Digital twin system reflecting the impact of electric vehicle charging station and construction method of the digital twin system

Abstract

A digital twin system reflecting the impact of an electric vehicle charging station includes a first server that collects and processes charging data, which is charging-related data, from terminals installed at each of the charging stations of multiple electric vehicles; wherein the first server calculates the impact degree, which is the degree of an area affecting from the installation location of the charging station for each of the charging stations of the multiple electric vehicles, using the charging data.

Description

Technology Field

[0001] The embodiments disclosed in this specification relate to a digital twin system that reflects the impact of an electric vehicle charging station and a method for constructing the digital twin system. Background Technology

[0002] A digital twin is a technology that digitally replicates real-world physical objects, processes, people, places, systems, and devices in a virtual world. By modeling and simulating real-world dynamics, this technology can enable performance improvement, problem-solving, and innovative development.

[0004] The main features of a digital twin are as follows.

[0005] (1) Real-time data synchronization: Digital twins use real-time data to continuously reflect the state of the actual object.

[0006] (2) Advanced Simulation and Modeling: Model complex systems and environments to simulate various scenarios.

[0007] (3) Predictive analysis: Predicts future performance or status through data analysis.

[0008] (4) Decision support: Supports effective decision-making based on real-time data and simulation results.

[0009] (5) Improvement and Optimization: Improve and optimize systems or processes through continuous monitoring and analysis.

[0010] Digital twins are being utilized in various fields such as manufacturing, construction, automotive, aerospace, healthcare, and urban planning, enabling more sophisticated and efficient system design and operation.

[0012] However, since the primary purpose of a digital twin is to improve devices or processes used in specific situations, the domains in which it is operated are often closed due to the limited number of users. For example, when utilizing digital twin technology to improve industrial production processes, only relevant personnel need to verify the results; therefore, the digital twin is not implemented to allow access via open networks such as the internet, nor is there a need for it.

[0014] However, in the case of a digital twin system related to electric vehicle charging stations, it is necessary to implement it so that it can be verified through an open network such as the internet, as it must be easily accessible to a large number of electric vehicle users. The problem to be solved

[0015] The embodiments disclosed in this specification are embodiments intended to solve the technical problems described above, and the purpose is to provide a digital twin system that reflects the impact of an electric vehicle charging station verifiable through an open network such as the Internet, and a method for constructing said digital twin system. means of solving the problem

[0016] A digital twin system reflecting the impact of an electric vehicle charging station includes: a first server that collects and processes charging data, which is charging-related data, from terminals installed at each of the charging stations of multiple electric vehicles; and a second server that receives the impact information for each charging station and the location information or address information of the charging stations of the multiple electric vehicles from the first server, and generates area information to indicate the area affected by the charging station centered on the location of the charging station using the impact information for each charging station.

[0018] Specifically, the first server calculates an influence degree, which is the degree of an area affected from the installation location of each charging station of the plurality of electric vehicles, using at least a portion of the charging data.

[0019] In addition, the second server may configure an area on a map into a grid divided into multiple cells, and generate recommendation information that recommends an installation area for additional charging stations by utilizing the ratio of the area where the influence of the surrounding charging station does not reach each of the multiple cells and the influence of the surrounding charging station. Effects of the invention

[0020] According to the digital twin system reflecting the impact of an electric vehicle charging station and the method for constructing the digital twin system of the embodiments disclosed in this specification, the impact of an electric vehicle charging station that can be verified through an open network such as the Internet can be reflected.

[0021] In addition, according to the digital twin system reflecting the impact of electric vehicle charging stations of the embodiments disclosed in this specification and the method for constructing the digital twin system, it is possible to efficiently recommend additional charging station installation areas. Brief explanation of the drawing

[0022] FIG. 1 is a configuration diagram of a digital twin system reflecting the impact of an electric vehicle charging station according to one embodiment. Figure 2 is an example of monitoring area information generated by a second server through a user terminal. Figure 3 is an example of monitoring recommendation information generated by a second server through a user terminal. FIG. 4 is a flowchart of a method for constructing a digital twin system reflecting the impact of an electric vehicle charging station according to one embodiment. Specific details for implementing the invention

[0023] Hereinafter, a digital twin system reflecting the impact of an electric vehicle charging station according to embodiments of the present disclosure and a method for constructing said digital twin system will be described in detail with reference to the attached drawings. It should be understood that the following embodiments of the present disclosure are merely intended to embody the present disclosure and do not limit or restrict the scope of the rights of the present disclosure. Anything that can be easily inferred by a person skilled in the art to which the present disclosure pertains from the detailed description and embodiments of the present disclosure is interpreted as falling within the scope of the rights of the present disclosure.

