CN115469226A - Real-time safety early warning method for electric vehicle power battery based on operation big data - Google Patents
Real-time safety early warning method for electric vehicle power battery based on operation big data Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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Abstract
The invention discloses a real-time safety early warning method for an electric vehicle power battery based on big operation data, which comprises the following steps: s1, reading data, namely reading the total current, the total SOC and the monomer voltage of past historical data of a power battery; s2, data cleaning, namely cleaning missing data, repeated value data and error data; s3, analyzing data, extracting voltage values at different charging moments, and establishing an OCV-SOC curve; s4, identifying parameters, namely identifying the parameters of the real-time collected data by using a Rint model through an OCV-SOC curve obtained by fitting to obtain the direct current internal resistance of a charging section and the direct current internal resistance of a discharging section; and S5, safety early warning is carried out, and early warning is carried out on the internal resistance of the charging section and the internal resistance of the discharging section. The invention has the beneficial effects that: the time-space two-dimensional safety early warning method based on the internal resistance information can effectively diagnose the specific time when the fault occurs, can also diagnose the specific single battery with the fault, and effectively realizes the safe and accurate early warning of the battery system.
Description
Technical Field
The invention relates to the field of power battery safety of new energy automobiles, in particular to a real-time safety early warning method for a power battery of an electric automobile based on operation big data.
Background
Electric vehicles have gained wide acceptance in the automotive industry due to their efficient performance and their contribution to solving environmental problems such as greenhouse gas emissions and global warming. The power battery is a main energy storage device of the electric automobile, so that safe and reliable operation of the battery is very important, and the battery is aged when being used as the battery like other energy storage devices, and is mainly reflected in capacity attenuation and power decline (internal resistance increase). Therefore, the reliable operation of the Battery is based on accurate Battery parameters and state estimation, and a Battery Management System (BMS) can monitor the Battery state in real time. On the other hand, with the advent of the big data era, the safety of electric vehicles, charging piles, cloud big data and the like through vehicle-side-end multi-field coupling monitoring is widely concerned, and how to establish a safety model of a power battery by using the big data is an urgent industrial problem to be solved.
Currently, safety research on power batteries is mostly established under specific conditions in laboratories, and an empirical model, an analytic model or a neural network model is established by using collected information of an accurate sensor under specific working conditions as a characterization parameter of battery safety, so as to obtain good state quantities such as direct current internal resistance, state of Charge (SOC), state of health (Stateof health, SOH), and peak power state (StateofPower, SOP). But there is great difference in the data and the laboratory data that real vehicle data and the big data platform in high in the clouds control, and its concrete characteristics show: 1. the sampling frequency of the large running data is often 0.1Hz or even lower, and the sampling frequency is not constant, which results in discontinuous and even large difference of battery states of adjacent sampling points, and some classical state identification methods such as recursive least square method for parameter identification require that the influence of the electric quantity consumed or absorbed by the battery in a unit sampling interval on the SOC is approximately zero, the temperature of the battery in the unit sampling interval is constant, and other harsh conditions, so the method relying on the assumption that the state quantity is slowly changed cannot be applied to the large running data of the actual vehicle. 2. Laboratory accessible accelerated life test charge-discharge under great multiplying power accomplishes power battery's full life cycle fast, and the real vehicle data of big data platform control often operate decades and just reach power battery's life-span limit, will lead to real vehicle operation operating mode to be difficult to through experimental means reappear under the big data platform control. 3. Due to the complexity of actual operation conditions and environment, the actual operation conditions are difficult to simulate under laboratory conditions, a series of problems such as quality degradation and the like can occur in acquired data under the influence of various uncertain conditions, and complex processing such as data cleaning and the like is required before modeling.
Against the background of the above problems, in an electric vehicle that runs large data, the Open Circuit Voltage (OCV) of the vehicle is difficult to characterize according to the change of SOC, and parameters related to battery safety, such as internal resistance, are difficult to identify. Therefore, a real-time safety early warning method for the power battery of the electric automobile based on the operation big data is urgently needed.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a real-time safety early warning method for an electric vehicle power battery based on large operation data.
