CN116449209A - Actual operation energy storage lithium capacitance prediction method based on LSTM - Google Patents
Actual operation energy storage lithium capacitance prediction method based on LSTM Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000004146 energy storage Methods 0.000 title claims abstract description 31
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 30
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 30
- 238000007600 charging Methods 0.000 claims abstract description 32
- 238000004590 computer program Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000032683 aging Effects 0.000 description 5
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 4
- 229910001416 lithium ion Inorganic materials 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 210000004027 cell Anatomy 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000000157 electrochemical-induced impedance spectroscopy Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010280 constant potential charging Methods 0.000 description 1
- 238000010277 constant-current charging Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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Abstract
The invention relates to an LSTM-based actual operation energy storage lithium capacitance prediction method. The method comprises the following steps: s1, extracting charging characteristics, calculating a Person correlation coefficient of the charging characteristics and battery attenuation, and selecting the charging characteristics as target characteristics according to the correlation; s2, inputting the target characteristics at the current moment into a characteristic prediction LSTM model to predict the target characteristics at the next moment; s3, inputting the predicted target characteristic at the next moment into a capacity prediction LSTM model, and predicting the capacity of the battery public voltage interval at the next moment. According to the LSTM-based actual operation energy storage lithium capacitance prediction method, the characteristic prediction LSTM model and the capacity prediction LSTM model are combined, so that the combined prediction of the characteristics and the capacity is realized, and the prediction accuracy is improved. Meanwhile, the capacity of the public voltage interval is used for replacing the complete capacity of the battery, so that the application range of the prediction method is widened.
Description
Technical Field
The invention relates to the technical field of battery capacity prediction, in particular to an actual operation energy storage lithium capacity prediction method based on an LSTM (Long Short Time Memory, long and short time memory network).
Background
In recent years, energy storage power stations have become more popular, for several reasons: 1. the energy storage power station can adjust the peak valley of the power grid, which is beneficial to reducing the maximum load of the power grid; 2. the energy storage power station can use the retired battery of the electric automobile, so the development of the electric automobile also promotes the energy storage power station; 3. the instability and intermittence of the wind power station and the matched energy storage power station on the power generation side of the photoelectric station can be effectively relieved.
The lithium ion battery can be aged gradually in the use process, and the capacity is reduced, and the internal resistance is increased. Capacity fade from battery aging is nonlinear and difficult to predict. The capacity of the lithium ion battery is accurately predicted, the service life information is obtained in advance, and the battery is replaced and maintained in time, so that the method has important significance in avoiding dangerous accidents, reducing operation cost and the like.
Currently, there is increasing research on capacity prediction of lithium ion batteries. Summarizing, capacity prediction can be divided into two parts, the selection of raw data and the construction of a capacity prediction model.
The selection of the original data refers to the selection of the historical operation data of the battery, including basic information such as capacity, voltage, temperature, internal resistance, etc., or higher-order EIS (electrochemical impedance spectroscopy), IC (capacity increment) curve, DV (differential voltage) curve, etc.
Constructing the capacity prediction model refers to constructing the model according to the corresponding relation between the original data and the capacity. The model can be divided into an empirical formula model, a mathematical model and a data driving model.
Patent CN115407210a discloses a method for predicting the capacity of a lithium ion battery based on a battery capacity prediction model, which only uses the information of capacity in terms of selecting characteristics and cannot accurately reflect the aging of the battery. Only 7 empirical formulas are constructed in terms of constructing the model, but the empirical formulas can only be empirical formulas, and can be self-rounded on limited data, but are difficult to expand to more batteries.
Patent CN115166561a discloses a lithium battery life prediction method based on a CNN-GRU combined neural network, which selects only 4 characteristics (constant current charging time interval, constant voltage charging time interval, discharge temperature peak time and cycle number) in terms of selecting characteristics, and the characteristics are too few to accurately reflect battery aging. Although a CNN-GRU combined neural network is used in the aspect of constructing a model, characteristics and capacity are not separately considered. Meanwhile, more data are needed, and the circulation data used in actual use account for about 36% of the total circulation number. The principle of predicting as much information as possible using as little data as possible is violated.
In summary, the existing battery capacity prediction method has simple selection characteristics and does not fully reflect battery aging information; meanwhile, the constructed model has poor interpretability and requires more data to use.
