CN115097319A - Power battery pack fault online diagnosis method and system - Google Patents
Power battery pack fault online diagnosis method and system Download PDFInfo
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- CN115097319A CN115097319A CN202210872840.1A CN202210872840A CN115097319A CN 115097319 A CN115097319 A CN 115097319A CN 202210872840 A CN202210872840 A CN 202210872840A CN 115097319 A CN115097319 A CN 115097319A
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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]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Abstract
The invention belongs to the technical field of power battery packs, and provides a power battery pack fault online diagnosis method and system. Acquiring voltage data of the power battery pack according to a set time interval; constructing the voltage data into a plurality of first voltage time sequences, comparing the voltage of each first voltage time sequence with a preset voltage threshold value, and preliminarily diagnosing whether the power battery pack has faults or not; when the power battery pack is preliminarily diagnosed to have no fault, phase space reconstruction is carried out on all the first voltage time sequences to obtain a plurality of second voltage time sequences; calculating the similarity between any two second voltage time sequences by using a fuzzy function, and further calculating the fuzzy entropy value of each second voltage time sequence in sequence; and finally diagnosing the faults and the occurrence time of the power battery pack in each set time interval on line according to the comparison result of the fuzzy entropy value and the preset fuzzy entropy value threshold.
Description
Technical Field
The invention belongs to the technical field of power battery packs, and particularly relates to a power battery pack fault online diagnosis method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The lithium ion battery pack is a non-linear, multiple time-varying and multiple space-time scale hybrid system, and along with the interaction influence of various factors such as the environment and working conditions of the battery, serious inconsistency can occur inside the battery pack, the battery tends to age, and the serious inconsistency is generally represented by that the internal resistance is continuously increased, the capacity is gradually attenuated, the safe charging and discharging boundary is difficult to find, abuse faults such as overcharge, overdischarge, short circuit and the like are easily caused, further thermal runaway is caused, great economic loss is caused, and the personal safety is seriously threatened. Therefore, a fault diagnosis method is needed to timely and efficiently troubleshoot the power battery pack. In the existing fault diagnosis method, the diagnosis method based on data driving gradually becomes a hotspot in the battery field due to the advantages of no model characteristic, convenient operation, strong applicability, good nonlinear mapping and the like. The entropy-based method can detect the time and the position of the battery fault in real time only by the acquired time sequence of the battery voltage, and is very suitable for application of power batteries and energy storage systems.
However, the inventor finds that the current entropy-based method is sensitive to the quantity and quality of short voltage sequences, low in prediction precision and poor in robustness, fault detection information is easy to lose, lose and the like, the probability of misdiagnosis and missed diagnosis is greatly increased, and potential safety hazards are more prominent.
Disclosure of Invention
In order to solve the technical problems in the background art, the invention provides a method and a system for online diagnosing the faults of the power battery pack, which can accurately and stably diagnose the early faults and the occurrence time of the lithium ion battery under different battery states and measurement noise interference while realizing parameter optimization by introducing a fuzzy membership function, and provide guarantee for uncontrollable factors in practical application.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a power battery pack fault online diagnosis method, which comprises the following steps:
acquiring voltage data of the power battery pack according to a set time interval;
constructing the voltage data into a plurality of first voltage time sequences, comparing the voltage of each first voltage time sequence with a preset voltage threshold value, and preliminarily diagnosing whether the power battery pack has faults or not;
when the power battery pack is preliminarily diagnosed to have no fault, phase space reconstruction is carried out on all the first voltage time sequences to obtain a plurality of second voltage time sequences;
calculating the similarity between any two second voltage time sequences by using a fuzzy membership function, and further sequentially calculating the fuzzy entropy value of each second voltage time sequence;
and finally diagnosing the faults and the occurrence time of the power battery pack in each set time interval on line according to the comparison result of the fuzzy entropy value and the preset fuzzy entropy value threshold.
As an embodiment, the voltage data is constructed into a plurality of first voltage time series according to a sliding window of a preset length.
