CN114152880A - Soft package battery sensor fault online detection method - Google Patents

Soft package battery sensor fault online detection method Download PDF

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CN114152880A
CN114152880A CN202210118609.3A CN202210118609A CN114152880A CN 114152880 A CN114152880 A CN 114152880A CN 202210118609 A CN202210118609 A CN 202210118609A CN 114152880 A CN114152880 A CN 114152880A
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CN114152880B (en
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王耀南
冯运
毛建旭
朱青
张辉
莫洋
钟杭
江一鸣
谭浩然
李玲
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Hunan University
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Abstract

The invention discloses a soft package battery sensor fault on-line detection method, which comprises the steps of measuring and recording battery input current and terminal voltage data, acquiring surface temperature data of a soft package battery through a thermocouple sensor, and training and verifying a preset prediction model according to a principal component matrix, an input current vector and a terminal voltage data vector by using a rolling time domain on-line learning method on the premise of not knowing information of a system model and parameters to obtain an estimated value of the temperature data at a corresponding moment; and obtaining a fault detection residual according to the thermocouple temperature data measured value and the temperature data estimated value at the corresponding moment, and obtaining a detection result according to the fault detection residual, a preset residual evaluation function and a preset fault detection threshold. And the sensor fault detection of the soft package lithium ion battery is realized by using a small number of thermocouples and current and voltage sensors on the premise of not knowing an accurate model.

Description

Soft package battery sensor fault online detection method
Technical Field
The invention belongs to the technical field of fault detection, and particularly relates to an online fault detection method for a soft package battery sensor.
Background
Compared with square and cylindrical batteries, the soft package lithium ion battery has the characteristics of high energy density, high safety, high flexibility and low cost, is a power source of new energy automobiles, and has the operation safety which is a key concern in the scientific research community and the industry. The battery management system monitors, evaluates and manages the battery state information through various sensors (temperature, voltage, current, etc.), and is important for high performance, long service life and safe operation of the battery. However, the number of the sensors is large, and when a fault occurs, the battery management system cannot accurately obtain the state information of the battery system, so that the battery is irreversibly damaged or even has potential safety hazards. At present, a fault detection method based on a battery internal thermodynamic model needs to know information of a system model and parameters in advance, and in practical application, an accurate model is often difficult to obtain and various interferences exist, so that application and popularization of the method are restricted. Therefore, it is highly desirable to develop a data-driven online detection method for the failure of the pouch battery sensor.
Disclosure of Invention
Aiming at the technical problems, the invention provides an online detection method for the faults of a soft package battery sensor.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a soft package battery sensor fault online detection method comprises the following steps:
step S100: measuring and recording battery input current and terminal voltage data, acquiring surface temperature data of the soft package battery through a thermocouple sensor, expressing the input current and terminal voltage data into a preset vector form to obtain an input current vector and a terminal voltage data vector, and expressing the surface temperature data of the soft package battery into a preset matrix form to obtain a temperature data matrix;
step S200: obtaining a singular value matrix by adopting a KL decomposition method for the temperature data matrix, selecting the largest singular value from the singular value matrix, calculating to obtain a corresponding basis function matrix, and obtaining a principal element matrix according to the basis function matrix and the temperature data matrix;
step S300: training and verifying a preset prediction model by using a rolling time domain online learning method according to a principal component matrix, an input current vector and a terminal voltage data vector to obtain an estimated value of temperature data at a corresponding moment;
step S400: the method comprises the steps of obtaining a thermocouple temperature data measured value at a moment, obtaining a fault detection residual according to the thermocouple temperature data measured value and a temperature data estimated value at a corresponding moment, and obtaining a detection result according to the fault detection residual, a preset residual evaluation function and a preset fault detection threshold.
Preferably, in step S100, the input current and terminal voltage data are expressed in a preset vector form, and the surface temperature data of the pouch battery is expressed in a preset matrix form, specifically:
Figure 63534DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 118078DEST_PATH_IMAGE002
and
Figure 328349DEST_PATH_IMAGE003
respectively a measured terminal voltage data vector and a current data vector,
Figure 101132DEST_PATH_IMAGE004
in order to be a step of time,
Figure 376256DEST_PATH_IMAGE005
is composed of
Figure 550885DEST_PATH_IMAGE006
A matrix of temperature data measured by individual thermocouples.
Preferably, step S200 includes:
step S210: obtaining a matrix C according to the temperature data matrix, and performing singular value decomposition on the matrix C to obtain a singular value matrix;
step S220: selecting the largest from the matrix of singular values
Figure 682789DEST_PATH_IMAGE007
Singular values based on the temperature data matrix and maximum
Figure 880553DEST_PATH_IMAGE007
Obtaining a basis function matrix by using elements corresponding to the singular values; and obtaining a principal component matrix according to the basis function matrix and the temperature data matrix.
