Dynamic power distribution method of direct current charging pile

Abstract

The invention discloses a dynamic power distribution method for a direct current charging pile, and relates to the technical field of power distribution of the direct current charging pile, and the method specifically comprises the following steps: obtaining the dynamic power characteristic information of all electric vehicles which are accessed to the direct current charging pile for charging in real time, and carrying out the analysis after obtaining, respectively generating a fluctuation risk index and a dynamic load adaptability coefficient of each electric vehicle; and constructing a fluctuation sensitivity evaluation model for the generated fluctuation risk index and the dynamic load adaptability coefficient of each electric vehicle, generating a fluctuation sensitivity evaluation coefficient of each electric vehicle, analyzing the fluctuation sensitivity evaluation coefficient after generation, and evaluating the sensitivity degree of each electric vehicle to the bus voltage fluctuation. And the electric vehicles are divided into high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles according to evaluation results. The problem that charging of sensitive vehicles is interrupted due to the fact that the direct-current charging pile cannot accurately distribute power is solved, and the stability and resource utilization efficiency of a charging system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution of a DC charging pile, and in particular to a dynamic power distribution method of a DC charging pile. Background Art

[0002] A DC charging pile is a device that provides DC fast charging for electric vehicles. It can directly convert the power of the power grid into DC power that is suitable for the vehicle battery, and significantly shorten the charging time through high power output. It is widely used in electric vehicle charging stations, bus hubs and other high-load scenarios. Since DC charging piles usually provide charging services for multiple vehicles at the same time, and the battery capacity, state of charge and charging requirements of different vehicles are different, the load distribution during the charging process is complex and unbalanced. If the power distribution is not optimized, some vehicles may charge too slowly or even fail to meet the demand, and some charging pile modules may be damaged due to overload operation. In addition, when the power supply capacity of the power grid is limited or fluctuates greatly, unreasonable power distribution will cause waste of electricity and reduce the overall resource utilization. Therefore, power distribution for DC charging piles, especially by dynamically adjusting the distribution strategy to maximize charging efficiency, balance equipment load and minimize energy consumption, can not only improve the operating efficiency and stability of the system, but also effectively extend the service life of the equipment and ensure user experience.

[0003] The existing dynamic power allocation technology of DC charging piles usually achieves reasonable power allocation by monitoring various status information during the charging process. During the charging process, the system obtains the battery charging status, current charging demand and charging priority of each vehicle in real time, and monitors the available power of the charging pile and the power supply status of the power grid. Based on this information, the system dynamically adjusts the charging power of each vehicle, such as giving priority to providing more power to vehicles with high charging demand, while appropriately reducing the power allocation to vehicles with low charging demand to avoid resource waste and uneven load. In this process, the system optimizes the allocation scheme in real time according to the actual situation. For example, after the vehicle is charged, the released power is redistributed to other vehicles to ensure that the power resources of the charging pile can be used efficiently. In addition, when there is a fluctuation in the power grid or the equipment load exceeds the safe range, the system can flexibly adjust the charging power to ensure the safety of equipment operation and charging efficiency. The entire allocation process is centered on real-time monitoring and flexible adjustment to ensure a dynamic balance between vehicle charging demand and charging pile load.

[0004] The prior art has the following deficiencies: When multiple electric vehicles are connected to a DC charging pile for charging at the same time, due to the difference in power demand and charging stages between vehicles, the DC bus voltage of the charging pile may fluctuate slightly. For example, when a vehicle enters the high-power charging stage, its instantaneous current demand increases, causing the bus voltage to drop for a short time; and when another vehicle is close to being fully charged, its demand decreases, which may cause the bus voltage to rise. Although this fluctuation is small (for example, ±5V), for some electric vehicles, especially those with a small tolerance range for bus voltage fluctuations (such as vehicles with strict battery management system protection mechanisms), the charging protection mechanism may be triggered to interrupt charging. When allocating power, the existing dynamic power allocation technology for DC charging piles only adjusts according to the total power demand of each vehicle, and fails to evaluate and dynamically monitor the sensitivity of vehicles to DC bus voltage fluctuations in real time, resulting in the inability to identify which vehicles are at greater risk of fluctuations, and then allocating too high or too low power to these sensitive vehicles. The inaccuracy of this allocation strategy may not only cause sensitive vehicles to frequently interrupt charging, affecting their charging efficiency, but also interfere with the power allocation of other vehicles due to the triggering of charging protection, reducing the stability and resource utilization efficiency of the entire charging system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a dynamic power allocation method for a DC charging pile to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a dynamic power allocation method for a DC charging pile, specifically comprising the following steps: When multiple electric vehicles are connected to a DC charging pile for charging at the same time, the fluctuation degree of the DC bus voltage is detected in real time, and the dynamic power allocation process is triggered when the voltage fluctuation exceeds the set range; Real-time acquisition of dynamic power characteristic information of all electric vehicles connected to DC charging piles for charging, and analysis after acquisition to generate fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle; A fluctuation sensitivity assessment model is constructed for the generated fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle, and a fluctuation sensitivity assessment coefficient of each electric vehicle is generated. After the generation, an analysis is performed to assess the sensitivity of each electric vehicle to bus voltage fluctuations, and each electric vehicle is divided into a highly sensitive vehicle, a medium sensitive vehicle, and a low sensitive vehicle according to the assessment results; According to the classification results of each electric vehicle, corresponding power allocation measures are taken for high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles respectively; Continuously monitor bus voltage fluctuations and the charging status of each electric vehicle, and dynamically adjust the fluctuation sensitivity assessment model and power allocation strategy based on real-time monitoring results. At the same time, store and analyze historical allocation data to optimize model parameters and strategy settings, and improve the long-term adaptability and stability of the system.

[0008] Preferably, the dynamic power characteristic information of all electric vehicles connected to the DC charging pile for charging is obtained in real time, and analyzed after acquisition to generate the fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle respectively, which specifically includes the following steps: Real-time acquisition of dynamic power characteristic information of all electric vehicles connected to DC charging piles for charging, and pre-processing after acquisition; Extracting voltage fluctuation response information and dynamic power adaptation information from the pre-processed dynamic power characteristic information of all electric vehicles; The extracted voltage fluctuation response information and dynamic power adaptation information are analyzed to generate the fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle respectively.

