Charging load prediction method for chained highway electric vehicle charging scene

Abstract

The invention relates to a charging load prediction method for a chained expressway electric vehicle charging scene. The method comprises the following steps: determining the time when an electric vehicle enters an expressway and the charge state of the electric vehicle entering a starting point of the expressway according to data of the electric vehicle entering the expressway within a day; for each service area on the expressway, an electric vehicle charging decision model constructed based on the charge state of the starting point entering the expressway is adopted to judge whether each electric vehicle is charged when entering the service area, if so, the charging power of the electric vehicle is calculated based on the time of the electric vehicle entering the expressway, and if not, the charging power of the electric vehicle is calculated. And superposing the charging power of all the charged electric vehicles to obtain the charging load of the electric vehicles in the service area. According to the invention, accurate prediction of the electric vehicle charging load of each service area of the chain type highway is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging load prediction, and in particular to a charging load prediction method for chain highway electric vehicle charging scenarios. Background Art

[0002] With the global energy transition and the rapid development of the electric vehicle (EV) industry, highways, as critical infrastructure for long-distance travel, are facing the challenge of rapidly growing charging demand. The widespread adoption of EVs has placed higher demands on the energy supply of highway service areas. In particular, the uncertainty in the temporal and spatial distribution of charging loads has placed higher demands on user-side load forecasting technologies.

[0003] Electric vehicle charging load forecasting is fundamental to highway energy supply system planning. Unlike traditional fuel vehicles, electric vehicle charging demand is affected by a variety of uncertain factors, including traffic volume, time of entry into the highway, state of charge (SOC), and charging behavior. Existing patent document CN119151159A discloses a method and system for predicting electric vehicle charging load that considers traffic flow. However, this solution only considers traffic flow information and does not account for the random nature of electric vehicle travel status and charging demand. Therefore, directly applying this solution to chained highway electric vehicle charging scenarios would result in insufficient charging load forecast accuracy. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a charging load prediction method for chain highway electric vehicle charging scenarios, which can improve the accuracy of charging load prediction.

[0005] The technical solution adopted by the present invention to solve the technical problem is to provide a charging load prediction method for a chain highway electric vehicle charging scenario, comprising the following steps:

[0006] Determine the time when electric vehicles enter the expressway and the state of charge at the starting point of entering the expressway based on the data of electric vehicles entering the expressway during the day;

[0007] For each service area on the highway, an electric vehicle charging decision model constructed based on the charge state at the starting point of entering the highway is used to determine whether each electric vehicle should be charged when entering the service area. If charging is required, the charging power of the electric vehicle is calculated based on the time when the electric vehicle enters the highway, and the charging power of all charged electric vehicles is superimposed to obtain the charging load of the electric vehicles in the service area.

[0008] Determining the time when an electric vehicle enters an expressway based on the data of electric vehicles entering the expressway during the day specifically includes:

[0009] Based on the daily traffic flow data of electric vehicles on the highway, a probability distribution model of the time when electric vehicles enter the highway is generated;

[0010] Based on the probability distribution model of the time when electric vehicles enter the highway, the Monte Carlo method is used to obtain the time when electric vehicles enter the highway within a day.

[0011] The method of generating a probability distribution model of the time when electric vehicles enter the highway based on the traffic flow data of electric vehicles on the highway within a day specifically includes:

[0012] Construct a traffic flow matrix based on the number of electric vehicles entering the highway per hour;

[0013] Calculate the probability density matrix of electric vehicles based on the traffic flow matrix;

[0014] Based on the distribution characteristics of the probability density matrix of electric vehicles, a probability distribution model of the time when electric vehicles enter the highway within a day is obtained.

[0015] The method uses the Monte Carlo method based on the time probability distribution model of electric vehicles entering the highway to obtain the time when electric vehicles enter the highway within a day. Specifically, based on the time probability distribution model of electric vehicles entering the highway, the Monte Carlo method is used for random sampling to generate random number samples uniformly distributed within a preset range, and the uniformly distributed random number samples are inversely transformed through the time probability distribution model of the highway to convert the uniformly distributed random number samples into samples of the time probability distribution model of the highway to obtain the time when electric vehicles enter the highway within a day.