Preferably, the matrix C obtained in step S210 according to the temperature data matrix is specifically:
Figure 772416DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 535973DEST_PATH_IMAGE005
in the form of a matrix of temperature data,
Figure 42041DEST_PATH_IMAGE004
is the time step;
in step S210, singular value decomposition is performed on the matrix C, specifically:
Figure 789417DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 203081DEST_PATH_IMAGE010
the matrix is a singular value matrix, and the matrixes U and V are matrixes calculated after decomposition according to the characteristic values;
s220 specifically comprises the following steps:
Figure 289985DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 293187DEST_PATH_IMAGE012
in the form of a matrix of basis functions,
Figure 262280DEST_PATH_IMAGE013
is a main element matrix, and the main element matrix,
Figure 682897DEST_PATH_IMAGE014
is a matrix
Figure 686625DEST_PATH_IMAGE015
Middle correspondence
Figure 331233DEST_PATH_IMAGE007
The number of the elements with the maximum singular value, the principal elements and the eigenvectors after dimensionality reduction is
Figure 990885DEST_PATH_IMAGE007
And satisfy the conditions
Figure 762663DEST_PATH_IMAGE016
。
Preferably, step S300 includes:
step S310: on-line learning method using rolling time domain, each principal element in principal element matrix is subjected to on-line learning according to principal element matrix, input current vector and terminal voltage data vector
Figure 620897DEST_PATH_IMAGE017
Training alone, for principal elements
Figure 374090DEST_PATH_IMAGE018
Of 1 at
Figure 583354DEST_PATH_IMAGE019
An input matrix of a training window is
Figure 142512DEST_PATH_IMAGE020
，
Figure 58515DEST_PATH_IMAGE021
First action of
Figure 294193DEST_PATH_IMAGE022
Second action
Figure 725174DEST_PATH_IMAGE023
By analogy, action m
Figure 25706DEST_PATH_IMAGE024
；
Wherein the content of the first and second substances,
Figure 124112DEST_PATH_IMAGE025
is the length of the training window, each training window having therein
Figure 281424DEST_PATH_IMAGE025
The number of the batches is one,
Figure 137384DEST_PATH_IMAGE026
is the length of data in each batch, first row
Figure 54656DEST_PATH_IMAGE027
Is the training data of the first batch, and so on, the second batch
Figure 741989DEST_PATH_IMAGE025
The line is the first
Figure 273465DEST_PATH_IMAGE025
Training data for individual batches;
step S320: the training algorithm adopted for training each batch of data is a lasso algorithm corresponding to the first batch
Figure 679038DEST_PATH_IMAGE028
In a window
Figure 321372DEST_PATH_IMAGE029
The objective function for the individual batch parameter estimation is:
Figure 128791DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 880103DEST_PATH_IMAGE031
representing the value of the variable at which the following equation is minimized,
Figure 710656DEST_PATH_IMAGE032
is composed of
Figure 218997DEST_PATH_IMAGE033
First of the matrix
Figure 615344DEST_PATH_IMAGE034
The rows of the image data are, in turn,
Figure 488622DEST_PATH_IMAGE035
and
Figure 603208DEST_PATH_IMAGE036
are respectively the first
Figure 665973DEST_PATH_IMAGE034
Each batch of parameter vectors to be estimated and estimated,
Figure 120088DEST_PATH_IMAGE037
and
Figure 226585DEST_PATH_IMAGE038
for the basis function terms and the regularization coefficients,
Figure 828467DEST_PATH_IMAGE039
and
Figure 616295DEST_PATH_IMAGE040
respectively representing 2-norm and 1-norm of Euclidean space;
step S330: after training
Figure 252812DEST_PATH_IMAGE028
After the window's data, verify the second with the following inputs
Figure 467893DEST_PATH_IMAGE028
A preset prediction model trained for each window:
Figure 71919DEST_PATH_IMAGE041
corresponding preset prediction model verification output
Figure 194595DEST_PATH_IMAGE042
Is composed of
Figure 623303DEST_PATH_IMAGE043
The calculation formula of (c) is:
Figure 337181DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 648076DEST_PATH_IMAGE045
is as follows
Figure 777706DEST_PATH_IMAGE025
Estimating parameter vectors of each batch;
step S340: to pairIn the first place
Figure 139549DEST_PATH_IMAGE028
Per window preset predictive model validation output
Figure 696432DEST_PATH_IMAGE046
And obtaining an estimated value of corresponding temperature data by combining the basis function matrix, specifically:
Figure 494624DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 490262DEST_PATH_IMAGE048
is composed of
Figure 893561DEST_PATH_IMAGE049
Time of day
Figure 683663DEST_PATH_IMAGE050
An estimate of the temperature data for each thermocouple,
Figure 221348DEST_PATH_IMAGE051
is composed of
Figure 692780DEST_PATH_IMAGE049
Time of day
Figure 747324DEST_PATH_IMAGE007
An estimate of the value of the individual pivot,
Figure 973906DEST_PATH_IMAGE012
is a basis function matrix;
step S350: when it comes to
Figure 949952DEST_PATH_IMAGE028
A first training window
Figure 21813DEST_PATH_IMAGE025
After the data of each batch is trained, the data of the second batch is obtained
Figure 399705DEST_PATH_IMAGE028
The training window moves to
Figure 16762DEST_PATH_IMAGE052
A training window, continuing from
Figure 542422DEST_PATH_IMAGE052
The first batch of the training window begins to train to the second batch
Figure 152395DEST_PATH_IMAGE052
A first training window
Figure 384793DEST_PATH_IMAGE025
And (5) carrying out batch processing until all the principal elements are trained.
