CN115951231A - Automobile power battery fault early warning method based on single battery voltage correlation - Google Patents

Automobile power battery fault early warning method based on single battery voltage correlation Download PDF

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CN115951231A
CN115951231A CN202310239436.5A CN202310239436A CN115951231A CN 115951231 A CN115951231 A CN 115951231A CN 202310239436 A CN202310239436 A CN 202310239436A CN 115951231 A CN115951231 A CN 115951231A
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voltage
single battery
time
battery
time window
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相里康
贾泳强
龚贤武
马建
马宇骋
张昭
吕晶晶
邱志鹏
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Changan University
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Abstract

The invention provides an automobile power battery fault early warning method based on single battery voltage correlation, which comprises the following steps: acquiring voltage data of each single battery of the vehicle based on the time sequence after data cleaning; determining the length of a time window, acquiring voltage data of each single battery in the current window, and calculating the voltage correlation coefficient sum of each single battery and other single batteries at each moment based on a sliding window by using a correlation coefficient algorithm; and (3) performing statistics on the voltage correlation coefficients of the single batteries by using a Z score of a statistical tool, obtaining abnormal voltage coefficients A of the single batteries at each moment based on a sliding window, setting an abnormal coefficient fault judgment threshold, and realizing power battery voltage inconsistency fault early warning based on the abnormal coefficients of the voltage correlation coefficients of the single batteries. The invention has the advantages of data simplification and easy acquisition, can realize real-time early warning of the thermal runaway fault of the vehicle, and can position the single battery with the fault.

