Method, system, device and storage medium for constructing load characteristics of electric bicycle

Abstract

The present invention discloses a method, system, device and storage medium for constructing an electric bicycle load characteristic, which belongs to the field of electric bicycle charging management and includes the following processes: obtaining baseline electric energy data of an electric bicycle when it is not charged at home, and actual household consumption data when it is charged at home; when the electric bicycle is charged at an outdoor charging pile, obtaining output electric energy data of the charging pile and charging battery status data of the electric bicycle; loading the output electric energy data of the charging pile as a disturbance to the baseline electric energy data to generate virtual household electric energy consumption data; fusing the virtual household electric energy consumption data with the actual household consumption data to generate household consumption data to be processed; extracting grid-side charging load characteristics; performing cluster analysis on charging battery status data based on a hierarchical clustering model to extract charging battery load characteristics; and performing feature fusion on the grid-side charging load characteristics and the charging battery load characteristics to construct a comprehensive load characteristic of the electric bicycle.

Description

Technical Field

[0001] The present invention belongs to the field of electric bicycle charging management and relates to a method, system, device and storage medium for constructing load characteristics of an electric bicycle. Background Art

[0002] With the increasing awareness of environmental protection and the development of urban transportation, electric bicycles have become a convenient and environmentally friendly means of short-distance travel. According to statistics, the number of electric bicycles in use continues to grow rapidly and has become one of the important choices for residents' daily travel.

[0003] The charging behavior of electric bicycles significantly impacts the load characteristics of the power grid. For one thing, the charging time and power of electric bicycles are random and dispersed. When a large number of electric bicycles are charged together, this can lead to localized overload on the power grid, impacting its stability and reliability. Furthermore, electric bicycles can be charged in a variety of ways, including at charging stations and at home, each of which has different impacts on the power grid. Therefore, accurately characterizing the load characteristics of electric bicycles is crucial for rational grid planning, optimizing power resource allocation, and improving grid operational efficiency.

[0004] Currently, research on e-bike load characteristics primarily focuses on single charging scenarios or simple load analysis. For example, some studies only consider the load of e-bikes charging at outdoor charging stations, ignoring the impact of home charging on household electricity loads. Other studies fail to fully consider the impact of the e-bike's rechargeable battery status on load characteristics. Furthermore, existing load feature extraction methods are mostly based on simple statistical analysis or traditional clustering algorithms, which struggle to accurately capture the complex load characteristics of e-bikes.

[0005] In practice, the lack of accurate e-bike load characteristics makes it difficult for grid operators to effectively guide and manage e-bike charging behavior, resulting in large fluctuations in grid load and increased costs and risks in grid operation. Furthermore, e-bike users are unable to obtain more reasonable charging recommendations, which impacts their experience and battery life. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, system, device and storage medium for constructing the load characteristics of electric bicycles, thereby improving the accuracy and reliability of the load characteristics extraction of electric bicycles and facilitating the implementation of subsequent applications.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for constructing load characteristics of an electric bicycle includes the following steps:

[0009] Obtain baseline electricity consumption data for e-bikes when they are not charged at home, and actual household consumption data when they are charged at home;

[0010] When the electric bicycle is charging at an outdoor charging station, the output power data of the charging station and the charging battery status data of the electric bicycle are obtained;

[0011] The output power data of the charging pile is loaded into the baseline power data as a disturbance to generate virtual household power consumption data;

[0012] The virtual household electricity consumption data is integrated with the actual household consumption data to generate the household consumption data to be processed;

[0013] Cluster analysis is performed on the household consumption data to be processed based on the fuzzy clustering model to extract the charging load characteristics on the grid side;

[0014] Perform cluster analysis on rechargeable battery status data based on a hierarchical clustering model to extract rechargeable battery load characteristics;

[0015] The charging load characteristics of the grid side and the charging battery load characteristics are fused to construct the comprehensive load characteristics of the electric bicycle.

[0016] Preferably, cluster analysis is performed on the household consumption data to be processed based on the fuzzy clustering model to extract the specific process of the grid-side charging load characteristics: initialize the number of clusters, define the fuzzy membership matrix and the cluster center matrix; iteratively calculate the fuzzy membership degree of the household consumption data to be processed at each time point, and update the cluster center; when the iteration stop condition is met, output the cluster center as the grid-side charging load characteristics.