[0016] The method of determining the state of charge of the starting point of the expressway according to the data of electric vehicles entering the expressway within a day is specifically as follows: using a uniform distribution model to determine the state of charge of the starting point of the expressway within a day.

[0017] The electric vehicle charging decision model is specifically as follows: ,in, For the Electric vehicles arrived The state of charge of the electric vehicle when entering the service area is determined by the state of charge of the electric vehicle at the starting point of the highway. For the Electric vehicles arrived The distance traveled when there are service areas is When it is the last service area, is the total length of the expressway, It represents the amount of electricity consumed by the nth electric car for every 1 km it travels. represents the battery capacity of the nth electric car, is the charging coefficient, which is defined as the lowest value of the charge state of the electric vehicle that the user can accept when driving from the current service area to the next service area; when the electric vehicle charging decision model is met, it means that the electric vehicle needs to be charged when entering the service area.

[0018] The calculation of the charging power of the electric vehicle and the superposition of the charging powers of all the charged electric vehicles to obtain the charging load of the electric vehicles in the service area specifically include:

[0019] Calculate the charging start time and charging end time of each electric vehicle that needs to be charged based on the time when the electric vehicle enters the highway;

[0020] Determining a charging power and time relationship model for each electric vehicle that needs to be charged according to a charging start time and a charging end time of each electric vehicle that needs to be charged;

[0021] The charging power of each electric vehicle that needs to be charged in each time period is calculated to obtain the charging load of the service area in each time period.

[0022] The calculation method of the charging start time of each electric vehicle that needs to be charged is: The calculation method of the charging end time of each electric vehicle that needs to be charged is: ,in, and Respectively The charging start time and charging end time of each electric vehicle; For the The time it takes for an electric car to enter the highway, For the Electric vehicles arrived The distance traveled when visiting a service area, For the The speed of an electric car; For the Electric car in the The charging time of each service area is expressed as: , For the Electric vehicles arrived The state of charge at the service area, represents the battery capacity of the nth electric car, For the The charging power of an electric vehicle.

[0023] The technical solution adopted by the present invention to solve the technical problem is to provide a charging load prediction device for chain highway electric vehicle charging scenarios, comprising:

[0024] a determination module, for determining the time when the electric vehicle enters the expressway and the state of charge of the starting point of entering the expressway based on the data of the electric vehicles entering the expressway during the day;

[0025] The prediction module is used to determine, for each service area on the highway, whether each electric vehicle should be charged when entering the service area using an electric vehicle charging decision model built based on the charge state at the starting point of entering the highway. If charging is required, the charging power of the electric vehicle is calculated based on the time when the electric vehicle enters the highway, and the charging power of all charged electric vehicles is superimposed to obtain the charging load of the electric vehicles in the service area.

[0026] The technical solution adopted by the present invention to solve its technical problem is: providing an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, implementing the steps of the above-mentioned charging load prediction method for the chain highway electric vehicle charging scenario.

[0027] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the charging load prediction method for the chain highway electric vehicle charging scenario are implemented.

[0028] Beneficial effects

[0029] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the existing technology: the present invention determines the time when electric vehicles enter the expressway and the charge state of the starting point of the expressway based on the data of electric vehicles entering the expressway during the day, fully considering the uncertainty characteristics of the electric vehicle travel time and charge state, and realizing accurate prediction of the electric vehicle charging load in each service area of ​​the chain expressway. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a charging load prediction method for a chain highway electric vehicle charging scenario according to a first embodiment of the present invention;

[0031] Figure 2 It is a highway topology map in the first embodiment of the present invention. DETAILED DESCRIPTION

[0032] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0033] The first embodiment of the present invention relates to a charging load prediction method for a chain highway electric vehicle charging scenario, such as Figure 1 As shown, the following steps are included:

[0034] Step 1: Determine the time and state of charge at the starting point of each EV's entry into the highway based on daily data from EVs entering the highway. This step, based on the random distribution of EV charging loads on chained highways, uses a Monte Carlo method to simulate the randomness of EVs' time, space, and battery SOC.

[0035] The time when electric vehicles enter the highway is determined by the following method:

[0036] Based on the daily traffic flow data of electric vehicles on the highway, a probability distribution model of the time when electric vehicles enter the highway is generated.