Preferably, the step S400 of obtaining a fault detection residual according to the thermocouple temperature data measurement value and the temperature data estimation value at the corresponding time includes:
Figure 687598DEST_PATH_IMAGE053
wherein the content of the first and second substances,
Figure 684242DEST_PATH_IMAGE054
in order to detect the residual error for the fault,
Figure 301168DEST_PATH_IMAGE055
is a measurement of the temperature data of the thermocouple,
Figure 450390DEST_PATH_IMAGE056
the temperature data estimation value of the corresponding moment is obtained;
defining a time-domain transform
Figure 127359DEST_PATH_IMAGE057
The following fault detection residuals are obtained:
Figure 96452DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure 313806DEST_PATH_IMAGE059
is composed of
Figure 520797DEST_PATH_IMAGE060
Fault detection residual at time.
Preferably, the step S400 of obtaining a detection result according to the fault detection residual, the preset residual evaluation function and the preset fault detection threshold includes:
step S410: obtaining a residual error evaluation value according to the fault detection residual error and a preset residual error evaluation function, specifically:
Figure 181716DEST_PATH_IMAGE061
step S420: when the residual evaluation value is larger than the preset fault detection threshold value, the method indicates that
Figure 638105DEST_PATH_IMAGE060
There is a fault at any moment, otherwise,
Figure 596834DEST_PATH_IMAGE060
the method has no fault at any moment, and specifically comprises the following steps:
Figure 455069DEST_PATH_IMAGE062
wherein the content of the first and second substances,
Figure 473840DEST_PATH_IMAGE063
is composed of
Figure 417526DEST_PATH_IMAGE060
The residual evaluation value at the time of the time,
Figure 494459DEST_PATH_IMAGE064
is a preset fault detection threshold.
Preferably, step S400 further comprises:
step S430: when the residual evaluation value is larger than the supremum of the peak norm of the Euclidean space of the fault detection residual, switching the preset training mode of the prediction model to an off-line mode, and when the preset training mode of the prediction model is in the off-line mode, estimating the parameter vector
Figure 207200DEST_PATH_IMAGE065
The real-time updating is not performed any more, and the numerical value of the moment before switching is kept; otherwise, the preset training mode of the prediction model is continuously kept in the online mode.
According to the soft package battery sensor fault on-line detection method, the input current and terminal voltage data of the battery are measured and recorded, the surface temperature data of the soft package battery are collected through the thermocouple sensor, and on the premise that the information of a system model and parameters is not known, a preset prediction model is trained and verified according to a principal component matrix, an input current vector and a terminal voltage data vector by using a rolling time domain on-line learning method to obtain an estimated value of the temperature data at the corresponding moment; and obtaining a fault detection residual error according to the thermocouple temperature data measured value and the temperature data estimated value at the corresponding moment, obtaining a detection result according to the fault detection residual error, a preset residual error evaluation function and a preset fault detection threshold value, and realizing the sensor fault detection of the soft package lithium ion battery according to the detection result.
Drawings
Fig. 1 is a flowchart of a method for online detecting a fault of a pouch battery sensor according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an online learning method of a rolling time domain according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a distribution of 20 thermocouples provided in accordance with an embodiment of the present invention;
FIG. 4 shows an input current under no fault condition according to an embodiment of the present invention
Figure 131294DEST_PATH_IMAGE066
A curve;
FIG. 5 shows a schematic view of the present inventionEmbodiments provide for real terminal voltage without failure
Figure 827855DEST_PATH_IMAGE067
A curve;
fig. 6 is a failure detection result in a mode switching manner for a first failure according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a failure detection result in a continuous online update mode for a first failure according to an embodiment of the present invention;
fig. 8 is a failure detection result in a mode switching manner for a second failure according to an embodiment of the present invention;
fig. 9 is a failure detection result in the continuous online update mode for the second failure in an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention is further described in detail below with reference to the accompanying drawings.
In one embodiment, as shown in fig. 1, an online detection method for a pouch cell sensor fault includes the following steps:
step S100: measuring and recording battery input current and terminal voltage data, acquiring surface temperature data of the soft package battery through a thermocouple sensor, expressing the input current and terminal voltage data into a preset vector form to obtain an input current vector and a terminal voltage data vector, and expressing the surface temperature data of the soft package battery into a preset matrix form to obtain a temperature data matrix;
step S200: obtaining a singular value matrix by adopting a KL decomposition method on the temperature data matrix, and selecting the largest singular value matrix
Figure 128386DEST_PATH_IMAGE069
Calculating the singular values to obtain corresponding basis function matrixes, and obtaining a principal element matrix according to the basis function matrixes and the temperature data matrix;
step S300: training and verifying a preset prediction model by using a rolling time domain online learning method according to a principal component matrix, an input current vector and a terminal voltage data vector to obtain an estimated value of temperature data at a corresponding moment;
step S400: the method comprises the steps of obtaining a thermocouple temperature data measured value at a moment, obtaining a fault detection residual according to the thermocouple temperature data measured value and a temperature data estimated value at a corresponding moment, and obtaining a detection result according to the fault detection residual, a preset residual evaluation function and a preset fault detection threshold.
