A charging station vehicle charging power scheduling method

Abstract

This application relates to the technical field of charging power scheduling, and discloses a method for scheduling vehicle charging power at a charging station, including: S1, acquiring basic vehicle information, scenario information, and user information, and acquiring the priority of each charging vehicle; S2, acquiring the charging acceptance factor of each charging vehicle based on the basic vehicle information and scenario information, establishing an objective function based on the charging acceptance factors of all charging vehicles; performing rapid iteration after setting constraints to obtain the pre-allocated power for each charging vehicle, and smoothing the pre-allocated power to obtain the allocated power for each vehicle; S3, acquiring a dynamic period based on the basic vehicle information and priorities of all charging vehicles in the charging station, and performing step S2 according to the dynamic period. This invention allocates power to each vehicle based on priority, ensuring the total power of the charging station while maximizing the appropriate charging power for different charging vehicles.

Description

Technical Field

[0001] This application relates to the technical field of charging power scheduling, and in particular to a method for scheduling vehicle charging power at a charging station. Background Technology

[0002] With the rapid growth of electric vehicle ownership, public DC fast charging stations, as crucial energy replenishment infrastructure, are receiving increasing attention for their operational efficiency and user experience. In a typical multi-pile charging station, transformer capacity, power distribution lines, and heat dissipation capabilities jointly determine the station's total available power limit. When multiple electric vehicles are charging simultaneously, the sum of their power requests often exceeds the total power limit. Therefore, dynamic allocation of charging power is necessary to avoid overload and improve overall service capacity.

[0003] Existing charging station power allocation mainly employs the following technical solutions: First, fixed power allocation, where each charging pile is pre-assigned a fixed power value, which is supplied regardless of the actual needs of the vehicle. This method is simple to implement, but it cannot be adjusted according to the actual needs of the vehicle or the load situation within the station, easily leading to some piles being idle and some vehicles charging too slowly, resulting in low resource utilization. Second, average or proportional allocation, which distributes all available total power equally to vehicles currently charging, or according to a proportion of the vehicle's maximum requested power. This approach ignores the differentiated needs of vehicles such as remaining battery power and dwell time, resulting in vehicles urgently needing charging not receiving more power, and fails to incorporate operational strategies and user experience factors. Third, charging stations employ a two-stage strategy of "queue priority + power allocation," first determining the order in which vehicles obtain charging piles through a priority queue, and then allocating power according to a fixed proportion or average method. However, the lack of coordination between the two stages means that even if a charging vehicle has a lower priority than waiting vehicles, it may still occupy a higher power, causing high-priority vehicles to be unable to charge quickly even if they obtain a charging pile. Therefore, how to accurately and reasonably allocate power according to the status of different vehicles is the fundamental problem that this invention aims to solve. Summary of the Invention

[0004] To accurately and reasonably allocate power according to the different states of vehicles, this application provides a method for scheduling vehicle charging power at a charging station, which adopts the following technical solution:

[0005] A method for scheduling vehicle charging power at a charging station includes:

[0006] S1. Obtain basic vehicle information, scenario information, and user information, and obtain the priority of each charging vehicle;

[0007] S2. Obtain the charging acceptance factor of each charging vehicle based on the vehicle's basic information and scenario information. Establish an objective function based on the charging acceptance factors of all charging vehicles. After setting constraints, perform rapid iteration to obtain the pre-allocated power of each charging vehicle. Smooth the pre-allocated power to obtain the allocated power of each vehicle.

[0008] S3. Obtain the dynamic cycle based on the basic vehicle information and priority of all charging vehicles in the charging station, and proceed with step S2 according to the dynamic cycle.

[0009] Optionally, the vehicle basic information includes SOC, SOH, the maximum allowable rate of the battery, and the optimal efficiency rate of the battery;

[0010] The scenario information includes the charging temperature;

[0011] The process of obtaining the charging acceptance factor includes:

[0012] Obtain the SOC dependency factor corresponding to the scope of the SOC;

[0013] Health status factors were calculated based on SOH.

