Charging pile output power control method

Abstract

The present invention discloses a method for controlling the output power of a charging pile, which specifically relates to the technical field of charging pile management, and includes the following steps: by collecting real-time operating information of the charging station, analyzing the overall load fluctuation, and judging whether there are early signs of excessive operating fluctuation intensity. If an abnormality is identified, further collect the operating information of each charging pile, extract characteristic data, and perform stability and adaptability evaluation. Import the evaluation results into a fuzzy reasoning model to divide the charging piles into high-quality and low-quality operating states. Perform an abnormality analysis based on the division results: in a single abnormal scenario, the charging piles in a high-quality operating state maintain a constant current and constant voltage strategy, and the charging piles in a low-quality operating state switch to an adaptive power adjustment strategy; in an overall abnormal scenario, all charging piles are switched to an adaptive power adjustment strategy to improve operating efficiency and stability; the present invention can dynamically adjust strategies according to different operating scenarios to meet the needs under different operating conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile management, and more specifically, to a charging pile output power control method. Background Art

[0002] With the rapid development of new energy vehicles, charging piles, as an important infrastructure, play a key role in the popularization of new energy vehicles through their construction and management. However, there are many challenges in the overall operation of charging stations, including frequent load fluctuations, uneven response delays, and operational abnormalities that are difficult to predict in advance. For example, during peak hours, the operating load of charging piles will increase rapidly, resulting in excessive load fluctuations, which not only threatens the stability of the power grid, but may also affect charging efficiency and user experience. In addition, there may be differences in the ability of charging piles to respond to input commands. Some charging piles experience response delays due to aging hardware or insufficient software optimization, further affecting their operating quality.

[0003] In the prior art, solutions for the overall operation and management of charging stations often rely on fixed charging strategies and lack the ability to dynamically adjust strategies based on actual operating conditions. Although the constant current and constant voltage strategy is suitable for charging piles with high-quality operation, it is difficult to maintain the stability of the system under low-quality operation conditions. On the other hand, although the adaptive power regulation strategy can improve the adaptability of the charging pile, it has a greater impact on the system load and lacks an accurate scenario triggering mechanism. Therefore, how to accurately switch the charging strategy according to the operating status of the charging pile through real-time data analysis and intelligent division mechanism has become a key technical problem to improve the overall efficiency and operational stability of the charging station. Summary of the invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The charging pile output power control method comprises the following steps:

[0006] Collect the real-time operation information of the charging station to obtain the charging station operation information set, and perform the overall load fluctuation analysis of the charging station based on the charging station operation information set, and judge whether there are early signs of excessive operation fluctuation intensity in the overall operation management of the charging station through the overall load fluctuation analysis results of the charging station;

[0007] After identifying early signs of excessive fluctuations in the overall operation and management of the charging station, the operation information of each charging pile is collected and features are extracted. Stability and adaptability assessments are performed based on the extracted feature data. The two assessment results are then imported into a pre-trained fuzzy inference model to classify the charging piles into low-quality and high-quality operation states.

[0008] An abnormality analysis is performed based on the charging pile classification results. If the abnormality analysis results trigger a single abnormal scenario, charging strategy one is used for charging piles in high-quality operating states, and charging strategy two is used for charging piles in low-quality operating states. If the abnormality analysis results trigger an overall abnormal scenario, charging strategy two is used for both charging piles in high-quality and low-quality operating states.

[0009] In a preferred embodiment, the extracted characteristic data includes a load data set and a response data set. A stability assessment is performed based on the load data set to obtain a power load dynamic balancing index for measuring the degree of power balancing of the charging pile during dynamic load changes. An adaptability assessment is performed based on the response data set to obtain a response delay adaptability index for measuring the degree of adaptability of the charging pile to input instructions.

[0010] In a preferred embodiment, the two evaluation results are imported into a pre-trained fuzzy inference model, and the charging pile is divided into a low-quality operation state and a high-quality operation state, which means:

[0011] The power load dynamic balance index, the response delay adaptability index, and the proportion of vehicles charging at the same time to vehicles charging at the charging station are taken as input variables of fuzzy reasoning, and the type of charging pile classification is taken as the output variable. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the operating quality under different combinations of data types. The fuzzified input variables are inferred through fuzzy rules to determine the classification type that is suitable for the charging pile.

[0012] In a preferred embodiment, triggering a single abnormal scenario means: recording the ratio of the number of charging piles classified into low-quality operating states to the number of charging piles classified into high-quality operating states as an abnormal value, and if the abnormal value is less than or equal to a preset scenario threshold, the current charging station operation scenario is judged as a single abnormal scenario;

[0013] Triggering an overall abnormal scenario means that if the abnormal value is greater than a preset scenario threshold, the current charging station operation scenario is judged as an overall abnormal scenario.