Description

Automobile power battery fault early warning method based on single battery voltage correlation
Technical Field
The invention belongs to the technical field of automobile power batteries, and particularly relates to an automobile power battery fault early warning method based on single battery voltage correlation.
Background
Under the dual pressure of environmental pollution and energy crisis, energy conservation and emission reduction become common responsibilities of countries in the world, and new energy automobiles represented by electric automobiles become the first choice of future transportation tools. The power battery system is used as a key and core component of the electric automobile, and the safety and the reliability of the power battery system directly influence the comprehensive performance of the electric automobile to a great extent. The lithium ion battery is the most widely used energy storage element in the new energy automobile industry at present by virtue of the advantages of high power, high energy density, long cycle life, low self-discharge rate and the like. However, in recent years, as a lithium ion battery system fails under extreme operating conditions and harsh environments, and thus a fire accident is occurring, safety thereof is receiving more and more attention. Safety accidents characterized by thermal runaway occur occasionally, and hidden dangers are brought to the life and property safety of the public.
Thermal runaway of electric vehicles has uncertainty, and the possibility of thermal runaway occurs during charging at rest, during driving, and during parking. In addition, the method is also sudden, and no obvious abnormality is detected by the monitoring data and the background data of the battery management system before thermal runaway occurs. Therefore, early warning of thermal runaway of the power battery of the electric automobile is very important for ensuring the safety of the electric automobile.
At present, the research on the thermal runaway early warning method of the battery mainly focuses on a power battery system fault diagnosis method developed based on laboratory conditions or a model simulation test carried out by using related software. Such research is often large in calculation amount, low in diagnosis accuracy, poor in universality of diagnosis targets, insufficient in robustness characteristics of noise from a system and an environment and system uncertainty, and not ideal in actual application effect on electric vehicles. Therefore, a fault diagnosis method with high accuracy and high reliability for a power battery system of a real vehicle is yet to be developed to improve the safety of the electric vehicle.
Based on the method, the automobile power battery fault early warning method based on the voltage correlation of the single battery is provided.
Disclosure of Invention
The invention provides a method for early warning the fault of an automobile power battery based on the voltage correlation of a single battery, aiming at overcoming the defects in the prior art, and aims to solve the problems in the background art.
In order to solve the technical problems, the invention adopts the technical scheme that: the automobile power battery fault early warning method based on the voltage correlation of the single battery comprises the following steps:
s1, acquiring voltage data of each single battery of the vehicle based on time sequence after data cleaning,
the method specifically comprises the following steps:
s11, firstly, cleaning the voltage signal of the power battery monomer of the vehicle, deleting empty data and data larger than 5V in the data, and then selecting the data;
s12, extracting voltage data of each single battery of the vehicle based on the time sequence, and constructing a voltage data set U as follows:
Figure SMS_1
、
wherein, U is a voltage data set of all the single batteries based on a time sequence;
u i is voltage data of No. i single battery based on time series;
k is the number of the single batteries in the power battery pack;
s2, determining the length of a time window, acquiring voltage data of each single battery in the current window, and calculating the voltage correlation coefficient sum of each single battery and other single batteries at each moment based on a sliding window by using a correlation coefficient algorithm;
s3, performing Z fraction on the voltage correlation coefficient of each single battery obtained in the S2 by using a statistical tool, and obtaining a voltage abnormal coefficient A of each single battery at each moment based on a sliding window;
s4: and setting an abnormal coefficient fault judgment threshold, and realizing power battery voltage inconsistency fault early warning based on the abnormal coefficient of the voltage correlation coefficient of each single battery.
Further, S2 specifically includes the following steps:
s21, determining from
Figure SMS_2
Time of day to>
Figure SMS_3
Time window length of time instant>
Figure SMS_4
；
Wherein the content of the first and second substances,
Figure SMS_5
is greater than or equal to at moment>
Figure SMS_6
Time of day;
s22, constructing
Figure SMS_7
Is reached at moment>
Figure SMS_8
At the moment in time the voltage data set of the individual cell->
Figure SMS_9
：
Figure SMS_10
、
