CN116404212A - Capacity equalization control method and system for zinc-iron flow battery system - Google Patents
Capacity equalization control method and system for zinc-iron flow battery system Download PDFInfo
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Abstract
The application relates to the field of intelligent control, and particularly discloses a capacity balance control method and a system of a zinc-iron flow battery system.
Description
Technical Field
The application relates to the field of intelligent control, and in particular relates to a capacity balance control method and system of a zinc-iron flow battery system.
Background
As an energy storage battery, a zinc-iron flow battery has been widely paid attention to and studied in recent years due to its characteristics of excellent safety, reliability, low cost, and the like. Unlike conventional batteries, zinc-iron flow batteries require pumps to drive the circulation of energy storage materials within the stack to effect conversion between electrical and chemical energy. However, due to the semi-deposition type structure of the zinc-iron flow battery, the problem of capacity imbalance among all electric stacks can be caused, and the operation efficiency and stability of the system are affected.
Accordingly, an optimized zinc-iron flow battery system capacity balancing control system is desired.
Disclosure of Invention
The present application has been made in order to solve the above technical problems. The embodiment of the application provides a capacity balance control method and a system for a zinc-iron flow battery system, which are used for accurately controlling charge and discharge parameters of the zinc-iron flow battery system by adopting a neural network model based on deep learning to mine time sequence cooperative association relations of all data items in external data of the battery, so that stable operation of the system under different working conditions is ensured, and the performance of the battery is improved.
According to one aspect of the present application, there is provided a zinc-iron flow battery system capacity equalization control system, comprising:
the external data acquisition module is used for acquiring battery external data of a plurality of preset time points in a preset time period, wherein the battery external data comprise battery inlet pressure, flow and temperature;
the parameter time sequence association distribution module is used for arranging the battery external data of the plurality of preset time points into a battery external parameter input matrix according to the time dimension and the parameter sample dimension;
the matrix segmentation module is used for carrying out local matrix segmentation on the battery external parameter input matrix to obtain a plurality of local battery external parameter matrixes;
The parameter local time sequence correlation module is used for respectively passing the plurality of local battery external parameter matrixes through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors;
the global association coding module is used for enabling the plurality of local battery external parameter feature vectors to pass through a context coder based on a converter to obtain classification feature vectors; and
and the charge and discharge power control module is used for passing the classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased.
In the capacity equalization control system of the zinc-iron flow battery system, the parameter time sequence association distribution module is used for: arranging the battery external data of the plurality of preset time points into a plurality of row vectors according to the dimension of the parameter sample; and two-dimensionally arranging the row vectors according to the time dimension to obtain the battery external parameter input matrix.
In the capacity equalization control system of the zinc-iron flow battery system, the parameter local time sequence association module is used for: each layer of the convolutional neural network model used as the filter performs the following steps on input data in forward transfer of the layer: carrying out convolution processing on input data to obtain a convolution characteristic diagram; pooling the convolution feature images based on a feature matrix to obtain pooled feature images; performing nonlinear activation on the pooled feature map to obtain an activated feature map; wherein the output of the last layer of the convolutional neural network as a filter is the plurality of local battery external parameter feature vectors, and the input of the first layer of the convolutional neural network as a filter is the plurality of local battery external parameter matrices.
In the capacity equalization control system of the zinc-iron flow battery system, the global association coding module comprises: a context encoding unit, configured to pass the plurality of local battery external parameter feature vectors through a context encoder based on a converter to obtain a plurality of context local battery external parameter feature vectors; an optimization factor calculation unit, configured to calculate gaussian regression uncertainty factors of the feature vectors of the external parameters of the respective contextual local batteries to obtain a plurality of gaussian regression uncertainty factors; the weighted optimization unit is used for weighted optimization of the context local battery external parameter feature vectors by taking the plurality of Gaussian regression uncertainty factors as weighting coefficients so as to obtain a plurality of optimized context local battery external parameter feature vectors; and the optimization fusion unit is used for cascading the plurality of optimization context local battery external parameter feature vectors to obtain the classification feature vector.
In the capacity equalization control system of a zinc-iron flow battery system, the context coding unit includes: the query vector construction subunit is used for carrying out one-dimensional arrangement on the plurality of local battery external parameter feature vectors to obtain global battery external parameter feature vectors; a self-attention subunit, configured to calculate a product between the global battery external parameter feature vector and a transpose vector of each of the plurality of local battery external parameter feature vectors to obtain a plurality of self-attention correlation matrices; the normalization subunit is used for respectively performing normalization processing on each self-attention correlation matrix in the plurality of self-attention correlation matrices to obtain a plurality of normalized self-attention correlation matrices; the attention calculating subunit is used for obtaining a plurality of probability values through a Softmax classification function by each normalized self-attention correlation matrix in the normalized self-attention correlation matrices; and the attention applying subunit is used for weighting each local battery external parameter characteristic vector in the local battery external parameter characteristic vectors by taking each probability value in the plurality of probability values as a weight so as to obtain the context semantic local battery external parameter characteristic vectors.
In the capacity balance control system of the zinc-iron flow battery system, the optimization factor calculation unit is used for: calculating the Gaussian regression uncertainty factors of the external parameter feature vectors of the local battery of each context according to the following optimization formula to obtain a plurality of Gaussian regression uncertainty factors; wherein, the optimization formula is:
wherein v is ij Is the feature value of the j-th position of the i-th context local battery external parameter feature vector in the context local battery external parameter feature vectors, L is the length of the feature vector, mu i Sum sigma i 2 The mean and variance of each position characteristic value set in the i-th context local battery external parameter characteristic vector are respectively that log is a logarithmic function value based on 2, and w i Is an ith gaussian regression uncertainty factor of the plurality of gaussian regression uncertainty factors.
