CN112986829B - Battery differential pressure abnormity threshold value confirmation method and system based on big data and cloud computing - Google Patents
Battery differential pressure abnormity threshold value confirmation method and system based on big data and cloud computing Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention relates to the technical field of big data processing, in particular to a battery differential pressure abnormity threshold confirmation method and system based on big data and cloud computing. The method comprises the following steps: s1, collecting pressure difference data of a plurality of groups of batteries; s2, discretizing the pressure difference data; s3, constructing a Poisson distribution model and obtaining an abnormal threshold. The system is used for realizing the method. The method and the device can preferably establish the pressure difference threshold value by means of the big data sample, so that the limitation of the prior method for establishing the abnormal threshold value by adopting an empirical formula can be overcome.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a battery differential pressure abnormity threshold confirmation method and system based on big data and cloud computing.
Background
The battery voltage difference is the maximum difference of voltages between battery cores of the lithium battery in the discharging and charging processes, and is a key index for reflecting the quality of the battery. Due to the insufficient voltage protection and high voltage protection mechanisms inside the battery, the influence of the voltage difference of the battery on the performance of the battery is mainly reflected in that: in the discharging process, the battery cell with the lowest nominal voltage reaches a power-shortage protection threshold value first, so that the whole battery pack stops discharging, and the whole battery pack still has discharging capacity at the moment; in the charging process, the electric core with the highest nominal voltage firstly reaches a high-voltage protection threshold value to further cause the whole battery pack to stop charging, and at the moment, the whole battery pack is still in an electric quantity underfill state actually.
Because of the deviation of the production process of the battery core and the difference of the interior of the interface when the battery cores are connected in series in the installation process, a small amount of pressure difference exists in a brand-new battery, and the theoretical battery pressure difference of 0 is almost impossible in practice, so that a pressure difference detection mechanism is arranged in the prior art to detect the pressure difference abnormity of the battery.
Currently, most of existing differential pressure detection is to manually set a threshold value of a battery differential pressure to detect the differential pressure, for example, the threshold value of the battery differential pressure is set to be 200mv, and when the differential pressure of any battery cell of a battery pack is detected to exceed 200mv, it is determined that the battery pack has an abnormal differential pressure. The detection method based on the method has the following disadvantages:
1. setting the threshold value by manual experience is unreliable
The method is mainly characterized in that the pressure difference abnormal thresholds of batteries with different capacities and different materials are different, manual experience has limitations, rules in the battery pressure difference cannot be better and fully excavated, and therefore misjudgment cases are increased easily;
2. noise points easily appear when differential pressure data is read in monitoring of battery
This is mainly reflected in that there may be noise in the data of the cell voltage read during monitoring or the cell voltage fluctuates instantaneously, and a noise point easily breaks through a threshold of the battery voltage difference, thereby increasing false judgment cases.
Disclosure of Invention
The invention provides a battery differential pressure abnormity threshold value confirmation method based on big data and cloud computing, which can overcome the defect of larger limitation caused by the fact that the differential pressure threshold value is established by experience in the prior art.
The battery differential pressure abnormity threshold value confirmation method based on big data and cloud computing comprises the following steps:
s1, collecting pressure difference data of multiple groups of batteries
In the step, the charging and discharging of the lithium batteries with a plurality of models and a plurality of loss states are respectively collectedEstablishing a differential pressure data set N, N = tone in the differential pressure data in the process
I belongs to N, j belongs to N, h =0 or 1},
；
wherein i represents a model class of the battery, j represents a state of wear class of the battery, h =0 represents a state of charge, and h =1 represents a state of discharge; that is to say that the first and second electrodes,
the differential pressure data sequence is acquired by the battery in the ith model and the jth loss state under the charging state,
the voltage difference data sequence is acquired by the battery in the ith type and the jth loss state under the discharge state;
wherein the content of the first and second substances,
representing a sequence of pressure differential data
The L-th element in (1), n is a pressure difference data sequence
The total number of elements in (1);
s2, discretizing the pressure difference data
In this step, for any pressure difference data sequence
All carry out discretization processing to obtain a discretization pressure difference data sequence
，
={
}; wherein the content of the first and second substances,
⌊
⌋，⌊
⌋ is a round-down operation,
setting a pressure difference interval;
s3, constructing a Poisson distribution model and acquiring an abnormal threshold value
In the step, a Poisson distribution model is constructed to respectively carry out data sequence on all the pressure differences
Carrying out treatment;
the probability density function of the constructed poisson distribution model is,
wherein the content of the first and second substances,
，
representing a sequence of discretized pressure differential data
Middle value
The probability of occurrence;
in this step, based on the formula
Obtaining discrete value of threshold value less than or equal to (1-p)
，
Representing a sequence of discretized pressure differential data
Is less than or equal to the numerical value
P is a set abnormal rate;
in this step, based on the formula F = D ″
An anomaly threshold F is obtained.
