CN110940921A - Multi-fault diagnosis method and system of lithium ion battery string based on correction variance - Google Patents
Multi-fault diagnosis method and system of lithium ion battery string based on correction variance Download PDFInfo
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- G—PHYSICS
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- 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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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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- G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
- G01R19/16566—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533
- G01R19/16576—Circuits and arrangements for comparing voltage or current with one or several thresholds and for indicating the result not covered by subgroups G01R19/16504, G01R19/16528, G01R19/16533 comparing DC or AC voltage with one threshold
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- G—PHYSICS
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- 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
- G01R31/3835—Arrangements for monitoring battery or accumulator variables, e.g. SoC involving only voltage measurements
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Abstract
The invention discloses a multi-fault diagnosis method and a multi-fault diagnosis system of a lithium ion battery string based on a corrected variance, wherein a voltage time sequence of the lithium ion battery string is measured; judging whether each voltage value in the sequence is greater than or equal to a maximum set threshold value, if so, indicating that the corresponding time point is an overcharge fault; otherwise, judging whether the voltage value smaller than the maximum set threshold value in the sequence is larger than or equal to the minimum set threshold value, if so, indicating that the over-discharge fault exists at the corresponding time point; otherwise, calculating the variance of the voltage time sequence; calculating a correction coefficient; calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient; judging whether the improvement variance is suddenly increased, if so, judging that the time range corresponding to the sudden increase is overcharge or open circuit fault; if not, judging whether the improved variance is suddenly reduced, if so, judging whether the improved variance is over-discharged or short-circuit fault in a time range corresponding to the sudden reduction; if not, returning to the starting step.
Description
Technical Field
The disclosure relates to the technical field of battery fault diagnosis, in particular to a multi-fault diagnosis method and system of a lithium ion battery string based on a correction variance.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The popularization and development of new energy electric vehicles play an important role in the exhaustion of fossil energy, environmental pollution and the suppression of greenhouse effect at present. As a vehicle-mounted power support of an electric vehicle, the performance of a lithium ion battery is crucial to the power performance, economy and safety of the whole vehicle. According to the report statistics, in more than 40 electric automobile safety accidents occurred since 2019, the accident causes have a commonality and are all caused by lithium ion batteries equipped in the automobile. Battery failure is mainly caused by three reasons: one is that the electrochemical reaction in the lithium ion battery is extremely complex and is very sensitive to the environmental temperature and the aging of the battery; secondly, a lithium ion battery string is usually composed of thousands of completely inconsistent battery monomers; and thirdly, mechanical abuse, electrical abuse and thermal abuse of the power battery. Therefore, the method has important significance for simply, efficiently, accurately and stably diagnosing the early-stage faults of the battery and preventing the occurrence of the safety accidents of the battery.
In the course of implementing the present disclosure, the inventors found that the following technical problems exist in the prior art:
the current battery fault diagnosis methods can be classified into ① fault diagnosis methods based on adaptive threshold comparison, ② fault diagnosis methods based on analytical models, mainly including unscented extended Kalman Filter (UKF) methods, ③ fault diagnosis methods based on data drive, mainly including correlation coefficient methods, neural network methods, fuzzy logic methods, and support vector machine methods.
Disclosure of Invention
In order to solve the deficiencies of the prior art, the present disclosure provides a multi-fault diagnosis method and system of a lithium ion battery string based on a corrected variance; the method can accurately diagnose the fault of the battery without a model, efficiently solve the problem that the initial fault type and time of the lithium ion battery cannot be quickly, accurately and stably diagnosed, and has the following advantages compared with the traditional fault diagnosis method based on data driving: 1) a simple fault detection scheme based on battery voltage variance is provided to predict initial battery faults when the battery has no obvious abnormal phenomenon. 2) A correction factor theta representing voltage fluctuation information is introduced to detect the type of fault (including battery short, open, etc.) and time. 3) The battery data is updated by adopting the sliding window, the diagnosis sensitivity to the fault is kept, the calculation efficiency is high, the cost is low, and the real-time implementation is convenient.