Compared with other fault detection methods, the fault detection method provided by the invention only needs sensor data, and does not need to know the accurate information of the system model. And the sensor fault detection of the soft package lithium ion battery is realized by using a small number of thermocouples and current and voltage sensors on the premise of not knowing an accurate model.
In one embodiment, in step S100, the input current and terminal voltage data are expressed in a preset vector form, and the surface temperature data of the pouch battery is expressed in a preset matrix form, specifically:
Figure 961213DEST_PATH_IMAGE070
wherein the content of the first and second substances,
Figure 869257DEST_PATH_IMAGE002
and
Figure 990797DEST_PATH_IMAGE003
respectively a measured terminal voltage data vector and a current data vector,
Figure 157336DEST_PATH_IMAGE004
in order to be a step of time,
Figure 579090DEST_PATH_IMAGE005
is composed of
Figure 110566DEST_PATH_IMAGE006
A matrix of temperature data measured by individual thermocouples.
Specifically, the current and voltage data are input into the battery thermal distribution model, and the temperature measurement data are output.
In one embodiment, step S200 includes:
step S210: obtaining a matrix C according to the temperature data matrix, and performing singular value decomposition on the matrix C to obtain a singular value matrix;
step S220: selecting the largest from the matrix of singular values
Figure 516139DEST_PATH_IMAGE007
Singular values based on the temperature data matrix and maximum
Figure 424052DEST_PATH_IMAGE007
Obtaining a basis function matrix by using elements corresponding to the singular values; and obtaining a principal component matrix according to the basis function matrix and the temperature data matrix.
In one embodiment, the matrix C obtained in step S210 according to the temperature data matrix is specifically:
Figure 480739DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 979853DEST_PATH_IMAGE005
in the form of a matrix of temperature data,
Figure 810406DEST_PATH_IMAGE004
is the time step;
in step S210, singular value decomposition is performed on the matrix C, specifically:
Figure 318748DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 715094DEST_PATH_IMAGE010
the matrix is a singular value matrix, and the matrixes U and V are matrixes calculated after decomposition according to the characteristic values;
s220 specifically comprises the following steps:
Figure 588372DEST_PATH_IMAGE071
wherein the content of the first and second substances,
Figure 719270DEST_PATH_IMAGE012
in the form of a matrix of basis functions,
Figure 500144DEST_PATH_IMAGE013
is a main element matrix, and the main element matrix,
Figure 219839DEST_PATH_IMAGE014
is a matrix
Figure 326335DEST_PATH_IMAGE015
Middle correspondence
Figure 928218DEST_PATH_IMAGE007
The number of the elements with the maximum singular value, the principal elements and the eigenvectors after dimensionality reduction is
Figure 716045DEST_PATH_IMAGE007
And satisfy the conditions
Figure 593041DEST_PATH_IMAGE016
。
Specifically, for
Figure 604860DEST_PATH_IMAGE072
The matrix adopts a Karhunen-Loe've (KL) decomposition method, and the specific steps are that the matrix is subjected to
Figure 631722DEST_PATH_IMAGE073
(the superscript T represents the transpose of the matrix) and Singular Value Decomposition (SVD) is performed to obtain
Figure 285557DEST_PATH_IMAGE074
Wherein, in the step (A),
Figure 714264DEST_PATH_IMAGE075
as singular valuesThe elements on the diagonal line of the matrix are arranged from large to small in singular value, and the maximum element is selected
Figure 162563DEST_PATH_IMAGE007
The singular values and the corresponding basis function matrix and principal component matrix are calculated as:
Figure 489770DEST_PATH_IMAGE076
wherein the content of the first and second substances,
Figure 619400DEST_PATH_IMAGE012
in the form of a matrix of basis functions,
Figure 699352DEST_PATH_IMAGE013
is a main element matrix, and the main element matrix,
Figure 584131DEST_PATH_IMAGE014
is a matrix
Figure 585585DEST_PATH_IMAGE015
Middle correspondence
Figure 315644DEST_PATH_IMAGE007
The number of the elements with the maximum singular value, the principal elements and the eigenvectors after dimensionality reduction is
Figure 764949DEST_PATH_IMAGE007
And satisfy the conditions
Figure 555050DEST_PATH_IMAGE016
。
In one embodiment, step S300 includes:
step S310: on-line learning method using rolling time domain, each principal element in principal element matrix is subjected to on-line learning according to principal element matrix, input current vector and terminal voltage data vector
Figure 43800DEST_PATH_IMAGE017
Training alone, for principal elements
Figure 577550DEST_PATH_IMAGE018
Of 1 at
Figure 632093DEST_PATH_IMAGE019
An input matrix of a training window is
Figure 796358DEST_PATH_IMAGE020