[0009] Preferably, the logic for obtaining the volatility risk index of each electric vehicle is as follows: The voltage fluctuation response information is extracted from the pre-processed dynamic power characteristic information of all electric vehicles, including the actual fluctuation amplitude of the bus voltage at different times within a period of time, the rate of change of the battery terminal voltage of each electric vehicle, and the standard deviation of the battery terminal voltage of each electric vehicle, and calibrated as , and , Indicates that within a period of time The actual fluctuation amplitude of bus voltage at the moment, Indicates that within a period of time Moment The rate of change of the battery terminal voltage of an electric vehicle, Indicates that within a period of time Moment The standard deviation of the battery terminal voltage of an electric vehicle, , , and are all positive integers; Calculate the volatility risk index of each electric vehicle. The specific calculation formula is as follows: In the formula, For the Volatility risk index of electric vehicles.

[0010] Preferably, the logic for obtaining the dynamic load adaptability coefficient of each electric vehicle is as follows: The dynamic power adaptation information is extracted from the preprocessed dynamic power characteristic information of all electric vehicles, including the current charging power of each electric vehicle at different times within a period of time, the rate of change of the charging current of each electric vehicle, and the rate of change of the internal impedance of the battery of each electric vehicle, and they are calibrated as , and , Indicates that within a period of time Moment The current charging power of an electric vehicle, Indicates that within a period of time Moment The rate of change of charging current of an electric vehicle, Indicates that within a period of time Moment The rate of change of the internal impedance of an electric vehicle battery, , , and are all positive integers; Calculate the dynamic load adaptability coefficient of each electric vehicle. The specific calculation formula is as follows: In the formula, For the Dynamic load adaptability coefficient of an electric vehicle.

[0011] Preferably, a fluctuation sensitivity assessment model is constructed for the generated fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle to generate a fluctuation sensitivity assessment coefficient of each electric vehicle, specifically comprising the following steps: The volatility risk index, dynamic load adaptability coefficient and corresponding volatility sensitivity assessment coefficient of several electric vehicles generated in the past period of time are collected and calibrated as , and , Indicates the number of fluctuation risk indexes, dynamic load adaptability coefficients and corresponding fluctuation sensitivity assessment coefficients of several electric vehicles generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; The multivariate regression model is selected as the volatility sensitivity assessment model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Using the finalized regression coefficient, the constructed volatility sensitivity assessment model is input with the volatility risk index of each electric vehicle generated in real time. and dynamic load adaptability factor , generate the fluctuation sensitivity assessment coefficient of each electric vehicle in real time .

[0012] Preferably, the generated fluctuation sensitivity assessment coefficients of each electric vehicle are The pre-set threshold range of the volatility sensitivity assessment coefficient The comparison is carried out, and the sensitivity of each electric vehicle to bus voltage fluctuation is evaluated based on the comparison results. Based on the evaluation results, each electric vehicle is divided into highly sensitive vehicles, medium sensitive vehicles and low sensitive vehicles. The specific comparison analysis and classification are as follows: like , the sensitivity of the electric vehicle to bus voltage fluctuation is low, then the electric vehicle is classified as a low-sensitivity vehicle; like , the electric vehicle is moderately sensitive to bus voltage fluctuations, and is classified as a moderately sensitive vehicle; like , the electric vehicle is highly sensitive to bus voltage fluctuations, and is classified as a highly sensitive vehicle.

[0013] Preferably, according to the classification results of each electric vehicle, corresponding power allocation measures are taken for high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles respectively, specifically: For electric vehicles classified as low-sensitivity vehicles, the power allocation measures taken are as follows: prioritize reducing the dynamic power adjustment range of the vehicle to ensure the stability of its existing charging process, and reduce the power resources allocated to the vehicle when there is a power demand conflict in the system, while continuously monitoring the changes in its power demand to ensure the stability of the overall charging process; For electric vehicles classified as medium-sensitive vehicles, the power allocation measures taken are as follows: according to the preset power allocation rules, balanced power resources are allocated to the vehicle to ensure the stability of its charging process, and the allocation ratio is dynamically adjusted according to the real-time power demand and bus voltage fluctuations to take into account the charging efficiency of the vehicle and the overall power balance of the system. At the same time, its sensitivity status is regularly evaluated to ensure the optimization of the power allocation strategy; For electric vehicles classified as highly sensitive vehicles, the power allocation measures taken are as follows: giving priority to ensuring their charging stability, reducing the risk of triggering protection mechanisms due to fluctuations by limiting the bus voltage fluctuation amplitude and allocating smooth power resources, while monitoring their charging status and battery-end response characteristics in real time.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention can accurately quantify the sensitivity of each electric vehicle to bus voltage fluctuations by introducing a fluctuation risk index and a dynamic load adaptability coefficient, and constructing a fluctuation sensitivity assessment model based on a multiple regression model. Unlike the prior art that allocates resources only based on total power demand, the present invention can comprehensively evaluate the vehicle's fluctuation adaptability from both static and dynamic dimensions, and provide accurate fluctuation sensitivity assessment results. This accuracy can not only identify highly sensitive vehicles, take protective measures in a timely manner, and avoid charging interruptions, but also effectively prevent the system operation efficiency from being reduced due to unreasonable resource allocation. On the whole, the present invention provides a scientific basis for power allocation strategies through sensitivity classification and quantification, thereby improving resource utilization and system stability.

[0015] 2. The present invention realizes dynamic adjustment of the power allocation strategy by continuously monitoring bus voltage fluctuations and vehicle charging status information, so as to adapt to the complex and changeable working conditions during the charging process. For example, when the bus voltage fluctuation exceeds the threshold, the system will automatically trigger the power allocation adjustment process, recalculate the vehicle's fluctuation sensitivity assessment coefficient in real time, and prioritize power resources. Highly sensitive vehicles are given priority protection, and low-sensitivity vehicles have their allocated resources appropriately reduced, thereby reducing the charging protection risk caused by fluctuations. At the same time, the dynamic adjustment strategy of the present invention enables the charging pile system to maintain stable operation even when power resources are limited, providing vehicles with an efficient and safe charging environment, and solving the problem that traditional solutions are insufficient in dealing with fluctuation risks.