In the present invention, a large number of battery differential pressure samples can be collected in step S1, the sample data collected in step S1 can be preferably processed in step S2, and the differential pressure abnormality threshold of the battery can be preferably obtained according to the distribution characteristics of the battery differential pressure in step S3.
In step S1, when sample data is collected, the sampled data can be sampled according to the model, the loss state, the charge state, and the discharge state, so that the same collected differential pressure data sequence is obtained
The data in (1) can be data in the same model, the same loss and the same charge-discharge state, so that the subsequent differential pressure abnormal threshold value obtained according to the step S3 can have better pertinence, and the method of the invention can be directed toThe corresponding differential pressure abnormal threshold is formulated according to the batteries in different states, so that the pertinence of the method can be improved better, and the whole method is more scientific and reasonable by artificially eliminating differential pressure differences caused by the different states of the batteries.
Through the step S2, the discretization of the data collected in the step S1 can be preferably realized, and the data collected in the step S1 can be preferably converted into statistically processable data, so that the processing of the step S3 can be preferably facilitated.
In step S3 of the present invention, constructing a poisson distribution model can preferably realize the discretization of the pressure difference data sequence
By setting the abnormal rate p, the maximum tolerable threshold discrete value can be preferably obtained
And then, the abnormal threshold value F can be acquired through conversion.
In this example, the determination of the differential pressure abnormal threshold of the battery in different states can be realized based on the idea of big data and on the statistical means, which can overcome the limitation caused by setting the threshold based on manual experience. The method disclosed by the invention can be pertinently suitable for the distribution characteristics of the battery differential pressure by constructing the Poisson distribution model, so that the method is scientific and reasonable.
Preferably, in step S1, any pressure difference data
Are all obtained by the following steps of,
step S11, continuously sampling the differential pressure data of the battery at a set sampling interval T, and further acquiring a differential pressure time sequence array of the corresponding battery;
step S12, selecting 2m +1 data of t time and m times before and after t time in the pressure difference time sequence array for moving average operationCalculating to obtain corresponding pressure difference data
。
In the present invention, through steps S11 and S12, the sampled data can be extracted from a time series, and the sampled data can be obtained after being processed by a moving average method, so that the influence caused by noise points can be preferably reduced.
Preferably, in step S1, the total number of charges or total number of discharges of the battery in any loss state is determined based on the total number of charges or total number of discharges of the battery in the loss state, and the total number of charges or total number of discharges of the battery in any loss state is within the range of the total number of charges or the range of the total number of discharges corresponding to the loss state. Thereby, the classification of the batteries of the same loss state can be preferably realized.
Preferably, in step S1, the pressure difference data set N stores all collected pressure difference data, and when pressure difference data is newly added, the pressure difference data set N is updated; in step S2, when the differential pressure data set N is updated, discretizing the newly added data; in step S3, when the differential pressure data set N is updated, the abnormality threshold F is updated based on the updated differential pressure data set N. Therefore, the sample database can be enriched step by step better, and the method provided by the invention has better learning ability.
The invention also provides a battery differential pressure abnormity threshold value confirmation system based on big data and cloud computing, which is used for any battery differential pressure abnormity threshold value confirmation method based on big data and cloud computing; which comprises
The terminal detection unit is used for periodically sampling the current voltage of each battery cell of the battery in the charging and discharging processes and sending the current voltage to the terminal processing unit;
the terminal processing unit is used for calculating the difference value between the highest value and the lowest value of the data acquired by the terminal detection unit in each sampling period and sending the difference value to the remote server; and
and the remote server is used for storing and processing the data sent by the terminal processing unit so as to obtain the abnormal threshold F.
Through the system, the acquisition and processing of the pressure difference data and the determination of the abnormal threshold value can be preferably realized.
Preferably, the terminal processing unit is further configured to receive the anomaly threshold F computed and obtained at the remote server end in real time, and the terminal processing unit is capable of sending an alarm instruction to the remote server end when the difference is higher than the anomaly threshold F. Therefore, the abnormal threshold F at the battery end can be updated in real time when the number of samples is increased, and real-time monitoring of the battery differential pressure abnormality can be better realized.