[0017] Preferably, cluster analysis is performed on the rechargeable battery status data based on a hierarchical clustering model to extract the rechargeable battery load characteristics. The specific process is: preprocessing the rechargeable battery status data; clustering each independent sample in the rechargeable battery status data using an agglomerative hierarchical clustering algorithm, and then extracting the rechargeable battery load characteristics.

[0018] Preferably, the specific process of clustering each independent sample in the rechargeable battery status data using the agglomerative hierarchical clustering algorithm is as follows: each independent sample in the rechargeable battery status data is regarded as a separate cluster, and in each iteration, the distance between all clusters is calculated, and the two clusters with the closest distance are merged into a new cluster, and the iterative merging process is repeated until the stopping condition is met.

[0019] Preferably, the process of extracting the load characteristics of the rechargeable battery is: determining the distribution and characteristics of the rechargeable battery status data in different clusters, extracting representative features from each cluster, and explaining the behavior patterns and performance of the rechargeable battery under different load conditions based on the clustering results and the extracted features.

[0020] Preferably, the grid-side charging load characteristics and the charging battery load characteristics are subjected to feature fusion to construct the comprehensive load characteristics of the electric bicycle. The specific process is as follows: the grid-side charging load characteristics and the charging battery load characteristics are spliced ​​into a feature matrix; the covariance matrix of the feature matrix is ​​calculated, and the principal component space is constructed according to the covariance matrix; the spliced ​​feature matrix is ​​projected into the principal component space to generate the fused comprehensive load characteristics.

[0021] Preferably, when constructing the principal component space, the covariance matrix is ​​first subjected to eigendecomposition to obtain eigenvalues ​​and corresponding eigenvectors, the corresponding eigenvectors are selected according to the size of the eigenvalues, and the principal component space is constructed using the principal components corresponding to the selected eigenvectors.

[0022] A system for constructing load characteristics of an electric bicycle, comprising:

[0023] The electricity consumption data acquisition module is used to obtain the baseline electricity consumption data of the electric bicycle when it is not charged at home, and the actual household consumption data when it is charged at home;

[0024] A charging data acquisition module is used to obtain the output power data of the charging pile and the charging battery status data of the electric bicycle when the electric bicycle is charging at an outdoor charging pile;

[0025] A virtual household power consumption data module is used to load the output power data of the charging pile as a disturbance to the baseline power data to generate virtual household power consumption data;

[0026] A household consumption data module to be processed is used to fuse virtual household energy consumption data with actual household consumption data to generate household consumption data to be processed;

[0027] The grid-side charging load feature extraction module is used to perform cluster analysis on the household consumption data to be processed based on the fuzzy clustering model and extract the grid-side charging load features;

[0028] The rechargeable battery load feature extraction module is used to perform cluster analysis on the rechargeable battery status data based on the hierarchical clustering model and extract the rechargeable battery load features;

[0029] The comprehensive load feature construction module is used to fuse the charging load features of the grid side and the charging battery load features to construct the comprehensive load features of the electric bicycle.

[0030] A computer device 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, the steps of the method for constructing the load characteristics of an electric bicycle are implemented.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for constructing the load characteristics of an electric bicycle.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention is based on setting a fuzzy clustering model to perform cluster analysis on the household consumption data to be processed to extract the charging load characteristics of the grid side. The fuzzy clustering model can more comprehensively describe the clustering structure of the household consumption data to be processed by assigning fuzzy membership degrees of each cluster to each data point, including those data points that may be on the edge of multiple clusters, which helps to more accurately capture the charging load characteristics of the grid side. Based on setting a hierarchical clustering model, cluster analysis is performed on the charging battery status data to extract the charging battery load characteristics, providing the load characteristics of the charging battery under different conditions. This hierarchical structure helps to understand the changing trends and potential problems of battery performance. Since the fuzzy clustering model and the hierarchical clustering model are optimized for different characteristics of the data respectively, the present invention fully utilizes the advantages of these two feature analysis methods to construct a feature fusion strategy, improve the accuracy and reliability of feature extraction, and construct a comprehensive load characteristic of the electric bicycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a method for constructing load characteristics of an electric bicycle in Example 1 of the present invention;

[0035] Figure 2 This is a flow chart of the method for constructing the load characteristics of an electric bicycle in Example 2 of the present invention. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0037] Example 1:

[0038] like Figure 1 FIG. 1 shows a method for constructing the load characteristics of an electric bicycle according to this embodiment, which includes the following steps:

[0039] S100, obtaining baseline power data of the electric bicycle when it is not charged at home, and actual household consumption data when it is charged at home.