[0014] In a preferred embodiment, charging strategy one is a constant current and constant voltage charging strategy, and charging strategy two is an adaptive power regulation strategy.

[0015] In a preferred embodiment, performing overall load fluctuation analysis of a charging station refers to:

[0016] The time series data of the power grid load within a fixed time window is obtained from the charging station operation information set, and then the mean value and standard deviation corresponding to the time series data of the power grid load are calculated respectively.

[0017] In a preferred embodiment, judging whether there are early signs of excessive operation fluctuation intensity during the overall operation management of the charging station through the overall load fluctuation analysis result of the charging station refers to:

[0018] An allowable load fluctuation range is set for the charging station in advance. The load fluctuation range consists of fluctuation tolerance one and fluctuation tolerance two. The average value corresponding to the time series data of the power grid load is compared with fluctuation tolerance one, and the standard deviation corresponding to the time series data of the power grid load is compared with fluctuation tolerance two. If the average value corresponding to the time series data of the power grid load falls within fluctuation tolerance one and the standard deviation corresponding to the time series data of the power grid load falls within fluctuation tolerance two, it means that in the overall operation and management of the charging station, there are no early signs of excessive operation fluctuation intensity. If the average value corresponding to the time series data of the power grid load does not fall within fluctuation tolerance one and the standard deviation corresponding to the time series data of the power grid load falls within fluctuation tolerance two, it means that in the overall operation and management of the charging station, there are early signs of excessive operation fluctuation intensity.

[0019] In a preferred embodiment, the logic for obtaining the power load dynamic balancing index is:

[0020] Collect the power output data of the charging pile within the time window T , n is the number of sampling points in the time window, and the power difference between each two adjacent sampling points is calculated : i represents the index position of the data point;

[0021] ; i represents the starting position where the power change occurs;

[0022] Then define the volatility directional factor :

[0023] ; Calculate the power fluctuation amplitude with directionality :

[0024] ;

[0025] The time window T is divided into K sliding time windows, and the formula for calculating the dynamic balance index of power load is:

[0026] ; α, β and γ are all preset non-zero adjustment coefficients, PLB is the power load dynamic balance index, is the sum of the fluctuation amplitudes of the kth sliding time window, is the square sum of the fluctuation amplitude of the kth sliding time window, is the maximum power output.

[0027] In a preferred embodiment, the logic for obtaining the response delay adaptability index is:

[0028] Collect the target response time of the charging pile within the time window T and actual response time , calculate the delay difference of each response :

[0029] ; n is the number of sampling points in the time window, i represents the i-th response;

[0030] Introducing nonlinear weighting function : ;

[0031] Calculate the changing trend of continuous response delay: ;

[0032] Then calculate the overall trend weight : ;

[0033] Calculate the variance of the delay difference : ; is the average value of the response delay difference, i represents the delay difference position from the 1st to the nth time;

[0034] The calculation formula of the response delay adaptability index is:

[0035] ; RDA is the response delay adaptability index, λ and θ are both non-zero adjustment coefficients, The maximum target response time.

[0036] Technical effects and advantages of the present invention:

[0037] The present invention can comprehensively analyze the operation quality of charging piles and identify potential abnormal conditions by evaluating the load dynamic balance index and response delay adaptability index of charging piles. By dynamically adjusting the charging strategy, especially combining the fuzzy logic reasoning model to classify the operation status of charging piles, the volatility of the overall operation of the charging station can be effectively reduced to ensure the stability of the power grid.

[0038] The present invention divides the charging station operation scenarios into single abnormal scenarios and overall abnormal scenarios through abnormality analysis. In the single abnormal scenario, the strategy is switched only for the charging piles in the low-quality operation state, while the charging piles in the high-quality operation state continue to use the existing strategy, thereby reducing unnecessary system intervention and optimizing resource allocation efficiency. For the overall abnormal scenario, the strategy is adjusted uniformly to quickly restore the system balance. By evaluating the response delay adaptability index, the present invention can accurately identify the charging piles with slow response and switch to the adaptive power adjustment strategy, significantly improving its response speed and charging efficiency, which can not only reduce the user's waiting time, but also improve the satisfaction of the charging service.