As an embodiment, the power battery pack is preliminarily diagnosed as having a fault when the voltage in the first voltage time series is lower than a preset voltage threshold.
In one embodiment, the fuzzy membership function is an exponential function, and the similarity between the two second voltage time series is calculated by an exponential function of the maximum absolute distance between the two second voltage time series.
In one embodiment, when the fuzzy entropy value of a certain second voltage time series is greater than a preset fuzzy entropy value threshold, the fault of the power battery pack in a corresponding set time interval is diagnosed online, and the related second voltage time series and the calculated fuzzy entropy value are transmitted to the front-end equipment together.
The invention provides a power battery pack fault online diagnosis system in a second aspect, which comprises:
the voltage data acquisition module is used for acquiring voltage data of the power battery pack according to a set time interval;
the fault preliminary diagnosis module is used for constructing the voltage data into a plurality of first voltage time sequences, comparing the voltage of each first voltage time sequence with a preset voltage threshold value and preliminarily diagnosing whether the power battery pack has faults or not;
the phase space reconstruction module is used for performing phase space reconstruction on all the first voltage time sequences to obtain a plurality of second voltage time sequences when the power battery pack is preliminarily diagnosed to be free of faults;
the fuzzy entropy calculation module is used for calculating the similarity between any two second voltage time sequences by using a fuzzy membership function so as to sequentially calculate the fuzzy entropy of each second voltage time sequence;
and the fault final diagnosis module is used for finally diagnosing the faults and the occurrence time of the power battery pack in each set time interval on line according to the comparison result of the fuzzy entropy value and a preset fuzzy entropy value threshold.
In one embodiment, the voltage data is constructed into a plurality of first voltage time series according to a sliding window with a preset length in the fault preliminary diagnosis module.
In one embodiment, the fault preliminary diagnosis module preliminarily diagnoses that the power battery pack has a fault when the voltage in the first voltage time series is lower than a preset voltage threshold.
In one embodiment, in the fuzzy entropy calculation module, the fuzzy membership function is an exponential function, and the similarity between two second voltage time series is calculated by the exponential function of the maximum absolute distance between the two second voltage time series.
In one embodiment, in the fault final diagnosis module, when the fuzzy entropy value of a certain second voltage time series is greater than a preset fuzzy entropy value threshold, a fault of the power battery pack within a corresponding set time interval is diagnosed online, and the related second voltage time series and the calculated fuzzy entropy value are transmitted to the front-end equipment together.
Compared with the prior art, the invention has the beneficial effects that:
(1) the online diagnosis method for the faults of the power battery pack has the advantages of no model characteristic, convenience in operation, strong applicability and the like according to the numerical value change of the fuzzy entropy, can detect the positions of the faults of the batteries under the condition of no model, and can accurately detect and position the early faults of the batteries and the occurrence time of the early faults according to the time of the early forms of the faults calculated by the sampling frequency.
(2) The method calculates the fuzzy entropy value by introducing the fuzzy membership function, has convenient and accurate parameter setting, strong anti-interference performance and no need of considering the influence of the inconsistency of the battery, changes the discrete degree of data due to the introduction of the fuzzy function in practical application, has certain anti-interference performance and stable diagnosis result.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a detailed flowchart of a power battery pack fault online diagnosis method according to embodiment 1 of the present invention;
FIG. 2(a) and FIG. 2(b) are the entropy of the sample and the membership function in embodiment 1 of the present invention, respectively;
fig. 3(a) and fig. 3(b) are a voltage time sequence of the 8-node series module in the embodiment 1 of the present invention under the UDDS working condition and a new voltage time sequence after adding 50% snr;
fig. 4(a), fig. 4(b), and fig. 4(c) are schematic diagrams of battery pack fault detection effects of the conventional sample entropy under different parameters (N-5, r-0.094; N-10, r-0.094; N-5, r-0.098);
fig. 5(a), fig. 5(b), and fig. 5(c) are schematic diagrams of the battery pack failure detection effect under different sliding windows according to embodiment 1 of the present invention (N-15, r-0.094; N-5, r-0.094; N-30, r-0.094);
fig. 6(a) and fig. 6(b) are schematic diagrams of the battery pack fault detection effect of embodiment 1 of the present invention under different similarities (N-15, r-0.08; N-15, r-0.15);
fig. 7(a) and fig. 7(b) are schematic diagrams of battery pack fault detection effects of the conventional sample entropy and the embodiment 1 of the present invention after adding 50% snr (N-15, r-0.094), respectively.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. 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.
