CN116400244B - Abnormality detection method and device for energy storage battery - Google Patents
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Abstract
The invention provides an abnormality detection method and device of an energy storage battery, and relates to the technical field of energy storage batteries, wherein the method comprises the following steps: performing signal decomposition on initial voltage signals of all battery monomers in the energy storage battery to obtain a plurality of modal signals; determining a first characteristic signal inconsistent with the running state of the battery cell according to the similarity measurement of the corresponding time sequence of each modal signal; based on the size of a moving window of the display platform corresponding to the initial voltage signal, extracting characteristic signals of the plurality of modal signals to obtain second characteristic signals of each battery cell; clustering the first characteristic signals and the second characteristic signals based on a preset signal strength threshold value to obtain a plurality of signal clusters with different signal strengths; and whether an abnormal battery monomer exists in the energy storage battery is further determined, so that various faults of the energy storage battery are accurately identified based on a signal cluster corresponding to the energy storage battery, early warning is realized, and the safety of the actual operation of the energy storage battery is improved.
Description
Technical Field
The present invention relates to the field of energy storage batteries, and in particular, to a method and apparatus for detecting an abnormality of an energy storage battery, an electronic device, and a storage medium.
Background
In recent years, as a typical energy storage device involving a complex electrochemical reaction/transmission mechanism, an energy storage battery has a high potential safety hazard, on one hand, the energy storage battery can generate mechanical, electrical and thermal abuse, such as overcharge, overdischarge, overheat and the like, in the actual operation process, and the rapid degradation of the battery performance, even internal short circuit, is easy to cause safety problems. On the other hand, when the large-scale energy storage field is applied, a large number of battery monomers in the energy storage battery form a battery pack, a battery pack and even a battery cluster, a large number of connecting components exist, the complexity of the system is greatly increased, the probability of various faults is increased, and potential safety hazards are increased.
In the related art, most of existing battery fault detection methods need to be modeled in an accurate battery, the calculated amount is high, the self-adaptive capacity of an abnormal detection threshold is poor, the diagnosis reliability is low, and the method is not suitable for being applied to an actual battery management system (Battery Management System, BMS), so that a lightweight abnormal detection method of an energy storage battery is needed.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first object of the present invention is to provide an abnormality detection method for an energy storage battery, so as to accurately identify various faults of the energy storage battery, realize early warning, and improve the safety of the actual operation of the energy storage battery.
A second object of the present invention is to provide an abnormality detection device for an energy storage battery.
A third object of the present invention is to propose an electronic device.
A fourth object of the present invention is to propose a non-transitory computer readable storage medium storing computer instructions.
To achieve the above object, an embodiment of a first aspect of the present invention provides an abnormality detection method for an energy storage battery, where the energy storage battery includes at least one battery cell, the method includes:
acquiring initial voltage signals of each battery cell, and performing signal decomposition on the initial voltage signals to obtain a plurality of modal signals corresponding to each initial voltage signal;
determining a first characteristic signal inconsistent with the running state of the battery monomer in a plurality of modal signals according to the similarity measurement of the corresponding time sequence of each modal signal;
based on the size of a moving window of the display platform corresponding to the initial voltage signal, extracting characteristic signals of the plurality of modal signals to obtain second characteristic signals of each battery cell;
clustering the first characteristic signals and the second characteristic signals based on a preset signal strength threshold value to obtain a plurality of signal clusters with different signal strengths;
and determining whether an abnormal battery monomer exists in the energy storage battery according to the signal cluster.
To achieve the above object, an embodiment of a second aspect of the present invention provides an abnormality detection device for an energy storage battery, wherein the energy storage battery includes at least one battery cell, the device including:
the acquisition module is used for acquiring initial voltage signals of all the battery monomers and carrying out signal decomposition on the initial voltage signals so as to obtain a plurality of modal signals corresponding to the initial voltage signals;
the first determining module is used for determining a first characteristic signal which is inconsistent with the running state of the battery monomer in the plurality of modal signals according to the similarity measurement of the corresponding time sequence of each modal signal;
the extraction module is used for extracting the characteristic signals of the plurality of modal signals based on the size of the moving window of the display platform corresponding to the initial voltage signal so as to obtain second characteristic signals of each battery cell;
the clustering module is used for clustering the first characteristic signals and the second characteristic signals based on a preset signal strength threshold value to obtain a plurality of signal clusters with different signal strengths;
and the second determining module is used for determining whether an abnormal battery monomer exists in the energy storage battery according to the signal cluster.
