A visual real-time simulation method for charging load of electric vehicle charging station

Abstract

The present application relates to the technical field of electric vehicle charging load modeling, and discloses a visual real-time simulation method for charging load of an electric vehicle charging station, comprising the following steps: setting relevant parameters of the charging station; determining the probability distribution characteristics or data statistical characteristics of relevant variables of the electric vehicle charging demand; starting a simulation process; each charging pile and the charging station will establish and continuously maintain their own information list during the simulation process; based on the maintained information list, the simulation system visually presents the real-time status of the charging station and continues the simulation of the next time period. The present application can simulate the real-time dynamics of the charging load through real-time simulation technology, which can help technical personnel more accurately understand the details of the charging load and provide an experimental platform for researchers to study the fine real-time charging control of the charging station.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging load modeling technology, and specifically to a visualized real-time simulation method for electric vehicle charging station charging load. Background Technology

[0002] The rapid growth in the number of electric vehicles in recent years has become a significant social phenomenon. According to statistics from the China Association of Automobile Manufacturers, sales of pure electric vehicles in China reached 5.365 million units in 2022, representing a year-on-year increase of 81.6%. The widespread adoption of electric vehicles, coupled with the flexible and dispersed charging behavior of users, will place enormous pressure on the power supply and distribution systems. The charging load of electric vehicles is a concentrated reflection of their impact on the power system. Therefore, accurate simulation of the charging load of electric vehicles is a necessary foundation for ensuring the healthy and stable operation of the power system and is of great significance for building a friendly and interactive social ecosystem between vehicles and users. This invention focuses on load simulation technology within charging station facilities closely related to electric vehicles.

[0003] Existing charging station load modeling technologies can be divided into the following two categories:

[0004] 1. Establish a probability distribution of electric vehicle user behavior habits using statistical methods, and then use Monte Carlo sampling to obtain the average distribution of charging station load. For example, the following technical solutions exist in the prior art:

[0005] (1) After determining the probability distribution characteristics of the charging power probability of a single vehicle, the initial state of charge distribution, the charging start time and the charging duration, Monte Carlo simulation is used to obtain the daily charging load curve of the charging station and the average number of charging vehicles per day.

[0006] (2) Gaussian fitting of charging behavior is obtained by performing Gaussian fitting on electric vehicle user behavior data, and then Monte Carlo algorithm is used to simulate the fitting model to obtain the average daily load curve of charging station.

[0007] 2. Utilizing data-driven prediction algorithms to forecast charging station load can better adapt to the diverse random and nonlinear factors in real-world environments. The following technical solutions exist in this regard:

[0008] By preprocessing the data, we constructed a dataset of charging power and a dataset of the number of vehicles charging per unit time period. We then used a deep learning-based LSTM model for data training and prediction to finally obtain an estimate of the charging load at the charging station.

[0009] However, these existing technical solutions typically obtain the load curve of the charging station over a long period of time in a single transaction, making it difficult to capture the dynamic status of the charging station in real time. Therefore, they lack readability and interpretability for practical technicians. Summary of the Invention

[0010] To address the aforementioned technical problems, this invention provides a visualized real-time simulation method for electric vehicle charging station charging load. The method divides the day into discrete time periods and executes the following actions within each period: removing electric vehicles that have reached their parking period, connecting electric vehicles waiting to charge, having the charging pile output corresponding power according to charging instructions, and processing the charging demands of arriving electric vehicles. Simultaneously, the system can monitor key information at both the electric vehicle and charging station levels. Finally, the system can also retrieve historical electric vehicle state of charge, historical charging station load, and historical charging station flow data to generate relevant graphs.

[0011] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0012] A method for real-time simulation of charging load at electric vehicle charging stations, comprising the following steps:

[0013] S1: Set relevant parameters for the charging station;

[0014] S2: Determine the probability distribution characteristics or statistical characteristics of variables related to electric vehicle charging demand;

[0015] S3: Based on the relevant parameters of the charging station and the probability distribution characteristics or statistical data characteristics of electric vehicle user behavior, the simulation process is initiated. During the simulation, each charging pile and charging station will establish and continuously maintain its own information list. The simulation process specifically includes the following steps:

[0016] S31: At the beginning of each time period t in the simulation process: the charging station removes electric vehicles that have reached their parking period, and the charging pile where the removed electric vehicle is located updates its own information list; the charging station connects each electric vehicle that arrived at the charging station during the previous time period t-1 to an idle charging pile, and the charging pile where the connected electric vehicle is located updates its own information list.