In a first aspect, the present disclosure provides a method for multi-fault diagnosis of lithium ion battery strings based on a modified variance;
the multi-fault diagnosis method of the lithium ion battery string based on the corrected variance comprises the following steps:
measuring a voltage time sequence of the lithium ion battery string to be diagnosed;
judging whether each voltage value in the voltage time sequence is greater than or equal to a maximum set threshold value, if so, indicating that the lithium ion battery string to be diagnosed is in overcharge fault at the corresponding time point; otherwise, the next step is carried out:
judging whether a voltage value smaller than the maximum set threshold in the voltage time sequence is larger than or equal to the minimum set threshold, if so, indicating that the lithium ion battery string to be diagnosed has an overdischarge fault at the corresponding time point; otherwise, entering the next step;
calculating the variance of the voltage time series; calculating a correction coefficient; calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient;
judging whether the improvement variance is suddenly increased or not, if so, indicating that the lithium ion battery string to be diagnosed is overcharged or broken circuit fault within the time range corresponding to the sudden increase; if not, entering the next step;
judging whether the improved variance is suddenly reduced or not, if so, indicating that the lithium ion battery string to be diagnosed is over-discharge or short-circuit fault within the time range corresponding to the sudden reduction; and if not, returning to the step of measuring the voltage time sequence of the lithium ion battery string to be diagnosed.
In a second aspect, the present disclosure also provides a multiple fault diagnosis system for a lithium ion battery string based on a modified variance;
the multi-fault diagnosis system of the lithium ion battery string based on the corrected variance comprises:
a measurement module configured to: measuring a voltage time sequence of the lithium ion battery string to be diagnosed;
a first determination module configured to: judging whether each voltage value in the voltage time sequence is greater than or equal to a maximum set threshold value, if so, indicating that the lithium ion battery string to be diagnosed is in overcharge fault at the corresponding time point; otherwise, entering a second judging module:
a second determination module configured to: judging whether a voltage value smaller than the maximum set threshold in the voltage time sequence is larger than or equal to the minimum set threshold, if so, indicating that the lithium ion battery string to be diagnosed has an overdischarge fault at the corresponding time point; otherwise, entering an improved variance calculation module;
an improved variance calculation module configured to: calculating the variance of the voltage time series; calculating a correction coefficient; calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient;
a third determination module configured to: judging whether the improvement variance is suddenly increased or not, if so, indicating that the lithium ion battery string to be diagnosed is overcharged or broken circuit fault within the time range corresponding to the sudden increase; if not, entering a fourth judgment module;
a fourth determination module configured to: judging whether the improved variance is suddenly reduced or not, if so, indicating that the lithium ion battery string to be diagnosed is over-discharge or short-circuit fault within the time range corresponding to the sudden reduction; and if not, returning to the step of measuring the voltage time sequence of the lithium ion battery string to be diagnosed.
In a third aspect, the present disclosure also provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method of the first aspect.
In a fourth aspect, the present disclosure also provides a computer-readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of the first aspect.
Compared with the prior art, the beneficial effect of this disclosure is:
1. an accurate lithium ion battery equivalent circuit model does not need to be constructed;
2. the algorithm operation cost is low;
3. the battery fault type and the fault occurrence time can be efficiently and accurately diagnosed, and the damage caused by the fault occurrence can be favorably prevented;
4. the robustness is strong, and the online real-time implementation is easy.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flow chart of a modified variance based algorithm according to a first embodiment of the present disclosure;
FIG. 2 is a diagram of a battery voltage sequence measured under UDDS operating conditions according to a first embodiment of the present disclosure;
FIG. 3 is a multi-fault diagnosis diagram of a lithium ion battery based on a conventional variance algorithm according to a first embodiment of the present disclosure;
FIG. 4 is a multi-fault diagnosis diagram of a lithium ion battery based on a modified variance algorithm according to a first embodiment of the present disclosure;
fig. 5(a) -5 (d) are multiple fault diagnosis graphs of the corrected variance under different size sliding windows according to the first embodiment of the disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In the first embodiment, the present embodiment provides a multi-fault diagnosis method for a lithium ion battery string based on a corrected variance;
as shown in fig. 1, the method for diagnosing multiple faults of a lithium ion battery string based on a corrected variance includes:
s1: measuring a voltage time sequence of the lithium ion battery string to be diagnosed;
s2: judging whether each voltage value in the voltage time sequence is greater than or equal to a maximum set threshold value, if so, indicating that the lithium ion battery string to be diagnosed is in overcharge fault at the corresponding time point; otherwise, proceed to S3:
s3: judging whether a voltage value smaller than the maximum set threshold in the voltage time sequence is larger than or equal to the minimum set threshold, if so, indicating that the lithium ion battery string to be diagnosed has an overdischarge fault at the corresponding time point; otherwise, go to S4;
s4: calculating the variance of the voltage time series; calculating a correction coefficient; calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient;
s5: judging whether the improvement variance is suddenly increased or not, if so, indicating that the lithium ion battery string to be diagnosed is overcharged or broken circuit fault within the time range corresponding to the sudden increase; if not, go to S6;
s6: judging whether the improved variance is suddenly reduced or not, if so, indicating that the lithium ion battery string to be diagnosed is over-discharge or short-circuit fault within the time range corresponding to the sudden reduction; if not, return to S1.
