CN117310537A - Energy storage system health assessment and optimization method and system - Google Patents
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- 238000004146 energy storage Methods 0.000 title claims abstract description 83
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- 238000005457 optimization Methods 0.000 title claims abstract description 24
- 239000011159 matrix material Substances 0.000 claims abstract description 101
- 239000013598 vector Substances 0.000 claims abstract description 33
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- 238000011156 evaluation Methods 0.000 claims description 14
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention provides a health assessment and optimization method and system for an energy storage system, and relates to the field of energy storage. Comprising the following steps: acquiring operation parameters of an nth battery pack in the energy storage system at different moments; according to the operation parameters of the nth battery pack at different moments, a first matrix Z is constructed, and each row of vector of the first matrix corresponds to the operation parameters of the nth battery pack at one moment; determining the absolute health state of the nth battery pack according to the first matrix; constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment; determining the relative health status of the nth battery pack according to the second matrix; and correcting the absolute health state by using the relative health state to obtain the target health state of the nth battery pack. The accuracy of the assessment of the state of health of the corresponding battery is improved by adding the secondary matrix, i.e. by adding the consideration of the interactions between the individual batteries within the energy storage system.
Description
Technical Field
The invention relates to the technical field of energy storage, in particular to a method and a system for health evaluation and optimization of an energy storage system.
Background
Energy storage systems are typically composed of multiple battery packs, but the decay laws of the different battery packs are not uniform.
Solving for the state of health of individual battery packs is well established prior art, but in an energy storage system, there is a coordinated relationship between the individual battery packs.
When the state of health of the battery pack in one vehicle energy storage system is evaluated, if the state of health of the battery pack is solved according to the operation parameters of a single battery pack, the problem that the accuracy of solving the obtained state of health of the battery pack is lower exists.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, the invention provides a multi-parameter comprehensive analysis energy storage power station state monitoring method and system, so as to realize more accurate and rapid discovery of abnormal working states of an energy storage power station.
In a first aspect, the present invention provides a method for health assessment and optimization of an energy storage system, comprising:
acquiring operation parameters of an nth battery pack in an energy storage system at different moments, wherein the operation parameters comprise: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance;
according to the operation parameters of the nth battery pack at different moments, a first matrix Z is constructed, each row of vector of the first matrix corresponds to the operation parameters of the nth battery pack at one moment, and the operation parameters of the nth battery pack at the ith moment comprise: the current voltage value Un i, the current temperature value Tn i, the current energy storage capacity Cn i, the current power value Wn i, the current cycle number An i and the current internal resistance Rn i;
determining an absolute health state of the nth battery pack according to the first matrix;
constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment;
determining the relative health status of the nth battery pack according to the second matrix;
and correcting the absolute health state by using the relative health state to obtain a target health state of the nth battery pack, wherein the target health state is a health state evaluation value of the nth battery pack in the energy storage system at the current moment.
Further, the determining the absolute health status of the nth battery pack according to the first matrix includes:
normalizing the first matrix to obtain a third matrix;
and inputting the third matrix into a pre-trained health state evaluation model to obtain the absolute health state of the nth battery pack.
Further, before the third matrix is input into the health state evaluation model, the method further includes:
acquiring historical sample data of all the battery packs in the energy storage system in a can bus communication mode;
after data cleaning treatment is carried out on the historical sample data, calculating the aggregation degree of the sample data in each dimension at different moments, wherein the aggregation degree is obtained by solving an average value through a support vector; establishing a corresponding relation between sample data of each dimension, storing the corresponding relation in a table of a MySQL database, and inputting the processed historical behavior data into a preset initial health state model;
calculating related adjustment factors through the initial health state model, and training the health state model by adopting a support vector machine method on the basis of meeting the performance index threshold; continuously updating the prediction result into a known performance index data sequence, performing correlation analysis, retraining by expanding a training set according to different correlation degrees, and dynamically updating the health state model;
the polymerization degree is obtained by solving an average value through a support vector, and the solving formula is as follows:
wherein b is the degree of polymerization, x i 、y i For training samples, (x) S ，y s ) For any support vector, s= { i|ai > 0, i=1, 2,..m } is the subscript set of all support vectors, a i The Lagrangian multiplier is used, and T is the time.
Further, the determining the relative health status of the nth battery pack according to the second matrix includes:
normalizing the second matrix to obtain a fourth matrix;
solving the characteristic value of the fourth matrix;
and determining the relative health state of the nth battery pack according to the characteristic value of the fourth matrix.