[0017] S32: During each time period t in the simulation process, each charging pile outputs a specific power according to its local state and updates its own information list;

[0018] S33: At the end of each time period t in the simulation process, the charging pile sends the information in its information list back to the charging station management platform; the charging station management platform counts the charging data of electric vehicles arriving at the charging station within time period t and updates the information list of the charging station.

[0019] S4: Based on the information list maintained in step S3, the simulation system visualizes the real-time status of the charging station and continues the simulation for the next time period.

[0020] Furthermore, the relevant parameters of the charging station include: simulation step size τ, simulation duration T, number of charging piles N, and charging power P of the charging piles. chr The charging efficiency η and the maximum state of charge threshold S of the charging pile max The probability distribution characteristics include: the number K of electric vehicles arriving at the charging station, and the battery capacity B of the electric vehicles. cap The initial state of charge S of an electric vehicle ini The expected state of charge S of electric vehicles exp Total number of parking periods D for electric vehicles tot .

[0021] Furthermore, in step S2, determining the probability distribution characteristics of variables related to electric vehicle charging demand specifically includes:

[0022] The number of electric vehicles K arriving at the charging station within a single time period follows a Poisson distribution P(·), where the mean and variance of K are variables with respect to time period t:

[0023]

[0024] Where n represents the total number of electric vehicles arriving at the charging station during all time periods; the mean λ(t) is obtained from actual data statistics and reflects the change in the electric vehicle arrival rate at different times of the day.

[0025] Battery capacity B of a single electric vehicle cap It follows a discrete distribution Pr(·) across various battery capacities:

[0026] Pr(B cap =b k ) = p k k = 1, 2, 3, ...;

[0027] Where k represents a certain battery specification, b k and p k Let represent the battery capacity and probability corresponding to the k-th battery specification, respectively;

[0028] The initial state of charge S of a single electric vehicle ini Follows a log-normal distribution

[0029]

[0030] Where s is a random variable representing the state of charge. and μ are constants representing the minimum and maximum initial state of charge, respectively. s and σ s These are the logarithmic mean and logarithmic standard deviation of the random variable, respectively.

[0031] The expected state of charge S of a single electric vehicle exp Obey a uniform distribution

[0032]

[0033] in, and These are constants representing the upper and lower bounds of the desired charged state, respectively.

[0034] Total number of parking periods for a single electric vehicle D tot for:

[0035]

[0036] Among them, D nes For necessary berthing time, i.e. D sch The schedulable berthing time follows a normal distribution.

[0037]

[0038] d is a random variable with schedulable time. and Here, μ represents the minimum schedulable time and the maximum schedulable time, respectively. d and σ d These are the mean and standard deviation of the random variable, respectively.

[0039] Furthermore, the information list in step S3 has the following structure:

[0040] For a single charging station, denoted by 'i', the charging station's information list includes the following: the number of 'F' indicating whether a vehicle is parked there. i Parked vehicle serial number j, vehicle battery capacity Initial state of charge of vehicle Vehicle Expected State of Charge Real-time state of charge of vehicle S j Total number of parking periods for vehicles Number of remaining parking periods for vehicles Vehicle historical state of charge data {S j Charging power of charging piles Where the charging pile marker i satisfies 1≤i≤N, and the parking vehicle sequence j is the order in which the vehicle arrives at the charging station after the simulation starts; when Fi =0 indicates that the charging station is idle; when F i =1 indicates that a vehicle has connected to or parked at the charging station;

[0041] The overall information list of the charging station includes the following: the number of vehicles K arriving at the charging station during the current period and their charging demand data, the charging load level L at the charging station during the current period, the historical charging load {L} of the charging station, and the historical arrival flow {K} of the charging station.

