CN118131054B - Intelligent monitoring method and system for state of charge of sodium ion battery - Google Patents
Intelligent monitoring method and system for state of charge of sodium ion battery Download PDFInfo
- Publication number
- CN118131054B CN118131054B CN202410044730.5A CN202410044730A CN118131054B CN 118131054 B CN118131054 B CN 118131054B CN 202410044730 A CN202410044730 A CN 202410044730A CN 118131054 B CN118131054 B CN 118131054B
- Authority
- CN
- China
- Prior art keywords
- discharge
- subsequence
- sequence
- outlier
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 229910001415 sodium ion Inorganic materials 0.000 title claims abstract description 54
- FKNQFGJONOIPTF-UHFFFAOYSA-N Sodium cation Chemical compound [Na+] FKNQFGJONOIPTF-UHFFFAOYSA-N 0.000 title claims abstract description 48
- 238000012544 monitoring process Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 32
- 239000011159 matrix material Substances 0.000 claims abstract description 53
- 108010076504 Protein Sorting Signals Proteins 0.000 claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 238000006243 chemical reaction Methods 0.000 abstract description 5
- 238000012360 testing method Methods 0.000 abstract description 2
- 238000007599 discharging Methods 0.000 description 11
- 230000008859 change Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 230000005355 Hall effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009831 deintercalation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009830 intercalation Methods 0.000 description 1
- 230000002687 intercalation Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
-
- 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
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The invention relates to the technical field of electrical performance testing, in particular to an intelligent monitoring method and system for the state of charge of a sodium ion battery, wherein the method comprises the following steps: acquiring a sodium ion initial discharge signal sequence consisting of current values; constructing a discharge signal differential sequence, dividing the discharge signal differential sequence to obtain all subsequences of the discharge signal differential sequence, and obtaining a discharge outlier matrix of each subsequence; acquiring discrete sequences of elements in each subsequence according to the discharge outlier matrix, and constructing a discharge outlier trend correlation matrix of each subsequence according to the discrete sequences; acquiring the discharge global correlation deviation degree of each sub-sequence, and further acquiring the discharge deviation tolerance of each sub-sequence; and acquiring an adjusting threshold according to the discharge deviation tolerance, and monitoring the state of charge of sodium ions. The invention aims to solve the problem of inaccurate monitoring results caused by different chemical reaction balance states in batteries at different stages.
Description
Technical Field
The invention relates to the technical field of electrical performance testing, in particular to an intelligent monitoring method and system for the state of charge of a sodium ion battery.
Background
With the continuous development of new energy technology, a sodium ion battery is used as a high-density battery, and gradually becomes a hot spot for the current energy storage research. Has the characteristics of novel high efficiency, environmental protection, safety and the like. In the use process of the sodium ion battery, the monitoring of the charge state is important to ensure the normal operation of the battery and prolong the service life of the battery.
The state of charge monitoring method of the conventional technology mainly depends on physical parameters such as current, voltage and the like to judge. The method for monitoring the abnormality of the current signal in the discharging process of the sodium ion battery by adopting a Local Outlier Factor (LOF) algorithm is more commonly used. The algorithm will typically calculate a LOF value for each data point and empirically set a threshold value, with data points greater than the threshold value being considered outliers. However, the current fluctuation conditions are different in different stages in the discharging process of the sodium ion battery, namely, the chemical reaction equilibrium state inside the battery is different in different stages, so that the current output in different states is caused. Therefore, the use of a fixed size threshold may have an impact on the anomaly monitoring results.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent monitoring method and system for the state of charge of a sodium ion battery, and the adopted technical scheme is as follows:
In a first aspect, an embodiment of the present invention provides a method for intelligently monitoring a state of charge of a sodium ion battery, where the method includes the following steps:
Acquiring a sodium ion initial discharge signal sequence consisting of current values;
constructing a discharge signal differential sequence according to the initial discharge signal sequence, dividing the discharge signal differential sequence to obtain all subsequences of the discharge signal differential sequence, and acquiring a discharge outlier matrix of each subsequence according to elements in the subsequences of the discharge signal differential sequence; acquiring discrete sequences of elements in each subsequence according to the discharge outlier matrix, and constructing a discharge outlier trend correlation matrix of each subsequence according to pearson correlation coefficients among the discrete sequences of the elements; acquiring the discharge global correlation deviation degree of each sub-sequence according to the discharge outlier trend correlation matrix, and acquiring the discharge deviation tolerance of each sub-sequence according to the discharge global correlation deviation degree;
and acquiring an adjustment threshold according to the discharge deviation tolerance, and monitoring the state of sodium ion charge according to the adjustment threshold and a local outlier factor algorithm.