[0016] 3. The present invention not only focuses on the real-time power allocation problem, but also optimizes the model parameters and strategy settings by storing and analyzing historical allocation data, thereby improving the long-term adaptability of the system. By mining the volatility risk index, dynamic load adaptability coefficient, and the effect of the power allocation strategy in historical data, the present invention can dynamically adjust the key parameters in the evaluation model (such as regression coefficients and sensitivity thresholds), thereby gradually improving the system's adaptability to different vehicle characteristics and working conditions. In addition, the analysis of historical data can reveal potential patterns and problems in resource allocation, and further improve the scientific nature of the power allocation strategy. This design with data-driven optimization as the core can not only improve the efficiency and stability of the system operation, but also provide strong support for the expansion of the system to more complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 The present invention is a flow chart of a method for dynamic power distribution of a DC charging pile. DETAILED DESCRIPTION

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0020] The present invention provides Figure 1 A dynamic power allocation method for a DC charging pile shown in the figure specifically comprises the following steps: When multiple electric vehicles are connected to a DC charging pile for charging at the same time, the fluctuation degree of the DC bus voltage is detected in real time, and the dynamic power allocation process is triggered when the voltage fluctuation exceeds the set range; Real-time detection of the fluctuation degree of DC bus voltage can be achieved through high-precision voltage sensors and monitoring modules installed inside the charging pile. The voltage sensor continuously collects real-time voltage data of the bus and transmits the collected voltage signal to the software control system of the charging pile at a high frequency (such as multiple times per second). The voltage fluctuation analysis module in the software system compares these voltage data with the reference voltage value of the bus, and accurately evaluates the degree of voltage fluctuation by dynamically calculating the instantaneous change amplitude and fluctuation trend of the voltage (for example, calculating the short-term change rate of voltage through the sliding window method). In addition, the software system can combine historical fluctuation data and vehicle access data for trend prediction, further improving the accuracy and real-time performance of fluctuation detection.

[0021] When the voltage fluctuation analysis module detects that the fluctuation amplitude of the bus voltage exceeds the set safety range (for example, ±5V), the event trigger module in the software system will immediately send out an abnormal signal. This signal can trigger the preset dynamic power allocation process in the system. The specific implementation method is that the abnormal signal will start the power allocation strategy module to re-collect the data of all currently connected vehicles, including the dynamic power characteristic information of the vehicle (such as voltage fluctuation tolerance range and real-time power demand), and at the same time call the fluctuation sensitivity assessment model to generate the vehicle's fluctuation sensitivity assessment coefficient, and readjust the power allocation plan according to the assessment results. The entire triggering and allocation adjustment process is completed in an automated manner in the software system without manual intervention to ensure that the system can respond quickly and adjust in real time.

[0022] Real-time detection of bus voltage fluctuations and triggering of the dynamic power allocation process are key steps in solving technical problems, because bus voltage fluctuations directly affect the charging stability of the vehicle, especially for sensitive vehicles with a small tolerance range for voltage fluctuations, which are more likely to interrupt charging due to fluctuations triggering the protection mechanism. By detecting the degree of fluctuation in real time, abnormalities can be discovered and remedial measures can be initiated as soon as the problem occurs. Triggering the dynamic power allocation process can adjust the power allocation strategy in real time according to the sensitivity of the vehicle, giving priority to the stability of highly sensitive vehicles, while optimizing the fluctuation adaptability of less sensitive vehicles. This mechanism not only improves the accuracy of power allocation, but also avoids the phenomenon of frequent interruptions of charging for sensitive vehicles, ensuring the stability of the entire system and resource utilization efficiency. In addition, this software-based automation method can quickly respond to complex fluctuation scenarios, ensuring that the charging pile system can still operate efficiently when multiple vehicles are connected, and solving the problem of lack of dynamic monitoring and response capabilities in existing technical solutions.

[0023] Real-time acquisition of dynamic power characteristic information of all electric vehicles connected to DC charging piles for charging, and analysis after acquisition to generate fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle; In this embodiment, the dynamic power characteristic information of all electric vehicles connected to the DC charging pile for charging is obtained in real time, and analyzed after acquisition to generate the fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle respectively, which specifically includes the following steps: Real-time acquisition of dynamic power characteristic information of all electric vehicles connected to DC charging piles for charging, and pre-processing after acquisition; Real-time acquisition of dynamic power characteristics information of all electric vehicles connected to the DC charging pile for charging can be accomplished through the collaboration of the built-in sensors in the charging pile and the vehicle communication module. First, the sensors in the charging pile collect key physical parameters in real time, including the real-time voltage (such as terminal voltage), real-time current (such as charging current) of the vehicle, and the current power demand of the vehicle. These data are transmitted to the control system of the charging pile at a high frequency (such as multiple times per second). In addition, through the battery management system (BMS) interface of the vehicle, standard communication protocols (such as CAN protocol or dedicated protocol) are used to obtain the internal state information of the battery, including impedance value, temperature and power, and transmit it to the charging pile software control system in real time. Finally, the data acquisition module integrates the sensor data and the BMS uploaded data to form dynamic power characteristics information with a unified timestamp for subsequent analysis.

[0024] The purpose of preprocessing the dynamic power characteristic information is to improve the accuracy and consistency of the data and ensure the reliability of the subsequent analysis results. The raw data may have noise interference (such as sensor measurement error), data missing (such as communication delay) or timestamp misalignment. The preprocessing is automatically completed by the software and includes the following steps: First, use the anomaly detection algorithm to clean the data, remove the noise beyond the reasonable range, and smooth the data fluctuations by mean filtering or median filtering. Secondly, interpolate and fill in the missing values, such as using linear interpolation to fill the missing data segments, or use historical model prediction to fill in large-scale data loss. Then, the data from different sources are time-aligned by the time synchronization algorithm to ensure the consistency of the data at the same time point. Finally, data standardization is performed to normalize the data of different units or ranges to a unified scale to provide high-quality input data for subsequent parameter calculation and analysis models.

[0025] Extracting voltage fluctuation response information and dynamic power adaptation information from the pre-processed dynamic power characteristic information of all electric vehicles; The extracted voltage fluctuation response information and dynamic power adaptation information are analyzed to generate the fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle respectively.