[0042] Furthermore, step S31 specifically includes:

[0043] At the beginning of each time period t in the simulation process, the charging pile is based on the number of remaining parking time periods. To determine whether to remove an electric vehicle that has reached its parking period: when Remove electric vehicles that have reached their parking period from the charging station; update the charging station information list as follows: Set the marker value F i If the value is 0, then the information related to the vehicle in the charging station information list, i.e., j, S j , (S j}, all set to Null, charging pile charging power constant;

[0044] At the beginning of each time period t in the simulation process, if a charging pile connects to an electric vehicle, the charging pile updates its information list as follows: Set the marker F. i Set to 1, and set the parking vehicle number j and vehicle battery capacity to 1. Initial state of charge of vehicle Vehicle Expected State of Charge Total number of parking periods for vehicles Adjust from Null to the actual value of the connected vehicle, vehicle real-time state of charge S j The value was updated from Null to Number of remaining parking periods for vehicles The value was updated from Null to Vehicle historical state of charge data {S j The charging power of the charging station is updated from null to an empty array. constant.

[0045] Furthermore, step S32 specifically includes:

[0046] During each time period t in the simulation, each charging pile outputs a constant power charging power, with the value of the charging power being 0 or a positive number. The charging power is then recorded in the charging pile's information list. The value is updated to the current value, and other information in the charging pile's information list is updated as follows:

[0047] When the number of markers F i When = 1, update the vehicle's real-time state of charge S according to the following formula. j Number of remaining parking periods for vehicles Vehicle historical state of charge data {S j} and historical charging power data of charging piles The rest of the information list remains unchanged:

[0048]

[0049] When the number of markers F i When the value is 0, the information list of the charging station and the content related to the vehicle remain null.

[0050] Furthermore, step S33 specifically includes:

[0051] At the end of each time period t in the simulation process, each charging pile will increase its charging power. The data is then sent back to the charging station. The charging station tracks the number of electric vehicles (K) arriving within time period t, stores the demand data for each electric vehicle in dictionary form, and updates the charging station's information list using the following formula: charging load level L, historical charging load {L}, and historical arrival flow {K}.

[0052]

[0053] Furthermore, when the simulation system visualizes the real-time status of the charging station in step S4, it specifically includes: visualizing the overall occupancy of charging piles or parking spaces, visualizing the local information of charging piles, visualizing the electric vehicles that arrive and wait for charging within a time period and their demand data, and visualizing the global information of the charging station.

[0054] The simulation interface of the simulation system is divided into a charging area, a waiting area, and a global information area. The charging area displays the occupancy status of charging piles or parking spaces at the current time and allows users to view the real-time information and historical charge status of connected electric vehicles. The waiting area displays the number of electric vehicles that have arrived at the charging station and are waiting to charge at the current time, as well as electric vehicle demand information. The global information area displays time information, global load information of the charging station, and allows users to view the historical load and historical flow data of the charging station.

[0055] The specific method for visualizing the overall occupancy of charging piles or parking spaces is to use different visual elements to distinguish between idle charging piles and charging piles with different output power that have electric vehicles connected.

[0056] The specific method for visualizing local information of charging piles, demand data of electric vehicles waiting to be charged, and global information of charging stations is as follows: by inputting mouse and keyboard commands, the real-time information of the element being viewed or the legend of the historical data of the element being viewed can be displayed on the simulation interface.

[0057] Compared with the prior art, the beneficial technical effects of the present invention are:

[0058] Traditional charging station load modeling techniques largely rely on probabilistic modeling methods or data-driven approaches to obtain long-term statistical characteristics of the charging load. This invention, however, uses real-time simulation technology to simulate the real-time dynamics of the charging load. This advantage helps technicians understand the details of charging load occurrence more accurately and provides an experimental platform for researchers to study precise real-time charging station control. Compared to the disadvantages of traditional simulation techniques, such as high operational difficulty and low information readability, this invention has excellent visualization, operability, and data integration capabilities. It not only allows technicians to understand the operation within the charging station more clearly but also provides professionals with convenient functions such as charging load monitoring and historical data viewing. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the overall simulation method in this invention.

[0060] Figure 2 This is a schematic diagram illustrating the mean variation of the Poisson distribution used in the embodiments of the present invention;

[0061] Figure 3 This is a schematic diagram showing the distribution of the initial state of charge of an electric vehicle in an embodiment of the present invention;

[0062] Figure 4 This is a schematic diagram of the different blocks included in the simulation interface in the embodiments of the present invention;

[0063] Figure 5 This is a schematic diagram illustrating the design and function of the charging area in the simulation interface of this invention.