Drawings
Fig. 1 is a schematic flowchart of a method for confirming an abnormal threshold value of a battery differential pressure in embodiment 1;
FIG. 2 is a diagram showing the distribution characteristics of the cell differential pressure;
fig. 3 is a block diagram schematically illustrating a system for confirming an abnormal threshold value of differential pressure between batteries in embodiment 1.
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.
Example 1
Referring to fig. 1, the present embodiment provides a battery differential pressure abnormality threshold value confirmation method based on big data and cloud computing, which includes the following steps:
s1, collecting pressure difference data of multiple groups of batteries
In the step, pressure difference data of multiple models of lithium batteries in multiple loss states in the charging and discharging processes are respectively collected, and a pressure difference data set N, N = &isestablished
I belongs to N, j belongs to N, h =0 or 1},
；
whereinI represents a model type of the battery, j represents a state of wear category of the battery, h =0 represents a state of charge, and h =1 represents a state of discharge; that is to say that the first and second electrodes,
the differential pressure data sequence is acquired by the battery in the ith model and the jth loss state under the charging state,
the voltage difference data sequence is acquired by the battery in the ith type and the jth loss state under the discharge state;
wherein the content of the first and second substances,
representing a sequence of pressure differential data
The L-th element in (1), n is a pressure difference data sequence
The total number of elements in (1);
s2, discretizing the pressure difference data
In this step, for any pressure difference data sequence
All carry out discretization processing to obtain a discretization pressure difference data sequence
，
={
}; wherein the content of the first and second substances,
⌊
⌋，⌊
⌋ is a round-down operation,
setting a pressure difference interval;
s3, constructing a Poisson distribution model and acquiring an abnormal threshold value
In the step, a Poisson distribution model is constructed to respectively carry out data sequence on all the pressure differences
Carrying out treatment;
the probability density function of the constructed poisson distribution model is,
wherein the content of the first and second substances,
，
representing a sequence of discretized pressure differential data
Middle value
The probability of occurrence;
in this step, based on the formula
Obtaining discrete value of threshold value less than or equal to (1-p)
，
Representing a sequence of discretized pressure differential data
Is less than or equal to the numerical value
P is a set abnormal rate;
in this step, based on the formula F = D ″
An anomaly threshold F is obtained.
FIG. 2 is a diagram showing the distribution characteristics of the cell differential pressure; the distribution characteristic diagram is obtained by counting the pressure difference values of 1000 battery samples of the same model and the same loss state, the ordinate of the distribution characteristic diagram represents the distribution quantity of the samples, and the abscissa of the distribution characteristic diagram represents the magnitude of the pressure difference. Based on the distribution characteristic diagram, the distribution characteristic of the battery differential pressure presents obvious 'left convex and right concave' type distribution, the type distribution is different from the conventional normal distribution, and therefore when the battery with abnormal differential pressure is screened based on big data, the judgment is difficult to be carried out by applying the conventional 3 sigma criterion.
In this embodiment, a large number of battery differential pressure samples can be collected in step S1, the sample data collected in step S1 can be preferably processed in step S2, and the differential pressure abnormality threshold of the battery can be preferably obtained according to the distribution characteristics of the battery differential pressure in step S3.
In step S1, when sample data is collected, the sampled data can be sampled according to the model, the loss state, the charge state, and the discharge state, so that the same collected differential pressure data sequence is obtained
The data in (1) can be of the same type, the same loss and the sameAnd waiting for the data in the charging and discharging states, so that the subsequent differential pressure abnormal threshold value obtained according to the step S3 can have better pertinence, and thus the method of the embodiment can make the corresponding differential pressure abnormal threshold value according to the batteries in different states in a targeted manner, so that the pertinence of the method of the embodiment can be better improved, and the whole method is more scientific and reasonable by artificially eliminating the differential pressure difference caused by the different states of the batteries.
Through the step S2, the discretization of the data collected in the step S1 can be preferably realized, and the data collected in the step S1 can be preferably converted into statistically processable data, so that the processing of the step S3 can be preferably facilitated.
In step S3 of the present embodiment, constructing a poisson distribution model can preferably realize the discretization of the pressure difference data sequence
By setting the abnormal rate p, the maximum tolerable threshold discrete value can be preferably obtained
And then, the abnormal threshold value F can be acquired through conversion.
In this example, the determination of the differential pressure abnormal threshold of the battery in different states can be realized based on the idea of big data and on the statistical means, which can overcome the limitation caused by setting the threshold based on manual experience. In addition, the method of the embodiment is scientific and reasonable as the Poisson distribution model is constructed, and the method can be pertinently suitable for the distribution characteristics of the battery pressure difference.