，
Figure 319875DEST_PATH_IMAGE021
First action of
Figure 594998DEST_PATH_IMAGE022
Second action
Figure 35207DEST_PATH_IMAGE023
By analogy, action m
Figure 901532DEST_PATH_IMAGE024
；
Wherein the content of the first and second substances,
Figure 364874DEST_PATH_IMAGE025
is the length of the training window, each training window having therein
Figure 240426DEST_PATH_IMAGE025
The number of the batches is one,
Figure 472825DEST_PATH_IMAGE026
is the length of data in each batch, first row
Figure 762248DEST_PATH_IMAGE027
Is the training data of the first batch, and so on, the second batch
Figure 775203DEST_PATH_IMAGE025
The line is the first
Figure 392129DEST_PATH_IMAGE025
Training data for individual batches;
step S320: the training algorithm adopted for training each batch of data is a lasso algorithm corresponding to the first batch
Figure 275772DEST_PATH_IMAGE028
In a window
Figure 15058DEST_PATH_IMAGE029
The objective function for the individual batch parameter estimation is:
Figure 187413DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 889921DEST_PATH_IMAGE031
representing the value of the variable at which the following equation is minimized,
Figure 159228DEST_PATH_IMAGE032
is composed of
Figure 741519DEST_PATH_IMAGE033
First of the matrix
Figure 463488DEST_PATH_IMAGE034
The rows of the image data are, in turn,
Figure 218954DEST_PATH_IMAGE035
and
Figure 280451DEST_PATH_IMAGE036
are respectively the first
Figure 610807DEST_PATH_IMAGE034
Each batch of parameter vectors to be estimated and estimated,
Figure 757755DEST_PATH_IMAGE037
and
Figure 51333DEST_PATH_IMAGE038
for basis function terms and regularization systemsThe number of the first and second groups is,
Figure 29653DEST_PATH_IMAGE039
and
Figure 219326DEST_PATH_IMAGE040
respectively representing 2-norm and 1-norm of Euclidean space;
step S330: after training
Figure 650307DEST_PATH_IMAGE028
After the window's data, verify the second with the following inputs
Figure 763888DEST_PATH_IMAGE028
A preset prediction model trained for each window:
Figure 534398DEST_PATH_IMAGE041
corresponding preset prediction model verification output
Figure 957289DEST_PATH_IMAGE042
Is composed of
Figure 875566DEST_PATH_IMAGE043
The calculation formula of (c) is:
Figure 714209DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 981636DEST_PATH_IMAGE045
is as follows
Figure 513111DEST_PATH_IMAGE025
Estimating parameter vectors of each batch;
step S340: for the first
Figure 918685DEST_PATH_IMAGE028
Presetting of individual windowsIs output by the predictive model verification
Figure 623336DEST_PATH_IMAGE046
And obtaining an estimated value of corresponding temperature data by combining the basis function matrix, specifically:
Figure 368438DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 883864DEST_PATH_IMAGE048
is composed of
Figure 511154DEST_PATH_IMAGE049
Time of day
Figure 691600DEST_PATH_IMAGE050
An estimate of the temperature data for each thermocouple,
Figure 619105DEST_PATH_IMAGE051
is composed of
Figure 289121DEST_PATH_IMAGE049
Time of day
Figure 606969DEST_PATH_IMAGE007
An estimate of the value of the individual pivot,
Figure 637111DEST_PATH_IMAGE012
is a basis function matrix;
step S350: when it comes to
Figure 419122DEST_PATH_IMAGE028
A first training window
Figure 260039DEST_PATH_IMAGE025
After the data of each batch is trained, the data of the second batch is obtained
Figure 65184DEST_PATH_IMAGE028
The training window moves to
Figure 915329DEST_PATH_IMAGE052
A training window, continuing from
Figure 37000DEST_PATH_IMAGE052
The first batch of the training window begins to train to the second batch
Figure 252080DEST_PATH_IMAGE052
A first training window
Figure 341259DEST_PATH_IMAGE025
And (5) carrying out batch processing until all the principal elements are trained.
Specifically, for the principal elements after the dimension reduction, a new online learning method of a rolling time domain is proposed for model training and prediction, and a schematic diagram of the method is shown in fig. 2.
In one embodiment, the step S400 of obtaining the fault detection residual according to the thermocouple temperature data measurement value and the temperature data estimation value at the corresponding time includes:
Figure 932777DEST_PATH_IMAGE053
wherein the content of the first and second substances,
Figure 423802DEST_PATH_IMAGE054
in order to detect the residual error for the fault,
Figure 809784DEST_PATH_IMAGE055
is a measurement of the temperature data of the thermocouple,
Figure 386258DEST_PATH_IMAGE056
the temperature data estimation value of the corresponding moment is obtained;
defining a time-domain transform
Figure 830403DEST_PATH_IMAGE057
The following fault detection residuals are obtained:
Figure 910354DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure 732817DEST_PATH_IMAGE059
is composed of
Figure 796588DEST_PATH_IMAGE060
Fault detection residual at time.