[0026] In this embodiment, the logic for obtaining the volatility risk index of each electric vehicle is as follows: The voltage fluctuation response information is extracted from the pre-processed dynamic power characteristic information of all electric vehicles, including the actual fluctuation amplitude of the bus voltage at different times within a period of time, the rate of change of the battery terminal voltage of each electric vehicle, and the standard deviation of the battery terminal voltage of each electric vehicle, and calibrated as , and , Indicates that within a period of time The actual fluctuation amplitude of bus voltage at the moment, Indicates that within a period of time Moment The rate of change of the battery terminal voltage of an electric vehicle, Indicates that within a period of time Moment The standard deviation of the battery terminal voltage of an electric vehicle, , , and are all positive integers; Extracting the voltage fluctuation response information from the pre-processed dynamic power characteristic information of all electric vehicles can be completed through the software data processing module of the charging pile system. First, the system locates the data fields related to voltage fluctuation from the dynamic power characteristic information, such as the time series of bus voltage and the time series of vehicle battery terminal voltage. For the actual fluctuation amplitude of the bus voltage, the system uses the time window to split the bus voltage data, for example, divide the continuous time series into windows of fixed length (such as 1 second), calculate the maximum and minimum values ​​of the bus voltage in each window, and use the difference between the two as the actual fluctuation amplitude. For the rate of change of the vehicle battery terminal voltage, the system calculates the derivative of the terminal voltage time series, that is, at each time point, calculates the difference between the two data points before and after and divides it by the time interval to generate a change rate sequence. Finally, for the standard deviation of the vehicle battery terminal voltage, the system calculates the standard deviation of the battery terminal voltage in each time window to reflect the long-term stability of the voltage fluctuation. The entire extraction process is automatically completed by the software, and the extraction results are stored in a standardized data format for subsequent calculation and analysis.

[0027] Actual fluctuation amplitude of bus voltage: Bus voltage data is collected in real time by high-precision voltage sensors inside the charging pile. The sensors record the instantaneous value of bus voltage at a high frequency (e.g., multiple times per second) and upload these values ​​to the software control module of the charging pile. The collected data is input into the system as a time series, and then the actual fluctuation amplitude is calculated by the maximum and minimum values ​​within a dynamic window (e.g., 1 second).

[0028] The rate of change of the vehicle battery terminal voltage: The battery terminal voltage data is obtained in real time through the charging pile and the vehicle communication module. The vehicle's battery management system (BMS) uploads the instantaneous value of the terminal voltage to the charging pile system through the CAN protocol or other standard protocols. After receiving the terminal voltage time series, the software control module uses the differential algorithm to calculate the rate of change, that is, at each time point, the difference between adjacent data points is calculated and divided by the time interval to generate a complete change rate sequence.

[0029] Standard deviation of vehicle battery terminal voltage: Battery terminal voltage data is also uploaded to the charging pile control system through the BMS. After collecting the continuous terminal voltage time series, the system divides the time window (such as 1 second or 5 seconds) and calculates the standard deviation of the terminal voltage in each window to reflect the fluctuation stability of the vehicle battery terminal voltage. The calculation of the standard deviation is based on statistical methods, and is completed by summing the square deviations of the data points in each window, taking the mean, and then taking the square root.

[0030] Calculate the volatility risk index of each electric vehicle. The specific calculation formula is as follows: In the formula, For the Volatility risk index of electric vehicles.

[0031] This formula is obtained by integrating the actual fluctuation amplitude of the bus voltage ( ), the rate of change of the vehicle battery terminal voltage ( ) and the standard deviation of the battery terminal voltage ( ), comprehensively evaluate the vehicle's sensitivity to bus voltage fluctuations. First, The product of the fluctuation amplitude and the terminal voltage change rate is partially smoothed by square root operation, which not only captures the fluctuation intensity but also avoids the influence of extreme value amplification on the result; secondly, By mapping the long-term stability of the voltage fluctuation at the vehicle end through the natural logarithm, the nonlinear effect when the standard deviation is too large is reduced, and the cumulative effect of voltage fluctuation on sensitivity is reflected; finally, by summing and averaging, the formula is ensured to comprehensively evaluate the fluctuation performance in multiple time periods and balance the differences between time periods. The overall calculation logic is reasonably designed, which can comprehensively and smoothly quantify the fluctuation risk, providing an accurate and scientific basis for vehicle sensitivity assessment.

[0032] No. Volatility risk index of electric vehicles The size of directly reflects the sensitivity of the vehicle to bus voltage fluctuations. The larger the index, the more easily the charging stability of the vehicle is affected when facing bus voltage fluctuations. This is because the fluctuation risk index comprehensively considers the fluctuation amplitude of the bus voltage, the rate of change of the vehicle battery terminal voltage and the long-term stability (standard deviation), and quantifies the dynamic response capability and anti-fluctuation capability of the vehicle under fluctuating conditions. A higher fluctuation risk index usually means that the vehicle responds more violently to bus voltage fluctuations, which is manifested as a greater rate of change of terminal voltage or poorer terminal voltage stability. Therefore, it is identified as a vehicle that is more sensitive to fluctuations in the sensitivity assessment. Through this index, the sensitivity level of each vehicle can be clearly distinguished, providing a scientific basis for the formulation of subsequent power allocation strategies, and giving priority to protecting the charging stability of highly sensitive vehicles.

[0033] In this embodiment, the logic for obtaining the dynamic load adaptability coefficient of each electric vehicle is as follows: The dynamic power adaptation information is extracted from the preprocessed dynamic power characteristic information of all electric vehicles, including the current charging power of each electric vehicle at different times within a period of time, the rate of change of the charging current of each electric vehicle, and the rate of change of the internal impedance of the battery of each electric vehicle, and they are calibrated as , and , Indicates that within a period of time Moment The current charging power of an electric vehicle, Indicates that within a period of time Moment The rate of change of charging current of an electric vehicle, Indicates that within a period of time Moment The rate of change of the internal impedance of an electric vehicle battery, , , and are all positive integers; Extracting dynamic power adaptation information from the pre-processed dynamic power characteristic information of all electric vehicles is completed by the automated data processing module of the charging pile control system. First, the system locks the data fields related to charging power, current and internal impedance of the battery from the dynamic power characteristic information, and stores and marks them in time series. Then, the system divides the data by time window, for example, setting each 1 second as a window, extracting the current charging power of the vehicle in each time window, and directly reading the power data at that moment in combination with the real-time monitoring values ​​of voltage and current. At the same time, the system calculates the rate of change of charging current at each time point by analyzing the time series of current data, and this process is completed by the differential algorithm module of the software. Finally, the system calculates the degree of impedance change in each time window for the internal impedance time series data uploaded by the battery management system (BMS), and generates the internal impedance change rate. These extracted data are output as dynamic power adaptation information in a unified format for subsequent sensitivity assessment.

[0034] Current charging power: The terminal voltage and charging current of the vehicle are collected in real time through the voltage and current sensors built into the charging pile. The power data is directly calculated and recorded as a time series by the charging pile software system. The charging pile system will obtain this data at a high frequency (such as multiple times per second) to ensure that dynamic changes are fully captured.

[0035] Charging current change rate: Charging current data is collected in real time by sensors. The system compares the data point by point and analyzes the change amplitude of the current at different time points. This change rate reflects the vehicle's responsiveness to dynamic power adjustment. The entire process is automatically processed by the software's data analysis module to generate a continuous current change rate sequence.