Further, the constructing a discharge signal differential sequence according to the initial discharge signal sequence includes:
and processing the initial discharge signal sequence by using a first-order backward differential method to obtain a discharge signal differential sequence.
Further, the dividing the discharge signal differential sequence to obtain all sub-sequences of the discharge signal differential sequence includes:
and taking a sequence formed by elements in each second in the discharge signal differential sequence as a subsequence in the discharge signal differential sequence.
Further, the obtaining the discharge outlier matrix of each sub-sequence includes:
for each subsequence of the discharge differential sequence, calculating the absolute value of the difference between the ith element and the jth element in the subsequence, calculating the difference between the largest element value and the smallest element value in the subsequence, calculating the product of the standard deviation of all element values in the subsequence and the difference, taking the ratio of the absolute value of the difference to the product as the element of the ith row and the jth column in the discharge outlier matrix of each subsequence, and arranging all elements according to the positions of the elements in the discharge outlier matrix to obtain the discharge outlier matrix of each subsequence.
Further, the obtaining the discrete sequence of each element in each sub-sequence includes:
For the discharge outlier matrix of each sub-sequence, taking the sequence composed of the elements of the ith row in the discharge outlier matrix as the discrete sequence of the ith element in the sub-sequence.
Further, the constructing a discharge outlier trend correlation matrix of each sub-sequence includes:
For each subsequence of the discharge signal differential sequence, calculating the Pearson correlation coefficient between the discrete sequences of the ith element and the jth element in the subsequence as the element of the ith row and the jth column in the discharge outlier trend correlation matrix of the subsequence, and calculating the Pearson correlation coefficient between all elements in the subsequence to obtain the discharge outlier trend correlation matrix of each subsequence.
Further, the obtaining the discharge global correlation deviation degree of each sub-sequence includes:
For each subsequence of the discharge signal differential sequence, calculating the difference between the maximum value and the minimum value of the elements in the subsequence, calculating the ratio of each element in the subsequence to the difference, calculating the sum of all the ratios in the subsequence, calculating the norm of the discharge outlier trend correlation matrix of the subsequence, and taking the product of the norm and the sum as the discharge global correlation bias degree of each subsequence.
Further, the obtaining the discharge deviation tolerance of each sub-sequence specifically includes:
For each subsequence of the discharge differential sequence, calculating the absolute value of the difference between the average value of all elements in the previous subsequence and the average value of all elements in the next subsequence of the subsequence, calculating the interval time between the previous subsequence and the next subsequence of the subsequence, calculating the ratio between the absolute value of the difference and the interval, and taking the ratio of the ratio and the discharge global correlation deviation degree of the subsequence as the discharge deviation tolerance of each subsequence.
Further, the obtaining the adjustment threshold, and monitoring the state of charge of the sodium ions according to the adjustment threshold and the local outlier factor algorithm, includes:
Calculating the product of a normalized value of the discharge variation tolerance of each subsequence and a preset experience threshold value as an adjustment threshold value of each subsequence, calculating the average value of the adjustment threshold values of all the subsequences, calculating the LOF value of each current value by using a local outlier factor algorithm, and taking the current value with the LOF value larger than the average value as an abnormal current signal, otherwise, a normal current signal.
In a second aspect, an embodiment of the present invention further provides a sodium ion battery state of charge intelligent monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The invention has at least the following beneficial effects:
The invention provides a method and a system for intelligently monitoring the state of charge of a sodium ion battery, which are used for analyzing current signals of the sodium ion battery during discharging, the state of charge of the sodium ion battery is monitored to avoid irrecoverable losses. Specifically, a differential sequence is constructed through an initial discharge signal, then a sub-sequence is divided, a current differential signal value of each sub-sequence is analyzed, a discharge outlier matrix is constructed, and a pearson correlation coefficient between each element in the sub-sequence is calculated to obtain a discharge outlier trend correlation matrix, so that a discharge global correlation bias sequence is further determined. Finally, the discharge deviation tolerance under different current states is obtained, so that when the local outlier factor is adopted for abnormal monitoring, the algorithm can self-adaptively adjust the threshold value according to the self characteristics of the sodium ion battery during discharge, the problem of inaccurate monitoring results caused by different chemical reaction balance states in the battery at different stages is solved, and the accuracy of abnormal monitoring is further improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of steps of a method for intelligently monitoring a state of charge of a sodium ion battery according to an embodiment of the present invention;
Fig. 2 is a flow chart of adjustment threshold acquisition.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent monitoring method and system for the state of charge of the sodium ion battery according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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.