To achieve the above object, an embodiment of a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
To achieve the above object, an embodiment of a fourth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method according to the first aspect.
According to the abnormality detection method, the abnormality detection device, the electronic equipment and the storage medium for the energy storage battery, which are provided by the embodiment of the invention, the initial voltage signals of all battery monomers in the energy storage battery are subjected to signal decomposition so as to obtain a plurality of modal signals; determining a first characteristic signal inconsistent with the running state of the battery cell according to the similarity measurement of the corresponding time sequence of each modal signal; based on the size of a moving window of the display platform corresponding to the initial voltage signal, extracting characteristic signals of the plurality of modal signals to obtain second characteristic signals of each battery cell; clustering the first characteristic signals and the second characteristic signals based on a preset signal strength threshold value to obtain a plurality of signal clusters with different signal strengths; and whether an abnormal battery monomer exists in the energy storage battery is further determined, so that various faults of the energy storage battery are accurately identified based on a signal cluster corresponding to the energy storage battery, early warning is realized, and the safety of the actual operation of the energy storage battery is improved.
Additional aspects and advantages 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 foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of an abnormality detection method for an energy storage battery according to an embodiment of the present invention;
fig. 2 is a flow chart of another abnormality detection method for an energy storage battery according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an abnormality detection device for an energy storage battery according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The technical scheme of the invention is to acquire, store, use, process and the like data, which all meet the relevant regulations of national laws and regulations.
The following describes an abnormality detection method, an abnormality detection device, an electronic device, and a storage medium of an energy storage battery according to an embodiment of the present invention with reference to the accompanying drawings.
Fig. 1 is a flow chart of an abnormality detection method of an energy storage battery according to an embodiment of the present invention, where the energy storage battery includes at least one battery cell.
As shown in fig. 1, the method comprises the steps of:
step 101, obtaining initial voltage signals of each battery cell, and performing signal decomposition on the initial voltage signals to obtain a plurality of modal signals corresponding to each initial voltage signal.
Alternatively, the energy storage battery may be a lithium ion battery, but is not limited thereto, and the embodiment is not particularly limited thereto.
In some embodiments, an implementation manner of obtaining an initial voltage signal of each battery cell and performing signal decomposition on the initial voltage signal to obtain a plurality of mode signals corresponding to each initial voltage signal may be: the method comprises the steps of obtaining initial voltage signals of all battery monomers, carrying out signal decomposition on the initial voltage signals to obtain a plurality of inherent mode functions corresponding to all initial voltage signals, and taking all the inherent mode functions as mode signals under the condition that all the inherent mode functions become monotone or meet preset function termination standards, so that accurate decomposition of the initial voltage signals of all the battery monomers is realized.
Specifically, when the energy storage battery is a lithium ion battery, an initial voltage signal of each battery cell in the lithium ion battery can be obtained through a voltage detection device, and then the initial voltage signal can be subjected to signal decomposition by combining with empirical mode decomposition (empirical mode decomposition, EMD) to obtain a plurality of inherent mode functions (intrinsic mode function, IMF) corresponding to each initial voltage signal, and if the frequency of the suspected IMF has a local maximum value, repeating the steps; if the suspected IMF becomes monotonic or meets some termination criteria, then the IMF is considered to be the final modal signal.
Among other things, a variational mode decomposition (Variational mode decomposition, VMD) algorithm may be used for an Intrinsic Mode Function (IMF) that relates to the response of corresponding external stimuli or internal states in different frequency bands. VMDs are used to evaluate the bandwidth of k components and they are independently distributed around k center frequencies, i.e., k IMFs. Since the number of IMFs is not less than 2, IMFs in different main frequency bands are roughly divided into a dynamic part and a static part. As a transient response to an input current brought about by the participation of the energy storage battery in peak shaving and frequency modulation, dynamic components in the higher frequency bands are believed to carry critical information characterizing a particular anomaly. In contrast, the difference in IMF in the lowest frequency band may be regarded as a main manifestation of cell state inconsistency.
Using the hilbert transform in the frequency domain, the collected initial voltage signal time series can be converted into a constrained variation optimization problem as shown in equation (1):
wherein: u (u) k All modes representing the initial voltage signal; omega k Representing the center frequency thereof; f represents the original signal, delta represents the dirac distribution; t represents a sampling time sequence; * Representing a convolution operator. After introducing the quadratic penalty term and the Lagrangian multiplier, the expression of the constraint variation optimization problem is as shown in equation (2):
wherein: alpha represents the hyper-parameters of the data fidelity constraints. It can be solved by the alternative direction of the multipliers. In the spectrum, the generated IMF is as shown in formula (3):
wherein: f (ω), u i (omega), lambda (omega) andrespectively represent y (t), y i (t), lambda (t) and->Is a fourier transform of (a). The time domain component may be obtained from the initial voltage signal as the real part of the inverse fourier transform with a wiener filter structure.