[0025] First, FIG. 1 shows a configuration diagram of a digital twin system (100) reflecting the influence of an electric vehicle charging station according to one embodiment.

[0026] As can be seen from FIG. 1, a digital twin system (100) reflecting the influence of an electric vehicle charging station according to one embodiment is configured to include a charging station terminal (10), a first server (20), a second server (30), and a user terminal (40). For reference, in this specification, an electric vehicle charging station may be configured to include at least one charging device.

[0027] Additionally, each of the charging station terminal (10), the first server (20), the second server (30), and the user terminal (40) may be configured to include memory and at least one processor as a type of computing device.

[0029] A charging station terminal (10) is installed at each charging station, and when an electric vehicle is charged, the charging station terminal (10) transmits charging data, which is data related to the charging, to the first server (20). Specifically, the charging station terminal (10) can transmit charging data, which is data related to all charging, such as open data that can be collected via the internet or in the form of a specific data dump file, to the first server (20). The transmission of charging data from the charging station terminal (10) to the first server (20) may be performed periodically or limited to once, depending on the type of charging data.

[0031] For example, charging data may include many of the following: identification information of the charging station, location information of the charging station, address information of the charging station, charging time and date, charging power amount, charging start period, charging end time, and weather information.

[0033] The first server (20) can be described as a data server and plays the role of collecting and processing charging data, which is data related to charging, from charging station terminals (10) installed at each of the charging stations of multiple electric vehicles.

[0034] The first server (20) may be configured to include a database (21), an impact calculation module (22), and a data sharing API (Application Programming Interface) module (23).

[0035] In the database (21), charging data collected from the charging station terminal (10) and result data calculated by the impact calculation module (22) can be stored as storage devices.

[0036] In addition, the impact calculation module (22) and the data sharing API module (23) can each be implemented in the form of a computer program executed by a processor.

[0037] Specifically, the impact calculation module (22) calculates the impact level, which is the degree of the area affected from the installation location of the charging station for each charging station, using at least a portion of the charging data. The impact level calculation can be performed using the charging data during a pre-set first period. That is, the impact level can be updated every first period. The impact level calculated by the impact calculation module (22) is transmitted to the second server (30) along with the identification information of the charging station.

[0038] The data sharing API module (23) transmits data stored in the database (21), excluding the influence, to the second server (30).

[0040] The second server (30) receives data from the first server (20) and can receive map information and weather information, etc. from the map server and the weather server. The second server (30) can provide simulation and modeling using map information and weather information, etc. based on the data received from the first server (20). That is, the second server (30) is a server that implements a digital twin and can provide web services available via the internet, including a 3D model of a specific area or process.

[0041] The web service provided by the second server (30) is connected to the first server (20) in two ways to exchange data. The first method is to connect via the internet with the data sharing API module (23) and receive RESTful data using the HTTP protocol. The second method is to connect via the internet to create a bidirectional socket channel and, through this, send and receive data with the impact calculation module (22). The second server (30) provides all data received from the first server (20) to the user terminal (40) through an interface including a 3D model. At this time, the user can access the second server (30) using the user terminal (40) and use the service.

[0043] Below, we will specifically explain the method of calculating the impact by the impact calculation module (22) of the first server (20).

[0045] The impact calculation module (22) can cluster charging stations of multiple electric vehicles into multiple clusters by using first data, which is result data obtained by processing at least a portion of the charging data during a first period pre-set for each charging station. The first data may include multiple items among N items regarding the total number of chargings, the total amount of charging power, and the number of chargings for N charging power ranges.