Disclosure of Invention
Therefore, the invention aims to provide the LSTM-based actual operation energy storage lithium capacitance prediction method which has wide application range and high prediction accuracy.
In order to solve the technical problems, the invention provides an LSTM-based actual operation energy storage lithium capacitance prediction method, which comprises the following steps:
s1, extracting charging characteristics, calculating a Person correlation coefficient of the charging characteristics and battery attenuation, and selecting the charging characteristics as target characteristics according to the correlation;
s2, inputting the target characteristics at the current moment into a characteristic prediction LSTM model to predict the target characteristics at the next moment;
s3, inputting the predicted target characteristic at the next moment into a capacity prediction LSTM model, and predicting the capacity of the battery public voltage interval at the next moment.
In one embodiment of the present invention, the feature prediction LSTM model includes a first sequence input layer, a first LSTM layer, a first full connection layer, and a first regression output layer connected in sequence.
In one embodiment of the present invention, the capacity prediction LSTM model includes a second sequence input layer, a second LSTM layer, a second full connection layer, a discard layer, a third full connection layer, and a second regression output layer that are sequentially connected.
In one embodiment of the present invention, the target feature includes a number of charges, a total charge amount, a charge duration, an average current, an average temperature, a charge process temperature rise, a charge start voltage, a charge end voltage, an a/B peak IC value, an a valley voltage, an a valley IC value, an area under 0.003V near a B peak of an IC curve (0.003V to the right of the B peak voltage).
In one embodiment of the invention, the battery common voltage interval is 3.3V-3.4V.
In one embodiment of the invention, the method further comprises the steps of:
and S4, observing the attenuation of the complete capacity of the battery according to the predicted capacity of the battery public voltage interval at the next moment.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when the program is executed.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described above.
The invention also provides a processor for running a program, wherein the program runs to execute the method of any one of the above.
The invention also provides an LSTM-based actual operation energy storage lithium capacitance prediction system, which comprises:
the feature calculation module is used for extracting charging features, calculating Person correlation coefficients of the charging features and battery attenuation, and selecting the charging features as target features according to the correlation;
the characteristic prediction LSTM model is used for receiving the target characteristic at the current moment and predicting the target characteristic at the next moment;
and the capacity prediction LSTM model is used for receiving the predicted target characteristic at the next moment and predicting the capacity of the battery common voltage interval at the next moment.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the LSTM-based actual operation energy storage lithium capacitance prediction method, the characteristic prediction LSTM model and the capacity prediction LSTM model are combined, so that the combined prediction of the characteristics and the capacity is realized, and the prediction accuracy is improved. Meanwhile, the capacity of the public voltage interval is used for replacing the complete capacity of the battery, so that the application range of the prediction method is widened.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention, as well as the preferred embodiments thereof, together with the following detailed description of the invention, given by way of illustration only, together with the accompanying drawings.
Drawings
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings, in which
FIG. 1 is a SOC-OCV curve of a battery;
FIG. 2 is a flow chart of an LSTM-based method for predicting the capacity of an actual operating stored energy lithium in an embodiment of the present invention;
FIG. 3 is an IC plot of a battery;
FIG. 4 is a technical roadmap of an LSTM-based actual operating stored energy lithium capacitance prediction method in an embodiment of the invention;
fig. 5 shows the actual and predicted values of 15 cells after 150 cycles in an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
Example 1
Since the discharge of the energy storage power station is determined according to the demand side, the energy storage power station is extremely unstable. The charging is set by the system, and the working condition is stable, so that the invention uses the charging data for observing and predicting the attenuation of the battery.
In addition, since the cells in the energy storage power station operate in series, the current is the same and the voltage is different for different cells. For uniformity, the capacity of the common voltage part is selected as a prediction target.
It is assumed that the full capacity of a battery should be 2.5V-3.65V partial discharge capacity. To demonstrate that the selected common voltage capacity can be used for observing and predicting the battery attenuation, the SOC-OCV curve (see FIG. 1) of the battery is measured, and from the proportional relationship (formula 1), we can observe the attenuation of the complete capacity of the battery according to the common voltage interval capacity.