[0014] The temperature influence factor is calculated based on the charging temperature.

[0015] The charging rate limit factor is calculated based on the currently requested charging rate, the maximum allowable charging rate of the battery, and the optimal efficiency rate of the battery.

[0016] Obtain the cumulative product of the SOC dependence factor, health status factor, temperature influence factor, charging rate limiting factor, and voltage plateau saturation factor. ,pass Obtain the charging acceptance factor for each charging vehicle. , This is the minimum value for the charge acceptance factor.

[0017] Optionally, the process of establishing the objective function includes:

[0018] The charging power of each charging vehicle As a variable, the charging power of each vehicle The weighted charging power of each charging vehicle is obtained by multiplying the priority and charging acceptance factors.

[0019] According to charging power Given the power allocated to the charging vehicle at the previous moment, calculate the power change coefficient for each charging vehicle.

[0020] The weighted charging power of all charging vehicles is summed to obtain the baseline weighted power; the power change coefficients of all charging vehicles are summed, and the summed value of the power change coefficients is adjusted by the first smoothing coefficient to obtain the power change smoothing term.

[0021] The objective function is to subtract the power change smoothing term from the baseline weighted power.

[0022] Optionally, the pre-allocated power acquisition process for each charging vehicle includes:

[0023] Establish constraints, including:

[0024] The total power is calculated by summing the charging power of all charging vehicles, and a total power limit is set.

[0025] Set an upper limit and a lower limit for the charging power of each charging vehicle;

[0026] To perform rapid iterations, including:

[0027] Calculate the initial weighted demand for each charging vehicle;

[0028] Determine whether the cumulative charging power of all charging vehicles exceeds the total power limit:

[0029] If the limit is not exceeded, then the charging power of each charging vehicle is set to the upper limit value.

[0030] If the power exceeds the limit, a power ratio compression allocation will be performed to obtain... , , This is the lower limit of power;

[0031] Determine whether , This is the upper limit of power;

[0032] If yes, then record the excess amount. The excess amount Δ is redistributed according to the initial weighted demand to vehicles that have not yet reached their limit, iterating until all vehicles have reached their limit. ; Obtain pre-allocated power.

[0033] Optionally, the process of smoothing the pre-allocated power includes:

[0034] The second smoothing coefficient is obtained based on the SOC change rate. The allocated power for each vehicle is then calculated based on the second smoothing coefficient, the pre-allocated power, and the allocated power at the previous moment.

[0035] Optionally, the vehicle basic information includes SOC, battery capacity, current charging power, and task type;

[0036] The scenario information includes the waiting time before charging, the dwell time when the target battery level is exceeded, and the charging station scenario mode.

[0037] The user information includes membership level;

[0038] The priority acquisition process for each charging vehicle includes:

[0039] Priority factors are obtained based on SOC, current charging power, task type, pre-charging waiting time, dwell time exceeding the target charge level, and VIP level; the weighted ratio of the priority factors is obtained according to the charging station scenario mode, and the weighted sum of all priority factors is obtained according to the weighted ratio.

[0040] The penalty factor is determined based on the dwell time exceeding the target battery level.

[0041] The priority of each charging vehicle is obtained by subtracting the penalty factor from the weighted sum of the priority factors.

[0042] Optionally, the priority factors include power urgency, dwell time factor, absolute shortage factor, power efficiency factor, waiting fairness factor, membership level factor, and emergency task factor.

[0043] Optionally, the charging station scenario modes include normal mode, peak congestion mode, off-peak promotion mode, and emergency task mode.

[0044] Optionally, the process of obtaining the dynamic period includes:

[0045] Set the minimum allowable period tmin, the maximum allowable period tmax, and the reference period tb;

[0046] Determine if a preset event has occurred:

[0047] If a preset event occurs, the time point ts when the preset event occurs will be used as the start time point of the next dynamic cycle, and ts+tmin will be used as the end time point of the next dynamic cycle.

