Method and system for multi-section power distribution of direct current charging pile

Abstract

The invention discloses a DC charging pile multi-segment power distribution method and system, and relates to the technical field of DC charging pile power distribution, and the method comprises the following steps: setting an initial monitoring frequency at a first charging stage of a DC charging pile according to historical charging data and monitoring demands, collecting current data when the battery of the electric vehicle is charged at the initial monitoring frequency; on the basis of data acquisition, a time sliding monitoring window is introduced, and current data in the monitoring window form a current data set according to a time sequence. By setting the initial monitoring frequency and combining the sliding window and the neural network model, intelligent monitoring and anomaly prediction of current data are achieved, in the potential anomaly stage, the monitoring frequency is dynamically improved, the charging power is reduced, the abnormal current is accurately captured, the battery performance is protected, and the service life is prolonged. The system efficiency is optimized while the charging safety is guaranteed, and reliable technical support is provided for a high-power charging scene.

Description

Technical Field

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

[0002] Multi-stage power distribution of DC charging piles means dividing the charging process into multiple stages according to the charging requirements and status of the electric vehicle battery (such as SOC status, temperature, charging curve, etc.), and using different power output strategies in different stages to optimize charging efficiency, protect battery life and ensure safety. For example, when the battery SOC is low (such as 0-30%), a higher power is used to quickly replenish the battery; in the middle stage (such as 30-80%), the power is gradually reduced to reduce heat and battery loss; and in the high SOC stage (such as 80%-100%), charging is completed at a lower power or constant voltage mode to accurately control the power input. This method can balance the charging speed and battery life, and is suitable for the optimized design of smart DC charging piles.

[0003] When a DC charging pile charges a battery at a low SOC stage (such as 0-30%), a constant current charging mode is usually used, that is, a high constant current (high power) is used to quickly replenish the power. This is because the internal resistance of the battery is low at this stage and it can safely withstand a large current, thereby achieving efficient charging. However, high-power charging also brings potential risks. If the current control accuracy of the charging pile is insufficient, causing the current to exceed the maximum design tolerance of the battery, it may cause internal overheating of the battery and material degradation, thereby causing irreversible damage to the battery. In extreme cases, this excessive current may trigger thermal runaway, causing serious consequences such as fire or explosion.

[0004] In order to solve the above problems, the existing technology usually uses a current sensor to monitor the current when the battery is charging, and when the current when the battery is charging exceeds the set value, measures are taken immediately. However, when the existing technology performs current monitoring, it usually uses a fixed monitoring frequency to monitor the current in real time, and the monitoring frequency is usually not set very high. The main benefit of doing so is to balance real-time performance and system resource consumption; however, when the current control of the charging pile is abnormal, continuing to use this monitoring frequency may miss the rapidly rising current peak, resulting in the abnormal current not being detected and controlled in time. Continuous excessive current will cause overheating inside the battery, aggravate material decomposition and degradation, cause irreversible damage, shorten the battery life, and even make it scrapped.

[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 method and system for multi-stage power distribution of a DC charging pile. By setting the initial monitoring frequency, efficient use of system resources is achieved, and combined with a sliding window and a neural network model, the current data is dynamically analyzed, the operating trend is accurately predicted, and the normal operating stage and the potential abnormal stage are effectively distinguished, thereby realizing intelligent monitoring. In the potential abnormal stage, dynamically increasing the monitoring frequency significantly improves the ability to capture abnormal currents, avoiding battery damage or safety accidents due to response delays. At the same time, the charging power is appropriately reduced to reduce the current load, protect battery performance, and extend its service life. On the basis of ensuring charging safety, the monitoring accuracy and operating efficiency of the system are comprehensively optimized, providing a reliable and intelligent technical solution for high-power charging scenarios 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 method for multi-stage power distribution of a DC charging pile, comprising the following steps: According to historical charging data and monitoring requirements, an initial monitoring frequency is set in the first charging stage of the DC charging pile, and the current data of the electric vehicle battery when charging is collected at the initial monitoring frequency; On the basis of data collection, a time sliding monitoring window is introduced, and the current data in the monitoring window is organized into a current data set in time sequence, so that the current data set is continuously updated with the progress of time; In the monitoring window, after abnormal analysis of the current data in the current data set, the analyzed features are input into the pre-trained neural network model, and the current operation trend of the electric vehicle battery during charging is predicted through the neural network model; Based on the output results of the neural network model, the current situation in the monitoring window is divided into a normal operation stage and a potential abnormal operation stage; For the current situation classified as the normal operation stage, the current of the electric vehicle battery during charging continues to be monitored in real time at the set initial monitoring frequency; For current conditions that are classified as potential abnormal operation stages, the actual monitoring frequency of current monitoring is dynamically increased based on the prediction results of the neural network model to achieve accurate monitoring of abnormal current conditions, reduce charging power and reduce current load.