In step S2 of the present embodiment, the set pressure difference interval D can be determined according to actual conditions, and in the present embodiment, D =10mv is taken as an example for description, and when the set pressure difference interval D is 10mv, the pressure difference data series
Element (1) of
In the interval [0,10 ]]Time-based discretization pressure difference data sequence
Corresponding elements in (1)
The value of (1) is 0, the pressure difference data sequence
Element (1) of
In the interval [10,20 ]]Time-based discretization pressure difference data sequence
Corresponding elements in (1)
The numerical value of (1) is equal to the numerical value of (1), and so on, so that the pressure difference data sequence is discretized
The numerical value of any one element of (a) is a non-negative integer, so that the processing in step S3 can be preferably facilitated.
In step S3 of the present embodiment, the abnormality rate p can be set to the factory abnormality rate of the same type of battery, for example, if the factory abnormality rate of a certain type of battery is 0.01, the corresponding abnormality rate p can be set to 0.01. It is understood that the abnormality rate p in the present embodiment can also be derived based on statistics of the batteries in the same state. Based on such setting of the abnormality rate p, it can be understood that the threshold setting concept of the method in the present embodiment is: in the case where the samples are sufficient, the probability of the differential pressure abnormality of the battery tends to be a fixed value, so in the present embodiment, the abnormality threshold value to be set for the battery in a certain state can be preferably determined by processing based on the large data.
In step S1 of the present embodiment, any pressure difference data
Are all obtained by the following steps of,
step S11, continuously sampling the differential pressure data of the battery at a set sampling interval T, and further acquiring a differential pressure time sequence array of the corresponding battery;
step S12, selecting 2m +1 data of t time and m times before and after t time in the pressure difference time sequence to perform moving average operation so as to obtain corresponding pressure difference data
。
In this embodiment, through steps S11 and S12, the sampled data can be extracted from a time series, and the sampled data can be obtained after being processed by a moving average method, so that the influence caused by noise points can be preferably reduced.
In step S11 of the present embodiment, the sampling interval T can be set to, for example, 5S, and missing of data can be preferably prevented by setting the sampling interval on the order of seconds.
In step S12 of the present embodiment, the time t can be selected as the time t when the pressure difference is the largest in the pressure difference time series.
In step S1 of this embodiment, the total current charging times or the total current discharging times are used as the basis for determining the depletion state, and the total charging times or the total discharging times for the battery in any depletion state are within the range of the total charging times or the range of the total discharging times corresponding to the depletion state. Thereby, the classification of the batteries of the same loss state can be preferably realized. Specifically, for example, if the number of charges is set to 0 to 3 and the number of discharges is set to 1 to 4, which is the first loss state, i.e., j =1, in step S1, only the battery cells corresponding to 0 to 3 and 1 to 4 of the number of discharges at the same time are set to the first loss state.
In step S1 of this embodiment, in step S1, the pressure difference data set N stores all the collected pressure difference data, and when newly adding pressure difference data, the pressure difference data set N is updated; in step S2, when the differential pressure data set N is updated, discretizing the newly added data; in step S3, when the differential pressure data set N is updated, the abnormality threshold F is updated based on the updated differential pressure data set N. Therefore, the sample database can be enriched step by step better, and the method of the embodiment can have better learning ability.
With reference to fig. 3, based on the method in the present embodiment, the present embodiment further provides a battery differential pressure abnormality threshold value confirmation system based on big data and cloud computing, which is used to implement the battery differential pressure abnormality threshold value confirmation method based on big data and cloud computing in the present embodiment; it includes:
the terminal detection unit is used for periodically sampling the current voltage of each battery cell of the battery in the charging and discharging processes and sending the current voltage to the terminal processing unit;
the terminal processing unit is used for calculating the difference value between the highest value and the lowest value of the data acquired by the terminal detection unit in each sampling period and sending the difference value to the remote server; and
and the remote server is used for storing and processing the data sent by the terminal processing unit so as to obtain the abnormal threshold F.
Through the system in the embodiment, the acquisition and processing of the pressure difference data and the determination of the abnormal threshold value can be preferably realized.
In this embodiment, the terminal detection unit can preferably achieve the acquisition of the original data in step S1, the terminal processing unit can preferably achieve the acquisition of the final pressure difference data in step S1, and the remote server can preferably achieve the establishment of the pressure difference data set N in step S1 and the implementation of steps S2 and S3 by using the processing capability of cloud computing.
