System, method and computer-readable storage medium for monitoring electric vehicle chargers through digital twins

Abstract

A system, method, and computer-readable storage medium for managing charging stations via digital avatars are disclosed. This system constructs a simulated electric vehicle charging station environment using digital avatar models, enabling pre-construction planning, charging power allocation, and energy management strategy simulation. Once completed, the charging station can be managed visually. Within the charging station, managed objects such as charging piles and wiring configurations are represented by digital avatar models. A visual page displays real-time information about each object and its corresponding location for the manager's review. Each digital avatar has a simulator; combinations of simulators allow for the verification of management strategies, such as simulating the maximum number of charging piles the charging station can accommodate, simulating charging power allocation, and calculating cost reductions during off-peak hours. This achieves a manageable, simulable, verifiable, and executable management environment.

Description

Technical Field

[0001] This invention relates to a management simulation system, and more particularly to a management simulation system for electric vehicle charging stations established using digital avatar technology, along with a corresponding method and a computer-readable storage medium. Prior Technology

[0002] In recent years, with technological advancements and rising environmental awareness, the usage rate of electric vehicles such as buses, cars, and motorcycles has gradually increased. To promote the widespread adoption of electric vehicles, China has begun planning the construction of electric vehicle charging stations or piles in various locations, including public parking lots or roadside parking spaces, highway rest areas, supermarkets, and gas stations. In the future, the demand for charging stations from community residents will continue to increase to make charging electric vehicles more convenient.

[0003] Currently, there are existing charging pile management technologies. The charging process includes member authentication, uploading charging information to the management system, and billing. However, existing technologies lack simulation technology for charging station operation behaviors, such as vehicle charging behavior, energy management, and total electricity cost prediction, leading to management challenges.

[0004] It is evident that the aforementioned conventional technologies still have many shortcomings and are not well-designed, and urgently need improvement. Summary of the Invention

[0005] This invention provides a system for managing charging stations through digital avatars. It utilizes digital avatar technology to create models of various devices such as charging stations and power line configurations, uses these models to correspond with the actual operating devices, and transmits the actual device information to the model display.

[0006] This invention also provides a method for managing charging stations through digital avatars. In the aforementioned system, each digital avatar model within a charging station is equipped with a simulator to simulate the behavior of that avatar model. There is also strategic simulation, such as charging scheduling, which simulates strategies for charging stations that have not yet been built or have already been physically built (using the digital avatars of the charging stations). For example, newly built but not yet operational charging stations can use charging station operation simulation technology to examine their future operational status. If it is necessary to adjust the charging schedule, this system can also be used to continuously simulate and correct the schedule to achieve the optimal charging schedule, saving costs without affecting the actual operation of the charging station.

[0007] The system for managing charging piles via digital avatars, which can achieve the above-mentioned invention objectives, includes: a charging station configuration unit for inputting the charging pile data structure and location configuration, and storing the above information in a database; an energy management unit for arranging the charging mode of each charging pile according to the charging scheduling mechanism, and detecting whether each charging pile has a fault or abnormality; a simulator management unit for managing the simulated behavior of various types of avatars; and a display unit for presenting the charging pile system content in a three-dimensional visual model.

[0008] This invention also provides a method for managing charging stations through digital avatars. This method uses digital avatars to create a virtual charging station by establishing the architecture of all objects (equipment) and their environment within the charging station. If a physical charging station is also established, the correspondence between its objects (equipment) and the equipment in the virtual charging station is set. When the physical device information is uploaded to the system, the corresponding real-time information can be viewed on the display unit. If a physical charging station has not yet been established, the virtual charging station can be previewed on the display unit, and then each avatar simulator can be used to simulate all the actions and operations within the charging station.

[0009] In another embodiment, the present invention provides a system for managing charging stations via digital avatars, including a display unit, a charging station configuration unit, a simulator management unit, an energy management unit, and a data management unit. The display unit provides a visual page for users to input charging station information; the charging station configuration unit establishes associations between the charging station and its charging piles, and between the charging station and its digital avatars based on the information; the simulator management unit selects the simulators used by each digital avatar, wherein the visual page displays the operation of the simulated environment of the charging station based on the simulation operation of each simulator; the energy management unit manages the energy management strategy of the charging station; and the data management unit receives sensing data transmitted by the charging piles and transmits the sensing data to the energy management unit to trigger the operation of the energy management strategy, or provides the sensing data to the training model of the plurality of simulators in the simulator management unit.

