CN114879049A - Power battery consistency safety state evaluation method - Google Patents

Power battery consistency safety state evaluation method Download PDF

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CN114879049A
CN114879049A CN202210664642.6A CN202210664642A CN114879049A CN 114879049 A CN114879049 A CN 114879049A CN 202210664642 A CN202210664642 A CN 202210664642A CN 114879049 A CN114879049 A CN 114879049A
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consistency
power battery
characteristic
state
safety state
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闫文
万鑫铭
赵岩
王澎
程端前
张怒涛
抄佩佩
蒲云川
杨飞
王振宇
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China Automotive Engineering Research Institute Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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Abstract

The invention relates to the technical field of power battery evaluation, in particular to a power battery consistency safety state evaluation method, which comprises the following steps: extracting a plurality of charging data segments capable of reflecting the vehicle state; calculating the standard deviation characteristic and the variance entropy consistency characteristic of the monomer voltage of each charging data segment to obtain a characteristic value; acquiring the covering times of a charging process of covering a target preset interval in the full life cycle of the vehicle, and combining the covering times with the first correction times and the second correction times to respectively obtain a reference sample and an evaluation sample; constructing the characteristic values into a characteristic matrix, and performing unsupervised training after processing to divide the characteristic values into two types to obtain a confusion matrix; constructing a power battery consistency safety state quantitative calculation model; and constructing a state evaluation alarm grade model, and outputting a grade result to represent the safety state of the power battery. The method and the system can timely warn the potential risks of the vehicle and avoid the accident risk caused by the abnormal state evolution of the vehicle.