Step 102, determining a first characteristic signal inconsistent with the running state of the battery cell in the plurality of modal signals according to the similarity measure of the corresponding time sequence of each modal signal.
It can be appreciated that after decomposing the initial voltage signal to obtain a plurality of corresponding modal signals IMF, the initial voltage signal can be effectively decomposed by the VMD to meet different analysis requirements, and the influence of the offset caused by the inconsistent states of the battery cells is smaller. Features of the IMF are then extracted and selected, and a distance-based evaluation of the state inconsistency is performed.
In particular, in case of an energy storage BATTERY application in an industrial BATTERY management system (BATTERY MANAGEMENT SYSTEM, BMS), since the computing power of industrial BMS hardware is temporarily insufficient to support the online implementation of the model-based BATTERY state estimation method, a time-series similarity measure may be used to describe BATTERY inconsistencies, such as euclidean distance (Euclidean Distance, ED), dynamic time warping (Dynamic Time Warping, DTW), and singular value decomposition (Singular Value Decomposition, SVD), which is not particularly limited in this embodiment.
In particular, euclidean Distance (ED), dynamic Time Warping (DTW), and Singular Value Decomposition (SVD). However, SVD is more suitable for processing data from a variety of sensors because it risks erroneous decisions in cases where the time series is not multivariate. Since the present invention uses only the initial voltage signal, ED and DTW are used, assuming that a certain voltage segment observed with N discrete sampling moments of M cells is represented by formula (4):
and average voltage sequenceAs redundancy for distance calculation. To evaluate the difference between the jth cell voltage signal (j=1, 2, …, M) and the average voltage sequence, the sum of the absolute values of the differences for every two corresponding points in equation (5) is calculated as the overall ED:
the DTW distance calculation with the minimum warp path W is as shown in equation (6):
wherein: w (w) k = (a, b) represents v calculated using ED j Points in (a)a andthe detailed rule of which can be expressed as (7):
w 1 ＝(1，1)w k ＝(N，N)
w k ＝(a，b)w k-1 ＝(a′，b′) (7)
wherein: 0.ltoreq.a-a '. Ltoreq.1, 0.ltoreq.b-b'. Ltoreq.1
The similarity measure of the corresponding time series of each modal signal can be divided into segments in a charging or discharging scene according to the time stamp, current and speed in the uploading record. By using the VMD algorithm to obtain the components of each segment to evaluate the battery cell state inconsistency before feature extraction, the static components in the lowest frequency band can be effectively used for battery state inconsistency estimation.
Furthermore, in addition to Incremental Capacity Analysis (ICA) by curve features related to aging mechanisms, DTW distance can also replace filtering algorithms, which are more resolvable than ED.
And 103, extracting characteristic signals of the plurality of modal signals based on the size of the moving window of the display platform corresponding to the initial voltage signal so as to obtain second characteristic signals of each battery cell.
In some embodiments, the battery with internal short circuit or thermal runaway is not necessarily the battery with the worst consistency, so that diagnosis is required to be performed on the dynamic component of the battery cell, so that the characteristic signals of the plurality of modal signals are extracted based on the size of the moving window of the display platform corresponding to the initial voltage signal, so as to obtain the second characteristic signal of each battery cell, and realize the diagnosis on the dynamic component of each battery cell.
In particular, the feature signal extraction may be performed on a plurality of modal signals based on a graphic Device Interface (Graphics Device Interface, GDI) to obtain a second feature signal of each battery cell, for example, the size N of a moving window of the GDI corresponding to the display platform may be determined to prevent small fault signals from being ignored when extracting signal features from a longer similar voltage sequence, and a Dimensionless Index (DI) is sensitive to abnormal signals rather than to operating conditions, and thus is widely used in industrial diagnosis. In a battery fault diagnosis study based on current signals, correlation coefficients and information entropy are called key signal features, but DI formulas cannot express both correctly. Because they are dimensionless in nature, there is a lack of generalized construction formulas for feature construction. Therefore, in order to construct the dimensionless signal feature, the generalized dimensionless index GDI structure is performed according to formula (8):
wherein z is i (v),s j (v) A function representing a vector v (t) of input signals collected from a single or multiple sources for simple processing; p (·) represents the probability density function of the signal value; l (L) i ,m j Representing different normal numbers, n z ,n s Representing the order of GDI. The integral in equation (8) can be calculated by moving the discrete voltage signal in the window. The GDI extraction steps are as follows:
a) To enhance the flexibility of the formula, z is used i (·),s j (. Cndot.) processing components of the input voltage vector decomposition, z i (·),s j (. Cndot.) can be a variety of simple functions such as taking logarithms, center deviations, absolute values, data normalization, etc.
