Charging control method and device for photovoltaic charging station

Abstract

The present invention relates to the field of charging control technology, and specifically, to a charging control method and device for a photovoltaic charging station. It includes capturing the dynamic changes in the values ​​of influencing variables, combining them with actual charging needs, and obtaining the judgment priority of each influencing variable in different time periods; capturing the dynamic changes in the values ​​of influencing variables, combining them with actual charging needs, and obtaining the judgment priority of each influencing variable in different time periods. The present invention updates the weight scores of the hybrid energy of the charging station in real time, and converts the hybrid energy according to the weights to adapt to the charging needs of different service areas in different time periods. It can avoid congestion, maximize the use of photovoltaic power generation, reduce operating costs, and achieve sustainable development of green energy.

Description

Technical Field

[0001] The present invention relates to the field of charging control technology, and in particular to a charging control method and device for a photovoltaic charging station. Background Art

[0002] The photovoltaic charging station is a comprehensive energy facility that integrates photovoltaic power generation, energy storage system and electric vehicle charging piles. Its core goal is to provide low-carbon, efficient and flexible charging services for electric vehicles through the synergy of renewable energy and energy storage technology.

[0003] In order to meet the charging demand, the existing photovoltaic storage charging stations need to adopt a variety of hybrid energy sources for charging supply, including photovoltaic power supply among new energy sources and grid power supply. That is, when photovoltaic power supply cannot meet the current charging demand, the grid power supply is directly supplied to the vehicle. This is because photovoltaic power supply is affected by external conditions and has poor stability. However, compared with grid power supply, the advantage of photovoltaic power supply is that it is a green energy, and grid power supply needs to consider the grid electricity price in the current time period and can only be purchased for power supply. Therefore, photovoltaic power supply needs to be given priority when powering the vehicle.

[0004] At the same time, the power supply speeds of different hybrid energy sources in the photovoltaic storage charging station are different. When dealing with the power supply work in the service area, it is necessary to consider the current charging vehicle flow to prevent the charging speed from being too slow, causing congestion in the charging area, and then causing congestion in the entire service area.

[0005] Therefore, reasonable planning of the distribution and conversion of different energy sources in photovoltaic charging stations has become an urgent problem that needs to be solved today. Summary of the invention

[0006] The purpose of the present invention is to provide a charging control method and device for a photovoltaic charging station to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, one of the objects of the present invention is to provide a charging control method for a photovoltaic charging station, comprising the following steps:

[0008] S1. Monitor the charging status of the current charging station and obtain the real-time charging efficiency during the charging process , and the charging efficiency Make a determination;

[0009] When charging efficiency If the charging demand of the charging vehicle is not met, the photovoltaic power supply of the hybrid energy of the charging station will not be responded;

[0010] When charging efficiency If the charging requirements of the charging vehicle are met, then jump to step S2;

[0011] S2, obtaining the light storage status of the charging station and the influencing variables under the charging status;

[0012] S3. Collect the traffic flow fluctuations in the service area and predict the peak charging time period of the charging station , valley time period And the mean time period ;

[0013] S4. Capture the dynamic changes in the values ​​of influencing variables, and obtain the judgment priority of each influencing variable in different time periods in combination with the actual charging demand;

[0014] S5. Based on the determination results of the influencing variables, the power supply priority of the hybrid energy of the charging station is obtained.

[0015] As a further improvement of the technical solution, the charging demand in S1 includes the output value of the photovoltaic array, the power of the adapted charging vehicle, and the stable values ​​of the voltage and current under the charging state;

[0016] The calculation formula of the photovoltaic array output value is as follows:

[0017] ;

[0018] in is the photovoltaic array output value, The conversion efficiency of the battery components of the charging station, is the photovoltaic array area, is the solar radiation on the inclined surface of the photovoltaic module, and T is the current temperature.

[0019] As a further improvement of the technical solution, the hybrid energy of the charging station in S1 includes photovoltaic power supply, energy storage power supply and grid power supply;

[0020] Among them, photovoltaic power supply is that photovoltaic modules convert sunlight into photovoltaic electricity, and directly supply power to vehicles through photovoltaic electricity;

[0021] Energy storage power supply stores photovoltaic power in energy storage equipment or converts photovoltaic power into other forms of energy for storage, and directly supplies power to vehicles through stored power;

[0022] The grid power supply is a traditional power system that completes vehicle power supply through the transmission of voltage and current.