[0048] Otherwise, obtain the power change rate factor based on the rate of change of the total charging output power in the previous dynamic cycle; obtain the priority fluctuation factor based on the priority fluctuation status of all charging vehicles in the previous dynamic cycle; obtain the charging stage factor based on the proportion of charging vehicles in the constant voltage stage in the previous dynamic cycle; obtain the reference cycle based on the power change rate factor, priority fluctuation factor and charging stage factor; and determine the duration of the dynamic cycle based on the relationship between the reference cycle and the minimum and maximum allowable cycles.

[0049] Optionally, the preset event includes:

[0050] New vehicles are connected to charging stations;

[0051] The vehicle has finished charging and is disconnected;

[0052] The priority score of any vehicle changes more than the threshold.

[0053] Received power grid demand response instruction.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] This invention dynamically acquires the priority of each charging vehicle in a charging station and allocates power to each vehicle based on the priority. Through rapid iteration, it ensures the total power of the charging station while making different charging vehicles receive appropriate pre-charging power as much as possible. At the same time, by smoothing the pre-allocated power, it avoids the impact of power abrupt changes on the hardware. Through the dynamic cycle acquisition process, it can dynamically adjust according to the operating status of the charging station to achieve better overall performance. Attached Figure Description

[0056] Figure 1 This is a flowchart of the steps in the charging station vehicle charging power scheduling method of the present invention. Detailed Implementation

[0057] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0058] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0059] This application discloses a method for scheduling vehicle charging power at a charging station, referring to... Figure 1The process includes: S1. Obtaining basic vehicle information, scenario information, and user information, and obtaining the priority of each charging vehicle; S2. Obtaining the charging acceptance factor of each charging vehicle based on the basic vehicle information and scenario information, and establishing an objective function based on the charging acceptance factors of all charging vehicles; after setting constraints, performing rapid iteration to obtain the pre-allocated power of each charging vehicle, and smoothing the pre-allocated power to obtain the allocated power of each vehicle; S3. Obtaining the dynamic cycle based on the basic vehicle information and priority of all charging vehicles in the charging station, and performing step S2 according to the dynamic cycle; This embodiment dynamically obtains the priority of each charging vehicle in the charging station, allocates power to each vehicle based on the priority, and through rapid iteration, ensures the total power of the charging station while making different charging vehicles receive appropriate pre-charging power as much as possible; at the same time, through the smoothing of the pre-allocated power, the impact of power mutation on the hardware is avoided; in addition, through the process of obtaining the dynamic cycle, it is possible to dynamically adjust according to the operating status of the charging station to achieve better overall performance.

[0060] In one embodiment, a priority acquisition process for each charging vehicle is provided. The basic vehicle information used in this process includes SOC, battery capacity, current charging power, and task type; scenario information includes pre-charging waiting time, dwell time exceeding the target charge level, and charging station scenario mode; user information includes membership level; priority acquisition includes: firstly, obtaining priority factors based on SOC, battery capacity, current charging power, task type, pre-charging waiting time, dwell time exceeding the target charge level, and VIP level; priority factors include charge urgency, dwell time factor, absolute shortage factor, power efficiency factor, waiting fairness factor, membership level factor, and emergency task factor.

[0061] Among them, the urgency of power is determined based on the State of Charge (SOC). The lower the SOC, the higher the demand for power. Therefore, the calculation model for the urgency of power is as follows: ,in, The low battery reward factor is set to 0.3 in this embodiment. The value is 1 when the SOC is less than 15, and 0 otherwise. Through the low power reward strategy, extra points can be obtained when the charging power is low, thereby improving the rationality of power allocation.

[0062] Furthermore, the dwell time factor is determined based on the dwell time, and the calculation model for the dwell time factor is as follows: In the model This represents the baseline value for the stay time. A higher score is given for stay times below this baseline value. In this embodiment... =15min, This represents a reference value for the dwell time, in this embodiment. =30min, so the dwell time factor can encourage vehicles at charging stations to charge quickly and leave quickly.