Further, after said correcting said absolute state of health using said relative state of health to obtain a target state of health for said nth battery, said method further comprises:
generating alarm information when the target health state of the nth battery pack is smaller than a preset health state threshold value;
and sending the alarm information to a target terminal and performing visual display.
In a second aspect, the present application provides an energy storage system health assessment and optimization system, the system comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the operation parameters of an nth battery pack in an energy storage system at different moments, and the operation parameters comprise: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance;
the first construction module is configured to construct a first matrix Z according to operation parameters of the nth battery pack at different moments, where each row vector of the first matrix corresponds to an operation parameter of the nth battery pack at a moment, and the operation parameters of the nth battery pack at the ith moment include: the current voltage value Un i, the current temperature value Tn i, the current energy storage capacity Cn i, the current power value Wn i, the current cycle number An i and the current internal resistance Rn i;
a first determining module, configured to determine an absolute health state of the nth battery pack according to the first matrix;
the second construction module is used for constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment;
a second determining module, configured to determine a relative health status of the nth battery pack according to the second matrix;
and the correction module is used for correcting the absolute health state by using the relative health state to obtain a target health state of the nth battery pack, wherein the target health state is a health state evaluation value of the nth battery pack in the energy storage system at the current moment.
Further, the first determining module includes:
the first obtaining submodule is used for carrying out normalization processing on the first matrix to obtain a third matrix;
and the second obtaining submodule is used for inputting the third matrix into a pre-trained health state evaluation model to obtain the absolute health state of the nth battery pack.
Further, the first determining module includes:
acquiring historical sample data of all the battery packs in the energy storage system in a can bus communication mode;
after data cleaning treatment is carried out on the historical sample data, calculating the aggregation degree of the sample data in each dimension at different moments, wherein the aggregation degree is obtained by solving an average value through a support vector; establishing a corresponding relation between sample data of each dimension, storing the corresponding relation in a table of a MySQL database, and inputting the processed historical behavior data into a preset initial health state model;
calculating related adjustment factors through the initial health state model, and training the health state model by adopting a support vector machine method on the basis of meeting the performance index threshold; continuously updating the prediction result into a known performance index data sequence, performing correlation analysis, retraining by expanding a training set according to different correlation degrees, and dynamically updating the health state model;
the polymerization degree is obtained by solving an average value through a support vector, and the solving formula is as follows:
wherein b is the degree of polymerization, x i 、y i For training samples, (x) S ，y s ) For any support vector, s= { i|ai > 0, i=1, 2,..m } is the subscript set of all support vectors, a i The Lagrangian multiplier is used, and T is the time.
Further, the third obtaining submodule is used for carrying out normalization processing on the second matrix to obtain a fourth matrix;
a solving sub-module, configured to solve a eigenvalue of the fourth matrix;
and the determining submodule is used for determining the relative health state of the nth battery pack according to the eigenvalue of the fourth matrix.
Further, the energy storage system health assessment and optimization system further comprises:
the generation module is used for generating alarm information when the target health state of the nth battery pack is smaller than a preset health state threshold value;
and the sending module is used for sending the alarm information to the target terminal and carrying out visual display.
In the embodiment of the application, the improvement points are mainly as follows: (1) the first matrix and the second matrix have their meanings. (2) correcting the absolute health state using the relative health state. (3) training method of health state evaluation model.
The invention provides a health evaluation and optimization method of an energy storage system, which comprises the following steps: acquiring operation parameters of an nth battery pack in an energy storage system at different moments, wherein the operation parameters comprise: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance; according to the operation parameters of the nth battery pack at different moments, a first matrix Z is constructed, each row of vector of the first matrix corresponds to the operation parameters of the nth battery pack at one moment, and the operation parameters of the nth battery pack at the ith moment comprise: the current voltage value Un i, the current temperature value Tn i, the current energy storage capacity Cn i, the current power value Wn i, the current cycle number An i and the current internal resistance Rn i; determining an absolute health state of the nth battery pack according to the first matrix; constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment; determining the relative health status of the nth battery pack according to the second matrix; and correcting the absolute health state by using the relative health state to obtain a target health state of the nth battery pack, wherein the target health state is a health state evaluation value of the nth battery pack in the energy storage system at the current moment. The accuracy of the assessment of the state of health of the corresponding battery is improved by adding the secondary matrix, i.e. by adding the consideration of the interactions between the individual batteries within the energy storage system.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for health assessment and optimization of an energy storage system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a health evaluation and optimization system of an energy storage system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the present invention provides a method for evaluating and optimizing health of an energy storage system, which includes:
step 101, obtaining operation parameters of an nth battery pack in an energy storage system at different moments, wherein the operation parameters comprise: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance;
step 102, constructing a first matrix Z according to the operation parameters of the nth battery pack at different moments, where each row of vector of the first matrix corresponds to the operation parameter of the nth battery pack at one moment, and the operation parameters of the nth battery pack at the ith moment include: the current voltage value Un i, the current temperature value Tn i, the current energy storage capacity Cn i, the current power value Wn i, the current cycle number An i and the current internal resistance Rn i;
step 103, determining the absolute health state of the nth battery pack according to the first matrix;
104, constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment;
step 105, determining the relative health status of the nth battery pack according to the second matrix;
and 106, correcting the absolute health state by using the relative health state to obtain a target health state of the nth battery pack, wherein the target health state is a health state evaluation value of the nth battery pack in the energy storage system at the current moment.