[0023] As a further improvement of the technical solution, the influencing variables in S2 include charging speed, average power supply, grid electricity price on the day, and energy storage capacity;

[0024] Among them, the charging speed is the ratio of the total amount of electricity charged by the vehicle to the charging time;

[0025] The average power supply is the ratio of historical power consumption to historical charging vehicles;

[0026] The power grid electricity price on that day is the unit price per kilowatt-hour of electricity under the power grid supply status;

[0027] The energy storage capacity is the total energy storage capacity under the energy storage power supply state.

[0028] As a further improvement of the technical solution, the method for predicting the time period of the charging station in S3 includes the following steps:

[0029] S3.1. Obtain the number of charging stations in the current service area;

[0030] S3.2. Obtain the number of vehicles waiting in line and the maximum capacity of the charging area, calculate the ratio of the number of vehicles waiting in line to the maximum capacity of the charging area, and mark it as the capacity ratio ;

[0031] S3.3. Formulate the accommodation section , as well as , and with the peak time period , valley time period And the mean time period Perform one-to-one matching;

[0032] S3.4. Determine the accommodation ratio Belongs to the segment and matches the corresponding time period.

[0033] As a further improvement of the technical solution, the method for obtaining the determination priority of each influencing variable in different time periods in S4 includes the following steps:

[0034] S4.1. Collect historical power supply information and obtain the weights of different influencing variables in the corresponding time period influencing power supply work;

[0035] S4.2. Arrange the influencing variables in different time periods in order of weight.

[0036] As a further improvement of the technical solution, the method for obtaining the power supply priority of the hybrid energy of the charging station in S5 includes the following steps:

[0037] S5.1. According to the weight sorting, the initial weight scores of each rank are specified, and the thresholds of the influencing variable values ​​corresponding to each initial weight score are formulated to determine the value of the influencing variable and the size of the threshold;

[0038] When the value of the influencing variable is greater than the threshold, the current influencing variable is assigned an initial weight score;

[0039] When the value of the influencing variable is less than the threshold, jump to step S5.2;

[0040] S5.2. Define error variables , error variable The value range is ;

[0041] S5.3. Calculate the real-time weight score of each influencing variable = initial weight score and error variable The product of

[0042] S5.4. Count the sum of the weighted scores of the influencing variables corresponding to each hybrid energy source, and compare them. Mark the hybrid energy source with the highest sum of weighted scores as the most suitable hybrid energy source for the current time period, and replace it adaptively.

[0043] As a further improvement of this technical solution, the error variable defined in S5.2 is The method comprises the following steps:

[0044] S5.2.1. Define the unit value change of each influencing variable ;

[0045] S5.2.2. Calculate the difference between the influencing variable value and the threshold, and calculate the difference and the unit value change Multiples, marked as ;

[0046] S5.2.3. Establishing initial error variables and unit change error variable , calculate the real-time error variable = initial error variable -Unit change error variable and The product of .

[0047] The second object of the present invention is to provide a device for implementing a charging control method of a photovoltaic charging station, comprising an application layer, a platform layer, a network layer and a terminal layer;

[0048] The terminal layer is used to monitor the changes in vehicle flow in the service area and the charging status of the charging station. In the present invention, a camera monitoring device is mainly used to obtain the vehicle flow situation in the service area in real time;

[0049] The network layer is used to build a network channel, receive monitoring data from the terminal layer, and transmit the monitoring data to the platform layer;

[0050] The platform layer processes and analyzes the monitoring data, obtains the hybrid energy distribution relationship of the charging station in the current service area, matches the corresponding hybrid energy for power supply in different time periods, and transmits the processed instruction information to the application layer;

[0051] The application layer is used to execute instruction information and make hybrid energy charging changes.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] In the charging control method and device of the photovoltaic charging station, the weight score of the hybrid energy of the charging station is updated in real time, and the hybrid energy is converted according to the weight to adapt to the charging needs of different service areas in different time periods. This can avoid the occurrence of congestion and maximize the use of photovoltaic power generation, reduce operating costs, and achieve sustainable development of green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is the overall process step diagram of the present invention;