[0008] Preferably, the initial monitoring frequency refers to a fixed data collection frequency preset when a DC charging pile performs constant current charging on an electric vehicle battery, and is used to monitor changes in the battery charging current.

[0009] Preferably, the sliding window means that during the data acquisition process, a fixed time length is used as the window range, a part of the data is extracted from the continuous current data stream for processing and analysis, and the window moves forward continuously over time, and each movement updates the data contained therein.

[0010] Preferably, within the monitoring window, abnormal analysis is performed on the current data in the current data set, and the specific steps are as follows: The ratio of the amplitude and frequency of the sudden increase in the peak value in the current waveform, the rate of continuous increase of the current over a period of time and the cumulative amplitude are extracted from the current data set. After analyzing the extracted features, the peak quantization reference value and the continuous increase quantization reference value are generated respectively. The peak quantization reference value reflects the instantaneous abnormal increase degree and frequency of the charging current; the continuous increase quantization reference value reflects the long-term deviation of the current from the stable value.

[0011] Preferably, after obtaining the peak quantization reference value and the continuous rise quantization reference value obtained by abnormal analysis of the current data in the current data set, the peak quantization reference value and the continuous rise quantization reference value are input into a pre-trained neural network model, and a current potential abnormality index is generated by the model. The abnormal operating current condition of the electric vehicle battery during charging is predicted by the current potential abnormality index.

[0012] Preferably, the current potential abnormality index generated when predicting the current operation trend of the electric vehicle battery during charging by the neural network model under the monitoring window is compared and analyzed with the preset current potential abnormality index reference threshold, and the current situation in the monitoring window is divided. The specific division steps are as follows: If the current potential abnormality index is greater than the current potential abnormality index reference threshold, the current situation in the monitoring window is divided into the potential abnormal operation stage; if the current potential abnormality index is less than the current potential abnormality index reference threshold, the current situation in the monitoring window is divided into the potential abnormal operation stage.

[0013] Preferably, the specific steps of extracting the ratio of the amplitude and frequency of the spike in the current waveform from the current data set and analyzing the extracted features in the monitoring window to generate the spike quantization reference value are as follows: The spike features in the current waveform are extracted from the current data set, including the spike increase amplitude and the spike increase frequency. The spike increase amplitude is defined as the maximum instantaneous change value of the current between two adjacent time points. The calculation expression is: ,in, For time The current value, For time The current value, j Indicates the time point index. is the spike increase magnitude, which is used to quantify the severity of a single spike change. is the sampling time interval; the spike frequency is defined as the ratio of the number of spike events in the monitoring window to the window length, and the calculation expression is: ,in, is the frequency of spikes, indicating the density of abnormal fluctuations. is the duration of the monitoring window, is the count of spike burst events; Introducing time weighting factor , which is used to enhance the weight of recent peak events. The calculation expression of the time weighting coefficient is: ,in, is the weight decay factor used to control time sensitivity; The peak quantization reference value is calculated by combining the time weighting coefficient through a nonlinear fusion formula. The calculation expression is: , where It is the peak quantification reference value, which quantifies the degree of sudden increase of charging current and its frequency. and are the weights of the normalized spike burst amplitude and the normalized spike burst frequency, respectively, to adjust the influence of the spike burst amplitude and the spike burst frequency. The calculation expressions of the normalized spike burst amplitude and the normalized spike burst frequency are: ， in, and They represent the normalized spike burst magnitude and the normalized spike burst frequency, respectively. and They represent the maximum values ​​of the spike increase magnitude and spike increase frequency within the monitoring window respectively.

[0014] Preferably, the specific steps of extracting the rate and cumulative amplitude of the continuous increase of the current over a period of time from the current data set, and analyzing the extracted features within the monitoring window to generate a continuous increase quantitative reference value are as follows: Within the monitoring window, the current data set Analyze in time series order to extract the continuous rising rate and cumulative amplitude of the current over a period of time. It is defined as the ratio of the cumulative value of the current change rate within the monitoring window to time, and the expression is: , where It indicates the continuous rate of increase of current over a period of time. and Indicates the number of consecutive k and k +1 current value at a time point, n Indicates the total number of time points in the monitoring window. Indicates that only the rising part of the current is considered, ignoring the falling or stable changes; the cumulative amplitude It is defined as the cumulative sum of all current rise changes within the monitoring window, and the expression is: , where Indicates the cumulative amplitude of the current over a period of time. The cumulative amplitude indicates the accumulation of all increments of the current rise, reflecting the cumulative amplitude of the current deviation from the initial value; By measuring the continuous increase rate of current over a period of time and cumulative amplitude Nonlinear combination analysis is performed to generate a continuously rising quantitative reference value, which reflects the trend of the current deviating from the stable value in the long term. The expression for generating the continuously rising quantitative reference value is: , where Indicates a continuously rising quantitative reference value, used to quantify the abnormal degree of long-term deviation of the current from the stable value. and The continuous rising rate and cumulative amplitude The weights of the continuous rising rate and cumulative amplitude are used to adjust the impact of the results.