The invention provides a health evaluation and optimization method of an energy storage system, which comprises the following steps: acquiring operation parameters of an nth battery pack in an energy storage system at different moments, wherein the operation parameters comprise: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance; according to the operation parameters of the nth battery pack at different moments, a first matrix Z is constructed, each row of vector of the first matrix corresponds to the operation parameters of the nth battery pack at one moment, and the operation parameters of the nth battery pack at the ith moment comprise: current voltage value Un i, current temperature value Tn i, current energy storage capacity Cn i, current power value Wn i, current cycle number Ani and current internal resistance Rni; determining an absolute health state of the nth battery pack according to the first matrix; constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment; determining the relative health status of the nth battery pack according to the second matrix; and correcting the absolute health state by using the relative health state to obtain a target health state of the nth battery pack, wherein the target health state is a health state evaluation value of the nth battery pack in the energy storage system at the current moment. The accuracy of the assessment of the state of health of the corresponding battery is improved by adding the secondary matrix, i.e. by adding the consideration of the interactions between the individual batteries within the energy storage system.
Further, the determining the absolute health status of the nth battery pack according to the first matrix includes:
normalizing the first matrix to obtain a third matrix;
and inputting the third matrix into a pre-trained health state evaluation model to obtain the absolute health state of the nth battery pack.
Further, before the third matrix is input into the health state evaluation model, the method further includes:
acquiring historical sample data of all the battery packs in the energy storage system in a can bus communication mode;
after data cleaning treatment is carried out on the historical sample data, calculating the aggregation degree of the sample data in each dimension at different moments, wherein the aggregation degree is obtained by solving an average value through a support vector; establishing a corresponding relation between sample data of each dimension, storing the corresponding relation in a table of a MySQL database, and inputting the processed historical behavior data into a preset initial health state model;
calculating related adjustment factors through the initial health state model, and training the health state model by adopting a support vector machine method on the basis of meeting the performance index threshold; continuously updating the prediction result into a known performance index data sequence, performing correlation analysis, retraining by expanding a training set according to different correlation degrees, and dynamically updating the health state model;
the polymerization degree is obtained by solving an average value through a support vector, and the solving formula is as follows:
wherein b is the degree of polymerization, x i 、y i For training samples, (x) S ，y s ) For any support vector, s= { i|ai > 0, i=1, 2,..m } is the subscript set of all support vectors, a i The Lagrangian multiplier is used, and T is the time.
Further, the determining the relative health status of the nth battery pack according to the second matrix includes:
normalizing the second matrix to obtain a fourth matrix;
solving the characteristic value of the fourth matrix;
and determining the relative health state of the nth battery pack according to the characteristic value of the fourth matrix.
Further, after said correcting said absolute state of health using said relative state of health to obtain a target state of health for said nth battery, said method further comprises:
generating alarm information when the target health state of the nth battery pack is smaller than a preset health state threshold value;
and sending the alarm information to a target terminal and performing visual display.