Figure SMS_11
、
Wherein, the first and the second end of the pipe are connected with each other,
Figure SMS_12
is the first->
Figure SMS_13
A cell voltage data set at each time window, device for selecting or keeping>
Figure SMS_14
；
Figure SMS_15
The voltage values of the ith row and the jth single battery under the h time window are represented;
Figure SMS_16
is the j-th cell voltage vector under the h-th time window;
s23, calculating the sum of voltage correlation coefficients of the j unit cell and other unit cells in the h time window
Figure SMS_17
Comprises the following steps:
Figure SMS_18
、
Figure SMS_19
wherein it is present>
Figure SMS_20
The voltage correlation coefficient of the jth single battery and the Kth single battery under the h time window is obtained;
Figure SMS_21
is the average value of the number j monomer voltage vectors under the h time window;
Figure SMS_22
is the standard deviation of the j-th cell voltage vector under the h-th time window;
s24, voltage data set in h time window
Figure SMS_23
The voltage correlation coefficient of the single battery is as follows:
Figure SMS_24
、/>
s25, calculating the sum of the voltage correlation coefficients of each single battery and other single batteries under all time windows according to S23-S24 as follows:
Figure SMS_25
、
wherein n is the number of time windows.
Further, S3 specifically includes the following steps:
s31, calculating the voltage abnormity coefficient of the j unit battery based on the Z fraction under the h time window
Figure SMS_26
Comprises the following steps:
Figure SMS_27
、
wherein the content of the first and second substances,
Figure SMS_28
is the average correlation coefficient value of all the cell voltages in the h time window;
Figure SMS_29
is the standard deviation of all cell voltage correlation coefficient values under the h-th time window;
s32, voltage abnormity coefficients of all single batteries based on Z fraction in h time window
Figure SMS_30
Comprises the following steps:
Figure SMS_31
、
s33, calculating voltage abnormity coefficients A of all the single batteries based on the Z fraction under all the time windows according to S31-S32, wherein the voltage abnormity coefficients A are as follows:
Figure SMS_32
。
further, S4 specifically includes the following steps:
s41, setting an abnormal coefficient fault judgment threshold value Z;
s42, performing threshold value judgment on the abnormal coefficient values of the correlation coefficients of all the single batteries under all the time windows, and if the abnormal coefficient values do not exceed the threshold values, indicating that the vehicle has no risk of thermal runaway power battery failure;
if the threshold value is exceeded, the risk of thermal runaway power battery failure is indicated, the corresponding single battery code is recorded, and the alarm time is determined.
Compared with the prior art, the invention has the following advantages:
the voltage data of the single battery of the electric automobile is used, and the method has the advantage of data simplification; moreover, the voltage data of the single battery is real-time operation data of the electric automobile, and the method has the advantage of easy acquisition; secondly, the invention is not influenced by the voltage value of the single battery, and does not need to carry out classification analysis according to the running working condition and the charging and discharging working condition of the vehicle; the invention can realize real-time early warning of the thermal runaway fault of the vehicle and can position the single battery with the fault.
Drawings
Fig. 1 is a flow chart of the electric vehicle power battery fault early warning based on real vehicle data according to the present invention:
FIG. 2 is voltage data of each unit cell of the vehicle based on time series in the embodiment of the present invention;
FIG. 3 is a voltage relationship value of a single battery under each time window in the embodiment of the present invention;
fig. 4 is the voltage abnormality coefficient values of the unit cells at the respective time windows in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, the present invention provides a technical solution: the automobile power battery fault early warning method based on the voltage correlation of the single battery comprises the following steps:
s1, acquiring voltage data of each single battery of the vehicle based on time sequence after data cleaning,
the method specifically comprises the following steps:
s11, firstly, cleaning the voltage signal of the power battery monomer of the vehicle, deleting empty data and data larger than 5V in the data, and then selecting the data;
s12, extracting voltage data of each single battery of the vehicle based on the time sequence, and constructing a voltage data set U as follows:
Figure SMS_33
、
wherein, U is a voltage data set of all the single batteries based on a time sequence;
u i is voltage data of No. i single battery based on time series;
k is the number of the single batteries in the power battery pack.
S2, determining the length of a time window, acquiring voltage data of each single battery in the current window, and calculating the sum of voltage correlation coefficients of each single battery and other single batteries at each moment based on a sliding window by using a correlation coefficient algorithm;