[0010] In one embodiment, the system further includes a database, wherein the charging station configuration unit is configured to store the attributes and associations of the plurality of digital clones in the database, and the data management unit is configured to store the sensing data transmitted by the charging pile in the database.

[0011] In one embodiment, the energy management strategy includes at least one of a scheduled charging strategy, a charging mode strategy, and a power distribution control strategy, for controlling at least one of the following: charging schedule, scheduled charging, power limitation, and dynamic power distribution of the charging station.

[0012] In one embodiment, the simulator management unit is reused to enable the plurality of simulators to simulate the operation of the charging station according to the energy management strategy set or adjusted by the user, so as to optimize the energy management strategy.

[0013] For example, the energy management strategy of a paid charging station can be to schedule charging based on conditions (such as VIP members enjoying the highest charging efficiency, and users manually setting the fastest charging efficiency to 10KW), or the energy management strategy of a private charging station can be to use a charging scheduling simulator to charge during characteristic off-peak hours, such as users manually setting the start charging time to 00:00. Alternatively, the system can use members' previous historical data as input and employ artificial intelligence technology to set the optimal charging efficiency; similarly, the system can use the previous historical data of a private charging station as input and employ artificial intelligence technology to set the optimal start charging time.

[0014] In one embodiment, each simulator is a model, logic operation, or fixed form generated by artificial intelligence training.

[0015] For example, an electric vehicle scheduling simulator can be a model trained by artificial intelligence; another example is a current simulator that can provide alternating current or direct current and can set the maximum wattage it can provide, such as 14.4 kW. Yet another example is a current simulator that uses a convolutional neural network (CNN) model to provide data: alternating current, whose maximum wattage can be input from previous historical data, and output its maximum wattage, such as 14.4 kW, through artificial intelligence technology.

[0016] The present invention also provides a method for managing charging stations through digital clones, comprising: providing a visualization page for users to input information about the charging station; establishing a connection between the charging station and its charging piles and between the charging station and its digital clones based on the information; selecting a simulator used by each digital clone, and then having the visualization page display the operation of the simulated environment of the charging station based on the simulated operation of each simulator; and triggering the operation of the energy management strategy of the charging station based on the sensing data transmitted by the charging pile, or providing the sensing data to the training model of the plurality of simulators.

[0017] The present invention also provides a computer-readable storage medium that stores a plurality of instructions, which are read by a processing unit, processor, computer or server to execute the above-described method for managing charging stations through digital clones.

[0018] This invention utilizes digital avatar technology to create a charging station composed of various device avatars. Furthermore, it employs artificial intelligence (AI) generative modeling technology to train a behavior simulator for each avatar. For example, the charging pile avatar uses a current simulator (AC / DC / maximum wattage), the electric vehicle scheduling simulator assigns various vehicle avatars to the charging station, and the arriving vehicle avatars use a battery simulator. A power consumption calculation simulator calculates the total power consumption of the charging station in real time. If the current total power consumption is at a critical value, an alarm is triggered to prevent a power outage. An electricity cost prediction simulator calculates unbilled electricity costs (electricity costs vary depending on the time of day).

[0019] The above can help charging stations adjust the charging schedule simulator (energy management) to achieve the best charging schedule, thereby saving costs or maximizing the utilization efficiency of the charging station.

[0020] Furthermore, the use of clone simulation during the adjustment process allows for the management of charging stations without affecting the actual operation of the charging stations, in order to predict whether the new charging scheduling scheme can achieve optimal charging efficiency. Simple Explanation of the Diagram

[0021] Figure 1 is a block diagram of the system for managing charging piles through digital clones according to the present invention.

[0022] Figure 2 is an application flowchart of the method for managing charging piles through digital clones according to the present invention.

[0023] Figure 3 is a schematic diagram of an application example of the above-described system of the present invention.