[0039] The present invention uses a fuzzy logic reasoning model to intelligently process multi-dimensional input data, comprehensively considering factors such as load stability, response adaptability, and the proportion of charging vehicles, effectively solving the problem that traditional methods are difficult to deal with complex scenarios. The ability to dynamically adjust the charging strategy improves the overall operational safety of the charging station. By reasonably allocating loads and optimizing power output strategies, the present invention can reduce the long-term operating pressure of equipment and reduce equipment losses caused by excessive fluctuations or improper charging strategies, thereby extending the service life of charging equipment and reducing maintenance costs.

[0040] The present invention can dynamically adjust strategies according to different operating scenarios, and can provide efficient management methods to meet the needs of different operating conditions, whether in the case of a surge in load during peak hours or in the case of low response demand during off-peak hours. The dynamic control mechanism of the present invention can adapt to the power fluctuation scenarios of new energy power generation (such as photovoltaic and wind power), improve the ability of new energy and charging piles to operate in coordination, and provide support for a green and low-carbon energy management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0042] Figure 1 It is a schematic diagram of the charging pile output power control method in the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Reference Figure 1 The following examples are obtained:

[0045] Embodiment 1: A charging pile output power control method comprises the following steps:

[0046] Collect the real-time operation information of the charging station to obtain the charging station operation information set. The real-time operation information is the basis for subsequent analysis and is used to reflect the overall load distribution and dynamic changes of the charging station. By collecting real-time data such as grid load and charging pile power output, the operation status of the entire charging station can be dynamically understood, providing necessary data support for identifying excessive load fluctuations, which is the key prerequisite for judging abnormalities. According to the charging station operation information set, the overall load fluctuation analysis of the charging station is carried out. The overall load fluctuation analysis results of the charging station are used to determine whether there are early signs of excessive operation fluctuation intensity in the overall operation and management of the charging station; analyze the change trend and fluctuation amplitude of the charging station load to identify whether it meets the preset operation range. Excessive load fluctuation may bring pressure to the power grid, so evaluating the fluctuation is a prerequisite for ensuring the safe operation of the power grid and charging stations. Through load fluctuation analysis, potential early abnormal signs can be screened out to provide direction for subsequent operations. By judging the average and standard deviation of load fluctuations, it is determined whether the charging station has the hidden danger of excessive operation fluctuation intensity, identify risks in advance, and trigger subsequent charging pile operation evaluation to avoid system instability or equipment damage caused by load fluctuations, and avoid the impact of excessive fluctuation intensity on user experience, such as extended charging time or reduced efficiency.

[0047] After identifying early signs of excessive operating fluctuations in the overall operation and management of the charging station, the operating information of each charging pile is collected and feature extraction is performed. Stability and adaptability assessments are performed based on the extracted feature data. The two assessment results are then imported into a pre-trained fuzzy inference model to divide the charging piles into low-quality operating states and high-quality operating states.

[0048] Go deep into the charging pile level, collect the operation information of each charging pile (such as load, response delay, etc.), provide basic data for individual evaluation, extract key features (such as power fluctuation amplitude, response delay) from the operation data, and provide high-quality data for subsequent stability and adaptability evaluation, so as to find the performance differences between charging piles and formulate targeted charging strategies; calculate the power load dynamic balance index through load data, evaluate the stability of charging piles during dynamic load changes, and charging piles with low stability may have problems such as large load fluctuations and unreasonable power distribution, provide stability input data for fuzzy reasoning model, and judge the operation quality of charging piles. Calculate the response delay adaptability index through response data, evaluate the execution efficiency and adaptability of charging piles to input commands, and charging piles with poor adaptability may perform poorly in dynamic load adjustment or user command response, provide adaptability input data for fuzzy reasoning model, and complete quality division together with stability evaluation.

[0049] The stability index, adaptability index and other related variables (such as the proportion of charging vehicles) are comprehensively analyzed through the fuzzy reasoning model to obtain a more accurate quality classification. The fuzzy reasoning model can handle the fuzziness and uncertainty of the input variables and generate clear output results. Charging piles in high-quality operating conditions may be able to maintain the original strategy, while charging piles in low-quality operating conditions need to be optimized.

[0050] An abnormality analysis is performed based on the charging pile classification results. If the abnormality analysis results trigger a single abnormal scenario, charging strategy one is used for charging piles in high-quality operating states, and charging strategy two is used for charging piles in low-quality operating states. If the abnormality analysis results trigger an overall abnormal scenario, charging strategy two is used for both charging piles in high-quality and low-quality operating states.

[0051] By using the ratio of the number of charging piles in high-quality and low-quality operation states, we can quantify the abnormality of the current charging station and classify the abnormal scenarios:

[0052] Single abnormal scenario: When the proportion of charging piles in low-quality operation status is low, only the charging piles in low-quality operation status need to be optimized.