In order to achieve the purpose, the invention is realized by the following technical scheme: a real-time safety early warning method for an electric vehicle power battery based on big operation data comprises the following steps:
s1, reading data, namely reading the total current, the total SOC and the monomer voltage of past historical data of a power battery;
s2, data cleaning, namely cleaning missing data, repeated value data and error data;
s3, analyzing data, extracting voltage values at different charging moments, recording SOC values at corresponding moments, and establishing an OCV-SOC curve;
s4, identifying parameters, namely identifying the parameters of the real-time collected data by using a Rint model through an OCV-SOC curve obtained by fitting to obtain the direct current internal resistance of a charging section and the direct current internal resistance of a discharging section;
and S5, safety early warning, namely, respectively adopting an entropy weight method and a variation coefficient method to carry out safety early warning on the internal resistance of the charging segment in a space dimension and a time dimension, and setting a threshold value for the internal resistance of the discharging segment.
Further, in S2, aiming at the data volume of the missing value being less than or equal to 4 sampling points, a nearest neighbor interpolation method is adopted to complement the data, and the missing data volume being greater than 4 sampling points is directly removed; only one valid value is reserved for a duplicate value; and for the data with wrong contents, when the voltage data is zero, performing condition judgment on the current data at the moment, if the current at the moment and the last sampling point change by less than 3% of the maximum charge and discharge current, replacing the voltage with the voltage at the last sampling point, and if the current change value is greater than 3% of the maximum charge and discharge current, directly rejecting the voltage at the moment.
Further, in S4, a hash mapping is established to calculate internal resistance, an OCV-SOC curve obtained by fitting is fitted to obtain an OCV specific value corresponding to each SOC, the SOC is stored in a key vector keys of the hash table, the OCV is stored in a value vector value of the hash table, total current, cell voltage, and total SOC collected in real time are obtained, and charging or discharging is determined according to change of the SOC.
Further, in the charging process, the direct current internal resistance of each single body is identified on line, and the calculation formula is as follows:
OCV(k)＝Hashmap(SOC(k)) (I)
where OCV (k) is the open circuit voltage at the k-th time, SOC (k) is the state of charge at the k-th time, hashmap is the hash map established above, R (k) is the DC internal resistance at the k-th time, U t (k) Is the order of the kth timeTerminal voltage of the body cell.
Further, in S4, in the discharging process, the direct current internal resistance of the discharging section is indirectly obtained by combining a filtering algorithm and a least square characteristic curve.
Further, the early warning method of the charging segment in the S5 in the time dimension includes:
according to the charging condition of the long-term running of the electric automobile, the charging strategy which is often selected is multi-stage constant current charging, but statistics shows that the charging mode with a large multiplying power is often adopted in the 70% -90% SOC interval, the change of ohmic internal resistance in the interval is not obvious, and if abnormal information occurs in the interval with the inconspicuous change of ohmic internal resistance, whether a fault occurs is more easily detected, so that the interval is selected as a research object. And carrying out two-step internal resistance consistency safety early warning of time dimension and space inconsistency dimension on the plurality of charging segments.
Firstly, selecting the internal resistance of all monomers in 70% -90% SOC interval:
wherein j represents the number of the single batteries, and i represents the ith sampling point of different SOCs;
then, each row of the matrix R is normalized, and the calculation formula is:
in the formula r i And (3) representing the row vector of the R matrix, and obtaining a dimensionless R _ normal matrix after processing:
calculating the mean and standard deviation M of the R _ normal row vector i And S i ：
Wherein n represents the number of single batteries;
finally, the variation coefficients V of different sampling points are obtained i ：
The result of the coefficient of variation is that the uniformity of each battery cell is good within 0.15, and when the coefficient of variation at a certain time is greater than 0.15, the inconsistency of the battery cells occurs at the time.
Further, the early warning method of the charging segment in the space dimension in the S5 comprises the following steps:
firstly, selecting n single batteries and m sampling moments to establish a matrix X ij (i =1,2, \8230;, n; j =1,2, \8230;, m), then the matrix column vector is normalized, and the specific gravity p of the ith cell at the j-th sampling point at that moment is calculated ij ：
Then, an entropy e at the j-th time is calculated j ：
Wherein the content of the first and second substances,
calculating information entropy redundancy d according to entropy value j ：
d j ＝1-e j
And (3) carrying out weight calculation on the redundancy:
the sum of all the time points j is calculated by each single cell to obtain the comprehensive score s of each single cell i ：
And finally, subtracting the average value of the total score of each battery monomer and the scores of all the monomers to obtain a deviation degree delta s, wherein the larger the deviation degree is, the larger the deviation degree of the monomer from most monomers is reflected, and judging whether the monomer is abnormal or not according to the deviation degree.