In one embodiment, the obtaining the detection result according to the fault detection residual, the preset residual evaluation function and the preset fault detection threshold in step S400 includes:
step S410: obtaining a residual error evaluation value according to the fault detection residual error and a preset residual error evaluation function, specifically:
Figure 464329DEST_PATH_IMAGE061
step S420: when the residual evaluation value is larger than the preset fault detection threshold value, the method indicates that
Figure 929946DEST_PATH_IMAGE060
There is a fault at any moment, otherwise,
Figure 470780DEST_PATH_IMAGE060
the method has no fault at any moment, and specifically comprises the following steps:
Figure 693950DEST_PATH_IMAGE062
wherein the content of the first and second substances,
Figure 493279DEST_PATH_IMAGE063
is composed of
Figure 547823DEST_PATH_IMAGE060
The residual evaluation value at the time of the time,
Figure 446509DEST_PATH_IMAGE064
is a preset fault detection threshold.
In one embodiment, step S400 further comprises:
step S430: when the residual evaluation value is larger than the supremum of the peak norm of the Euclidean space of the fault detection residual, switching the preset training mode of the prediction model to an off-line mode, and when the preset training mode of the prediction model is in the off-line mode, estimating the parameter vector
Figure 484872DEST_PATH_IMAGE065
The real-time updating is not performed any more, and the numerical value of the moment before switching is kept; otherwise, the preset training mode of the prediction model is continuously kept in the online mode.
Specifically, the fault detection threshold is obtained by a Kernel Density Estimation (KDE)
Figure 806001DEST_PATH_IMAGE077
The specific steps are to adopt Gaussian to check an evaluation function under the condition of no fault
Figure 918313DEST_PATH_IMAGE078
Is estimated, defining a significance level
Figure 50217DEST_PATH_IMAGE079
Time of day corresponding
Figure 310297DEST_PATH_IMAGE078
Detecting thresholds for faults
Figure 389112DEST_PATH_IMAGE077
。
In a detailed embodiment, an experimental platform comprising a battery test cabinet, a thermostat and a battery management system is set up to collect battery surface temperature data, an experimental object is a LiFePO 4/graphite soft package lithium ion battery, the length, the width and the thickness of the battery are respectively 0.24 meter, 0.18 meter and 0.00783 meter (the thickness is negligible), and the total thickness is 0.24 meter, 0.18 meter and 0.00783 meter20 thermocouples were located on the surface, as schematically shown in FIG. 3. FIG. 4 and FIG. 5 show the input current in the fault-free case
Figure 418248DEST_PATH_IMAGE080
And true terminal voltage
Figure 471785DEST_PATH_IMAGE081
Curve line. The following two types of sensor faults are injected respectively:
failure 1: constant measurement bias of 1K was injected from 2500 seconds for thermocouples No. 10 to No. 15 in fig. 3;
and (3) failure 2: the end-to-end voltage measurement sensor injects a constant measurement bias of 3V starting at 2500 seconds.
Fig. 6 and 7 show the results of failure detection in the case where the mode switching manner in step S430 is adopted for failure 1) and the continuous online update mode is adopted, respectively, and fig. 8 and 9 show the results of failure detection in the case where the mode switching manner in step S430 is adopted for failure 2) and the continuous online update mode is adopted, respectively. Comparing fig. 6 and 7, and fig. 8 and 9, it can be seen that the Fault Detection Rate (FDR) can be effectively improved by using the mode switching manner proposed in this patent, and table 1 is a specific value of the Fault detection rate under different situations.
Figure 422424DEST_PATH_IMAGE082
TABLE 1 Fault detection results for different training modes
Figure 101667DEST_PATH_IMAGE083
Compared with other fault detection methods, the fault detection method of the soft package battery sensor only needs sensor data, and does not need to know accurate information of a system model. Compared with the method adopting continuous online updating, the mode switching method provided by the invention can effectively improve the fault detection rate. And the sensor fault detection of the soft package lithium ion battery is realized by using a small number of thermocouples and current and voltage sensors on the premise of not knowing an accurate model.
The online fault detection method for the soft package battery sensor provided by the invention is described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the core concepts of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (8)

1. The online detection method for the fault of the soft package battery sensor is characterized by comprising the following steps:
step S100: measuring and recording battery input current and terminal voltage data, acquiring surface temperature data of the soft package battery through a thermocouple sensor, expressing the input current and the terminal voltage data into a preset vector form to obtain an input current vector and a terminal voltage data vector, and expressing the surface temperature data of the soft package battery into a preset matrix form to obtain a temperature data matrix;
step S200: obtaining a singular value matrix by adopting a KL decomposition method for the temperature data matrix, selecting the largest singular value from the singular value matrix, calculating to obtain a corresponding basis function matrix, and obtaining a principal element matrix according to the basis function matrix and the temperature data matrix;
step S300: training and verifying a preset prediction model according to the principal component matrix, the input current vector and the terminal voltage data vector by using a rolling time domain online learning method to obtain an estimated value of temperature data at a corresponding moment;
step S400: the method comprises the steps of obtaining a thermocouple temperature data measured value at a moment, obtaining a fault detection residual according to the thermocouple temperature data measured value and the temperature data estimated value at the corresponding moment, and obtaining a detection result according to the fault detection residual, a preset residual evaluation function and a preset fault detection threshold.