[0063] The absolute shortage factor is based on the battery capacity B of the charging vehicle and the target charging capacity. And with SOC determined, its calculation model is as follows: Therefore, the larger the absolute deficit, the more time and resources are needed, which in turn increases the score of the absolute deficit factor. At the same time, since it is not advisable to over-prioritize under high load, logarithmic compression can make the score of the absolute deficit factor more objective and accurate.

[0064] Power efficiency factor It is equal to the ratio of the current charging power to the upper limit of the requested charging power. If the current power has dropped to below 80% of the requested power, it means that the charging process is coming to an end, and the power efficiency factor score will decrease at this time.

[0065] The fairness factor is based on the waiting time before charging. It is determined that its calculation model is as follows: When the waiting time before charging is 0, A score of 0 indicates that the longer the waiting time before charging, the higher the score, which can improve the fairness of waiting from the perspective of charging station operation.

[0066] The membership level factor and emergency task factor are determined based on whether the user is a member and whether the task is an emergency, respectively. When the user is a member, the membership level factor is 1, otherwise it is 0; when the task is an emergency, the emergency task factor is 1, otherwise it is 0. The weighting ratio of priority factors is obtained according to the charging station scenario mode, and the weighted sum of all priority factors is obtained according to the weighting ratio. The charging station scenario modes include regular mode, peak congestion mode, off-peak promotion mode and emergency task mode. Different charging station scenario modes have different weighting ratios. The table below is a weighting factor selection comparison table integrated based on historical charging station data in this embodiment.

[0067] Weighting factors Battery urgency Dwell time factor Absolute deficit factor Power efficiency factor Waiting for fairness Membership level factor Emergency mission factor Normal mode 2.0 1.5 0.5 1.0 1.0 0.3 3.0 Peak congestion mode 2.5 2.0 0.2 1.5 1.2 0.1 3.0 Off-peak promotion model 1.0 1.0 0.5 0.5 0.5 0.5 2.0 Emergency Mission Mode 1.0 0.5 0.2 0.5 0 0 10.0

[0068] Then, based on the dwell time exceeding the target battery level. The penalty factor is obtained, which mainly targets charging vehicles that still occupy charging positions after reaching the target charging level of SOC. Its calculation model is as follows: The priority factor is obtained by subtracting the penalty factor from the weighted sum of the priority factors. The priority of each charging vehicle is then used to facilitate the reasonable allocation of power for each charging vehicle in the future.

[0069] In one embodiment, a process for obtaining a charging acceptance factor is provided. This process uses basic vehicle information including State of Charge (SOC), State of Health (SOH), the maximum allowable battery rate, and the optimal battery efficiency rate; scenario information includes charging temperature; the acquisition process includes: firstly, obtaining a SOC dependency factor based on the range of SOC; this can be obtained through a SOC dependency factor lookup function, which is: This mapping function allows for full power acceptance in the early stages, a slow decrease in the middle stages, and an exponential decay in the later stages, thus better matching the charging curve of lithium batteries.

[0070] The health status factor is then calculated based on the State of Health (SOH). SOH represents battery health, with a value ranging from 0 to 1, and can be read from the Battery Management System (BMS). The calculation model for the health status factor is as follows: ,in, The value is 1.5, which is used to evaluate the power acceptance capability of a charging vehicle based on battery health.

[0071] Then, the temperature influence factor is calculated based on the charging temperature. Since the optimal charging temperature for lithium batteries is 15~35°C, both excessively low and excessively high temperatures will limit the charging power. Therefore, this embodiment uses an asymmetric Gaussian function: ,in, , The degree to which power is acceptable is judged by temperature.

