Intelligent power distribution control method and system for new energy charging pile

Abstract

The invention relates to the field of power distribution, and discloses an intelligent power distribution control method and system for a new energy charging pile, and the method comprises the following steps: collecting various parameters of the charging pile and a connected vehicle in real time through a sensor and other devices, and transmitting the parameters to a central control system; and analyzing and processing the received data by using an intelligent scheduling algorithm so as to determine an optimal power distribution strategy. The power output of the charging pile is adjusted according to the strategy, and dynamic adjustment is performed in real time according to voltage and current changes to ensure that power distribution does not cause overload or instability of a power grid. According to the invention, intelligent and efficient power distribution can be realized, and safe and reliable charging service is provided for the electric vehicle.

Description

Technical Field

[0001] The present invention relates to the field of power distribution, and in particular to an intelligent power distribution control method and system for a new energy charging pile. Background Art

[0002] New energy charging piles, also known as electric vehicle charging stations or electric vehicle power supply equipment, are devices that provide electric energy to electric vehicles, enabling them to store enough electricity to support their operation. This type of equipment is usually installed in public places such as public buildings, parking lots, shopping malls, and operating vehicle charging stations, as well as private places such as residential areas. Its power input end is connected to the AC power grid, and the power output end with a charging plug is connected to the car for charging. During the charging process, the charging pile will dynamically adjust the output power according to the real-time status of the battery to avoid damage to the battery due to overcharging. It can also automatically identify the type of connected electric vehicle and the battery status, and automatically adjust the charging mode and output power according to the charging requirements of different models and batteries, thereby improving the charging efficiency. It is necessary to use new energy charging piles to perform intelligent power distribution control on vehicles because there are differences in the charging requirements of different models and batteries, and traditional charging methods often cannot be flexibly adjusted according to these differences. Through intelligent power distribution control, the charging pile can automatically identify the type of connected electric vehicle and the battery status, and automatically adjust the charging mode and output power according to this information to meet the charging requirements of different models and batteries. This intelligent charging method can not only improve the charging efficiency, but also better protect the battery and extend its service life. Therefore, an intelligent power distribution control method and system for a new energy charging pile are proposed. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an intelligent power distribution control method and system for a new energy charging pile.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides an intelligent power distribution control method for a new energy charging pile, comprising the following steps: Collect charging parameters of new energy charging piles and connected vehicles, and upload them to the central control system for data preprocessing of charging parameters; Construct a power demand model, use the power demand model to analyze the real-time charging parameters of the charging pile and the target vehicle's sequential charging status, and design a power allocation strategy for the new energy charging pile based on the analysis results; Output power allocation strategy, and conduct fault analysis and tracing on new energy charging piles during the power allocation strategy output process.

[0005] Furthermore, in a preferred embodiment of the present invention, the charging parameters of the new energy charging pile and the connected vehicle are collected and uploaded to the central control system for data preprocessing of the charging parameters, specifically: Obtain a new energy charging pile, and determine the range of various charging parameters of the new energy charging pile, which are calibrated as the charging parameter range of the new energy charging pile, wherein the charging parameter range of the new energy charging pile includes the range of input voltage, input current, output voltage, and output current; Marking the vehicle being connected to the new energy charging pile as a target vehicle, and determining the battery status of the target vehicle, wherein the battery status includes the remaining power and the real-time temperature of the battery; Obtain the real-time charging parameters of the new energy charging pile, obtain the central control system of the new energy charging pile, calibrate it as the target central control system, and control the target central control system to collect and store the real-time charging parameters of the new energy charging pile and the battery status of the target vehicle in real time; In the target central control system, all stored parameters are cleaned to remove abnormal and invalid values ​​in the parameters. At the same time, all parameters are processed in a unified data format and time series conversion, so that the data format of all parameters is converted into a time series format, and the real-time charging parameters of the new energy charging pile in the time series format are obtained, which are calibrated as the real-time charging parameters of the charging pile time series, and the battery status of the target vehicle in the time series format is obtained, which is calibrated as the time series charging status of the target vehicle.