In the above-mentioned zinc-iron flow battery system capacity equalization control system, the charge-discharge power control module includes: the full-connection coding unit is used for carrying out full-connection coding on the classification characteristic vectors by using a plurality of full-connection layers of the classifier so as to obtain coded classification characteristic vectors; and the classification result generating unit is used for enabling the coding classification feature vector to pass through a Softmax classification function of the classifier to obtain the classification result.
According to another aspect of the present application, there is provided a capacity equalization control method of a zinc-iron flow battery system, including:
acquiring battery external data of a plurality of preset time points in a preset time period, wherein the battery external data comprises battery inlet pressure, flow and temperature;
arranging the battery external data of the plurality of preset time points into a battery external parameter input matrix according to a time dimension and a parameter sample dimension;
performing local matrix segmentation on the battery external parameter input matrix to obtain a plurality of local battery external parameter matrixes;
respectively passing the local battery external parameter matrixes through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors;
passing the plurality of local battery external parameter feature vectors through a converter-based context encoder to obtain a classification feature vector; and
and the classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased.
According to still another aspect of the present application, there is provided an electronic apparatus including: a processor; and a memory having stored therein computer program instructions that, when executed by the processor, cause the processor to perform the zinc-iron flow battery system capacity balancing control method as described above.
According to yet another aspect of the present application, there is provided a computer readable medium having stored thereon computer program instructions which, when executed by a processor, cause the processor to perform the zinc-iron flow battery system capacity balancing control method as described above.
Compared with the prior art, the capacity balance control method and system for the zinc-iron flow battery system provided by the application have the advantages that the time sequence cooperative association relation of each data item in the external data of the battery is dug out by adopting the neural network model based on deep learning, so that the charge and discharge parameters of the zinc-iron flow battery system are accurately controlled, the stable operation of the system under different working conditions is ensured, and the performance of the battery is improved.
Drawings
The foregoing and other objects, features and advantages of the present application will become more apparent from the following more particular description of embodiments of the present application, as illustrated in the accompanying drawings. The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate the application and not constitute a limitation to the application. In the drawings, like reference numerals generally refer to like parts or steps.
Fig. 1 is a schematic view of a scenario of a capacity balancing control system of a zinc-iron flow battery system according to an embodiment of the present application;
FIG. 2 is a block diagram of a zinc-iron flow battery system capacity equalization control system according to an embodiment of the present application;
FIG. 3 is a system architecture diagram of a zinc-iron flow battery system capacity balancing control system according to an embodiment of the present application;
FIG. 4 is a flow chart of convolutional neural network coding in a zinc-iron flow battery system capacity equalization control system according to an embodiment of the present application;
FIG. 5 is a block diagram of a global association coding module in a zinc-iron flow battery system capacity equalization control system according to an embodiment of the present application;
FIG. 6 is a flow chart of a method of controlling capacity equalization of a zinc-iron flow battery system according to an embodiment of the present application;
fig. 7 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Summary of the application
As described above, unlike conventional batteries, zinc-iron flow batteries require a pump to drive the circulation of an energy storage substance inside the stack to effect conversion between electrical and chemical energy. However, due to the semi-deposition type structure of the zinc-iron flow battery, the problem of capacity imbalance among all electric stacks can be caused, and the operation efficiency and stability of the system are affected. Accordingly, an optimized zinc-iron flow battery system capacity balancing control system is desired.
It should be understood that, since the zinc-iron flow is different from the all-vanadium flow battery, it belongs to a half-deposition type flow battery, and the uniformity of zinc deposition among the battery pieces is realized through system equalization in the operation process, so that the risk of membrane puncture caused by excessive zinc deposition of local battery pieces is avoided. Therefore, in order to ensure that the system capacity is maximized as much as possible, it is necessary to ensure that the capacities among the stacks are uniform as much as possible, and thus it is necessary to optimize the battery performance by monitoring the parameters of the stacks in real time.
Accordingly, considering that external data parameters operating during the charge and discharge of the battery have an important influence on the performance of the battery in the process of actually performing the monitoring of the stack parameters, it is desirable to control the charge and discharge parameters based on the variation of the external data parameters to ensure the stability of the system and the superiority of the battery performance. Based on this, in the technical scheme of the application, in order to realize the balanced control of the capacity of the zinc-iron flow battery system, it is desirable to adjust the charge and discharge parameters of the zinc-iron flow battery system, such as charge and discharge power, charge and discharge time, charge and discharge efficiency, according to external data such as battery inlet pressure, flow rate, temperature, etc., so as to ensure the stable operation of the system under different working conditions. However, it is considered that each data item in the external data of the battery has a respective dynamic change rule in the time dimension, and each data item has a time sequence dynamic association relationship. Therefore, in the process, the difficulty is how to fully express the time sequence collaborative association characteristic information of each data item in the external data of the battery, so as to accurately control the charge and discharge parameters of the zinc-iron flow battery system, thereby ensuring the stable operation of the system under different working conditions and improving the performance of the battery.
In recent years, deep learning and neural networks have been widely used in the fields of computer vision, natural language processing, text signal processing, and the like. The development of deep learning and neural networks provides new solutions and schemes for mining time sequence collaborative association characteristic information of each data item in the external data of the battery.
Specifically, in the technical solution of the present application, first, battery external data including a battery inlet pressure, a flow rate, and a temperature at a plurality of predetermined time points within a predetermined period of time is acquired. Next, considering that there is a time-series cooperative correlation between the battery inlet pressure, flow rate and temperature, in order to enable sufficient expression of time-series cooperative correlation characteristic information of the battery external data, it is first necessary to construct a correlation matrix of time-series distribution information of each data item in the battery external data. Specifically, the battery external data of the plurality of preset time points are arranged into a battery external parameter input matrix according to a time dimension and a parameter sample dimension, so that time sequence distribution information of each data item in the battery external data is integrated.