Further, in the step S3, voltage values at the starting time of the plurality of charging segments, the time when the current is zero during the operation process and exceeds 30min, and the time when the charging is finished and the charging is sufficiently settled are extracted.
Compared with the prior art, the invention has the beneficial effects that:
1. the conventional OCV-SOC curve is only obtained by performing an OCV experiment or Hybrid Pulse Power Characteristics (HPPCs) under an offline condition in a laboratory and is difficult to obtain from a real vehicle.
2. The method can effectively identify the direct current internal resistance of the power battery in the charging process and the discharging process on line, and has small calculation complexity and high accuracy; in addition, the time-space two-dimensional safety early warning method based on the internal resistance information can effectively diagnose the specific time when the fault occurs and the specific single battery with the fault, and effectively realize the safety and accurate early warning of the battery system.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 shows the results of the cell voltage data before cleaning;
FIG. 3 is the result after the cell voltage data has been washed;
FIG. 4 is a fitted OCV-SOC curve;
FIG. 5 is a plot of identified internal charging resistances versus SOC;
FIG. 6 is a plot of identified internal discharge resistance versus SOC;
FIG. 7 shows the coefficient of variation at different times;
FIG. 8 shows the deviation of different monomers;
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and these equivalents also fall within the scope of the present application.
As shown in fig. 1, a real-time safety early warning method for an electric vehicle power battery based on big operation data comprises the following steps:
s1, reading data, namely reading total current, total SOC (state of charge) and monomer voltage of past historical data of a power battery;
s2, data cleaning, namely cleaning missing data, repeated value data and error data;
s3, analyzing data, extracting voltage values at different charging moments, recording SOC values at corresponding moments, and establishing an OCV-SOC curve;
s4, identifying parameters, namely identifying the parameters of real-time collected data by using a Rint (battery equivalent circuit model) model through an OCV-SOC curve obtained by fitting to obtain the direct current internal resistance of a charging section and the direct current internal resistance of a discharging section;
and S5, safety early warning, namely, respectively adopting an entropy weight method and a variation coefficient method to carry out safety early warning on the internal resistance of the charging segment in a space dimension and a time dimension, and setting a threshold value for the internal resistance of the discharging segment.
The pile big data has the problems of missing value, repeated value, content error and the like, so that the pile big data cannot be directly used, the data cleaning work needs to be carried out, and the clear method comprises the following steps: the data is complemented by adopting a nearest neighbor interpolation method aiming at the data quantity of the missing value being less than or equal to 4 sampling points, and the missing data quantity being more than 4 sampling points is directly eliminated; for a duplicate value, only one valid value is retained; and for the data with wrong contents, when the voltage data is zero, performing condition judgment on the current data at the moment, if the current at the moment and the last sampling point change by less than 3% of the maximum charging and discharging current, replacing the voltage with the voltage at the last sampling point, and if the current change value is greater than 3% of the maximum charging and discharging current, directly rejecting the voltage at the moment, wherein the data before and after cleaning is shown in fig. 2, a graph a is before cleaning, and a graph b is after cleaning.
Further, in S4, a hash mapping is established to calculate internal resistance, an OCV-SOC curve obtained by fitting is fitted to obtain an OCV specific value corresponding to each SOC, the SOC is stored in a key vector keys of the hash table, the OCV is stored in a value vector value of the hash table, total current, cell voltage, and total SOC collected in real time are obtained, and charging or discharging is determined according to change of the SOC.
Further, for the online identification of the direct current internal resistance of each monomer in the charging process, the calculation formula is as follows:
OCV(k)＝Hashmap(SOC(k))
where OCV (k) is the open circuit voltage at the k-th time, SOC (k) is the state of charge at the k-th time, hashmap is the hash map established above, R (k) is the DC internal resistance at the k-th time, U t (k) Is the cell terminal voltage at the kth time.