the method specifically comprises the following steps:
s21, determining from
Figure SMS_34
Is reached at moment>
Figure SMS_35
Time window length of time of day->
Figure SMS_36
；
Wherein, the first and the second end of the pipe are connected with each other,
Figure SMS_37
is greater than or equal to at moment>
Figure SMS_38
Time of day;
s22, constructing from
Figure SMS_39
Time of day to>
Figure SMS_40
At the moment in time the voltage data set of the individual cell->
Figure SMS_41
：
Figure SMS_42
、
Figure SMS_43
、
Wherein the content of the first and second substances,
Figure SMS_44
is the first->
Figure SMS_45
The cell voltage data set at each time window, device for selecting or keeping>
Figure SMS_46
；
Figure SMS_47
The voltage values of the ith row and the jth single battery under the h time window are represented;
Figure SMS_48
is the j-th cell voltage vector under the h-th time window;
s23, calculating the sum of voltage correlation coefficients of the j unit cell and other unit cells in the h time window
Figure SMS_49
Comprises the following steps:
Figure SMS_50
、/>
Figure SMS_51
、
wherein the content of the first and second substances,
Figure SMS_52
the voltage correlation coefficient of the j-th single battery and the K-th single battery under the h-th time window is obtained;
Figure SMS_53
is the average value of the number j monomer voltage vectors under the h time window;
Figure SMS_54
is the standard deviation of the j number monomer voltage vector under the h time window;
Figure SMS_55
is the standard deviation of the No. K monomer voltage vector under the h time window;
s24, voltage data set in h time window
Figure SMS_56
Is not only a sheetThe voltage correlation coefficient of the bulk battery is as follows:
Figure SMS_57
、
s25, calculating the sum of the voltage correlation coefficients of each single battery and other single batteries under all time windows according to S23-S24 as follows:
Figure SMS_58
、
wherein n is the number of time windows.
S3, obtaining the voltage abnormal coefficient A of each single battery at each moment on the basis of a sliding window by using a statistical tool Z score of the voltage correlation coefficient of each single battery obtained in the S2;
the method specifically comprises the following steps:
s31, calculating the voltage abnormity coefficient of the j unit battery based on the Z fraction under the h time window
Figure SMS_59
Comprises the following steps:
Figure SMS_60
、
wherein, the first and the second end of the pipe are connected with each other,
Figure SMS_61
is the average correlation coefficient value of all the cell voltages in the h-th time window;
Figure SMS_62
is the standard deviation of all cell voltage correlation coefficient values under the h-th time window;
s32, voltage abnormity coefficients of all single batteries based on Z fraction in h time window
Figure SMS_63
Comprises the following steps:
Figure SMS_64
、
s33, calculating voltage abnormity coefficients A of all the single batteries based on the Z fraction under all the time windows according to the steps S31-S32 as follows:
Figure SMS_65
。/>
s4: setting an abnormal coefficient fault judgment threshold value, and realizing power battery voltage inconsistency fault early warning based on the abnormal coefficient of each single battery voltage correlation coefficient, wherein the method specifically comprises the following steps:
s41, setting an abnormal coefficient fault judgment threshold value Z;
s42, performing threshold value judgment on the abnormal coefficient values of the correlation coefficients of all the single batteries in all the time windows, and if the abnormal coefficient values do not exceed the threshold values, indicating that the vehicle has no risk of thermal runaway power battery failure;
if the threshold value is exceeded, the risk of thermal runaway power battery failure is indicated, the corresponding single battery code is recorded, and the alarm time is determined.
Fig. 2 is voltage data of each battery cell of a thermal runaway accident vehicle in time series, wherein the thermal runaway occurs when the vehicle has severe water diving at the last stage, and the vehicle is in a normal state before the thermal runaway accident vehicle. Taking the length of the time window =100, fig. 3 shows the voltage correlation coefficient value of each unit cell. Setting a threshold value Z =, and fig. 4 is abnormal coefficient value data of the voltage correlation coefficient of each unit cell;
it can be seen that the voltage abnormality coefficient value of a certain single battery exceeds-5 at the beginning, which indicates that the vehicle is at risk of thermal runaway, and the voltage abnormality coefficient value of the No. 86 single battery exceeds-5 after positioning the battery as No. 29 single battery and a few seconds later.
After 46 minutes, the vehicle had thermal runaway.
In conclusion, the electric vehicle power battery thermal runaway early warning method based on real vehicle data can well achieve early warning of vehicle thermal runaway faults, locate the faulty battery and the occurrence time, and cannot generate false alarm when the vehicle has no thermal runaway faults.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (4)