[0053] Overall abnormal scenario: When the proportion of charging piles in low-quality operation status is high, the charging strategy needs to be adjusted as a whole.

[0054] Through accurate classification, the most appropriate optimization scheme is selected to avoid unnecessary large-scale strategy adjustments. Charging piles with low-quality operation status are optimized without affecting the operation of charging piles with high-quality operation status, reducing unnecessary global intervention, and focusing on optimizing problem piles to improve overall operation efficiency. When the overall situation is abnormal, the charging strategy of all charging piles is optimized to quickly restore the operation quality of the charging station, avoid service interruption or grid overload caused by overall abnormality, and protect users and equipment.

[0055] Charging strategy 1 (constant current and constant voltage charging strategy) and charging strategy 2 (adaptive power regulation strategy)

[0056] Differentiated response to problems:

[0057] Constant current and constant voltage charging strategy: suitable for charging piles in high-quality operating conditions, running smoothly and without the need for complex dynamic adjustments.

[0058] Adaptive power regulation strategy: Usually applicable to charging piles in low-quality operation, it improves operation stability and adaptability by dynamically adjusting power output. By selecting the appropriate strategy, the stability and efficiency of the charging process are guaranteed.

[0059] The extracted feature data includes load data sets and response data sets. Stability evaluation is performed based on the load data sets to obtain the power load dynamic balance index used to measure the degree of power balance of the charging pile during dynamic load changes. If the power fluctuation is small and the output is stable, it means that the charging pile has good dynamic balance ability during operation. Abnormalities in the load data set (such as excessive fluctuations or frequent jumps) may indicate that there are equipment failures or regulation problems in the charging pile. Through the evaluation, unreasonable phenomena in load operation can be discovered in a timely manner. Stability evaluation directly determines whether the charging pile can be classified as a high-quality operating state. Charging piles with low stability may need to optimize strategies to reduce fluctuations and improve the stability of power output. Stable power output can reduce the impact on the power grid and vehicle batteries, extend the service life of equipment and batteries, and help ensure the safety and experience of users during the charging process.

[0060] An adaptability evaluation is performed based on the response data set to obtain a response delay adaptability index used to measure the adaptability of the charging pile to the input command. The timeliness and accuracy of the command response are important indicators for measuring the performance of the charging pile, which directly affects the user's charging experience. If the response delay of the charging pile is too large or fluctuates significantly, it may indicate that there are problems with the communication protocol, device hardware or control algorithm. Through adaptability evaluation, the response mechanism of the charging pile can be optimized in a targeted manner. Highly adaptable charging piles can quickly respond to changes in grid load and adjust output power to cooperate with peak shaving and valley filling strategies. It also has better adaptability to the dynamic power regulation requirements of new energy access (such as photovoltaic or wind power). The charging pile has high response efficiency and can quickly complete power allocation in multi-user scenarios, reducing waiting time and improving overall charging efficiency.

[0061] Importing the two evaluation results into the pre-trained fuzzy inference model and classifying the charging pile into low-quality operation state and high-quality operation state means:

[0062] The power load dynamic balance index, the response delay adaptability index, and the proportion of vehicles charging at the same time to vehicles charging at the charging station are taken as input variables of fuzzy reasoning, and the type of charging pile classification is taken as the output variable. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the operating quality under different combinations of data types. The fuzzified input variables are inferred through fuzzy rules to determine the classification type that is suitable for the charging pile.

[0063] The input variables include the power load dynamic balance index, the response delay adaptability index, and the ratio of vehicles charging at the same time to vehicles charging at the charging station. These variables may have certain continuity and volatility, so directly using exact values ​​for classification may lead to instability of the results. Fuzzy logic first fuzzifies these variables, that is, mapping exact values ​​to fuzzy sets, such as "high", "medium", "low" and other descriptive categories. For example: the power load dynamic balance index can be divided into "high stability", "medium stability" and "low stability". The response delay adaptability index can be divided into "high adaptability", "medium adaptability" and "low adaptability". The vehicle proportion can be divided into "high proportion", "medium proportion" and "low proportion". Fuzzification is achieved through membership functions, which define the correspondence between input variable values ​​and fuzzy sets, so that each input value can partially belong to different fuzzy sets.

[0064] Fuzzy rules are the core of the entire reasoning process and are used to describe the operating quality under different combinations of input variables. For example: if the power load dynamic balance index is "high stability", the response delay adaptability index is "high adaptability", and the vehicle proportion is "low proportion", the charging pile operation quality is "high quality". If the power load dynamic balance index is "medium stability", the response delay adaptability index is "low adaptability", and the vehicle proportion is "high proportion", the charging pile operation quality is "low quality".