Example one
Referring to fig. 1, the embodiment provides an online fault diagnosis method for a power battery pack, which specifically includes the following steps:
step 1: and acquiring voltage data of the power battery pack according to a set time interval.
The sampling frequency of a certain battery module is 1s, the working condition of the whole UDDS lasts for 1336s, the collected voltage point is shown in fig. 3(a), it is noted that the voltage collection frequency is determined according to the actual situation and the experience of an engineer, and the improper collection frequency can cause the failure of the whole fault diagnosis system.
The battery is not particularly limited, and includes, but is not limited to, a battery module, a single battery cell, and the like connected in series.
Step 2: constructing the voltage data into a number of first voltage time series V 1 (i) And time-sequencing each first voltage V 1 (i) Comparing the voltage with a preset voltage threshold value, and primarily diagnosing whether the power battery pack has faults or not;
and constructing the voltage data into a plurality of first voltage time sequences according to a sliding window with a preset length.
Sliding the window: when calculating the fuzzy entropy, selecting a voltage time sequence with a certain length as a window, automatically defining the length of the window according to the requirement, moving to the positive direction of the original voltage time sequence according to the set step length after the calculation is finished, and calculating in sequence. The window with the above-mentioned properties is a sliding window.
And when the voltage in the first voltage time sequence is lower than a preset voltage threshold value, primarily diagnosing that the power battery pack has faults.
And step 3: when the power battery pack is preliminarily diagnosed not to have faults, all the first voltage time sequences V are subjected to time sequence 1 (i) Phase space reconstruction is carried out to obtain a plurality of second voltage time sequences V 2 (i)。
In particular, the time series V is applied to all the first voltages 1 (i) Phase space reconstruction is carried out to obtain a plurality of second voltage time sequences V 2 (i) The detailed process comprises the following steps:
step 3.1: selecting a sliding window with the length of N according to the measured battery voltage data of the power battery pack, wherein a voltage vector V in the sliding window 1 (i) Can be expressed as:
V 1 (i)＝[v 1 (i)，v 1 (i+1)，...，v 1 (i+m-1)]，i＝1，2，...，N-m+1 (1)
where m is the embedding dimension and is typically set to 2 or 3 to accurately measure the change in cell voltage.
Step 3.2: for voltage time sequence V 1 (i) Carrying out phase space reconstruction to obtain a new voltage time sequence V 2 (i) Comprises the following steps:
V 2 (i)＝[v 1 (i)，v 1 (i+1)，...，v 1 (i+m-1)]-v 0 (i)，i＝1，2，...，N-m+1 (2)
in the formula (I), the compound is shown in the specification,
is the mean value.
And 4, step 4: and calculating the similarity between any two second voltage time sequences by using the fuzzy membership function, and further calculating the fuzzy entropy value of each second voltage time sequence in sequence.
In one embodiment, the fuzzy membership function is an exponential function, and the similarity between the two second voltage time series is calculated by an exponential function of the maximum absolute distance between the two second voltage time series.
The specific process of calculating the similarity between any two second voltage time sequences by using the fuzzy membership function is as follows:
defining two voltage time sequences V 2 (i) And V 2 (j) The maximum absolute distance between is:
using the exponential function a (x) as the fuzzy membership function:
the voltage time series V 2 (i) And V 2 (j) The similarity between them can be expressed as:
where r is the similarity tolerance and is typically set between 10% and 25% of the standard deviation of the data.