Then, it is considered that the time-series correlation characteristic in the time dimension due to the individual data items in the battery external data, that is, the battery inlet pressure, flow rate, and temperature, is implicit correlation characteristic information of a small scale, and the battery inlet pressure, flow rate, and temperature exhibit different correlation characteristics at different sample dimensions and different time period spans. Therefore, in the technical scheme of the application, in order to fully perform time sequence collaborative correlation feature expression of each data item in the battery external data, the battery external parameter input matrix is further subjected to local matrix segmentation to obtain a plurality of local battery external parameter matrices, and a convolution neural network model which is used as a filter and has excellent performance in local implicit correlation feature extraction is used for performing feature mining on the plurality of local battery external parameter matrices, so that local time sequence implicit correlation feature information of the parameter data of the battery inlet pressure, flow and temperature under different time and sample spans is respectively extracted, and a plurality of local battery external parameter feature vectors are obtained.
Further, for each local battery external parameter matrix, the time sequence local correlation features about the battery inlet pressure, flow rate and temperature in each local battery external parameter feature vector have a correlation relationship based on the whole battery external parameter input matrix. That is, the respective local battery external parameter feature vectors have associated feature information therebetween based on the time and sample dimension entirety of the respective data items in the battery external data. Therefore, in order to sufficiently express the time sequence collaborative correlation characteristic of each data item in the battery external data, in the technical scheme of the application, the plurality of local battery external parameter characteristic vectors are further encoded in a context encoder based on a converter so as to extract local correlation characteristics of the battery inlet pressure, flow and temperature in time and sample dimension based on global context semantic correlation characteristic information, so that classification characteristic vectors are obtained.
And then, further classifying the classifying feature vector by a classifier to obtain a classifying result for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased. That is, in the technical solution of the present application, the labels of the classifier include that the charge-discharge power of the zinc-iron flow battery system should be increased (first label) and that the charge-discharge power of the zinc-iron flow battery system should be decreased (second label), wherein the classifier determines to which classification label the classification feature vector belongs through a soft maximum function. It should be noted that the first tag p1 and the second tag p2 do not include the concept of artificial setting, and in fact, during the training process, the computer model does not have the concept of "the charge-discharge power of the zinc-iron flow battery system should be increased or decreased", which is only two kinds of classification tags, and the probability that the output characteristics are under the two classification tags, that is, the sum of p1 and p2 is one. Therefore, the classification result that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased is actually converted into the classification probability distribution conforming to the natural rule through the classification label, and the physical meaning of the natural probability distribution of the label is essentially used instead of the language text meaning that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased. It should be understood that, in the technical scheme of the application, the classification label of the classifier is a control strategy label that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased, so after the classification result is obtained, the charge and discharge power value of the zinc-iron flow battery system can be adaptively adjusted based on the classification result, so as to perform adaptive control of the charge and discharge parameters of the zinc-iron flow battery system, thereby ensuring stable operation of the system under different working conditions and improving the performance of the battery.
In particular, in the technical solution of the present application, in consideration of possible introduction of source data noise in the data acquisition process of the battery external data, after the local matrix segmentation is performed on the time-sample two-dimensional matrix, the source data noise may cross in the time-sample dimension, and after the local correlation feature extraction by the convolutional neural network model serving as a filter and the global context correlation encoding of the local correlation feature by the context encoder based on the converter, the gaussian distribution error uncertainty of the respective feature distribution is further introduced into the plurality of context local battery external parameter feature vectors obtained by the context encoder based on the converter, so that, in consideration of the classification feature vector, the classification feature vector is obtained by directly cascading the plurality of context local battery external parameter feature vectors, the direct superposition of the gaussian distribution error uncertainty may also cause the classification regression error of the classification feature vector, and affect the accuracy of the classification result obtained by the classifier.
Based on this, in the technical solution of the present application, each of the plurality of context local battery external parameter feature vectors is calculated separately, for example, denoted as V i Is expressed as:
l is the length of the feature vector, μ i Sum sigma i 2 Respectively the feature sets v ij ∈V i Mean and variance of (v), where v ij Is the feature vector V i Is the eigenvalue of the j-th position of (c), and log is the base 2 logarithm.
Here, for the agnostic regression of the classification feature vector, which may be caused by the distribution uncertainty information of the respective integrated feature set of each of the plurality of context local battery external parameter feature vectors, scalar measurement of the statistical characteristics of the feature set is performed by using the mean value and the variance of the statistical quantization parameter, so that the normal distribution cognitive mode of the feature representation of the source data noise is expanded to an unknown distribution regression mode, and the migration learning based on natural distribution transfer on the feature set scale is realized, so that the classification feature vector is obtained by weighting each context local battery external parameter feature vector by the gaussian regression uncertainty factor respectively and cascading the weighted feature vector, and the uncertainty correction of each context local battery external parameter feature vector based on self calibration when the classification feature vector is formed can be realized, so that the classification regression error existing in the classification feature vector is corrected, and the accuracy of the classification result obtained by the classifier of the classification feature vector is improved. Therefore, the self-adaptive control of the charge and discharge parameters of the zinc-iron flow battery system can be accurately performed in real time based on the external environment condition of the actual battery, so that the stable operation of the system under different working conditions is ensured, and the performance of the battery is improved.
Based on this, the application provides a zinc-iron flow battery system capacity balance control system, which includes: the external data acquisition module is used for acquiring battery external data of a plurality of preset time points in a preset time period, wherein the battery external data comprise battery inlet pressure, flow and temperature; the parameter time sequence association distribution module is used for arranging the battery external data of the plurality of preset time points into a battery external parameter input matrix according to the time dimension and the parameter sample dimension; the matrix segmentation module is used for carrying out local matrix segmentation on the battery external parameter input matrix to obtain a plurality of local battery external parameter matrixes; the parameter local time sequence correlation module is used for respectively passing the plurality of local battery external parameter matrixes through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors; the global association coding module is used for enabling the plurality of local battery external parameter feature vectors to pass through a context coder based on a converter to obtain classification feature vectors; and the charge and discharge power control module is used for enabling the classification feature vector to pass through a classifier to obtain a classification result, wherein the classification result is used for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased.