[0065] Through the fuzzified input variables and the defined fuzzy rules, the fuzzy reasoning model calculates the matching degree of the input variable combination with the rule conditions. The matching degree reflects the degree of compliance of the input variables under the rule conditions and serves as the fuzzy value of the output variable. For example: if a certain input combination partially matches two rules, the reasoning model will comprehensively consider these matching degrees and calculate the membership value of the fuzzified output variable. This process is usually implemented through algorithms such as "maximum minimum method" or "weighted average method". The output of the reasoning is a fuzzy set, which needs to be converted into a clear classification result through defuzzification. Commonly used defuzzification methods include the centroid method and the maximum membership method. The output result after defuzzification is the operating status classification of the charging pile, such as "high-quality operating status" or "low-quality operating status".

[0066] Through fuzzy logic reasoning, multi-dimensional complex data can be converted into classification results that are easy to understand and operate: load stability, response adaptability and vehicle proportion are taken into account at the same time, avoiding the one-sidedness of a single dimension. Fuzzy logic can handle the fuzziness and uncertainty of input variables and has strong fault tolerance. The classification results can be directly used for optimization decisions on charging strategies. Charging piles in high-quality operating states implement constant current and constant voltage strategies, and charging piles in low-quality operating states switch to adaptive power regulation strategies.

[0067] The power load dynamic balance index focuses on the stability of power output, and the response delay adaptability index focuses on the ability to respond quickly to commands. The two evaluations together reflect the comprehensive operation quality of the charging pile, avoiding the limitations of single-dimensional evaluation. The stability and adaptability evaluation results are imported into the fuzzy reasoning model, and combined with the proportion of vehicles charging at the same time to vehicles charging at the charging station, the charging pile can be accurately divided into high-quality or low-quality operation status. Optimize the charging strategy according to the classification results to ensure more efficient resource allocation. Charging piles in high-quality operation status continue to implement the constant current and constant voltage charging strategy or switch to the adaptive power adjustment strategy to maintain stable operation. Charging piles in low-quality operation status switch to the adaptive power adjustment strategy to improve their operation stability and adaptability. Through classification management, global intervention is reduced and the overall operation efficiency of the charging station is improved. Charging piles that operate stably and respond promptly can provide more reliable and efficient services. Stability and adaptability evaluation can also discover potential problems, which helps to maintain and optimize in time and extend the service life of equipment.

[0068] Triggering a single abnormal scenario means: the ratio of the number of charging piles in low-quality operation state to the number of high-quality operation state is recorded as an abnormal value. If the abnormal value is less than or equal to the preset scenario threshold, the current charging station operation scenario is judged as a single abnormal scenario; triggering an overall abnormal scenario means: if the abnormal value is greater than the preset scenario threshold, the current charging station operation scenario is judged as an overall abnormal scenario. The logic of triggering single abnormal scenarios and overall abnormal scenarios quantifies the operation state of the charging station by the ratio of the number of charging piles in high-quality to low-quality operation states (abnormal value), which helps to accurately judge the scope and severity of the abnormality. Single abnormal scenarios (abnormal values ​​below the threshold) indicate that the problem is limited to a few charging piles in low-quality operation states, which can be optimized in a targeted manner without affecting the overall operation; overall abnormal scenarios (abnormal values ​​above the threshold) indicate that the problem is global and the strategy of all charging piles needs to be adjusted uniformly. This design can effectively distinguish between local and overall problems, avoid excessive intervention, and improve operation management efficiency and service quality.

[0069] Charging strategy one is a constant current and constant voltage charging strategy, and charging strategy two is an adaptive power regulation strategy.

[0070] Carrying out overall load fluctuation analysis of charging stations means:

[0071] The time series data of the power grid load within a fixed time window is obtained from the charging station operation information set, and then the mean value and standard deviation corresponding to the time series data of the power grid load are calculated respectively.