b) Direction l i ,m j Assigning various positive constants to ensure diversity;
c) The order of GDI may be defined by an order constant n z ,n s Modulation because the numerator and denominator of GDI should have the same dimensions, regardless of the constant and signal assigned, n z ,n s Respectively setting to different values to ensure that GDI molecules and denominators have the same dimension;
the main parameter values n, l, m in the present invention are positive odd, positive odd and positive even (e.g. n= 3,l =3, m=2), respectively, and the GDI derived from the diversified parameter assignments can be further used for subsequent feature fusion.
Step 104, clustering the first characteristic signal and the second characteristic signal based on a preset signal strength threshold to obtain a plurality of signal clusters with different signal strengths.
In some embodiments, based on a preset signal strength threshold, the clustering processing is performed on the first characteristic signal and the second characteristic signal, so as to obtain a plurality of signal clusters with different signal strengths.
The preset signal strength threshold may be determined by combining an application scenario of the energy storage battery, or may be set by a technician, which is not specifically limited in this embodiment.
Step 105, determining whether an abnormal battery monomer exists in the energy storage battery according to the signal cluster.
In some embodiments, according to the signal clusters, one implementation manner of determining whether an abnormal battery monomer exists in the energy storage battery may be to select an abnormal cluster in the signal clusters based on the cluster characteristics of each signal cluster, and then determine that the battery monomer corresponding to the abnormal cluster is abnormal according to the abnormal cluster, so that early warning is implemented and the safety, stability and reliability of the actual operation of the battery system are improved under the condition that the abnormal monomer exists.
According to the abnormality detection method for the energy storage battery, disclosed by the embodiment of the invention, the initial voltage signals of all battery monomers in the energy storage battery are subjected to signal decomposition to obtain a plurality of modal signals; determining a first characteristic signal inconsistent with the running state of the battery cell according to the similarity measurement of the corresponding time sequence of each modal signal; based on the size of a moving window of the display platform corresponding to the initial voltage signal, extracting characteristic signals of the plurality of modal signals to obtain second characteristic signals of each battery cell; clustering the first characteristic signals and the second characteristic signals based on a preset signal strength threshold value to obtain a plurality of signal clusters with different signal strengths; and whether an abnormal battery monomer exists in the energy storage battery is further determined, so that various faults of the energy storage battery are accurately identified based on a signal cluster corresponding to the energy storage battery, early warning is realized, and the safety of the actual operation of the energy storage battery is improved.
In order to clearly illustrate the above embodiment, fig. 2 is a flow chart of another abnormality detection method for an energy storage battery according to an embodiment of the present invention.
Step 201, obtaining initial voltage signals of each battery cell, and performing signal decomposition on the initial voltage signals to obtain a plurality of modal signals corresponding to each initial voltage signal.
Step 202, determining a first characteristic signal inconsistent with the running state of the battery cell in the plurality of modal signals according to the similarity measure of the corresponding time sequence of each modal signal.
And 203, extracting characteristic signals of the plurality of modal signals based on the size of the moving window of the display platform corresponding to the initial voltage signal, so as to obtain second characteristic signals of each battery cell.
It should be noted that, regarding the specific implementation of steps 201 to 202, reference may be made to the related description in the above embodiments.
And 204, performing data size dimension reduction processing of the time features on the first feature signal and the second feature signal to obtain a first target feature signal and a second target feature signal after dimension reduction.
In some embodiments, in the case of feature signal extraction of a plurality of modal signals based on a Graphics Device Interface (GDI), many feature points in the original feature space are likely to be identified as outliers due to the large number of feature sequences generated using the GDI. However, this situation can be effectively alleviated by implementing density-based clustering on the feature space of dimension reduction, and thus, a manifold learning method can be used to dimension-reduce features to comprehensively utilize key information in each feature dimension as an example.