[0036] Internal impedance change rate: The internal impedance data is uploaded to the charging pile system by the vehicle's battery management system (BMS) in real time. These data record the internal impedance state of the battery during the charging process. The software identifies the adaptability of the vehicle's internal battery to power fluctuations by analyzing the impedance changes between different time points. The preprocessed impedance change rate is stored as a time series for subsequent dynamic adaptability analysis.

[0037] Calculate the dynamic load adaptability coefficient of each electric vehicle. The specific calculation formula is as follows: In the formula, For the Dynamic load adaptability coefficient of an electric vehicle.

[0038] This formula is designed to comprehensively evaluate the adaptability of each electric vehicle in dynamic power distribution and quantify its response characteristics to dynamic load adjustment. Each calculation step in the formula reflects scientificity and pertinence: First, It represents the square of the product of the vehicle's current charging power and the charging current change rate, which amplifies the impact of dynamic current changes on the vehicle's power demand and reflects the vehicle's load characteristics in dynamic power adjustment. It represents the dynamic response capability of the vehicle's internal impedance change, and normalizes the impedance change amplitude through square and square root operations, thereby balancing the impact of impedance data on the overall calculation. Taking the logarithm, the interference of extreme values ​​on the results is smoothed nonlinearly, while enhancing the differentiation of dynamic response capabilities. Finally, all time points are summed and averaged, and the dynamic changes in a short period of time are integrated into a unified indicator to comprehensively measure the adaptability of the vehicle over the entire time period. The design logic of the formula is reasonable, which not only captures the instantaneous dynamic characteristics, but also enhances the robustness of the results through normalization and smoothing, providing an accurate basis for dynamic load adaptability analysis.

[0039] No. Dynamic load adaptability coefficient of an electric vehicle The size of directly reflects the vehicle's responsiveness and adaptability to bus voltage fluctuations in a dynamic power distribution environment. The larger the coefficient, the higher the vehicle's sensitivity to dynamic adjustments during charging and the weaker its adaptability. This is because the dynamic load adaptability coefficient comprehensively considers the vehicle's current charging power, the rate of change of charging current, and the rate of change of the battery's internal impedance, and comprehensively measures the vehicle's electrical characteristics and internal adaptability in dynamic adjustments. When the coefficient is high, it means that the vehicle's load response to dynamic power adjustment is more intense, and the internal impedance has a weaker buffering capacity for fluctuations, so it is more sensitive to bus voltage fluctuations; a lower coefficient means that the vehicle can adapt to dynamic power distribution more stably. Through the size of this coefficient, the fluctuation sensitivity of each electric vehicle under dynamic power adjustment can be accurately evaluated, providing a scientific basis for optimizing the power distribution strategy.

[0040] A fluctuation sensitivity assessment model is constructed for the generated fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle, and a fluctuation sensitivity assessment coefficient of each electric vehicle is generated. After the generation, an analysis is performed to assess the sensitivity of each electric vehicle to bus voltage fluctuations, and each electric vehicle is divided into a highly sensitive vehicle, a medium sensitive vehicle, and a low sensitive vehicle according to the assessment results; In this embodiment, a fluctuation sensitivity assessment model is constructed based on the generated fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle to generate a fluctuation sensitivity assessment coefficient of each electric vehicle, specifically including the following steps: The volatility risk index, dynamic load adaptability coefficient and corresponding volatility sensitivity assessment coefficient of several electric vehicles generated in the past period of time are collected and calibrated as , and , Indicates the number of fluctuation risk indexes, dynamic load adaptability coefficients and corresponding fluctuation sensitivity assessment coefficients of several electric vehicles generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; The collection of the volatility risk index, dynamic load adaptability coefficient and corresponding volatility sensitivity assessment coefficient of several electric vehicles generated in the past period of time can be realized through the historical data storage module in the charging pile control system. First, when each electric vehicle completes the calculation of the volatility risk index and dynamic load adaptability coefficient of real-time charging, the system will automatically store these data together with the timestamp and vehicle identification (such as vehicle ID) generated by it in the database. The volatility sensitivity assessment coefficient, as the final output of the model calculation, will also be recorded and archived synchronously. In order to improve the query efficiency and integrity of the data, the storage module adopts a hierarchical storage method, in which the indicators generated in real time enter the temporary storage area in chronological order, and are transferred to the long-term storage area after timed batch archiving. In addition, the system will combine the management software of the charging pile, and regularly extract data within the specified time range in the past (such as the past month) from the database through an automated script, and classify and filter the data according to the vehicle ID and timestamp to form a complete historical data set for subsequent model training and analysis. This process is completed automatically by the software system to ensure the real-time, accuracy and consistency of data collection.

[0041] The range of is limited to positive integers greater than or equal to 3 because the number of regression coefficients in the target equation is three ( , and ), and the calculation of these regression coefficients needs to be determined by the least squares method or other optimization methods. ), there will not be enough equations to solve the three regression coefficients, resulting in the inability to find a unique solution to the system of equations. Therefore, at least three sample data are needed to form a system of equations to ensure that the regression coefficients can be uniquely determined. At the same time, more sample data can not only enhance the robustness of the regression model, but also reduce errors through more samples, and improve the model's fit and generalization ability to real data. Therefore, Limiting it to a positive integer greater than or equal to 3 not only meets the basic mathematical solution requirements, but also provides more data support for model optimization and performance improvement, ensuring that the calculation of the regression coefficient is accurate and statistically significant.

[0042] The multivariate regression model is selected as the volatility sensitivity assessment model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; The multiple regression model is a statistical analysis method used to study the linear relationship between multiple independent variables (such as volatility risk index and dynamic load adaptability coefficient) and dependent variables (such as volatility sensitivity assessment coefficient). In this model, the regression equation ,Will and The weight contribution is quantified as the regression coefficient and ,and The reason for choosing the multivariate regression model as the volatility sensitivity assessment model is that it can quantify and The impact on volatility sensitivity assessment ensures that the model can summarize patterns from historical data and optimize the regression coefficient by minimizing errors (such as the deviation between the actual value and the predicted value). In addition, the specific meaning of the regression coefficient is as follows: It indicates the linear influence of volatility risk index on volatility sensitivity assessment coefficient. The larger the value, the The more important the contribution to sensitivity; Indicates the influence of dynamic load adaptability coefficient on sensitivity assessment; Indicates when and The initial sensitivity value when all are zero. The model is trained by historical data to determine , and The optimal value of can improve the prediction accuracy of the model and provide a scientific basis for subsequent sensitivity analysis.