[0024] Figure 4 is an application flowchart of the method for managing charging piles through digital clones according to the present invention. Implementation

[0025] According to one embodiment, the present invention provides a method for managing charging piles through digital avatars (hereinafter referred to as the method) and a system 10 for managing charging piles through digital avatars (hereinafter referred to as the system), wherein the method is executed by the system.

[0026] Please refer to Figure 1, which is a block diagram of this system. This system uses digital avatar technology to create digital avatars (hereinafter referred to as avatars) of multiple charging stations 11, 12 and multiple charging piles 111, 112, 121, 122 within them, and to establish the relationships (hierarchy) between the avatars.

[0027] Charging station 11 includes charging station information 11a, and charging station 12 includes charging station information 12a. For example, charging station information 11a and 12a may include the name of charging station 11 or 12 (e.g., XX service area), basic information of the charging station operator (e.g., XX charging station 02-12345678), total number of charging piles (e.g., 20), total number of charging guns (e.g., 40), number of charging parking spaces (e.g., 40), and business hours (e.g., 0:00~24:00), etc.

[0028] Each charging station contains data and device status information. For example, charging station 111 contains data 111a and device status information 111b, while charging station 121 contains data 121a and device status information 121b. For instance, data 111a and 121a may include the charging time of charging stations 111 and 121, or the voltage, current, and power consumption of each charging gun. Device status information 111b and 121b may include the AC or DC power supply, output voltage, output power, and charging status of charging stations 111 and 121, such as charging in progress / charging complete / standby / occupied / faulty / abnormal, or various statuses of each charging gun.

[0029] In one embodiment, the data and device status information are described as follows.

[0030] Charging Station 111: Kaohsiung Sankewang Fengshan No. 1

[0031] Data 111a: Charging time 1 hour 33 minutes 3 seconds

[0032] Device status information 111b: AC power, voltage 220V, power 7KW, charging.

[0033] Charging Station 121: No. 3, Jian Gong Computer Room Station, Kaohsiung Erkewang

[0034] Data 121a: Charging time 24 minutes and 7 seconds

[0035] Device status information 121b: DC power, voltage 380V, power 22KW, charging complete.

[0036] Another charging station (not shown in the picture): Taichung Xingtong East No. 2

[0037] Data: Charging time --

[0038] Equipment status information: AC power --, Voltage --, Output power --, Abnormal

[0039] In addition, the system includes a data management unit 101, an energy management unit 102, a simulator management unit 103, a database 104, a display unit 105, and a charging station configuration unit 106, which are interconnected.

[0040] Each unit 101-103, 105 and 106 of this system 10 can be hardware or software; if it is hardware, it can be a processing unit, processor, computer or server with data processing and computing capabilities; if it is software, it can include instructions executable by the processing unit, processor, computer or server, and can be installed on the same hardware device or distributed on multiple different hardware devices.

[0041] The charging station configuration unit 106 is used to establish the mutual association between the charging station and the charging pile's clones and the aforementioned clones. The established clones, their attributes, and their associations are stored in the database 104. The establishment of each clone, its attributes, and its associations can be completed by the user through the display unit 105. The physical charging station and its charging pile must correspond to the clones established above.

[0042] The data management unit 101 is used to receive data uploaded from physical charging stations and their charging piles (e.g., charging piles 111 and 112 of charging station 11). The raw data is stored in the database 104 or transferred to the energy management unit 102 for data calculation with the total electricity cost prediction model. In addition to receiving data from physical charging stations and their charging piles, it can also receive test data provided by the simulator management unit 103.

[0043] The energy management unit 102 is used to set energy-related parameters. These parameters can be set manually or by a simulator to manage the charging schedule, dynamic current sharing charging, or power distribution control of the charging station.

[0044] The simulator management unit 103 manages the behavioral simulators of each entity, including the name, type, operation, accuracy, and training of each simulator. The simulator can be a generative artificial intelligence (AI) model used to simulate the battery specifications, remaining battery power, and arrival time of an electric vehicle; it can also be a simple logical operation, such as calculating the total electricity cost using a fixed pricing formula. The aforementioned simulators include simulated behaviors of physical equipment (i.e., physical charging stations and their charging piles) and policy entities.

