by using the equivalent circuit model 400 established in the step S1 and by using the ground-based least square method 0 、R 1 、C 1 、R 2 And C 2 Equivalent circuit models under different fault conditions can be obtained, namely a battery multi-fault model 600 as shown in fig. 4.
S42: obtaining residual errors of all battery monomers of an actual battery;
s43: by using the multiple fault model technique 600, as shown in fig. 4, the probability of different faults occurring is generated, the fault with the highest probability is the fault occurring in the single battery, and the fault types include, but are not limited to, overcharge, overdischarge, and battery short circuit.
Further, the covariance of the residual at different detection time points can be expressed as:
wherein
Representing the linearized output vector estimated at the current estimation state. In the history of measurement data Z (t) i-1 )＝[z T (t 1 ),...,z T (t i-1 )]Based on the above, the conditional probability density of the nth fault model can be expressed as: f. of z(k)a,Z(k-1) (z k |a n ,Z k-1 )＝β n exp(ο)
Wherein the content of the first and second substances,
wherein l-1 denotes the measurement dimension and has
Wherein r is n A residual signal representing the nth fault model.
Further, the conditional probability of the nth subsystem may be expressed as:
wherein p is j Represents the conditional probability of the jth system, j 1, 2. In the above equation, the conditional probability p of the fault model at the previous time n And (k-1) weighting the conditional probability density of the previous moment, and after calculating the weighted probability of each model, obtaining the weight of the model at the current moment through normalization processing. If a certain model parameter is consistent with the actual system parameter or the matching degree is higher, the model is the type of the system fault, for example, the model can be overcharge or overdischarge.
Referring to fig. 6, in the present embodiment, the present invention provides a battery system fault diagnosis system 100, including: at least one battery 300 and a battery management system 200 are connected to the battery for performing fault diagnosis on the battery 300, wherein the battery management system 200 may include a current sensor 201, a voltage sensor 202 and an upper computer 203, and the current sensor 201 and the voltage sensor 202 are connected to the processing chip 203.
In the present embodiment, the battery system 300 includes a plurality of batteries, and the batteries may be lithium ion batteries.
Referring to fig. 6, in the present embodiment, the current sensor 201 and the voltage sensor 202 are connected to at least one battery 300 to obtain the real-time voltage and current values of the battery 300, and further, the current sensor 201 and the voltage sensor 202 are connected to the host computer 203 for parameter identification.
In some embodiments, the battery system fault diagnosis system 100 may be external to the battery 300.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. A battery system fault diagnosis method, comprising:
establishing a battery equivalent circuit model in a healthy state, and identifying parameters;
respectively acquiring the battery equivalent circuit model after the parameter identification and the frequency response value of each battery monomer in the actual battery system;
diagnosing whether each battery monomer in the actual battery system has a fault or not through the battery equivalent circuit model, the frequency response value of each battery monomer and the dynamic threshold value, wherein the method comprises the following steps:
establishing an amplitude square coherent function as a test statistic test for the frequency response values of the battery equivalent circuit model and each single battery in the actual battery system;
setting an estimation threshold value, setting the mean value of the test statistic test in unit time T as mu and the variance as sigma 2 The dynamic threshold th is obtained according to the chebyshev inequality, and is expressed as follows:
th＝μ i -εσ i
wherein, mu i Is the dynamic mean value, σ i Is the dynamic standard deviation, i represents the ith data;
if the square coherence function value of the amplitude is less than or equal to the dynamic threshold value, the corresponding battery monomer in the battery system breaks down;
and establishing a multi-fault model through the battery equivalent circuit model, and judging the fault type by using a multi-fault model technology.
2. The battery system failure diagnosis method according to claim 1,
the battery system includes a plurality of batteries.
3. The battery system failure diagnosis method according to claim 1,
the equivalent circuit model comprises capacitance caused by charge diffusion, capacitance caused by charge transfer, ohmic internal resistance of the battery, resistance caused by charge diffusion and resistance caused by charge transfer.
4. The battery system failure diagnosis method according to claim 1,
the method for acquiring the frequency response values of the single batteries in the battery equivalent circuit model and the actual battery system comprises the following steps:
exciting the battery equivalent circuit model and an actual battery system by using current;
acquiring the terminal voltage of a battery equivalent circuit model and the terminal voltage of each single battery of an actual battery system;
and performing Fourier transform on the obtained terminal voltage to obtain a battery equivalent circuit model and a frequency response value of each single battery of the actual battery system.
5. The battery system fault diagnosis method according to claim 4, wherein the current is a sinusoidal current.
6. The method for diagnosing the faults of the battery system according to claim 1, wherein the establishing of the multiple fault models through the battery equivalent battery circuit model to judge the fault types comprises:
establishing a multi-fault model according to the battery equivalent circuit model;
the fault type is determined by a multiple fault model technique.
7. The method as claimed in claim 1, wherein the fault model is obtained by performing parameter identification under a fault condition of the battery using the equivalent circuit model.
8. The battery system fault diagnosis method according to claim 1, wherein the fault types include overcharge, overdischarge, and a battery internal short circuit.
9. A battery system fault diagnosis system, characterized by comprising:
at least one battery;
the battery management system is connected with the battery and is used for carrying out fault diagnosis on the battery;
the battery management system establishes a battery equivalent circuit model and carries out parameter identification;
after the battery management system identifies parameters, acquiring a frequency response value of each single battery of the battery equivalent circuit model and the actual battery system;
the method for diagnosing whether each single battery in the actual battery system has a fault through the battery equivalent circuit model, the frequency response value and the dynamic threshold of each single battery in the actual battery system by the battery management system comprises the following steps:
establishing an amplitude square coherent function as a test statistic test for the equivalent circuit model and the frequency response value of each single battery in the actual battery system;
setting an estimation threshold value, setting the mean value of the test statistic test in unit time T as mu and the variance as sigma 2 The dynamic threshold th is obtained from the chebyshev inequality, and is expressed as follows:
th＝μ i -εσ i
wherein, mu i Is the dynamic mean value, σ i Is the dynamic standard deviation, i represents the ith data;
if the square coherence function value of the amplitude is less than or equal to the dynamic threshold value, the corresponding single battery in the battery system breaks down;
the battery management system utilizes the battery equivalent circuit model to establish a multi-fault model so as to judge the fault type.
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| CN112285569B (en) * | 2020-10-29 | 2022-02-01 | 哈尔滨工业大学（威海） | Electric vehicle fault diagnosis method based on dynamic threshold model |
| KR20230047709A (en) * | 2021-10-01 | 2023-04-10 | 주식회사 엘지에너지솔루션 | Apparatus and method for detecting short-circuit current in battery system |
| CN114252772B (en) * | 2021-12-22 | 2023-09-05 | 中国科学院电工研究所 | Internal short circuit diagnosis method and system for lithium ion battery |
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