2. The method according to claim 1, wherein in step S100, the input current and the terminal voltage data are expressed in a preset vector form, and the surface temperature data of the pouch battery is expressed in a preset matrix form, specifically:
Figure 630344DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 104051DEST_PATH_IMAGE002
and
Figure 73144DEST_PATH_IMAGE003
respectively a measured terminal voltage data vector and a current data vector,
Figure 493761DEST_PATH_IMAGE004
in order to be a step of time,
Figure 248221DEST_PATH_IMAGE005
is composed of
Figure 96092DEST_PATH_IMAGE006
A matrix of temperature data measured by individual thermocouples.
3. The method according to claim 2, wherein step S200 comprises:
step S210: obtaining a matrix C according to the temperature data matrix, and performing singular value decomposition on the matrix C to obtain a singular value matrix;
step S220: selecting the largest of the singular value matrices
Figure 552481DEST_PATH_IMAGE007
A singular value according to the temperatureData matrix and maximum
Figure 573526DEST_PATH_IMAGE007
Obtaining a basis function matrix by using elements corresponding to the singular values; and obtaining a principal component matrix according to the basis function matrix and the temperature data matrix.
4. The method according to claim 3, wherein the obtaining of the matrix C according to the temperature data matrix in step S210 specifically includes:
Figure 635023DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 450533DEST_PATH_IMAGE005
in the form of a matrix of temperature data,
Figure 643485DEST_PATH_IMAGE004
is the time step;
in step S210, singular value decomposition is performed on the matrix C, specifically:
Figure 405905DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 384225DEST_PATH_IMAGE010
the matrix is a singular value matrix, and the matrixes U and V are matrixes calculated after decomposition according to the characteristic values;
s220 specifically comprises the following steps:
Figure 370636DEST_PATH_IMAGE011
wherein the content of the first and second substances,
Figure 4880DEST_PATH_IMAGE012
in the form of a matrix of basis functions,
Figure 102149DEST_PATH_IMAGE013
is a main element matrix, and the main element matrix,
Figure 685708DEST_PATH_IMAGE014
is a matrix
Figure 46282DEST_PATH_IMAGE015
Middle correspondence
Figure 964559DEST_PATH_IMAGE007
The number of the elements with the maximum singular value, the principal elements and the eigenvectors after dimensionality reduction is
Figure 865519DEST_PATH_IMAGE007
And satisfy the conditions
Figure 756115DEST_PATH_IMAGE016
。
5. The method of claim 4, wherein step S300 comprises:
step S310: on-line learning method using rolling time domain, according to the principal component matrix, the input current vector and the terminal voltage data vector, each principal component in the principal component matrix
Figure 349907DEST_PATH_IMAGE017
Training alone, for principal elements
Figure 693164DEST_PATH_IMAGE018
Of 1 at
Figure 396152DEST_PATH_IMAGE019
An input matrix of a training window is
Figure 734729DEST_PATH_IMAGE020
，
Figure 233843DEST_PATH_IMAGE021
First action of
Figure 64396DEST_PATH_IMAGE022
Second action
Figure 323470DEST_PATH_IMAGE023
By analogy, action m
Figure 985396DEST_PATH_IMAGE024
；
Wherein the content of the first and second substances,
Figure 858674DEST_PATH_IMAGE025
is the length of the training window, each training window having therein
Figure 973260DEST_PATH_IMAGE025
The number of the batches is one,
Figure 19714DEST_PATH_IMAGE026
is the length of data in each batch, first row
Figure 739408DEST_PATH_IMAGE027
Is the training data of the first batch, and so on, the second batch
Figure 95172DEST_PATH_IMAGE025
The line is the first
Figure 634738DEST_PATH_IMAGE025
Training data for individual batches;
step S320: the training algorithm adopted for training each batch of data is a lasso algorithm corresponding to the first batch
Figure 484882DEST_PATH_IMAGE028
In a window
Figure 855821DEST_PATH_IMAGE029
The objective function for the individual batch parameter estimation is:
Figure 70901DEST_PATH_IMAGE030
wherein the content of the first and second substances,
Figure 425659DEST_PATH_IMAGE031
representing the value of the variable at which the following equation is minimized,
Figure 751598DEST_PATH_IMAGE032
is composed of
Figure 993355DEST_PATH_IMAGE033
First of the matrix
Figure 441654DEST_PATH_IMAGE034
The rows of the image data are, in turn,
Figure 955812DEST_PATH_IMAGE035
and
Figure 147759DEST_PATH_IMAGE036
are respectively the first
Figure 696552DEST_PATH_IMAGE034
Each batch of parameter vectors to be estimated and estimated,
Figure 50173DEST_PATH_IMAGE037
and
Figure 366141DEST_PATH_IMAGE038
for the basis function terms and the regularization coefficients,
Figure 96200DEST_PATH_IMAGE039