In particular, assuming that the data points (first and second characteristic signals) are uniformly sampled from a low-dimensional manifold in a high-dimensional euclidean space, manifold learning is used to recover the low-dimensional manifold structure from the high-dimensional sampled data. By finding a low-dimensional manifold with a corresponding embedding map in the high-dimensional space, dimension reduction of the temporal features can be achieved. In manifold learning, the Laplace feature mapping (LE) is adopted to realize dimension reduction. The original similarity between points in each local region is calculated and desirably maintained in a low-dimensional space. The LE method generates dimension reduction features as follows:
a) Determining nearest neighbor using optimal weights
At some point in the fusion of the feature sequences F, a weighted similarity calculated by the following equation (9) can be usedTo establish its element f i Is the k nearest neighbors of (a):
wherein: k represents the dimension of the feature at this time, so the similarity matrix can be formulated as equation (10):
in the case where W is not a symmetric matrix, W may be further processed as (11):
b) Construction of objective functions
The objective function is constructed to maintain similarity in a low dimensional space as in equation (12):
let d=diag (D) 1 ,d 2 ，...d n ) In whichi-1, 2..n, whereby D-W is a positive semi-definite matrix and the objective function is expressed as formula (13):
the optimization problem therefore becomes equation (14):
c) The solution optimization problem is set to m=d-W, so that one eigenvalue of M is 0 and all values of the corresponding eigenvector are 1. After eigenvalue decomposition, the corresponding eigenvectors consisting of eigenvalues from the second minimum to the (d+1) th minimum are embedded into the d-dimensional matrix Y as output.
Step 205, clustering the first target feature signal and the second target feature signal based on a preset signal strength threshold to obtain a plurality of signal clusters with different signal strengths.
In some embodiments, the clustering of the first target feature signal and the second target feature signal based on the preset signal strength threshold may be performed to obtain a plurality of signal clusters with different signal strengths, which may be based on a density clustering method, and the clustering of the first target feature signal and the second target feature signal may be performed in combination with the preset signal strength threshold, so as to implement accurate clustering of the signal clusters.
In particular, the clustering rules of the density clustering method may be used to collect coherent features characterizing similar responses of the cells by forming various clusters. To obtain clusters of signals of various features to clearly detect and locate anomalies, the clusters are grouped according to the following assumptions:
a) The normal instance (normal signal strength value) belongs to one of the signal clusters, and the abnormal instance (abnormal signal strength value) does not belong to any signal cluster;
b) The normal instance is close to the nearest signal cluster centroid, and the abnormal instance is far from the nearest signal cluster centroid;
c) Normal instances belong to large and dense signal clusters, while abnormal instances belong to small or sparse signal clusters.
For the clustering algorithm satisfying a), the clustering algorithm is to obtain clusters from the feature sequences with all signal strength thresholds preset. Furthermore, if anomalies in the data themselves form clusters, one anomaly value itself may become the centroid and the only member of a cluster, and the method satisfying b) cannot detect such anomalies. Thus, clustering is performed with c), i.e. if the second characteristic signal corresponding to the cell initial voltage signal belongs to a cluster whose size and/or density is below the signal intensity threshold, the cell initial voltage signal is considered to be abnormal. Because the actual energy storage battery failure is likely to be caused by a very small number of battery cells, no timely warning or catastrophic accident is caused, density-based clustering is employed to find abnormal signals in the second characteristic signal. With a minimum and optimized density-based clustering by centroid distance, a cluster that contains very few points in the temporal feature sequence (less than 2 points at the sampling instant) may suggest potential anomalies in the battery cells, which reduces the differences that occur in the decision process using diversified features.
In summary, the complementary correction can be realized by the traditional method based on the signal light threshold value, so as to effectively eliminate erroneous judgment. Due to certain unavoidable drastic fluctuations in operating conditions or measurement noise, moments with fairly discrete distribution characteristics are prone to false alarms by alarm thresholds. However, the corresponding second characteristic signal anomalies of the battery cells that were not correctly detected at a certain moment are corrected by a density-based clustering method.
Step 206, determining whether an abnormal battery monomer exists in the energy storage battery according to the signal cluster.
According to the abnormality detection method for the energy storage battery, disclosed by the embodiment of the invention, the initial voltage signals of all battery monomers in the energy storage battery are subjected to signal decomposition to obtain a plurality of modal signals; determining a first characteristic signal inconsistent with the running state of the battery cell according to the similarity measurement of the corresponding time sequence of each modal signal; based on the size of a moving window of the display platform corresponding to the initial voltage signal, extracting characteristic signals of the plurality of modal signals to obtain second characteristic signals of each battery cell; performing data size dimension reduction processing of time features on the first abnormal signal and the second abnormal signal to obtain a first target feature signal and a second target feature signal after dimension reduction; and clustering the first target characteristic signals and the second target characteristic signals based on a preset signal intensity threshold value to obtain a plurality of signal clusters with different signal intensities. The method comprises the steps of carrying out a first treatment on the surface of the And whether an abnormal battery monomer exists in the energy storage battery is further determined, so that various faults such as gradual change, sudden and the like are accurately and effectively identified based on a signal cluster corresponding to the energy storage battery, early warning is realized, and the safety, stability and reliability of the actual operation of the battery system are improved.