[0043] By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Optimize the regression coefficients by minimizing the error between the predicted values ​​and the actual values , and The value of is to ensure that the multivariate regression model can accurately fit the historical data, thereby improving the predictive ability of the future volatility sensitivity assessment coefficient. The reason for this is that the core goal of the regression model is to extract the relationship pattern between variables through historical data, and minimizing the error allows the model to find the optimal regression coefficient, so that the predicted results are as close to the actual results as possible, reducing deviations and improving the robustness of the model. The specific implementation method is to use the least squares method through the software to calculate the sum of the squares of the difference between the predicted value and the actual value in the regression equation, and optimize the regression coefficient while ensuring that the square error is minimized. The entire process is automatically completed by the software's optimization algorithm, and through iterative adjustment , and The value of is used to find the coefficient combination that minimizes the error function, and finally generate an optimized regression model that can accurately reflect the data pattern.

[0044] Using the finalized regression coefficient, the constructed volatility sensitivity assessment model is input with the volatility risk index of each electric vehicle generated in real time. and dynamic load adaptability factor , generate the fluctuation sensitivity assessment coefficient of each electric vehicle in real time .

[0045] In this embodiment, the generated fluctuation sensitivity evaluation coefficients of each electric vehicle are The pre-set threshold range of the volatility sensitivity assessment coefficient The comparison is carried out, and the sensitivity of each electric vehicle to bus voltage fluctuation is evaluated based on the comparison results. Based on the evaluation results, each electric vehicle is divided into highly sensitive vehicles, medium sensitive vehicles and low sensitive vehicles. The specific comparison analysis and classification are as follows: like , the sensitivity of the electric vehicle to bus voltage fluctuation is low, then the electric vehicle is classified as a low-sensitivity vehicle; This situation means that the vehicle is less sensitive to bus voltage fluctuations and can better adapt to bus voltage fluctuations. This low sensitivity indicates that the vehicle has strong stability during dynamic power allocation, and the battery terminal voltage and internal impedance have little effect on voltage fluctuations. In this case, the vehicle has a low demand for dynamic power allocation of the charging pile and is not easily disturbed by fluctuations. The system can give priority to allocating more power resources to highly sensitive vehicles, thereby optimizing the overall power allocation strategy.

[0046] like , the electric vehicle is moderately sensitive to bus voltage fluctuations, and is classified as a moderately sensitive vehicle; This situation means that the vehicle is moderately sensitive to bus voltage fluctuations. The vehicle has a certain response to bus fluctuations, but its overall adaptability is still good. It is a type that requires moderate attention but does not require priority protection. In this case, the system needs to reasonably allocate power resources to ensure that the charging process of medium-sensitive vehicles will not be damaged by fluctuations, and at the same time reserve more adjustment space for highly sensitive vehicles in the optimized allocation.

[0047] like , the electric vehicle is highly sensitive to bus voltage fluctuations, and is classified as a highly sensitive vehicle.

[0048] This situation means that the vehicle is highly sensitive to bus voltage fluctuations, and it is easy for fluctuations to trigger protection mechanisms or cause charging interruptions. This high sensitivity shows that the vehicle is extremely dependent on fluctuations in dynamic power distribution, and its battery terminal voltage and internal impedance are extremely sensitive to fluctuations. In this case, the system needs to give priority to protecting the charging stability of highly sensitive vehicles, which may be achieved by reducing the power fluctuation amplitude, distributing more stable power, or increasing its power supply to ensure a smooth charging process, thereby avoiding chain interference with the power distribution of other vehicles.

[0049] Pre-set threshold range for volatility sensitivity assessment coefficient It can be determined dynamically by analyzing historical data and combining statistical methods. Specifically, the software first extracts a large number of historical data sets from the database, including the volatility risk index, dynamic load adaptability coefficient, and the corresponding generated volatility sensitivity assessment coefficient of each electric vehicle. Then, through the statistical analysis module, the historical data The value distribution is processed, such as calculating the mean and standard deviation of the distribution. According to the application requirements, the appropriate interval division method can be selected. In addition, historical data can also be analyzed through clustering analysis (such as K-means algorithm). The software classifies the data and automatically generates classification thresholds for highly sensitive, medium sensitive, and low sensitive vehicles. Finally, the software outputs the threshold intervals according to the set rules and Data is classified in real time to ensure the dynamic adaptability and accuracy of thresholds.

[0050] According to the classification results of each electric vehicle, corresponding power allocation measures are taken for high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles respectively; In this embodiment, according to the classification results of each electric vehicle, corresponding power allocation measures are taken for high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles respectively, specifically: For electric vehicles classified as low-sensitivity vehicles, the specific power allocation measures are as follows: prioritize reducing the dynamic power adjustment range of the vehicle to ensure the stability of its existing charging process, and reduce the power resources allocated to the vehicle when there is a power demand conflict in the system, and allocate more power resources to vehicles with higher sensitivity. At the same time, continuously monitor the changes in its power demand to ensure the stability of the overall charging process; For low-sensitivity vehicles, the main goal of power allocation is to reduce the dynamic power adjustment range to ensure the stability of their existing charging process, while giving priority to more resources for highly sensitive vehicles when power resources are tight. This can be achieved through the software scheduling system of the charging pile. Specifically, the system marks low-sensitivity vehicles as "low-priority vehicles" by determining the fluctuation sensitivity evaluation coefficient of the vehicle. In the actual power allocation process, the system sets the dynamic power adjustment range of these vehicles to a lower range, for example, by controlling the upper and lower limits of the output power to reduce frequent adjustments caused by fluctuations. At the same time, when power demand conflicts, the system will give priority to reducing the power output of low-sensitivity vehicles, such as by dynamically adjusting the power allocation table to reallocate some resources to higher priority vehicles. The purpose of this is to ensure that the charging pile resources are optimally utilized, and at the same time, low-sensitivity vehicles will not affect the stability of charging due to the reduction in power adjustment due to their strong adaptability.