In the embodiment, the fuzzy entropy value is calculated by introducing the fuzzy membership function, so that the setting range of parameters such as the sliding window N and the similar tolerance r is wide, the selection is flexible, the influence of inconsistency among batteries is not considered, the discrete degree of data is changed due to the introduction of the fuzzy function in practical application, certain interference resistance is realized, and the diagnosis result is stable.
Specifically, the process of calculating the fuzzy entropy value of each second voltage time series is as follows:
for each i, average it
The expression is as follows:
defining:
based on the above calculation, the expression of the fuzzy entropy of each voltage time series is:
FuzzyEn(m，r，N)＝lnφ m (r)-lnφ m+1 (r) (8)
and 5: and finally diagnosing the faults and the occurrence time of the power battery pack in each set time interval on line according to the comparison result of the fuzzy entropy value and the preset fuzzy entropy value threshold.
When the fuzzy entropy value of a certain second voltage time sequence is larger than a preset fuzzy entropy value threshold value, the fault of the power battery pack in a corresponding set time interval is diagnosed on line, and the related second voltage time sequence and the calculated fuzzy entropy value are transmitted to the front-end equipment together.
The method can be used as a substitute of a similar entropy algorithm to a certain extent, and as a foundation stone of the existing diagnosis system based on the entropy method, the stability of fault diagnosis is improved, more possibilities are provided for parameter selection in practical application, and the robustness is strong.
The method and effects of the embodiments are illustrated below by way of comparison and specific examples:
as shown in FIG. 5(a), the fuzzy entropy value has a peak phenomenon. The specific reasons are as follows: as shown in fig. 2(b), the fuzzy entropy uses an exponential function as a fuzzy membership function to describe the degree of similarity between two vectors, and the larger the distance between two points is, the lower the degree of similarity is. For example, in fig. 2(b), at three points d1, d2 and the initial point d0, it is obvious that the similarity between d1 and d0 is greater than the similarity between d2 and d0, and the similarity between the two points is different, and is not a non-0, i.e. 1 phenomenon of the unit step function in the conventional entropy method, as shown in fig. 2 (a). In addition, in fig. 5(a), the entropy value of (c) is smaller than (i) and is about (i) 2/3, so that the set threshold can be determined according to the peak (i) and the effect of the actual working condition. Therefore, the battery is shown to have voltage fluctuation phenomenon in the initial discharge and discharge process, and the fuzzy membership function can reflect the details of voltage data change, so that the parameter selection is more free.
The range of the similarity tolerance r is relatively broad. As shown in fig. 2(b), as the similarity tolerance r increases, the slope of the exponential function becomes lower, and the corresponding fuzzy entropy value gradually decreases, but the overall trend does not change. When a certain fluctuation occurs in the system, the fuzzy entropy value of the voltage can also generate corresponding fluctuation like an electrocardiogram, and the voltage is reflected to change suddenly. Fig. 5(a), 5(b) and 5(c) show that the overall trend of the fuzzy entropy is still not changed obviously in the process of changing the similarity tolerance r from 0.08 to 0.15, the fault diagnosis capability of the system is not influenced, and only the setting of the threshold value is changed along with the change of the parameter.
The size of the sliding window N is widely selected. As shown in fig. 5(a), 6(a) and 6(b), in the process of changing the sliding window N from 5 to 30, the overall trend of the fuzzy entropy does not change significantly, the fault point obviously exceeds the preset threshold value, and the diagnostic capability does not have the phenomena of sensitivity reduction and the like along with the change of the N value. It should be noted that the value of N must not be too large, otherwise the entire diagnostic system will be disabled.
The limitation of the existing sample entropy parameter selection range is obvious, and as shown in fig. 4(a), 4(b) and 4(c), very accurate parameters are often required to be set for different application scenarios, which undoubtedly greatly reduces the robustness of the algorithm. In MATLAB software, a trial and error method is used for further verifying that the parameter selection range of the similarity r is not ideal whether the sliding window N is adopted or the similarity r is adopted, and only every 0.003 span has great influence on the diagnostic performance.