1. The automobile power battery fault early warning method based on the voltage correlation of the single battery is characterized by comprising the following steps of:
s1, acquiring voltage data of each single battery of the vehicle based on time sequence after data cleaning,
the method specifically comprises the following steps:
s11, firstly, cleaning the voltage signal of the power battery monomer of the vehicle, deleting empty data and data larger than 5V in the data, and then selecting the data;
s12, extracting voltage data of each single battery of the vehicle based on the time sequence, and constructing a voltage data set U as follows:
Figure QLYQS_1
、
wherein U is a time series based voltage data set of all the cells;
u i is voltage data of No. i single battery based on time series;
k is the number of the single batteries in the power battery pack;
s2, determining the length of a time window, acquiring voltage data of each single battery in the current window, and calculating the sum of voltage correlation coefficients of each single battery and other single batteries at each moment based on a sliding window by using a correlation coefficient algorithm;
s3, performing Z fraction on the voltage correlation coefficient of each single battery obtained in the S2 by using a statistical tool, and obtaining a voltage abnormal coefficient A of each single battery at each moment based on a sliding window;
s4: and setting an abnormal coefficient fault judgment threshold, and realizing power battery voltage inconsistency fault early warning based on the abnormal coefficient of the voltage correlation coefficient of each single battery.
2. The automobile power battery fault early warning method based on single battery voltage correlation as claimed in claim 1, wherein S2 specifically comprises the following steps:
s21, determining from
Figure QLYQS_2
Is reached at moment>
Figure QLYQS_3
Time window length of time instant>
Figure QLYQS_4
；
Wherein, the first and the second end of the pipe are connected with each other,
Figure QLYQS_5
is greater than or equal to at moment>
Figure QLYQS_6
Time of day;
s22, constructing from
Figure QLYQS_7
Time of day to>
Figure QLYQS_8
At the moment in time the voltage data set of the individual cell->
Figure QLYQS_9
：
Figure QLYQS_10
Figure QLYQS_11
、
Wherein the content of the first and second substances,
Figure QLYQS_12
is a first->
Figure QLYQS_13
The cell voltage data set at each time window, device for selecting or keeping>
Figure QLYQS_14
；
Figure QLYQS_15
The voltage values of the ith row and the jth single battery under the h time window are represented;
Figure QLYQS_16
is the j-th cell voltage vector under the h-th time window;
s23, calculating the sum of voltage correlation coefficients of the jth single battery and other single batteries under the h time window
Figure QLYQS_17
Comprises the following steps:
Figure QLYQS_18
、
Figure QLYQS_19
、/>
wherein the content of the first and second substances,
Figure QLYQS_20
the voltage correlation coefficient of the jth single battery and the Kth single battery under the h time window is obtained;
Figure QLYQS_21
is the average value of the number j monomer voltage vectors under the h time window;
Figure QLYQS_22
is the standard deviation of the j number monomer voltage vector under the h time window;
Figure QLYQS_23
is the standard deviation of the No. K monomer voltage vector under the h time window;
s24, voltage data set in h time window
Figure QLYQS_24
The voltage correlation coefficient of the single battery is as follows:
Figure QLYQS_25
、
s25, calculating the sum of the voltage correlation coefficients of each single battery and other single batteries under all time windows according to S23-S24 as follows:
Figure QLYQS_26
、
wherein n is the number of time windows.
3. The automobile power battery fault early warning method based on single battery voltage correlation as claimed in claim 2, wherein S3 specifically comprises the following steps:
s31, calculating the voltage abnormity coefficient of the j unit battery based on the Z fraction under the h time window
Figure QLYQS_27
Comprises the following steps:
Figure QLYQS_28
、
wherein the content of the first and second substances,
Figure QLYQS_29
is the average correlation coefficient value of all the cell voltages in the h-th time window;
Figure QLYQS_30
is the standard deviation of all cell voltage correlation coefficient values under the h-th time window;
s32, voltage abnormity coefficients of all single batteries based on Z fraction in h time window
Figure QLYQS_31
Comprises the following steps:
Figure QLYQS_32
、
s33, calculating voltage abnormity coefficients A of all the single batteries based on the Z fraction under all the time windows according to the steps S31-S32 as follows:
Figure QLYQS_33
。
4. the automobile power battery fault early warning method based on single battery voltage correlation as claimed in claim 3, wherein S4 specifically comprises the following steps:
s41, setting an abnormal coefficient fault judgment threshold value Z;
s42, performing threshold value judgment on the abnormal coefficient values of the correlation coefficients of all the single batteries in all the time windows, and if the abnormal coefficient values do not exceed the threshold values, indicating that the vehicle has no risk of thermal runaway power battery failure;
if the threshold value is exceeded, the risk of thermal runaway power battery failure is indicated, the corresponding single battery code is recorded, and the alarm time is determined.
CN202310239436.5A 2023-03-14 2023-03-14 Automobile power battery fault early warning method based on single battery voltage correlation Pending CN115951231A (en)

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CN116968556A (en) * 2023-07-26 2023-10-31 北京科技大学 Power and energy storage battery fault diagnosis method based on fuzzy entropy

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CN114583301A (en) * 2022-04-29 2022-06-03 国网浙江省电力有限公司电力科学研究院 Power station thermal runaway early warning method and system based on safety characteristic parameter representation system
CN114942387A (en) * 2022-07-20 2022-08-26 湖北工业大学 Real data-based power battery fault online detection method and system
CN115257379A (en) * 2022-08-05 2022-11-01 长安大学 Electric vehicle power battery thermal runaway early warning method based on real vehicle data

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CN114583301A (en) * 2022-04-29 2022-06-03 国网浙江省电力有限公司电力科学研究院 Power station thermal runaway early warning method and system based on safety characteristic parameter representation system
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