[0046] Here, the number of charges per N charging power intervals is the number of charges per N interval after dividing the power charged in one go into N intervals according to size. Additionally, N is a natural number greater than or equal to 2.

[0047] For example, the charging power amount range may be divided into six ranges, such as 0~10 kWh, 10~20 kWh, 20~30 kWh, 30~40 kWh, 40~50 kWh, and 50 kWh or more. In this case, the first data may include multiple items among the total number of charges, total charging power amount, the number of charges in the charging power amount range of 0~10 kWh, the number of charges in the charging power amount range of 10~20 kWh, the number of charges in the charging power amount range of 20~30 kWh, the number of charges in the charging power amount range of 30~40 kWh, the number of charges in the charging power amount range of 40~50 kWh, and the number of charges in the charging power amount range of 50 kWh or more.

[0048] For reference, the total number of charges is data used to derive similarity in usage frequency of charging stations, and the total amount of charging power is data used to derive similarity in usage of charging stations. In addition, the number of charges in the 0–10 kWh range and the number of charges in the 10–20 kWh range are, respectively, data used to derive similarity in the number of charges in the low range. Furthermore, the number of charges in the 20–30 kWh range and the number of charges in the 30–40 kWh range are, respectively, data used to derive similarity in the number of charges in the middle range. Also, the number of charges in the 40–50 kWh range and the number of charges in the 50 kWh or higher range are, respectively, data used to derive similarity in the number of charges in the high range.

[0050] The impact calculation module (22) uses first data from a first period for each charging station to cluster multiple electric vehicle charging stations into multiple clusters using each of multiple clustering algorithms, and selects and uses a clustering model based on the clustering algorithm with the best clustering performance, i.e., multiple clusters.

[0052] That is, the impact calculation module (22) uses each item of the first data to perform a clustering process so that charging stations with similar data can be classified.

[0053] The reason for performing clustering before calculating the impact of each charging station is to reduce the computational process required to derive the impact of each charging station. For example, to calculate the impact of 500 charging stations, the process of calculating the impact for each charging station must be performed in the same number of steps as the number of charging stations; however, if the charging stations are classified into clusters showing similarity and the impact is derived for each cluster, the process required is significantly reduced.

[0055] Specifically, clustering can be performed through the following process.

[0056] Using each item of the first data, a clustering model capable of classifying charging stations into M clusters is simultaneously generated for each of the following multiple clustering algorithms. Here, M is a natural number greater than or equal to 10.

[0057] - K-Means Clustering

[0058] - Affinity Propagation Clustering

[0059] - Mean Shift Clustering

[0060] - Spectral Clustering

[0061] Agglomerative Clustering

[0062] - Density-Based Spatial Clustering

[0063] - OPTICS Clustering

[0064] - Birch Clustering

[0065] - K-Modes Clustering

[0067] In addition, the impact calculation module (22) compares the clustering models derived for each of the multiple clustering algorithms and finally selects the clustering model with the best performance and uses the cluster.

[0069] The impact calculation module (22) calculates the impact for each cluster using second data, which is the average value of each item of first data for all charging stations included in the cluster during the first period. For example, if cluster 1 includes a first charging station and a second charging station, each of the average values ​​of each item of first data calculated for each of the first charging station and the second charging station becomes each item of second data.

[0070] Specifically, the impact calculation module (22) can apply a preset weight to each item of the second data for all charging stations included in the cluster, for each cluster. That is, the impact calculation module (22) can sort the sum of the values ​​obtained by multiplying each item of the second data by a preset weight and assign a higher impact value sequentially from the lowest to the highest sum value, or assign a lower impact value sequentially from the highest to the lowest sum value.

[0071] That is, the impact calculation module (22) calculates the average value of each item of the first data for each cluster to calculate the impact for each cluster. In addition, the impact calculation module (22) multiplies the average value of each item of the first data by a preset weight and sorts the summed values ​​in order of highest or lowest order, and can sequentially assign an impact according to the sorted order. For example, if there are 50 clusters, the impact can be assigned from 0 to 49.

[0072] For reference, the impact calculated for each cluster of the impact calculation module (22) is assigned as the impact of each charging station included in that cluster. That is, charging stations belonging to the same cluster have the same impact value.