[0015] Preferably, for the current condition classified as a potential abnormal operation stage, the actual monitoring frequency of the current monitoring is dynamically increased according to the prediction result of the neural network model, and the specific steps are as follows: In the potential abnormal operation stage, the dynamic monitoring frequency adjustment factor is calculated according to the current potential abnormal index generated by the neural network model prediction and the preset current potential abnormal index reference threshold. The dynamic monitoring frequency adjustment factor is determined according to the magnitude of the current potential abnormal index exceeding the current potential abnormal index reference threshold. The calculation expression is: , where is the potential abnormality index of the current in the current monitoring window, which is predicted by the neural network and quantifies the degree of abnormal risk. is the reference threshold of the current potential abnormality index, which is used to distinguish between normal and potential abnormal operation stages. To adjust the coefficient, control the sensitivity of the dynamic monitoring frequency adjustment factor, is a hyperbolic tangent function, used to smoothly monitor the change of the frequency adjustment factor; In order to obtain the dynamic monitoring frequency adjustment factor After that, the actual monitoring frequency is calculated according to the initial monitoring frequency. The calculation expression is: , where is the initial monitoring frequency, indicating the basic monitoring frequency of the current during normal operation. The actual monitoring frequency after dynamic adjustment is used for current monitoring at the current potential abnormal stage; In the potential abnormal stage, according to the actual monitoring frequency calculated , optimize the time interval of data collection, and adjust the data update strategy of the monitoring window in real time, so that the data points in the window are denser and the abnormal change trend is captured more finely. The expression of the optimized data collection time interval is: , where is the optimized data collection time interval; Based on the updated monitoring window data, the real-time collected data is further analyzed to optimize the power output of the charging pile. The actual charging power of the optimized charging pile is: , where The actual charging power after dynamic adjustment will be reduced as the abnormal risk increases to protect the battery safety. is the rated charging power, which indicates the charging power value under normal conditions. Sensitivity parameter for power adjustment.

[0016] A system for multi-stage power distribution of a DC charging pile, comprising an initial monitoring module, a data updating module, an abnormal prediction module, a stage division module, a normal monitoring module and a dynamic adjustment module; The initial monitoring module sets the initial monitoring frequency in the first charging stage of the DC charging pile according to the historical charging data and monitoring requirements, and collects the current data of the electric vehicle battery during charging at the initial monitoring frequency; The data update module introduces a time sliding monitoring window based on data acquisition, and organizes the current data in the monitoring window into a current data set in chronological order, so that the current data set is continuously updated over time; The abnormality prediction module performs abnormal analysis on the current data in the current data set within the monitoring window, inputs the analyzed features into a pre-trained neural network model, and predicts the current operation trend of the electric vehicle battery when charging through the neural network model; The stage division module divides the current situation in the monitoring window into a normal operation stage and a potential abnormal operation stage based on the output results of the neural network model; The normal monitoring module, for the current situation classified as the normal operation stage, continues to monitor the current when the electric vehicle battery is charged in real time at the set initial monitoring frequency; The dynamic adjustment module dynamically increases the actual monitoring frequency of current monitoring for current conditions classified as potential abnormal operation stages based on the prediction results of the neural network model, achieves accurate monitoring of abnormal current conditions, and reduces charging power to reduce current load.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention achieves efficient use of system resources by setting the initial monitoring frequency, and combines the sliding window and neural network model to dynamically analyze the current data, accurately predict the operating trend, and effectively distinguish the normal operation stage from the potential abnormal stage, thereby realizing intelligent monitoring. In the potential abnormal stage, dynamically increasing the monitoring frequency significantly improves the ability to capture abnormal currents and avoids battery damage or safety accidents caused by response delays. At the same time, the charging power is appropriately reduced to reduce the current load, protect the battery performance, and extend its service life. On the basis of ensuring charging safety, the monitoring accuracy and operating efficiency of the system are comprehensively optimized, providing a reliable and intelligent technical solution for high-power charging scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] Figure 1 The present invention is a method flow chart of a multi-stage power distribution method for a DC charging pile.

[0020] Figure 2 The present invention is a module schematic diagram of a multi-stage power distribution system for a DC charging pile. DETAILED DESCRIPTION

[0021] 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.