[0045] In one embodiment, entity classes include, for example, a charging pile simulator and a battery simulator, while strategy classes include, for example, a power calculation simulator, a total electricity cost prediction simulator, and an electric vehicle arrival time simulator.

[0046] Database 104 is used to store various digital clone-related information, such as models, clones, and associations, as well as the correspondence between clones and physical devices, and the corresponding simulators.

[0047] Display unit 105 is used to display appropriate database data for user viewing. Display unit 105 is a visual page presentation that can be accessed via the Internet.

[0048] Please refer to Figures 2 to 4. Figures 2 and 4 are flowcharts illustrating the application of this method, and Figure 3 is a schematic diagram of a case study of this system applied to a physical charging station 33. Charging station 33 includes charging station data 331, charging piles 332 and 333, and transaction records 332a and 333a for the charging guns of charging piles 332 and 333. In Figure 3, Type1, Type2, CCS1, and TPC represent the specifications of the charging guns of charging piles 332 and 333 and the charging ports of the electric vehicle 37.

[0049] First, in step 21, the charging station manager 36 can set the charging station configuration through the three-dimensional visualization page 31 displayed on the display unit 105.

[0050] In step 22, the charging station configuration unit 106 creates a charging station clone, which includes information on charging piles and other charging stations, as well as a charging station configuration diagram, and stores this data in the database 104.

[0051] In step 23, the contents of database 104 are available for users to view the charging station management status via the Internet or local area network 32 and using the 3D visualization page 31.

[0052] Additionally, in step 41, the simulator management unit 103 performs AI training or settings according to the target to generate the simulator in the simulator management unit 103.

[0053] In one embodiment, a company has multiple electric company vehicles in its factory area and has its own charging station. This charging station needs an off-peak charging schedule to save on electricity costs. This schedule would allow most vehicles to charge during off-peak hours, from 00:00 to 09:00 Monday to Friday and all day Saturday and Sunday. However, this off-peak charging schedule must ensure that all electric vehicles are fully charged during the commuting hours when they are needed, or, based on demand, ensure that 80% of the electric vehicles are fully charged, and the remaining 20% ​​are fully charged before noon. Therefore, the aforementioned "AI training or setting based on the target" can generate a model using AI training that "minimizes electricity costs and can complete charging of 20 vehicles before 07:00 the following morning," which can be used as a simulator by the charging station's clone. That is, the charging station clone can set its simulator to the aforementioned model in step 44. Furthermore, the training data for the aforementioned model can be the sensing data transmitted by the charging piles (e.g., how many vehicles have connected to the charging piles and started charging, the current remaining battery power of the vehicles, etc.).

[0054] In step 42, after the simulator is generated, the simulator management unit 103 stores information such as the simulator's name, type, operation, and accuracy in the database 104 for the clone to select and use.

[0055] In step 43, if the simulator is found to be inaccurate or the rules have changed, the simulator is regenerated.

[0056] In step 44, the simulator management unit 103 sets up a simulator for each charging station clone, stores the simulator information corresponding to each charging station clone in the database 104, and also stores the algorithm or AI model used by each simulator in the database 104.

[0057] In step 45, when the charging station starts to simulate operation, under the control of the simulator management unit 103, the simulator of the charging station clone used in the operation scenario will start to operate.

[0058] Continuing with the example of a company's own charging station in its factory area, if the number of electric vehicles in the factory area increases, the original off-peak charging schedule cannot ensure that all electric vehicles can complete charging by 07:00 during working hours. In this case, the schedule must be readjusted and a new simulator must be generated. For example, it can be adjusted so that some electric vehicles charge during peak or off-peak hours, while the rest charge during off-peak hours.

[0059] In one embodiment, when creating the charging station clone in step 22, multiple clones can be generated, namely, a charging scheduling clone, an electric vehicle arrival simulation clone, and one or more charging pile clones. Furthermore, in step 41, four simulators can be generated: a current-sharing charging scheduling simulator (using the maximum capacity that the charging station can handle regardless of peak or off-peak times), an off-peak charging scheduling simulator (scheduling a majority of electric vehicles to charge during off-peak times), an arrival charging simulator (scheduling electric vehicles with various charging port specifications and various battery remaining capacities to arrive at the charging station at different times), and a charging pile failure simulator (the charging gun cannot charge).