[0074] The second server (30) receives data from the first server (20) including information on the impact of each charging station and location information or address information of charging stations for multiple electric vehicles, and uses the impact of each charging station to generate area information to indicate the area affected by the charging station centered on the location of the charging station. The information on the impact of each charging station is transmitted from the impact calculation module (22) to the second server (30) as a data set of the charging station's identification information and the impact.

[0075] In addition, the data sharing API module (23) can transmit to the second server (30) a data set of identification information of the charging station and location information or address information.

[0076] FIG. 2 is an example of monitoring area information generated by the second server (30) through a user terminal (40). However, map information is omitted in FIG. 2.

[0077] Area information indicating the region affected by the charging station can be represented in the form of a circle, and the color or type of the circumference, or the color or pattern of the interior, can be expressed differently depending on the size of the area. In this case, the radius of the circle can be calculated as {value of the charging station's influence × A} + B. Here, A and B are each pre-set coefficients.

[0079] The second server (30) can transmit weather information received from the weather server to the impact calculation module (22) of the first server (20). Here, the weather information may include multiple items among temperature, humidity, wind direction, wind speed, precipitation, fine dust, ultrafine dust, and ultraviolet radiation levels. In addition, the weather information may be current weather information, or weather information for a past point in interest or a future point in interest.

[0080] The first server (20) can calculate the impact by further utilizing weather information. Specifically, the first server (20) can calculate the impact by utilizing the similarity between the weather information and the weather information at the time of charging at the corresponding charging station. For example, the first server (20) can calculate the impact by utilizing only charging data for charging that was performed within a certain range of similarity between the weather information and the weather information at the time of charging at the corresponding charging station.

[0081] The second server (30) can further transmit traffic environment, infrastructure status, etc. to the impact calculation model of the first server (20). The first server (20) can further calculate the impact using the traffic environment, infrastructure status, etc. Specifically, the first server (20) can calculate the impact using the similarity between the traffic environment, infrastructure status, etc. and the traffic environment, infrastructure status, etc. at the time of charging of the charging station.

[0082] That is, the second server (30) can replicate and implement weather information, traffic environment, infrastructure status, etc., and can derive the optimal area for installing electric vehicle charging stations within the environment.

[0084] Specifically, the second server (30) can generate recommendation information that recommends an additional charging station installation area by configuring the area on the map into a grid divided into multiple cells of a preset size and using the ratio of the area where the influence of the surrounding charging station does not reach each of the multiple cells and the influence of the surrounding charging station.

[0085] FIG. 3 is an example of monitoring recommendation information generated by the second server (30) through a user terminal (40). However, map information is omitted in FIG. 3.

[0087] Specifically, the second server (30) can generate recommendation information that recommends an area within a cell region that is not affected by the influence of surrounding charging stations as an additional charging station installation area, provided that the influence of surrounding charging stations is greater than or equal to the first value and the influence of surrounding charging stations does not extend to an area greater than or equal to a preset area within the cell region. Alternatively, the second server (30) can generate recommendation information that recommends an additional charging station installation area for the cell region if the influence of surrounding charging stations is less than the second value and the influence of surrounding charging stations does not extend to the cell region at all. Here, the first value is a value with a relatively high influence, and the second value is a value with a relatively low influence. That is, the first value is greater than the second value.

[0089] The user terminal (40) connects to the second server (30) and can monitor various charging data, charging station information, impact information, area information based on impact information, recommendation information, etc. on map information in a digital twin environment.

[0091] The overall operation of the digital twin system (100) reflecting the influence of the electric vehicle charging station according to the following embodiment will be summarized.

[0093] The first server (20) collects and stores charging data in the database (21). At this time, if the data is updated periodically, the collection period is stored at regular time intervals. In the case of data that is not updated periodically, the first server (20) stores it only once.

[0094] In addition, the impact level for each charging station is calculated through the impact level calculation module (22). At this time, the impact level can be calculated by updating it at predetermined times on a daily basis, and charging data from all electric vehicle charging stations belonging to the predetermined period is used. Once the impact level is calculated, it can be considered that the preparation of all data to be provided to the second server (30) has been completed by the first server (20).