[0022] The present invention provides Figure 1 A method for distributing multi-stage power of a DC charging pile is shown, comprising the following steps: According to historical charging data and monitoring requirements, an initial monitoring frequency is set in the first charging stage of the DC charging pile (when the SOC is low), and the current data of the electric vehicle battery during charging is collected at the initial monitoring frequency; The initial monitoring frequency refers to the pre-set fixed data collection frequency when the DC charging pile charges the electric vehicle battery with a constant current, which is used to monitor the changes in the battery charging current. The initial monitoring frequency determines the time interval for current data collection. For example, if data is collected once per second, the initial monitoring frequency is 1Hz. It is the basic parameter for real-time monitoring during the charging process. It is usually set to a moderate fixed value at the beginning of charging or when the system is running normally to balance the real-time nature of the data and the efficiency of resource utilization.

[0023] On the basis of data collection, a time sliding monitoring window is introduced, and the current data in the monitoring window is organized into a current data set in time sequence, so that the current data set is continuously updated with the progress of time; The introduction of a time-sliding monitoring window can dynamically organize current data within a certain time range into a current data set, which is continuously updated over time. The role of this mechanism is to capture the latest change trend of the current during the charging process in real time, ensuring that subsequent abnormal analysis and prediction are based on the current charging status rather than lagging or outdated data. The dynamic movement of the monitoring window enables the system to continuously obtain the latest data features without adding additional data storage and processing burdens, thereby improving the real-time and accuracy of the analysis, and providing basic support for rapid response to potential abnormalities.

[0024] Sliding window means that during the data acquisition process, a fixed time length is used as the window range, a part of the data is extracted from the continuous current data stream for processing and analysis, and the window moves forward continuously over time, and each movement updates the data contained in it. The setting of the sliding window needs to take into account real-time and data representativeness: the window length should be short enough to capture the latest current change trend and meet real-time monitoring needs, while being long enough to cover the typical fluctuation cycle of the charging current to avoid missing key patterns or abnormal features. The step size of the window sliding determines the frequency of data updates and is usually set to a part of the window length to ensure continuity and efficiency.

[0025] In the monitoring window, after abnormal analysis of the current data in the current data set, the analyzed features are input into the pre-trained neural network model, and the current operation trend of the electric vehicle battery during charging is predicted through the neural network model; In the monitoring window, the current data in the current data set is analyzed for abnormalities. The specific steps are as follows: The ratio of the amplitude and frequency of the sudden increase in the peak value in the current waveform, the rate of continuous increase of the current over a period of time, and the cumulative amplitude are extracted from the current data set. After analyzing the extracted features, the peak quantization reference value and the continuous increase quantization reference value are generated respectively. The peak quantization reference value is used to reflect the degree and frequency of the instantaneous abnormal increase in the charging current, and the possible overload risk or control disorder problem of the system is evaluated. The continuous increase quantization reference value is used to reflect the long-term deviation of the current from the stable value, and to evaluate whether the battery or charging system has entered a potential overload or unstable state.

[0026] After obtaining the peak quantization reference value and the continuous rise quantization reference value obtained by abnormal analysis of the current data in the current data set, the peak quantization reference value and the continuous rise quantization reference value are input into a pre-trained neural network model, and a current potential abnormality index is generated through the model. The abnormal operating current situation of the electric vehicle battery during charging is predicted through the current potential abnormality index.

[0027] A pre-trained neural network model refers to a model obtained after sufficient training with a historical current data set and its features (such as spike quantization reference values, continuous rise quantization reference values, etc.). The model has a certain generalization ability and can learn the complex laws and potential patterns of current anomalies from input features. During the training process, the neural network continuously optimizes its parameters by processing a large amount of labeled data (including normal current and abnormal current samples), so that it can accurately distinguish between normal operating conditions and abnormal operating conditions. The structure of the model usually includes multiple layers of neuron units (such as multilayer perceptrons or convolutional neural networks), which mine deep associations in the input data through nonlinear transformations, thereby realizing the recognition of complex abnormal patterns.

[0028] In the above application, the peak quantization reference value and the continuous rise quantization reference value are used as inputs to the neural network model. Combined with historical training data, the model can learn how to map these indices to the current potential anomaly index. This mapping relationship takes into account the interaction between features, such as whether a short-term surge in the peak quantization reference value is accompanied by a continuous upward trend, or the contribution of different feature combinations to the anomaly. Through the deep structure of the neural network, the model can capture the nonlinear patterns hidden in the input features, making the prediction of complex current anomalies more accurate.

[0029] The core significance of this neural network model is to improve the accuracy and real-time performance of predicting abnormalities. First, through pre-training, the neural network can identify complex abnormal patterns that are difficult to capture with traditional rules or statistical methods. For example, the single abnormalities of the spike quantitative reference value and the continuously rising quantitative reference value may not be significant, but their combined abnormalities may indicate that the charging current is entering a dangerous area. Through the nonlinear combination analysis of the neural network, the model can extract key patterns from multidimensional features to generate an accurate current potential abnormality index.