[0055] Figure 2 A diagram showing the steps of a method for predicting a time period of a charging station according to the present invention;

[0056] Figure 3 A step diagram of a method for obtaining the determination priority of each influencing variable in different time periods according to the present invention;

[0057] Figure 4 A step diagram of a method for obtaining a power supply priority of hybrid energy of a charging station according to the present invention;

[0058] Figure 5 The error variables defined in the present invention are Method steps diagram;

[0059] Figure 6 It is the overall device structure diagram of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] See also Figure 1 As shown, one of the purposes of the present invention is to provide a charging control method for a photovoltaic charging station, the specific contents of which are as follows:

[0062] This method can adapt to the charging control of energy distribution supply under different power supply conditions. First of all, for photovoltaic power supply, only when it reaches a stable state can it be considered to respond to the start, otherwise it will seriously affect the charging efficiency. Therefore, it is necessary to monitor the charging status of the current charging station and obtain the real-time charging efficiency during the charging process. , and the charging efficiency Make a determination;

[0063] When charging efficiency If the charging demand of the charging vehicle is not met, the photovoltaic power supply of the hybrid energy of the charging station will not be responded, where the hybrid energy of the charging station includes photovoltaic power supply, energy storage power supply and grid power supply;

[0064] Only when the charging efficiency Only when the charging needs of charging vehicles are met can photovoltaic power supply be selected in conjunction with other energy sources.

[0065] It is worth noting that the charging demand in the present invention includes the photovoltaic array output value, the power of the charging vehicle, and the stable values ​​of voltage and current under charging state. The photovoltaic array output value calculation formula here is as follows:

[0066] ;

[0067] in is the photovoltaic array output value, The conversion efficiency of the battery components of the charging station, is the photovoltaic array area, is the solar radiation on the inclined surface of the photovoltaic module, and T is the current temperature.

[0068] In terms of the power that is suitable for charging vehicles, fast charging of vehicles usually requires a power of tens to hundreds of kilowatts. This power state can only be met under sufficient sunlight. Therefore, if the current charging power cannot reach the above range, the charging efficiency will not be able to meet the current vehicle's fast power supply work.

[0069] For the stable values ​​of voltage and current under charging state, under different charging conditions, the corresponding charging voltage and current have a stable range of variation. When exceeding this range, the corresponding charging efficiency will decrease. Therefore, the stable values ​​of voltage and current are also the reference standards for measuring whether the current photovoltaic power supply meets the power supply demand.

[0070] Different types of photovoltaic storage charging stations correspond to the photovoltaic array output value, the power of the adapted charging vehicle, and the stable values ​​of the voltage and current in the charging state. Therefore, the corresponding values ​​need to be obtained for judgment during the specific judgment work. Only when the photovoltaic array output value, the power of the adapted charging vehicle, and the stable values ​​of the voltage and current in the charging state meet the corresponding values, can the photovoltaic power supply be determined as the reference energy power supply.

[0071] Since the three energy sources are affected by different conditions, when allocating the three power supply methods, it is necessary to obtain the photovoltaic storage status of the charging station and the influencing variables under the charging status, including charging speed, average power supply, daily grid electricity price and energy storage capacity.

[0072] Different variables need to be considered under different traffic conditions. For example, when the number of vehicles waiting to be charged is about to exceed the number of vehicles parked in the service area, the first priority is the charging speed. Therefore, it is necessary to predict the number of vehicles parked in the service area in advance, that is, to collect the floating state of the traffic flow in the service area and predict the peak charging time period of the charging station. , valley time period And the mean time period , and capture the dynamic changes of the values ​​of the influencing variables, and combine them with the actual charging needs to obtain the judgment priority of each influencing variable in different time periods.