[0006] Furthermore, in a preferred embodiment of the present invention, the power demand model is constructed, and power scheduling analysis is performed on the real-time charging parameters of the charging pile sequence and the target vehicle sequence charging state through the power demand model, and a power allocation strategy for the new energy charging pile is designed based on the analysis results, specifically: Based on the real-time charging parameters of the charging pile, the real-time output power of the new energy charging pile is calculated. At the same time, based on the real-time charging status of the target vehicle, the real-time charging rate and SOC of the target vehicle are calculated. The Lasso regression algorithm is introduced to construct an initial Lasso regression model, and the real-time output power of the new energy charging pile and the real-time charging rate and SOC of the target vehicle are formulated into a training set and a test set; The training set is imported into the Lasso regression initial model, and the historical data network is introduced, and the conventional value of the regularization parameter α when constructing the power demand model is retrieved from the historical data network and calibrated as the target regularization parameter α conventional value; The target regularization parameter α is set to a normal value in the initial Lasso regression model, and the training set is fitted with the minimum data in the initial Lasso regression model to make the parameters in the training set approach zero. At the same time, the L1 regularization term is introduced to constrain the fitting coefficients of the parameters of the training set when the training set is fitted with the minimum data. A standard fitting coefficient is preset. When the fitting coefficient of the parameter in the training set is equal to the standard fitting coefficient, the minimization data fitting of the training set is stopped, and the trained Lasso regression initial model is output and calibrated as the power demand model to be analyzed. A coordinate descent method is introduced into the power demand model to be analyzed, and a convergence state analysis is performed on the test set based on the coordinate descent method. If the convergence state of the test set is within the preset range, the power demand model to be analyzed is calibrated as the power demand standard model. If the convergence state of the test set is not within the preset range, the power demand model to be analyzed is subjected to k-fold cross validation of the conventional value of the regularization parameter α, thereby adjusting the conventional value of the regularization parameter α to obtain the power demand standard model. Combined with the standard power demand model, the power scheduling analysis is carried out on the real-time charging parameters of the charging pile and the target vehicle's sequential charging status, and the power allocation strategy of the new energy charging pile is designed.

[0007] Further, in a preferred embodiment of the present invention, the power demand standard model is combined to perform power scheduling analysis on the real-time charging parameters of the charging pile sequence and the target vehicle sequence charging state, and a power allocation strategy for the new energy charging pile is designed, specifically: Preset the maximum charging time, run the power demand standard model, predict the output power change trend of the new energy charging pile after charging the target vehicle within the maximum charging time, and calibrate it as the charging pile output power change trend. At the same time, calculate the charging power change trend of the target vehicle within the maximum charging time based on the power demand model standard model, and calibrate it as the charging power change trend of the target vehicle; Analyze the changing trend of the charging pile output power and the charging power of the target vehicle to determine the maximum real-time output power of the new energy charging pile and the maximum real-time charging power of the target vehicle. If the maximum real-time output power of the new energy charging pile is constantly greater than the maximum real-time charging power of the target vehicle, generate a first power allocation strategy. The first power allocation strategy is to control the output power of the new energy charging pile to the target vehicle so that the output power of the new energy charging pile is equal to the maximum real-time charging power of the target vehicle; If the maximum real-time output power of the new energy charging pile is not constant and is greater than the maximum real-time charging power of the target vehicle, a second power allocation strategy is generated; The second power allocation strategy is to control the new energy charging pile to output power to the target vehicle so that the output power is constant and output at the maximum real-time output power of the new energy charging pile; A quantity analysis is performed on the target vehicles, and based on the quantity analysis result, a strategy optimization is performed on the first power allocation strategy and the second power allocation strategy.

[0008] Furthermore, in a preferred embodiment of the present invention, the target vehicles are analyzed quantitatively, and based on the quantitative analysis results, the first power allocation strategy and the second power allocation strategy are optimized, specifically: Performing a quantity analysis on the target vehicles, if the number of the target vehicles is equal to 1, there is no need to optimize the first power allocation strategy and the second power allocation strategy, and the first power allocation strategy and the second power allocation strategy can be directly output; If the number of target vehicles is greater than 1, the SOC of all target vehicles is analyzed to determine the remaining power of all target vehicles, and the remaining power of all target vehicles is analyzed. The target vehicles are sorted according to the ascending principle to obtain a sorting table of the remaining power of the target vehicles; Preset a target vehicle dangerous power level, analyze the target vehicle remaining power ranking table, and mark the target vehicle whose remaining power level is less than the target vehicle dangerous power level on the target vehicle remaining power ranking table as a target vehicle with dangerous power level; When it is determined that the number of target vehicles is greater than 1, the first power allocation strategy is output to all target vehicles with dangerous power levels, until the remaining power of all target vehicles with dangerous power levels is greater than the dangerous power level of the target vehicles; If the first power allocation strategy can be output to a single target vehicle, but cannot be output to multiple target vehicles, a third power allocation strategy is generated, wherein the third power allocation strategy is, based on the target vehicle remaining power ranking table, to output full power to the target vehicle ranked first, and to output average power to other target vehicles; If only the second power allocation strategy can be output for a single vehicle, a fourth power allocation strategy is generated, wherein the fourth power allocation strategy is, based on the target vehicle remaining power ranking table, outputting the second power allocation strategy to the target vehicle ranked first, and ranking other target vehicles for processing.