[0037] To obtain the time when electric vehicles enter the highway, this embodiment generates a probability distribution model of the time when EVs enter the highway based on the daily traffic flow data of electric vehicles on the highway. The specific details are as follows:

[0038] Construct a traffic flow matrix based on the number of electric vehicles entering the highway per hour , as follows:

[0039] ;

[0040] in, Indicates in The number of electric vehicles entering the highway during a period. Based on the traffic flow matrix , forming the probability density matrix of electric vehicles , which is expressed as:

[0041] ;

[0042] in, Indicates that electric vehicles The probability of entering the highway in the time period is equal to The number of electric vehicles entering the highway during the period Divided by the total number of electric vehicles entering the highway on a given day, it is calculated as follows:

[0043] ;

[0044] The probability density matrix for electric vehicles The distribution characteristics of are fitted to obtain the time probability density function (i.e., time probability distribution model) of electric vehicles entering the highway within a day. Assuming that the distribution conforms to the mixed Gaussian distribution, the time probability density function is expressed as:

[0045] ;

[0046] This formula reflects the time coordinate axis of a single electric vehicle. The probability density at time is .in, is the number of components of the mixed Gaussian distribution, representing each sub-model in the mixed Gaussian model The number of and denote the mean and variance of the sub-model respectively, is the mixing coefficient, which represents the weight of each sub-model.

[0047] Based on the probability distribution model of the time when electric vehicles enter the highway, the Monte Carlo method is used to obtain the time when electric vehicles enter the highway within a day: , using the Monte Carlo method for random sampling, generating a random number sample uniformly distributed in [0, 24], and then using the probability density function Perform an inverse transformation to convert a uniformly distributed random number sample into a target distribution Sample.

[0048] Assume there is If electric vehicles enter the highway in one day, the matrix , which is expressed as:

[0049] ;

[0050] in, Representative The time it takes for an EV to enter the highway.

[0051] In this step, the state of charge of the electric vehicle entering the starting point of the highway is determined by the following method:

[0052] Electric vehicles can only travel in one direction on highways and need to pass through toll booths and service areas along the way. Since electric vehicles can only be charged in service areas, the highway topology network for electric vehicle charging is described as a straight line with various service areas along the way for energy supply. The topology network is as follows: Figure 2shown.

[0053] Before entering the highway, electric vehicle owners will keep the state of charge (SOC) of the electric vehicle at a high level. Therefore, assuming that the SOC of the electric vehicle at the starting point of the highway is uniformly distributed in [0.6, 1], within a day The initial SOC of an electric vehicle at the starting point of a highway can be calculated using the matrix express:

[0054] ;

[0055] Where, Indicates the The SOC of an electric vehicle at the starting point of the highway.

[0056] Step 2: For each service area on the highway, an electric vehicle charging decision model constructed based on the charge state at the starting point of entering the highway is used to determine whether each electric vehicle should be charged when entering the service area. If charging is required, the charging power of the electric vehicle is calculated based on the time when the electric vehicle enters the highway, and the charging power of all charged electric vehicles is superimposed to obtain the charging load of the electric vehicles in the service area.

[0057] When an electric vehicle arrives at a service area, the user may not necessarily choose to charge. Charging will only occur when the vehicle's SOC falls below a certain level. Furthermore, when deciding whether to charge in the current service area, the user must consider not only the vehicle's current SOC but also whether the next service area can be reached. To determine the electric vehicle's charging behavior and the time it takes to charge, this embodiment introduces an electric vehicle charging decision model. When the conditions in the electric vehicle charging decision model are met, the electric vehicle begins charging; otherwise, it leaves the service area.

[0058] In order to determine whether the electric vehicle should be charged when it arrives at the service area, this embodiment introduces the charging coefficient , which is defined as the lowest SOC value of the electric vehicle that the user can accept when driving from the current service area to the next service area. Therefore, the electric vehicle charging decision model of this embodiment can be expressed as:

[0059] ;

[0060] Where, For the Electric vehicles arrived The state of charge of the electric vehicle when entering the service area is determined by the state of charge of the electric vehicle at the starting point of the highway. For the Electric vehicles arrived The distance traveled when there are service areas is When it is the last service area, is the total length of the expressway, It represents the amount of electricity consumed by the nth electric car for every 1 km it travels. Represents the battery capacity of the nth electric vehicle.