[0073] like Figure 2 As shown, the method for predicting the time period of a charging station includes the following contents:

[0074] First, get the number of charging stations in the current service area, that is, how many vehicles can be charged at the same time, get the number of vehicles waiting in line, and get the maximum capacity of the charging area. Calculate the ratio of the number of vehicles waiting in line to the maximum capacity of the charging area, and mark it as the capacity ratio. , formulate the accommodation section , as well as , different accommodation sections correspond to different time periods, where Corresponding valley time period , Corresponding mean time period , Corresponding peak time period , determine the accommodation ratio Belongs to the segment and matches the corresponding time period;

[0075] It is worth noting that different service areas have different power supply requirements, and the corresponding maximum capacity of the charging area is different. The final accommodation sections will also be different. Therefore, when defining the accommodation sections, it is necessary to determine them based on the actual situation of the service area.

[0076] In addition, if Figure 3 As shown in the figure, in the process of obtaining the determination priority of each influencing variable in different time periods, it is necessary to first collect the historical power supply situation and obtain the weight of different influencing variables affecting the power supply work in the corresponding time period. The traffic volume that needs to be powered is small, as it is during the peak period. To store electric energy, the energy storage capacity is given priority to this influencing variable, that is, photovoltaic electric energy storage is required during the power supply process. At this time, if photovoltaic power supply cannot meet the storage demand, that is, photovoltaic power supply can only meet the current vehicle power supply demand and cannot generate excess electric energy for storage, then it is necessary to replace the grid power supply, and the electric energy generated by the photovoltaic components is used for storage until the energy storage device stores full electric energy, and then converted to photovoltaic power supply;

[0077] During this time period, the second thing to consider is the charging speed, to ensure the charging speed, then the grid electricity price of the day, and finally the average power supply. The internal weights are ranked from large to small, namely, energy storage capacity, charging speed, grid electricity price on the day, and average power supply;

[0078] In the mean time period In the calculation, this time period is required to have the longest time proportion compared to other time periods, so the first consideration is the grid electricity price of the day to save the overall power supply cost. In this time period, the weights of various influencing variables are the grid electricity price of the day, charging speed, energy storage capacity and average power supply from large to small;

[0079] Finally, for the peak time period At this time, what needs to be considered is how to quickly meet the power supply needs, ensure the normal power supply of the service area, and avoid congestion. Therefore, the charging speed is given priority, followed by the energy storage capacity and the average power supply. This is because if the current charging speed through energy storage power supply is the fastest, the energy storage power supply will be used for power supply processing during this period, but the storage capacity is limited. Although electricity can be supplied through photovoltaic modules, if the supply capacity is lower than the power supply, the storage capacity will gradually decrease. At this time, it is necessary to replace the other two power supply methods for power supply processing, that is, compare the charging speeds of the two power supply methods, and finally consider the power grid electricity price of the day.

[0080] Furthermore, after completing the priority determination of the influencing variables in each time period, it is necessary to carry out the conversion of various hybrid energies according to different situations, such as Figure 4 As shown, during the power supply process, the power supply priority of each energy supply needs to be updated in real time. First, the initial weight score of each sequence is specified according to the weight sorting, such as the average time period In the weight order, the grid electricity price, charging speed, energy storage capacity and average power supply are the same day, and the corresponding initial weight scores are as well as ,and > > > , and then each influence weight corresponds to a threshold. Only when the value of the influencing variable is not lower than the threshold, the corresponding weight score can be assigned. At the same time, when the value of the influencing variable is lower than the threshold, the error variable will be defined. , where the error variable The value range is , and the error variable It is inversely proportional to the difference between the influencing variable value and the threshold value, that is, the larger the difference, the smaller the corresponding error variable, such as Figure 5 As shown, the specific calculation steps are as follows:

[0081] First, define the unit value change of each influencing variable ;

[0082] When the value of the influencing variable is lower than the threshold, the difference between the two is obtained, and the difference and the unit value change are calculated. Multiples (round off if not exactly divided), marked as ;

[0083] Formulate initial error variables and unit change error variable , calculate the real-time error variable = initial error variable Unit change error variable and The product of

[0084] It is worth noting that when the error variable When <0, it means that the current mixed energy is not suitable for the current time period and the remaining mixed energy needs to be replaced.