[0009] Furthermore, in a preferred embodiment of the present invention, the output power allocation strategy and the fault analysis and tracing of the new energy charging pile are carried out during the output of the power allocation strategy, specifically: The first power allocation strategy, the second power allocation strategy, the third power allocation strategy and the fourth power allocation strategy are collectively referred to as target power allocation strategies, and the new energy charging pile is controlled to output the target power allocation strategy to all target vehicles; In the process of outputting the target power allocation strategy to all target vehicles, the voltage and current fluctuations output by the new energy charging pile are monitored in real time, and a voltage and current fluctuation schematic diagram is constructed according to the voltage and current fluctuations output by the new energy charging pile; Analyze the voltage and current fluctuation schematic diagram to determine whether the voltage and current are not within the preset range when fluctuating. If so, perform image recognition on the charging interface and connecting wire of the new energy charging pile, and determine whether the charging interface and connecting wire of the new energy charging pile are damaged based on the image recognition; If so, the reason why the voltage and current are not within the preset range when fluctuating is defined as a physical reason of the charging pile; Otherwise, the reason why the voltage and current are not within the preset range when fluctuating is defined as the reason for abnormal grid load.

[0010] The second aspect of the present invention further provides an intelligent power distribution control system for a new energy charging pile, the intelligent power distribution control system comprising a memory and a processor, the memory storing an intelligent power distribution control method, and when the intelligent power distribution control method is executed by the processor, the following steps are implemented: Collect charging parameters of new energy charging piles and connected vehicles, and upload them to the central control system for data preprocessing of charging parameters; build a power demand model, use the power demand model to perform power scheduling analysis on the real-time charging parameters of the charging piles and the target vehicle's sequential charging status, and design a power allocation strategy for new energy charging piles based on the analysis results; Output power allocation strategy, and conduct fault analysis and tracing on new energy charging piles during the power allocation strategy output process.

[0011] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: various parameters of the charging pile and the connected vehicle are collected in real time through sensors and other equipment and transmitted to the central control system. The received data is analyzed and processed using an intelligent scheduling algorithm to determine the optimal power allocation strategy. The power output of the charging pile is adjusted according to the strategy, and dynamically adjusted in real time according to voltage and current changes to ensure that power distribution does not cause overload or instability of the power grid. The present invention can achieve intelligent and efficient power distribution and provide safe and reliable charging services for electric vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0013] Figure 1 A flow chart showing an intelligent power distribution control method for a new energy charging pile is shown; Figure 2 A flow chart of a method for constructing a power demand model and designing a power allocation strategy is shown; Figure 3 A program view of an intelligent power distribution control system for a new energy charging pile is shown. DETAILED DESCRIPTION

[0014] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0015] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0016] Figure 1 A flow chart of an intelligent power distribution control method for a new energy charging pile is shown, comprising the following steps: S102: Collect charging parameters of the new energy charging pile and the connected vehicle, and upload them to the central control system to perform data preprocessing on the charging parameters; S104: construct a power demand model, perform power scheduling analysis on the real-time charging parameters of the charging pile and the target vehicle's sequential charging status through the power demand model, and design a power allocation strategy for the new energy charging pile based on the analysis results; S106: Output the power allocation strategy, and perform fault analysis and source tracing on the new energy charging pile during the power allocation strategy output process.

[0017] Furthermore, in a preferred embodiment of the present invention, the charging parameters of the new energy charging pile and the connected vehicle are collected and uploaded to the central control system for data preprocessing of the charging parameters, specifically: Obtain a new energy charging pile, and determine the range of various charging parameters of the new energy charging pile, which are calibrated as the charging parameter range of the new energy charging pile, wherein the charging parameter range of the new energy charging pile includes the range of input voltage, input current, output voltage, and output current; Marking the vehicle being connected to the new energy charging pile as a target vehicle, and determining the battery status of the target vehicle, wherein the battery status includes the remaining power and the real-time temperature of the battery; Obtain the real-time charging parameters of the new energy charging pile, obtain the central control system of the new energy charging pile, calibrate it as the target central control system, and control the target central control system to collect and store the real-time charging parameters of the new energy charging pile and the battery status of the target vehicle in real time; In the target central control system, all stored parameters are cleaned to remove abnormal and invalid values ​​in the parameters. At the same time, all parameters are processed in a unified data format and time series conversion, so that the data format of all parameters is converted into a time series format, and the real-time charging parameters of the new energy charging pile in the time series format are obtained, which are calibrated as the real-time charging parameters of the charging pile time series, and the battery status of the target vehicle in the time series format is obtained, which is calibrated as the time series charging status of the target vehicle.

