Super capacitor system control method and system based on integral control adjustment
By constructing feature space and clustering, determining the integral gain factor and coefficient, the segment control of the supercapacitor system is realized, solving the system instability and slow response caused by improper setting of the integral gain coefficient, and improving the accuracy and stability of control adjustment.
Patent Information
- Application Number
- CN202510393637.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-08
AI Technical Summary
In the existing supercapacitor system control methods, setting the integral gain coefficient too large may lead to instability of the system, and too small will cause slow response speed, which will not effectively eliminate steady-state errors, affecting the stability and control accuracy of the system.
By obtaining the voltage and current signal data sequences in the supercapacitor, building feature spaces and clustering, class clusters are obtained, integral gain factors and coefficients are determined according to the distribution of class clusters, segment control and adjustment are performed, and system control is used in different segments.
It improves the control and regulation accuracy of the supercapacitor system, reduces the transition response caused by steady-state error, and ensures the stability and response speed of the system at different time periods.
Smart Images
Figure CN120276312A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing, and in particular, to a control method and system for a supercapacitor system based on integral control regulation. Background Art
[0002] A supercapacitor is an electrical energy storage device with a high capacitance value, capable of providing rapid charge and discharge, having characteristics such as high power density and long life, and is widely used in fields such as electric vehicle energy recovery, instantaneous electrical energy supplementation, and backup power supplies. In order to monitor the supercapacitor, various data need to be collected through sensors. Due to the instability of charge transfer during the electrochemical reaction process, the interaction between the battery and the capacitor, the electromagnetic interference of the external environment, and the noise of the internal circuit of the system, the collected data has poor credibility. Therefore, it is necessary to use various data in the supercapacitor system for denoising processing to improve the measurement accuracy of the sensor, ensure the accuracy of signals such as the voltage and current of the capacitor; optimize the control system to maintain the stability and efficiency of the charge and discharge process; improve the reliability of the system, reduce the risk of failures caused by noise; reduce false alarms, ensure the accuracy of fault diagnosis; and improve the user experience, making the monitoring data more stable and credible. Therefore, denoising and filtering of supercapacitor data play a very important role.
[0003] In conventional technologies, the control and regulation of the supercapacitor system can use integral regulation in the PID control algorithm to control and eliminate the steady-state error in the supercapacitor system; usually, the integral gain coefficient is set manually. An overly large integral gain coefficient can well eliminate the steady-state error, but an overly large integral gain may cause the system to be unstable. Especially in the case where the feedback loop is long or there is a delay, an overly large integral action may amplify the error, making it more difficult for the system to maintain stability; while an overly small integral gain coefficient has a slow response speed and cannot effectively eliminate the steady-state error. Summary of the Invention
[0004] The present invention provides a control method and system for a supercapacitor system based on integral control regulation to solve existing problems.
[0005] The object of the present invention can be achieved by the following technical solutions: The first aspect of the present invention is to provide a control method for a supercapacitor system based on integral control regulation, including: Obtain each signal data sequence in the supercapacitor; wherein, the signal data sequence includes a voltage signal data sequence and a current signal data sequence; According to the change difference between the data adjacent to the left and right of each data in each signal data sequence, obtain the degree of change intensity of each data in each signal data sequence; according to the degree of change intensity of each data in each signal data sequence, the voltage signal data sequence and the current signal data sequence, construct a feature space, and map the data in the voltage signal data sequence and the current signal data sequence in the feature space to obtain a number of data points; cluster the number of data points in the feature space to obtain a number of clusters; According to the distribution of all data points in each cluster, obtain the integral gain factor of each cluster; By mapping the integral gain factor of each cluster, obtain the integral gain coefficient of each cluster; according to a number of clusters, divide the time series into a number of time series segments, and according to the integral gain coefficient of each cluster, obtain the integral gain coefficient of each time series segment; perform sectional control and adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments.
[0006] Further, the obtaining of each signal data sequence in the supercapacitor includes: Obtain various signal data in the supercapacitor through various sensors at a preset time interval to divide each type of signal data, obtain a number of data, and according to the time sequence, form a group of sequences with the number of data, denoted as each signal data sequence.
[0007] Further, the obtaining of the degree of change intensity of each data in each signal data sequence according to the change difference between the data adjacent to the left and right of each data in each signal data sequence includes:
[0008] wherein, represents the th data in each signal data sequence, represents the th data in each signal data sequence, represents the degree of change intensity of the th data in each signal data sequence, is the absolute value symbol.