[0085] The corresponding weight score = initial weight score and error constant The product of the weighted scores of the influencing variables corresponding to each hybrid energy is calculated and compared. The hybrid energy with the highest weighted score is marked as the most suitable hybrid energy for the current time period, and is replaced adaptively. For example, the average time period In the weight order, the grid electricity price, charging speed, energy storage capacity and average power supply are the same day, and the corresponding initial weight scores are as well as ,and > > > , since the grid electricity price of the day is considered only when the grid is supplying power, for photovoltaic power supply and energy storage power supply, the corresponding initial weight scores are the same and both are higher than the grid power supply, so in the mean time period The grid power supply is not considered in the process. In order to make further selection, it is necessary to determine the weight scores of the other three influencing variables and calculate the final sum of the weight scores. When the final sum of the weight scores of photovoltaic power supply is greater than the sum of the weight scores of energy storage power supply, photovoltaic power supply is selected as the current average time period. The power supply energy within the

[0086] It is worth noting that when the final weight score of energy storage power supply is greater than the weight score of photovoltaic power supply, in the initial state, energy storage power supply is selected as the current average time period. At the same time, since the energy storage power supply has limited energy storage capacity, and when the average power supply exceeds the energy supply of the photovoltaic components, the corresponding energy storage capacity will gradually decrease, that is, the sum of the corresponding weight scores will decrease until it is lower than the sum of the weight scores of the photovoltaic power supply. At this time, a secondary conversion is required to select the photovoltaic power supply as the current average time period. The power supply energy in the system is used until the energy storage capacity is restored to its initial state.

[0087] like Figure 6 As shown, a device for a charging control method of a photovoltaic charging station is also provided, which includes an application layer, a platform layer, a network layer and a terminal layer;

[0088] The terminal layer is used to monitor the changes in vehicle flow in the service area and the charging status of the charging station. In the present invention, the camera monitoring equipment is mainly used to obtain the vehicle flow in the service area in real time, that is, to distinguish fuel vehicles from new energy vehicles by new energy license plates, and to divide the charging area, waiting area and queuing area for zone monitoring, and to collect the background power supply data of the charging station in real time, and obtain the corresponding photovoltaic power generation, energy storage power and power grid supply status (including the power grid electricity price on the day) and other data information;

[0089] The network layer is used to build a network channel, receive monitoring data from the terminal layer, and transmit the monitoring data to the platform layer, using the 5G network to build a network channel and transmit the monitoring data of the terminal layer in real time;

[0090] The platform layer processes and analyzes the monitoring data, obtains the hybrid energy distribution relationship of the charging stations in the current service area, matches the corresponding hybrid energy for power supply in different time periods, and transmits the processed instruction information to the application layer, that is, updates the weight scores of the three power supply modes of photovoltaic power supply, energy storage power supply and grid power supply in different time periods in real time, and uses the power supply mode with the highest weight score as the best power supply mode in the current time period, and performs adaptive power supply mode replacement;

[0091] The application layer is used to execute instruction information and make hybrid energy charging changes.

[0092] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A charging control method for a photovoltaic charging station, characterized in that: The steps include: S1. Monitor the charging status of the current charging station and obtain the real-time charging efficiency during the charging process , and the charging efficiency Make a determination; When charging efficiency If the charging demand of the charging vehicle is not met, the photovoltaic power supply of the hybrid energy of the charging station will not be responded; When charging efficiency If the charging requirements of the charging vehicle are met, then jump to step S2; The charging requirements in S1 include the output value of the photovoltaic array, the power of the adapted charging vehicle, and the stable values ​​of the voltage and current under the charging state; The calculation formula of the photovoltaic array output value is as follows: ； in is the photovoltaic array output value, The conversion efficiency of the battery components of the charging station, is the photovoltaic array area, is the solar radiation on the inclined surface of the photovoltaic module, and T is the current temperature; S2, obtaining the light storage status of the charging station and the influencing variables under the charging status; S3. Collect the traffic flow fluctuations in the service area and predict the peak charging time period of the charging station , valley time period And the mean time period ; S4. Capture the dynamic changes in the values ​​of influencing variables, and obtain the judgment priority of each influencing variable in different time periods in combination with the actual charging demand; The method for obtaining the determination priority of each influencing variable in different time periods in S4 includes the following steps: S4.