Further, in S4, in the discharging process, the direct current internal resistance of the discharging section is indirectly obtained by using a filtering algorithm in combination with the least square characteristic curve, in the discharging process, due to the complexity of the discharging working condition of the electric vehicle, the charging current symbol is defined to be negative, the discharging current symbol is defined to be positive, a normal discharging process in which the current is greater than 0 and a braking energy recovery in which the current is less than 0 exist in the driving process, and current jump caused by other working conditions also exists, in order to ensure the validity of the discharging internal resistance identification, filtering processing needs to be performed on the current jump moment, and multiple off-line experiments verify that the current of 0.1C (C refers to (current) multiplying power of the battery) is selected as the boundary condition. And setting the current with the current absolute value smaller than 0.1C multiplying power as 0, and calculating the obtained internal resistance to be infinite according to the formula, namely not referring. After filtering by the method, the direct current internal resistance at the discharging moment can be identified by using a mode of identifying the direct current internal resistance in the charging stage. It should be noted that, due to the complexity of the discharge condition, the discharge direct current internal resistance is only used as a detection reference index of the safety pre-warning.
Further, the early warning method of the charging segment in the S5 on the time dimension is as follows:
first, selecting the internal resistances of all the monomers in the 70% -90% SOC interval:
j represents the number of single batteries, and i represents the ith sampling point of different SOC;
then, each row of the matrix R is normalized by the calculation formula:
in the formula r i The row vector of the R matrix is processed to obtain a dimensionless R _ normal matrix:
calculating the mean and standard deviation M of the R _ normal row vectors i And S i ：
Wherein n represents the number of the single batteries;
finally, the variation coefficients V of different sampling points are obtained i ：
The result of the coefficient of variation is that the uniformity of each battery cell is good within 0.15, and when the coefficient of variation at a certain time is greater than 0.15, the inconsistency of the battery cells occurs at the time.
Further, the early warning method of the charging segment in the space dimension in the S5 includes:
firstly, selecting n single batteries and m sampling moments to establish a matrix X ij (i =1,2, \8230;, n; j =1,2, \8230;, m), then the matrix column vector is normalized, and the specific gravity p of the ith cell at the j-th sampling point at that moment is calculated ij ：
Then, an entropy value e at the j-th time is calculated j ：
Wherein the content of the first and second substances,
calculating the information entropy redundancy d according to the entropy value j ：
d j ＝1-e j
And (3) carrying out weight calculation on the redundancy:
the sum of all the j moments is calculated by each single cell to obtain the comprehensive score s of each single cell i ：
And finally, subtracting the average value of the comprehensive score of each battery monomer and the scores of all the monomers to obtain a deviation degree delta s, wherein the larger the deviation degree is, the larger the deviation degree of the monomer from most of the monomers is, judging whether the monomer is abnormal or not according to the deviation degree, and through experimental trial and error, setting the threshold value of the deviation degree to be 4.
Further, in the step S3, voltage values at the starting time of 30 charging segments, the time when the current is zero during the operation process and exceeds 30min, and the time when the charging is finished and the charging is sufficiently settled are extracted.
Claims (9)
1. A real-time safety early warning method for an electric vehicle power battery based on big operation data is characterized by comprising the following steps:
s1, reading data, namely reading total current, total SOC (state of charge) and monomer voltage of past historical data of a power battery;
s2, data cleaning, namely cleaning missing data, repeated value data and error data;
s3, analyzing data, extracting voltage values at different charging moments, recording SOC values at corresponding moments, and establishing an OCV-SOC curve;
s4, identifying parameters, namely identifying the parameters of the real-time collected data by using a Rint model through an OCV-SOC curve obtained by fitting to obtain the direct current internal resistance of a charging section and the direct current internal resistance of a discharging section;
and S5, safety early warning, namely, respectively adopting an entropy weight method and a variation coefficient method to carry out safety early warning on the internal resistance of the charging segment in a space dimension and a time dimension, and setting a threshold value for the internal resistance of the discharging segment.
2. The real-time safety early warning method for the power battery of the electric automobile based on the big running data as claimed in claim 1, characterized in that in S2, aiming at the data volume of the missing value is less than or equal to 4 sampling points, a nearest neighbor interpolation method is adopted to complement the data, and the missing data volume is directly removed when the data volume is greater than 4 sampling points; only one valid value is reserved for a duplicate value; and for the data with wrong contents, when the voltage data is zero, performing condition judgment on the current data at the moment, if the current at the moment and the last sampling point change by less than 3% of the maximum charge and discharge current, replacing the voltage with the voltage at the last sampling point, and if the current change value is greater than 3% of the maximum charge and discharge current, directly rejecting the voltage at the moment.