[0072] Then, based on the currently requested charging rate Maximum allowable rate of battery and optimal battery efficiency The charging rate limiting factor is calculated to describe the deviation of the current requested charging rate from the battery's optimal charging rate. The calculation model is as follows: When the requested power exceeds the optimal power, the acceptance rate decreases linearly-exponentially; when the requested power is below the optimal power, the factor is 1, which means that the battery can fully accept that power.

[0073] Finally, the cumulative product of the SOC dependence factor, health status factor, temperature influence factor, charging rate limitation factor, and voltage plateau saturation factor is obtained. In power distribution, to avoid excessively penalizing vehicles with low state of equilibrium (SOH) or high temperatures, by... Implement lower limit protection. As the minimum charging acceptance factor, this embodiment sets it to 0.2, thereby obtaining the charging acceptance factor for each charging vehicle. .

[0074] In one embodiment, the process of establishing the objective function includes: calculating the charging power of each charging vehicle. As a variable, the charging power of each vehicle The weighted charging power of each vehicle is obtained by multiplying the weighted power by the priority and charging acceptance factors. Then, based on the charging power... and the power allocated to the charging vehicle at the previous moment. Calculate the power variation coefficient for each charging vehicle. The weighted charging power of all charging vehicles is summed to obtain a baseline weighted power; the power variation coefficients of all charging vehicles are summed and then passed through a first smoothing coefficient. Adjust the accumulated value of the power change coefficient to obtain the power change smoothing term. The objective function is to subtract the power change smoothing term from the baseline weighted power, i.e., the objective function is: The first smoothing coefficient in this embodiment Set to 0.1, in the objective function To prevent division by zero, let n be the number of charging vehicles, i∈[1,n]. The priority score for the i-th charging vehicle. Let be the charging acceptance factor for the i-th charging vehicle.

[0075] Based on the objective function, the process of obtaining the pre-allocated power for each charging vehicle includes: first, establishing constraints, including: using the cumulative charging power of all charging vehicles as the total power, and setting an upper limit for the total power. ,Right now The total power limit is set based on the relevant operating parameters of the charging station; then, a power limit value is set for the charging power of each charging vehicle. and power lower limit The lower power limit ensures the minimum charging rate, while the upper power limit is set according to the vehicle's performance parameters.

[0076] Then, rapid iterations are performed, including: calculating the initial weighted demand for each charging vehicle; the weighted demand weights for each vehicle are: Determine if the cumulative charging power of all charging vehicles exceeds the total power limit: if not, set the charging power of each charging vehicle to the power limit; if it exceeds, perform power ratio compression allocation, through: get , Ensure that each vehicle reaches at least the minimum power limit; determine whether... If so, then record the excess amount. The excess amount Δ is redistributed according to the initial weighted demand to vehicles that have not yet reached their limit, iterating until all vehicles have reached their limit. Obtain pre-allocated power .

[0077] In one embodiment, the process of smoothing the pre-allocated power includes: obtaining a second smoothing coefficient based on the SOC change rate. Second smoothing coefficient The range is [0.3, 0.7]. The higher the SOC change rate, the larger the second smoothing coefficient, thus improving the rapid response capability. The allocated power for each vehicle is calculated based on the second smoothing coefficient, the pre-allocated power, and the allocated power at the previous moment. The smoothing process is as follows: This process can prevent power oscillations.

[0078] In one embodiment, the process of obtaining the dynamic cycle includes: first, setting the minimum allowable cycle tmin, the maximum allowable cycle tmax, and the base cycle tb. These parameters are set based on empirical data from different scenario types. Then, it is determined whether a preset event has occurred. Preset events include: a new vehicle connecting to the charging pile, a vehicle completing charging and disconnecting, any vehicle's priority score changing beyond a threshold, and receiving a grid demand response command. If a preset event occurs, the time point ts of the preset event is used as the start time point of the next dynamic cycle, and ts+tmin is used as the end time point of the next dynamic cycle. That is, when a preset event occurs, power allocation is directly executed, and the next dynamic cycle is adjusted to the shortest possible value to achieve rapid adjustment. Otherwise, the change rate of the total charging output power in the previous dynamic cycle is used as the starting time point. The calculation model for the power change rate factor is as follows: , To ensure a fast response cycle, in this embodiment... =2s, For slow response periods, this embodiment =30s, , , These are the high, medium, and low thresholds for power change rate, set based on empirical data. In this embodiment, they are used in conjunction with these thresholds. =50kW / s, =20kW / s, =5kW / s.

[0079] Then, the priority fluctuation factor is obtained based on the priority fluctuation status of all charging vehicles in the previous dynamic cycle. The priority standard deviation of all charging vehicles in the previous dynamic cycle is compared with the threshold. The threshold is set according to empirical data. In this embodiment, the threshold is set to 2.0. If the standard deviation is greater than the threshold, the priority fluctuation factor is set to the fast response period. Otherwise, the priority fluctuation factor is set to the maximum allowable period tmax.

[0080] Then, the charging stage factor is obtained based on the proportion of charging vehicles in the constant voltage stage in the previous dynamic cycle. If the proportion of charging vehicles in the constant voltage stage exceeds half, the charging stage factor is set to a short period dedicated to the constant voltage stage. In this embodiment, the short period dedicated to the constant voltage stage is set to 3s; otherwise, the priority fluctuation factor is set to the maximum allowable period tmax.

[0081] Finally, a reference period is obtained based on the power change rate factor, priority fluctuation factor, and charging stage factor. The duration of the dynamic period is determined based on the relationship between the reference period and the minimum and maximum allowable periods. The minimum value among the power change rate factor, priority fluctuation factor, and charging stage factor is selected as the reference period. If the reference period is greater than or equal to the maximum allowable period tmax, the dynamic period is the maximum allowable period tmax; if the reference period is less than or equal to the minimum allowable period tmin, the dynamic period is the minimum allowable period tmin; otherwise, the dynamic period is the reference period. Through the above process, the duration of the cycle can be dynamically adjusted based on the state of the charging station, ensuring response speed while improving control stability and achieving better overall performance.

[0082] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for scheduling vehicle charging power at a charging station, characterized in that, include: S1. Obtain basic vehicle information, scenario information, and user information, and obtain the priority of each charging vehicle; S2. Obtain the charging acceptance factor of each charging vehicle based on the vehicle's basic information and scenario information. Establish an objective function based on the charging acceptance factors of all charging vehicles. After setting constraints, perform rapid iteration to obtain the pre-allocated power of each charging vehicle. Smooth the pre-allocated power to obtain the allocated power of each vehicle. S3. Obtain the dynamic cycle based on the basic vehicle information and priority of all charging vehicles in the charging station, and proceed with step S2 according to the dynamic cycle.

2. The method for scheduling vehicle charging power at a charging station according to claim 1, characterized in that, The vehicle's basic information includes SOC, SOH, the maximum allowable rate of the battery, and the optimal efficiency rate of the battery. The scenario information includes the charging temperature; The process of obtaining the charging acceptance factor includes: Obtain the SOC dependency factor corresponding to the scope of the SOC; Health status factors were calculated based on SOH. The temperature influence factor is calculated based on the charging temperature. The charging rate limit factor is calculated based on the currently requested charging rate, the maximum allowable charging rate of the battery, and the optimal efficiency rate of the battery. Obtain the cumulative product of the SOC dependence factor, health status factor, temperature influence factor, and charging rate limitation factor. ,pass Obtain the charging acceptance factor for each charging vehicle. , This is the minimum value for the charge acceptance factor.

3. The method for scheduling vehicle charging power at a charging station according to claim 1, characterized in that, The process of establishing the objective function includes: The charging power of each charging vehicle As a variable, the charging power of each vehicle The weighted charging power of each charging vehicle is obtained by multiplying the priority and charging acceptance factors. According to charging power Given the power allocated to the charging vehicle at the previous moment, calculate the power change coefficient for each charging vehicle. The weighted charging power of all charging vehicles is summed to obtain the baseline weighted power; the power change coefficients of all charging vehicles are summed, and the summed value of the power change coefficients is adjusted by the first smoothing coefficient to obtain the power change smoothing term. The objective function is to subtract the power change smoothing term from the baseline weighted power.

4. The method for scheduling vehicle charging power at a charging station according to claim 3, characterized in that, The pre-allocated power acquisition process for each charging vehicle includes: Establish constraints, including: The total power is calculated by summing the charging power of all charging vehicles, and a total power limit is set. Set an upper limit and a lower limit for the charging power of each charging vehicle; To perform rapid iterations, including: Calculate the initial weighted demand for each charging vehicle; Determine whether the cumulative charging power of all charging vehicles exceeds the total power limit: If the limit is not exceeded, then the charging power of each charging vehicle is set to the upper limit value. If the power exceeds the limit, a power ratio compression allocation will be performed to obtain... , , This is the lower limit of power; Determine whether , This is the upper limit of power; If yes, then record the excess amount. The excess amount Δ is redistributed according to the initial weighted demand to vehicles that have not yet reached their limit, iterating until all vehicles have reached their limit. ; Obtain pre-allocated power.

5. A method for scheduling vehicle charging power at a charging station according to claim 4, characterized in that, The process of smoothing pre-allocated power includes: The second smoothing coefficient is obtained based on the SOC change rate. The allocated power for each vehicle is then calculated based on the second smoothing coefficient, the pre-allocated power, and the allocated power at the previous moment.

6. The method for scheduling vehicle charging power at a charging station according to claim 1, characterized in that, The vehicle's basic information includes SOC, battery capacity, current charging power, and task type; The scenario information includes the waiting time before charging, the dwell time when the target battery level is exceeded, and the charging station scenario mode. The user information includes membership level; The priority acquisition process for each charging vehicle includes: Priority factors are obtained based on SOC, current charging power, task type, pre-charging waiting time, dwell time exceeding the target charge level, and VIP level; the weighted ratio of the priority factors is obtained according to the charging station scenario mode, and the weighted sum of all priority factors is obtained according to the weighted ratio. The penalty factor is determined based on the dwell time exceeding the target battery level. The priority of each charging vehicle is obtained by subtracting the penalty factor from the weighted sum of the priority factors.

7. A method for scheduling vehicle charging power at a charging station according to claim 6, characterized in that, The priority factors include power urgency, dwell time factor, absolute shortage factor, power efficiency factor, waiting fairness factor, membership level factor, and emergency task factor.

8. A method for scheduling vehicle charging power at a charging station according to claim 6, characterized in that, The charging station scenario modes include regular mode, peak congestion mode, off-peak promotion mode, and emergency task mode.

9. A method for scheduling vehicle charging power at a charging station according to claim 1, characterized in that, The process of obtaining the dynamic period includes: Set the minimum allowable period tmin, the maximum allowable period tmax, and the reference period tb; Determine if a preset event has occurred: If a preset event occurs, the time point ts when the preset event occurs will be used as the start time point of the next dynamic cycle, and ts+tmin will be used as the end time point of the next dynamic cycle. Otherwise, obtain the power change rate factor based on the rate of change of the total charging output power in the previous dynamic cycle; obtain the priority fluctuation factor based on the priority fluctuation status of all charging vehicles in the previous dynamic cycle; obtain the charging stage factor based on the proportion of charging vehicles in the constant voltage stage in the previous dynamic cycle; obtain the reference cycle based on the power change rate factor, priority fluctuation factor and charging stage factor; and determine the duration of the dynamic cycle based on the relationship between the reference cycle and the minimum and maximum allowable cycles.

10. A method for scheduling vehicle charging power at a charging station according to claim 9, characterized in that, The preset events include: New vehicles are connected to charging stations; The vehicle has finished charging and is disconnected; The priority score of any vehicle changes more than the threshold. Received power grid demand response instruction.

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