Fig. 1 is a schematic view of a scenario of a capacity balancing control system of a zinc-iron flow battery system according to an embodiment of the present application. As shown in fig. 1, in this application scenario, battery inlet pressures at a plurality of predetermined time points within a predetermined period are acquired by a pressure sensor (e.g., V1 as illustrated in fig. 1); acquiring, by a flow sensor (e.g., V2 as illustrated in fig. 1), battery inlet flow at a plurality of predetermined time points over a predetermined period of time; and acquiring, by a temperature sensor (e.g., V3 as illustrated in fig. 1), battery inlet temperatures at a plurality of predetermined time points within a predetermined period of time. The battery external data is then input to a server (e.g., S in fig. 1) that is deployed with a disinfection algorithm for I CU intensive care, where the server is capable of processing the input data with the disinfection algorithm for I CU intensive care to generate a decoded value representing a recommended fan power value for the current point in time.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Exemplary System
Fig. 2 is a block diagram of a zinc-iron flow battery system capacity balancing control system according to an embodiment of the present application. As shown in fig. 2, a zinc-iron flow battery system capacity equalization control system 300 according to an embodiment of the present application includes: an external data acquisition module 310; a parameter timing correlation distribution module 320; a matrix splitting module 330; a parameter local timing correlation module 340; a global association encoding module 350; and a charge-discharge power control module 360.
The external data acquisition module 310 is configured to acquire external data of the battery at a plurality of predetermined time points within a predetermined period, where the external data of the battery includes a battery inlet pressure, a flow rate, and a temperature; the parameter time sequence association distribution module 320 is configured to arrange the battery external data at the plurality of predetermined time points into a battery external parameter input matrix according to a time dimension and a parameter sample dimension; the matrix splitting module 330 is configured to split the local matrix of the battery external parameter input matrix to obtain a plurality of local battery external parameter matrices; the parameter local time sequence correlation module 340 is configured to pass the plurality of local battery external parameter matrices through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors; the global association encoding module 350 is configured to pass the plurality of local battery external parameter feature vectors through a context encoder based on a converter to obtain a classification feature vector; and the charge-discharge power control module 360 is configured to pass the classification feature vector through a classifier to obtain a classification result, where the classification result is used to indicate that the charge-discharge power of the zinc-iron redox flow battery system should be increased or decreased.
Fig. 3 is a system architecture diagram of a zinc-iron flow battery system capacity balancing control system according to an embodiment of the present application. As shown in fig. 3, in the network architecture, first, battery external data including a battery inlet pressure, a flow rate and a temperature at a plurality of predetermined time points within a predetermined period of time is acquired through the external data acquisition module 310; next, the parameter time sequence association distribution module 320 arranges the battery external data at a plurality of predetermined time points acquired by the external data acquisition module 310 into a battery external parameter input matrix according to a time dimension and a parameter sample dimension; the matrix segmentation module 330 performs local matrix segmentation on the battery external parameter input matrix obtained by the parameter time sequence association distribution module 320 to obtain a plurality of local battery external parameter matrices; then, the parameter local time sequence correlation module 340 respectively passes the plurality of local battery external parameter matrices obtained by the matrix segmentation module 330 through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors; the global association encoding module 350 passes the plurality of local battery external parameter feature vectors obtained by the parameter local timing association module 340 through a context encoder based on a converter to obtain classification feature vectors; further, the charge-discharge power control module 360 passes the classification feature vector through a classifier to obtain a classification result, where the classification result is used to indicate that the charge-discharge power of the zinc-iron flow battery system should be increased or decreased.
Specifically, during the operation of the capacity balancing control system 300 of the zinc-iron flow battery system, the external data acquisition module 310 is configured to acquire external data of the battery at a plurality of predetermined time points within a predetermined period of time, where the external data of the battery includes a pressure, a flow rate, and a temperature of an inlet of the battery. It should be understood that, considering that the external data parameters running during the charging and discharging of the battery have an important effect on the battery performance in the actual monitoring process of the pile parameters, in the technical scheme of the application, the capacity balance control of the zinc-iron flow battery system is realized by adaptively adjusting the charging and discharging parameters of the zinc-iron flow battery system, such as the charging and discharging power, the charging and discharging time, the charging and discharging efficiency and the like, based on the external data parameters of the battery. Thus, first, the battery inlet pressure at a plurality of predetermined time points within a predetermined period of time can be acquired by the pressure sensor; acquiring, by a flow sensor, battery inlet flows at a plurality of predetermined time points within a predetermined period of time; and acquiring, by the temperature sensor, battery inlet temperatures at a plurality of predetermined time points within a predetermined period of time.
Specifically, during the operation of the capacity balancing control system 300 of the zinc-iron flow battery system, the parameter time sequence association distribution module 320 is configured to arrange the external data of the battery at the plurality of predetermined time points into an external parameter input matrix of the battery according to a time dimension and a parameter sample dimension. In view of the fact that there is a time-series cooperative correlation between the battery inlet pressure, flow rate and temperature, in order to enable sufficient expression of time-series cooperative correlation characteristic information of the battery external data, it is first necessary to construct a correlation matrix of time-series distribution information of each data item in the battery external data. Specifically, first, the battery external data at the plurality of predetermined time points are arranged into a plurality of row vectors according to a parameter sample dimension; and two-dimensionally arranging the row vectors according to the time dimension to obtain the battery external parameter input matrix.
Specifically, in the operation process of the capacity balancing control system 300 of the zinc-iron flow battery system, the matrix splitting module 330 is configured to perform local matrix splitting on the battery external parameter input matrix to obtain a plurality of local battery external parameter matrices. Considering that the time-series correlation characteristic of the battery inlet pressure, flow rate and temperature in the time dimension is implicit correlation characteristic information of a small scale due to each data item in the battery external data, and the battery inlet pressure, flow rate and temperature exhibit different correlation characteristics in different sample dimensions and different time period spans. Therefore, in the technical scheme of the application, in order to fully perform the time sequence collaborative correlation feature expression of each data item in the external data of the battery, the external parameter input matrix of the battery is further subjected to local matrix segmentation so as to obtain a plurality of local external parameter matrices of the battery.