[0009] Further, the constructing of a feature space according to the degree of change intensity of each data in each signal data sequence, the voltage signal data sequence and the current signal data sequence, and mapping the data in the voltage signal data sequence and the current signal data sequence in the feature space to obtain a number of data points includes: Obtain the degree of change of each data in the voltage signal data sequence and the degree of change of each data in the current signal data sequence according to the process of obtaining the degree of change of each data in each signal data sequence; Construct a feature space with the degree of change of each data in the voltage signal data sequence as the horizontal axis and the degree of change of each data in the current signal data sequence as the vertical axis; map all the degrees of change of each data in the voltage and current signal data sequences into the feature space to obtain several data points in the feature space.
[0010] Further, clustering the several data points in the feature space to obtain several clusters, including: Cluster the several data points in the feature space by the K-means clustering algorithm to obtain several clusters.
[0011] Further, obtaining the integral gain factor of each cluster according to the distribution of all data points in each cluster, including:
[0012] In the formula, represents the distance between the th data point in each cluster and the origin of the feature space, represents the number of all data points in each cluster, represents the integral gain factor of each cluster.
[0013] Further, obtaining the integral gain coefficient of each cluster by mapping the integral gain factor of each cluster; dividing the time series into several time series segments according to several clusters, and obtaining the integral gain coefficient of each time series segment according to the integral gain coefficient of each cluster, including: Map all the integral gain factors of all clusters into the integral gain coefficient interval, and take the mapped result of the integral gain factor of each cluster as the integral gain coefficient of each cluster; wherein, the integral gain coefficient interval is a preset; According to the divided clusters, connect the moments when the data points corresponding to all adjacent moments in the time series belong to the same cluster, and divide the time series into several time series segments; take the integral gain coefficient of the cluster corresponding to each time series segment as the integral gain coefficient of each time series segment.
[0014] The second aspect of the present invention is to provide a supercapacitor system control system based on integral control regulation, including: Data acquisition module: used to obtain each signal data sequence in the supercapacitor; wherein, the signal data sequence includes a voltage signal data sequence and a current signal data sequence; Data processing module: used to obtain the change severity of each data in each signal data sequence according to the change difference between the left and right adjacent data of each data in each signal data sequence; construct a feature space according to the change severity of each data in each signal data sequence, the voltage signal data sequence and the current signal data sequence, map the data in the voltage signal data sequence and the current signal data sequence in the feature space, and obtain a number of data points; cluster the number of data points in the feature space to obtain a number of clusters; Analysis module: used to obtain the integral gain factor of each cluster according to the distribution of all data points in each cluster; Adjustment module: used to obtain the integral gain coefficient of each cluster by mapping the integral gain factor of each cluster; divide the time series into several time series segments according to several clusters, and obtain the integral gain coefficient of each time series segment according to the integral gain coefficient of each cluster; perform sectional control adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments.
[0015] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method of the supercapacitor system based on integral control adjustment.
[0016] The fourth aspect of the present invention is to provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the control method of the supercapacitor system based on integral control adjustment.
[0017] Compared with the prior art, the beneficial effects of the present invention are: obtaining the change severity of each data in each signal data sequence according to the change difference between the left and right adjacent data of each data in each signal data sequence, improving the accuracy of the analysis of the data change severity; constructing a feature space according to the change severity and obtaining a number of data points; clustering the number of data points in the feature space to obtain a number of clusters, improving the accuracy of the approximate analysis of data errors; obtaining the integral gain factor of each cluster according to the distribution of all data points in each cluster; mapping the integral gain factor to obtain the integral gain coefficient of each cluster; dividing the time series into several time series segments according to several clusters, using the segments for adjustment, reducing the transient response caused by the difference in steady-state errors at different times, and obtaining the integral gain coefficient of each time series segment according to the integral gain coefficient of each cluster; performing sectional control adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments, improving the accuracy of the control adjustment of the supercapacitor system. Description of the Drawings
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.
[0019] Figure 1 It is a schematic flow chart of the steps of a control method for a supercapacitor system based on integral control regulation provided by the present invention; Figure 2 It is a schematic module flow chart of a control system for a supercapacitor system based on integral control regulation provided by the present invention. Detailed implementation manners
[0020] In order to enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.