[0030] Secondly, the adaptive learning ability of the neural network enables it to dynamically adjust the prediction performance according to the actual data. For example, when the type of electric vehicle battery, the model of charging pile or the charging environment conditions change, the model can maintain high efficiency through continuous optimization. This flexibility is particularly suitable for environments with complex and changing charging scenarios. The current potential anomaly index generated by the neural network model provides a quantitative reference value for the charging system. Therefore, the pre-trained neural network model can not only accurately predict anomalies, but also provide core technical guarantees for the operation of the intelligent charging system through in-depth analysis and decision support.

[0031] When charging an electric vehicle battery through a DC charger, a high ratio of the amplitude and frequency of the spike in the current waveform usually indicates that the DC charger may have potential current anomalies when charging the electric vehicle battery. The amplitude of the spike reflects the degree to which the current changes dramatically in a short period of time, while the frequency ratio refers to the frequency of such drastic changes. If this ratio is high, it may indicate that there is a problem with the current output stability of the charging system, such as delayed response of the charging station's control module, unstable current regulation, or external interference (such as grid fluctuations) affecting the charging process. Continuous current spikes will not only increase the heat accumulation of the battery and affect its life, but may also pose a threat to the safety of the charging equipment and battery.

[0032] The specific steps of extracting the ratio of the amplitude and frequency of the spike in the current waveform from the current data set and analyzing the extracted features within the monitoring window to generate the spike quantization reference value are as follows: The spike features in the current waveform are extracted from the current data set, including the spike increase amplitude and the spike increase frequency. The spike increase amplitude is defined as the maximum instantaneous change value of the current between two adjacent time points. The calculation expression is: ,in, For time The current value, For time The current value, j Indicates the time point index. is the spike increase magnitude, which is used to quantify the severity of a single spike change. is the sampling time interval; the spike frequency is defined as the ratio of the number of spike events in the monitoring window to the window length, and the calculation expression is: ,in, is the frequency of spikes, indicating the density of abnormal fluctuations. is the duration of the monitoring window, is the count of spike burst events; Introducing time weighting factor , which is used to enhance the weight of recent peak events. The calculation expression of the time weighting coefficient is: ,in, is the weight decay factor used to control time sensitivity; The peak quantization reference value is calculated by combining the time weighting coefficient through a nonlinear fusion formula. The calculation expression is: , where It is the peak quantification reference value, which quantifies the degree of sudden increase of charging current and its frequency. and are the weights of the normalized spike burst amplitude and the normalized spike burst frequency, respectively, to adjust the influence of the spike burst amplitude and the spike burst frequency. The calculation expressions of the normalized spike burst amplitude and the normalized spike burst frequency are: ， in, and They represent the normalized spike burst magnitude and the normalized spike burst frequency, respectively. and They represent the maximum values ​​of the spike increase magnitude and the spike increase frequency in the monitoring window respectively; The nonlinear fusion formula increases the sensitivity to high-amplitude and high-frequency spikes through characteristic power operations, thereby improving the accuracy of anomaly detection.

[0033] It can be seen from the peak quantization reference value that, under the monitoring window, the greater the performance value of the peak quantization reference value generated by analyzing the amplitude and frequency ratio characteristics of the peak sudden increase in the current waveform extracted from the current data set, the greater the hidden danger of potential current abnormality when the DC charging pile charges the electric vehicle battery, and vice versa. The peak quantization reference value comprehensively evaluates the degree of severe current fluctuations and the frequency of their occurrence during the charging process by quantifying the amplitude and frequency ratio of the sharp sudden increase in the current waveform. When the peak quantization reference value is large, it means that the current has increased sharply and fluctuated multiple times in a short period of time, which may be caused by abnormal control system response, circuit fluctuations or hardware failures, indicating that the potential overload risk or system imbalance problem is more serious. When the peak quantization reference value is small, it means that the current fluctuation is small and stable, and the charging process is safer and smoother. Therefore, the peak quantization reference value can be used as an important indicator to evaluate the stability of the charging current and provide an early warning basis for potential abnormalities.

[0034] The current continues to rise at a high rate and cumulative amplitude over a period of time, indicating that there is a potential current anomaly when the DC charging pile charges the electric vehicle battery. This phenomenon usually means that the current output of the charging system fails to remain constant, which may be due to an abnormal controller of the charging pile, delayed feedback from the battery management system (BMS), or a line fault that causes the current regulation to be out of control. The continuously rising current will increase the thermal load of the battery, leading to safety hazards such as internal material degradation and thermal runaway. At the same time, the accumulation of too fast or too large current may exceed the design tolerance of the battery, which will have an adverse effect on the battery life and the stability of the charging equipment.