Further, the calculating the variance of the voltage time series means: the mean of the sum of squares of the differences between each sample value and the mean of all sample values.
It should be understood that the variance of the voltage time series is calculated by the formula:
wherein, variance (N) represents the variance of the voltage time series; v (i) represents the cell voltage at the ith sampling point; n represents the length of a sliding window of the voltage time sequence, and is a positive integer;
represents the average voltage;
V(i)＝[V(i),V(i+1),...,V(N)](1)
where v (i) represents a battery voltage vector.
It should be appreciated that for an online implementation, the variance of the battery voltage sequence should be calculated in real time. Thus, a sliding window is used to update the battery data in real time and maintain the variance sensitivity to faults. In particular, the variance at each instant is calculated from the cell voltage in the previous historical sliding window. It is noted that N in equation (1) represents the sliding window size, which essentially determines the variance and the sensitivity of the variance to faults. According to equations (2) - (3), for the same battery voltage sequence, the smaller the sliding window, the larger the variance, indicating higher sensitivity to failure, and therefore, a smaller sliding window may provide better accuracy of battery failure diagnosis. However, when the sliding window size is below 2, the variance is constant at 0 regardless of any abnormal change in voltage, which means that the proposed method cannot detect any malfunction when the sliding window size is too small. Theoretically, the optimal sliding window size is 2, and the variance can achieve strong robustness and high-efficiency calculation on measurement noise and interference.
It is noted that the variance of the battery voltage sequence is always non-negative regardless of a sudden rise or a sudden fall in the battery voltage. Conventional variance based methods are therefore unable to predict multiple battery failure types. In fact, detecting different battery failure types and predicting the time at which the failure occurs are essential to battery safety. In order to accommodate the multiple fault diagnosis requirements of the battery system, the variance algorithm needs to be improved. Therefore, a correction coefficient θ representing voltage fluctuation information is introduced to detect the type of battery failure and predict the time at which the failure occurs.
Further, the specific step of calculating the correction coefficient includes:
when the voltage value at the moment t is smaller than the average value of the voltage in the current sliding window, the value of the correction coefficient is-1;
when the voltage value at the moment t is larger than the average value of the voltage in the current sliding window, the value of the correction coefficient is 1;
when the voltage value at the moment t is equal to the average value of the voltage in the current sliding window, the value of the correction coefficient is 0;
it should be understood that the specific calculation formula for calculating the correction coefficient is as follows:
wherein θ is a correction coefficient; v (t) is the real-time battery voltage value at time t, VavgIs the average voltage of the cell in a sliding window.
Further, based on the variance of the voltage time series and the correction coefficient, calculating an improved variance of the voltage time series; the method comprises the following specific steps:
and multiplying the variance of the voltage time sequence by the correction coefficient to obtain a product, namely the improved variance of the voltage time sequence.
It is understood that based on the variance of the voltage time series and the correction coefficient, a modified variance of the voltage time series is calculated; the calculation formula is as follows:
S＝θ×Variance(N) (4)
where S represents the improved variance of the voltage time series.