The invention provides a method and a system for intelligently monitoring the state of charge of a sodium ion battery, which are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for intelligently monitoring a state of charge of a sodium ion battery according to an embodiment of the invention is shown, the method includes the following steps:
and S001, collecting a current value of the sodium ion battery in a charge-discharge period, and obtaining a sodium ion initial discharge signal sequence.
Sodium ion batteries are rechargeable batteries that utilize sodium ions to conduct intercalation and deintercalation reactions between the positive and negative electrodes to achieve charge and discharge. In order to realize intelligent monitoring of the state of charge of the sodium ion battery, the change of current during discharging can be adopted.
In the embodiment, the current value of the sodium ion battery during discharging is obtained by adopting the hall effect current sensor of the ACS712 chip, in order to facilitate analysis and meet the nyquist sampling theorem, the signal is resampled every 0.01s, the complete charge-discharge period of the battery is taken as a sampling period, and the sequence formed by the current values obtained in each sampling period according to the time sequence is recorded as the sodium ion initial discharge signal sequence.
Thus, a sodium ion initial discharge signal sequence is obtained.
And step S002, analyzing the current signal change characteristics when the sodium ion battery is discharged, obtaining subsequences in the discharge signal differential sequence, and obtaining the discharge deviation tolerance of each subsequence.
Sodium ion batteries are divided into three phases upon discharge, including an initial phase, a steady state, and a final phase. In the initial stage of discharging, the initial potential difference between the anode and the cathode of the sodium ion battery is overlarge, and chemical reactions in the battery are not balanced yet, so that the output current can be gradually reduced to the size of stable operation from a higher-starting current state, and current fluctuation is usually larger in the stage; in the stable phase of the sodium ion battery, the current output is kept relatively stable, so that the current fluctuation is relatively small; in the final stage of the sodium ion battery discharge, the current is further reduced to near zero because the energy storage of the battery is about to be exhausted and a larger current output cannot be maintained, and the current fluctuation is usually larger in this stage. At the same time, the fluctuation of the current is increased when the load in the circuit changes.
In calculating the outliers of the current signal, the threshold of the Local Outlier Factor (LOF) algorithm should be adaptively adjusted according to the fluctuation of the current.
To emphasize the change of the signal before analyzing the sodium ion battery current signal, the low frequency component in the signal is eliminated, thereby suppressing the interference caused by the low frequency noise. In this embodiment, a first-order backward differential method is used to construct a discharge signal differential sequence for the initial discharge signal sequence, and is not described herein.
Because the current signal frequency is higher and the signal fluctuation is smaller in a short time, the embodiment divides the discharge signal differential sequence to obtain a subsequence and then analyzes the subsequence. Wherein, in the sub-sequence division, every 1s of data is set as one sub-sequence. In order to analyze the current signal fluctuation from a global perspective, a discharge outlier matrix is ultimately constructed for each sub-sequence of signals. The specific calculation of each element in the discharge outlier matrix of each sub-sequence is as follows:
wherein, Elements representing the ith row and jth column in the discharge outlier matrix of the jth subsequence in the discharge signal differential sequence C; Representing the value of the ith element in the nth subsequence in the discharge signal differential sequence; Representing the value of the jth element in the t-th subsequence in the discharge signal differential sequence; representing standard deviation of all element values in the t sub-sequence in the discharge signal differential sequence; Representing the maximum element value in the t-th subsequence in the discharge signal differential sequence; Representing the minimum element value in the t-th subsequence in the differential sequence of discharge signals.
In a discharge outlier matrix B t of the t-th subsequence in the discharge signal differential sequence C, the i-th row element represents the difference between the i-th element value of the t-th subsequence in the discharge signal differential sequence and other element values in the subsequence; dividing the difference between the maximum value and the minimum value of the subsequence by the difference between the maximum value and the minimum value of the subsequence to normalize the data, mapping the current signal to between (0, 1), and eliminating the difference caused by different subsequences when constructing each element in the discharge outlier matrix B t; meanwhile, the standard deviation of the subsequence is divided to highlight the abnormal value in the current signal, so that the abnormal value is more deviated from other values, and the monitoring is easy.