As shown in fig. 2, the present application implementation provides an energy storage system health assessment and optimization system, the system comprising:
the obtaining module 201 is configured to obtain operation parameters of an nth battery pack in the energy storage system at different moments, where the operation parameters include: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance;
a first construction module 202, configured to construct a first matrix Z according to the operation parameters of the nth battery pack at different moments, where each row vector of the first matrix corresponds to the operation parameter of the nth battery pack at one moment, and the operation parameters of the nth battery pack at the ith moment include: the current voltage value Un i, the current temperature value Tn i, the current energy storage capacity Cn i, the current power value Wn i, the current cycle number An i and the current internal resistance Rn i;
a first determining module 203, configured to determine an absolute health status of the nth battery pack according to the first matrix;
a second construction module 204, configured to construct a second matrix W according to operation parameters of all battery packs in the energy storage system at the same moment;
a second determining module 205, configured to determine a relative health status of the nth battery pack according to the second matrix;
and the correction module 206 is configured to correct the absolute state of health by using the relative state of health to obtain a target state of health of the nth battery pack, where the target state of health is an evaluation value of the state of health of the nth battery pack in the energy storage system at the current time.
Further, the first determining module includes:
the first obtaining submodule is used for carrying out normalization processing on the first matrix to obtain a third matrix;
and the second obtaining submodule is used for inputting the third matrix into a pre-trained health state evaluation model to obtain the absolute health state of the nth battery pack.
Further, the first determining module includes:
acquiring historical sample data of all the battery packs in the energy storage system in a can bus communication mode;
after data cleaning treatment is carried out on the historical sample data, calculating the aggregation degree of the sample data in each dimension at different moments, wherein the aggregation degree is obtained by solving an average value through a support vector; establishing a corresponding relation between sample data of each dimension, storing the corresponding relation in a table of a MySQL database, and inputting the processed historical behavior data into a preset initial health state model;
calculating related adjustment factors through the initial health state model, and training the health state model by adopting a support vector machine method on the basis of meeting the performance index threshold; continuously updating the prediction result into a known performance index data sequence, performing correlation analysis, retraining by expanding a training set according to different correlation degrees, and dynamically updating the health state model;
the polymerization degree is obtained by solving an average value through a support vector, and the solving formula is as follows:
wherein b is the degree of polymerization, x i 、y i For training samples, (x) S ，y s ) For any support vector, s= { i|ai > 0, i=1, 2,..m } is the subscript set of all support vectors, a i The Lagrangian multiplier is used, and T is the time.
Further, the third obtaining submodule is used for carrying out normalization processing on the second matrix to obtain a fourth matrix;
a solving sub-module, configured to solve a eigenvalue of the fourth matrix;
and the determining submodule is used for determining the relative health state of the nth battery pack according to the eigenvalue of the fourth matrix.
Further, the energy storage system health assessment and optimization system further comprises:
the generation module is used for generating alarm information when the target health state of the nth battery pack is smaller than a preset health state threshold value;
and the sending module is used for sending the alarm information to the target terminal and carrying out visual display.
It should be noted that, for details not disclosed in the energy storage system health evaluation and optimization system of the present embodiment, please refer to details disclosed in the embodiments of the energy storage system health evaluation and optimization method of the present embodiment, and are not described herein.
Claims (10)
1. A method for health assessment and optimization of an energy storage system, comprising:
acquiring operation parameters of an nth battery pack in an energy storage system at different moments, wherein the operation parameters comprise: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance;
according to the operation parameters of the nth battery pack at different moments, a first matrix Z is constructed, each row of vector of the first matrix corresponds to the operation parameters of the nth battery pack at one moment, and the operation parameters of the nth battery pack at the ith moment comprise: current voltage value Uni, current temperature value Tni, current energy storage capacity Cni, current power value Wni, current number of cycles Ani, and current internal resistance Rni;
determining an absolute health state of the nth battery pack according to the first matrix;
constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment;
determining the relative health status of the nth battery pack according to the second matrix;
and correcting the absolute health state by using the relative health state to obtain a target health state of the nth battery pack, wherein the target health state is a health state evaluation value of the nth battery pack in the energy storage system at the current moment.
2. The energy storage system health assessment and optimization method of claim 1, wherein said determining an absolute state of health of the nth battery pack according to the first matrix comprises:
normalizing the first matrix to obtain a third matrix;
and inputting the third matrix into a pre-trained health state evaluation model to obtain the absolute health state of the nth battery pack.