Specifically, during the operation of the capacity balancing control system 300 of the zinc-iron flow battery system, the parameter local time sequence correlation module 340 is configured to pass the local battery external parameter matrices through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors. In other words, in the technical solution of the present application, the convolutional neural network model as a filter with excellent performance in the aspect of local implicit relevant feature extraction is used to perform feature mining of the plurality of local battery external parameter matrices, so as to extract local time sequence implicit relevant feature information of the parameter data of the battery inlet pressure, flow and temperature under different time and sample spans, thereby obtaining a plurality of local battery external parameter feature vectors. In one particular example, the convolutional neural network includes a plurality of neural network layers that are cascaded with one another, wherein each neural network layer includes a convolutional layer, a pooling layer, and an activation layer. In the coding process of the convolutional neural network, each layer of the convolutional neural network carries out convolutional processing based on a convolutional kernel on input data by using the convolutional layer in the forward transmission process of the layer, carries out pooling processing on a convolutional feature map output by the convolutional layer by using the pooling layer and carries out activation processing on the pooling feature map output by the pooling layer by using the activation layer.
Fig. 4 is a flowchart of convolutional neural network coding in a zinc-iron flow battery system capacity equalization control system according to an embodiment of the present application. As shown in fig. 4, during the convolutional neural network encoding process. Comprises the following steps: each layer of the convolutional neural network model used as the filter performs the following steps on input data in forward transfer of the layer: s210, carrying out convolution processing on input data to obtain a convolution characteristic diagram; s220, pooling the convolution feature map based on a feature matrix to obtain a pooled feature map; s230, performing nonlinear activation on the pooled feature map to obtain an activated feature map; wherein the output of the last layer of the convolutional neural network as a filter is the plurality of local battery external parameter feature vectors, and the input of the first layer of the convolutional neural network as a filter is the plurality of local battery external parameter matrices.
Specifically, during operation of the zinc-iron flow battery system capacity balancing control system 300, the global association encoding module 350 is configured to pass the plurality of local battery external parameter feature vectors through a context encoder based on a converter to obtain a classification feature vector. It should be appreciated that for each of the local battery external parameter matrices, the time-series local correlation features in each of the local battery external parameter feature vectors with respect to the battery inlet pressure, flow rate, and temperature have a correlation relationship based on the battery external parameter input matrix as a whole. That is, the respective local battery external parameter feature vectors have associated feature information therebetween based on the time and sample dimension entirety of the respective data items in the battery external data. Therefore, in order to sufficiently express the time sequence cooperative correlation characteristics of each data item in the battery external data, in the technical scheme of the application, the following is further adopted And encoding the plurality of local battery external parameter feature vectors in a context encoder based on a converter to extract local associated features of the battery inlet pressure, flow and temperature in time and sample dimensions based on global context semantic associated feature information, so as to obtain classification feature vectors. In particular, in the technical solution of the present application, in consideration of possible introduction of source data noise in the data acquisition process of the battery external data, after the local matrix segmentation is performed on the time-sample two-dimensional matrix, the source data noise may cross in the time-sample dimension, and after the local correlation feature extraction by the convolutional neural network model serving as a filter and the global context correlation encoding of the local correlation feature by the context encoder based on the converter, the gaussian distribution error uncertainty of the respective feature distribution is further introduced into the plurality of context local battery external parameter feature vectors obtained by the context encoder based on the converter, so that, in consideration of the classification feature vector, the classification feature vector is obtained by directly cascading the plurality of context local battery external parameter feature vectors, the direct superposition of the gaussian distribution error uncertainty may also cause the classification regression error of the classification feature vector, and affect the accuracy of the classification result obtained by the classifier. Based on this, in the technical solution of the present application, each of the plurality of context local battery external parameter feature vectors is calculated separately, for example, denoted as V i Is expressed as:
wherein v is ij Is the feature value of the j-th position of the i-th context local battery external parameter feature vector in the context local battery external parameter feature vectors, L is the length of the feature vector, mu i Sum sigma i 2 Each bit in the i-th context local battery external parameter feature vectorSetting the mean and variance of the eigenvalue set, log is the log function value based on 2, w i Is an ith gaussian regression uncertainty factor of the plurality of gaussian regression uncertainty factors. Here, for the agnostic regression of the classification feature vector, which may be caused by the distribution uncertainty information of the respective integrated feature set of each of the plurality of context local battery external parameter feature vectors, scalar measurement of the statistical characteristics of the feature set is performed by using the mean value and the variance of the statistical quantization parameter, so that the normal distribution cognitive mode of the feature representation of the source data noise is expanded to an unknown distribution regression mode, and the migration learning based on natural distribution transfer on the feature set scale is realized, so that the classification feature vector is obtained by weighting each context local battery external parameter feature vector by the gaussian regression uncertainty factor respectively and cascading the weighted feature vector, and the uncertainty correction of each context local battery external parameter feature vector based on self calibration when the classification feature vector is formed can be realized, so that the classification regression error existing in the classification feature vector is corrected, and the accuracy of the classification result obtained by the classifier of the classification feature vector is improved. Therefore, the self-adaptive control of the charge and discharge parameters of the zinc-iron flow battery system can be accurately performed in real time based on the external environment condition of the actual battery, so that the stable operation of the system under different working conditions is ensured, and the performance of the battery is improved.