[0095] The next step is to use web services by connecting to the second server (30) through a user terminal (40) that is a web browser compatible terminal. At this time, the second server (30) performs two processes simultaneously. First, data is provided to the user through the user terminal (40) with an intuitive interface along with a 3D model. Next, all weather information, traffic environment, infrastructure status, etc., that the user adjusts on the digital twin of the second server (30) through the user terminal (40) are transmitted in real time to the first server (20) and can be used as data to calculate the impact.

[0097] The following describes a method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station according to an embodiment. Since the method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station according to an embodiment is implemented by the digital twin system (100) described above, it is obvious that it includes all features of the digital twin system (100) without separate explanation. Furthermore, each step of the method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station can be implemented by at least one processor of at least one computing device as a method of operation of at least one computing device. That is, the method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station can be implemented in the form of a computer program executed by at least one processor, and each step included in the method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station can be implemented by at least one processor.

[0099] FIG. 4 shows a flowchart of a method for constructing a digital twin system (100) that reflects the influence of an electric vehicle charging station according to one embodiment.

[0100] As can be seen from FIG. 4, a method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station according to one embodiment comprises: a step (S10) in which a first server (20) collects charging data, which is data related to charging, from a charging station terminal (10) installed at each of the charging stations of a plurality of electric vehicles; a step (S20) in which the first server (20) calculates an impact degree, which is the degree of an area affected from the installation location of the charging station for each charging station, using at least a portion of the charging data, which is data related to charging, collected from the charging station terminal (10) installed at each of the charging stations of a plurality of electric vehicles; and a step (S30) in which a second server (30) receives data including the impact degree information for each charging station and the location information or address information of the charging stations of a plurality of electric vehicles from the first server (20), and generates area information to indicate the area affected by the charging station centered on the location of the charging station using the impact degree for each charging station.

[0101] In addition, the method for constructing a digital twin system (100) that reflects the influence of an electric vehicle charging station according to one embodiment may further include the step (S40) in which a second server (30) forms a grid in which an area on a map is divided into a plurality of cells of a preset size, and generates recommendation information that recommends an additional charging station installation area using the ratio of an area where the influence of a charging station around each of the plurality of cells does not reach and the influence of a charging station around the cell.

[0103] Step S20 may include: a step of clustering multiple electric vehicle charging stations into multiple clusters using first data during a first period for each charging station (S21); a step of calculating an impact degree for each cluster using first data for each of the entire charging stations included in the cluster (S22); and a step of assigning the calculated impact degree for the cluster to the impact degree of the charging stations included in the cluster (S23).

[0104] The first data may include a number of items among the total number of charges, the total amount of power charged, and N items regarding the number of charges for each of the N power charge intervals. In addition, the number of charges for each of the N power charge intervals is the number of times the amount of power charged in one charge is divided into N intervals according to size, and then charged for each of the N intervals. Here, N is a natural number greater than or equal to 2.

[0105] Specifically, step S21 may include: a step of clustering multiple electric vehicle charging stations into multiple clusters by each of multiple clustering algorithms; and a step of setting a clustering model by the clustering algorithm with the best clustering performance as the final multiple clusters.

[0107] In addition, the method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station according to one embodiment may further include the step (S50) in which the second server (30) transmits at least one of weather information, traffic environment, and infrastructure status to the first server (20).

[0108] In step S20, the first server (20) can calculate the impact by using the similarity between weather information and the weather information at the time of charging at the corresponding charging station. Additionally, in step S20, the first server (20) can calculate the impact by further using the traffic environment, infrastructure status, etc. Specifically, the first server (20) can calculate the impact by using the similarity between the traffic environment, infrastructure status, etc. and the traffic environment, infrastructure status, etc. at the time of charging at the corresponding charging station.

[0109] In addition, the method for constructing a digital twin system (100) that reflects the impact of an electric vehicle charging station according to one embodiment may further include the step (S60) of a user terminal (40) connecting to a second server (30).

[0111] According to the digital twin system (100) reflecting the impact of an electric vehicle charging station according to the embodiments and the method for constructing the digital twin system (100), it can be seen that the impact of an electric vehicle charging station that can be verified through an open network such as the internet can be reflected, and the installation area of ​​additional charging stations can be efficiently recommended.