[0061] When the above electric vehicle charging decision model is met, the electric vehicle will perform the charging operation. Electric car in the When a service area performs charging operation, the charging start time can be expressed as:

[0062] ;

[0063] When Electric car in the When the charging operation of a service area is completed, the charging completion time can be expressed as:

[0064] ;

[0065] in, and Respectively The charging start time and charging end time of each electric vehicle; For the Electric vehicles arrived The distance traveled when visiting a service area, For the The speed of electric cars, For the Electric car in the Charging time at each service area.

[0066] In this embodiment, when the electric vehicle selects When the first service area starts charging, the charging power is approximately constant, and each charge will fully charge the battery, that is, the SOC is 1, then the first Electric car in the Charging time in service areas The calculation formula can be expressed as:

[0067] ;

[0068] Where, Indicates the The charging power of an electric vehicle. Based on this formula, within one day The charging time of electric vehicles in each service area can be calculated using a matrix express:

[0069] .

[0070] Determine the Electric car in the After the charging start time and charging end time of the service area, The relationship between the charging power and time of an electric vehicle can be expressed as:

[0071] ;

[0072] In the formula, the calculation cycle of the electric vehicle charging power is controlled within the range of 24 hours a day, so The value range is 0 to 24.

[0073] The time step of the design calculation is 1 hour, so a day will be divided into 24 time periods, and the calculation of the time of each electric vehicle in each time period is carried out separately. The charging power of each service area can be obtained by summarizing the The charging load of a service area can be expressed as:

[0074] ;

[0075] Based on this formula, The distribution of charging load in each service area in 24 periods of a day can form a matrix , which is expressed as:

[0076] ;

[0077] When the traversal is completed, the electric vehicle charging load data of all service areas will be obtained, and the charging load forecast will be completed.

[0078] This paper uses a Monte Carlo method to fully account for the uncertainty of the SOC of electric vehicles entering highways and introduces an electric vehicle charging decision model to improve this. This allows for accurate prediction of the electric vehicle charging load at each service area on a chain of highways. Furthermore, the present invention only requires the total number of electric vehicles entering the highway during the day to be input to determine the distribution of electric vehicle charging load at each time period within the day.

[0079] A second embodiment of the present invention relates to a charging load prediction device for a chain highway electric vehicle charging scenario, comprising:

[0080] a determination module, for determining the time when the electric vehicle enters the expressway and the state of charge of the starting point of entering the expressway based on the data of the electric vehicles entering the expressway during the day;

[0081] The prediction module is used to determine, for each service area on the highway, whether each electric vehicle should be charged when entering the service area using an electric vehicle charging decision model built based on the charge state at the starting point of entering the highway. If charging is required, the charging power of the electric vehicle is calculated based on the time when the electric vehicle enters the highway, and the charging power of all charged electric vehicles is superimposed to obtain the charging load of the electric vehicles in the service area.

[0082] The determination module includes a time determination unit, which generates a time probability distribution model of electric vehicles entering the highway based on traffic flow data of electric vehicles on the highway within a day; based on the time probability distribution model of electric vehicles entering the highway, uses the Monte Carlo method to obtain the time when electric vehicles enter the highway within a day.

[0083] When the time determination unit generates a probability distribution model of the time when electric vehicles enter the expressway based on the traffic flow data of electric vehicles on the expressway within a day, the method specifically includes:

[0084] Construct a traffic flow matrix based on the number of electric vehicles entering the highway per hour;

[0085] Calculate the probability density matrix of electric vehicles based on the traffic flow matrix;

[0086] Based on the distribution characteristics of the probability density matrix of electric vehicles, a probability distribution model of the time when electric vehicles enter the highway within a day is obtained.

[0087] The time determination unit uses the Monte Carlo method based on the time probability distribution model of the electric vehicle entering the highway to obtain the time when the electric vehicle enters the highway within a day. Specifically, based on the time probability distribution model of the electric vehicle entering the highway, the Monte Carlo method is used to perform random sampling to generate random number samples uniformly distributed within a preset range, and then an inverse transformation is performed through the time probability distribution model of the highway to convert the uniformly distributed random number samples into samples of the time probability distribution model of the highway to obtain the time when the electric vehicle enters the highway within a day.