[0022] In response to the problems in the background art, a control method and system for a supercapacitor system based on integral control regulation are studied and designed, which has important practical significance.
[0023] As Figure 1 shown, the first aspect of the present invention is to provide a control method for a supercapacitor system based on integral control regulation, including the following steps: Step S001: Collect each signal data sequence in the supercapacitor.
[0024] It should be noted that in order to achieve the health assessment of the supercapacitor, the prediction of its service life, the optimization of the control accuracy of the supercapacitor, and the fault diagnosis of the supercapacitor, it is necessary to collect various signal data of the supercapacitor through various sensors, and analyze the various signal data of the supercapacitor to achieve the health assessment, life prediction, control optimization, and fault diagnosis of the supercapacitor, so as to ensure its efficient and stable operation in various application scenarios.
[0025] Specifically, various signal data in the supercapacitor are obtained through various sensors; among them, the various signal data in the supercapacitor include: current signal data, voltage signal data, temperature signal data, internal resistance signal data, and power signal data. Among them, at a preset time interval each type of signal data is divided to obtain a number of data, and in chronological order, the number of data is formed into a group sequence, denoted as each type of signal data sequence. Among them, the signal data sequences include a voltage signal data sequence and a current signal data sequence.
[0026] Among them, in this embodiment, the preset time interval seconds, where in this embodiment, the preset time interval is not specifically limited, and the implementer can determine it according to the specific situation.
[0027] Among them, the current signal data is collected by a current sensor, the voltage signal data is collected by a voltage sensor, the temperature signal data is collected by a temperature sensor, the internal resistance signal data is simply calculated by an AC impedance spectrometer, and the power signal data is collected by a power meter.
[0028] So far, each type of signal data sequence in the supercapacitor is obtained.
[0029] Step S002: According to the change difference between the left and right adjacent data in each signal data sequence, obtain the change severity of each data in each signal data sequence; according to the change severity, construct a feature space, and map the data in the voltage signal data sequence and the current signal data sequence in the feature space to obtain a number of data points; cluster the number of data points in the feature space to obtain a number of clusters.
[0030] It should be noted that during the charging and discharging process of the supercapacitor, the voltage and current change relatively violently, so the steady-state error of the supercapacitor system is different in different time periods of charging and discharging.
[0031] Further, it should be noted that when the voltage or current data changes slightly in a section, a smaller integral gain coefficient can be used for adjustment in this section; when the voltage or current data changes significantly in a section, a larger integral gain coefficient can be used for adjustment in this section; in this way, the influence caused by the change of the steady-state error during the operation of the entire system can be avoided.
[0032] Specifically, according to the change difference between the left and right adjacent data of each data in each signal data sequence, the change severity of each data in each signal data sequence is obtained; the change severity of each data in each signal data sequence is specifically expressed by the formula:
[0033] In the formula, represents the th data in each signal data sequence, represents the th data in each signal data sequence, represents the change severity of the th data in each signal data sequence, is the absolute value symbol.
[0034] Among them, represents the difference between the left and right adjacent data of each data in each signal data sequence. When the difference is larger, the change of the data is larger, that is, the existing steady-state error is also larger; when the difference is smaller, the change of the data is smaller, that is, the existing steady-state error is also smaller.
[0035] It should be noted that in order to better use the integral gain coefficient for system control adjustment, different integral gain coefficients are used for adjustment in different sections. For the division of sections, it can be divided according to the change severity of each data in the signal data sequence, that is, the data with similar change severity are divided into one section, so as to use an integral gain coefficient for system control adjustment.
[0036] Further, it should be noted that the charging and discharging processes of the super capacitor are analyzed together with the change severity of the data in the current and voltage signal data sequences.
[0037] Specifically, according to the process of obtaining the change severity of each data in each signal data sequence, the change severity of each data in the voltage signal data sequence and the change severity of each data in the current signal data sequence are obtained.
[0038] Construct a feature space with the degree of change of each data in the voltage signal data sequence as the horizontal axis and the degree of change of each data in the current signal data sequence as the vertical axis; map the degree of change of each data in the voltage and current signal data sequences onto the feature space to obtain several data points in the feature space.
[0039] Cluster the several data points in the feature space through the K-means clustering algorithm to obtain several clusters; among them, the number of clusters is obtained by the elbow method; among them, both the K-means clustering algorithm and the elbow method are well-known technologies and will not be specifically elaborated here.
[0040] Thus, several clusters in the feature space are obtained.