[0072] The overall load fluctuation analysis results of the charging station are used to determine whether there are early signs of excessive fluctuation intensity during the overall operation and management of the charging station:

[0073] An allowable load fluctuation range is set for the charging station in advance. The load fluctuation range consists of fluctuation tolerance one and fluctuation tolerance two. The average value corresponding to the time series data of the power grid load is compared with fluctuation tolerance one, and the standard deviation corresponding to the time series data of the power grid load is compared with fluctuation tolerance two. If the average value corresponding to the time series data of the power grid load falls within fluctuation tolerance one and the standard deviation corresponding to the time series data of the power grid load falls within fluctuation tolerance two, it means that in the overall operation and management of the charging station, there are no early signs of excessive operation fluctuation intensity. If the average value corresponding to the time series data of the power grid load does not fall within fluctuation tolerance one and the standard deviation corresponding to the time series data of the power grid load falls within fluctuation tolerance two, it means that in the overall operation and management of the charging station, there are early signs of excessive operation fluctuation intensity.

[0074] The logic for obtaining the power load dynamic balance index is:

[0075] Collect the power output data of the charging pile within the time window T , n is the number of sampling points in the time window, and the power difference between each two adjacent sampling points is calculated : i represents the index position of the data point;

[0076] ; i represents the starting position of the power change; the power output difference between two adjacent sampling points is calculated to measure the change amplitude of the power output. The size of the difference can reflect the stability of the power output: the smaller the difference, the more stable the power fluctuation; the larger the difference, the more violent the fluctuation. is the power output value at the i-th moment. is the power output value at the i+1th moment. is the power change value between the i-th and i+1-th sampling points. Using the difference between adjacent points can directly reflect the change in power output in a short period of time, avoiding complex calculations. The power difference is the basis for subsequent calculations of the fluctuation direction and fluctuation amplitude.

[0077] Then define the volatility directional factor :

[0078] ; Used to distinguish the direction of power fluctuation (increase, decrease or no change) and provide directional support for the calculation of fluctuation amplitude. is the fluctuation direction factor, which takes values ​​of 1, -1 or 0, and the power change value Indicates the change in the current power output. The direction of the fluctuation may affect the overall operating stability. For example, a continuously increasing or continuously decreasing power fluctuation may have different effects on the system, so the direction needs to be recorded. The design of taking the value 0 avoids the interference of no change points on subsequent calculations.

[0079] Calculate the power fluctuation amplitude with directionality :

[0080] ; Combine the power difference and the fluctuation direction factor to calculate the fluctuation amplitude with direction. Through absolute value processing, ensure that the fluctuation amplitude is positive, which is convenient for subsequent accumulation and analysis. The fluctuation amplitude indicates the absolute value and directionality of power change. The fluctuation amplitude is a key indicator for subsequent stability analysis. , which can fully reflect the characteristics of power fluctuations, and the use of absolute values ​​avoids the interference of direction on amplitude accumulation.

[0081] The time window T is divided into K sliding time windows, and the formula for calculating the dynamic balance index of power load is:

[0082] ; α, β and γ are all preset non-zero adjustment coefficients, which respectively control the influence of fluctuation amplitude, square sum and total power on the calculation results. PLB is the power load dynamic balance index, which is used to measure the stability of the charging pile during the dynamic change of load. The closer the power load dynamic balance index value is to 1, the more stable the power output is; the smaller the value is, the more violent the fluctuation is. is the sum of the fluctuation amplitudes of the kth sliding time window, is the square sum of the fluctuation amplitude of the kth sliding time window, is the maximum value of power output, used for normalization.

[0083] The logic for obtaining the response delay adaptability index is:

[0084] Collect the target response time of the charging pile within the time window T and actual response time , calculate the delay difference of each response :

[0085] ; n is the number of sampling points in the time window, i represents the i-th response; the difference between the actual response time and the target response time is calculated to measure the response speed of the charging pile to the input command. The difference can reflect the response efficiency of the command. The smaller the difference, the faster the response, and the larger the difference, the more serious the delay. is the delay difference of the ith response, is the actual time of the ith response, is the target time of the ith response, i is the sampling point index in the time series, indicating the number of each response.

[0086] In order to amplify the impact of large delays, a nonlinear weighting function is introduced : ; Nonlinear weighting of response delays is performed to amplify the impact of longer delays while reducing the impact of overly fast responses. Different weightings are applied to positive delays (too slow response) and negative delays (too fast response) to adapt to different operating scenarios. is the weighted value of the ith response. A logarithmic function is used for positive delays to amplify the impact of large delays and highlight serious problems. A linear function is used for negative delays to moderately reduce the impact of too fast responses and avoid unnecessary optimization interventions on the system.

[0087] Calculate the changing trend of continuous response delay: ; Calculate the difference between two consecutive response delays to determine whether the delay is improving or worsening. A positive trend (positive value) indicates an increase in delay, and a negative trend (negative value) indicates a decrease in delay. is the changing trend of the ith response delay, is the delay difference of the i-1th response.