[0051] For electric vehicles classified as medium-sensitive vehicles, the power allocation measures taken are as follows: according to the preset power allocation rules, balanced power resources are allocated to the vehicle to ensure the stability of its charging process, and the allocation ratio is dynamically adjusted according to the real-time power demand and bus voltage fluctuations to take into account the charging efficiency of the vehicle and the overall power balance of the system. At the same time, its sensitivity status is regularly evaluated to ensure the optimization of the power allocation strategy; For medium-sensitive vehicles, the goal of power allocation is to balance their charging needs and the overall stability of the system while ensuring the dynamic flexibility of power allocation. This can be achieved by dynamically adjusting the allocation rules based on real-time data through the software control system of the charging pile. Specifically, the system will set a default power allocation value for medium-sensitive vehicles (for example, based on the average demand of the vehicle during the charging phase), and dynamically adjust it based on the real-time monitored vehicle power demand, bus voltage fluctuations, and the allocation status of other vehicles. For example, when the bus voltage fluctuates greatly, the system can moderately reduce the power output of medium-sensitive vehicles to avoid impacting highly sensitive vehicles; when power resources are sufficient, the system can appropriately increase the power allocation to medium-sensitive vehicles to improve charging efficiency. In addition, the system will regularly analyze the operating status of medium-sensitive vehicles, re-evaluate their sensitivity levels, and ensure that the allocation strategy always meets the actual needs of the vehicle. The purpose of this is to optimize the resource allocation of the entire system while taking into account the charging efficiency of medium-sensitive vehicles and maintaining a dynamic balance in the overall operation.

[0052] For electric vehicles classified as highly sensitive vehicles, the power allocation measures taken are as follows: giving priority to ensuring their charging stability, reducing the risk of triggering protection mechanisms due to fluctuations by limiting the bus voltage fluctuation amplitude and allocating smooth power resources, while monitoring their charging status and battery-side response characteristics in real time, and increasing power resources when necessary to meet their needs for a stable charging environment, ensuring that they complete the charging process safely and efficiently under highly sensitive conditions.

[0053] For highly sensitive vehicles, the core goal of power allocation is to prioritize their charging stability and avoid triggering the protection mechanism due to bus voltage fluctuations. This is achieved by implementing a priority protection strategy for highly sensitive vehicles through the software control system of the charging pile. Specifically, the system monitors the fluctuation sensitivity assessment coefficient, terminal voltage fluctuation and real-time charging demand of highly sensitive vehicles in real time, and adjusts them in combination with the dynamic fluctuation amplitude of the bus voltage. First, the system adjusts the smoothness of power output, such as using a smaller power adjustment step or a higher frequency smooth fluctuation, to ensure that the output power variation amplitude is always within the tolerance range of highly sensitive vehicles. Secondly, when the overall power of the system is insufficient, the system will prioritize the power allocation of highly sensitive vehicles, such as by reducing the power resources of medium-sensitive and low-sensitive vehicles to make room for them. In addition, the system will dynamically adjust the charging mode of highly sensitive vehicles (such as switching to constant voltage charging mode) in combination with real-time monitoring data to further reduce the risk of fluctuations. The purpose of this is to maximize the protection of the charging safety of highly sensitive vehicles while avoiding interference with the normal charging of other vehicles in the system due to the triggering of fluctuation protection.

[0054] Continuously monitor bus voltage fluctuations and the charging status of each electric vehicle, and dynamically adjust the fluctuation sensitivity assessment model and power allocation strategy based on real-time monitoring results. At the same time, store and analyze historical allocation data to optimize model parameters and strategy settings, and improve the long-term adaptability and stability of the system.

[0055] The continuous monitoring of bus voltage fluctuations and the charging status information of each electric vehicle can be achieved through the real-time data acquisition module and monitoring software system of the charging pile. The monitoring of bus voltage fluctuations depends on the high-precision voltage sensor installed in the charging pile, which collects real-time data of bus voltage at a fixed frequency (such as multiple times per second) and uploads the data to the control system of the charging pile. The charging status information of each electric vehicle (such as real-time power demand, terminal voltage, current, battery internal impedance, etc.) is obtained through the communication interface with the vehicle battery management system (BMS), and these data are transmitted to the control system through standardized protocols (such as CAN protocol). The data processing module in the monitoring system will integrate these data in real time and generate unified time series information for subsequent analysis and adjustment. The purpose of this is to ensure that the system can dynamically perceive bus voltage fluctuations and the operating status of each electric vehicle, and provide basic data support for subsequent model optimization and strategy adjustment.

[0056] Dynamically adjusting the fluctuation sensitivity assessment model and power allocation strategy based on real-time monitoring results can be accomplished through the intelligent control system and model update module of the charging pile. First, the system analyzes the real-time monitored bus voltage fluctuations and vehicle charging status information, inputs the new data into the existing fluctuation sensitivity assessment model (such as the multivariate regression model), and recalculates the fluctuation sensitivity assessment coefficient of each vehicle. Then, the vehicles are classified according to the new fluctuation sensitivity assessment coefficient value, and the power allocation strategy is dynamically adjusted in combination with the current power resource status of the system. For example, for highly sensitive vehicles, the system will further limit the power fluctuation amplitude and give priority to ensuring its charging stability; for low-sensitivity vehicles, the power allocation amplitude may be reduced to optimize resource utilization. The purpose of this is to improve the model's adaptability to changing working conditions through real-time updates and dynamic adjustments, while ensuring that the entire system can still operate efficiently in complex environments.

[0057] The storage and analysis of historical distribution data can be completed through the database and data analysis module in the charging pile control system. The system will store the key data recorded in each power distribution process (such as FRI, DLAC, FSEC, power distribution ratio, bus voltage fluctuation amplitude, etc.) in the database according to the timestamp. After the stored data is cleaned and standardized, it enters the analysis module. The analysis module uses data mining and statistical methods (such as trend analysis, regression analysis, cluster analysis, etc.) to explore patterns and potential problems in the power distribution process, and optimizes model parameters based on the analysis results, such as re-adjusting the threshold interval or regression coefficient of FSEC. At the same time, the system can test the effect of the new strategy through simulation, and deploy the optimization results to actual control after verification. The purpose of this is to use historical data to summarize experience, improve the accuracy of the fluctuation sensitivity assessment model and the scientific nature of the power distribution strategy, thereby improving the long-term adaptability and operational stability of the system.