The improvement variance rises suddenly, namely that the change value of the improvement variance is larger than a set threshold value in a set time range, and the improvement variance at the later time point is larger than the improvement variance at the earlier time point.
The improvement variance is suddenly reduced, namely the change value of the improvement variance is larger than a set threshold value in a set time range, and the improvement variance at the later time point is smaller than the improvement variance at the earlier time point.
As shown in FIG. 2, three lithium ion batteries (No. B)1,B2,B3) In the sequence of battery voltages in the presence of short and open faults under urban road circulation (UDDS) conditions, it can be observed that the battery voltages have an inconsistency, and B3Is lower than B1And B2(ii) a However, because the same charge and discharge currents are used, their voltage fluctuation tendencies are consistent. At 41.95 seconds of UDDS cycle, B3An open circuit fault occurred, resulting in a sudden increase in the battery voltage to 3.512V. At 54.54 seconds, one wire is used to short circuit B2Lasting approximately 0.13 seconds, during which time the battery voltage suddenly drops by 0.487V. It is noted that when the short-circuit fault disappears, the battery voltage will also return to normal. At 69.53 seconds, B1An open circuit fault occurs, resulting in a sudden increase in battery voltage of 0.414V. It is worth mentioning that all battery voltages cannot trigger the cut-off voltage of the battery during charging and discharging when the battery is in failure.
As shown in fig. 3, based on the result of the fault diagnosis of the battery voltage sequence of the conventional variance, with the sliding window size N set to 100, it can be clearly observed that when a fault occurs due to a sudden change in voltage, the variance of the battery voltage at that time suddenly increases (exceeds 0.03); however, the variance value is 0 when the battery voltage is normal, which indicates that the studied variance-based fault diagnosis method does not falsely trigger an alarm under normal voltage conditions. The variance algorithm appears very sensitive to abnormal voltages compared to the battery voltage sequence chart shown in fig. 2, which indicates that the variance algorithm is promising to detect battery failure even if the battery voltage is within a safe range. However, the multi-fault diagnosis result of the lithium ion battery based on the conventional variance shown in fig. 3 shows that the variance value of the battery voltage sequence is always non-negative regardless of any type of fault of the battery, and therefore, the multi-fault diagnosis method of the lithium ion battery based on the conventional variance cannot distinguish the fault type of the battery.
As shown in fig. 4, the multiple fault diagnosis result of the battery voltage series based on the corrected variance under the same condition. According to the battery voltage and the average voltage, a correction coefficient theta is introduced, and the corrected variance can effectively predict sudden change of the battery voltage and further identify the fault type. In FIG. 2B3 Open circuit fault ① occurs at 41.95 seconds because VB3(t)＝3.519V>Vavg(t) 3.429V, when a positive variance is first generated, ① in FIG. 4, when the fault disappears in 44.05 seconds, because VB3(t)＝3.153V<Vavg(t) 3.316V, a negative square difference then occurs; b is2It is worth mentioning that the time of occurrence and disappearance of abnormal voltage and the time of increase and decrease of the corrected variance are completely coincident, and therefore, the time of occurrence of the fault can be accurately predicted based on the multiple fault diagnosis method of correcting variance1The corrected variance of the battery voltage suddenly increases in the positive direction, indicating that this is an open circuit fault. The proposed multiple fault diagnosis method of a lithium ion battery string based on a corrected variance is summarized, which can effectively predict the type of fault of a battery and the time when the fault occurs.
For an online implementation, the variance of the battery voltage sequence should be calculated in real time. Thus, real-time updating of battery data and maintaining variance sensitivity to faults may be performed using a sliding window. In particular, the variance at each instant is calculated from the cell voltage in the previous historical sliding window. Note that N in the formula (1) represents the sliding window size. The sliding window size essentially determines the variance and the sensitivity of the variance to faults. According to equations (2) - (3), for the same battery voltage sequence, the smaller the sliding window, the larger the variance value, indicating the higher sensitivity of the variance to the fault, and therefore, a smaller sliding window can provide higher accuracy of diagnosis of the battery fault. However, according to equations (2) - (3), when the sliding window size is below 2, the variance value is constantly 0 regardless of any abnormal change in voltage, which means that the proposed method cannot detect any malfunction when the sliding window size is too small. Theoretically, the optimal sliding window size is 2, and the variance can achieve high-efficiency calculation and strong robustness on measurement noise and interference.