[0018] It should be noted that after determining the new energy charging pile and the target vehicle, it is necessary to use the central control system to integrate and control the relevant parameters of the new energy charging pile and the target vehicle, collect and unify the data format. The relevant parameters are the charging parameter range of the new energy charging pile and the battery status of the target vehicle. The real-time charging parameters of the new energy charging pile and the real-time charging status of the target vehicle can be determined based on the relevant parameters. When adjusting and allocating the output power, the above conditions determine how much power is allocated and in what order.

[0019] Furthermore, in a preferred embodiment of the present invention, the output power allocation strategy and the fault analysis and tracing of the new energy charging pile are carried out during the output of the power allocation strategy, specifically: The first power allocation strategy, the second power allocation strategy, the third power allocation strategy and the fourth power allocation strategy are collectively referred to as target power allocation strategies, and the new energy charging pile is controlled to output the target power allocation strategy to all target vehicles; In the process of outputting the target power allocation strategy to all target vehicles, the voltage and current fluctuations output by the new energy charging pile are monitored in real time, and a voltage and current fluctuation schematic diagram is constructed according to the voltage and current fluctuations output by the new energy charging pile; Analyze the voltage and current fluctuation schematic diagram to determine whether the voltage and current are not within the preset range when fluctuating. If so, perform image recognition on the charging interface and connecting wire of the new energy charging pile, and determine whether the charging interface and connecting wire of the new energy charging pile are damaged based on the image recognition; If so, the reason why the voltage and current are not within the preset range when fluctuating is defined as a physical reason of the charging pile; Otherwise, the reason why the voltage and current are not within the preset range when fluctuating is defined as the reason for abnormal grid load.

[0020] It should be noted that when the new energy charging pile outputs power to the target vehicle, if the voltage and current of the charging pile fluctuate abnormally, that is, the voltage and current are abnormally large or abnormally small, it is judged that there may be a problem in the new energy key pile, which leads to abnormal voltage and current fluctuations during the output power. Usually, the abnormal voltage and current fluctuations are caused by the failure of the charging head of the charging pile or the abnormal load connected to the power grid. Therefore, through image analysis, it is judged whether there is a failure of the charging head. If so, it is a physical reason for the charging pile, otherwise it is caused by abnormal load of the power grid.

[0021] Figure 2 A flow chart of a method for constructing a power demand model and designing a power allocation strategy is shown, comprising the following steps: S202: construct a power demand model, perform power scheduling analysis on the real-time charging parameters of the charging pile and the target vehicle's sequential charging status through the power demand model, and design a power allocation strategy for the new energy charging pile based on the analysis results; S204: Combined with the power demand standard model, power scheduling analysis is performed on the real-time charging parameters of the charging pile and the target vehicle's sequential charging status, and a power allocation strategy for the new energy charging pile is designed; S206: Performing a quantity analysis on the target vehicles, and optimizing the first power allocation strategy and the second power allocation strategy based on the quantity analysis result.