ΔSOC /100％ ＝C part /C full (1)
Wherein ΔSOC is the state of charge variation, C part For the selected common voltage partial capacity, C full Is the full capacity of the battery.
Further, in this embodiment, the 3.3-3.4V portion is selected as the common voltage interval (selected according to the common voltage capacity ratio and the battery charging characteristics).
From equation (1) and FIG. 1, it can be calculated that the 3.3-3.4V capacity in this example is 55.5% of the full capacity. The capacity referred to below, unless otherwise indicated, is 3.3-3.4V capacity.
Based on this, referring to fig. 2, this embodiment discloses a method for predicting the actual operation stored energy lithium capacity based on LSTM, which includes the following steps:
s1, extracting charging characteristics, calculating a Person correlation coefficient of the charging characteristics and battery attenuation, and selecting the charging characteristics as target characteristics according to the correlation;
specifically, the coordinates of each peak and valley of the IC curve are mainly (as shown in fig. 3), and the battery cycle number, the average temperature of the charging process, the current and the total charging capacity are auxiliary (determined according to the sensors of the operating power station). The IC curve is derived from the Q-V curve of the battery, and the IC curve converts a slowly-changing voltage platform into an obvious peak, so that the IC curve is widely applied to battery aging analysis.
Specifically, 15 features which are relatively related to the attenuation of the battery are finally obtained as target features through Person correlation calculation, wherein the target features comprise the charging times, the total charging amount, the charging duration, the average current, the average temperature, the charging process temperature rise, the charging starting voltage, the charging ending voltage, the A/B peak IC value, the A valley voltage, the A valley IC value and the area under 0.003V near the B peak of the IC curve.
S2, inputting the target characteristics at the current moment into a characteristic prediction LSTM model to predict the target characteristics at the next moment;
specifically, the characteristic prediction LSTM model comprises a first sequence input layer, a first LSTM layer, a first full connection layer and a first regression output layer which are sequentially connected. The first LSTM layer is used for learning the correlation between the time sequence and the sequence data, the first full-connection layer integrates the features learned by the previous layer and outputs data with specified length, and the first regression output layer is used for calculating the semi-mean square error loss of regression tasks so as to evaluate the prediction effect.
And S3, inputting the predicted target characteristic at the next moment into a capacity prediction LSTM model, and predicting the capacity of the battery public voltage interval at the next moment.
Specifically, the capacity prediction LSTM model comprises a second sequence input layer, a second LSTM layer, a second full connection layer, a discarding layer, a third full connection layer and a second regression output layer which are sequentially connected. The difference between the capacity prediction LSTM model and the characteristic prediction LSTM model is that: the second fully-connected layer, the discarding layer and the third fully-connected layer are used for replacing the first fully-connected layer, wherein the discarding layer is used for effectively avoiding overfitting. Because the greater the number of neurons in the LSTM layer in the capacity predictive LSTM model, the more neurons, the more information learned, and more likely overfitting, a discard layer is added to suppress the occurrence of overfitting.
Referring to fig. 4, a technical scheme of the LSTM-based actual operation energy storage lithium capacitance prediction method in an embodiment of the present invention is shown. Wherein BAT is an abbreviation of BAT (1 to n represent BATTERY numbers); alpha is a feature vector (subscript indicates the number of cycles), and the vector size is 1 x 15; c is capacity (subscript indicates number of cycles).
Further, the actual operation energy storage lithium capacitance prediction method based on LSTM in the invention further comprises the following steps:
and S4, observing the attenuation of the complete capacity of the battery according to the predicted capacity of the battery public voltage interval at the next moment.
In order to verify the effectiveness of the LSTM-based actual operation energy storage lithium capacitance prediction method, two LSTM models in the invention are realized by software MATLAB and run on a notebook computer configured by 7 generation i5 and GTX 1050.
The capacity prediction of 15 batteries after 150 cycles is realized. The average relative error is 5.35%, corresponding to only 0.036% capacity error offset per step prediction. The effect is good. Compared with a single LSTM method for directly predicting capacity (average relative error is 25.8%), the double LSTM model method greatly improves the prediction accuracy. The predictive effect is referred to in fig. 5.