[0060] Accordingly, in step 44, the charging schedule can be set to use the equalization charging or off-peak charging simulator, the electric vehicle arrival simulation can be set to use the arrival charging simulator, and the charging pile can be set to use the charging pile fault simulator, and one of the following operating examples can be used.

[0061] Example 1: The physical charging station has been built but is not yet in use. Therefore, an electric vehicle arrival simulation clone is used to simulate various electric vehicles entering the station for charging, i.e., simulating the future operation. Then, a 3D visualization page 31 is used to monitor the real-time information of the charging station, such as that there are currently ten charging guns charging, two charging guns occupied (the electric vehicle has finished charging but has not left), and two charging guns reserved, etc.

[0062] Example 2: The charging station is operational and there are actual electric vehicles arriving to charge. Therefore, the charging schedule within the station uses a current-sharing charging schedule simulator to ensure that each charging station uses the maximum available current to charge the arriving electric vehicles.

[0063] Example 3: A company has its own charging station at its factory site to charge its electric company vehicles, but wants to save on electricity costs. Therefore, it uses an off-peak charging scheduling simulator to schedule the charging piles 1 to 5 for 24-hour charging, and the charging piles 6 to 14 for charging during the off-peak hours of 00:00-09:00. If it is found that this charging schedule cannot meet the usage needs during the working hours of 07:00-20:00, the charging schedule must be adjusted (for example, by increasing the number of charging piles that charge 24 hours a day).

[0064] Example 4: Simulate charging station alarm events by having three charging piles (e.g., the eighth, ninth, and thirteenth charging pile clones) use a charging pile fault simulator. Therefore, the fault of the eighth charging pile clone is overheating (error code 0x2000), the fault of the ninth charging pile clone is low leakage current protection (error code 0x2100), and the fault of the thirteenth charging pile clone is overcurrent (error code 0x1000). The fault codes can be included in the device status information 111b and 121b. In addition to the aforementioned error codes, the charging pile clones can generate other error codes, such as overvoltage (0x8000).

[0065] Returning to the process in Figure 2, in step 24, when a charging station entity corresponding to the aforementioned charging station clone is established, the charging station configuration unit 106 maps the clone to its corresponding entity one by one and stores this mapping information in the database 104. When the charging station starts to operate, the corresponding device will transmit data (such as information about electric vehicles arriving at the station for charging, such as battery specifications and remaining power) to the database 104 for storage.

[0066] In step 25, the energy management unit 102 receives relevant data and configures the current energy (power) according to the currently set charging schedule and charging mode. If necessary, it can control the power consumption.

[0067] For example, in the current dynamic current sharing charging system, the instantaneous total power is calculated. If the current total power has reached a critical value, and another electric vehicle enters the charging station at this time, the output power of each charging station will be reduced to avoid power outages. As another example, in the current conditional charging system, if the above situation occurs, VIP charging stations will be given priority to provide full-load power, while the power of other charging stations will be reduced.

[0068] In step 26, the total electricity consumption is calculated using the total electricity cost prediction simulator of one of the simulator management units 103 based on the electricity consumption time and billing method (e.g., progressive electricity price, time-based electricity price in two or three tiers, or summer electricity price, etc.). Alternatively, the billing method of the total electricity cost prediction simulator can be adjusted in step 26a before executing step 26.

[0069] In step 29, the data transmitted during the operation of the charging station in step 24 can be provided to the simulator that needs to be regenerated, such as the battery simulator and the electric vehicle arrival time simulator, so that the simulator can simulate richer and more diverse behaviors.

[0070] In step 23, the charging station manager 36 can view real-time display information 341 of the physical / simulated charging station through the 3D visualization page 31 and the charging station management page 34, such as the charging station availability status and charging pile information (e.g., the battery status of a vehicle that is currently charging). In addition, the charging station manager 36 can also view statistical analysis data 342 of the simulated / physical charging station, such as charging station charging reports and charging pile status reports.