[0022] Furthermore, in a preferred embodiment of the present invention, the power demand model is constructed, and power scheduling analysis is performed on the real-time charging parameters of the charging pile sequence and the target vehicle sequence charging state through the power demand model, and a power allocation strategy for the new energy charging pile is designed based on the analysis results, specifically: Based on the real-time charging parameters of the charging pile, the real-time output power of the new energy charging pile is calculated. At the same time, based on the real-time charging status of the target vehicle, the real-time charging rate and SOC of the target vehicle are calculated. The Lasso regression algorithm is introduced to construct an initial Lasso regression model, and the real-time output power of the new energy charging pile and the real-time charging rate and SOC of the target vehicle are formulated into a training set and a test set; The training set is imported into the Lasso regression initial model, and the historical data network is introduced, and the conventional value of the regularization parameter α when constructing the power demand model is retrieved from the historical data network and calibrated as the target regularization parameter α conventional value; The target regularization parameter α is set to a normal value in the initial Lasso regression model, and the training set is fitted with the minimum data in the initial Lasso regression model to make the parameters in the training set approach zero. At the same time, the L1 regularization term is introduced to constrain the fitting coefficients of the parameters of the training set when the training set is fitted with the minimum data. A standard fitting coefficient is preset. When the fitting coefficient of the parameter in the training set is equal to the standard fitting coefficient, the minimization data fitting of the training set is stopped, and the trained Lasso regression initial model is output and calibrated as the power demand model to be analyzed. A coordinate descent method is introduced into the power demand model to be analyzed, and a convergence state analysis is performed on the test set based on the coordinate descent method. If the convergence state of the test set is within the preset range, the power demand model to be analyzed is calibrated as the power demand standard model. If the convergence state of the test set is not within the preset range, the power demand model to be analyzed is subjected to k-fold cross validation of the conventional value of the regularization parameter α, thereby adjusting the conventional value of the regularization parameter α to obtain the power demand standard model. It should be noted that the power demand model is a model that predicts the changes in output power and charging power of charging piles and vehicles during the charging process. According to the changes in output power and charging power, the best power allocation strategy can be designed to achieve intelligent and efficient power allocation, and provide safe and reliable charging services for electric vehicles. The Lasso regression algorithm is required to build the model. The Lasso regression algorithm is a regularization technique for linear regression models. It improves the performance of the model by introducing the L1 regularization term, especially in feature selection and preventing overfitting. The purpose of model training can be achieved by inputting data into the Lasso regression initial model for minimization fitting. Among them, the formula for fitting data is: ; in, is the vector of response variables, is the feature matrix, indicating the i The feature vector of the samples, is the vector of regression coefficients, that is, the model parameters that need to be solved, n is the number of samples, p is the number of features, and α is the regularization parameter used to control the strength of regularization. α The larger the value, the stronger the regularization effect, and the coefficients of more features will be compressed to zero. The regular value of the target regularization parameter α can be retrieved from the historical data network. After the fitting process is completed, it is necessary to introduce a test set to test whether the prediction ability of the model is qualified, that is, based on the coordinate descent method, the convergence state analysis of the test set is performed in the model. If the convergence state is within the preset value range, it proves that the model prediction ability is qualified. If not, it is necessary to adjust the regular value of the target regularization parameter α. Lasso regression can automatically select features that contribute less to the model and reduce their coefficients to zero while fitting the data, thereby preventing overfitting and improving the generalization ability of the model. Therefore, according to the Lasso regression method, a predictable model is constructed. A model that predicts the output power and charging power changes of charging piles and vehicles during charging, that is, a power demand standard model.

[0023] Further, in a preferred embodiment of the present invention, the power demand standard model is combined to perform power scheduling analysis on the real-time charging parameters of the charging pile sequence and the target vehicle sequence charging state, and a power allocation strategy for the new energy charging pile is designed, specifically: Preset the maximum charging time, run the power demand standard model, predict the output power change trend of the new energy charging pile after charging the target vehicle within the maximum charging time, and calibrate it as the charging pile output power change trend. At the same time, calculate the charging power change trend of the target vehicle within the maximum charging time based on the power demand model standard model, and calibrate it as the charging power change trend of the target vehicle; Analyze the changing trend of the charging pile output power and the charging power of the target vehicle to determine the maximum real-time output power of the new energy charging pile and the maximum real-time charging power of the target vehicle. If the maximum real-time output power of the new energy charging pile is constantly greater than the maximum real-time charging power of the target vehicle, generate a first power allocation strategy. The first power allocation strategy is to control the output power of the new energy charging pile to the target vehicle so that the output power of the new energy charging pile is equal to the maximum real-time charging power of the target vehicle; If the maximum real-time output power of the new energy charging pile is not constant and is greater than the maximum real-time charging power of the target vehicle, a second power allocation strategy is generated; Among them, the second power allocation strategy is to control the new energy charging pile to output power to the target vehicle so that the output power is constant and output at the maximum real-time output power of the new energy charging pile.

[0024] It should be noted that the change trend of the charging pile output power and the change trend of the target vehicle charging power reflect the power change of the new energy charging pile when charging the vehicle, and different power allocation strategies are designed according to the power change. Among them, the charging strategy of a single vehicle is given priority. If the real-time maximum output power of the new energy charging pile is constantly greater than the real-time maximum charging power of the target vehicle, it proves that there will be no shortage of power when the new energy charging pile outputs power for charging. On the contrary, if the real-time maximum output power of the new energy charging pile is not constantly greater than the real-time maximum charging power of the target vehicle, there may be a period of insufficient power supply, thereby generating different power allocation strategies.