Fig. 5 is a block diagram of a global association coding module in a zinc-iron flow battery system capacity balancing control system according to an embodiment of the present application. As shown in fig. 5, the global associated coding module 350 includes: a context encoding unit 351 configured to pass the plurality of local battery external parameter feature vectors through a context encoder based on a converter to obtain a plurality of context local battery external parameter feature vectors; an optimization factor calculation unit 352 configured to calculate gaussian regression uncertainty factors of the feature vectors of the external parameters of the respective contextual local batteries to obtain a plurality of gaussian regression uncertainty factors; a weighted optimization unit 353 for performing weighted optimization on the plurality of context local battery external parameter feature vectors with the plurality of gaussian regression uncertainty factors as weighting coefficients to obtain a plurality of optimized context local battery external parameter feature vectors; and an optimization fusion unit 354 configured to concatenate the plurality of optimization context local battery external parameter feature vectors to obtain the classification feature vector. Wherein the context encoding unit 351 includes: the query vector construction subunit is used for carrying out one-dimensional arrangement on the plurality of local battery external parameter feature vectors to obtain global battery external parameter feature vectors; a self-attention subunit, configured to calculate a product between the global battery external parameter feature vector and a transpose vector of each of the plurality of local battery external parameter feature vectors to obtain a plurality of self-attention correlation matrices; the normalization subunit is used for respectively performing normalization processing on each self-attention correlation matrix in the plurality of self-attention correlation matrices to obtain a plurality of normalized self-attention correlation matrices; the attention calculating subunit is used for obtaining a plurality of probability values through a Softmax classification function by each normalized self-attention correlation matrix in the normalized self-attention correlation matrices; and the attention applying subunit is used for weighting each local battery external parameter characteristic vector in the local battery external parameter characteristic vectors by taking each probability value in the plurality of probability values as a weight so as to obtain the context semantic local battery external parameter characteristic vectors.
Specifically, during the operation of the capacity balancing control system 300 of the zinc-iron flow battery system, the charge-discharge power control module 360 is configured to pass the classification feature vector through a classifier to obtain a classification result, where the classification result is used to indicate that the charge-discharge power of the zinc-iron flow battery system should be increased or decreased. That is, after the classification feature vector is obtained, it is further passed through a classifier to obtain a classification result for indicating that the charge-discharge power of the zinc-iron flow battery system should be increased or decreased. Specifically, the classifier includes a plurality of fully connected layers and a Softmax layer cascaded with a last fully connected layer of the plurality of fully connected layers. In the classification processing of the classifier, multiple full-connection encoding is carried out on the classification feature vectors by using multiple full-connection layers of the classifier to obtain encoded classification feature vectors; further, the encoded classification feature vector is input to a Softmax layer of the classifier, i.e., the encoded classification feature vector is classified using the Softmax classification function to obtain a classification label. In the technical scheme of the application, the labels of the classifier comprise a first label which is used for increasing the charge and discharge power of the zinc-iron flow battery system and a second label which is used for decreasing the charge and discharge power of the zinc-iron flow battery system, wherein the classifier determines which classification label the classification feature vector belongs to through a soft maximum function. It should be noted that the first tag p1 and the second tag p2 do not include the concept of artificial setting, and in fact, during the training process, the computer model does not have the concept of "the charge-discharge power of the zinc-iron flow battery system should be increased or decreased", which is only two kinds of classification tags, and the probability that the output characteristics are under the two classification tags, that is, the sum of p1 and p2 is one. Therefore, the classification result that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased is actually converted into the classification probability distribution conforming to the natural rule through the classification label, and the physical meaning of the natural probability distribution of the label is essentially used instead of the language text meaning that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased. It should be understood that, in the technical scheme of the application, the classification label of the classifier is a control strategy label that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased, so after the classification result is obtained, the charge and discharge power value of the zinc-iron flow battery system can be adaptively adjusted based on the classification result, so as to perform adaptive control of the charge and discharge parameters of the zinc-iron flow battery system, thereby ensuring stable operation of the system under different working conditions and improving the performance of the battery.
In summary, the capacity balance control system 300 of the zinc-iron flow battery system according to the embodiment of the application is illustrated, and by adopting a neural network model based on deep learning to mine the time sequence cooperative association relation of each data item in the external data of the battery, the accurate control of the charge and discharge parameters of the zinc-iron flow battery system is performed, so that the stable operation of the system under different working conditions is ensured, and the performance of the battery is improved.
As described above, the capacity balancing control system of the zinc-iron flow battery system according to the embodiment of the application can be implemented in various terminal devices. In one example, the zinc-iron flow battery system capacity equalization control system 300 according to embodiments of the present application may be integrated into a terminal device as a software module and/or hardware module. For example, the zinc-iron flow battery system capacity equalization control system 300 may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the zinc-iron flow battery system capacity equalization control system 300 can also be one of a number of hardware modules of the terminal device.
Alternatively, in another example, the zinc-iron flow battery system capacity equalization control system 300 and the terminal device may be separate devices, and the zinc-iron flow battery system capacity equalization control system 300 may be connected to the terminal device through a wired and/or wireless network and transmit the interaction information in a agreed data format.
Exemplary method
Fig. 6 is a flow chart of a method of controlling capacity equalization of a zinc-iron flow battery system according to an embodiment of the present application. As shown in fig. 6, the capacity balancing control method of the zinc-iron flow battery system according to the embodiment of the application includes the steps of: s110, acquiring battery external data of a plurality of preset time points in a preset time period, wherein the battery external data comprise battery inlet pressure, flow and temperature; s120, arranging the battery external data of the plurality of preset time points into a battery external parameter input matrix according to a time dimension and a parameter sample dimension; s130, carrying out local matrix segmentation on the battery external parameter input matrix to obtain a plurality of local battery external parameter matrixes; s140, the local battery external parameter matrixes are respectively passed through a convolutional neural network model serving as a filter to obtain local battery external parameter feature vectors; s150, passing the plurality of local battery external parameter feature vectors through a context encoder based on a converter to obtain classification feature vectors; and S160, passing the classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased.