[0058] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0059] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0060] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0061] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0062] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0063] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0065] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A dynamic power allocation method for a DC charging pile, characterized in that: The specific steps include: When multiple electric vehicles are connected to a DC charging pile for charging at the same time, the fluctuation degree of the DC bus voltage is detected in real time, and the dynamic power allocation process is triggered when the voltage fluctuation exceeds the set range; Real-time acquisition of dynamic power characteristic information of all electric vehicles connected to DC charging piles for charging, and analysis after acquisition to generate fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle; A fluctuation sensitivity assessment model is constructed for the generated fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle, and a fluctuation sensitivity assessment coefficient of each electric vehicle is generated. After the generation, an analysis is performed to assess the sensitivity of each electric vehicle to bus voltage fluctuations, and each electric vehicle is divided into a highly sensitive vehicle, a medium sensitive vehicle, and a low sensitive vehicle according to the assessment results; According to the classification results of each electric vehicle, corresponding power allocation measures are taken for high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles respectively; Continuously monitor bus voltage fluctuations and the charging status of each electric vehicle, and dynamically adjust the fluctuation sensitivity assessment model and power allocation strategy based on real-time monitoring results. At the same time, store and analyze historical allocation data to optimize model parameters and strategy settings, and improve the long-term adaptability and stability of the system.

2. The dynamic power allocation method of a DC charging pile according to claim 1, characterized in that: The dynamic power characteristic information of all electric vehicles connected to the DC charging pile for charging is obtained in real time, and analyzed after acquisition to generate the fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle respectively, which specifically includes the following steps: Real-time acquisition of dynamic power characteristic information of all electric vehicles connected to DC charging piles for charging, and pre-processing after acquisition; Extracting voltage fluctuation response information and dynamic power adaptation information from the pre-processed dynamic power characteristic information of all electric vehicles; The extracted voltage fluctuation response information and dynamic power adaptation information are analyzed to generate the fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle respectively.

3. The dynamic power allocation method of a DC charging pile according to claim 2, characterized in that: The logic for obtaining the volatility risk index of each electric vehicle is as follows: The voltage fluctuation response information is extracted from the pre-processed dynamic power characteristic information of all electric vehicles, including the actual fluctuation amplitude of the bus voltage at different times within a period of time, the rate of change of the battery terminal voltage of each electric vehicle, and the standard deviation of the battery terminal voltage of each electric vehicle, and calibrated as , and , Indicates that within a period of time The actual fluctuation amplitude of bus voltage at the moment, Indicates that within a period of time Moment The rate of change of the battery terminal voltage of an electric vehicle, Indicates that within a period of time Moment The standard deviation of the battery terminal voltage of an electric vehicle, , , and All are positive integers; Calculate the volatility risk index of each electric vehicle. The specific calculation formula is as follows: In the formula, For the Volatility risk index of electric vehicles.

4. The dynamic power allocation method for a DC charging pile according to claim 3, characterized in that: The logic for obtaining the dynamic load adaptability coefficient of each electric vehicle is as follows: The dynamic power adaptation information is extracted from the preprocessed dynamic power characteristic information of all electric vehicles, including the current charging power of each electric vehicle at different times within a period of time, the rate of change of the charging current of each electric vehicle, and the rate of change of the internal impedance of the battery of each electric vehicle, and they are calibrated as , and , Indicates that within a period of time Moment The current charging power of an electric vehicle, Indicates that within a period of time Moment The rate of change of charging current of an electric vehicle, Indicates that within a period of time Moment The rate of change of the internal impedance of an electric vehicle battery, , , and All are positive integers; Calculate the dynamic load adaptability coefficient of each electric vehicle. The specific calculation formula is as follows: In the formula, For the Dynamic load adaptability coefficient of an electric vehicle.

5. The method for dynamic power allocation of a DC charging pile according to claim 4, characterized in that: A fluctuation sensitivity assessment model is constructed based on the generated fluctuation risk index and dynamic load adaptability coefficient of each electric vehicle to generate a fluctuation sensitivity assessment coefficient of each electric vehicle, specifically including the following steps: The volatility risk index, dynamic load adaptability coefficient and corresponding volatility sensitivity assessment coefficient of several electric vehicles generated in the past period of time are collected and calibrated as , and , Indicates the number of fluctuation risk indexes, dynamic load adaptability coefficients and corresponding fluctuation sensitivity assessment coefficients of several electric vehicles generated in the past period of time. , is a positive integer, and the data collected in the past period of time forms a historical data set; The multivariate regression model is selected as the volatility sensitivity assessment model, and is trained through historical data sets to determine the value of the regression coefficient according to the formula: In the formula, , and is the regression coefficient; By minimizing the error between the predicted value and the actual value, the regression coefficient is optimized and finally determined. , and The value of Using the finalized regression coefficient, the constructed volatility sensitivity assessment model is input with the volatility risk index of each electric vehicle generated in real time. and dynamic load adaptability factor , generate the fluctuation sensitivity assessment coefficient of each electric vehicle in real time .

6. A method for dynamic power allocation of a DC charging pile according to claim 5, characterized in that: The generated fluctuation sensitivity assessment coefficients of each electric vehicle The pre-set fluctuation sensitivity assessment coefficient threshold range The comparison is carried out, and the sensitivity of each electric vehicle to bus voltage fluctuation is evaluated based on the comparison results. Based on the evaluation results, each electric vehicle is divided into highly sensitive vehicles, medium sensitive vehicles and low sensitive vehicles. The specific comparison analysis and classification are as follows: like , the sensitivity of the electric vehicle to bus voltage fluctuation is low, then the electric vehicle is classified as a low-sensitivity vehicle; like , the electric vehicle is moderately sensitive to bus voltage fluctuations, and is classified as a moderately sensitive vehicle; like , the electric vehicle is highly sensitive to bus voltage fluctuations, and is classified as a highly sensitive vehicle.

7. A method for dynamic power allocation of a DC charging pile according to claim 6, characterized in that: According to the classification results of each electric vehicle, corresponding power allocation measures are taken for high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles, specifically: For electric vehicles classified as low-sensitivity vehicles, the power allocation measures taken are as follows: prioritize reducing the dynamic power adjustment range of the vehicle to ensure the stability of its existing charging process, and reduce the power resources allocated to the vehicle when there is a power demand conflict in the system, while continuously monitoring the changes in its power demand to ensure the stability of the overall charging process; For electric vehicles classified as medium-sensitive vehicles, the power allocation measures taken are as follows: according to the preset power allocation rules, balanced power resources are allocated to the vehicle to ensure the stability of its charging process, and the allocation ratio is dynamically adjusted according to the real-time power demand and bus voltage fluctuations to take into account the charging efficiency of the vehicle and the overall power balance of the system. At the same time, its sensitivity status is regularly evaluated to ensure the optimization of the power allocation strategy; For electric vehicles classified as highly sensitive vehicles, the power allocation measures taken are as follows: giving priority to ensuring their charging stability, reducing the risk of triggering protection mechanisms due to fluctuations by limiting the bus voltage fluctuation amplitude and allocating smooth power resources, while monitoring their charging status and battery-end response characteristics in real time.

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