[0071] Through the charging station operation simulation page 35, the charging station manager 36 can view the future operation of the charging station before the physical charging station is completed or before it is officially put into operation, or conduct a trial of the charging station after designing a new energy management strategy 343 (such as generating a charging schedule simulator, characterized by features such as off-peak charging, current sharing charging, or priority charging). The specific steps are as follows: First, in step 27, set or adjust the energy management strategy 343.

[0072] Using the charging pile simulator 353 in the simulator management unit 103, the form and operation of each charging pile in the above-mentioned charging station are simulated. For example, the first charging pile is characterized by AC charging, J1772 (Type 1) specification and Mennekes (Type 2) specification, while the second charging pile is characterized by DC charging, CSS1 specification.

[0073] In step 28, the battery simulator and electric vehicle arrival time simulator 351 in the simulator management unit 103 are used to simulate electric vehicle arrival charging with various batteries and remaining power or arrival charging time.

[0074] For example, three electric vehicles, A, B, and C, can be simulated. Vehicle A has a Type 1 charging port, 20% battery remaining, and will immediately enter the station, scheduled to leave only when fully charged. Vehicle B has a CSS1 charging port, 25% battery remaining, and will enter the station 30 minutes later, scheduled to leave only when fully charged. Vehicle C has a TPC charging port, 10% battery remaining, and will enter the station 5 minutes later, scheduled to leave only when fully charged. The above describes the battery status and arrival time of the three vehicles, based on real-world sensor data.

[0075] The above vehicles are charged according to the charging schedule or charging mode 352 set by the charging schedule simulator. Different energy management strategies will result in different charging results.

[0076] For example, using equalization charging, since there is only one fast charging (CSS1, TPC) gun position left, if car C enters the station to charge, car B will not be able to charge if it enters 30 minutes later. For example, using scheduled charging, one fast charging (CSS1, TPC) gun position is reserved for car B, but because its reservation time is 30 minutes later, it cannot be used by any other vehicle during the waiting period (for example, car C can use it within 30 minutes, but it cannot be used to charge car C). For example, using user priority charging, if car C arrives first before car B arrives, it can be charged first, but the charging gun will stop supplying power to car B 10 minutes before the reservation time (please ask car B to check out and leave) or the charging power will be given priority to car B within 20 minutes so that car B can finish charging before car C arrives.

[0077] For example, if the original energy management strategy is equalization charging, and now two vehicles are connected to the charging station, the energy management unit 102 will detect that one of the vehicles is a VIP customer and then change the energy management strategy to give priority to the user, prioritizing the output of the maximum power to that vehicle.

[0078] In one embodiment, the above-described actual model can be generated through artificial intelligence training.

[0079] If the charging station is on company property and there is no scheduled charging issue, then an off-peak charging energy management strategy can be used in step 27. However, if all vehicles use off-peak charging, they may not be able to be fully charged before the next day's shift. Therefore, the off-peak charging vehicle schedule can be continuously adjusted, and the total electricity cost prediction simulator can be used to calculate the results to select and adopt the most suitable strategy (e.g., the cheapest total electricity cost or the fastest way to charge all electric vehicles).

[0080] In one embodiment, the above-described actual model can be generated through artificial intelligence training.

[0081] Furthermore, in one embodiment, each simulator is a model, logical operation, or fixed type generated through artificial intelligence training. The following five examples illustrate this:

[0082] I. Simulator Name / Type: Charging Scheduling Simulator / Artificial Intelligence Model.

[0083] Description: A strategy simulator used to develop charging plans for different types of charging stations. Model input: An artificial intelligence model.

[0084] Time, date, historical charging records (charging time, charging amount, vehicle type), real-time electricity price, and grid load data.

[0085] Model training method: reinforcement learning.

[0086] Feature engineering: peak charging demand periods (off-peak), average charging time (current sharing), and user priority.

[0087] Simulator use case: As mentioned above, private charging stations schedule charging during periods of low electricity prices, while paid charging stations prioritize high-flow charging for premium members.

[0088] II. Simulator Name / Type: Total Electricity Cost Prediction Simulator / Logical Arithmetic Operations.