The obtained discharge outlier matrix of each subsequence is thus obtained in this embodiment, where B t is the discharge outlier matrix of the t-th subsequence in the discharge signal differential sequence.
The discharge outlier matrix for each sub-sequence reflects the differences between the element values at each time instant in the current sub-sequence and the element values at other time instants in the sub-sequence. The ith row in the discharge outlier matrix is taken as the discrete sequence β i of the ith element in the corresponding subsequence.
Further, in order to analyze the anomaly degree of each element in each subsequence in the discharge signal differential sequence, the embodiment constructs a discharge outlier trend correlation matrix according to the discrete sequence in the discharge outlier matrix of each subsequence, specifically:
wherein, Elements of an ith row and a jth column in a discharge outlier trend correlation matrix of a nth subsequence in the discharge signal differential sequence are represented; ρ () represents calculating pearson correlation coefficients for two sequences; Representing i discrete sequences in a discharge outlier matrix of the t-th subsequence; and j discrete sequences in the discharge outlier matrix representing the t sub-sequence.
The element value of the (i, j) position in the t-th subsequence discharge outlier trend correlation matrix D t in the discharge signal differential sequence represents the pearson correlation coefficient between the i-th discrete sequence and the j-th discrete sequence in the subsequence discharge outlier matrix B t, that is, the discrete correlation degree between the element value at the i-th moment and the element value at the j-th moment in the subsequence, and the larger the discrete correlation degree is, the larger the correlation degree between the element values is.
Thus far, the present embodiment obtains all discharge outlier trend correlation matrices D 1,…,Dt,…,DN of the sodium ion battery, where D t is the discharge outlier trend correlation matrix of the t-th subsequence.
Since the fluctuation of the current signal is small in a short time during the discharging process of the battery, the discharging global correlation deviation gamma t of the t-th sub-sequence is calculated according to the discharging outlier trend correlation matrix D t of the t-th sub-sequence, and is specifically calculated as follows:
Wherein, gamma t represents the discharge global correlation deviation degree of the t sub-sequence of the discharge signal differential sequence; the I D t||F represents the F norm of the discharge outlier trend correlation matrix D t of the t sub-sequence in the discharge signal differential sequence, and represents the abnormal degree of discharge signal fluctuation of the time period where the sub-sequence is located; m represents the number of elements of the t-th subsequence in the discharge signal differential sequence; An ith element representing an ith subsequence in the discharge signal differential sequence; representing the maximum value of the element in the t sub-sequence in the discharge signal differential sequence; Representing the minimum value of the elements in the t-th subsequence in the discharge signal differential sequence C.
When the F norm of the discharge outlier trend correlation matrix D t of the t-th subsequence in the discharge signal differential sequence C is larger, the current signal fluctuation is more obvious in the subsequence time period, namely, the discharge global correlation deviation degree of the subsequence is larger, otherwise, the current signal fluctuation is weaker in the subsequence time period, namely, the discharge global correlation deviation degree of the subsequence is smaller; when the sum of the elements of the t-th subsequence in the discharge signal differential sequence C is larger, the larger the adjacent current signal difference in the subsequence time period is, namely the larger the discharge global correlation deviation degree of the subsequence is, otherwise, the smaller the adjacent current signal difference in the subsequence time period is, namely the smaller the discharge global correlation deviation degree of the subsequence is. The sum of the maximum element and the minimum element of the t-th subsequence in the discharge signal differential sequence C is divided to normalize the data, and eliminate the signal difference of different subsequences.
And thus, obtaining the discharge global correlation deviation degree of all sub-sequences in the discharge signal differential sequence.
When the abnormal monitoring of the current change during the sodium ion discharge is performed by adopting a Local Outlier Factor (LOF) algorithm, the signals have different degrees of interference due to different stages of the circuit and different load changes. Thus, the sensitivity in anomaly monitoring of the current signal should be different over different time periods, where the sensitivity of the local outlier algorithm is adjusted by calculating its current bias tolerance for each sub-sequence in the current signal. The discharge deviation tolerance is constructed and is calculated as follows:
wherein, alpha t represents the discharge deviation tolerance of the t sub-sequence in the discharge signal differential sequence; gamma t represents the discharge global correlation deviation degree of the t sub-sequence in the discharge signal differential sequence; the average value of all elements of the (t+1) th subsequence in the discharging signal differential sequence C when the sodium ion battery is discharged is shown; The average value of all elements of the t-1 sub-sequence in the discharging signal differential sequence C when the sodium ion battery is discharged is shown; Δt is the interval time between every two sub-sequences in the discharge signal differential sequence C.