Description

Power battery consistency safety state evaluation method
Technical Field
The invention relates to the technical field of power battery evaluation, in particular to a power battery consistency safety state evaluation method.
Background
Compared with the existing fuel-powered automobile, the new energy automobile has short development history, the development of each technology of the new energy automobile is not as mature as the fuel-powered automobile, and the research and research on the new energy automobile and the power battery thereof are far less thorough than the research and research on the fuel-powered automobile. The actual operation of the new energy automobile has the characteristics of variable environment, complex scene, multidimensional data, redundancy, isomerism, strong coupling and the like, great challenges are brought to exploring and mining the safety state of the power automobile contained in the large data, and the state evaluation of the new energy automobile power battery of the strong coupling complex system is difficult to realize through mechanism analysis.
Because the new energy automobile uploads the operation data according to the standard GB-32960 during the service period, and the representation signals such as voltage are a representation quantity of the comprehensive state of the power battery, the difference degree of the states of the power battery during the service period can be further quantized by analyzing and mining the safety characteristics of the historical operation data of the power automobile.
Therefore, in order to find the safety problem of the power automobile in time, a complex and coupled electrochemical mechanism is not considered, and the overall signal safety degree of the power battery system is described directly by calculating the data characteristics of the characterization signals, so that the safety state of the battery system is quantized, the rapid quantization and evaluation of the safety state of the battery are realized, and the power automobile is guided to perform safety early warning.
Disclosure of Invention
The invention aims to provide a power battery consistency safety state evaluation method to rapidly quantify and evaluate the safety state of a power battery.
The method for evaluating the consistency safety state of the power battery in the scheme comprises the following steps:
acquiring historical operation data of a vehicle to be evaluated, and extracting a plurality of charging data segments capable of reflecting the vehicle state from the historical operation data;
calculating the standard deviation characteristic and the variance entropy consistency characteristic of the monomer voltage of each charging data segment to obtain a characteristic value;
step three, acquiring the covering times of the charging process of the vehicle in the full life cycle covering target preset interval, multiplying the covering times by a first parameter to obtain a first correction time, taking the characteristic value of the first correction time in the charging data segment as a reference sample, multiplying the covering times by a second parameter to obtain a second correction time, and taking the characteristic value of the second correction time in the charging data segment as an evaluation sample;
constructing the characteristic values into a characteristic matrix, deleting the characteristic values of which the quantiles are more than 99 quantiles in the characteristic matrix, carrying out unsupervised training of a preset algorithm on the characteristic matrix, and dividing the characteristic matrix into two types to obtain a confusion matrix;
constructing a power battery consistency safety state quantitative calculation model according to the confusion matrix, and calculating to obtain a consistency state safety evaluation score through the consistency safety state quantitative calculation model;
and step six, constructing a state evaluation alarm grade model according to the consistency state safety evaluation score, outputting a grade result, and representing the safety state of the power battery by using the grade result.
The beneficial effect of this scheme is:
the charging data in the historical operating data of the new energy automobile are sliced to obtain charging data fragments, the charging characteristics are extracted, an evaluation score is obtained by combining a consistency state safety evaluation method, a state evaluation alarm grade is finally output, the potential risk of the automobile is early warned in time, and the condition that the abnormal state of the automobile evolves into more serious accident risk is avoided.
Further, in the first step, an OCV curve of the battery of the vehicle to be evaluated is obtained, and a charging data segment is extracted for an SOC interval on the OCV curve.
The beneficial effects are that: the safety state of the vehicle can be accurately reflected on the premise of keeping the corresponding characteristics.
Further, in the second step, the calculation formula of the standard deviation feature is as follows:
Figure BDA0003691142230000021
the calculation formula of the variance entropy consistency characteristic is as follows:
Figure BDA0003691142230000022
the beneficial effects are that: through calculation of standard deviation characteristics, the discrete degree of data can be analyzed quickly and accurately, and through definition of variance entropy for describing the disorder degree of the system, the voltage difference situation among all the monomers of the power battery system can be described, namely the consistency is good and bad, the smaller the variance entropy is, the smaller the voltage difference of the monomers is, the better the consistency is, and the safer the battery system is.
Further, in the third step, the number of times of covering is represented by n, the first parameter is represented by α, the second parameter is represented by β, the first number of times of correction is n × α, and the second number of times of correction is n × β.
