Charging pile control method and system for large-current new energy heavy truck

Abstract

The invention relates to the technical field of charging piles, in particular to a charging pile control method and system for a large-current new energy heavy truck, and the method comprises the steps: building a demand power fluctuation curve of a plurality of charging piles in a charging station based on the charging power data of the charging station at a plurality of historical time points; determining a power overall limit time period and overall limit power of the overall power limit time period based on the theoretical maximum power and the demand power fluctuation curve; determining individual limited power of each charging pile based on the temperature prediction data of the current day; constructing an optimization model based on the total limited power and the individual limited power, and solving the optimization model to obtain the optimal operation power of the plurality of charging piles in the charging station; and performing power limitation control based on the optimal operation power of the plurality of charging piles in the charging station. The total power of the charging station and the individual power of the charging station can be limited under the condition that charging is not affected, power failure of a trigger protection mechanism is avoided, heating is reduced, electric energy is saved, and heat dissipation pressure is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and in particular to a charging pile control method and system for a high-current new energy heavy-duty truck. Background Art

[0002] High-current heavy-duty truck charging piles are specially designed for new energy heavy-duty trucks. They have the characteristics of ultra-high power output and complex operating environment.

[0003] Due to the significant energy consumption of high-current heavy truck charging stations, unstable power exchange between the charging station and the upper grid can cause grid frequency fluctuations and even trigger power outages. Furthermore, as power increases, the heating of the charging station increases significantly, leading to a decrease in charging efficiency. Summary of the Invention

[0004] In view of this, an object of the present invention is to provide a charging pile control method and system for a large-current new energy heavy-duty truck to solve the problems in the background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A charging pile control method for a high-current new energy heavy-duty truck of the present invention comprises the following steps:

[0007] Obtain the theoretical maximum power that the charging station can obtain from the upper power grid, obtain the charging power data of multiple charging piles in the charging station at multiple historical time points, and obtain the current daily temperature forecast data;

[0008] Constructing a demand power fluctuation curve for multiple charging piles in a charging station based on the charging power data of the charging station at multiple historical time points;

[0009] Determining the overall power limit time period and the overall power limit of the overall power limit time period based on the theoretical maximum power and the required power fluctuation curve; and determining the individual power limit of each charging pile based on the temperature forecast data of the current day;

[0010] An optimization model is constructed based on the overall limited power and the individual limited power, and the optimization model is solved to obtain the optimal operating power of multiple charging piles in the charging station; and power limit control is performed based on the optimal operating power of multiple charging piles in the charging station.

[0011] In one embodiment of the present application, the charging power data includes the power values ​​of multiple charging piles. The power demand fluctuation curve of multiple charging piles in the charging station is constructed based on the charging power data of the charging station at multiple historical time points, including:

[0012] Calculate the total power at each historical time point Where i is the serial number of the charging pile, and t is the historical time point;

[0013] The total power of multiple historical time points Mapping into a two-dimensional coordinate system, wherein the two-dimensional coordinate system includes a daily time axis and a power data axis;

[0014] The total power of multiple historical time points in the two-dimensional coordinate system Perform density clustering to obtain multiple clusters;

[0015] Calculating the total power variance and the total power mean in each cluster, and removing outliers in each cluster based on the total power variance and the total power mean to obtain a target cluster, wherein the total power variance in the target cluster is less than or equal to a preset variance threshold;

[0016] The time range and total power mean of the target cluster are calculated, and the total power mean values ​​corresponding to the time ranges of multiple target clusters are fitted to obtain the demand power fluctuation curves of multiple charging piles in the charging station.

[0017] In one embodiment of the present application, outliers in each cluster are removed based on the total power variance and the total power mean to obtain a target cluster, including:

[0018] S1, comparing the total power variance with a set variance threshold;

[0019] S2, when the total power variance is greater than the set variance threshold, calculate the difference between the total power of each data point in the cluster and the total power mean, and remove the data point with the largest difference as an outlier, and return to S1 until the total power variance is less than or equal to the set variance threshold;

[0020] S3: When the total power variance is less than or equal to a set variance threshold, a target cluster is obtained.

[0021] In one embodiment of the present application, determining the overall power limited time period and the overall limited power of the overall power limited time period based on the theoretical maximum power and the required power fluctuation curve includes:

[0022] Based on the theoretical maximum power Construct an intercept line and map the intercept line to the coordinate system of the demand power fluctuation curve, wherein the intercept line is Where y is the Y-axis coordinate of the coordinate system, T represents the time period, and P0 is the redundancy;

[0023] Based on the intersection of the intercept line and the required power fluctuation curve as the overall power limit time period, the overall limited power of the overall power limit time period is

[0024] In one embodiment of the present application, determining the individual limited power of each charging pile based on the current daily temperature forecast data includes:

[0025] Extracting temperature values ​​at multiple predicted time points from the current daily temperature prediction data;

[0026] Input the temperature values ​​at multiple predicted time points and the set charging pile power into a pre-built charging pile heating prediction model to obtain the predicted temperature;

[0027] The predicted temperature is compared with a preset temperature threshold. When the predicted temperature is greater than the preset temperature threshold, the charging pile power is adjusted, and the current daily temperature prediction data and the set charging pile power are input into a pre-built charging pile heating prediction model to obtain the predicted temperature, until the target predicted temperature and the target charging pile power corresponding to the target predicted temperature are obtained, and the target charging pile power is used as the individual limited power.

[0028] In one embodiment of the present application, the method for constructing the charging pile heating prediction model includes:

[0029] Acquire sample data, wherein the sample data includes outdoor temperature, operating power of the charging pile, and temperature of the charging pile;

[0030] Constructing training data using the charging pile temperature as a label;

[0031] The artificial neural network is trained based on the training data until the loss function is fitted to obtain a charging pile heating prediction model, wherein the mathematical expression of the loss function is:

[0032]

[0033] In the formula, Loss is the loss, n is the amount of training data, i is the training data number, y i is the label of the i-th training data, is the predicted value of the i-th training data.

[0034] In one embodiment of the present application, constructing an optimization model based on the overall limited power and the individual limited power includes:

[0035] Constructing constraints, wherein the constraints include:

[0036]

[0037] 0≤P i ≤P max

[0038] Where, P i is the charging power of the i-th charging pile, P total_max is the overall limited power, P max Limit power for individuals;

[0039] Construct an objective function of minimizing heat and maximizing power, wherein the objective function is:

[0040]

[0041] Where λ is the trade-off parameter;

[0042] An optimization model is constructed based on the constraints and the objective function.

[0043] The present application also provides a charging pile control system for a high-current new energy heavy-duty truck, the system comprising:

[0044] The acquisition module is used to obtain the theoretical maximum power obtained by the charging station from the upper power grid, obtain the charging power data of multiple charging piles in the charging station at multiple historical time points, and obtain the temperature forecast data of the current day;

[0045] A feature extraction module is used to construct a demand power fluctuation curve for multiple charging piles in the charging station based on the charging power data of the charging station at multiple historical time points;

[0046] a limited power determination module, configured to determine an overall power limited time period and an overall limited power for the overall power limited time period based on the theoretical maximum power and the required power fluctuation curve; and to determine an individual limited power for each charging pile based on the current daily temperature forecast data;

[0047] A limit control module is configured to construct an optimization model based on the overall limit power and the individual limit power, and solve the optimization model to obtain the optimal operating power of multiple charging piles in the charging station; and perform power limit control based on the optimal operating power of multiple charging piles in the charging station.

[0048] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method as described in any one of the above items is implemented.

[0049] The present application also provides an electronic terminal, comprising: a processor and a memory;

[0050] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal performs any of the above methods.

[0051] The beneficial effects of the present invention are as follows: a charging pile control method and system for a large-current new energy heavy-duty truck of the present invention, in this application, a demand power fluctuation curve of multiple charging piles in a charging station is constructed based on the charging power data of the charging station at multiple historical time points; the overall power limit time period and the overall limited power of the overall power limit time period are determined based on the theoretical maximum power and the demand power fluctuation curve; and the individual limited power of each charging pile is determined based on the current daily temperature forecast data; an optimization model is constructed based on the overall limited power and the individual limited power, and the optimization model is solved to obtain the optimal operating power of multiple charging piles in the charging station; and power limiting control is performed based on the optimal operating power of multiple charging piles in the charging station. The present application can limit the overall power of the charging station and the individual power of the charging station without affecting charging, avoid triggering the protection mechanism to cut off the power, and reduce heat generation at the same time to save electricity and reduce heat dissipation pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0053] Figure 1 This is a flow chart of a charging pile control method for a high-current new energy heavy-duty truck shown in one embodiment of the present application;

[0054] Figure 2 The power demand fluctuation curve in one embodiment of the present application;

[0055] Figure 3 is a schematic diagram of an image of a filter element unit in one embodiment of the present application;

[0056] Figure 4 This is a structural diagram of a charging pile control system for a high-current new energy heavy-duty truck shown in one embodiment of the present application. DETAILED DESCRIPTION

[0057] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0058] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention. Therefore, the drawings only show the layers related to the present invention and are not drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer can be changed arbitrarily, and the layer layout type may also be more complicated.

[0059] In the following description, numerous details are set forth to provide a more thorough explanation of the embodiments of the present invention; however, it is apparent to one skilled in the art that the embodiments of the present invention may be practiced without these specific details.

[0060] Figure 1 This is a flow chart of a method for controlling a charging pile of a large current new energy heavy truck shown in one embodiment of the present application. Figure 1 As shown, this application may include steps S110 to S140:

[0061] S110, obtaining the theoretical maximum power obtained by the charging station from the upper power grid, obtaining charging power data of multiple charging piles in the charging station at multiple historical time points, and obtaining temperature forecast data for the current day;

[0062] The theoretical maximum power is the design value of the upper-level power grid. In some cases, the theoretical maximum power is not fixed. For example, it is 2500kW during peak hours and 3000kW during off-peak hours.

[0063] The current daily temperature forecast data can be extracted through weather forecast data.

[0064] S120, constructing a demand power fluctuation curve for multiple charging piles in the charging station based on the charging power data of the charging station at multiple historical time points;

[0065] In this application, the power demand pattern of the charging station is found by constructing a demand power fluctuation curve. The specific process is as follows:

[0066] S121, calculate the total power at each historical time point Where i is the serial number of the charging pile, and t is the historical time point;

[0067] By collecting the real-time power data of all charging piles in the charging station at historical time points (such as every hour, every minute), and calculating the total power based on the basic circuit principle, the total power is the superposition of the power of all charging piles, that is,

[0068] S122, the total power of multiple historical time points Mapping into a two-dimensional coordinate system, wherein the two-dimensional coordinate system includes a daily time axis and a power data axis;

[0069] The total power data at each historical point in time is mapped into a two-dimensional coordinate system, with the horizontal axis representing the daily time (e.g., the specific time of day) and the vertical axis representing the power data. This mapping process is based on the mathematical principles of coordinate systems (e.g., rectangular coordinates), with the point (t, P) representing the total power tP at each time point t.

[0070] S123, the total power of multiple historical time points in the two-dimensional coordinate system Perform density clustering to obtain multiple clusters;

[0071] A density-based clustering algorithm (such as DBSCAN) is used to divide data points into clusters based on their distribution density. The time and power characteristics of the data within the cluster are similar, which represents the approximate total power distribution within a time interval.

[0072] S124, calculating the total power variance and the total power mean in each cluster, and removing outliers in each cluster based on the total power variance and the total power mean to obtain a target cluster, wherein the total power variance in the target cluster is less than or equal to a preset variance threshold;

[0073] The variance threshold is used to filter out clusters with smaller variances, retain stable power patterns, and eliminate transient anomalies (such as charging pile failures or false positives). This reduces the interference of noise on subsequent analysis and improves the accuracy of the fitting curve. Specifically, the process of eliminating outliers includes:

[0074] S1, comparing the total power variance with a set variance threshold;

[0075] S2, when the total power variance is greater than the set variance threshold, calculate the difference between the total power of each data point in the cluster and the total power mean, and remove the data point with the largest difference as an outlier, and return to S1 until the total power variance is less than or equal to the set variance threshold;

[0076] If the overall variance is greater than the variance threshold, the data is relatively dispersed and may contain outliers. In this case, remove the data with the largest deviation from the mean. Repeat this process until you obtain clusters with a high degree of clustering.

[0077] S3: When the total power variance is less than or equal to a set variance threshold, a target cluster is obtained.

[0078] When the overall power variance is less than or equal to the variance threshold, it indicates that the data within the cluster is relatively concentrated, and thus can reflect the power pattern of the corresponding time period.

[0079] S125 , calculating the time range and total power mean of the target cluster, and fitting the total power mean values ​​corresponding to the time ranges of multiple target clusters to obtain demand power fluctuation curves of multiple charging piles in the charging station.

[0080] First, extract the corresponding time range (e.g., power data within a certain time period) for each target cluster. Calculate the mean μ of the total power within the target cluster as the typical power value for that time period. Based on the means and time ranges of multiple target clusters, use regression analysis (e.g., linear regression, polynomial fitting) or time series models (e.g., ARIMA) to fit the charging station's demand power fluctuation curve.

[0081] For example, suppose the center of the time range of the target cluster is t1, t2, ...t n , the corresponding means are μ1, μ2, ...μ n , then the fitting function can be expressed as:

[0082] P(t)=a0+a1t+a2t 2

[0083] Substitute each set of samples (t1, μ1), (t2, μ2), ... (t1, μ1) into the above formula for fitting, and obtain the fitting parameters a0, a1, and a2 to obtain the demand power fluctuation curve. Figure 2 The power demand fluctuation curve in one embodiment of the present application is obtained by fitting. Figure 2 shown.

[0084] S130: Determine an overall power limit time period and an overall power limit for the overall power limit time period based on the theoretical maximum power and the required power fluctuation curve; and determine an individual power limit for each charging pile based on the current daily temperature forecast data;

[0085] (1) Overall limited power

[0086] In this embodiment, in order to avoid triggering the power-off protection mechanism when the total power of the charging station is greater than the exchange power of the upper power grid, this application sets an overall power limit during the peak demand period of the charging station. This overall power limit is used to limit the charging power of all charging piles in the charging station. The specific process includes:

[0087] S1301, based on the theoretical maximum power Construct an intercept line and map the intercept line to the coordinate system of the demand power fluctuation curve, wherein the intercept line is Where y is the Y-axis coordinate of the coordinate system, T represents the time period, and P0 is the redundancy;

[0088] S1302: Based on the intersection of the intercept line and the required power fluctuation curve as the overall power limit time period, the overall power limit of the overall power limit time period is

[0089] Figure 3 FIG. 1 is a schematic diagram of a process for determining a power overall limited time period in an embodiment of the present application, such as Figure 3 As shown, this application uses the interception line as The power demand fluctuation curve is intersected and the intersecting time period is used as the overall power limit time period. If there is no intersection, it means that there is no need to limit the overall power.

[0090] (2) Individual limited power

[0091] The higher the charging power of the charging station and the higher the ambient temperature, the more heat will be generated and difficult to dissipate. When the operating temperature is high, the power of the charging station will be reduced. In view of this, this application sets a maximum power limit for the charging station based on the predicted or real-time ambient temperature to avoid overheating of the charging pile. The specific process includes:

[0092] S1311, extracting temperature values ​​at multiple predicted time points from the temperature prediction data of the current day;

[0093] S1312, inputting the temperature values ​​at multiple predicted time points and the set charging pile power into a pre-built charging pile heating prediction model to obtain a predicted temperature;

[0094] S1313, compare the predicted temperature with the preset temperature threshold. When the predicted temperature is greater than the preset temperature threshold, adjust the charging pile power, and return to inputting the current daily temperature prediction data and the set charging pile power into the pre-built charging pile heating prediction model to obtain the predicted temperature, until the target predicted temperature and the target charging pile power corresponding to the target predicted temperature are obtained, and the target charging pile power is used as the individual limited power.

[0095] In this application, a deep learning model is used to fit the relationship between the two-dimensional data composed of ambient temperature and charging power and the heating temperature of the charging pile. A loop rule is set to perform multiple checks to find the limited power value of each charging pile at different time points. The specific process is as follows: the predicted temperature T pre With the temperature threshold T thr (such as 85℃) for comparison, if T pre >T thr , then reduce the maximum power of the charging pile (such as from 120kW to 90kW), and then re-predict until T pre ≤T thr , and the maximum power of the charging pile is 90kW.

[0096] The construction process of the above charging pile heating prediction model is as follows:

[0097] Acquire sample data, wherein the sample data includes outdoor temperature, operating power of the charging pile, and temperature of the charging pile;

[0098] Constructing training data using the charging pile temperature as a label;

[0099] The artificial neural network is trained based on the training data until the loss function is fitted to obtain a charging pile heating prediction model, wherein the mathematical expression of the loss function is:

[0100]

[0101] In the formula, Loss is the loss, n is the amount of training data, i is the training data number, y i is the label of the i-th training data, is the predicted value of the i-th training data.

[0102] In the above process, the gradient descent method is used for iteration.

[0103] S140: constructing an optimization model based on the overall limited power and the individual limited power, and solving the optimization model to obtain optimal operating powers of multiple charging piles in the charging station; and performing power limit control based on the optimal operating powers of multiple charging piles in the charging station.

[0104] Finally, in order to obtain a more reasonable limit value, under the above restrictions, this application constructs an optimization model to calculate the optimal solution. The optimization model is as follows:

[0105] Constructing constraints, wherein the constraints include:

[0106]

[0107] 0≤P i ≤P max

[0108] Where, P i is the charging power of the i-th charging pile, P total_max is the overall limited power, P max The power is limited for each individual; and an objective function of minimizing heat generation and maximizing power is constructed, wherein the objective function is:

[0109]

[0110] Where λ is the trade-off parameter;

[0111] An optimization model is constructed based on the constraints and the objective function.

[0112] The objective function is quadratic and the constraints are linear, so a quadratic programming solver can be used directly. Finally, charging is performed based on the optimal power obtained for each charging station.

[0113] The present invention provides a charging pile control method for a high-current new energy heavy-duty truck. In this application, based on the charging power data of the charging station at multiple historical time points, a demand power fluctuation curve for multiple charging piles in the charging station is constructed; based on the theoretical maximum power and the demand power fluctuation curve, the overall power limit time period and the overall limited power of the overall power limit time period are determined; and based on the current daily temperature forecast data, the individual limited power of each charging pile is determined; based on the overall limited power and the individual limited power, an optimization model is constructed, and the optimization model is solved to obtain the optimal operating power of multiple charging piles in the charging station; and power limit control is performed based on the optimal operating power of multiple charging piles in the charging station. This application can limit the overall power of the charging station and the individual power of the charging station without affecting charging, avoiding triggering a power outage due to a protection mechanism, while reducing heat generation to save electricity and reduce heat dissipation pressure.

[0114] like Figure 4 As shown, the present application also provides a charging pile control system for a large current new energy heavy truck, the system comprising:

[0115] The acquisition module is used to obtain the theoretical maximum power obtained by the charging station from the upper power grid, obtain the charging power data of multiple charging piles in the charging station at multiple historical time points, and obtain the temperature forecast data of the current day;

[0116] A feature extraction module is used to construct a demand power fluctuation curve for multiple charging piles in the charging station based on the charging power data of the charging station at multiple historical time points;

[0117] a limited power determination module, configured to determine an overall power limited time period and an overall limited power for the overall power limited time period based on the theoretical maximum power and the required power fluctuation curve; and to determine an individual limited power for each charging pile based on the current daily temperature forecast data;

[0118] A limit control module is configured to construct an optimization model based on the overall limit power and the individual limit power, and solve the optimization model to obtain the optimal operating power of multiple charging piles in the charging station; and perform power limit control based on the optimal operating power of multiple charging piles in the charging station.

[0119] The present invention provides a charging pile control system for a high-current new energy heavy-duty truck. In this application, based on the charging power data of the charging station at multiple historical time points, a demand power fluctuation curve for multiple charging piles in the charging station is constructed; based on the theoretical maximum power and the demand power fluctuation curve, the overall power limit time period and the overall limited power of the overall power limit time period are determined; and based on the current daily temperature forecast data, the individual limited power of each charging pile is determined; based on the overall limited power and the individual limited power, an optimization model is constructed, and the optimization model is solved to obtain the optimal operating power of multiple charging piles in the charging station; and power limiting control is performed based on the optimal operating power of multiple charging piles in the charging station. This application can limit the overall power and individual power of the charging station without affecting charging, avoiding triggering a power outage due to a protection mechanism, while reducing heat generation to save electricity and reduce heat dissipation pressure.

[0120] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0121] The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0122] Regarding the computer-readable storage medium in this embodiment, those skilled in the art will appreciate that all or part of the steps in the aforementioned method embodiments can be implemented using hardware associated with the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps in the aforementioned method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0123] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used for communication, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes the various steps of the above method.

[0124] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0125] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0126] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a computer processor, the computer executes the aforementioned method. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device.

[0127] Another aspect of the present application provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the above embodiments.

[0128] The above embodiments are only preferred embodiments for fully illustrating the present application, and the protection scope of the present application is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art based on the present application are within the protection scope of the present application.

Claims

1. A charging pile control method for a large current new energy heavy truck, characterized in that: Including steps: Obtain the theoretical maximum power that the charging station can obtain from the upper power grid, obtain the charging power data of multiple charging piles in the charging station at multiple historical time points, and obtain the current daily temperature forecast data; Constructing a demand power fluctuation curve for multiple charging piles in a charging station based on the charging power data of the charging station at multiple historical time points; Determining the overall power limit time period and the overall power limit of the overall power limit time period based on the theoretical maximum power and the required power fluctuation curve; and determining the individual power limit of each charging pile based on the temperature forecast data of the current day; An optimization model is constructed based on the overall limited power and the individual limited power, and the optimization model is solved to obtain the optimal operating power of multiple charging piles in the charging station; and power limit control is performed based on the optimal operating power of multiple charging piles in the charging station.

2. The method for controlling a charging pile for a high-current new energy heavy truck according to claim 1, characterized in that: The charging power data includes the power values ​​of multiple charging piles The power demand fluctuation curve of multiple charging piles in the charging station is constructed based on the charging power data of the charging station at multiple historical time points, including: Calculate the total power at each historical time point Where i is the serial number of the charging pile, and t is the historical time point; The total power of multiple historical time points Mapping into a two-dimensional coordinate system, wherein the two-dimensional coordinate system includes a daily time axis and a power data axis; The total power of multiple historical time points in the two-dimensional coordinate system Perform density clustering to obtain multiple clusters; Calculating the total power variance and the total power mean in each cluster, and removing outliers in each cluster based on the total power variance and the total power mean to obtain a target cluster, wherein the total power variance in the target cluster is less than or equal to a preset variance threshold; The time range and total power mean of the target cluster are calculated, and the total power mean values ​​corresponding to the time ranges of multiple target clusters are fitted to obtain the demand power fluctuation curves of multiple charging piles in the charging station.

3. The method for controlling a charging pile for a high-current new energy heavy truck according to claim 2, characterized in that: Outliers in each cluster are removed based on the total power variance and the total power mean to obtain a target cluster, including: S1, comparing the total power variance with a set variance threshold; S2, when the total power variance is greater than the set variance threshold, calculate the difference between the total power of each data point in the cluster and the total power mean, and remove the data point with the largest difference as an outlier, and return to S1 until the total power variance is less than or equal to the set variance threshold; S3: When the total power variance is less than or equal to a set variance threshold, a target cluster is obtained.

4. The method for controlling a charging pile for a high-current new energy heavy truck according to claim 1, characterized in that: Determining the overall power limited time period and the overall limited power of the overall power limited time period based on the theoretical maximum power and the required power fluctuation curve includes: Based on the theoretical maximum power Construct an intercept line and map the intercept line to the coordinate system of the demand power fluctuation curve, wherein the intercept line is Where y is the Y-axis coordinate of the coordinate system, T represents the time period, and P0 is the redundancy; Based on the intersection of the intercept line and the required power fluctuation curve as the overall power limit time period, the overall limited power of the overall power limit time period is 5. The method for controlling a charging pile for a high-current new energy heavy truck according to claim 1, characterized in that: Determining the individual limited power of each charging pile based on the current daily temperature forecast data includes: Extracting temperature values ​​at multiple predicted time points from the current daily temperature prediction data; Input the temperature values ​​at multiple predicted time points and the set charging pile power into a pre-built charging pile heating prediction model to obtain the predicted temperature; The predicted temperature is compared with a preset temperature threshold. When the predicted temperature is greater than the preset temperature threshold, the charging pile power is adjusted, and the current daily temperature prediction data and the set charging pile power are input into a pre-built charging pile heating prediction model to obtain the predicted temperature, until the target predicted temperature and the target charging pile power corresponding to the target predicted temperature are obtained, and the target charging pile power is used as the individual limited power.

6. The method for controlling a charging pile for a high-current new energy heavy truck according to claim 5, characterized in that: The method for constructing the charging pile heating prediction model includes: Acquire sample data, wherein the sample data includes outdoor temperature, operating power of the charging pile, and temperature of the charging pile; Constructing training data using the charging pile temperature as a label; The artificial neural network is trained based on the training data until the loss function is fitted to obtain a charging pile heating prediction model, wherein the mathematical expression of the loss function is: In the formula, Loss is the loss, n is the amount of training data, i is the training data number, y i is the label of the i-th training data, is the predicted value of the i-th training data.

7. The method for controlling a charging pile for a high-current new energy heavy truck according to claim 1, characterized in that: Constructing an optimization model based on the overall limited power and the individual limited power, including: Constructing constraints, wherein the constraints include: 0≤P i ≤P max Where, P i is the charging power of the i-th charging pile, P total_max is the overall limited power, P max Limit power for individuals; Construct an objective function of minimizing heat and maximizing power, wherein the objective function is: Where λ is the trade-off parameter; An optimization model is constructed based on the constraints and the objective function.

8. A charging pile control system for a large current new energy heavy truck, characterized in that: The system comprises: The acquisition module is used to obtain the theoretical maximum power obtained by the charging station from the upper power grid, obtain the charging power data of multiple charging piles in the charging station at multiple historical time points, and obtain the temperature forecast data of the current day; A feature extraction module is used to construct a demand power fluctuation curve for multiple charging piles in the charging station based on the charging power data of the charging station at multiple historical time points; a limited power determination module, configured to determine an overall power limited time period and an overall limited power for the overall power limited time period based on the theoretical maximum power and the required power fluctuation curve; and to determine an individual limited power for each charging pile based on the current daily temperature forecast data; A limit control module is configured to construct an optimization model based on the overall limit power and the individual limit power, and solve the optimization model to obtain the optimal operating power of multiple charging piles in the charging station; and perform power limit control based on the optimal operating power of multiple charging piles in the charging station.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic terminal, characterized in that: include: processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal performs the method according to any one of claims 1 to 7.

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