[0088] Then calculate the overall trend weight : ; Calculate the average of all response delay trends to quantify the overall change trend of response delay. The trend weight can determine whether the delay is improving or worsening overall. is the overall trend weight for the response delay, is the changing trend of the ith response delay,

[0089] Calculate the variance of the delay difference : ; i represents the delay difference position from the 1st to the nth time; the variance of the response delay difference is calculated to reflect the fluctuation degree of the response delay. The larger the variance, the higher the instability of the response delay. is the average of the response delay differences.

[0090] The calculation formula of the response delay adaptability index is:

[0091] ; RDA is the response delay adaptability index, which is used to quantify the response capability and adaptability of the charging pile to input commands. The closer the value is to 1, the smaller the response delay, the lower the fluctuation, and the more stable the trend; the lower the value, the poorer the response adaptability. λ and θ are both non-zero adjustment coefficients, which are used to control the influence of variance and trend weight on the calculation results. The maximum target response time.

[0092] According to the charging pile classification results, an abnormality analysis is performed. If the abnormality analysis results trigger a single abnormal scenario, charging strategy 1 is used for charging piles in high-quality operation status, and charging strategy 2 is used for charging piles in low-quality operation status. If the abnormality analysis results trigger an overall abnormal scenario, charging strategy 2 is used for both high-quality and low-quality charging piles. The specific meaning is:

[0093] The abnormality analysis quantifies the ratio of high-quality and low-quality operation status of charging piles by counting the results of charging pile division, so as to judge whether there is an abnormality in the current charging station and the severity of the abnormality. The core of this analysis is to accurately evaluate whether the operation status of each charging pile in the charging station is controllable as a whole, so as to provide a scientific basis for subsequent strategy adjustments.

[0094] Single abnormal scenario: If the ratio of the number of charging piles in low-quality operation to the number of charging piles in high-quality operation is low (the abnormal value is less than or equal to the preset threshold), it means that the abnormality is limited to a few charging piles and the overall operation status is good.

[0095] In the case of single abnormality, the charging piles in high-quality operation maintain the original constant current and constant voltage strategy to ensure their stable operation. For the charging piles in low-quality operation, they are optimized by switching to the adaptive power regulation strategy, and the power output is dynamically adjusted to improve the operation status. Through local optimization, the interference to the charging piles in high-quality operation is reduced, and resources are concentrated to solve the individual charging piles with problems, thereby improving the overall operation efficiency of the charging station.

[0096] Overall abnormal scenario: If the ratio of the number of charging piles in low-quality operation status to the number of charging piles in high-quality operation status is high (the abnormal value is greater than the preset threshold), it means that the abnormality has spread to the entire system and the overall operation status needs to be comprehensively adjusted.

[0097] Comprehensive optimization: In overall abnormal scenarios, all charging piles (including high-quality and low-quality) switch to an adaptive power adjustment strategy to cope with overall abnormalities and ensure the safe operation of the charging station and load stability.

[0098] Reduce system risks: Unified adjustment strategies can quickly restore the overall operating quality of charging stations and avoid more serious consequences caused by the spread of abnormalities, such as load overload, grid fluctuations or equipment damage.

[0099] Charging strategy 1 (constant current and constant voltage charging strategy): It is more suitable for charging piles in high-quality operation, which can maintain stable charging output and improve charging efficiency. When the charging pile is in good condition, the constant current and constant voltage strategy can provide the best user experience while reducing the need for complex dynamic adjustment.