[0088] The determination module includes a state of charge determination unit, which uses a uniform distribution model to determine the state of charge of an electric vehicle entering a starting point of a highway within a day.

[0089] The electric vehicle charging decision model is specifically as follows: ,in, For the Electric vehicles arrived The state of charge of the electric vehicle when entering the service area is determined by the state of charge of the electric vehicle at the starting point of the highway. For the Electric vehicles arrived The distance traveled when there are service areas is When it is the last service area, is the total length of the expressway, It represents the amount of electricity consumed by the nth electric car for every 1 km it travels. represents the battery capacity of the nth electric car, is the charging coefficient, which is defined as the lowest value of the charge state of the electric vehicle that the user can accept when driving from the current service area to the next service area; when the electric vehicle charging decision model is met, it means that the electric vehicle needs to be charged when entering the service area.

[0090] The prediction module includes:

[0091] A time calculation unit, configured to calculate a charging start time and a charging end time for each electric vehicle that needs to be charged based on the time when the electric vehicle enters the highway;

[0092] A power-time determination unit, which determines a charging power-time relationship model for each electric vehicle that needs to be charged according to the charging start time and charging end time of each electric vehicle that needs to be charged;

[0093] The charging load calculation unit is used to calculate the charging power of each electric vehicle that needs to be charged in each time period to obtain the charging load of each time period in the service area.

[0094] The time calculation unit is Calculate the charging start time for each electric vehicle that needs to be charged, by Calculate the charging end time of each electric vehicle that needs to be charged, where: and Respectively The charging start time and charging end time of each electric vehicle; For the The time it takes for an electric car to enter the highway, For the Electric vehicles arrived The distance traveled when visiting a service area, For the The speed of an electric car; For the Electric car in the The charging time of each service area is expressed as: , For the Electric vehicles arrived The state of charge at the service area, represents the battery capacity of the nth electric car, For the The charging power of an electric vehicle.

[0095] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the charging load prediction method for a chain highway electric vehicle charging scenario of the first embodiment are implemented.

[0096] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the charging load prediction method for a chain highway electric vehicle charging scenario of the first embodiment are implemented.

[0097] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction method, which is implemented in the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0101] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A charging load prediction method for chain highway electric vehicle charging scenarios, characterized by: The following steps are involved: Determine the time when electric vehicles enter the expressway and the state of charge at the starting point of entering the expressway based on the data of electric vehicles entering the expressway during the day; For each service area on the highway, an electric vehicle charging decision model based on the state of charge at the starting point of entering the highway is used to determine whether each electric vehicle should be charged when entering the service area. If charging is required, the charging power of the electric vehicle is calculated based on the time the electric vehicle enters the highway, and the charging power of all charged electric vehicles is added together to obtain the charging load of the electric vehicles in the service area. The electric vehicle charging decision model is specifically as follows: ,in, For the Electric vehicles arrived The state of charge of the electric vehicle when entering the service area is determined by the state of charge of the electric vehicle at the starting point of the highway. For the Electric vehicles arrived The distance traveled when there are service areas is When it is the last service area, is the total length of the expressway, It represents the amount of electricity consumed by the nth electric car for every 1 km it travels. represents the battery capacity of the nth electric car, is the charging coefficient, which is defined as the lowest value of the charge state of the electric vehicle that the user can accept when driving from the current service area to the next service area; when the electric vehicle charging decision model is met, it means that the electric vehicle needs to be charged when entering the service area.

2. The charging load prediction method for chain highway electric vehicle charging scenarios according to claim 1 is characterized in that: Determining the time when an electric vehicle enters an expressway based on the data of electric vehicles entering the expressway during the day specifically includes: Based on the daily traffic flow data of electric vehicles on the highway, a probability distribution model of the time when electric vehicles enter the highway is generated; Based on the probability distribution model of the time when electric vehicles enter the highway, the Monte Carlo method is used to obtain the time when electric vehicles enter the highway within a day.