In one example, in the above method for controlling capacity balance of zinc-iron flow battery system, the step S120 includes: arranging the battery external data of the plurality of preset time points into a plurality of row vectors according to the dimension of the parameter sample; and two-dimensionally arranging the row vectors according to the time dimension to obtain the battery external parameter input matrix.
In one example, in the above method for controlling capacity balance of zinc-iron flow battery system, the step S140 includes: each layer of the convolutional neural network model used as the filter performs the following steps on input data in forward transfer of the layer: carrying out convolution processing on input data to obtain a convolution characteristic diagram; pooling the convolution feature images based on a feature matrix to obtain pooled feature images; performing nonlinear activation on the pooled feature map to obtain an activated feature map; wherein the output of the last layer of the convolutional neural network as a filter is the plurality of local battery external parameter feature vectors, and the input of the first layer of the convolutional neural network as a filter is the plurality of local battery external parameter matrices.
In one example, in the above method for controlling capacity balance of zinc-iron flow battery system, the step S150 includes: passing the plurality of local battery external parameter feature vectors through a converter-based context encoder to obtain a plurality of context local battery external parameter feature vectors; calculating Gaussian regression uncertainty factors of the external parameter feature vectors of the local batteries of the various contexts to obtain a plurality of Gaussian regression uncertainty factors; weighting and optimizing the external parameter feature vectors of the local battery in the plurality of contexts by taking the plurality of Gaussian regression uncertainty factors as weighting coefficients to obtain the external parameter feature vectors of the local battery in the plurality of optimized contexts; and cascading the plurality of optimization context local battery external parameter feature vectors to obtain the classification feature vector. Wherein passing the plurality of local battery external parameter feature vectors through a converter-based context encoder to obtain a plurality of context local battery external parameter feature vectors, comprising: one-dimensional arrangement is carried out on the plurality of local battery external parameter feature vectors so as to obtain global battery external parameter feature vectors; calculating the product between the global battery external parameter feature vector and the transpose vector of each local battery external parameter feature vector in the plurality of local battery external parameter feature vectors to obtain a plurality of self-attention association matrices; respectively carrying out standardization processing on each self-attention correlation matrix in the plurality of self-attention correlation matrices to obtain a plurality of standardized self-attention correlation matrices; obtaining a plurality of probability values by using a Softmax classification function through each normalized self-attention correlation matrix in the normalized self-attention correlation matrices; and weighting each local battery external parameter feature vector in the local battery external parameter feature vectors by taking each probability value in the probability values as a weight so as to obtain the context semantic local battery external parameter feature vectors. More specifically, computing the gaussian regression uncertainty factors for the respective contextual local battery external parameter feature vectors to obtain a plurality of gaussian regression uncertainty factors, comprising: calculating the Gaussian regression uncertainty factors of the external parameter feature vectors of the local battery of each context according to the following optimization formula to obtain a plurality of Gaussian regression uncertainty factors; wherein, the optimization formula is:
Wherein v is ij Is the feature value of the j-th position of the i-th context local battery external parameter feature vector in the context local battery external parameter feature vectors, L is the length of the feature vector, mu i Sum sigma i 2 The mean and variance of each position characteristic value set in the i-th context local battery external parameter characteristic vector are respectively that log is a logarithmic function value based on 2, and w i Is an ith gaussian regression uncertainty factor of the plurality of gaussian regression uncertainty factors.
In one example, in the above-mentioned method for controlling capacity balance of zinc-iron flow battery system, the step S160 includes: performing full-connection coding on the classification feature vectors by using a plurality of full-connection layers of the classifier to obtain coded classification feature vectors; and passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
In summary, the capacity balance control method of the zinc-iron flow battery system according to the embodiment of the application is explained, and the time sequence cooperative association relation of each data item in the external data of the battery is excavated by adopting a neural network model based on deep learning, so that the charge and discharge parameters of the zinc-iron flow battery system are accurately controlled, and the stable operation of the system under different working conditions is ensured, so that the performance of the battery is improved.
Exemplary electronic device
Next, an electronic device according to an embodiment of the present application is described with reference to fig. 7.
Fig. 7 illustrates a block diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 7, the electronic device 10 includes one or more processors 11 and a memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
The input means 13 may comprise, for example, a keyboard, a mouse, etc.
The output device 14 may output various information including the classification result and the like to the outside. The output means 14 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 10 that are relevant to the present application are shown in fig. 7 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In addition to the methods and apparatus described above, embodiments of the present application may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform steps in the functions of the zinc-iron flow battery system capacity balancing control method according to various embodiments of the present application described in the "exemplary systems" section of this specification.
The computer program product may write program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium, having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform steps in the functions of the zinc-iron flow battery system capacity balancing control method according to various embodiments of the present application described in the "exemplary systems" section of the present specification.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not intended to be limited to the details disclosed herein as such.
The block diagrams of the devices, apparatuses, devices, systems referred to in this application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent to the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (10)
1. A zinc-iron flow battery system capacity balance control system is characterized by comprising:
the external data acquisition module is used for acquiring battery external data of a plurality of preset time points in a preset time period, wherein the battery external data comprise battery inlet pressure, flow and temperature;
The parameter time sequence association distribution module is used for arranging the battery external data of the plurality of preset time points into a battery external parameter input matrix according to the time dimension and the parameter sample dimension;
the matrix segmentation module is used for carrying out local matrix segmentation on the battery external parameter input matrix to obtain a plurality of local battery external parameter matrixes;
the parameter local time sequence correlation module is used for respectively passing the plurality of local battery external parameter matrixes through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors;
the global association coding module is used for enabling the plurality of local battery external parameter feature vectors to pass through a context coder based on a converter to obtain classification feature vectors; and
and the charge and discharge power control module is used for passing the classification feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased.