In order to achieve the above embodiment, the present invention further provides an abnormality detection device for an energy storage battery.
Fig. 3 is a schematic structural diagram of an abnormality detection device for an energy storage battery according to an embodiment of the present invention.
As shown in fig. 3, the abnormality detection device 30 of the energy storage battery includes: the acquisition module 31, the first determination module 32, the extraction module 33, the clustering module 34 and the second determination module 35.
The acquiring module 31 is configured to acquire an initial voltage signal of each battery cell, and perform signal decomposition on the initial voltage signal to obtain a plurality of modal signals corresponding to each initial voltage signal;
a first determining module 32, configured to determine a first characteristic signal of the plurality of modal signals that is inconsistent with the running state of the battery cell according to a similarity measure of the corresponding time sequence of each of the modal signals;
the extracting module 33 is configured to extract the characteristic signals of the plurality of modal signals based on the size of the moving window of the display platform corresponding to the initial voltage signal, so as to obtain second characteristic signals of each battery cell;
the clustering module 34 is configured to perform clustering processing on the first characteristic signal and the second characteristic signal based on a preset signal strength threshold, so as to obtain a plurality of signal clusters with different signal strengths;
and a second determining module 35, configured to determine whether an abnormal battery monomer exists in the energy storage battery according to the signal cluster.
Further, in one possible implementation manner of the embodiment of the present invention, the obtaining module 31 is specifically configured to:
acquiring initial voltage signals of all the battery monomers, and performing signal decomposition on the initial voltage signals to obtain a plurality of inherent mode functions corresponding to the initial voltage signals;
and taking each natural mode function as a mode signal under the condition that each natural mode function becomes monotonous or meets the preset function termination standard.
Further, in one possible implementation manner of the embodiment of the present invention, the clustering module 34 is specifically configured to:
performing data size dimension reduction processing of time features on the first feature signal and the second feature signal to obtain a first target feature signal and a second target feature signal after dimension reduction;
and clustering the first target characteristic signals and the second target characteristic signals based on a preset signal intensity threshold value to obtain a plurality of signal clusters with different signal intensities.
Further, in one possible implementation manner of the embodiment of the present invention, the second determining module 35 is specifically configured to:
based on the cluster characteristics of each signal cluster, selecting an abnormal cluster in the signal clusters;
and determining that the battery monomer corresponding to the abnormal cluster is abnormal according to the abnormal cluster.
It should be noted that the foregoing explanation of the method embodiment is also applicable to the apparatus of this embodiment, and will not be repeated here.
According to the abnormality detection device for the energy storage battery, disclosed by the embodiment of the invention, the initial voltage signals of all battery monomers in the energy storage battery are subjected to signal decomposition so as to obtain a plurality of modal signals; determining a first characteristic signal inconsistent with the running state of the battery cell according to the similarity measurement of the corresponding time sequence of each modal signal; based on the size of a moving window of the display platform corresponding to the initial voltage signal, extracting characteristic signals of the plurality of modal signals to obtain second characteristic signals of each battery cell; clustering the first characteristic signals and the second characteristic signals based on a preset signal strength threshold value to obtain a plurality of signal clusters with different signal strengths; and whether an abnormal battery monomer exists in the energy storage battery is further determined, so that various faults of the energy storage battery are accurately identified based on a signal cluster corresponding to the energy storage battery, early warning is realized, and the safety of the actual operation of the energy storage battery is improved.
In order to achieve the above embodiment, the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aforementioned method.
To achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned method.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in a hardware manner or in a software functional module manner. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. An anomaly detection method for an energy storage battery, wherein the energy storage battery comprises at least one battery cell, the method comprising:
acquiring initial voltage signals of each battery cell, and performing signal decomposition on the initial voltage signals to obtain a plurality of modal signals corresponding to each initial voltage signal;
determining a first characteristic signal inconsistent with the running state of the battery monomer in a plurality of modal signals according to the similarity measurement of the corresponding time sequence of each modal signal;
based on the size of a moving window of the display platform corresponding to the initial voltage signal, extracting characteristic signals of the plurality of modal signals to obtain second characteristic signals of each battery cell;
clustering the first characteristic signals and the second characteristic signals based on a preset signal intensity threshold value to obtain a plurality of signal clusters with different signal intensities;
and determining whether an abnormal battery monomer exists in the energy storage battery according to the signal cluster.