1. Collect historical power supply information and obtain the weights of different influencing variables in the corresponding time period influencing power supply work; S4.

2. Arrange the influencing variables in different time periods in order of weight; S5. Combining the determination results of the influencing variables, obtaining the power supply priority of the hybrid energy of the charging station; The method for obtaining the power supply priority of the hybrid energy of the charging station in S5 comprises the following steps: S5.

1. According to the weight sorting, the initial weight scores of each rank are specified, and the thresholds of the influencing variable values ​​corresponding to each initial weight score are formulated to determine the value of the influencing variable and the size of the threshold; When the value of the influencing variable is greater than the threshold, the current influencing variable is assigned an initial weight score; When the value of the influencing variable is less than the threshold, jump to step S5.2; S5.

2. Define error variables , error variable The value range is ; S5.

3. Calculate the real-time weight score of each influencing variable = initial weight score and error variable The product of S5.

4. Count the sum of the weighted scores of the influencing variables corresponding to each hybrid energy source, and compare them. Mark the hybrid energy source with the highest sum of weighted scores as the most suitable hybrid energy source for the current time period, and replace it adaptively.

2. The charging control method of the photovoltaic charging station according to claim 1, characterized in that: The hybrid energy of the charging station in S1 includes photovoltaic power supply, energy storage power supply and grid power supply; Among them, photovoltaic power supply is that photovoltaic modules convert sunlight into photovoltaic electricity, and directly supply power to vehicles through photovoltaic electricity; Energy storage power supply stores photovoltaic power in energy storage equipment or converts photovoltaic power into other forms of energy for storage, and directly supplies power to vehicles through stored power; The grid power supply is a traditional power system that completes vehicle power supply through the transmission of voltage and current.

3. The charging control method of the photovoltaic charging station according to claim 1, characterized in that: The influencing variables in S2 include charging speed, average power supply, grid electricity price on the day, and energy storage capacity; Among them, the charging speed is the ratio of the total amount of electricity charged by the vehicle to the charging time; The average power supply is the ratio of historical power consumption to historical charging vehicles; The power grid electricity price on that day is the unit price per kilowatt-hour of electricity under the power grid supply status; The energy storage capacity is the total energy storage capacity under the energy storage power supply state.

4. The charging control method of the photovoltaic charging station according to claim 1, characterized in that: The method for predicting the time period of the charging station in S3 comprises the following steps: S3.

1. Obtain the number of charging stations in the current service area; S3.

2. Obtain the number of vehicles waiting in line and the maximum capacity of the charging area, calculate the ratio of the number of vehicles waiting in line to the maximum capacity of the charging area, and mark it as the capacity ratio ; S3.

3. Formulate the accommodation section , as well as , and with the peak time period , valley time period And the mean time period Perform one-to-one matching, where Corresponding valley time period , Corresponding mean time period , Corresponding peak time period ; S3.

4. Determine the accommodation ratio Belongs to the segment and matches the corresponding time period.

5. The charging control method of the photovoltaic charging station according to claim 4, characterized in that: The error variable is defined in S5.2 The method comprises the following steps: S5.2.

1. Define the unit value change of each influencing variable ; S5.2.

2. Calculate the difference between the influencing variable value and the threshold, and calculate the difference and the unit value change Multiples, marked as ; S5.2.

3. Establishing initial error variables and unit change error variable , calculate the real-time error variable = initial error variable -Unit change error variable and The product of .

6. A device for implementing the charging control method of the photovoltaic charging station according to claim 1, characterized in that: Includes application layer, platform layer, network layer and terminal layer; The terminal layer is used to monitor the changes in vehicle flow in the service area and the charging status of the charging station. The terminal layer uses a camera monitoring device to obtain the vehicle flow situation in the service area in real time; The network layer is used to build a network channel, receive monitoring data from the terminal layer, and transmit the monitoring data to the platform layer; The platform layer processes and analyzes the monitoring data, obtains the hybrid energy distribution relationship of the charging station in the current service area, matches the corresponding hybrid energy for power supply in different time periods, and transmits the processed instruction information to the application layer; The application layer is used to execute instruction information and make hybrid energy charging changes.

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