The beneficial effects are that: the character representation is carried out on each data, so that the calculation of the quantization process is smoother.
Further, the value range of the first parameter α is: (0.05, 0.5), wherein the value range of the second parameter is as follows: (0.01,0.2).
The beneficial effects are that: the number of features in the data set can be reduced and the calculation amount can be reduced by limiting the value ranges of the first parameter and the second parameter, and the difference between the reference sample and the evaluation data can be increased by taking values of the first parameter and the second parameter in different ranges.
Further, in the fourth step, the feature matrix is represented as X, and a confusion matrix obtained after unsupervised training of the feature matrix is as follows:
Figure BDA0003691142230000031
the beneficial effects are that: by calculating the confusion matrix, the feature matrix can be simply classified, and the processing speed of the historical operation data is improved.
Further, in the fifth step, the consistency safety state quantitative calculation model is:
Figure BDA0003691142230000032
wherein S represents a consistency state safety assessment score, # (X) of the assessment sample 0_1 )、#(X 0 )、#(X 1_1 )、#(X 1 ) Respectively represent (X) 0_1 )、X 0 、X 1_1 、X 1 The number of the samples is such that,
Figure BDA0003691142230000033
representing the mean of the variance features in the reference sample,
Figure BDA0003691142230000034
represents the mean of the variance entropy consistency feature in the reference sample,
Figure BDA0003691142230000035
representing the mean of the variance features in the evaluation sample,
Figure BDA0003691142230000036
means for variance entropy consistency feature means, μ sum, in the evaluation samples
Figure BDA0003691142230000037
Represents weight, and the value range of the weight is [0,1 ]]。
The beneficial effects are that: the consistency safety degree of the reference data and the data to be evaluated can be described in a quantification mode through quantification processing of the number of each sample, the safety degree of the two sections of data can be measured through quantification processing of the variance characteristic mean value and the variance entropy consistency characteristic mean value, the safety evaluation score can be obtained through quantification, and the evaluation accuracy is improved.
Further, in the sixth step, the state assessment alarm level model is:
Figure BDA0003691142230000038
the alarmLevel represents the alarm level obtained by evaluating the state of data to be evaluated, 0-level, 1-level, 2-level and 3-level alarms respectively correspond to no risk, low risk, medium risk and high risk, r is 1-S represents the vehicle risk, and r is 1 、r 2 、r 3 Respectively corresponding to a risk alarm threshold, and r 1 <r 2 <r 3 。
The beneficial effects are that: by quantitatively evaluating the consistency safety state of the power battery, risk factors can be more intuitively analyzed and found from a large amount of historical operating data.
Drawings
FIG. 1 is a block flow diagram of an embodiment of a method for evaluating consistency and safety status of a power battery according to the present invention;
FIG. 2 is an OCV curve diagram in an embodiment of the method for evaluating the consistency safety state of a power battery according to the present invention;
fig. 3 is a voltage data diagram of a single charging data segment in an embodiment of the method for evaluating consistency safety status of a power battery according to the invention.
Detailed Description
The following is a more detailed description of the present invention by way of specific embodiments.
Examples
The method for evaluating the consistency safety state of the power battery, as shown in fig. 1, comprises the following steps:
step one, obtaining historical operation data of a vehicle to be evaluated, obtaining an OCV curve of the battery of the vehicle to be evaluated, wherein the OCV curve is a reference tool of a power battery, each battery is provided by a manufacturer when the battery is on the market, extracting a plurality of charging data segments capable of reflecting the vehicle state from the historical operation data, for example, by taking 100 charging data segments as an example, voltage data of a single charging data segment is shown in fig. 3, defining a preset time period of the vehicle just beginning to be in service as a reference state of the vehicle safety state, namely, a default new vehicle is good, extracting charging data segments from an SOC interval on the OCV curve, namely, the extracted charging data segments are charging data containing 85% to 95% of electric quantity, and selecting the SOC interval according to the OCV curve is as follows: within this SOC interval, there is an abrupt adjustment of the voltage, corresponding to giving a typical excitation, and then the battery state can be represented based on the signal change of this excitation, as shown at the box in fig. 2.
Step two, calculating the standard deviation characteristic and the variance entropy consistency characteristic of the cell voltage of each charging data segment to obtain two characteristic values, and then obtaining a 100 × 2 characteristic value matrix after calculating 100 charging data segments, wherein the cell voltage is the voltage value of a cell, the cell voltage is represented as C, and the calculation formula of the standard deviation characteristic is as follows:
Figure BDA0003691142230000041
wherein x is ij Is the unit voltage value, x, of the jth battery cell at the ith moment i The average value of all the monomer voltages at the moment i is obtained;
the calculation formula of the variance entropy consistency characteristic is as follows:
Figure BDA0003691142230000051
wherein E is j Is the square of the cell voltage value.