When the discharge fluctuation abnormality in the ith sub-sequence period is larger, the more likely that noise exists in the period, the smaller the discharge deviation tolerance of the period should be, whereas the more likely that noise does not exist in the period, the larger the discharge deviation tolerance of the period should be; when the change rate of the discharge signal differential sequence in the ith sub-sequence period is larger, the period is possibly due to fluctuation caused by different states or load changes of the circuit, and larger interference is brought to abnormal monitoring, so that the discharge deviation tolerance of the period is larger, otherwise, the period is in different stable states, and the discharge deviation tolerance of the period is smaller.
Thus, the discharge deviation tolerance of each sub-sequence in the discharge signal differential sequence is obtained.
And step S003, monitoring the state of charge of sodium ions by adopting a local outlier factor monitoring method, and self-adaptively adjusting the threshold value of the local outlier factor according to the tolerance of discharge deviation.
The LOF value of each current value is calculated according to the local outlier algorithm, and the specific calculation mode is known in the art and will not be described herein.
Further, the current signal data in different time periods are used as the self-adaptive threshold value, and different threshold values are used for monitoring the state of charge of sodium ions. The calculation method of the adjustment threshold is as follows:
ρt＝Norm(αt)*ρ(0)
Wherein ρ t represents the adjustment threshold in the t-th sub-sequence period; alpha t represents the discharge deviation tolerance of the t-th sub-sequence time period in the discharge signal differential sequence; norm () represents normalizing the data; ρ (0) is the base empirical threshold and the empirical value is 2. The adjustment threshold acquisition flowchart is shown in fig. 2.
Further, the average value of all the adjustment thresholds is calculated, and a current signal with the LOF value larger than the average value of the adjustment thresholds is identified as an abnormal current signal, so that the state of charge of the sodium ion battery is intelligently monitored.
Based on the same inventive concept as the above method, the embodiment of the invention also provides an intelligent monitoring system for the state of charge of a sodium ion battery, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the above intelligent monitoring methods for the state of charge of the sodium ion battery when executing the computer program.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. The intelligent monitoring method for the state of charge of the sodium ion battery is characterized by comprising the following steps of:
Acquiring a sodium ion initial discharge signal sequence consisting of current values;
constructing a discharge signal differential sequence according to the initial discharge signal sequence, dividing the discharge signal differential sequence to obtain all subsequences of the discharge signal differential sequence, and acquiring a discharge outlier matrix of each subsequence according to elements in the subsequences of the discharge signal differential sequence; acquiring discrete sequences of elements in each subsequence according to the discharge outlier matrix, and constructing a discharge outlier trend correlation matrix of each subsequence according to pearson correlation coefficients among the discrete sequences of the elements; acquiring the discharge global correlation deviation degree of each sub-sequence according to the discharge outlier trend correlation matrix, and acquiring the discharge deviation tolerance of each sub-sequence according to the discharge global correlation deviation degree;
acquiring an adjustment threshold according to the discharge deviation tolerance, and monitoring the state of charge of sodium ions according to the adjustment threshold and a local outlier factor algorithm;
The obtaining the discharge outlier matrix of each sub-sequence comprises the following steps:
For each subsequence of the discharge differential sequence, calculating the absolute value of the difference between the ith element and the jth element in the subsequence, calculating the difference between the largest element value and the smallest element value in the subsequence, calculating the product of the standard deviation of all element values in the subsequence and the difference, taking the ratio of the absolute value of the difference to the product as the element of the ith row and the jth column in the discharge outlier matrix of each subsequence, and arranging all elements according to the positions of the elements in the discharge outlier matrix to obtain the discharge outlier matrix of each subsequence;
the obtaining the discrete sequence of each element in each subsequence comprises the following steps:
for the discharge outlier matrix of each subsequence, taking a sequence consisting of elements of the ith row in the discharge outlier matrix as a discrete sequence of the ith element in the subsequence;
the construction of the discharge outlier trend correlation matrix of each subsequence comprises the following steps:
For each subsequence of the discharge signal differential sequence, calculating a Pearson correlation coefficient between the i element and the discrete sequence of the j element in the subsequence as an element of the i row and the j column in a discharge outlier trend correlation matrix of the subsequence, and calculating the Pearson correlation coefficient between all elements in the subsequence to obtain the discharge outlier trend correlation matrix of each subsequence;
The obtaining the discharge global correlation deviation degree of each sub-sequence comprises the following steps:
for each subsequence of the discharge signal differential sequence, calculating the difference between the maximum value and the minimum value of the elements in the subsequence, calculating the ratio of each element to the difference in the subsequence, calculating the sum of all the ratios in the subsequence, calculating the norm of the discharge outlier trend correlation matrix of the subsequence, and taking the product of the norm and the sum as the discharge global correlation bias degree of each subsequence;
the obtaining the discharge deviation tolerance of each sub-sequence specifically comprises the following steps:
For each subsequence of the discharge differential sequence, calculating the absolute value of the difference between the average value of all elements in the previous subsequence and the average value of all elements in the next subsequence of the subsequence, calculating the interval time between the previous subsequence and the next subsequence of the subsequence, calculating the ratio between the absolute value of the difference and the interval time which is 2 times, and taking the ratio of the ratio and the discharge global correlation deviation degree of the subsequence as the discharge deviation tolerance of each subsequence;
the obtaining of the adjustment threshold value, the monitoring of the sodium ion charge state according to the adjustment threshold value and the local outlier factor algorithm comprises the following steps:
Calculating the product of a normalized value of the discharge variation tolerance of each subsequence and a preset experience threshold value as an adjustment threshold value of each subsequence, calculating the average value of the adjustment threshold values of all the subsequences, calculating the LOF value of each current value by using a local outlier factor algorithm, and taking the current value with the LOF value larger than the average value as an abnormal current signal, otherwise, a normal current signal.
2. The intelligent monitoring method for the state of charge of a sodium ion battery according to claim 1, wherein the constructing a discharge signal differential sequence according to the initial discharge signal sequence comprises:
and processing the initial discharge signal sequence by using a first-order backward differential method to obtain a discharge signal differential sequence.
3. The intelligent monitoring method for the state of charge of a sodium ion battery according to claim 1, wherein the dividing the differential sequence of discharge signals to obtain all sub-sequences of the differential sequence of discharge signals comprises:
and taking a sequence formed by elements in each second in the discharge signal differential sequence as a subsequence in the discharge signal differential sequence.
4. A sodium ion battery state of charge intelligent monitoring system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method of any of claims 1-3 when the computer program is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410044730.5A CN118131054B (en) | 2024-01-12 | 2024-01-12 | Intelligent monitoring method and system for state of charge of sodium ion battery |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410044730.5A CN118131054B (en) | 2024-01-12 | 2024-01-12 | Intelligent monitoring method and system for state of charge of sodium ion battery |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118131054A CN118131054A (en) | 2024-06-04 |
| CN118131054B true CN118131054B (en) | 2024-09-03 |
Family
ID=91233427
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410044730.5A Active CN118131054B (en) | 2024-01-12 | 2024-01-12 | Intelligent monitoring method and system for state of charge of sodium ion battery |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118131054B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115905992A (en) * | 2022-11-25 | 2023-04-04 | 国网江苏省电力有限公司电力科学研究院 | Electric power data preprocessing method, system, storage medium and computing device |
| US11809517B1 (en) * | 2022-09-21 | 2023-11-07 | Southwest Jiaotong University | Adaptive method of cleaning structural health monitoring data based on local outlier factor |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007098426A2 (en) * | 2006-02-17 | 2007-08-30 | Test Advantage, Inc. | Methods and apparatus for data analysis |