[0040] S200 , when the electric bicycle is charging at an outdoor charging pile, output power data of the charging pile and charging battery status data of the electric bicycle are obtained.

[0041] S300: Load the output power data of the charging pile as a disturbance to the baseline power data to generate virtual household power consumption data.

[0042] S400: Fusing virtual household power consumption data with actual household consumption data to generate household consumption data to be processed.

[0043] S500, performs cluster analysis on the household consumption data to be processed based on the fuzzy clustering model to extract the charging load characteristics on the grid side;

[0044] S600: Perform cluster analysis on the rechargeable battery status data based on a hierarchical clustering model to extract rechargeable battery load characteristics.

[0045] S700 performs feature fusion on the grid-side charging load characteristics and the charging battery load characteristics to construct the comprehensive load characteristics of the electric bicycle.

[0046] Example 2:

[0047] like Figure 2 As shown in FIG. 1 , the method for constructing the load characteristics of an electric bicycle according to this embodiment is described. This embodiment introduces the specific implementation of the solution of Example 1, and includes the following steps:

[0048] S1, based on edge devices, obtains the power consumption data of the smart meter when the electric bicycle is not charged at home, as well as the actual household consumption data of the smart meter when the electric bicycle is charged at home, and uses the power consumption data as the baseline power data.

[0049] S2, when the electric bicycle is charging at an outdoor charging pile, the output power data of the charging pile and the charging battery status data of the electric bicycle are obtained.

[0050] S3, loading the output power data of the charging pile into the baseline power data as a disturbance to obtain virtual household power consumption data.

[0051] S4: Merge the virtual household power consumption data and the actual household consumption data to generate household consumption data to be processed.

[0052] S5, based on the set fuzzy clustering model, cluster analysis is performed on the household consumption data to be processed to extract the charging load characteristics of the grid side.

[0053] S6, performing cluster analysis on the rechargeable battery status data based on a set hierarchical clustering model to extract rechargeable battery load characteristics.

[0054] S7, integrating the grid-side charging load characteristics and the charging battery load characteristics to construct a load characteristic of the electric bicycle when charging at home.

[0055] Optionally, in this embodiment, based on a set fuzzy clustering model, cluster analysis is performed on the household consumption data to be processed to extract grid-side charging load characteristics, including the following steps:

[0056] initialization:

[0057] Determine the number of clusters c (i.e., consider that there are several different load patterns in the household consumption data to be processed).

[0058] Initialize the fuzzy membership matrix U and cluster center matrix V.

[0059] Iterative calculation:

[0060] For each data point (household consumption data to be processed at each time point), calculate its fuzzy membership degree u of each cluster i,j This scheme calculates the distance between data points and cluster centers and optimizes it according to the objective function of fuzzy C-means.

[0061] The updated cluster center vj is usually the result of weighted average of the data points according to their fuzzy membership.

[0062] Repeat the iterative calculation until the stopping condition is met (such as the number of iterations reaches a preset value or the change in cluster center is less than a certain threshold, etc.).

[0063] Result analysis:

[0064] Each cluster center represents a load pattern, and the clustering results show the typical load characteristics of the household consumption data to be processed in different time periods.

[0065] The charging load characteristics on the grid side, such as charging time distribution and power variation, can be analyzed based on the clustering results.

[0066] Optionally, in this embodiment, cluster analysis is performed on the rechargeable battery status data based on a set hierarchical clustering model to extract rechargeable battery load characteristics, including the following process:

[0067] 1. Data Preprocessing

[0068] Data collection: First, collect the status data of the rechargeable battery under different conditions, such as voltage, current, temperature, and SOC (State of Charge).

[0069] Data cleaning: Remove outliers, missing values, or noise data to ensure the accuracy and completeness of charging battery status data.

[0070] Feature selection: Select relevant features based on the analysis goal. For example, if you are interested in the charging performance of the battery, voltage, current, and SOC are important features.

[0071] Data standardization / normalization: In order to eliminate the dimensional differences between different features, the charging battery status data is standardized or normalized.

[0072] 2. Setting up the hierarchical clustering model

[0073] Choose a hierarchical clustering algorithm: Common hierarchical clustering algorithms include agglomerative hierarchical clustering (bottom-up) and divisive hierarchical clustering (top-down). In most cases, agglomerative hierarchical clustering is more commonly used.

[0074] Determine the distance metric: Choose an appropriate distance metric (such as Euclidean distance or Manhattan distance) to calculate the similarity or distance between data points.