[0064] Figure 6 This is a schematic diagram illustrating the design and function of the waiting area of ​​the simulation interface in an embodiment of the present invention;

[0065] Figure 7 This is a schematic diagram illustrating the design and function of the global information area of ​​the simulation interface in this embodiment of the invention;

[0066] Figure 8 This is a schematic diagram of the overall design of the simulation interface in an embodiment of the present invention;

[0067] Figure 9 This is a schematic diagram of the typical daily load of a charging station under multiple simulations in an embodiment of the present invention. Detailed Implementation

[0068] The present invention will be further explained and described below with reference to the accompanying drawings to enable those skilled in the art to more deeply understand and implement the present invention. It is worth noting that the following embodiments are merely for the purpose of describing the present invention and are not intended to limit the scope of the present invention. The scope of protection of the present invention is defined by the appended claims; equivalent changes and modifications made without departing from the concept and principles of the present invention should all fall within the scope of protection of the present invention.

[0069] The real-time simulation method for visualized electric vehicle charging station charging load in this invention includes the following steps:

[0070] S1: Set the relevant parameters for the charging station, mainly including: simulation step size τ, simulation duration T, number of charging piles N, and charging power P of each charging pile. chr The charging efficiency η and the maximum state of charge threshold S of the charging pile max .

[0071] In one embodiment, the simulation step size τ is set to 15 minutes, the simulation duration T is set to 10,000 steps, the number of charging piles N in the charging station is set to 50, and the charging power P of the charging piles is set to... chr The charging station is equipped with two charging options: 0kW and 8kW (normal charging). The charging efficiency η is set to 0.96, and the maximum state of charge threshold S is... max Set it to 0.95.

[0072] S2: Determine the probability distribution characteristics or statistical characteristics of variables related to electric vehicle charging demand, mainly including: the number of electric vehicles K arriving at charging stations, and the battery capacity B of electric vehicles. cap Initial state of charge S ini Desired state of charge S exp and total berthing time D tot .

[0073] The number of electric vehicles K arriving at the charging station within a single time period follows a Poisson distribution given by formula (1); in one embodiment, the mean of this Poisson distribution varies with the 24 hours of a day as follows: Figure 2 As shown, since the simulation time interval step τ is 15 minutes, the mean of each time interval is 1 / 4 of the hourly mean.

[0074]

[0075] The battery specifications of a single electric vehicle follow a discrete distribution as shown in formula (2); in one embodiment, the capacities b1, b2, b3, and b4 of the four battery types are 20kWh, 40kWh, 60kWh, and 80kWh, respectively, and the corresponding probabilities p1, p2, p3, and p4 are 0.2, 0.4, 0.3, and 0.1, respectively.

[0076] Pr(B cap =b k ) = p k , k = 1, 2, 3, ... (2)

[0077] The initial state of charge S of a single electric vehicle ini It follows a log-normal distribution as shown in formula (3); in one embodiment, the minimum initial state of charge... and maximum initial state of charge The constants were set to 0.15 and 0.70 respectively, and the logarithmic mean μ s and logarithmic standard deviation σ s The results of sampling the probability distribution of this initial state of charge 1,000,000 times with values ​​set to -1.51 and 0.60 respectively are as follows: Figure 3 As shown.

[0078]

[0079] The expected state of charge S of a single electric vehicle exp Obeying the uniform distribution shown in formula (4); in one embodiment, the desired boundary of the charged state. and the Upper Realm The constants were set to 0.80 and 0.92 respectively.

[0080]

[0081] Total number of parking periods for a single electric vehicle D tot Due to the necessary berthing time D nes and schedulable berthing time D sch The composition is specifically calculated using formula (5). The necessary berthing time can be calculated as follows: Scheduling time D sch It follows a normal distribution as shown in formula (6). In one embodiment, the minimum schedulable time is... and maximum schedulable time The time was set to 1.5h and 10h respectively, with a mean of μ. d The duration is 3 hours, and the standard deviation is σ. d It takes 1 hour.