[0100] Charging strategy 2 (adaptive power adjustment strategy): Applicable to charging piles in low-quality operation status or overall abnormal scenarios, it can dynamically adjust power output and optimize the charging process according to the real-time operation status. Through real-time adaptation, it improves the operation stability of charging piles and reduces the impact of load fluctuations on the power grid and equipment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0105] 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 charging pile output power control method, characterized in that: The following steps are involved: Collect the real-time operation information of the charging station to obtain the charging station operation information set, and perform the overall load fluctuation analysis of the charging station based on the charging station operation information set, and judge whether there are early signs of excessive operation fluctuation intensity in the overall operation management of the charging station through the overall load fluctuation analysis results of the charging station; After identifying early signs of excessive fluctuations in the overall operation and management of the charging station, the operation information of each charging pile is collected and features are extracted. Stability and adaptability assessments are performed based on the extracted feature data. The two assessment results are then imported into a pre-trained fuzzy inference model to classify the charging piles into low-quality and high-quality operation states. An abnormality analysis is performed based on the charging pile classification results. If the abnormality analysis results trigger a single abnormal scenario, charging strategy 1 is used for charging piles in high-quality operation status, and charging strategy 2 is used for charging piles in low-quality operation status. If the abnormality analysis results trigger an overall abnormal scenario, charging strategy 2 is used for both charging piles in high-quality and low-quality operation status. The overall load fluctuation analysis results of the charging station are used to determine whether there are early signs of excessive fluctuation intensity during the overall operation and management of the charging station: An allowable load fluctuation range is set for the charging station in advance. The load fluctuation range consists of fluctuation tolerance one and fluctuation tolerance two. The average value corresponding to the time series data of the power grid load is compared with fluctuation tolerance one, and the standard deviation corresponding to the time series data of the power grid load is compared with fluctuation tolerance two. If the average value corresponding to the time series data of the power grid load falls within fluctuation tolerance one and the standard deviation corresponding to the time series data of the power grid load falls within fluctuation tolerance two, it means that in the overall operation and management of the charging station, there are no early signs of excessive operation fluctuation intensity. If the average value corresponding to the time series data of the power grid load does not fall within fluctuation tolerance one and the standard deviation corresponding to the time series data of the power grid load falls within fluctuation tolerance two, it means that in the overall operation and management of the charging station, there are early signs of excessive operation fluctuation intensity.

2. The charging pile output power control method according to claim 1, characterized in that: The extracted feature data include load data sets and response data sets. A stability evaluation is performed based on the load data sets to obtain a power load dynamic balancing index, which is used to measure the degree of power balancing of the charging pile during dynamic load changes. An adaptability evaluation is performed based on the response data sets to obtain a response delay adaptability index, which is used to measure the degree of adaptability of the charging pile to input commands.

3. The charging pile output power control method according to claim 2, characterized in that: Importing the two evaluation results into the pre-trained fuzzy inference model and classifying the charging pile into low-quality operation state and high-quality operation state means: The power load dynamic balance index, the response delay adaptability index, and the proportion of vehicles charging at the same time to vehicles charging at the charging station are taken as input variables of fuzzy reasoning, and the type of charging pile classification is taken as the output variable. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the operating quality under different combinations of data types. The fuzzified input variables are inferred through fuzzy rules to determine the classification type that is suitable for the charging pile.

4. The charging pile output power control method according to claim 3, characterized in that: Triggering a single abnormal scenario means that the ratio of the number of charging piles classified into low-quality operating states to the number of charging piles classified into high-quality operating states is recorded as an abnormal value. If the abnormal value is less than or equal to the preset scenario threshold, the current charging station operation scenario is judged as a single abnormal scenario. Triggering an overall abnormal scenario means that if the abnormal value is greater than a preset scenario threshold, the current charging station operation scenario is judged as an overall abnormal scenario.

5. The charging pile output power control method according to claim 4, characterized in that: Charging strategy one is a constant current and constant voltage charging strategy, and charging strategy two is an adaptive power regulation strategy.

6. The charging pile output power control method according to claim 5, characterized in that: Carrying out overall load fluctuation analysis of charging stations means: The time series data of the power grid load within a fixed time window is obtained from the charging station operation information set, and then the mean value and standard deviation corresponding to the time series data of the power grid load are calculated respectively.

7. The charging pile output power control method according to claim 6, characterized in that: The logic for obtaining the power load dynamic balance index is: Collect the power output data of the charging pile within the time window T , n is the number of sampling points in the time window, and the power difference between each two adjacent sampling points is calculated : i represents the index position of the data point; ; i represents the starting position where the power change occurs; Then define the volatility directional factor : ; Calculate the power fluctuation amplitude with directionality : ； The time window T is divided into K sliding time windows, and the formula for calculating the dynamic balance index of power load is: ; α, β and γ are all preset non-zero adjustment coefficients, PLB is the power load dynamic balance index, is the sum of the fluctuation amplitudes of the kth sliding time window, is the square sum of the fluctuation amplitude of the kth sliding time window, is the maximum power output.

8. The charging pile output power control method according to claim 7, characterized in that: The logic for obtaining the response delay adaptability index is: Collect the target response time of the charging pile within the time window T and actual response time , calculate the delay difference of each response : ; n is the number of sampling points in the time window, i represents the i-th response; Introducing nonlinear weighting function : ; Calculate the changing trend of continuous response delay: ; Then calculate the overall trend weight : ; Calculate the variance of the delay difference : ; i represents the delay difference position from the 1st to the nth time; The calculation formula of the response delay adaptability index is: ; RDA is the response delay adaptability index, λ and θ are both non-zero adjustment coefficients, The maximum target response time.

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