[0075] Set stopping conditions: Determine the stopping conditions of the hierarchical clustering process, such as reaching a preset number of clusters or the distance between clusters exceeding a certain threshold.

[0076] 3. Perform hierarchical clustering

[0077] Initialization: Each data point is considered as a separate cluster.

[0078] A data point refers to an independent sample of preprocessed rechargeable battery status data. These data points are collected from rechargeable battery status data under different conditions. In practice, rechargeable battery data such as voltage, current, temperature, and State of Charge (SOC) are collected. For example, when monitoring a batch of electric bicycle rechargeable batteries, the voltage, current, temperature, and SOC value of each battery are recorded at regular intervals (e.g., every 1 minute). Each set of recorded data constitutes a data point. Each data point is a multidimensional vector, the dimensionality of which depends on the number of selected features. If the four features (voltage, current, temperature, and SOC) are selected as the analysis basis during the feature selection phase, each data point is a four-dimensional vector, for example, [voltage, current, temperature, SOC]. At the beginning of the hierarchical clustering algorithm, each such data point is considered an independent cluster. As the algorithm iterates, the distance between each cluster (initially, each data point) is calculated, and then the two closest clusters are merged into a new cluster. This process is repeated until the preset stopping condition is met (such as reaching a preset number of clusters or the distance between clusters exceeds a certain threshold, etc.), thereby completing the cluster analysis of the charging battery status data and finally extracting the charging battery load characteristics.

[0079] Iterative merging: In each iteration, the distances between all clusters are calculated (usually the distance between the closest data points or the distance between cluster centers), and the two clusters with the closest distance are merged into a new cluster.

[0080] Update clustering: Repeat the above steps until the stopping condition is met.

[0081] 4. Extracting rechargeable battery load characteristics

[0082] Analyze clustering results: Observe the results of hierarchical clustering and determine the distribution and characteristics of the charging battery status data in different clusters.

[0083] Feature extraction: Extract representative features from each cluster, such as average voltage, maximum current, temperature variation range, SOC change rate, etc. These features can reflect the load characteristics of the rechargeable battery under different conditions.

[0084] Explain features: Based on the clustering results and extracted features, explain the behavior patterns and performance of rechargeable batteries under different load conditions.

[0085] Optionally, in this embodiment, the grid-side charging load characteristics and the charging battery load characteristics are integrated to construct the load characteristics of the electric bicycle when charging at home, including the following steps:

[0086] Construct a feature matrix: Combine the grid-side charging load characteristics and the rechargeable battery load characteristics into a large feature matrix as the raw data. Assuming the grid has m features and the rechargeable battery has n features, for p observation points (such as different time periods or charging events), the dimension of the feature matrix will be p × (m + n).

[0087] After the feature matrix is ​​constructed, principal component analysis is performed.

[0088] Calculate the covariance matrix: First, calculate the covariance matrix of the concatenated feature matrix. If the data has been standardized, then use the formula To calculate the covariance matrix A: equal to the transpose of the data matrix Multiply by the data matrix X and divide by the number of observations minus one, which is p-1.

[0089] Calculate eigenvalues ​​and eigenvectors: Perform eigendecomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors. The eigenvalues ​​represent the importance of each principal component (i.e., the variance contribution), while the eigenvectors define the direction of each principal component.

[0090] Select principal components: Select the principal components corresponding to the first k eigenvectors based on their eigenvalues, where k is an integer less than or equal to (m + n). The selection criterion can be that the cumulative contribution of the eigenvalues ​​reaches a certain threshold (such as 80% or 90%).

[0091] Constructing the principal component space: The principal component space is constructed using the principal components corresponding to the first k selected eigenvectors. These eigenvectors serve as the axes of the new coordinate system and are used to project the original data into this low-dimensional space.

[0092] Projecting the data: The original data (i.e., the concatenated feature matrix) is projected onto the principal component space to obtain the reduced-dimensional data. This is typically achieved by multiplying the original data by an eigenvector matrix (containing only the first k selected eigenvectors). The reduced-dimensional data is the fused e-bike load features.

[0093] In this embodiment, the comprehensive load characteristics of electric bicycles can be used to optimize the charging strategy of electric bicycles to reduce the load pressure on the power grid; in battery health management, the comprehensive load characteristics of electric bicycles can be used to predict the remaining life and performance degradation trend of the battery, so that maintenance measures can be taken in advance.