and
Figure 499499DEST_PATH_IMAGE040
respectively representing 2-norm and 1-norm of Euclidean space;
step S330: after training
Figure 289600DEST_PATH_IMAGE028
After the window's data, the following inputs are used to verify the second window
Figure 778351DEST_PATH_IMAGE028
A preset prediction model trained for each window:
Figure 577679DEST_PATH_IMAGE041
corresponding preset prediction model verification output
Figure 835485DEST_PATH_IMAGE042
Is composed of
Figure 547221DEST_PATH_IMAGE043
The calculation formula of (c) is:
Figure 320005DEST_PATH_IMAGE044
wherein the content of the first and second substances,
Figure 595128DEST_PATH_IMAGE045
is as follows
Figure 769757DEST_PATH_IMAGE025
Estimating parameter vectors of each batch;
step S340: for the said first
Figure 104924DEST_PATH_IMAGE028
Per window preset predictive model validation output
Figure 99425DEST_PATH_IMAGE046
And obtaining an estimated value of corresponding temperature data by combining the basis function matrix, specifically:
Figure 489824DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 253380DEST_PATH_IMAGE048
is composed of
Figure 759448DEST_PATH_IMAGE049
Time of day
Figure 506824DEST_PATH_IMAGE050
An estimate of the temperature data for each thermocouple,
Figure 920488DEST_PATH_IMAGE051
is composed of
Figure 7393DEST_PATH_IMAGE049
Time of day
Figure 497411DEST_PATH_IMAGE007
An estimate of the value of the individual pivot,
Figure 669766DEST_PATH_IMAGE012
is a basis function matrix;
step S350: when it comes to
Figure 152700DEST_PATH_IMAGE028
A first training window
Figure 94111DEST_PATH_IMAGE025
After the data of each batch is trained, the data of the second batch is obtained
Figure 738719DEST_PATH_IMAGE028
The training window moves to
Figure 460688DEST_PATH_IMAGE052
A training window, continuing from
Figure 419416DEST_PATH_IMAGE052
The first batch of the training window begins to train to the second batch
Figure 529848DEST_PATH_IMAGE052
A first training window
Figure 345358DEST_PATH_IMAGE025
And (5) carrying out batch processing until all the principal elements are trained.
6. The method of claim 5, wherein the step S400 of obtaining a fault detection residual according to the thermocouple temperature data measured value and the temperature data estimated value at the corresponding time comprises:
Figure 492305DEST_PATH_IMAGE053
wherein the content of the first and second substances,
Figure 317042DEST_PATH_IMAGE054
in order to detect the residual error for the fault,
Figure 29783DEST_PATH_IMAGE055
is a measurement of the temperature data of the thermocouple,
Figure 766926DEST_PATH_IMAGE056
the temperature data estimation value of the corresponding moment is obtained;
defining a time-domain transform
Figure 135590DEST_PATH_IMAGE057
The following fault detection residuals are obtained:
Figure 498438DEST_PATH_IMAGE058
wherein the content of the first and second substances,
Figure 331265DEST_PATH_IMAGE059
is composed of
Figure 691839DEST_PATH_IMAGE060
Fault detection residual at time.
7. The method according to claim 6, wherein obtaining the detection result according to the fault detection residual, the preset residual evaluation function and the preset fault detection threshold in step S400 includes:
step S410: obtaining a residual error evaluation value according to the fault detection residual error and a preset residual error evaluation function, specifically:
Figure 610117DEST_PATH_IMAGE061
step S420: when the residual evaluation value is larger than a preset fault detection threshold value, indicating that the residual evaluation value is larger than the preset fault detection threshold value
Figure 714339DEST_PATH_IMAGE060
There is a fault at any moment, otherwise,
Figure 916519DEST_PATH_IMAGE060
the method has no fault at any moment, and specifically comprises the following steps:
Figure 447995DEST_PATH_IMAGE062
wherein the content of the first and second substances,
Figure 587989DEST_PATH_IMAGE063
is composed of
Figure 292640DEST_PATH_IMAGE060
The residual evaluation value at the time of the time,
Figure 834480DEST_PATH_IMAGE064
is a preset fault detection threshold.
8. The method of claim 7, wherein step S400 further comprises:
step S430: when the residual error evaluation value is larger than the supremum of the peak norm of the Euclidean space of the fault detection residual error, switching the preset training mode of the prediction model to an off-line mode, and when the preset training mode of the prediction model is in the off-line mode, estimating the parameter vector
Figure 536856DEST_PATH_IMAGE065
The real-time updating is not performed any more, and the numerical value of the moment before switching is kept; otherwise, the preset training mode of the prediction model is continuously kept in the online mode.
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