[0089] Description: A strategy simulator used to predict the total electricity cost of charging stations.

[0090] Taipower's billing method is determined by different factors such as electricity category (e.g., residential, commercial, industrial) and the use of progressive electricity rates, electricity consumption periods (off-peak), and summer.

[0091] For example: For residential use, outside of summer, if the accumulated electricity consumption for the month that has not yet been billed is 400 kWh, the billing method is as follows: the first 120 kWh ($120 * $2.1) + the next 210 kWh ($210 * $2.68) + the remaining 70 kWh ($70 * $3.59) = $1066.10.

[0092] In addition, there were basic fees and other policy-related expenses at the time.

[0093] Simulator use case: Total electricity cost prediction as described above.

[0094] III. Simulator Name / Type: Charging Pile Fault Simulator / Fixed Type.

[0095] Description: An entity class simulator used to simulate charging pile fault states.

[0096] Error code.

[0097] Simulator use case: As mentioned above, a charging station.

[0098] IV. Simulator Name / Type: Charging Simulator / Artificial Intelligence Model.

[0099] Description: A digital clone entity simulator used to simulate the current output behavior of a charging station's charging gun.

[0100] Model inputs: time series (charging start and end times), environmental conditions (temperature, humidity), vehicle type, battery condition, and charging station status.

[0101] Model training method: Deep learning.

[0102] Feature engineering: time features, vehicle category feature coding, historical data (average charging current, charging time).

[0103] Simulator use case: As mentioned above, the Supercharger V3 is charging a Tesla with 20% battery remaining.

[0104] V. Simulator Name / Type: Battery Simulator / Artificial Intelligence Model.

[0105] Description: An entity class simulator used to simulate the battery of an electric vehicle.

[0106] Model inputs: battery usage data (charge and discharge cycle count, charging rate, discharging rate), environmental data (battery operating temperature, humidity), and battery data (battery voltage, current, internal resistance).

[0107] Model training method: Deep learning.

[0108] Feature engineering: battery health, remaining power.

[0109] Emulator use case: As mentioned above, the battery health is 90% with 20% remaining power.

[0110] The present invention also provides a computer-readable storage medium, such as memory, magnetic tape, magnetic disk, or optical disk. This computer-readable storage medium stores a plurality of instructions, which can be read by a processing unit, processor, computer, or server to execute the aforementioned method for managing charging stations via digital avatars. In one embodiment, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0111] The system, method, and computer-readable storage medium for managing charging stations via digital avatars provided by this invention have the following advantages compared to conventional technologies:

[0112] First, the present invention utilizes digital avatar technology for a charging pile management system, which establishes charging stations, internal charging piles and arriving vehicles as avatars in a virtual environment. Any avatar in the virtual environment can use a simulator to operate its behavior, or have the physical charging station equipment upload data and then present it in the avatar. The physical data can also be used as a source of training data for the simulator.

[0113] Secondly, this invention applies digital avatar technology to charging pile management, which can simulate physical equipment such as charging stations, charging piles, and electric vehicles. It can also simulate processes or behaviors such as electric vehicle arrival at the charging station, charging scheduling, and total electricity cost prediction. Therefore, managers can use avatar simulation, analysis, and prediction mechanisms to ensure that the charging station maintenance process achieves optimal results.

[0114] Third, the present invention achieves a management environment that is plannable, simulable, verifiable, and executable.

[0115] The above detailed description is a specific description of one feasible embodiment of the present invention. However, this embodiment is not intended to limit the patent scope of the present invention. All equivalent implementations or modifications that do not depart from the spirit of the present invention should be included in the patent scope of this case.

[0116] In conclusion, this invention is not only innovative in its technical concept, but also enhances the aforementioned multiple effects compared to conventional products. It fully meets the statutory requirements for novelty and inventiveness for an invention patent. Therefore, this application is filed in accordance with the law, and we respectfully request your office to approve this invention patent application to encourage invention. We are deeply grateful for your assistance.