The specific properties of the existing sample entropy are as follows: when the sliding window N is large, as shown by 10 in fig. 4(b), the frequency of misdiagnosis increases, and many misdiagnosis points tend to occur near the detected failure point. When the similarity r is larger or smaller, as shown as 0.098 in fig. 4(c), it is only 0.04 higher than the optimal parameter, and the phenomenon of losing fault information is serious, which is very likely to cause trouble in subsequent fault processing.
The immunity of the fuzzy entropy to white noise is verified below.
As shown in fig. 3(b), after white noise with a signal-to-noise ratio of 50% is added to the original voltage signal, the voltage signal is no longer smooth and fluctuates sharply. In this case, as shown in fig. 7(b), the fault diagnosis result graph shows that the fuzzy entropy curve does not change significantly as a whole, and the whole diagnosis system does not lose the diagnosis capability. But the local part has slight fluctuation, which also exactly verifies the analysis result, and the fuzzy entropy can reflect the details of the voltage data change.
However, the sensitivity of the existing sample entropy to the fault is extremely low after noise is introduced, and even the sample entropy value of the misdiagnosed point is higher than the fault point, as shown in fig. 7(a), the sample entropy has a severe influence on the diagnostic performance of the algorithm.
Example two
The embodiment provides a power battery pack fault online diagnosis system, which comprises the following modules:
(1) the voltage data acquisition module is used for acquiring voltage data of the power battery pack according to a set time interval;
(2) the fault preliminary diagnosis module is used for constructing the voltage data into a plurality of first voltage time sequences, comparing the voltage of each first voltage time sequence with a preset voltage threshold value and preliminarily diagnosing whether the power battery pack has faults or not;
in a specific implementation process, in the fault preliminary diagnosis module, the voltage data are constructed into a plurality of first voltage time sequences according to a sliding window with a preset length.
And in the fault preliminary diagnosis module, preliminarily diagnosing that the power battery pack has faults when the voltages in the first voltage time series are lower than a preset voltage threshold.
(3) The phase space reconstruction module is used for performing phase space reconstruction on all the first voltage time sequences to obtain a plurality of second voltage time sequences when the power battery pack is preliminarily diagnosed to be free of faults;
(4) the fuzzy entropy calculation module is used for calculating the similarity between any two second voltage time sequences by using a fuzzy membership function so as to sequentially calculate the fuzzy entropy of each second voltage time sequence;
in a specific implementation process, in the fuzzy entropy calculation module, the fuzzy membership function is an exponential function, and the similarity between the two second voltage time sequences is calculated by the exponential function of the maximum absolute distance between the two second voltage time sequences.
(5) And the fault final diagnosis module is used for finally diagnosing the faults and the occurrence time of the power battery pack in each set time interval on line according to the comparison result of the fuzzy entropy value and a preset fuzzy entropy value threshold.
In the fault final diagnosis module, when the fuzzy entropy value of a certain second voltage time sequence is greater than a preset fuzzy entropy threshold value, a fault of the power battery pack within a corresponding set time interval is diagnosed on line, and the related second voltage time sequence and the calculated fuzzy entropy value are transmitted to a front-end device (such as a mobile phone terminal or other terminal devices with a display function) together.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described herein again.
The online fault diagnosis method for the power battery pack provided by the invention can store a computer program. The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A power battery pack fault online diagnosis method is characterized by comprising the following steps:
acquiring voltage data of the power battery pack according to a set time interval;
constructing the voltage data into a plurality of first voltage time sequences, comparing the voltage of each first voltage time sequence with a preset voltage threshold value, and preliminarily diagnosing whether the power battery pack has faults or not;
when the power battery pack is preliminarily diagnosed to have no fault, phase space reconstruction is carried out on all the first voltage time sequences to obtain a plurality of second voltage time sequences;
calculating the similarity between any two second voltage time sequences by using a fuzzy membership function, and further calculating the fuzzy entropy value of each second voltage time sequence in sequence;
and finally diagnosing the faults and the occurrence time of the power battery pack in each set time interval on line according to the comparison result of the fuzzy entropy value and the preset fuzzy entropy value threshold.