As shown in fig. 5(a) -5 (d), the failure diagnosis results are obtained based on the modified variance algorithm under sliding windows of different sizes, where the sliding windows have respective sizes of (a) N-300, (b) N-60, (c) N-2, and (d) N-1. Compared to the variance value of 0.054 for the sliding window size of 100 in fig. 4, the variance value is reduced to 0.0423-0.0432 when the sliding window size is 300, which indicates that the larger the sliding window is, the less sensitive the proposed detection method is to abnormal voltage variations, thereby resulting in failure to detect a fault. When the sliding window size is reduced to 60 and 2, respectively, the variance values of the abnormal voltage variations are increased to 0.056 and 0.058, respectively, which indicates that the smaller the sliding window is, the more sensitive the proposed fault diagnosis method is to the abnormal voltage; however, as shown in fig. 5(d), when the sliding window size is reduced to 1, the studied failure diagnosis scheme will not work and any failure cannot be detected. Therefore, the experimental results confirm that the proposed multi-fault diagnosis method is most sensitive to the battery voltage abnormality diagnosis when 2 is the optimal sliding window size, i.e., N is 2.
The second embodiment also provides a multi-fault diagnosis system of the lithium ion battery string based on the corrected variance;
the multi-fault diagnosis system of the lithium ion battery string based on the corrected variance comprises:
a measurement module configured to: measuring a voltage time sequence of the lithium ion battery string to be diagnosed;
a first determination module configured to: judging whether each voltage value in the voltage time sequence is greater than or equal to a maximum set threshold value, if so, indicating that the lithium ion battery string to be diagnosed is in overcharge fault at the corresponding time point; otherwise, entering a second judging module:
a second determination module configured to: judging whether a voltage value smaller than the maximum set threshold in the voltage time sequence is larger than or equal to the minimum set threshold, if so, indicating that the lithium ion battery string to be diagnosed has an overdischarge fault at the corresponding time point; otherwise, entering an improved variance calculation module;
an improved variance calculation module configured to: calculating the variance of the voltage time series; calculating a correction coefficient; calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient;
a third determination module configured to: judging whether the improvement variance is suddenly increased or not, if so, indicating that the lithium ion battery string to be diagnosed is overcharged or broken circuit fault within the time range corresponding to the sudden increase; if not, entering a fourth judgment module;
a fourth determination module configured to: judging whether the improved variance is suddenly reduced or not, if so, indicating that the lithium ion battery string to be diagnosed is over-discharge or short-circuit fault within the time range corresponding to the sudden reduction; and if not, returning to the step of measuring the voltage time sequence of the lithium ion battery string to be diagnosed.
Further, the calculating the variance of the voltage time series means: the mean value of the sum of squares of the differences between each sample value and the mean value of all sample values;
further, the specific step of calculating the correction coefficient includes:
when the voltage value at the moment t is smaller than the average value of the voltage in the current sliding window, the value of the correction coefficient is-1;
when the voltage value at the moment t is larger than the average value of the voltage in the current sliding window, the value of the correction coefficient is 1;
when the voltage value at the moment t is equal to the average value of the voltage in the current sliding window, the value of the correction coefficient is 0;
further, based on the variance of the voltage time series and the correction coefficient, calculating an improved variance of the voltage time series; the method comprises the following specific steps:
and multiplying the variance of the voltage time sequence by the correction coefficient to obtain a product, namely the improved variance of the voltage time sequence.
In a third embodiment, the present embodiment further provides an electronic device, which includes a memory, a processor, and computer instructions stored in the memory and executed on the processor, where the computer instructions, when executed by the processor, implement the steps of the method in the first embodiment.