[0035] The specific steps of extracting the rate and cumulative amplitude of the continuous increase of current over a period of time from the current data set and analyzing the extracted features within the monitoring window to generate the continuous increase quantitative reference value are as follows: Within the monitoring window, the current data set Analyze in time series order to extract the continuous rising rate and cumulative amplitude of the current over a period of time. It is defined as the ratio of the cumulative value of the current change rate within the monitoring window to time, and the expression is: , where It indicates the continuous rate of increase of current over a period of time. and Indicates the number of consecutive k and k +1 current value at a time point, n Indicates the total number of time points in the monitoring window. It means that only the rising part of the current is considered, and the falling or stable changes are ignored. The continuous rising rate reflects the rate at which the current continues to rise per unit time. The higher the rate, the more likely there is a problem with the system's control ability. The cumulative amplitude It is defined as the cumulative sum of all current rise changes within the monitoring window, and the expression is: , where It indicates the cumulative amplitude of the current over a period of time. The cumulative amplitude represents the accumulation of all increments of the current rise, reflecting the cumulative amplitude of the current deviation from the initial value. The larger the cumulative amplitude, the more significant the fluctuation of the current during this time period, which may indicate potential abnormality of the charging system.

[0036] By measuring the continuous increase rate of current over a period of time and cumulative amplitude Nonlinear combination analysis is performed to generate a continuously rising quantitative reference value, which reflects the trend of the current deviating from the stable value in the long term. The expression for generating the continuously rising quantitative reference value is: , where It indicates the quantitative reference value of continuous rise, which is used to quantify the abnormal degree of long-term deviation of the current from the stable value. The larger the value, the more obvious the abnormal trend. The logarithmic transformation is used to compress the index range, enhance the sensitivity to small anomalies, and suppress the influence of the maximum value. and The continuous rising rate and cumulative amplitude The weights of the continuous rising rate and cumulative amplitude are used to adjust the impact of the results.

[0037] It can be seen from the continuous rising quantitative reference value that, under the monitoring window, the larger the performance value of the continuous rising quantitative reference value generated after analyzing the rate of continuous rise of the current over a period of time and the cumulative amplitude characteristics, the greater the hidden danger of potential current abnormality when the DC charging pile charges the electric vehicle battery, and vice versa. The continuous rising quantitative reference value comprehensively quantifies the rising rate and cumulative amplitude of the current in the monitoring window. A large performance value indicates that the current has a more significant trend of deviating from the stable value over a period of time, which may indicate that the control ability of the current regulation system is insufficient, or that the charging system has entered an overloaded or abnormal operating state. When the continuous rising quantitative reference value is small, it indicates that the current change tends to be stable, there is no significant deviation, and the charging system is in a relatively safe and normal operating state.

[0038] The neural network model is not specifically limited here, and can achieve the peak quantization reference value obtained after abnormal analysis of current data. and continuously rising quantitative reference values Comprehensive analysis to generate current potential anomaly index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; the current potential abnormality index The resulting calculation formula is: , where , The peak quantification reference values ​​are and continuously rising quantitative reference values The preset scaling factor of , Both are greater than 0.

[0039] It can be seen from the current potential anomaly index that, under the monitoring window, the greater the performance value of the peak quantization reference value generated after analyzing the ratio characteristics of the amplitude and frequency of the spike sudden increase in the current waveform extracted from the current data set, the greater the performance value of the continuous rise quantization reference value generated after analyzing the rate of continuous increase of current over a period of time and the cumulative amplitude characteristics, that is, under the monitoring window, the greater the performance value of the current potential anomaly index generated when predicting the current operation trend when charging the electric vehicle battery through the neural network model, the greater the hidden danger of potential current anomaly when the DC charging pile charges the electric vehicle battery, and vice versa, it indicates that the hidden danger of potential current anomaly when the DC charging pile charges the electric vehicle battery is smaller.

[0040] Based on the output results of the neural network model, the current situation in the monitoring window is divided into a normal operation stage and a potential abnormal operation stage; The current potential abnormality index generated when predicting the current operation trend of the electric vehicle battery charging under the monitoring window through the neural network model is compared and analyzed with the preset current potential abnormality index reference threshold, and the current situation in the monitoring window is divided. The specific division steps are as follows: If the current potential abnormality index is greater than the current potential abnormality index reference threshold, it indicates that there may be potential abnormality in the current when the DC charging pile charges the electric vehicle battery, and the current situation in the monitoring window is divided into the potential abnormal operation stage; if the current potential abnormality index is less than the current potential abnormality index reference threshold, it indicates that the current change when the DC charging pile charges the electric vehicle battery is in line with expectations and there is no obvious sign of abnormality, and the current situation in the monitoring window is divided into the potential abnormal operation stage.