Step three, acquiring the covering times of the charging process of the vehicle in the full life cycle covering target preset interval, wherein the covering times are represented as n, multiplying the covering times by a first parameter to obtain a first correction time, representing the first parameter as alpha, and the value range of the first parameter alpha is as follows: (0.05, 0.5), taking a characteristic value of the previous first correction times in the charging data segment as a reference sample, multiplying the covering times by a second parameter to obtain a second correction times, and expressing the second parameter as beta, wherein the value range of the second parameter is as follows: (0.01, 0.2), and taking the characteristic value of the second correction times in the charging data segment as an evaluation sample, wherein the second correction times is n x beta.
In step three, "front" means that the acquired charging data segment performs data acquisition according to the starting point of time, and "rear" means that the acquired charging data segment performs data acquisition from the ending point of time, for example: the acquired charging data segments are 100 data obtained by 100 times of charging, the data segments are arranged according to a time sequence, the first correction frequency can be set to be 20%, the second correction frequency can be set to be 10%, the first 20 data segments of the first correction frequency, namely the first 20 data segments of the 100 data segments, are taken as a comparison group, and the second 10 data segments of the second correction frequency, namely the last 10 data segments of the 100 data segments, are taken as evaluation samples.
Step four, constructing the characteristic value into a characteristic matrix, expressing the characteristic matrix as X, deleting the characteristic value which is more than 99 quantiles in the characteristic matrix, carrying out unsupervised training on the characteristic matrix by using a preset algorithm which is the existing K-Means algorithm, and dividing the X into two types, namely a 0 th type and a 1 st type respectively to obtain a confusion matrix, wherein the confusion matrix is as follows:
Figure BDA0003691142230000052
wherein X 0 For reference sample, X 1 To evaluate the samples.
Wherein, X 0_0 Denotes grouping the 0 component of the reference sample into class 0, X 0_1 Denotes grouping the 0 component of the reference sample into class 1, X 1_0 Denotes the classification of 1 component of the evaluation sample into class 0, X 1_1 Indicating that 1 component of the evaluation sample is classified into class 1.
And step five, constructing a power battery consistency safety state quantitative calculation model according to the confusion matrix on the basis of the reference sample and the evaluation sample in the confusion matrix, and calculating to obtain a consistency state safety evaluation score through the consistency safety state quantitative calculation model, wherein the consistency safety state quantitative calculation model is as follows:
Figure BDA0003691142230000053
wherein S represents a consistency state safety assessment score, # (X) of the assessment sample 0_1 )、#(X 0 )、#(X 1_1 )、#(X 1 ) Respectively represent (X) 0_1 )、X 0 、X 1_1 、X 1 The number of the samples is such that,
Figure BDA0003691142230000061
representing the mean of the variance features in the reference sample,
Figure BDA0003691142230000062
represents the mean of the variance entropy consistency feature in the reference sample,
Figure BDA0003691142230000063
representing the mean of the variance features in the evaluation sample,
Figure BDA0003691142230000064
means for variance entropy consistency feature means, μ sum, in the evaluation samples
Figure BDA0003691142230000065
Represents weight, and the value range of the weight is [0,1 ]]。
At the beginning, the state of the vehicle battery is good, namely two characteristic values of the reference sample are taken as an initial origin, the closer the two characteristic values calculated by the evaluation sample are to the initial origin, the closer the state of the surface vehicle battery is to the initial state, and the more the safety of the vehicle isGood results are obtained. The battery of the vehicle is increasingly poor along with the running use of the vehicle, data in the subsequent vehicle use process are used as evaluation samples to be classified, and then corresponding scores are calculated. I.e. with (X) 0_1 ) The higher the representative occupancy rate of classifying the evaluation sample 1 group into the 0 th group means that the evaluation sample is closer to the reference sample, and the smaller the difference between the evaluation sample and the reference sample is, the smaller the degree of separation is, the higher the safety of the vehicle is.
Step six, a state evaluation alarm grade model is built according to the consistency state safety evaluation score, namely the vehicle risk is obtained by subtracting the consistency state safety evaluation score from the total number, a grade result is output, namely the vehicle risk is represented as r-1-S, the safety state of the power battery is represented by the grade result, and the state evaluation alarm grade model is as follows:
Figure BDA0003691142230000066
the alarmLevel represents the alarm level obtained by evaluating the state of data to be evaluated, 0-level, 1-level, 2-level and 3-level alarms respectively correspond to no risk, low risk, medium risk and high risk, r is 1-S represents the vehicle risk, and r is 1 、r 2 、r 3 Respectively corresponding to a risk alarm threshold, and r 1 <r 2 <r 3 The risk alarm threshold is set according to actual requirements, such as 40, 60 and 80.
According to the method, the charging data fragments are obtained through slicing of historical charging data of the new energy automobile, the charging characteristic extraction is completed, the evaluation scores are obtained through a consistency state safety evaluation method, the state evaluation alarm level is finally output, the potential risk of the automobile is early warned in time, and the condition that the abnormal state of the automobile evolves into more serious accident risk is avoided.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.