2. The zinc-iron flow battery system capacity equalization control system of claim 1, wherein the parameter timing correlation distribution module is configured to:
arranging the battery external data of the plurality of preset time points into a plurality of row vectors according to the dimension of the parameter sample;
And two-dimensionally arranging the row vectors according to the time dimension to obtain the battery external parameter input matrix.
3. The zinc-iron flow battery system capacity equalization control system of claim 2, wherein the parameter local timing correlation module is configured to: each layer of the convolutional neural network model used as the filter performs the following steps on input data in forward transfer of the layer:
carrying out convolution processing on input data to obtain a convolution characteristic diagram;
pooling the convolution feature images based on a feature matrix to obtain pooled feature images; and
non-linear activation is carried out on the pooled feature map so as to obtain an activated feature map;
wherein the output of the last layer of the convolutional neural network as a filter is the plurality of local battery external parameter feature vectors, and the input of the first layer of the convolutional neural network as a filter is the plurality of local battery external parameter matrices.
4. The zinc-iron flow battery system capacity equalization control system of claim 3, wherein said global association coding module comprises:
a context encoding unit, configured to pass the plurality of local battery external parameter feature vectors through a context encoder based on a converter to obtain a plurality of context local battery external parameter feature vectors;
An optimization factor calculation unit, configured to calculate gaussian regression uncertainty factors of the feature vectors of the external parameters of the respective contextual local batteries to obtain a plurality of gaussian regression uncertainty factors;
the weighted optimization unit is used for weighted optimization of the context local battery external parameter feature vectors by taking the plurality of Gaussian regression uncertainty factors as weighting coefficients so as to obtain a plurality of optimized context local battery external parameter feature vectors; and
and the optimization fusion unit is used for cascading the plurality of optimization context local battery external parameter feature vectors to obtain the classification feature vector.
5. The zinc-iron flow battery system capacity equalization control system of claim 4, wherein said context encoding unit comprises:
the query vector construction subunit is used for carrying out one-dimensional arrangement on the plurality of local battery external parameter feature vectors to obtain global battery external parameter feature vectors;
a self-attention subunit, configured to calculate a product between the global battery external parameter feature vector and a transpose vector of each of the plurality of local battery external parameter feature vectors to obtain a plurality of self-attention correlation matrices;
The normalization subunit is used for respectively performing normalization processing on each self-attention correlation matrix in the plurality of self-attention correlation matrices to obtain a plurality of normalized self-attention correlation matrices;
the attention calculating subunit is used for obtaining a plurality of probability values through a Softmax classification function by each normalized self-attention correlation matrix in the normalized self-attention correlation matrices;
and the attention applying subunit is used for weighting each local battery external parameter characteristic vector in the local battery external parameter characteristic vectors by taking each probability value in the plurality of probability values as a weight so as to obtain the context semantic local battery external parameter characteristic vectors.
6. The zinc-iron flow battery system capacity equalization control system of claim 5, wherein the optimization factor calculation unit is configured to: calculating the Gaussian regression uncertainty factors of the external parameter feature vectors of the local battery of each context according to the following optimization formula to obtain a plurality of Gaussian regression uncertainty factors;
wherein, the optimization formula is:
wherein v is ij Is the feature value of the j-th position of the i-th context local battery external parameter feature vector in the context local battery external parameter feature vectors, L is the length of the feature vector, mu i Sum sigma i 2 Each bit in the i-th context local battery external parameter feature vectorSetting the mean and variance of the eigenvalue set, log is the log function value based on 2, w i Is an ith gaussian regression uncertainty factor of the plurality of gaussian regression uncertainty factors.
7. The zinc-iron flow battery system capacity equalization control system of claim 6, wherein said charge-discharge power control module comprises:
the full-connection coding unit is used for carrying out full-connection coding on the classification characteristic vectors by using a plurality of full-connection layers of the classifier so as to obtain coded classification characteristic vectors; and
and the classification result generation unit is used for passing the coding classification feature vector through a Softmax classification function of the classifier to obtain the classification result.
8. A capacity balance control method of a zinc-iron flow battery system is characterized by comprising the following steps:
acquiring battery external data of a plurality of preset time points in a preset time period, wherein the battery external data comprises battery inlet pressure, flow and temperature;
arranging the battery external data of the plurality of preset time points into a battery external parameter input matrix according to a time dimension and a parameter sample dimension;
Performing local matrix segmentation on the battery external parameter input matrix to obtain a plurality of local battery external parameter matrixes;
respectively passing the local battery external parameter matrixes through a convolutional neural network model serving as a filter to obtain a plurality of local battery external parameter feature vectors;
passing the plurality of local battery external parameter feature vectors through a converter-based context encoder to obtain a classification feature vector; and
and the classification feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased.
9. The method for controlling capacity equalization of a zinc-iron flow battery system according to claim 8, wherein the step of passing the plurality of local battery external parameter matrices through a convolutional neural network model as a filter to obtain a plurality of local battery external parameter feature vectors, respectively, comprises: each layer of the convolutional neural network model used as the filter performs the following steps on input data in forward transfer of the layer:
carrying out convolution processing on input data to obtain a convolution characteristic diagram;
pooling the convolution feature images based on a feature matrix to obtain pooled feature images; and
Non-linear activation is carried out on the pooled feature map so as to obtain an activated feature map;
wherein the output of the last layer of the convolutional neural network as a filter is the plurality of local battery external parameter feature vectors, and the input of the first layer of the convolutional neural network as a filter is the plurality of local battery external parameter matrices.
10. The method for controlling capacity balance of a zinc-iron flow battery system according to claim 9, wherein the classifying feature vector is passed through a classifier to obtain a classifying result, and the classifying result is used for indicating that the charge and discharge power of the zinc-iron flow battery system should be increased or decreased, and the method comprises the following steps:
performing full-connection coding on the classification feature vectors by using a plurality of full-connection layers of the classifier to obtain coded classification feature vectors; and
and the coding classification feature vector is passed through a Softmax classification function of the classifier to obtain the classification result.
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