[0025] Furthermore, in a preferred embodiment of the present invention, the target vehicles are analyzed quantitatively, and based on the quantitative analysis results, the first power allocation strategy and the second power allocation strategy are optimized, specifically: Performing a quantity analysis on the target vehicles, if the number of the target vehicles is equal to 1, there is no need to optimize the first power allocation strategy and the second power allocation strategy, and the first power allocation strategy and the second power allocation strategy can be directly output; If the number of target vehicles is greater than 1, the SOC of all target vehicles is analyzed to determine the remaining power of all target vehicles, and the remaining power of all target vehicles is analyzed. The target vehicles are sorted according to the ascending principle to obtain a sorting table of the remaining power of the target vehicles; Preset a target vehicle dangerous power level, analyze the target vehicle remaining power ranking table, and mark the target vehicle whose remaining power level is less than the target vehicle dangerous power level on the target vehicle remaining power ranking table as a target vehicle with dangerous power level; When it is determined that the number of target vehicles is greater than 1, the first power allocation strategy is output to all target vehicles with dangerous power levels, until the remaining power of all target vehicles with dangerous power levels is greater than the dangerous power level of the target vehicles; If the first power allocation strategy can be output to a single target vehicle, but cannot be output to multiple target vehicles, a third power allocation strategy is generated, wherein the third power allocation strategy is, based on the target vehicle remaining power ranking table, to output full power to the target vehicle ranked first, and to output average power to other target vehicles; If only the second power allocation strategy can be output for a single vehicle, a fourth power allocation strategy is generated, wherein the fourth power allocation strategy is, based on the target vehicle remaining power ranking table, outputting the second power allocation strategy to the target vehicle ranked first, and ranking other target vehicles for processing.

[0026] It should be noted that the power allocation strategy is different for different numbers of vehicles. When there is only one vehicle, the first power allocation strategy and the second power allocation strategy are directly output, and the judgment is made based on whether the maximum real-time output power of the new energy charging pile is constant and greater than the maximum real-time charging power of the target vehicle. If the number of vehicles is greater than 1, the order of vehicle charging needs to be considered. The current remaining power of the vehicle is given priority in vehicle charging. The vehicle with less remaining power is charged first, and the vehicle with a remaining power less than the preset value is charged first, so the remaining power ranking table of the target vehicle is constructed. The vehicle SOC is the remaining power of the vehicle. When the number of vehicles is greater than 1, the vehicle with a remaining power less than the preset value, that is, the target vehicle with dangerous power, is directly charged first to ensure that these vehicles pass the dangerous period of remaining power, and then analyze with other vehicles. When the first power allocation strategy can be output for only one vehicle, but not for multiple vehicles, it proves that the charging power of the new energy charging pile can only meet the full power operation of one vehicle, and the other needs to be output at an average power to ensure high efficiency. When a vehicle is fully charged after running at full power, it is powered off, and continues to select a vehicle for full power output based on the ranking table. When only the second power allocation strategy can be output for a single vehicle, proving that the new energy charging pile cannot output full power to any vehicle, the charging of other vehicles will be stopped, and the vehicle with the least remaining power will be charged first, and the charging will be arranged in sequence.

[0027] In addition, the intelligent power distribution control method of the new energy charging pile further includes the following steps: If there is a physical problem with the new energy charging pile, then in the historical data network, the solution for reinforcing and pressing the charging head of the new energy charging pile and replacing the damaged one is retrieved and output; After the charging head of the new energy charging pile is reinforced and pressed and damaged ones are replaced, if the voltage and current are still not within the preset range when fluctuating after the reinforcement, pressing and damaged ones are replaced, it is judged that the cause is abnormal grid load; If there is an abnormal reason for the grid load, the grid connected to the new energy charging pile is obtained, the load value of the grid is analyzed, and the load danger value and load standard value of the grid are preset; If the load value of the power grid is greater than the load standard value and less than the load danger value, load adjustment is performed within the power grid to reduce the charging power of the new energy charging pile as a whole. At the same time, a voltage stabilizer is configured and operated at the input end of the new energy charging pile to ensure that the voltage and current remain within the preset range when they fluctuate; If the load value of the power grid is greater than the load danger value, the new energy charging pile will stop outputting power until the load value of the power grid is less than the load danger value.

[0028] It should be noted that repairing and replacing the damaged charging head can help the voltage and current remain within the preset range when they fluctuate. If it still cannot be done, it is directly judged that the power grid load problem causes the abnormal output power of the charging pile. When the power grid load value is greater than the standard value and less than the dangerous value, the load can be adjusted and the voltage can be stabilized to ensure that the voltage and current remain within the preset range when they fluctuate. If the load value of the power grid is greater than the dangerous load value, continuing to charge may damage the vehicle, and the new energy charging pile should be stopped immediately until the load value of the power grid is less than the dangerous load value.

[0029] like Figure 3 As shown, the second aspect of the present invention further provides an intelligent power distribution control system for a new energy charging pile, the intelligent power distribution control system includes a memory 31 and a processor 32, the memory 31 stores an intelligent power distribution control method, and when the intelligent power distribution control method is executed by the processor 32, the following steps are implemented: Collect charging parameters of new energy charging piles and connected vehicles, and upload them to the central control system for data preprocessing of charging parameters; Construct a power demand model, use the power demand model to analyze the real-time charging parameters of the charging pile and the target vehicle's sequential charging status, and design a power allocation strategy for the new energy charging pile based on the analysis results; Output power allocation strategy, and conduct fault analysis and tracing on new energy charging piles during the power allocation strategy output process.