Claims (8)

1. The method for evaluating the consistency safety state of the power battery is characterized by comprising the following steps of:
acquiring historical operation data of a vehicle to be evaluated, and extracting a plurality of charging data segments capable of reflecting the vehicle state from the historical operation data;
calculating the standard deviation characteristic and the variance entropy consistency characteristic of the monomer voltage of each charging data segment to obtain a characteristic value;
step three, acquiring the covering times of the charging process of the vehicle in the full life cycle covering target preset interval, multiplying the covering times by a first parameter to obtain a first correction time, taking the characteristic value of the first correction time in the charging data segment as a reference sample, multiplying the covering times by a second parameter to obtain a second correction time, and taking the characteristic value of the second correction time in the charging data segment as an evaluation sample;
constructing the characteristic values into a characteristic matrix, deleting the characteristic values of which the quantiles are more than 99 quantiles in the characteristic matrix, carrying out unsupervised training of a preset algorithm on the characteristic matrix, and dividing the characteristic matrix into two types to obtain a confusion matrix;
constructing a power battery consistency safety state quantitative calculation model according to the confusion matrix, and calculating to obtain a consistency state safety evaluation score through the consistency safety state quantitative calculation model;
and step six, constructing a state evaluation alarm grade model according to the consistency state safety evaluation score, outputting a grade result, and representing the safety state of the power battery by using the grade result.
2. The power battery consistency safety state evaluation method according to claim 1, characterized in that: in the first step, an OCV curve of a battery of the vehicle to be evaluated is obtained, and a charging data segment is extracted from an SOC interval on the OCV curve.
3. The power battery consistency safety state evaluation method according to claim 2, characterized in that: in the second step, the calculation formula of the standard deviation features is as follows:
Figure FDA0003691142220000011
the calculation formula of the variance entropy consistency characteristic is as follows:
Figure FDA0003691142220000012
4. the power battery consistency safety state evaluation method according to claim 3, characterized in that: in the third step, the number of covering times is represented as n, the first parameter is represented as α, the second parameter is represented as β, the first correction time is n × α, and the second correction time is n × β.
5. The power battery consistency safety state evaluation method according to claim 4, characterized in that: the value range of the first parameter alpha is as follows: (0.05, 0.5), wherein the value range of the second parameter is as follows: (0.01,0.2).
6. The power battery consistency safety state evaluation method according to claim 5, characterized in that: in the fourth step, the feature matrix is represented as X, and the confusion matrix obtained after the feature matrix is subjected to unsupervised training is as follows:
Figure FDA0003691142220000021
7. the power battery consistency safety state evaluation method according to claim 5, characterized in that: in the fifth step, the consistency safety state quantitative calculation model is as follows:
Figure FDA0003691142220000022
wherein S represents a consistency state safety assessment score, # (X) of the assessment sample 0_1 )、#(X 0 )、#(X 1_1 )、#(X 1 ) Respectively represent (X) 0_1 )、X 0 、X 1_1 、X 1 The number of the samples is such that,
Figure FDA0003691142220000023
representing the mean of the variance features in the reference sample,
Figure FDA0003691142220000024
represents the mean of the variance entropy consistency feature in the reference sample,
Figure FDA0003691142220000025
representing the mean of the variance features in the evaluation sample,
Figure FDA0003691142220000026
means for variance entropy consistency feature means, μ sum, in the evaluation samples
Figure FDA0003691142220000027
Represents weight, and the value range of the weight is [0,1 ]]。
8. The power battery consistency safety state evaluation method according to claim 5, characterized in that: in the sixth step, the state assessment alarm grade model is as follows:
Figure FDA0003691142220000028
the alarmLevel represents the alarm level obtained by evaluating the state of data to be evaluated, 0-level, 1-level, 2-level and 3-level alarms respectively correspond to no risk, low risk, medium risk and high risk, r is 1-S represents the vehicle risk, and r is 1 、r 2 、r 3 Respectively corresponding to a risk alarm threshold, and r 1 <r 2 <r 3 。
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115728662A (en) * 2022-12-06 2023-03-03 北汽福田汽车股份有限公司 Battery fault risk judgment method and device and vehicle
CN118447600A (en) * 2024-06-28 2024-08-06 北京成功领行汽车技术有限责任公司 Fault diagnosis method based on vehicle-end T-BOX, electronic equipment and storage medium

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115728662A (en) * 2022-12-06 2023-03-03 北汽福田汽车股份有限公司 Battery fault risk judgment method and device and vehicle
CN115728662B (en) * 2022-12-06 2024-07-09 北汽福田汽车股份有限公司 Battery fault risk judging method and device and vehicle
CN118447600A (en) * 2024-06-28 2024-08-06 北京成功领行汽车技术有限责任公司 Fault diagnosis method based on vehicle-end T-BOX, electronic equipment and storage medium
CN118447600B (en) * 2024-06-28 2024-10-15 北京成功领行汽车技术有限责任公司 Fault diagnosis method based on vehicle-end T-BOX, electronic equipment and storage medium

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