3. The real-time safety early warning method for the power battery of the electric vehicle based on the big running data is characterized in that in S4, hash mapping is established to calculate internal resistance, an OCV-SOC curve obtained through fitting is used for obtaining specific OCV values corresponding to each SOC, the SOC is stored into a key vector keys of a Hash table, the OCV is stored into a value vector values of the Hash table, and total current, cell voltage and total SOC collected in real time are obtained.
4. The real-time safety early warning method for the power battery of the electric automobile based on the big operation data as claimed in claim 3, characterized in that: in the charging process, the direct current internal resistance of each monomer is identified on line, and the calculation formula is as follows:
OCV(k)＝Hashmap(SOC(k)) (I)
in formula (I), OCV (k) is the open-circuit voltage at the k-th moment, SOC (k) is the state of charge at the k-th moment, hashmap is the Hashmap established above, and in formula (II), R (k) is the direct-current internal resistance at the k-th moment, U (k) is the direct-current internal resistance at the k-th moment t (k) Is the order of the kth timeTerminal voltage of the body cell.
5. The real-time safety early warning method for the power battery of the electric automobile based on the big operation data as claimed in claim 4, wherein: and S4, in the discharging process, combining a filtering algorithm with a least square characteristic curve to indirectly obtain the direct current internal resistance of the discharging section.
6. The real-time safety early warning method for the power battery of the electric automobile based on the big operation data as claimed in claim 1, wherein the early warning method for the charging segment in the S5 in the time dimension is as follows:
firstly, selecting the internal resistance of all monomers in 70% -90% SOC interval:
wherein j represents the number of the single batteries, and i represents the ith sampling point of different SOCs;
then, each row of the matrix R is normalized, and the calculation formula is:
in the formula r i And (3) representing the row vector of the R matrix, and obtaining a dimensionless R _ normal matrix after processing:
calculating the mean and standard deviation M of the R _ normal row vector i And S i ：
Wherein n represents the number of the single batteries;
finally, the variation coefficients V of different sampling points are obtained i ：
The result of the coefficient of variation is that the uniformity of each battery cell is good within 0.15, and when the coefficient of variation at a certain time is greater than 0.15, the inconsistency of the battery cells occurs at the time.
7. The real-time safety early warning method for the power battery of the electric automobile based on the operation big data as claimed in claim 1, wherein the early warning method for the charging segment in the S5 in the spatial dimension is as follows:
firstly, selecting n single batteries and m sampling moments to establish a matrix X ij (i =1,2, \8230;, n; j =1,2, \8230;, m), then the matrix column vector is normalized, and the specific gravity p of the ith cell at the jth sampling point at that time is calculated ij ：
Then, an entropy value e at the j-th time is calculated j ：
Wherein the content of the first and second substances,
calculating information entropy redundancy d according to entropy value j ：
d j ＝1-e j
And (3) carrying out weight calculation on the redundancy:
the sum of all the time points j is calculated by each single cell to obtain the comprehensive score s of each single cell i ：
And finally, subtracting the average value of the total score of each battery monomer and the scores of all the monomers to obtain a deviation degree delta s, wherein the larger the deviation degree is, the larger the deviation degree of the monomer from most monomers is reflected, and judging whether the monomer is abnormal or not according to the deviation degree.
8. The real-time safety early warning method for the power battery of the electric automobile based on the big operation data as claimed in claim 1, characterized in that: and S3, extracting voltage values of the starting time of a plurality of charging segments, the time when the current is zero in the operation process and exceeds 30min, and the time after the charging is finished and the charging is fully settled.
9. The real-time safety early warning method for the electric vehicle battery based on the big operation data as claimed in claim 5, wherein: in the discharging process, the current with the current absolute value smaller than 0.1C multiplying power is set to be 0, and if the internal resistance obtained through calculation according to the formula (I) and the formula (II) is infinite, reference is not made.
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| CN116466241B (en) * | 2023-05-06 | 2024-03-26 | 重庆标能瑞源储能技术研究院有限公司 | Thermal runaway positioning method for single battery |
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