[0030] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent power distribution control method for a new energy charging pile, characterized in that: The following steps are involved: Collect charging parameters of new energy charging piles and connected vehicles, and upload them to the central control system for data preprocessing of charging parameters; Construct a power demand model, use the power demand model to analyze the real-time charging parameters of the charging pile and the target vehicle's sequential charging status, and design a power allocation strategy for the new energy charging pile based on the analysis results; Output power allocation strategy, and conduct fault analysis and tracing on new energy charging piles during the power allocation strategy output process.

2. According to the intelligent power distribution control method of a new energy charging pile described in claim 1, it is characterized in that: The charging parameters of the new energy charging pile and the connected vehicle are collected and uploaded to the central control system for data preprocessing of the charging parameters, specifically: Obtain a new energy charging pile, and determine the range of various charging parameters of the new energy charging pile, which are calibrated as the charging parameter range of the new energy charging pile, wherein the charging parameter range of the new energy charging pile includes the range of input voltage, input current, output voltage, and output current; Marking the vehicle being connected to the new energy charging pile as a target vehicle, and determining the battery status of the target vehicle, wherein the battery status includes the remaining power and the real-time temperature of the battery; Obtain the real-time charging parameters of the new energy charging pile, obtain the central control system of the new energy charging pile, calibrate it as the target central control system, and control the target central control system to collect and store the real-time charging parameters of the new energy charging pile and the battery status of the target vehicle in real time; In the target central control system, all stored parameters are cleaned to remove abnormal and invalid values ​​in the parameters. At the same time, all parameters are processed in a unified data format and time series conversion, so that the data format of all parameters is converted into a time series format, and the real-time charging parameters of the new energy charging pile in the time series format are obtained, which are calibrated as the real-time charging parameters of the charging pile time series, and the battery status of the target vehicle in the time series format is obtained, which is calibrated as the time series charging status of the target vehicle.

3. According to the intelligent power distribution control method of a new energy charging pile described in claim 1, it is characterized in that: The power demand model is constructed, and power scheduling analysis is performed on the real-time charging parameters of the charging pile and the target vehicle's sequential charging status through the power demand model, and a power allocation strategy for the new energy charging pile is designed based on the analysis results, specifically: Based on the real-time charging parameters of the charging pile, the real-time output power of the new energy charging pile is calculated. At the same time, based on the real-time charging status of the target vehicle, the real-time charging rate and SOC of the target vehicle are calculated. The Lasso regression algorithm is introduced to construct an initial Lasso regression model, and the real-time output power of the new energy charging pile and the real-time charging rate and SOC of the target vehicle are formulated into a training set and a test set; The training set is imported into the Lasso regression initial model, and the historical data network is introduced, and the conventional value of the regularization parameter α when constructing the power demand model is retrieved from the historical data network and calibrated as the target regularization parameter α conventional value; The target regularization parameter α is set to a normal value in the initial Lasso regression model, and the training set is fitted with the minimum data in the initial Lasso regression model to make the parameters in the training set approach zero. At the same time, the L1 regularization term is introduced to constrain the fitting coefficients of the parameters of the training set when the training set is fitted with the minimum data. A standard fitting coefficient is preset. When the fitting coefficient of the parameter in the training set is equal to the standard fitting coefficient, the minimization data fitting of the training set is stopped, and the trained Lasso regression initial model is output and calibrated as the power demand model to be analyzed. A coordinate descent method is introduced into the power demand model to be analyzed, and a convergence state analysis is performed on the test set based on the coordinate descent method. If the convergence state of the test set is within the preset range, the power demand model to be analyzed is calibrated as the power demand standard model. If the convergence state of the test set is not within the preset range, the power demand model to be analyzed is subjected to k-fold cross validation of the conventional value of the regularization parameter α, thereby adjusting the conventional value of the regularization parameter α to obtain the power demand standard model. Combined with the standard power demand model, the power scheduling analysis is carried out on the real-time charging parameters of the charging pile and the target vehicle's sequential charging status, and the power allocation strategy of the new energy charging pile is designed.