[0117]

[0118] 10: A system for managing charging stations via digital avatars

[0119] 101: Data Management Unit

[0120] 102: Energy Management Unit

[0121] 103: Emulator Management Unit

[0122] 104: Database

[0123] 105: Display Unit

[0124] 106: Charging Station Configuration Unit

[0125] 11, 12: Charging Station

[0126] 11a, 12a: Charging Station Information

[0127] 111, 112, 121, 122: Charging stations

[0128] 111a, 121a: Data and Information

[0129] 111b, 121b: Device status information

[0130] 21~26, 26a, 27~29, 41~45: Steps

[0131] 31: 3D Visual Page

[0132] 32: Internet or Local Area Network

[0133] 33: Charging Station

[0134] 331: Charging Station Data

[0135] 332, 333: Charging piles

[0136] 332a, 333a: Charging gun transaction records

[0137] 34: Charging Station Management Page

[0138] 341: Real-time information displayed at charging stations

[0139] 342: Statistical Analysis Data of Charging Stations

[0140] 343: Energy Management Strategy

[0141] 35: Operation Simulation Page

[0142] 351: Battery Simulator and Electric Vehicle Arrival Time Simulator

[0143] 352: Charging schedule or charging mode

[0144] 353: Charging Pile Simulator

[0145] 36: Charging station manager

[0146] 37: Electric vehicles

Claims

1. A system for managing charging stations via digital avatars, comprising: The display unit is used to provide a visual page for users to input information about the charging station; The charging station configuration unit is used to establish the association between the charging station and its charging pile, as well as the digital clones of the charging station and the charging pile, based on the information; the simulator management unit is used to select the simulator used by each digital clone, wherein the visualization page displays the operation of the simulated environment of the charging station based on the simulation operation of each simulator; the energy management unit is used to manage the energy management strategy of the charging station. And a data management unit, used to receive the sensing data transmitted by the charging pile, and then transmit the sensing data to the energy management unit to trigger the operation of the energy management strategy, or provide the sensing data to the training model of the multiple simulators of the simulator management unit.

2. The system for managing charging stations via digital avatars as described in claim 1 further includes a database, wherein, The charging station configuration unit is reused to store the attributes and associations of the plurality of digital clones in the database, and the data management unit is reused to store the sensing data transmitted by the charging pile in the database.

3. The system for managing charging stations via digital avatars as described in claim 1, wherein, The energy management strategy includes at least one of a scheduled charging strategy, a charging mode strategy, and a power distribution control strategy, used to control at least one of the following: charging schedule, scheduled charging, power limitation, and dynamic power distribution of the charging station.

4. The system for managing charging stations via digital avatars as described in claim 1, wherein, The simulator management unit is reused to enable the multiple simulators to simulate the operation of the charging station according to the energy management strategy set or adjusted by the user, so as to optimize the energy management strategy.

5. The system for managing charging stations via digital avatars as described in claim 1, wherein, Each simulator is a model or logical operation generated through artificial intelligence training.

6. A method for managing charging stations via digital avatars, comprising: Provides a visual page for users to input information about charging stations; Based on this information, the association between the charging station and its charging pile, as well as the digital clones of the charging station and the charging pile, is established; the simulator used by each digital clone is selected, and the visualization page displays the operation of the simulated environment of the charging station according to the simulated operation of each simulator; and the operation of the energy management strategy of the charging station is triggered according to the sensing data transmitted by the charging pile, or the sensing data is provided to the training model of the multiple simulators.

7. The method for managing charging stations via digital avatars as described in claim 6 further includes: Store the attributes of the multiple digital clones and the associated information in the database; And the sensing data transmitted by the charging pile is stored in the database.

8. The method for managing charging stations via digital avatars as described in claim 6, wherein, The energy management strategy includes at least one of a scheduled charging strategy, a charging mode strategy, and a power distribution control strategy, used to control at least one of the following: charging schedule, scheduled charging, power limitation, and dynamic power distribution of the charging station.

9. The method for managing charging stations via digital avatars as described in claim 6 further includes: The multiple simulators simulate the operation of the charging station according to the energy management strategy set or adjusted by the user, so as to optimize the energy management strategy.

10. A computer-readable storage medium storing a plurality of instructions that are read by a processing unit, processor, computer, or server to perform the method of managing a charging station via a digital avatar as described in any one of claims 6 to 9.

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