[0082]

[0083]

[0084] S3: Start the simulation process and establish a local information list for the charging piles and a global information list for the charging station platform. The information list has the following structure:

[0085] The information list for a single charging station marked with 'i' includes the following: whether there are vehicle parking markers (F). i Parked vehicle serial number j, vehicle battery capacity Initial state of charge of vehicle Vehicle Expected State of Charge Real-time state of charge of vehicle S j Total number of parking periods for vehicles Number of remaining parking periods for vehicles Vehicle historical state of charge data {S j Charging power of charging piles When F i =0 indicates that the charging station is idle; when F = 0, it means the charging station is idle. i =1 indicates that a vehicle has connected to or parked at the charging station. When F i When = 0, vehicle-related information such as vehicle serial number and vehicle historical charge state data does not exist, and is set to Null.

[0086] The overall information list of the charging station platform includes the following: the number of vehicles K arriving at the station during the current period and their charging demand data, the charging load level L at the station during the current period, the historical charging load {L} of the charging station, and the historical arrival flow {K} of the charging station.

[0087] In one embodiment, the information list of charging piles and charging platforms is implemented using a dictionary data structure.

[0088] Step S3 specifically includes the following steps:

[0089] S31: At the beginning of each time period during the simulation, the charging station removes vehicles that have reached their parking period. Remove vehicle charging station and update its local information list: Marker quantity F i ←0 sets all other vehicle-related information to Null, and the charging power of the charging station. It remains unchanged.

[0090] Additionally, at the beginning of each time period during the simulation, the charging station connects each electric vehicle that arrived at the charging station during the previous time period and was in a waiting state to an available charging pile. The charging pile for the connected vehicle updates its information list: setting the marker F. i ←1, Enter the parking vehicle number j and vehicle battery capacity Initial state of charge of vehicle Vehicle Expected State of Charge Total number of parking periods for vehicles The real-time state of charge of the vehicle is updated from Null to the actual value of the connected vehicle. The remaining parking time for the vehicle has been updated from Null to Vehicle historical state of charge data {S j The charging power of the charging station is updated from null to an empty array. It remains unchanged.

[0091] S32: During each time period of the simulation process, each charging station outputs its charging power. Implement constant power charging.

[0092] In one embodiment, the charging pile outputs a charging power of 0kW or 10kW for regular charging, and the charging power is controlled according to the "charge as soon as it arrives" principle: when a vehicle is connected or parked, the charging pile outputs a power of 10kW; when the connected vehicle reaches the expected power level or no vehicle is connected, the charging pile outputs a power of 0kW.

[0093] In other possible embodiments, the charging station can output more types of charging power or use specific algorithms for power control.

[0094] Each charging station then displays the charging power from its local information list. The numerical value is updated to the current output value, and other information in its local information list is also updated: when the marker value F i When = 1, update the vehicle's real-time state of charge S according to formula (7). j Number of remaining parking periods for vehicles Vehicle historical state of charge data {S j} and historical charging power data of charging piles The rest of the information remains unchanged.

[0095]

[0096] When the number of markers F i When the value is 0, vehicle-related information remains null.

[0097] S33: At the end of each time period in the simulation process, each charging station will display its power information. The data is sent back to the charging station platform. The charging station platform counts the number of electric vehicles K that arrive at the charging station and are waiting during the period, stores the demand data of each electric vehicle, and updates its global information list according to formula (8), including the charging load level L in the station, the historical charging load {L} of the charging station, and the historical arrival flow {K} of the charging station.

[0098]

[0099] S4: Based on the local and global information lists updated during the time period in steps S31, S32, and S33, the simulation system visualizes the real-time status of the charging station, including: visualization of the overall occupancy of charging piles or parking spaces, visualization of the local information of charging piles, visualization of the demand data of electric vehicles arriving and waiting for charging during the time period, and visualization of the global information of the charging station platform.

[0100] like Figure 4 As shown, in one embodiment of the present invention, the simulation system based on the Pygame application package divides the simulation interface into a charging area, a waiting area, and a global information area.

[0101] like Figure 5 As shown, in one embodiment of the present invention, the simulation system displays N=50 parking space elements on the charging area interface, and displays the electric vehicles connected in the current time period on the corresponding parking space elements; shaded squares represent idle charging piles or parking spaces, squares with black lightning bolt marks represent electric vehicles that are charging, and squares with white lightning bolt marks represent electric vehicles that are not charging, i.e., the charging pile outputs 0 power; by clicking on the vehicle element with the left or right mouse button, its real-time information or a graph of its historical charging state can be displayed.