[0041] Step S003: Obtain the integral gain factor of each cluster according to the distribution of all data points in each cluster.
[0042] It should be noted that when each cluster in the feature space is closer to the origin, it means that the degree of change of the data points in this cluster is slow, and a smaller integral gain coefficient can be used for adjustment; when each cluster in the feature space is farther from the origin, it means that the degree of change of the data points in this cluster is more intense, and a larger integral gain coefficient can be used for adjustment.
[0043] Specifically, obtain the integral gain factor of each cluster according to the distribution of all data points in each cluster; the integral gain factor of each cluster is specifically expressed by the formula:
[0044] In the formula, represents the distance between the th data point in each cluster and the origin of the feature space, represents the number of all data points in each cluster, represents the integral gain factor of each cluster.
[0045] Among them, when the distance between all data points in each cluster and the origin of the feature space is closer, it means that the integral gain factor to be adjusted is smaller; when the distance between all data points in each cluster and the origin of the feature space is farther, it means that the integral gain factor to be adjusted is larger.
[0046] Step S004: Obtain the integral gain coefficient of each cluster by mapping the integral gain factor of each cluster; divide the time series into several time series segments according to several clusters, and obtain the integral gain coefficient of each time series segment according to the integral gain coefficient of each cluster; perform partitioned control and adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments.
[0047] Preset an integral gain coefficient interval , where , ; Map all the integral gain factors of all clusters within the integral gain coefficient interval, and use the result of the mapped integral gain factor of each cluster as the integral gain coefficient of each cluster. Among them, in this embodiment, the low preset parameter , the high preset parameter , where, in this embodiment, for and no specific limitations are imposed, and implementers can determine according to specific circumstances.
[0048] According to the divided clusters, connect the moments when the data points corresponding to all adjacent moments in time series belong to the same cluster, and divide the time series into several time series segments; thus, the division of the time series segments is completed. Among them, each time series segment corresponds to an integral gain coefficient of a cluster.
[0049] Use the integral gain coefficient of the cluster corresponding to each time series segment as the integral gain coefficient of each time series segment. Perform sectional control and adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments.
[0050] As Figure 2 shown, the second aspect of the present invention is to provide a control system for a supercapacitor system based on integral control and adjustment, including the following modules: Data acquisition module 101: used to obtain each signal data sequence in the supercapacitor; among them, the signal data sequence includes a voltage signal data sequence and a current signal data sequence; Data processing module 102: used to obtain the degree of change intensity of each data in each signal data sequence according to the change difference between the left and right adjacent data of each data in each signal data sequence; construct a feature space according to the degree of change intensity of each data in each signal data sequence, the voltage signal data sequence, and the current signal data sequence, and map the data in the voltage signal data sequence and the current signal data sequence in the feature space to obtain several data points; perform clustering on the several data points in the feature space to obtain several clusters; Analysis module 103: used to obtain the integral gain factor of each cluster according to the distribution of all data points in each cluster; Adjustment module 104: used to obtain the integral gain coefficient of each cluster by mapping the integral gain factor of each cluster; divide the time series into several time series segments according to several clusters, and obtain the integral gain coefficient of each time series segment according to the integral gain coefficient of each cluster; perform sectional control and adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments.
[0051] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a control method for a supercapacitor system based on integral control regulation is implemented.
[0052] The fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor, implements a control method for a supercapacitor system based on integral control regulation.
[0053] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.
[0054] The present invention is described with reference to the flowcharts and / or block diagrams of methods, systems, and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.
[0055] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.
[0056] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide means for realizing the specified functions in one Figure 1 one flow or multiple flows and / or blocks Figure 1Steps of the functions specified in one or more boxes.
[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that it is still possible to modify the specific implementation manners of the present invention or make equivalent replacements. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the present invention.
Claims
1. A control method for a supercapacitor system based on integral control regulation, characterized in that, Including: Obtain each signal data sequence in the supercapacitor; wherein, the signal data sequence includes a voltage signal data sequence and a current signal data sequence; According to the change difference between the left and right adjacent data of each data in each signal data sequence, obtain the change severity of each data in each signal data sequence; according to the change severity of each data in each signal data sequence, the voltage signal data sequence and the current signal data sequence, construct a feature space, and map the data in the voltage signal data sequence and the current signal data sequence in the feature space to obtain a number of data points; cluster the number of data points in the feature space to obtain a number of clusters; According to the distribution of all data points in each cluster, obtain the integral gain factor of each cluster; By mapping the integral gain factor of each cluster, obtain the integral gain coefficient of each cluster; according to a number of clusters, divide the time series into a number of time series segments, and according to the integral gain coefficient of each cluster, obtain the integral gain coefficient of each time series segment; perform segmented control and adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments.