2. The method according to claim 1, wherein the obtaining an initial voltage signal of each of the battery cells and performing signal decomposition on the initial voltage signal to obtain a plurality of modal signals corresponding to each of the initial voltage signals includes:
acquiring initial voltage signals of all the battery monomers, and performing signal decomposition on the initial voltage signals to obtain a plurality of inherent mode functions corresponding to the initial voltage signals;
and taking each natural mode function as a mode signal under the condition that each natural mode function becomes monotonous or meets the preset function termination standard.
3. The method of claim 1, wherein clustering the first and second characteristic signals based on a preset signal strength threshold to obtain a plurality of signal clusters with different signal strengths, comprises:
performing data size dimension reduction processing of time features on the first feature signal and the second feature signal to obtain a first target feature signal and a second target feature signal after dimension reduction;
and clustering the first target characteristic signals and the second target characteristic signals based on a preset signal intensity threshold value to obtain a plurality of signal clusters with different signal intensities.
4. The method of claim 1, wherein determining whether an abnormal cell is present in the energy storage battery based on the signal clusters comprises:
based on the cluster characteristics of each signal cluster, selecting an abnormal cluster in the signal clusters;
and determining that the battery monomer corresponding to the abnormal cluster is abnormal according to the abnormal cluster.
5. An abnormality detection device for an energy storage battery, wherein the energy storage battery includes at least one battery cell, the device comprising:
the acquisition module is used for acquiring initial voltage signals of all the battery monomers and carrying out signal decomposition on the initial voltage signals so as to obtain a plurality of modal signals corresponding to the initial voltage signals;
the first determining module is used for determining a first characteristic signal which is inconsistent with the running state of the battery monomer in the plurality of modal signals according to the similarity measurement of the corresponding time sequence of each modal signal;
the extraction module is used for extracting the characteristic signals of the plurality of modal signals based on the size of the moving window of the display platform corresponding to the initial voltage signal so as to obtain second characteristic signals of each battery cell;
the clustering module is used for carrying out clustering processing on the first characteristic signals and the second characteristic signals based on a preset signal intensity threshold value so as to obtain a plurality of signal clusters with different signal intensities;
and the second determining module is used for determining whether an abnormal battery monomer exists in the energy storage battery according to the signal cluster.
6. The apparatus of claim 5, wherein the obtaining module is specifically configured to:
acquiring initial voltage signals of all the battery monomers, and performing signal decomposition on the initial voltage signals to obtain a plurality of inherent mode functions corresponding to the initial voltage signals;
and taking each natural mode function as a mode signal under the condition that each natural mode function becomes monotonous or meets the preset function termination standard.
7. The apparatus of claim 5, wherein the clustering module is specifically configured to:
performing data size dimension reduction processing of time features on the first feature signal and the second feature signal to obtain a first target feature signal and a second target feature signal after dimension reduction;
and clustering the first target characteristic signals and the second target characteristic signals based on a preset signal intensity threshold value to obtain a plurality of signal clusters with different signal intensities.