[0094] Example 3:

[0095] In this embodiment, a system for constructing the load characteristics of an electric bicycle is provided. The system for constructing the load characteristics of an electric bicycle can be used to implement the above-mentioned method for constructing the load characteristics of an electric bicycle. Specifically, the system for constructing the load characteristics of an electric bicycle includes a power consumption data acquisition module, a charging data acquisition module, a virtual household power consumption data module, a to-be-processed household consumption data module, a grid-side charging load characteristic extraction module, a rechargeable battery load characteristic extraction module, and a comprehensive load characteristic construction module.

[0096] Among them, the electricity consumption data acquisition module is used to obtain the baseline electricity data of the electric bicycle when it is not charged at home, as well as the actual household consumption data when it is charged at home.

[0097] The charging data acquisition module is used to obtain the output power data of the charging pile and the charging battery status data of the electric bicycle when the electric bicycle is charging at an outdoor charging pile.

[0098] The virtual household power consumption data module is used to load the output power data of the charging pile as a disturbance to the baseline power data to generate virtual household power consumption data.

[0099] The module for processing household consumption data is used to fuse virtual household electricity consumption data with actual household consumption data to generate household consumption data to be processed.

[0100] The grid-side charging load feature extraction module is used to perform cluster analysis on the household consumption data to be processed based on the fuzzy clustering model and extract the grid-side charging load features.

[0101] The rechargeable battery load feature extraction module is used to perform cluster analysis on the rechargeable battery status data based on a hierarchical clustering model and extract the rechargeable battery load features.

[0102] The comprehensive load characteristics construction module is used to fuse the charging load characteristics of the grid side and the charging battery load characteristics to construct the comprehensive load characteristics of the electric bicycle.

[0103] Example 4:

[0104] In this embodiment, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs), or a processor that can be used to store a computer program. Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the method for constructing the load characteristics of an electric bicycle, including: obtaining baseline power data of the electric bicycle when it is not charged at home, and actual household consumption data when it is charged at home; when the electric bicycle is charging at an outdoor charging pile, obtaining output power data of the charging pile and charging battery status data of the electric bicycle; loading the output power data of the charging pile as a disturbance to the baseline power data to generate virtual household power consumption data; fusing the virtual household power consumption data with the actual household consumption data to generate household consumption data to be processed; performing cluster analysis on the household consumption data to be processed based on a fuzzy clustering model to extract grid-side charging load characteristics; performing cluster analysis on the charging battery status data based on a hierarchical clustering model to extract charging battery load characteristics; and performing feature fusion on the grid-side charging load characteristics and the charging battery load characteristics to construct a comprehensive load characteristic of the electric bicycle.

[0105] Example 5:

[0106] In this embodiment, a computer-readable storage medium (Memory) is provided. The computer-readable storage medium is a memory device in a terminal device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides storage space, which stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by a processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0107] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the method for constructing the load characteristics of an electric bicycle in the above embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and execute the following steps: obtaining baseline power data of the electric bicycle when it is not charged at home, and actual household consumption data when it is charged at home; when the electric bicycle is charging at an outdoor charging pile, obtaining the output power data of the charging pile and the charging battery status data of the electric bicycle; loading the output power data of the charging pile as a disturbance to the baseline power data to generate virtual household power consumption data; fusing the virtual household power consumption data with the actual household consumption data to generate household consumption data to be processed; performing cluster analysis on the household consumption data to be processed based on a fuzzy clustering model to extract the charging load characteristics of the grid side; performing cluster analysis on the charging battery status data based on a hierarchical clustering model to extract the charging battery load characteristics; performing feature fusion on the charging load characteristics of the grid side and the charging battery load characteristics to construct a comprehensive load characteristic of the electric bicycle.

[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, optical storage, etc.) containing computer-usable program code.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0112] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0113] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0114] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description.