[0041] Dividing the system into stages helps it adopt corresponding monitoring strategies for different operating states and improve the flexibility and effectiveness of monitoring.

[0042] For the current situation classified as the normal operation stage, the current of the electric vehicle battery during charging continues to be monitored in real time at the set initial monitoring frequency; For the current conditions classified as the normal operation stage, the current of the electric vehicle battery during charging is continuously monitored in real time at the set initial monitoring frequency, in order to achieve efficient resource management and stable charging monitoring. By maintaining the initial monitoring frequency, the system can meet the needs of tracking normal current fluctuations while avoiding the increased computing pressure and energy consumption caused by excessively high monitoring frequencies, thereby optimizing the overall system performance. The current fluctuations are small during the normal operation stage, and maintaining the initial frequency can accurately capture the necessary change trends, ensure the smooth operation of the charging system, and reduce the burden on the hardware and data processing modules.

[0043] For current conditions that are classified as potential abnormal operation stages, the actual monitoring frequency of current monitoring is dynamically increased according to the prediction results of the neural network model, so as to accurately monitor abnormal current conditions and reduce charging power and current load. For the current conditions classified as potential abnormal operation stages, the actual monitoring frequency of current monitoring is dynamically increased according to the prediction results of the neural network model. The specific steps are as follows: In the potential abnormal operation stage, the dynamic monitoring frequency adjustment factor is calculated according to the current potential abnormal index generated by the neural network model prediction and the preset current potential abnormal index reference threshold. The dynamic monitoring frequency adjustment factor is determined according to the magnitude of the current potential abnormal index exceeding the current potential abnormal index reference threshold. The calculation expression is: , where is the potential abnormality index of the current in the current monitoring window, which is predicted by the neural network and quantifies the degree of abnormal risk. is the reference threshold of the current potential abnormality index, which is used to distinguish between normal and potential abnormal operation stages. To adjust the coefficient, control the sensitivity of the dynamic monitoring frequency adjustment factor, The larger the value of is, the more obvious the change in monitoring frequency is. It is a hyperbolic tangent function, which is used to smoothly monitor the change of the frequency adjustment factor dynamically to avoid excessive frequency adjustment due to excessively high potential abnormal current index; The degree of increase in monitoring frequency is dynamically calculated through the dynamic monitoring frequency adjustment factor to ensure flexible response to potential anomalies.

[0044] In order to obtain the dynamic monitoring frequency adjustment factor After that, the actual monitoring frequency is calculated according to the initial monitoring frequency. The calculation expression is: , where is the initial monitoring frequency, which indicates the basic monitoring frequency of the current during normal operation, usually a fixed value. The actual monitoring frequency after dynamic adjustment is used for current monitoring at the current potential abnormal stage; Dynamic actual monitoring frequency It directly determines the precision of monitoring. The higher the monitoring frequency, the more accurately it can capture subtle changes in current abnormalities.

[0045] In the potential abnormal stage, according to the actual monitoring frequency calculated , optimize the time interval of data collection, and adjust the data update strategy of the monitoring window in real time, so that the data points in the window are denser and the abnormal change trend is captured more finely. The expression of the optimized data collection time interval is: , where is the optimized data collection time interval; By adjusting the time interval, the data collection frequency can dynamically respond to potential abnormal situations, providing a denser sampling basis for subsequent real-time analysis.

[0046] Based on the updated monitoring window data, the real-time collected data is further analyzed to optimize the power output of the charging pile. The actual charging power of the optimized charging pile is: , where The actual charging power after dynamic adjustment will be reduced as the abnormal risk increases to protect the battery safety. is the rated charging power, which indicates the charging power value under normal conditions. Sensitivity parameters for power adjustment; While accurately monitoring, the current load is reduced by lowering the charging power to prevent the battery from overheating or overloading, forming a closed-loop strategy of monitoring and control.

[0047] For current conditions that are classified as potential abnormal operation stages, the steps of dynamically increasing the monitoring frequency and reducing the charging power can effectively improve the ability to capture current anomalies, while reducing the battery load to prevent further deterioration. The core role of this strategy is to obtain detailed data on current changes in a timely manner through accurate monitoring, so as to quickly identify the nature and trend of potential anomalies and provide high-resolution input for system decision-making. At the same time, by reducing the charging power and reducing the current load, it is possible to avoid internal battery overheating and material degradation, reduce the risk of thermal runaway and damage, and protect battery life. Using a higher monitoring frequency and power limiting strategy in the abnormal stage is the best choice to balance safety and efficiency, ensuring that the system always operates within the safety boundary before the potential risks are eliminated. Such a design not only improves the safety and stability of the charging process, but also provides reliable data support for subsequent abnormal handling and regulation.