In a fourth embodiment, the present embodiment further provides a computer-readable storage medium for storing computer instructions, and the computer instructions, when executed by a processor, perform the steps of the method in the first embodiment.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The multi-fault diagnosis method of the lithium ion battery string based on the corrected variance is characterized by comprising the following steps of:
measuring a voltage time sequence of the lithium ion battery string to be diagnosed;
judging whether each voltage value in the voltage time sequence is greater than or equal to a maximum set threshold value, if so, indicating that the lithium ion battery string to be diagnosed is in overcharge fault at the corresponding time point; otherwise, the next step is carried out:
judging whether a voltage value smaller than the maximum set threshold in the voltage time sequence is larger than or equal to the minimum set threshold, if so, indicating that the lithium ion battery string to be diagnosed has an overdischarge fault at the corresponding time point; otherwise, entering the next step;
calculating the variance of the voltage time series; calculating a correction coefficient; calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient;
judging whether the improvement variance is suddenly increased or not, if so, indicating that the lithium ion battery string to be diagnosed is overcharged or broken circuit fault within the time range corresponding to the sudden increase; if not, entering the next step;
judging whether the improved variance is suddenly reduced or not, if so, indicating that the lithium ion battery string to be diagnosed is over-discharge or short-circuit fault within the time range corresponding to the sudden reduction; and if not, returning to the step of measuring the voltage time sequence of the lithium ion battery string to be diagnosed.
2. The method of claim 1, wherein said calculating the variance of the voltage time series is: the mean of the sum of squares of the differences between each sample value and the mean of all sample values.
3. The method of claim 1, wherein said step of calculating a correction factor comprises:
when the voltage value at the moment t is smaller than the average value of the voltage in the current sliding window, the value of the correction coefficient is-1;
when the voltage value at the moment t is larger than the average value of the voltage in the current sliding window, the value of the correction coefficient is 1;
and when the voltage value at the moment t is equal to the average value of the voltage in the current sliding window, the value of the correction coefficient is 0.
4. The method according to claim 1, wherein a variance of improvement of the voltage time series is calculated based on the variance of the voltage time series and the correction coefficient; the method comprises the following specific steps:
and multiplying the variance of the voltage time sequence by the correction coefficient to obtain a product, namely the improved variance of the voltage time sequence.
5. The method of claim 1, wherein the improvement variance is raised, and the change value of the improvement variance is larger than the set threshold value within the set time range, and the improvement variance at the later time point is larger than the improvement variance at the earlier time point.
6. The method of claim 1, wherein the improvement variance is decreasing, and the change in the improvement variance is greater than a predetermined threshold value within a predetermined time range, and the improvement variance later in time is less than the improvement variance earlier in time.
7. The multi-fault diagnosis system of the lithium ion battery string based on the corrected variance is characterized by comprising the following steps:
a measurement module configured to: measuring a voltage time sequence of the lithium ion battery string to be diagnosed;
a first determination module configured to: judging whether each voltage value in the voltage time sequence is greater than or equal to a maximum set threshold value, if so, indicating that the lithium ion battery string to be diagnosed is in overcharge fault at the corresponding time point; otherwise, entering a second judging module:
a second determination module configured to: judging whether a voltage value smaller than the maximum set threshold in the voltage time sequence is larger than or equal to the minimum set threshold, if so, indicating that the lithium ion battery string to be diagnosed has an overdischarge fault at the corresponding time point; otherwise, entering an improved variance calculation module;
an improved variance calculation module configured to: calculating the variance of the voltage time series; calculating a correction coefficient; calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient;
a third determination module configured to: judging whether the improvement variance is suddenly increased or not, if so, indicating that the lithium ion battery string to be diagnosed is overcharged or broken circuit fault within the time range corresponding to the sudden increase; if not, entering a fourth judgment module;
a fourth determination module configured to: judging whether the improved variance is suddenly reduced or not, if so, indicating that the lithium ion battery string to be diagnosed is over-discharge or short-circuit fault within the time range corresponding to the sudden reduction; and if not, returning to the step of measuring the voltage time sequence of the lithium ion battery string to be diagnosed.