Claims

1. A method for constructing load characteristics of an electric bicycle, characterized in that: The following processes are included: Obtain baseline electricity consumption data for e-bikes when they are not charged at home, and actual household consumption data when they are charged at home; When the electric bicycle is charging at an outdoor charging station, the output power data of the charging station and the charging battery status data of the electric bicycle are obtained; The output power data of the charging pile is loaded into the baseline power data as a disturbance to generate virtual household power consumption data; The virtual household electricity consumption data is integrated with the actual household consumption data to generate the household consumption data to be processed; Cluster analysis is performed on the household consumption data to be processed based on the fuzzy clustering model to extract the charging load characteristics on the grid side; The specific process of extracting grid-side charging load characteristics is as follows: initializing the number of clusters, defining the fuzzy membership matrix and cluster center matrix; iteratively calculating the fuzzy membership degree of the household consumption data to be processed at each time point and updating the cluster center; when the iteration stop condition is met, outputting the cluster center as the grid-side charging load characteristics; Perform cluster analysis on rechargeable battery status data based on a hierarchical clustering model to extract rechargeable battery load characteristics; The specific process of extracting the rechargeable battery load characteristics is as follows: preprocessing the rechargeable battery status data; clustering each independent sample in the rechargeable battery status data using an agglomerative hierarchical clustering algorithm, and then extracting the rechargeable battery load characteristics; The specific process of clustering each independent sample is as follows: each independent sample in the charging battery status data is regarded as a separate cluster. In each iteration, the distance between all clusters is calculated, and the two clusters with the closest distance are merged into a new cluster. The iterative merging process is repeated until the stopping condition is met. Determine the distribution and characteristics of rechargeable battery status data in different clusters, extract representative features from each cluster, and interpret the behavior patterns and performance of rechargeable batteries under different load conditions based on the clustering results and extracted features; The charging load characteristics of the grid side and the charging battery load characteristics are fused to construct the comprehensive load characteristics of the electric bicycle.

2. The method for constructing the load characteristics of an electric bicycle according to claim 1, characterized in that: The specific process of fusing the grid-side charging load characteristics and the rechargeable battery load characteristics to construct the comprehensive load characteristics of the electric bicycle is as follows: the grid-side charging load characteristics and the rechargeable battery load characteristics are spliced ​​into a feature matrix; Calculate the covariance matrix of the feature matrix and construct the principal component space based on the covariance matrix; The spliced ​​feature matrix is ​​projected into the principal component space to generate the fused comprehensive load features.

3. The method for constructing the load characteristics of an electric bicycle according to claim 2, characterized in that: When constructing the principal component space, the covariance matrix is ​​first decomposed to obtain the eigenvalues ​​and corresponding eigenvectors. The corresponding eigenvectors are selected according to the size of the eigenvalues, and the principal component space is constructed using the principal components corresponding to the selected eigenvectors.

4. A system for constructing load characteristics of an electric bicycle, characterized in that: include: The electricity consumption data acquisition module is used to obtain the baseline electricity consumption data of the electric bicycle when it is not charged at home, and the actual household consumption data when it is charged at home; A charging data acquisition module is used to obtain the output power data of the charging pile and the charging battery status data of the electric bicycle when the electric bicycle is charging at an outdoor charging pile; A virtual household power consumption data module is used to load the output power data of the charging pile as a disturbance to the baseline power data to generate virtual household power consumption data; A household consumption data module to be processed is used to fuse virtual household energy consumption data with actual household consumption data to generate household consumption data to be processed; The grid-side charging load feature extraction module is used to perform cluster analysis on the household consumption data to be processed based on the fuzzy clustering model and extract the grid-side charging load features; The specific process of extracting grid-side charging load characteristics is as follows: initializing the number of clusters, defining the fuzzy membership matrix and cluster center matrix; iteratively calculating the fuzzy membership degree of the household consumption data to be processed at each time point and updating the cluster center; when the iteration stop condition is met, outputting the cluster center as the grid-side charging load characteristics; The rechargeable battery load feature extraction module is used to perform cluster analysis on the rechargeable battery status data based on the hierarchical clustering model and extract the rechargeable battery load features; The specific process of extracting the rechargeable battery load characteristics is as follows: preprocessing the rechargeable battery status data; clustering each independent sample in the rechargeable battery status data using an agglomerative hierarchical clustering algorithm, and then extracting the rechargeable battery load characteristics; The specific process of clustering each independent sample is as follows: each independent sample in the charging battery status data is regarded as a separate cluster. In each iteration, the distance between all clusters is calculated, and the two clusters with the closest distance are merged into a new cluster. The iterative merging process is repeated until the stopping condition is met. Determine the distribution and characteristics of rechargeable battery status data in different clusters, extract representative features from each cluster, and interpret the behavior patterns and performance of rechargeable batteries under different load conditions based on the clustering results and extracted features; The comprehensive load feature construction module is used to fuse the charging load features of the grid side and the charging battery load features to construct the comprehensive load features of the electric bicycle.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for constructing the load characteristics of an electric bicycle as claimed in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for constructing the load characteristics of an electric bicycle as claimed in any one of claims 1 to 3 are implemented.

Images