[0102] like Figure 6 As shown, in one embodiment of the present invention, the simulation system displays all electric vehicle elements waiting for charging in the current time period on the waiting area interface; by clicking on a vehicle element with the left mouse button, its demand information will pop up.

[0103] like Figure 7 As shown, in one embodiment of the present invention, the simulation system displays the current time period and the global load information of the charging station in the global information area; by clicking the relevant area with the left mouse button, a legend of the charging station's historical load data or historical flow data can be drawn.

[0104] Figure 8 The overall interface of the simulation implementation is displayed. Figure 9 The simulation shows the typical daily load of the charging station after multiple simulations.

[0105] For the flowchart of the simulation method in this invention, please refer to [link / reference]. Figure 1 .

[0106] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for real-time simulation of charging load at a visualized electric vehicle charging station, comprising the following steps: S1: Set the relevant parameters for the charging station, including: simulation step size. Simulation duration Number of charging piles at charging stations Charging power of charging piles Charging efficiency of charging piles and maximum state of charge threshold ; S2: Determine the probability distribution characteristics or statistical characteristics of variables related to electric vehicle charging demand, including: the number of electric vehicles arriving at charging stations. and the battery capacity of electric vehicles. Initial state of charge Desired state of charge and total berthing time ; S3: Based on the relevant parameters of the charging station and the probability distribution characteristics or statistical data characteristics of electric vehicle user behavior, the simulation process is initiated. During the simulation, each charging pile and charging station will establish and continuously maintain its own information list. The simulation process specifically includes the following steps: S31: At each time point in the simulation process Initially: The charging station removes electric vehicles that have reached their parking period, and the charging piles where the removed electric vehicles were located update their information lists; the charging station will update the previous time period. During this period, each electric vehicle that arrives at the charging station connects to an available charging pile, and the charging pile that connects to the electric vehicle updates its own information list. S32: At each time point in the simulation process During this period, each charging station outputs a specific power based on its local conditions and updates its own information list; S33: At each time point in the simulation process Finally, the charging pile sends the information from the charging pile information list back to the charging station management platform; the charging station management platform then compiles statistics for the time period. The system collects charging data of electric vehicles arriving at charging stations and updates the overall information list of charging stations. S4: Based on the information list maintained in step S3, the simulation system visualizes the real-time status of the charging station and continues the simulation for the next time period.

2. The method for real-time simulation of charging load at electric vehicle charging stations according to claim 1, characterized in that: When determining the probability distribution characteristics of variables related to electric vehicle charging demand in step S2, the specific steps include: Number of electric vehicles arriving at charging stations in a single time period Follows Poisson distribution , The mean and variance are related to the time period. Variables: ， ； in, This represents the total number of electric vehicles arriving at the charging station during all time periods; the mean. This data is based on actual statistics and reflects the changes in electric vehicle arrival rates at different times of the day. Battery capacity of a single electric vehicle It follows a discrete distribution across various battery capacities. : ， ； in, For a certain battery specification, and They represent the first The battery capacity and probability corresponding to each battery specification; Initial state of charge of a single electric vehicle Follows a log-normal distribution : ； in, Let be a random variable representing the state of charge. and These are constants representing the minimum and maximum initial state of charge, respectively. and These are the logarithmic mean and logarithmic standard deviation of the random variable, respectively. Expected state of charge of a single electric vehicle Obey a uniform distribution : ； in, and These are constants representing the upper and lower bounds of the desired charged state, respectively. Total number of parking periods for a single electric vehicle for: ； in, For necessary berthing time, i.e. ; The schedulable berthing time follows a normal distribution. : ； Let be a random variable with schedulable time. and These are constants representing the minimum schedulable time and the maximum schedulable time, respectively. and These are the mean and standard deviation of the random variable, respectively.