According to the LSTM-based actual operation energy storage lithium capacitance prediction method, the characteristic prediction LSTM model and the capacity prediction LSTM model are combined, so that the combined prediction of the characteristics and the capacity is realized, and the prediction accuracy is improved. Meanwhile, the capacity of the public voltage interval is used for replacing the complete capacity of the battery, so that the application range of the prediction method is widened.
Example two
The present embodiment discloses a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the steps of the method described in embodiment one when executing the program.
Example III
The present embodiment discloses a computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method described in the first embodiment.
Example IV
The present embodiment discloses a processor, where the processor is configured to execute a program, where the program executes the method described in the first embodiment.
Example five
The embodiment discloses an actual operation energy storage lithium capacitance prediction system based on LSTM, which comprises:
the feature calculation module is used for extracting charging features, calculating Person correlation coefficients of the charging features and battery attenuation, and selecting the charging features as target features according to the correlation;
the characteristic prediction LSTM model is used for receiving the target characteristic at the current moment and predicting the target characteristic at the next moment;
and the capacity prediction LSTM model is used for receiving the predicted target characteristic at the next moment and predicting the capacity of the battery common voltage interval at the next moment.
The actual operation energy storage lithium capacitance prediction system based on LSTM in the embodiment of the present invention is used to implement the foregoing actual operation energy storage lithium capacitance prediction method based on LSTM, so that the specific implementation of the system can be seen from the foregoing example section of the actual operation energy storage lithium capacitance prediction method based on LSTM, so that the specific implementation thereof can be referred to the description of the corresponding examples of the various sections, and will not be further described herein.
In addition, since the LSTM-based actual operation energy storage lithium capacitance prediction system of the present embodiment is used to implement the foregoing LSTM-based actual operation energy storage lithium capacitance prediction method, the function thereof corresponds to the function of the foregoing method, and the details thereof will not be repeated here.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (10)
1. The actual operation energy storage lithium capacitance prediction method based on LSTM is characterized by comprising the following steps of:
s1, extracting charging characteristics, calculating a Person correlation coefficient of the charging characteristics and battery attenuation, and selecting the charging characteristics as target characteristics according to the correlation;
s2, inputting the target characteristics at the current moment into a characteristic prediction LSTM model to predict the target characteristics at the next moment;
s3, inputting the predicted target characteristic at the next moment into a capacity prediction LSTM model, and predicting the capacity of the battery public voltage interval at the next moment.
2. The LSTM-based prediction method of actual operation energy storage lithium capacitance according to claim 1, wherein the characteristic prediction LSTM model includes a first sequence input layer, a first LSTM layer, a first full connection layer, and a first regression output layer connected in sequence.
3. The LSTM-based prediction method of actual operation energy storage lithium capacity according to claim 1, wherein the capacity prediction LSTM model includes a second sequence input layer, a second LSTM layer, a second full connection layer, a discard layer, a third full connection layer, and a second regression output layer that are sequentially connected.
4. The LSTM-based actually operative stored energy lithium capacity prediction method of claim 1, wherein the target characteristics include a number of charges, a total charge amount, a charge duration, an average current, an average temperature, a charge process temperature rise, a charge start voltage, a charge end voltage, an a/B peak IC value, an a valley voltage, an a valley IC value, an area at 0.003V near an IC curve B peak.
5. The LSTM based prediction method of actual operation energy storage lithium capacity according to claim 1, wherein the battery common voltage interval is 3.3V-3.4V.
6. The LSTM based prediction method of actual operation stored energy lithium capacity according to claim 1, further comprising the steps of:
and S4, observing the attenuation of the complete capacity of the battery according to the predicted capacity of the battery public voltage interval at the next moment.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when the program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 6.
9. A processor for running a program, wherein the program when run performs the method of any one of claims 1 to 6.
10. An LSTM-based actually operative stored energy lithium capacitance prediction system, comprising:
the feature calculation module is used for extracting charging features, calculating Person correlation coefficients of the charging features and battery attenuation, and selecting the charging features as target features according to the correlation;
the characteristic prediction LSTM model is used for receiving the target characteristic at the current moment and predicting the target characteristic at the next moment;
and the capacity prediction LSTM model is used for receiving the predicted target characteristic at the next moment and predicting the capacity of the battery common voltage interval at the next moment.
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