8. The apparatus of claim 5, wherein the second determining module is specifically configured to:
based on the cluster characteristics of each signal cluster, selecting an abnormal cluster in the signal clusters;
and determining that the battery monomer corresponding to the abnormal cluster is abnormal according to the abnormal cluster.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-4.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015059272A1 (en) * | 2013-10-24 | 2015-04-30 | Universite Libre De Bruxelles | Improved non-intrusive appliance load monitoring method and device |
| CN105510687A (en) * | 2015-12-24 | 2016-04-20 | 合肥工业大学 | Empirical mode decomposition-based voltage anomaly characteristic identification method |
| CN109167391A (en) * | 2018-10-11 | 2019-01-08 | 珠海吉瓦科技有限公司 | A kind of echelon battery energy storage power station energy management method and system based on set empirical mode decomposition |
| CN109635334A (en) * | 2018-11-12 | 2019-04-16 | 武汉科技大学 | Fault Diagnosis of Roller Bearings, system and medium based on particle group optimizing |
| WO2019161592A1 (en) * | 2018-02-26 | 2019-08-29 | 大连理工大学 | Method for automatically extracting structural modal parameters by clustering |
| CN113075554A (en) * | 2021-03-26 | 2021-07-06 | 国网浙江省电力有限公司电力科学研究院 | Lithium ion battery pack inconsistency identification method based on operation data |
| JP2021154115A (en) * | 2020-02-16 | 2021-10-07 | オリジン ワイヤレス, インコーポレイテッドOrigin Wireless, Inc. | Method, apparatus, and system for wireless monitoring |
| CN113866641A (en) * | 2021-09-06 | 2021-12-31 | 中国电力科学研究院有限公司 | Fault detection method and device for lithium ion battery |
| CN114559819A (en) * | 2022-01-25 | 2022-05-31 | 重庆标能瑞源储能技术研究院有限公司 | Electric vehicle battery safety early warning method based on signal processing |
| CN114660040A (en) * | 2022-03-10 | 2022-06-24 | 中国科学院青岛生物能源与过程研究所 | Method, device, medium and equipment for identifying microbial unicell species |
| WO2022198616A1 (en) * | 2021-03-26 | 2022-09-29 | 深圳技术大学 | Battery life prediction method and system, electronic device, and storage medium |
| CN115693734A (en) * | 2022-09-14 | 2023-02-03 | 国网河北省电力有限公司电力科学研究院 | Hybrid energy storage power distribution method, device, equipment and storage medium |
| CN115859584A (en) * | 2022-11-22 | 2023-03-28 | 中国科学院深圳先进技术研究院 | Battery life prediction method, prediction system and computer equipment |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160179923A1 (en) * | 2014-12-19 | 2016-06-23 | Xerox Corporation | Adaptive trajectory analysis of replicator dynamics for data clustering |
| US11663700B2 (en) * | 2019-06-29 | 2023-05-30 | Intel Corporation | Automatic elimination of noise for big data analytics |
| CN115166533A (en) * | 2022-07-26 | 2022-10-11 | 湖北工业大学 | Lithium ion battery fault diagnosis method based on segmented dimensionality reduction and outlier identification |
-
2023
- 2023-04-04 CN CN202310354705.2A patent/CN116400244B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015059272A1 (en) * | 2013-10-24 | 2015-04-30 | Universite Libre De Bruxelles | Improved non-intrusive appliance load monitoring method and device |
| CN105510687A (en) * | 2015-12-24 | 2016-04-20 | 合肥工业大学 | Empirical mode decomposition-based voltage anomaly characteristic identification method |
| WO2019161592A1 (en) * | 2018-02-26 | 2019-08-29 | 大连理工大学 | Method for automatically extracting structural modal parameters by clustering |
| CN109167391A (en) * | 2018-10-11 | 2019-01-08 | 珠海吉瓦科技有限公司 | A kind of echelon battery energy storage power station energy management method and system based on set empirical mode decomposition |
| CN109635334A (en) * | 2018-11-12 | 2019-04-16 | 武汉科技大学 | Fault Diagnosis of Roller Bearings, system and medium based on particle group optimizing |
| JP2021154115A (en) * | 2020-02-16 | 2021-10-07 | オリジン ワイヤレス, インコーポレイテッドOrigin Wireless, Inc. | Method, apparatus, and system for wireless monitoring |
| CN113075554A (en) * | 2021-03-26 | 2021-07-06 | 国网浙江省电力有限公司电力科学研究院 | Lithium ion battery pack inconsistency identification method based on operation data |
| WO2022198616A1 (en) * | 2021-03-26 | 2022-09-29 | 深圳技术大学 | Battery life prediction method and system, electronic device, and storage medium |
| CN113866641A (en) * | 2021-09-06 | 2021-12-31 | 中国电力科学研究院有限公司 | Fault detection method and device for lithium ion battery |
| CN114559819A (en) * | 2022-01-25 | 2022-05-31 | 重庆标能瑞源储能技术研究院有限公司 | Electric vehicle battery safety early warning method based on signal processing |
| CN114660040A (en) * | 2022-03-10 | 2022-06-24 | 中国科学院青岛生物能源与过程研究所 | Method, device, medium and equipment for identifying microbial unicell species |
| CN115693734A (en) * | 2022-09-14 | 2023-02-03 | 国网河北省电力有限公司电力科学研究院 | Hybrid energy storage power distribution method, device, equipment and storage medium |
| CN115859584A (en) * | 2022-11-22 | 2023-03-28 | 中国科学院深圳先进技术研究院 | Battery life prediction method, prediction system and computer equipment |
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|---|---|
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