8. The system of claim 7, wherein,
the variance of the voltage time series is calculated by: the mean value of the sum of squares of the differences between each sample value and the mean value of all sample values;
the specific step of calculating the correction coefficient includes:
when the voltage value at the moment t is smaller than the average value of the voltage in the current sliding window, the value of the correction coefficient is-1;
when the voltage value at the moment t is larger than the average value of the voltage in the current sliding window, the value of the correction coefficient is 1;
when the voltage value at the moment t is equal to the average value of the voltage in the current sliding window, the value of the correction coefficient is 0;
calculating an improved variance of the voltage time series based on the variance of the voltage time series and the correction coefficient; the method comprises the following specific steps:
and multiplying the variance of the voltage time sequence by the correction coefficient to obtain a product, namely the improved variance of the voltage time sequence.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of any one of claims 1 to 7.
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| CN201911266342.7A Withdrawn CN110940921A (en) | 2019-12-11 | 2019-12-11 | Multi-fault diagnosis method and system of lithium ion battery string based on correction variance |
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| CN112098850A (en) * | 2020-09-21 | 2020-12-18 | 山东工商学院 | Lithium ion battery voltage fault diagnosis method and system based on SDO algorithm |
| CN112147512A (en) * | 2020-09-17 | 2020-12-29 | 北京理工大学 | Diagnosis and separation method for short-circuit and abuse faults of lithium ion battery |
| CN112363059A (en) * | 2020-11-02 | 2021-02-12 | 山东大学 | Battery fault diagnosis method and system based on GM (1, 1) gray model |
| CN113067042A (en) * | 2021-03-15 | 2021-07-02 | 珠海旺远信息技术有限公司 | Energy storage device and fault prediction and diagnosis method |
| CN113791350A (en) * | 2021-08-06 | 2021-12-14 | 陕西汽车集团股份有限公司 | Battery failure prediction method |
| CN114139936A (en) * | 2021-11-29 | 2022-03-04 | 合肥安达创展科技股份有限公司 | Cloud intelligent early warning and repair reporting system based on exhibition item interactive data |
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| CN116400231A (en) * | 2023-06-09 | 2023-07-07 | 中国华能集团清洁能源技术研究院有限公司 | Battery multi-fault detection method and device of energy storage system and electronic equipment |
| CN116840731A (en) * | 2023-08-30 | 2023-10-03 | 中国华能集团清洁能源技术研究院有限公司 | Method and device for detecting faults of battery pack |
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| CN112147512A (en) * | 2020-09-17 | 2020-12-29 | 北京理工大学 | Diagnosis and separation method for short-circuit and abuse faults of lithium ion battery |
| CN112098850B (en) * | 2020-09-21 | 2024-03-08 | 山东工商学院 | Lithium ion battery voltage fault diagnosis method and system based on SDO algorithm |
| CN112098850A (en) * | 2020-09-21 | 2020-12-18 | 山东工商学院 | Lithium ion battery voltage fault diagnosis method and system based on SDO algorithm |
| CN112363059A (en) * | 2020-11-02 | 2021-02-12 | 山东大学 | Battery fault diagnosis method and system based on GM (1, 1) gray model |
| CN113067042A (en) * | 2021-03-15 | 2021-07-02 | 珠海旺远信息技术有限公司 | Energy storage device and fault prediction and diagnosis method |
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| CN113791350B (en) * | 2021-08-06 | 2024-05-14 | 陕西汽车集团股份有限公司 | Battery fault prediction method |
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| CN114966434A (en) * | 2022-07-29 | 2022-08-30 | 力高(山东)新能源技术有限公司 | Method for judging cell voltage deviation |
| CN114966434B (en) * | 2022-07-29 | 2022-10-28 | 力高(山东)新能源技术股份有限公司 | Method for judging cell voltage deviation |
| CN115061049B (en) * | 2022-08-08 | 2022-11-01 | 山东卓朗检测股份有限公司 | Method and system for rapidly detecting UPS battery fault of data center |
| CN115061049A (en) * | 2022-08-08 | 2022-09-16 | 山东卓朗检测股份有限公司 | Method and system for rapidly detecting UPS battery fault of data center |
| CN116400231A (en) * | 2023-06-09 | 2023-07-07 | 中国华能集团清洁能源技术研究院有限公司 | Battery multi-fault detection method and device of energy storage system and electronic equipment |
| CN116400231B (en) * | 2023-06-09 | 2023-10-03 | 中国华能集团清洁能源技术研究院有限公司 | Battery multi-fault detection method and device of energy storage system and electronic equipment |
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