2. The power battery pack fault online diagnosis method according to claim 1, wherein the voltage data is constructed into a plurality of first voltage time series according to a sliding window with a preset length.
3. The power battery pack fault online diagnosis method according to claim 1, wherein a fault of the power battery pack is preliminarily diagnosed when the voltages in the first voltage time series are lower than a preset voltage threshold.
4. The power battery pack fault online diagnosis method according to claim 1, wherein the fuzzy membership function is an exponential function, and the similarity between two second voltage time series is calculated from the exponential function of the maximum absolute distance between the two second voltage time series.
5. The power battery pack fault online diagnosis method according to claim 1, wherein when the fuzzy entropy value of a certain second voltage time series is greater than a preset fuzzy entropy threshold value, a fault of the power battery pack within a corresponding set time interval is diagnosed online, and the related second voltage time series and the calculated fuzzy entropy value are transmitted to the front-end equipment together.
6. A power battery pack fault online diagnosis system is characterized by comprising:
the voltage data acquisition module is used for acquiring voltage data of the power battery pack according to a set time interval;
the fault preliminary diagnosis module is used for constructing the voltage data into a plurality of first voltage time sequences, comparing the voltage of each first voltage time sequence with a preset voltage threshold value and preliminarily diagnosing whether the power battery pack has faults or not;
the phase space reconstruction module is used for performing phase space reconstruction on all the first voltage time sequences to obtain a plurality of second voltage time sequences when the power battery pack is preliminarily diagnosed to be free of faults;
the fuzzy entropy value calculation module is used for calculating the similarity between any two second voltage time sequences by using a fuzzy membership function so as to sequentially calculate the fuzzy entropy value of each second voltage time sequence;
and the fault final diagnosis module is used for finally diagnosing the faults and the occurrence time of the power battery pack in each set time interval on line according to the comparison result of the fuzzy entropy value and a preset fuzzy entropy value threshold.
7. The power battery pack fault online diagnosis system according to claim 6, wherein in the fault preliminary diagnosis module, the voltage data are constructed into a plurality of first voltage time series according to a sliding window of a preset length.
8. The power battery pack fault online diagnostic system of claim 6, wherein in the fault preliminary diagnosis module, a fault in the power battery pack is preliminarily diagnosed when a voltage in the first voltage time series falls below a preset voltage threshold.
9. The power battery pack fault online diagnosis system according to claim 6, wherein in the fuzzy entropy calculation module, the fuzzy membership function is an exponential function, and the similarity between two second voltage time series is calculated by the exponential function of the maximum absolute distance between the two second voltage time series.
10. The power battery pack fault online diagnosis system according to claim 6, wherein in the fault final diagnosis module, when the fuzzy entropy of a certain second voltage time series is greater than the preset fuzzy entropy threshold, the fault of the power battery pack within a corresponding set time interval is diagnosed online, and the related second voltage time series and the calculated fuzzy entropy are transmitted to the front-end equipment together.
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| CN116381419A (en) * | 2023-06-05 | 2023-07-04 | 中国南方电网有限责任公司超高压输电公司广州局 | Transmission line fault processing method, device, computer equipment and storage medium |
| CN116968556A (en) * | 2023-07-26 | 2023-10-31 | 北京科技大学 | Power and energy storage battery fault diagnosis method based on fuzzy entropy |
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| CN116106758B (en) * | 2023-03-23 | 2024-01-30 | 华能新能源股份有限公司山西分公司 | Battery fault diagnosis method and system based on data driving |
| CN116381419A (en) * | 2023-06-05 | 2023-07-04 | 中国南方电网有限责任公司超高压输电公司广州局 | Transmission line fault processing method, device, computer equipment and storage medium |
| CN116381419B (en) * | 2023-06-05 | 2023-11-07 | 中国南方电网有限责任公司超高压输电公司广州局 | Transmission line fault processing method, device, computer equipment and storage medium |
| CN116968556A (en) * | 2023-07-26 | 2023-10-31 | 北京科技大学 | Power and energy storage battery fault diagnosis method based on fuzzy entropy |
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