4. According to the intelligent power distribution control method of a new energy charging pile described in claim 3, it is characterized in that: The power demand standard model is combined to perform power scheduling analysis on the real-time charging parameters of the charging pile and the target vehicle's sequential charging status, and a power allocation strategy for the new energy charging pile is designed, specifically: Preset the maximum charging time, run the power demand standard model, predict the output power change trend of the new energy charging pile after charging the target vehicle within the maximum charging time, and calibrate it as the charging pile output power change trend. At the same time, calculate the charging power change trend of the target vehicle within the maximum charging time based on the power demand model standard model, and calibrate it as the charging power change trend of the target vehicle; Analyze the changing trend of the charging pile output power and the charging power of the target vehicle to determine the maximum real-time output power of the new energy charging pile and the maximum real-time charging power of the target vehicle. If the maximum real-time output power of the new energy charging pile is constantly greater than the maximum real-time charging power of the target vehicle, generate a first power allocation strategy. The first power allocation strategy is to control the output power of the new energy charging pile to the target vehicle so that the output power of the new energy charging pile is equal to the maximum real-time charging power of the target vehicle; If the maximum real-time output power of the new energy charging pile is not constant and is greater than the maximum real-time charging power of the target vehicle, a second power allocation strategy is generated; The second power allocation strategy is to control the new energy charging pile to output power to the target vehicle so that the output power is constant and output at the maximum real-time output power of the new energy charging pile; A quantity analysis is performed on the target vehicles, and based on the quantity analysis result, a strategy optimization is performed on the first power allocation strategy and the second power allocation strategy.

5. According to the intelligent power distribution control method of a new energy charging pile described in claim 4, it is characterized in that: The target vehicles are analyzed quantitatively, and based on the quantitative analysis results, the first power allocation strategy and the second power allocation strategy are optimized strategically, specifically: Performing a quantity analysis on the target vehicles, if the number of the target vehicles is equal to 1, there is no need to optimize the first power allocation strategy and the second power allocation strategy, and the first power allocation strategy and the second power allocation strategy can be directly output; If the number of target vehicles is greater than 1, the SOC of all target vehicles is analyzed to determine the remaining power of all target vehicles, and the remaining power of all target vehicles is analyzed. The target vehicles are sorted according to the ascending principle to obtain a sorting table of the remaining power of the target vehicles; Preset a target vehicle dangerous power level, analyze the target vehicle remaining power ranking table, and mark the target vehicle whose remaining power level is less than the target vehicle dangerous power level on the target vehicle remaining power ranking table as a target vehicle with dangerous power level; When it is determined that the number of target vehicles is greater than 1, the first power allocation strategy is output to all target vehicles with dangerous power levels, until the remaining power of all target vehicles with dangerous power levels is greater than the dangerous power level of the target vehicles; If the first power allocation strategy can be output to a single target vehicle, but cannot be output to multiple target vehicles, a third power allocation strategy is generated, wherein the third power allocation strategy is, based on the target vehicle remaining power ranking table, to output full power to the target vehicle ranked first, and to output average power to other target vehicles; If only the second power allocation strategy can be output for a single vehicle, a fourth power allocation strategy is generated, wherein the fourth power allocation strategy is, based on the target vehicle remaining power ranking table, outputting the second power allocation strategy to the target vehicle ranked first, and ranking other target vehicles for processing.

6. According to the intelligent power distribution control method of a new energy charging pile described in claim 1, it is characterized in that: The output power allocation strategy and the fault analysis and tracing of the new energy charging pile during the power allocation strategy output process are specifically as follows: The first power allocation strategy, the second power allocation strategy, the third power allocation strategy and the fourth power allocation strategy are collectively referred to as target power allocation strategies, and the new energy charging pile is controlled to output the target power allocation strategy to all target vehicles; In the process of outputting the target power allocation strategy to all target vehicles, the voltage and current fluctuations output by the new energy charging pile are monitored in real time, and a voltage and current fluctuation schematic diagram is constructed according to the voltage and current fluctuations output by the new energy charging pile; Analyze the voltage and current fluctuation schematic diagram to determine whether the voltage and current are not within the preset range when fluctuating. If so, perform image recognition on the charging interface and connecting wire of the new energy charging pile, and determine whether the charging interface and connecting wire of the new energy charging pile are damaged based on the image recognition; If so, the reason why the voltage and current are not within the preset range when fluctuating is defined as a physical reason of the charging pile; Otherwise, the reason why the voltage and current are not within the preset range when fluctuating is defined as the reason for abnormal grid load.

7. An intelligent power distribution control system for a new energy charging pile, characterized in that: The intelligent power distribution control system includes a memory and a processor, wherein the memory stores an intelligent power distribution control method program. When the intelligent power distribution control method program is executed by the processor, the steps of the intelligent power distribution control method for a new energy charging pile as described in any one of claims 1 to 6 are implemented.

Images