2. The control method of a supercapacitor system based on integral control regulation according to claim 1, characterized in that The obtaining each signal data sequence in the supercapacitor includes: Obtain various signal data in the supercapacitor through various sensors at preset time intervals to divide each type of signal data, obtain several data, and form a group of sequences in chronological order, which is denoted as each type of signal data sequence.
3. A control method for a supercapacitor system based on integral control regulation according to claim 1, characterized in that, The obtaining the change severity of each data in each signal data sequence according to the change difference between the left and right adjacent data of each data in each signal data sequence includes: In the formula, represents the th data in each signal data sequence, represents the th data in each signal data sequence, represents the degree of drastic change of the th data in each signal data sequence, is the absolute value symbol.
4. A control method for a supercapacitor system based on integral control regulation according to claim 3, wherein The constructing a feature space according to the change severity of each data in each signal data sequence, the voltage signal data sequence and the current signal data sequence, and mapping the data in the voltage signal data sequence and the current signal data sequence in the feature space to obtain a number of data points includes: According to the obtaining process of the change severity of each data in each signal data sequence, obtain the change severity of each data in the voltage signal data sequence and the change severity of each data in the current signal data sequence; Construct a feature space with the change severity of each data in the voltage signal data sequence as the horizontal axis and the change severity of each data in the current signal data sequence as the vertical axis; map all the change severities of the data in the voltage and current signal data sequences in the feature space to obtain a number of data points in the feature space.
5. A control method for a supercapacitor system based on integral control regulation according to claim 1, characterized in that The clustering the number of data points in the feature space to obtain a number of clusters includes: Cluster the number of data points in the feature space by the K-means clustering algorithm to obtain a number of clusters.
6. A control method for a supercapacitor system based on integral control regulation according to claim 1, characterized in that, The obtaining the integral gain factor of each cluster according to the distribution of all data points in each cluster includes: In the formula, represents the distance between the -th data point in each cluster and the origin of the feature space, represents the number of all data points in each cluster, represents the integral gain factor of each cluster.
7. A control method for a supercapacitor system based on integral control regulation according to claim 1, characterized in that The obtaining the integral gain coefficient of each cluster by mapping the integral gain factor of each cluster; according to a number of clusters, dividing the time series into a number of time series segments, and according to the integral gain coefficient of each cluster, obtaining the integral gain coefficient of each time series segment includes: Map all the integral gain factors of all clusters in the integral gain coefficient interval, and use the mapped result of the integral gain factor of each cluster as the integral gain coefficient of each cluster; wherein, the integral gain coefficient interval is a preset. According to the divided clusters, connect the moments when the data points corresponding to all adjacent moments in time series belong to the same cluster, and divide the time series into several time series segments; use the integral gain coefficient of the cluster corresponding to each time series segment as the integral gain coefficient of each time series segment.
8. A supercapacitor system control system based on integral control regulation, characterized in that, It includes: Data acquisition module: used to obtain each signal data sequence in the supercapacitor; among them, the signal data sequence includes a voltage signal data sequence and a current signal data sequence; Data processing module: used to obtain the change intensity of each data in each signal data sequence according to the change difference between the left and right adjacent data of each data in each signal data sequence; construct a feature space according to the change intensity of each data in each signal data sequence, the voltage signal data sequence and the current signal data sequence, and map the data in the voltage signal data sequence and the current signal data sequence in the feature space to obtain several data points; cluster the several data points in the feature space to obtain several clusters; Analysis module: used to obtain the integral gain factor of each cluster according to the distribution of all data points in each cluster; Adjustment module: used to obtain the integral gain coefficient of each cluster by mapping the integral gain factor of each cluster; divide the time series into several time series segments according to several clusters, and obtain the integral gain coefficient of each time series segment according to the integral gain coefficient of each cluster; perform partitioned control adjustment on the supercapacitor system according to the integral gain coefficients of all time series segments.
9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the control method of a supercapacitor system based on integral control adjustment according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the control method of a supercapacitor system based on integral control adjustment according to any one of claims 1-7.