| JP6585654B2 (en) * | 2017-05-01 | 2019-10-02 | 日本電信電話株式会社 | Determination apparatus, analysis system, determination method, and determination program |
| WO2019138286A1 (en) * | 2018-01-11 | 2019-07-18 | 株式会社半導体エネルギー研究所 | Abormality detection device for secondary battery, abnormality detection method, and program |
| JP7136439B2 (en) * | 2018-06-14 | 2022-09-13 | 学校法人早稲田大学 | Detection method and detection device |
| CN113742387A (en) * | 2020-05-29 | 2021-12-03 | 中兴通讯股份有限公司 | Data processing method, device and computer readable storage medium |
| CN113128098B (en) * | 2021-04-30 | 2024-05-17 | 国网陕西省电力公司电力科学研究院 | Concrete dam singular value detection method and equipment based on DE-LOF |
| CN113702843B (en) * | 2021-07-26 | 2022-05-20 | 南通大学 | Lithium battery parameter identification and SOC estimation method based on suburb optimization algorithm |
| US20230153311A1 (en) * | 2021-11-12 | 2023-05-18 | Google Llc | Anomaly Detection with Local Outlier Factor |
| CN114236393B (en) * | 2021-11-30 | 2023-07-07 | 上海瑞浦青创新能源有限公司 | Method and system for detecting battery abnormality on line based on big data |
| CN114758483B (en) * | 2022-04-14 | 2023-03-21 | 江苏国电南自海吉科技有限公司 | Dynamic intelligent early warning method for power equipment based on cosine similarity |
| CN115659799B (en) * | 2022-10-24 | 2023-05-16 | 国网浙江省电力有限公司电力科学研究院 | A Fault Diagnosis Method for Lithium Battery Energy Storage Power Station with Threshold Adaptive Function |
| CN116819352A (en) * | 2023-06-06 | 2023-09-29 | 重庆三峡时代能源科技有限公司 | Self-adaptive setting method, device and equipment for battery threshold value and storage medium |
| CN117074783B (en) * | 2023-10-12 | 2024-01-19 | 国网吉林省电力有限公司通化供电公司 | Real-time monitoring and early warning method for overheat state of power equipment |
-
2024
- 2024-01-12 CN CN202410044730.5A patent/CN118131054B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11809517B1 (en) * | 2022-09-21 | 2023-11-07 | Southwest Jiaotong University | Adaptive method of cleaning structural health monitoring data based on local outlier factor |
| CN115905992A (en) * | 2022-11-25 | 2023-04-04 | 国网江苏省电力有限公司电力科学研究院 | Electric power data preprocessing method, system, storage medium and computing device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118131054A (en) | 2024-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10422835B2 (en) | Innovative framework combining cycling and calendar aging models | |
| CN114861527A (en) | Lithium battery life prediction method based on time series characteristics | |
| CN113406521B (en) | Lithium battery health state online estimation method based on feature analysis | |
| US8405399B2 (en) | Method to predict min cell voltage from discrete min cell voltage output of stack health monitor | |
| CN117154268B (en) | SOC correction method, device and equipment based on water system sodium ion energy storage battery cabinet and storage medium | |
| US7489107B2 (en) | Optimal battery charging for damage mitigation | |
| CN117991030A (en) | Method and system for detecting health state of battery-changing cabinet | |
| CN116757337B (en) | House construction progress prediction system based on artificial intelligence | |
| CN115308608A (en) | All-vanadium redox flow battery voltage prediction method, device and medium | |
| CN118131054B (en) | Intelligent monitoring method and system for state of charge of sodium ion battery | |
| KR102805837B1 (en) | Method and apparatus for estimating a state of battery | |
| CN119125900A (en) | Method, device, storage medium and program product for determining charging state of battery pack | |
| CN117889581B (en) | Working condition parameter adjusting method in NMP waste gas recovery process | |
| CN117239759A (en) | Electric energy flow direction management system of grid-connected controller of hydrogen fuel cell energy supply system | |
| EP4231026A1 (en) | Battery management device and method | |
| CN118112422A (en) | Dual-time scale SVD-UKF-based battery state-of-charge estimation method | |
| CN112216883B (en) | Lithium battery equalization method, system, equipment and storage medium | |
| CN116859270A (en) | Battery capacity estimation model training and capacity estimation method, device and storage medium | |
| JP2008043147A (en) | Power supply system, control method and program for power supply system | |
| CN115308630A (en) | Attenuation analysis method for battery life | |
| Shen et al. | A model and data dual-driven framework for lithium-ion battery cycle life prediction integrating uncertainty model | |
| CN119674287B (en) | Lithium battery dynamic charging method based on user behavior and environmental conditions | |
| KR101711906B1 (en) | Dither-Correlation Maximum Power Point Tracking Method and System for Converter-Based Energy Harvesters | |
| US20250012867A1 (en) | Battery life estimation method and battery system | |
| CN117991109B (en) | Model training and charging state determining method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |