An orderly charging regulation method for a direct-current charging pile and a related device

Abstract

The application discloses an orderly charging regulation method of a direct-current charging pile and related devices, and relates to the technical field of charging control.The method comprises the following steps: performing charging time prediction based on acquired vehicle condition data and charging demand data; performing charging priority analysis of a vehicle to be charged based on the vehicle condition data and the expected charging time; performing space-time characteristic analysis of charging load based on the expected charging time and the charging priority sequence; performing power grid load condition prediction based on real-time power grid monitoring data in combination with the expected charging time and the space-time characteristic analysis data; constructing a power grid dispatching control strategy based on the power grid load condition prediction data and the charging priority sequence to generate an orderly charging strategy; and controlling the charging module to dynamically adjust parameters in the orderly charging process of each vehicle to be charged based on the orderly charging strategy.The application can ensure the completion of vehicle charging tasks while realizing accurate energy management and optimizing power grid load.

Description

Technical Field

[0001] The present invention relates to the field of charging control technology, and in particular to a method for orderly charging control of a DC charging pile and related devices. Background Art

[0002] my country's new energy industry is currently experiencing rapid development. As a result, new energy vehicles (NEVs), powered by electricity, have become the most popular type of vehicle. Charging service usage is also increasing year by year, with an increasing number of DC charging stations being used to deliver power to NEVs. The large number of DC charging stations connected to the power grid has a significant impact on the grid, and conventional, unordered charging management is no longer sufficient to meet vehicle charging needs. Therefore, a shift from unordered charging to organized charging is essential. Predicting vehicle charging time is crucial for organized charging at DC charging stations. Currently, this is typically achieved by looking up current in a meter, but this method suffers from insufficient accuracy, hindering effective control of organized charging. Furthermore, existing organized charging control methods lack analysis of the spatiotemporal characteristics of charging loads and charging priority, making it difficult to accurately predict grid load conditions. Consequently, the organized charging process can lead to increased peak loads, reduced power supply efficiency, and impacts on the safety and stability of the distribution network, hindering effective organized charging control. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology. The present invention provides a method and related devices for orderly charging control of DC charging piles, which can achieve precise energy management while ensuring the completion of vehicle charging tasks, optimize grid load, and achieve peak shaving and valley filling, so that the orderly charging control of DC charging piles can achieve more ideal results.

[0004] In order to solve the above technical problems, the present invention provides an orderly charging control method for a DC charging pile, which is applied to a main control module and a charging module of the DC charging pile, wherein the main control module is connected to the charging module; the method comprises:

[0005] The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0006] Performing a charging priority analysis on the vehicles to be charged based on the vehicle condition data and the expected charging time to obtain a charging priority sequence;

[0007] Performing a spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain spatiotemporal characteristic analysis data;

[0008] Based on the real-time grid monitoring data and the expected charging time and spatiotemporal characteristic analysis data, a grid load condition prediction is performed to obtain grid load condition prediction data;

[0009] Constructing a power grid dispatch control strategy based on the power grid load forecast data and the charging priority sequence, and generating an ordered charging strategy based on the power grid dispatch control strategy;

[0010] The charging module is controlled based on the orderly charging strategy to dynamically adjust parameters during the orderly charging process of each vehicle to be charged.

[0011] Optionally, the performing charging time prediction based on the vehicle status data and the charging demand data to obtain the expected charging time corresponding to each vehicle to be charged includes:

[0012] Calculating a charging preheating time and a charging duration reference value for a battery of each to-be-charged vehicle based on the vehicle condition data and the charging demand data;

[0013] Acquire historical charging data of a DC charging pile, and complete the historical charging data to obtain completed historical charging data;

[0014] Clustering is performed based on the completed historical charging data to obtain a clustering result, and curve fitting is performed based on the clustering result using a least squares method to obtain a charging fitting curve;

[0015] A correction coefficient corresponding to the charging duration reference value is matched based on the charging fitting curve, and an expected charging time corresponding to each to-be-charged vehicle is determined based on the correction coefficient, the charging preheating time, and the charging duration reference value.

[0016] Optionally, performing charging priority analysis on the vehicles to be charged based on the vehicle condition data and the expected charging time to obtain a charging priority sequence includes:

[0017] Determining a power impact factor of a DC charging pile based on the vehicle condition data and the expected charging time;

[0018] Performing power space analysis based on the vehicle status data and the expected charging time to obtain power space data for charging each vehicle to be charged;

[0019] performing joint clustering based on the power spatial data, vehicle condition data, and expected charging time to obtain a feature data set;

[0020] quantifying the contribution of the charging demand priority using a feature contribution matrix based on the feature data set to obtain a target feature contribution;

[0021] Based on the target feature contribution and the power impact factor, a charging priority analysis of the vehicles to be charged is performed using a preset total capacity constraint and a preset power allocation constraint to obtain a charging priority sequence.

[0022] Optionally, performing a spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain spatiotemporal characteristic analysis data includes:

[0023] Obtaining a vehicle type of each vehicle to be charged, and determining a charging time constraint and a charging space constraint based on the vehicle type using an expected charging time and a charging priority sequence;

[0024] Based on the completed historical charging data, time series regression coefficient analysis and spatial regression coefficient analysis are performed to obtain the target time series regression coefficient and target spatial regression coefficient;

[0025] The charging load distribution of the DC charging pile is obtained, and the spatiotemporal characteristic analysis of the charging load is performed using a spatiotemporal joint algorithm model based on the charging time constraint condition, the charging space constraint condition, the target time series regression coefficient, and the target space regression coefficient in combination with the charging load distribution to obtain spatiotemporal characteristic analysis data.

[0026] Optionally, performing grid load condition prediction based on the real-time grid monitoring data in combination with the expected charging time and spatiotemporal characteristic analysis data to obtain grid load condition prediction data includes:

[0027] Performing grid load fluctuation analysis based on the spatiotemporal characteristic analysis data to obtain grid load fluctuation data;

[0028] The power grid load fluctuation data and the real-time power grid monitoring data are input into a power grid load prediction model to predict the power grid load condition and obtain power grid load condition prediction data.

[0029] Optionally, constructing a power grid dispatch control strategy based on the power grid load prediction data and the charging priority sequence, and generating an ordered charging strategy based on the power grid dispatch control strategy, includes:

[0030] Constructing corresponding control constraints based on the grid load prediction data and the charging priority sequence;

[0031] Acquire historical power grid dispatch control strategy data, and generate corresponding learning strategy data using a reinforcement learning algorithm based on the power grid load forecast data and the charging priority sequence;

[0032] Updating the historical power grid dispatching control strategy data based on the learning strategy data to obtain updated power grid dispatching control strategy data;

[0033] Determining a power grid dispatch control strategy using control constraints based on the updated power grid dispatch control strategy data;

[0034] Generating a charging sequence combination based on the grid dispatch control strategy and the charging priority sequence, and constructing an initial charging strategy based on the charging sequence combination;

[0035] The initial charging strategy is simulated and analyzed to obtain simulation analysis results, and the initial charging strategy is adjusted based on the simulation analysis results to obtain an orderly charging strategy.

[0036] Optionally, the controlling the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy includes:

[0037] Based on the orderly charging strategy, the charging module is controlled to perform adaptive dynamic adjustment of charging power and charging time during the orderly charging process of each vehicle to be charged.

[0038] In addition, the present invention provides an orderly charging control device for a DC charging pile, which is applied to a main control module and a charging module of the DC charging pile, wherein the main control module is connected to the charging module; the device comprises:

[0039] Charging time prediction module: used for the main control module to obtain real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and to perform charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0040] Charging priority analysis module: used to analyze the charging priority of the vehicles to be charged based on the vehicle status data and the expected charging time, and obtain a charging priority sequence;

[0041] A spatiotemporal characteristic analysis module is configured to perform spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence, and obtain spatiotemporal characteristic analysis data;

[0042] A power grid load condition prediction module is used to predict the power grid load condition based on the real-time power grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data, and obtain power grid load condition prediction data;

[0043] An ordered charging strategy building module is used to build a power grid dispatch control strategy based on the power grid load forecast data and the charging priority sequence, and generate an ordered charging strategy based on the power grid dispatch control strategy;

[0044] Orderly charging process control module: used to control the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

[0045] In addition, the present invention provides an orderly charging control system for a DC charging pile, the system comprising a main control module and a charging module of the DC charging pile, the main control module being connected to the charging module, and the system being configured to execute the above-mentioned orderly charging control method for the DC charging pile.

[0046] In addition, the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned orderly charging control method of the DC charging pile.

[0047] In an embodiment of the present invention, the expected charging time for each vehicle to be charged is determined based on a correction factor and reference values ​​for preheating time and charging duration generated from vehicle status data and charging demand data, effectively improving charging time prediction accuracy. Charging priority analysis for vehicles to be charged is performed based on vehicle status data and expected charging time using feature contribution analysis, enhancing dynamic charging resource management and improving resource utilization efficiency. The spatiotemporal characteristics of charging load are analyzed based on expected charging time and charging priority sequences. This spatiotemporal characteristics analysis provides sufficiently accurate and comprehensive data support for subsequent grid load prediction. Grid load prediction based on real-time grid monitoring data combined with spatiotemporal characteristics analysis data improves grid load prediction accuracy and prevents significant deviations between the predicted grid load and actual conditions. A grid dispatch control strategy is constructed based on the predicted grid load data and charging priority sequences to generate an orderly charging strategy. This strategy controls the charging module to dynamically adjust parameters during the orderly charging process for each vehicle to be charged, ensuring the completion of vehicle charging tasks while achieving precise energy management, optimizing grid load, and achieving peak load shifting and valley filling, thereby achieving more ideal orderly charging control of DC charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 1 is a flow chart of an orderly charging control method for a DC charging pile in an embodiment of the present invention;

[0050] Figure 2 1 is a flow chart of a method for orderly charging control of a DC charging pile in another embodiment of the present invention;

[0051] Figure 3 Schematic diagram of the structure of the orderly charging control system of the DC charging pile in the embodiment of the present invention;

[0052] Figure 4 It is a schematic diagram of the structural composition of the orderly charging control device of the DC charging pile in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Example 1

[0055] See also Figure 1 , Figure 1 : is a flow chart of a method for orderly charging control of a DC charging pile in an embodiment of the present invention. The method is applied to a main control module and a charging module of a DC charging pile, wherein the main control module is connected to the charging module; the method comprises:

[0056] S11: The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0057] In the specific implementation process of the present invention, the charging time prediction is performed based on the vehicle status data and the charging demand data to obtain the expected charging time corresponding to each vehicle to be charged, including: calculating the charging preheating time and the charging duration reference value of the battery of each vehicle to be charged based on the vehicle status data and the charging demand data; obtaining historical charging data of the DC charging pile, and supplementing the historical charging data to obtain supplemented historical charging data; clustering is performed based on the supplemented historical charging data to obtain clustering results, and curve fitting is performed using the least squares method based on the clustering results to obtain a charging fitting curve; a correction coefficient corresponding to the charging duration reference value is matched based on the charging fitting curve, and the expected charging time corresponding to each vehicle to be charged is determined based on the correction coefficient, the charging preheating time and the charging duration reference value.

[0058] Specifically, the main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged. The real-time grid monitoring data includes the real-time current, real-time voltage, and real-time load of the grid. The vehicle status data includes the vehicle's remaining battery power, battery capacity, temperature, power demand, and state of charge. The charging demand data includes the expected dwell time and the expected battery state of charge value after charging. Based on the vehicle status data and charging demand data, the main control module calculates the charging preheating time and charging duration reference value of the battery of each vehicle to be charged. The main control module extracts battery temperature data from the vehicle status data, obtains the battery preheating rate under low-temperature charging conditions, calculates the charging preheating time based on the preheating rate using the battery temperature data and the preheating target temperature, extracts the battery remaining capacity and state of charge from the vehicle status data, and calculates the charging duration reference value based on the battery remaining capacity and state of charge. Acquire historical charging data of a DC charging pile, the historical charging data including charging time and corresponding charging percentage of various types of electric vehicles, and complete the historical charging data. Perform correlation analysis on the charging time and charging percentage using the Pearson coefficient to obtain a target Pearson correlation coefficient. Complete the historical charging data based on the target Pearson correlation coefficient to obtain completed historical charging data. Clustering is performed based on the completed historical charging data, and several historical charging curves are drawn according to the completed historical charging data. The initial slope of each historical charging curve is extracted, and the initial slope of a preset number of historical charging curves is randomly selected. The initial slope is used as the initial centroid, and the distance between the initial slope and the initial centroid of each historical charging curve is calculated respectively. The corresponding historical charging curves are clustered into a class with the smallest distance to form several clusters, and the centroids of the several clusters are recalculated and used as new clustering standards. The distances between each historical charging curve and the centroid of the cluster are recalculated to cluster again. The above process is repeated until the centroids calculated twice before and after do not change, and the calculation is stopped to obtain a clustering result. The least squares method is used to perform curve fitting based on the clustering result to obtain a charging fitting curve. Based on the correction coefficient corresponding to the charging duration reference value matched with the charging fitting curve, a corresponding target charging fitting curve is determined from a plurality of charging fitting curves according to the charging duration reference value, a correction coefficient is determined according to the target charging fitting curve, and an expected charging time corresponding to each to-be-charged vehicle is determined based on the correction coefficient, the charging preheating time and the charging duration reference value, thereby achieving accurate prediction of the charging time of the electric vehicle.

[0059] S12: Analyzing the charging priorities of the vehicles to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence;

[0060] In the specific implementation process of the present invention, the charging priority analysis of the vehicles to be charged is performed based on the vehicle condition data and the expected charging time to obtain a charging priority sequence, including: determining the power impact factor of the DC charging pile based on the vehicle condition data and the expected charging time; performing power space analysis based on the vehicle condition data and the expected charging time to obtain power space data for charging each vehicle to be charged; performing joint clustering based on the power space data, vehicle condition data and expected charging time to obtain a feature data set; quantifying the contribution of the charging demand priority based on the feature data set using a feature contribution matrix to obtain a target feature contribution; performing charging priority analysis on the vehicles to be charged based on the target feature contribution and the power impact factor using a preset total capacity constraint and a preset power allocation constraint to obtain a charging priority sequence.

[0061] Specifically, a power impact factor for a DC charging station is determined based on the vehicle condition data and expected charging time. The corresponding power impact factor is matched in a database based on the remaining battery capacity and expected charging time in the vehicle condition data. The power impact factor serves as the charging power impact value. A power space analysis is performed based on the vehicle condition data and expected charging time. Based on the power demand and expected charging time in the vehicle condition data, power space reserved for ensuring grid system stability is used to determine the power space data that can be allocated to each vehicle to be charged, thereby obtaining power space data for each vehicle to be charged. Joint clustering is performed based on the power space data, vehicle condition data, and expected charging time using a K-nearest neighbor algorithm. Related data is assigned to corresponding clusters to form feature categories, such as high power space - long charging time, thereby obtaining a feature dataset. Based on the feature dataset, a feature contribution matrix is ​​used to quantify the contribution of charging demand priorities. Key features are extracted from the feature dataset. The feature weights of the key features are weighted and aggregated using a feature weight aggregation model to obtain target feature weights. A feature contribution matrix is ​​constructed based on the target feature weights. The contribution of charging demand priorities is quantified based on the feature contribution matrix to obtain target feature contributions. Based on the target feature contribution and the power impact factor, the charging priority of the vehicles to be charged is analyzed using the preset total capacity constraint and the preset power allocation constraint. The preset total capacity constraint includes that the sum of the charging powers of the DC charging piles used shall not exceed the preset capacity, and the preset power allocation constraint allocates higher power to high-priority vehicles. According to the priority decision model, combined with the target feature contribution and the power impact factor, the charging priority of the vehicles to be charged is analyzed using the preset total capacity constraint and the preset power allocation constraint to obtain a charging priority sequence. The analysis of the charging priority can enhance the dynamic management capability of the charging resources, ensure the efficient use of power resources and the orderly progress of the charging process.

[0062] S13: performing a spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence to obtain spatiotemporal characteristic analysis data;

[0063] In the specific implementation process of the present invention, the spatiotemporal characteristic analysis of the charging load is performed based on the expected charging time and charging priority sequence to obtain spatiotemporal characteristic analysis data, including: obtaining the vehicle type of each vehicle to be charged, and determining the charging time constraint and the charging space constraint based on the vehicle type using the expected charging time and charging priority sequence; performing time series regression coefficient analysis and space regression coefficient analysis based on the completed historical charging data to obtain the target time series regression coefficient and the target space regression coefficient; obtaining the charging load distribution of the DC charging pile, and performing the spatiotemporal characteristic analysis of the charging load based on the charging time constraint, charging space constraint, target time series regression coefficient and target space regression coefficient in combination with the charging load distribution using a spatiotemporal joint algorithm model to obtain spatiotemporal characteristic analysis data.

[0064] Specifically, the vehicle type of each vehicle to be charged is obtained, including electric private cars, electric taxis, and official vehicles, and based on the vehicle type, the expected charging time and charging space constraints are determined using the expected charging time and charging priority sequence. The maximum charging delay time is determined based on the vehicle type and vehicle status data. The charging time constraint is determined based on the maximum charging delay time and the expected charging time. The power loss of the charging pile transfer is determined based on the vehicle type and vehicle status data. The charging space constraint is determined based on the power loss and the charging priority sequence. Time series regression coefficient analysis and spatial regression coefficient analysis are performed based on the completed historical charging data. The charging feature vector is extracted from the completed historical charging data. A time series impact analysis is performed based on the charging feature vector to obtain time series impact analysis data. A time series regression coefficient analysis is performed based on the time series impact analysis data using a time series analysis algorithm model to obtain a target time series regression coefficient. The spatial impact vector is extracted based on the completed historical charging data. The spatial regression coefficient analysis is performed based on the spatial impact vector in combination with a preset error term and a preset spatial impact factor to obtain a target spatial regression coefficient. The charging load distribution of the DC charging pile, i.e., the time-scale distribution of the charging load, is obtained. Based on the charging time constraints, charging space constraints, target time-series regression coefficients, and target spatial regression coefficients, the spatiotemporal characteristics of the charging load are analyzed using a spatiotemporal joint algorithm model. During the spatiotemporal analysis of the charging load, an error term is added to the spatiotemporal joint algorithm model to represent random errors or noise not captured by the model during the spatiotemporal analysis. This error term can include the influence of factors in the data not accounted for by the model, or random fluctuations in the data itself, to obtain spatiotemporal characteristics analysis data. The spatiotemporal characteristics analysis analyzes the time and space requirements for charging.

[0065] S14: Predicting a grid load condition based on the real-time grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data to obtain grid load condition prediction data;

[0066] In the specific implementation process of the present invention, the grid load situation is predicted based on the real-time grid monitoring data combined with the expected charging time and the spatiotemporal characteristic analysis data to obtain grid load situation prediction data, including: performing grid load fluctuation analysis based on the spatiotemporal characteristic analysis data to obtain grid load fluctuation data; inputting the grid load fluctuation data and the real-time grid monitoring data into the grid load prediction model to perform grid load situation prediction to obtain grid load situation prediction data.

[0067] Specifically, a grid load fluctuation analysis is performed based on the spatiotemporal characteristic analysis data. A grid load sequence analysis is performed based on the charging load analysis model using the spatiotemporal characteristic analysis data to obtain load sequence data. Bidirectional time series features of the spatiotemporal characteristic analysis data are determined based on the load sequence data. The weights of the data features at different time steps in the bidirectional time series features are determined based on a preset attention mechanism. Charging load characteristic analysis is performed using the weights of the data features at different time steps in the bidirectional time series features according to the charging load prediction model to obtain charging load characteristic analysis data. A grid load fluctuation analysis is performed based on the charging load characteristic analysis data in combination with peak time analysis and trough time analysis to obtain grid load fluctuation data. The grid load fluctuation data and real-time grid monitoring data are input into a grid load prediction model to predict grid load conditions. The grid load prediction model is a convergence model obtained by training a sample data set into a deep neural network model to obtain grid load condition prediction data.

[0068] S15: constructing a power grid dispatch control strategy based on the power grid load prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatch control strategy;

[0069] In the specific implementation process of the present invention, the grid dispatching control strategy is constructed based on the grid load condition prediction data and the charging priority sequence, and an orderly charging strategy is generated based on the grid dispatching control strategy, including: constructing corresponding control constraints based on the grid load condition prediction data and the charging priority sequence; obtaining historical grid dispatching control strategy data, and generating corresponding learning strategy data based on the grid load condition prediction data and the charging priority sequence using a reinforcement learning algorithm; updating the historical grid dispatching control strategy data based on the learning strategy data to obtain updated grid dispatching control strategy data; determining the grid dispatching control strategy based on the updated grid dispatching control strategy data using control constraints; generating a charging series combination based on the grid dispatching control strategy and the charging priority sequence, and constructing an initial charging strategy based on the charging series combination; performing simulation analysis on the initial charging strategy to obtain simulation analysis results, and adjusting the initial charging strategy based on the simulation analysis results to obtain an orderly charging strategy.

[0070] Specifically, corresponding control constraints are constructed based on the grid load prediction data and the charging priority sequence. The grid load condition and the charging priority sequence affect the grid's power dispatch. Therefore, corresponding control constraints need to be constructed based on the grid load prediction data and the charging priority sequence. Historical grid dispatch control strategy data is obtained, and corresponding learning strategy data is generated based on the grid load prediction data and the charging priority sequence using a reinforcement learning algorithm. The reinforcement learning algorithm uses the generated data to modify its own strategy data, selects a corresponding value function based on the grid load prediction data and the charging priority sequence, calculates an approximate value function based on the corresponding value function using the Bellman equation, iteratively updates the approximate value function, and performs data interaction based on the iteratively updated approximate value function to obtain corresponding learning strategy data. The historical grid dispatch control strategy data is updated based on the learning strategy data, that is, updated grid dispatch control strategy data is obtained by interacting with the learning strategy data and the historical grid dispatch control strategy data. Based on the updated grid dispatch control strategy data, a grid dispatch control strategy is determined using control constraints. In a simulation environment incorporating the control constraints, the updated grid dispatch control strategy data is iteratively updated until a predetermined number of iterations is reached. After each iteration, scenario execution data is obtained and incorporated into the next iteration to obtain the grid dispatch control strategy. A charging sequence combination is generated based on the grid dispatch control strategy and a charging priority sequence. A time-increasing charging sequence for power demand response is generated based on the grid dispatch control strategy and the charging priority sequence. An initial charging strategy is constructed based on the charging sequence combination. The charging sequence combination and the grid dispatch control strategy are combined to form the initial charging strategy. A simulation analysis is performed on the initial charging strategy to obtain simulation analysis results. Based on the simulation analysis results, the initial charging strategy is adjusted. Based on the simulation analysis results, a deep feedback analysis is performed on the initial charging strategy using a deep learning algorithm to obtain deep feedback analysis data. Improvement data for the initial charging strategy is determined based on the deep feedback analysis data. The initial charging strategy is adjusted based on the improvement data to obtain an ordered charging strategy.

[0071] S16: Based on the orderly charging strategy, the charging module is controlled to dynamically adjust parameters during the orderly charging process of each vehicle to be charged.

[0072] In the specific implementation process of the present invention, the orderly charging strategy is based on controlling the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged, including: based on the orderly charging strategy, controlling the charging module to adaptively and dynamically adjust the charging power and charging time during the orderly charging process of each vehicle to be charged.

[0073] Specifically, based on the orderly charging strategy, the charging module is controlled to adaptively and dynamically adjust the charging power and charging time during the orderly charging process of each vehicle to be charged, thereby ensuring the completion of the vehicle charging task while achieving precise energy management, optimizing the grid load, and realizing peak shaving and valley filling, so that the orderly charging regulation of the DC charging pile can achieve a more ideal effect.

[0074] In an embodiment of the present invention, the expected charging time for each vehicle to be charged is determined based on a correction factor and reference values ​​for preheating time and charging duration generated from vehicle status data and charging demand data, effectively improving charging time prediction accuracy. Charging priority analysis for vehicles to be charged is performed based on vehicle status data and expected charging time using feature contribution analysis, enhancing dynamic charging resource management and improving resource utilization efficiency. The spatiotemporal characteristics of charging load are analyzed based on expected charging time and charging priority sequences. This spatiotemporal characteristics analysis provides sufficiently accurate and comprehensive data support for subsequent grid load prediction. Grid load prediction based on real-time grid monitoring data combined with spatiotemporal characteristics analysis data improves grid load prediction accuracy and prevents significant deviations between the predicted grid load and actual conditions. A grid dispatch control strategy is constructed based on the predicted grid load data and charging priority sequences to generate an orderly charging strategy. This strategy controls the charging module to dynamically adjust parameters during the orderly charging process for each vehicle to be charged, ensuring the completion of vehicle charging tasks while achieving precise energy management, optimizing grid load, and achieving peak load shifting and valley filling, thereby achieving more ideal orderly charging control of DC charging piles.

[0075] Example 2

[0076] See also Figure 2 , Figure 2 : This is a flow chart of a method for orderly charging control of a DC charging pile in another embodiment of the present invention. The method is applied to a main control module and a charging module of a DC charging pile, wherein the main control module is connected to the charging module; the method comprises:

[0077] S201: The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0078] S202: Analyzing the charging priorities of the vehicles to be charged based on the vehicle status data and the expected charging time to obtain a charging priority sequence;

[0079] S203: Obtaining the vehicle type of each vehicle to be charged, and determining a charging time constraint and a charging space constraint based on the vehicle type using an expected charging time and a charging priority sequence;

[0080] S204: Performing time series regression coefficient analysis and spatial regression coefficient analysis based on the completed historical charging data to obtain target time series regression coefficients and target spatial regression coefficients;

[0081] S205: Obtaining a charging load distribution of a DC charging pile, and performing a spatiotemporal characteristic analysis of the charging load using a spatiotemporal joint algorithm model based on the charging time constraint, the charging space constraint, the target time series regression coefficient, and the target space regression coefficient in combination with the charging load distribution to obtain spatiotemporal characteristic analysis data;

[0082] S206: Predicting a grid load condition based on the real-time grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data to obtain grid load condition prediction data;

[0083] S207: Constructing a power grid dispatch control strategy based on the power grid load prediction data and the charging priority sequence, and generating an orderly charging strategy based on the power grid dispatch control strategy;

[0084] S208: Controlling the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

[0085] In an embodiment of the present invention, the expected charging time for each vehicle to be charged is determined based on a correction factor and reference values ​​for preheating time and charging duration generated from vehicle status data and charging demand data, effectively improving charging time prediction accuracy. Charging priority analysis for vehicles to be charged is performed based on vehicle status data and expected charging time using feature contribution analysis, enhancing dynamic charging resource management and improving resource utilization efficiency. The spatiotemporal characteristics of charging load are analyzed based on expected charging time and charging priority sequences. This spatiotemporal characteristics analysis provides sufficiently accurate and comprehensive data support for subsequent grid load prediction. Grid load prediction based on real-time grid monitoring data combined with spatiotemporal characteristics analysis data improves grid load prediction accuracy and prevents significant deviations between the predicted grid load and actual conditions. A grid dispatch control strategy is constructed based on the predicted grid load data and charging priority sequences to generate an orderly charging strategy. This strategy controls the charging module to dynamically adjust parameters during the orderly charging process for each vehicle to be charged, ensuring the completion of vehicle charging tasks while achieving precise energy management, optimizing grid load, and achieving peak load shifting and valley filling, thereby achieving more ideal orderly charging control of DC charging piles.

[0086] Example 3

[0087] See also Figure 3 , Figure 3 3 is a schematic diagram of the structural composition of the orderly charging control system of the DC charging pile in an embodiment of the present invention. The system includes a main control module 31 and a charging module 32 of the DC charging pile. The main control module 31 is connected to the charging module 32. The system is configured to execute the orderly charging control method of the DC charging pile in the above embodiment.

[0088] In the specific implementation of the present invention, the main control module 31 possesses intelligent control and communication functions, integrating advanced algorithms. The main control module can control the operation of the charging module and contactor closure, monitor and adjust the output voltage and current, and adjust the heat dissipation within the cabin. Based on a dual-processor design of a microcontroller unit and a microprocessor, it can achieve more complex data operations and more efficient and flexible application designs, supporting various module control modes such as full matrix, dual matrix, and ring to adapt to different application scenarios. The charging module 32 converts the input power into stable DC power that the battery can accept, and then connects the fuse, shunt, DC contactor, and charging gun to charge the electric vehicle.

[0089] at the same time, Figure 3 The orderly charging control system of the DC charging pile shown does not constitute a limitation on all components and may include more or fewer components than shown, or a combination of certain components. Specific implementation methods can be found in the above embodiments and will not be repeated here.

[0090] In an embodiment of the present invention, the expected charging time for each vehicle to be charged is determined based on a correction factor and reference values ​​for preheating time and charging duration generated from vehicle status data and charging demand data, effectively improving charging time prediction accuracy. Charging priority analysis for vehicles to be charged is performed based on vehicle status data and expected charging time using feature contribution analysis, enhancing dynamic charging resource management and improving resource utilization efficiency. The spatiotemporal characteristics of charging load are analyzed based on expected charging time and charging priority sequences. This spatiotemporal characteristics analysis provides sufficiently accurate and comprehensive data support for subsequent grid load prediction. Grid load prediction based on real-time grid monitoring data combined with spatiotemporal characteristics analysis data improves grid load prediction accuracy and prevents significant deviations between the predicted grid load and actual conditions. A grid dispatch control strategy is constructed based on the predicted grid load data and charging priority sequences to generate an orderly charging strategy. This strategy controls the charging module to dynamically adjust parameters during the orderly charging process for each vehicle to be charged, ensuring the completion of vehicle charging tasks while achieving precise energy management, optimizing grid load, and achieving peak load shifting and valley filling, thereby achieving more ideal orderly charging control of DC charging piles.

[0091] Example 4

[0092] See also Figure 4 , Figure 4 : This is a structural diagram of an orderly charging control device for a DC charging pile in an embodiment of the present invention. The device is applied to a main control module and a charging module of a DC charging pile, and the main control module is connected to the charging module; the device includes:

[0093] Charging time prediction module 41: used for the main control module to obtain real-time power grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and to perform charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged;

[0094] Charging priority analysis module 42: configured to perform charging priority analysis of the vehicles to be charged based on the vehicle status data and the expected charging time, and obtain a charging priority sequence;

[0095] A spatiotemporal characteristic analysis module 43 is configured to perform spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence, and obtain spatiotemporal characteristic analysis data;

[0096] The grid load condition prediction module 44 is configured to predict the grid load condition based on the real-time grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data, and obtain grid load condition prediction data;

[0097] An ordered charging strategy building module 45 is configured to build a power grid dispatch control strategy based on the power grid load prediction data and the charging priority sequence, and generate an ordered charging strategy based on the power grid dispatch control strategy;

[0098] The orderly charging process control module 46 is used to control the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

[0099] In the specific implementation process of the present invention, the specific implementation methods of the device items can be referred to the above embodiments, which will not be repeated here.

[0100] In an embodiment of the present invention, the expected charging time for each vehicle to be charged is determined based on a correction factor and reference values ​​for preheating time and charging duration generated from vehicle status data and charging demand data, effectively improving charging time prediction accuracy. Charging priority analysis for vehicles to be charged is performed based on vehicle status data and expected charging time using feature contribution analysis, enhancing dynamic charging resource management and improving resource utilization efficiency. The spatiotemporal characteristics of charging load are analyzed based on expected charging time and charging priority sequences. This spatiotemporal characteristics analysis provides sufficiently accurate and comprehensive data support for subsequent grid load prediction. Grid load prediction based on real-time grid monitoring data combined with spatiotemporal characteristics analysis data improves grid load prediction accuracy and prevents significant deviations between the predicted grid load and actual conditions. A grid dispatch control strategy is constructed based on the predicted grid load data and charging priority sequences to generate an orderly charging strategy. This strategy controls the charging module to dynamically adjust parameters during the orderly charging process for each vehicle to be charged, ensuring the completion of vehicle charging tasks while achieving precise energy management, optimizing grid load, and achieving peak load shifting and valley filling, thereby achieving more ideal orderly charging control of DC charging piles.

[0101] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for orderly charging control of a DC charging pile in any of the above embodiments is implemented. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. In other words, the storage device includes any medium that can store or transmit information in a readable form by a device (for example, a computer, a mobile phone), which can be a read-only memory, a disk or an optical disk, etc.

[0102] In addition, the above is a detailed introduction to the orderly charging control method and related devices of a DC charging pile provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for orderly charging control of a DC charging pile, characterized in that: A main control module and a charging module applied to a DC charging pile, wherein the main control module is connected to the charging module; the method includes: The main control module obtains real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and performs charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged; Performing a charging priority analysis on the vehicles to be charged based on the vehicle condition data and the expected charging time to obtain a charging priority sequence; Performing a spatiotemporal characteristic analysis of the charging load based on the expected charging time and charging priority sequence to obtain spatiotemporal characteristic analysis data includes: obtaining the vehicle type of each vehicle to be charged, and determining a charging time constraint and a charging space constraint based on the vehicle type using the expected charging time and charging priority sequence; performing a time series regression coefficient analysis and a space regression coefficient analysis based on the completed historical charging data to obtain a target time series regression coefficient and a target space regression coefficient; obtaining a charging load distribution of a DC charging pile, and performing a spatiotemporal characteristic analysis of the charging load based on the charging time constraint, the charging space constraint, the target time series regression coefficient, the target space regression coefficient, and the charging load distribution using a spatiotemporal joint algorithm model to obtain spatiotemporal characteristic analysis data; Performing grid load condition prediction based on the real-time grid monitoring data in combination with the expected charging time and spatiotemporal characteristic analysis data to obtain grid load condition prediction data, including: performing grid load fluctuation analysis based on the spatiotemporal characteristic analysis data to obtain grid load fluctuation data; inputting the grid load fluctuation data and the real-time grid monitoring data into a grid load prediction model to perform grid load condition prediction to obtain grid load condition prediction data; Constructing a power grid dispatch control strategy based on the power grid load forecast data and the charging priority sequence, and generating an ordered charging strategy based on the power grid dispatch control strategy; The charging module is controlled based on the orderly charging strategy to dynamically adjust parameters during the orderly charging process of each vehicle to be charged.

2. The method for orderly charging control of a DC charging pile according to claim 1, characterized in that: The charging time prediction based on the vehicle status data and the charging demand data to obtain the expected charging time corresponding to each vehicle to be charged includes: Calculating a charging preheating time and a charging duration reference value for a battery of each to-be-charged vehicle based on the vehicle condition data and the charging demand data; Acquire historical charging data of a DC charging pile, and complete the historical charging data to obtain completed historical charging data; Clustering is performed based on the completed historical charging data to obtain a clustering result, and curve fitting is performed based on the clustering result using a least squares method to obtain a charging fitting curve; A correction coefficient corresponding to the charging duration reference value is matched based on the charging fitting curve, and an expected charging time corresponding to each to-be-charged vehicle is determined based on the correction coefficient, the charging preheating time, and the charging duration reference value.

3. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The performing charging priority analysis of the vehicles to be charged based on the vehicle condition data and the expected charging time to obtain a charging priority sequence includes: Determining a power impact factor of a DC charging pile based on the vehicle condition data and the expected charging time; Performing power space analysis based on the vehicle status data and the expected charging time to obtain power space data for charging each vehicle to be charged; performing joint clustering based on the power spatial data, vehicle condition data, and expected charging time to obtain a feature data set; quantifying the contribution of the charging demand priority using a feature contribution matrix based on the feature data set to obtain a target feature contribution; Based on the target feature contribution and the power impact factor, a charging priority analysis of the vehicles to be charged is performed using a preset total capacity constraint and a preset power allocation constraint to obtain a charging priority sequence.

4. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The step of constructing a power grid dispatch control strategy based on the power grid load prediction data and the charging priority sequence, and generating an ordered charging strategy based on the power grid dispatch control strategy, includes: Constructing corresponding control constraints based on the grid load prediction data and the charging priority sequence; Acquire historical power grid dispatch control strategy data, and generate corresponding learning strategy data using a reinforcement learning algorithm based on the power grid load forecast data and the charging priority sequence; Updating the historical power grid dispatching control strategy data based on the learning strategy data to obtain updated power grid dispatching control strategy data; Determining a power grid dispatch control strategy using control constraints based on the updated power grid dispatch control strategy data; Generating a charging sequence combination based on the grid dispatch control strategy and the charging priority sequence, and constructing an initial charging strategy based on the charging sequence combination; The initial charging strategy is simulated and analyzed to obtain simulation analysis results, and the initial charging strategy is adjusted based on the simulation analysis results to obtain an orderly charging strategy.

5. The orderly charging control method of a DC charging pile according to claim 1, characterized in that: The control of the charging module based on the orderly charging strategy to dynamically adjust parameters during the orderly charging process of each vehicle to be charged includes: Based on the orderly charging strategy, the charging module is controlled to perform adaptive dynamic adjustment of charging power and charging time during the orderly charging process of each vehicle to be charged.

6. A device for orderly charging control of a DC charging pile, the device being applied to the method for orderly charging control of a DC charging pile according to any one of claims 1 to 5, characterized in that: A main control module and a charging module applied to a DC charging pile, wherein the main control module is connected to the charging module; the device comprises: Charging time prediction module: used for the main control module to obtain real-time grid monitoring data and vehicle status data and charging demand data of each vehicle to be charged, and to perform charging time prediction based on the vehicle status data and charging demand data to obtain the expected charging time corresponding to each vehicle to be charged; Charging priority analysis module: used to analyze the charging priority of the vehicles to be charged based on the vehicle status data and the expected charging time, and obtain a charging priority sequence; A spatiotemporal characteristic analysis module is configured to perform spatiotemporal characteristic analysis of the charging load based on the expected charging time and the charging priority sequence, and obtain spatiotemporal characteristic analysis data; A power grid load condition prediction module is used to predict the power grid load condition based on the real-time power grid monitoring data combined with the expected charging time and spatiotemporal characteristic analysis data, and obtain power grid load condition prediction data; An ordered charging strategy building module is used to build a power grid dispatch control strategy based on the power grid load forecast data and the charging priority sequence, and generate an ordered charging strategy based on the power grid dispatch control strategy; Orderly charging process control module: used to control the charging module to dynamically adjust parameters during the orderly charging process of each vehicle to be charged based on the orderly charging strategy.

7. An orderly charging control system for a DC charging pile, characterized in that: The system includes a main control module and a charging module of a DC charging pile, wherein the main control module is connected to the charging module. The system is configured to execute the orderly charging control method of a DC charging pile according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the orderly charging control method for a DC charging pile according to any one of claims 1 to 5.

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