In this embodiment, the terminal processing unit is further configured to receive an abnormal threshold F calculated and obtained at the remote server end in real time, and the terminal processing unit is capable of sending an alarm instruction to the remote server end when the difference is higher than the abnormal threshold F. Therefore, the abnormal threshold F at the battery end can be updated in real time when the number of samples is increased, and real-time monitoring of the battery differential pressure abnormality can be better realized.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (6)
1. The battery differential pressure abnormity threshold value confirmation method based on big data and cloud computing comprises the following steps:
s1, collecting pressure difference data of multiple groups of batteries
In the step, pressure difference data of multiple models of lithium batteries in multiple loss states in the charging and discharging processes are respectively collected, and a pressure difference data set N, N = &isestablished
I belongs to N, j belongs to N, h =0 or 1},
；
wherein i represents a model class of the battery, j represents a state of wear class of the battery, h =0 represents a state of charge, and h =1 represents a state of discharge; that is to say that the first and second electrodes,
the differential pressure data sequence is acquired by the battery in the ith model and the jth loss state under the charging state,
the voltage difference collected by the battery under the discharge state under the ith type and the jth loss state is shownA data sequence;
wherein the content of the first and second substances,
representing a sequence of pressure differential data
The L-th element in (1), n is a pressure difference data sequence
The total number of elements in (1);
s2, discretizing the pressure difference data
In this step, for any pressure difference data sequence
All carry out discretization processing to obtain a discretization pressure difference data sequence
，
={
}; wherein the content of the first and second substances,
⌊
⌋，⌊
⌋ is a round-down operation,
to set the pressure difference interval；
S3, constructing a Poisson distribution model and acquiring an abnormal threshold value
In the step, a Poisson distribution model is constructed to respectively carry out data sequence on all the pressure differences
Carrying out treatment;
the probability density function of the constructed poisson distribution model is,
wherein the content of the first and second substances,
，
representing a sequence of discretized pressure differential data
Middle value
The probability of occurrence;
in this step, based on the formula
Obtaining discrete value of threshold value less than or equal to (1-p)
，
Representing a sequence of discretized pressure differential data
Is less than or equal to the numerical value
P is a set abnormal rate;
in this step, based on the formula F = D ″
An anomaly threshold F is obtained.
2. The big data and cloud computing based battery pressure difference abnormality threshold value confirmation method according to claim 1, wherein: in step S1, any pressure difference data
Are all obtained by the following steps of,
step S11, continuously sampling the differential pressure data of the battery at a set sampling interval T, and further acquiring a differential pressure time sequence array of the corresponding battery;
step S12, selecting 2m +1 data of t time and m times before and after t time in the pressure difference time sequence to perform moving average operation so as to obtain corresponding pressure difference data
。
3. The big data and cloud computing based battery pressure difference abnormality threshold value confirmation method according to claim 1, wherein: in step S1, the total charging or discharging times of the battery in any loss state is within the total charging or discharging time range corresponding to the loss state, based on the total charging or discharging times as the determination criterion of the loss state.
4. The big data and cloud computing based battery pressure difference abnormality threshold value confirmation method according to claim 1, wherein: in step S1, the pressure difference data set N stores all the collected pressure difference data, and updates the pressure difference data set N when pressure difference data is newly added; in step S2, when the differential pressure data set N is updated, discretizing the newly added data; in step S3, when the differential pressure data set N is updated, the abnormality threshold F is updated based on the updated differential pressure data set N.
5. A big data and cloud computing-based battery differential pressure abnormity threshold confirmation system for realizing the big data and cloud computing-based battery differential pressure abnormity threshold confirmation method in any one of claims 1-4; the method is characterized in that: comprises that
The terminal detection unit is used for periodically sampling the current voltage of each battery cell of the battery in the charging and discharging processes and sending the current voltage to the terminal processing unit;
the terminal processing unit is used for calculating the difference value between the highest value and the lowest value of the data acquired by the terminal detection unit in each sampling period and sending the difference value to the remote server;
and the remote server is used for storing and processing the data sent by the terminal processing unit so as to obtain the abnormal threshold F.
6. The big data and cloud computing based battery pressure difference anomaly threshold validation system according to claim 5, wherein: the terminal processing unit is also used for receiving the abnormal threshold value F obtained by calculation at the remote server end in real time, and the terminal processing unit can send an alarm instruction to the remote server end when the difference value is higher than the abnormal threshold value F.
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