3. The method of claim 2, further comprising, prior to said inputting the third matrix into a state of health assessment model, obtaining an absolute state of health of the nth battery pack:
acquiring historical sample data of all the battery packs in the energy storage system in a can bus communication mode;
after data cleaning treatment is carried out on the historical sample data, calculating the aggregation degree of the sample data in each dimension at different moments, wherein the aggregation degree is obtained by solving an average value through a support vector; establishing a corresponding relation between sample data of each dimension, storing the corresponding relation in a table of a MySQL database, and inputting the processed historical behavior data into a preset initial health state model;
calculating related adjustment factors through the initial health state model, and training the health state model by adopting a support vector machine method on the basis of meeting the performance index threshold; continuously updating the prediction result into a known performance index data sequence, performing correlation analysis, retraining by expanding a training set according to different correlation degrees, and dynamically updating the health state model;
the polymerization degree is obtained by solving an average value through a support vector, and the solving formula is as follows:
wherein b is the degree of polymerization, x i 、y i For training samples, (x) S ，y s ) For any support vector, s= { i|ai > 0, i=1, 2,..m } is the subscript set of all support vectors, a i The Lagrangian multiplier is used, and T is the time.
4. The energy storage system health assessment and optimization method of claim 1, wherein said determining the relative health of the nth battery pack according to the second matrix comprises:
normalizing the second matrix to obtain a fourth matrix;
solving the characteristic value of the fourth matrix;
and determining the relative health state of the nth battery pack according to the characteristic value of the fourth matrix.
5. The energy storage system health assessment and optimization method of claim 1, wherein after said correcting said absolute state of health with said relative state of health to obtain a target state of health for said nth battery pack, said method further comprises:
generating alarm information when the target health state of the nth battery pack is smaller than a preset health state threshold value;
and sending the alarm information to a target terminal and performing visual display.
6. An energy storage system health assessment and optimization system based on the method of any one of claims 1-5, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the operation parameters of an nth battery pack in an energy storage system at different moments, and the operation parameters comprise: current voltage value, current temperature value, current energy storage capacity, current power value, current cycle number and current internal resistance;
the first construction module is configured to construct a first matrix Z according to operation parameters of the nth battery pack at different moments, where each row vector of the first matrix corresponds to an operation parameter of the nth battery pack at a moment, and the operation parameters of the nth battery pack at the ith moment include: current voltage value Uni, current temperature value Tni, current energy storage capacity Cni, current power value Wni, current number of cycles Ani, and current internal resistance Rni;
a first determining module, configured to determine an absolute health state of the nth battery pack according to the first matrix;
the second construction module is used for constructing a second matrix W according to the operation parameters of all battery packs in the energy storage system at the same moment;
a second determining module, configured to determine a relative health status of the nth battery pack according to the second matrix;
and the correction module is used for correcting the absolute health state by using the relative health state to obtain a target health state of the nth battery pack, wherein the target health state is a health state evaluation value of the nth battery pack in the energy storage system at the current moment.
7. The energy storage system health assessment and optimization system of claim 6, wherein the first determination module comprises:
the first obtaining submodule is used for carrying out normalization processing on the first matrix to obtain a third matrix;
and the second obtaining submodule is used for inputting the third matrix into a pre-trained health state evaluation model to obtain the absolute health state of the nth battery pack.
8. The energy storage system health assessment and optimization system of claim 7, wherein the first determination module comprises:
acquiring historical sample data of all the battery packs in the energy storage system in a can bus communication mode;
after data cleaning treatment is carried out on the historical sample data, calculating the aggregation degree of the sample data in each dimension at different moments, wherein the aggregation degree is obtained by solving an average value through a support vector; establishing a corresponding relation between sample data of each dimension, storing the corresponding relation in a table of a MySQL database, and inputting the processed historical behavior data into a preset initial health state model;
calculating related adjustment factors through the initial health state model, and training the health state model by adopting a support vector machine method on the basis of meeting the performance index threshold; continuously updating the prediction result into a known performance index data sequence, performing correlation analysis, retraining by expanding a training set according to different correlation degrees, and dynamically updating the health state model;
the polymerization degree is obtained by solving an average value through a support vector, and the solving formula is as follows:
wherein b is the degree of polymerization, x i 、y i For training samples, (x) S ，y s ) For any support vector, s= { i|ai > 0, i=1, 2,..m } is the subscript set of all support vectors, a i The Lagrangian multiplier is used, and T is the time.
9. The energy storage system health assessment and optimization system of claim 6, wherein the second determination module comprises:
a third obtaining sub-module, configured to normalize the second matrix to obtain a fourth matrix;
a solving sub-module, configured to solve a eigenvalue of the fourth matrix;
and the determining submodule is used for determining the relative health state of the nth battery pack according to the eigenvalue of the fourth matrix.
10. The energy storage system health assessment and optimization system of claim 6, further comprising:
the generation module is used for generating alarm information when the target health state of the nth battery pack is smaller than a preset health state threshold value;
and the sending module is used for sending the alarm information to the target terminal and carrying out visual display.
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