[0112] That is, according to the digital twin system (100) reflecting the impact of electric vehicle charging stations according to the embodiments and the method for constructing the digital twin system (100), the user can calculate the impact of other electric vehicle charging stations in the region and, based on this, identify an efficient installation location for a new electric vehicle charging station. At this time, the user can visually check the status of electric vehicle charging stations in the region in real time along with a 3D model via the web. Since the service can be utilized via the web, anyone can use the information, and through this, rapid expansion of electric vehicle charging stations can be expected. Explanation of the symbols

[0113] 100 : Digital Twin System 10 : Terminal for charging stations 20 : 1st Server 30 : 2nd server 40 : User terminal 21 : Database 22: Impact Calculation Module 23: Data Sharing API Module

Claims

Claim 1 A digital twin system reflecting the impact of electric vehicle charging stations, comprising: a first server that clusters the multiple electric vehicle charging stations into multiple clusters using first data for each charging station during a first period, and calculates the impact degree for each charging station, which is the degree of the area affecting from the installation location of the charging station, using the first data for each of the multiple clusters for all charging stations included in the cluster; A digital twin system comprising: a second server that receives the influence degree for each charging station and location information or address information of the charging stations of the plurality of electric vehicles from the first server, and generates area information to indicate the area where the influence of the charging station is applied, centered on the location of the charging station; wherein the second server comprises a grid in which an area on a map is divided into a plurality of cells, and generates recommendation information that recommends an installation area for an additional charging station using the ratio of the area where the influence degree of the surrounding charging station does not apply to each of the plurality of cells and the influence degree of the surrounding charging station, and the first data includes a plurality of items among a total number of charging items, a total charging amount item, and N items regarding the number of charging items for N charging amount intervals, wherein the number of charging items for N charging amount intervals is the number of times charging is performed for each of the N intervals after dividing the amount of power charged in one time into N intervals according to size, and N is a natural number greater than or equal to 2. Claim 2 delete Claim 3 delete Claim 4 A digital twin system according to claim 1, wherein the first server calculates the influence degree by using the average value of each item of the first data for all charging stations included in the corresponding cluster for each cluster. Claim 5 delete Claim 6 A digital twin system according to claim 1, wherein the second server transmits weather information to the first server, and the first server calculates the influence by further utilizing the similarity between the weather information and the weather information at the time of charging of the corresponding charging station. Claim 7 delete Claim 8 delete Claim 9 A method for constructing a digital twin system that reflects the impact of an electric vehicle charging station comprises: a first server calculating an impact degree, which is the degree of an area affected from the installation location of a charging station for each of the charging stations of multiple electric vehicles; a second server receiving the impact degree for each charging station and location information or address information of the charging stations of multiple electric vehicles from the first server, and generating area information to indicate the area affected by the charging station centered on the location of the charging station; and a second server configuring an area on a map into a grid divided into multiple cells, and generating recommendation information that recommends an additional charging station installation area using the ratio of the area not affected by the impact of the surrounding charging station for each of the multiple cells and the impact degree of the surrounding charging station; wherein the step of calculating the impact degree, which is the degree of an area affected from the installation location of the charging station, comprises a step of clustering the charging stations of multiple electric vehicles into multiple clusters using first data during a first period for each of the charging stations. A method for constructing a digital twin system, comprising: a step of calculating the influence degree for each of the clusters of the plurality of clusters using the first data for each of the entire charging stations included in the cluster; wherein the first data includes a plurality of items among a total number of charging items, a total charging amount item, and N items regarding the number of charging times for N charging amount intervals, wherein the number of charging times for N charging amount intervals is the number of times charging is performed for each of the N intervals after dividing the amount of power charged once into N intervals according to size, and N is a natural number greater than or equal to 2. Claim 10 delete Claim 11 delete Claim 12 In claim 9, the method for constructing the digital twin system further comprises the step of the second server transmitting weather information to the first server; wherein, in the step of calculating the impact, the impact is calculated by further utilizing the similarity between the weather information and the weather information at the time of charging of the corresponding charging station. Claim 13 delete

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