3. The charging load prediction method for chain highway electric vehicle charging scenarios according to claim 2 is characterized in that: The method of generating a probability distribution model of the time when electric vehicles enter the highway based on the traffic flow data of electric vehicles on the highway within a day specifically includes: Construct a traffic flow matrix based on the number of electric vehicles entering the highway per hour; Calculate the probability density matrix of electric vehicles based on the traffic flow matrix; Based on the distribution characteristics of the probability density matrix of electric vehicles, a probability distribution model of the time when electric vehicles enter the highway within a day is obtained.

4. The charging load prediction method for chain highway electric vehicle charging scenarios according to claim 2 is characterized in that: The method uses the Monte Carlo method based on the time probability distribution model of electric vehicles entering the highway to obtain the time when electric vehicles enter the highway within a day. Specifically, based on the time probability distribution model of electric vehicles entering the highway, the Monte Carlo method is used for random sampling to generate random number samples uniformly distributed within a preset range, and the uniformly distributed random number samples are inversely transformed through the time probability distribution model of the highway to convert the uniformly distributed random number samples into samples of the time probability distribution model of the highway to obtain the time when electric vehicles enter the highway within a day.

5. The charging load prediction method for chain highway electric vehicle charging scenarios according to claim 1 is characterized in that: The method of determining the state of charge of the starting point of the expressway according to the data of electric vehicles entering the expressway within a day is specifically as follows: using a uniform distribution model to determine the state of charge of the starting point of the expressway within a day.

6. The charging load prediction method for chain highway electric vehicle charging scenarios according to claim 1 is characterized in that: The calculation of the charging power of the electric vehicle and the superposition of the charging powers of all the charged electric vehicles to obtain the charging load of the electric vehicles in the service area specifically include: Calculate the charging start time and charging end time of each electric vehicle that needs to be charged based on the time when the electric vehicle enters the highway; Determining a charging power and time relationship model for each electric vehicle that needs to be charged according to a charging start time and a charging end time of each electric vehicle that needs to be charged; The charging power of each electric vehicle that needs to be charged in each time period is calculated to obtain the charging load of the service area in each time period.

7. The charging load prediction method for chain highway electric vehicle charging scenarios according to claim 6 is characterized in that: The calculation method of the charging start time of each electric vehicle that needs to be charged is: The calculation method of the charging end time of each electric vehicle that needs to be charged is: ,in, and Respectively The charging start time and charging end time of each electric vehicle; For the The time it takes for an electric car to enter the highway, For the Electric vehicles arrived The distance traveled when visiting a service area, For the The speed of an electric car; For the Electric car in the The charging time of each service area is expressed as: , For the Electric vehicles arrived The state of charge at the service area, represents the battery capacity of the nth electric car, For the The charging power of an electric vehicle.

8. A charging load prediction device for chain highway electric vehicle charging scenarios, characterized in that: include: a determination module, for determining the time when the electric vehicle enters the expressway and the state of charge of the starting point of entering the expressway based on the data of the electric vehicles entering the expressway during the day; The prediction module is used to determine, for each service area on the highway, whether each electric vehicle should be charged when entering the service area using an electric vehicle charging decision model constructed based on the charge state at the starting point of entering the highway. If charging is required, the charging power of the electric vehicle is calculated based on the time when the electric vehicle enters the highway, and the charging power of all charged electric vehicles is added together to obtain the charging load of the electric vehicles in the service area. The electric vehicle charging decision model is specifically as follows: ,in, For the Electric vehicles arrived The state of charge of the electric vehicle when entering the service area is determined by the state of charge of the electric vehicle at the starting point of the highway. For the Electric vehicles arrived The distance traveled when there are service areas is When it is the last service area, is the total length of the expressway, It represents the amount of electricity consumed by the nth electric car for every 1 km it travels. represents the battery capacity of the nth electric car, is the charging coefficient, which is defined as the lowest value of the charge state of the electric vehicle that the user can accept when driving from the current service area to the next service area; when the electric vehicle charging decision model is met, it means that the electric vehicle needs to be charged when entering the service area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the charging load prediction method for the chain highway electric vehicle charging scenario as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the charging load prediction method for a chain highway electric vehicle charging scenario as described in any one of claims 1 to 7 are implemented.

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