3. The method for real-time simulation of charging load at a visualized electric vehicle charging station according to claim 1, characterized in that, The information list in step S3 has the following structure: For a single charging station, The information list for charging stations includes the following: the number of markers indicating whether a vehicle is parked there. Parked vehicle serial number Vehicle battery capacity Initial state of charge of the vehicle Vehicle expected state of charge Real-time vehicle charge status Total number of parking periods for vehicles Number of remaining parking periods for vehicles Historical state of charge data of vehicles Charging power of charging piles Among them, charging pile markings satisfy Parked vehicle serial number This refers to the order in which the vehicles arrive at the charging station after the simulation starts; when When the charging station is idle, it indicates that the charging station is available. This indicates that a vehicle has connected to or parked at the charging station. The overall information list for the charging station includes the following: the number of vehicles arriving at the charging station during the current time period. And vehicle charging demand data, charging load level at charging stations during the current period. Historical charging load of charging stations Historical arrival traffic at charging stations .

4. The method for real-time simulation of charging load at electric vehicle charging stations according to claim 3, characterized in that, Step S31 specifically includes: At each time period of the simulation process Initially, the charging station is based on the number of remaining parking time periods. To determine whether to remove an electric vehicle that has reached its parking period: when Then remove electric vehicles that have reached their parking period from the charging station; update the charging station information list as follows: Set the marker quantity. Setting it to 0 will display the vehicle-related information in the charging station's information list. , , , , , , , All are set to Null, charging power of charging pile constant; At each time period of the simulation process Initially, if a charging station is connected to an electric vehicle, the charging station updates its information list as follows: [Setting a marker quantity] Set the number to 1, and assign the parking vehicle number to the correct number. Vehicle battery capacity Initial state of charge of the vehicle Vehicle expected state of charge Total number of parking periods for vehicles Adjust from Null to the actual value of the connected vehicle, the vehicle's real-time state of charge. The value was updated from Null to Number of remaining parking periods for vehicles The value was updated from Null to Historical state of charge data of vehicles Updated from null to an empty array, charging power of the charging station. constant.

5. The method for real-time simulation of charging load at electric vehicle charging stations according to claim 3, characterized in that, Step S32 specifically includes: At each time period of the simulation process During this period, each charging station outputs a constant power charging power, with the charging power value being 0 or a positive number. The charging power is then recorded in the charging station's information list. The value is updated to the current value, and other information in the charging pile's information list is updated as follows: When the number of tags Update the vehicle's real-time state of charge using the following formula. Number of remaining parking periods for vehicles Historical state of charge data of vehicles Historical charging power data of charging piles The rest of the information list remains unchanged: ； When the number of tags At that time, the information list of the charging station and the content related to the vehicle remain null.

6. The method for real-time simulation of charging load at electric vehicle charging stations according to claim 3, characterized in that, Step S33 specifically includes: At each time period of the simulation process Finally, each charging station will display its charging power. Data is sent back to the charging station. Charging station statistics period Number of electric vehicles arriving at charging stations The demand data for each electric vehicle is stored in dictionary form, and the information list of charging stations is updated according to the following formula, including the charging load level within the charging station. Historical charging load of charging stations Historical arrival traffic at charging stations : 。 7. The method for real-time simulation of charging load in a visualized electric vehicle charging station according to claim 1, characterized in that: In step S4, when the simulation system visualizes the real-time status of the charging station, it specifically includes: visualizing the overall occupancy of charging piles or parking spaces, visualizing the local information of charging piles, visualizing the electric vehicles that arrive and wait for charging within a time period and their demand data, and visualizing the global information of the charging station. The simulation interface of the simulation system is divided into a charging area, a waiting area, and a global information area. The charging area displays the occupancy status of charging piles or parking spaces at the current time and allows users to view the real-time information and historical charge status of connected electric vehicles. The waiting area displays the number of electric vehicles that have arrived at the charging station and are waiting to charge at the current time, as well as the electric vehicle demand information. The global information area displays time information, global load information of the charging station, and allows users to view the historical load and historical flow data of the charging station. The specific method for visualizing the overall occupancy of charging piles or parking spaces is to use different visual elements to distinguish between idle charging piles and charging piles with different output power that have electric vehicles connected. The specific method for visualizing local information of charging piles, demand data of electric vehicles waiting to be charged, and global information of charging stations is as follows: by inputting mouse and keyboard commands, the real-time information of the element being viewed pops up on the simulation interface or a legend of the historical data of the element being viewed is drawn.

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