[0048] Through the multi-stage power distribution solution of the DC charging pile, the problem of delay in capturing current anomalies caused by the traditional fixed monitoring frequency is solved, and the safety and efficiency of the charging process are improved. First, the initial monitoring frequency is set to ensure efficient use of system resources, and the combination of sliding window and neural network model can dynamically analyze current data and predict operating trends, accurately distinguish normal and potential abnormal stages, and realize intelligent monitoring. For the potential abnormal stage, dynamically increasing the monitoring frequency significantly enhances the accuracy of capturing abnormal currents and avoids battery damage or safety accidents caused by delayed response. At the same time, by reducing the charging power and reducing the current load, the battery is effectively protected and its life is extended. This solution not only improves the real-time monitoring capability of the charging process, but also optimizes the system operation efficiency while ensuring safety, providing reliable technical support for high-power charging scenarios.

[0049] The present invention provides Figure 2 A system for multi-stage power distribution of a DC charging pile shown includes an initial monitoring module, a data update module, an abnormal prediction module, a stage division module, a normal monitoring module and a dynamic adjustment module; The initial monitoring module sets the initial monitoring frequency in the first charging stage of the DC charging pile according to the historical charging data and monitoring requirements, and collects the current data of the electric vehicle battery during charging at the initial monitoring frequency; The data update module introduces a time sliding monitoring window based on data acquisition, and organizes the current data in the monitoring window into a current data set in chronological order, so that the current data set is continuously updated over time; The abnormality prediction module performs abnormal analysis on the current data in the current data set within the monitoring window, inputs the analyzed features into a pre-trained neural network model, and predicts the current operation trend of the electric vehicle battery when charging through the neural network model; The stage division module divides the current situation in the monitoring window into a normal operation stage and a potential abnormal operation stage based on the output results of the neural network model; The normal monitoring module, for the current situation classified as the normal operation stage, continues to monitor the current when the electric vehicle battery is charged in real time at the set initial monitoring frequency; The dynamic adjustment module dynamically increases the actual monitoring frequency of current monitoring according to the prediction results of the neural network model for the current conditions classified as potential abnormal operation stages, realizes accurate monitoring of abnormal current conditions, and reduces charging power and current load; A method for multi-stage power distribution of a DC charging pile provided in an embodiment of the present invention is implemented by the above-mentioned system for multi-stage power distribution of a DC charging pile. The specific method and process of the system for multi-stage power distribution of a DC charging pile are detailed in the embodiment of the above-mentioned method for multi-stage power distribution of a DC charging pile, which will not be repeated here.

[0050] 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.

[0051] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0052] 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.

[0053] 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.

[0054] 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 device 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0055] 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.

[0056] 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.

[0057] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0058] 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 method for multi-stage power distribution of a DC charging pile, characterized in that: The following steps are involved: According to historical charging data and monitoring requirements, an initial monitoring frequency is set in the first charging stage of the DC charging pile, and the current data of the electric vehicle battery when charging is collected at the initial monitoring frequency; On the basis of data collection, a time sliding monitoring window is introduced, and the current data in the monitoring window is organized into a current data set in time sequence, so that the current data set is continuously updated with the progress of time; In the monitoring window, after abnormal analysis of the current data in the current data set, the analyzed features are input into the pre-trained neural network model, and the current operation trend of the electric vehicle battery during charging is predicted through the neural network model; Based on the output results of the neural network model, the current situation in the monitoring window is divided into a normal operation stage and a potential abnormal operation stage; For the current situation classified as the normal operation stage, the current of the electric vehicle battery during charging continues to be monitored in real time at the set initial monitoring frequency; For current conditions that are classified as potential abnormal operation stages, the actual monitoring frequency of current monitoring is dynamically increased based on the prediction results of the neural network model to achieve accurate monitoring of abnormal current conditions, reduce charging power and reduce current load.

2. A method for multi-stage power distribution of a DC charging pile according to claim 1, characterized in that: The initial monitoring frequency refers to the pre-set fixed data collection frequency when the DC charging pile charges the electric vehicle battery with a constant current, which is used to monitor the changes in the battery charging current.

3. A method for multi-stage power distribution of a DC charging pile according to claim 1, characterized in that: Sliding window means that during the data acquisition process, a fixed time length is used as the window range, a part of the data is extracted from the continuous current data stream for processing and analysis, and the window moves forward continuously over time, and each movement updates the data contained therein.

4. The method for multi-stage power distribution of a DC charging pile according to claim 1, characterized in that: In the monitoring window, the current data in the current data set is analyzed for abnormalities. The specific steps are as follows: The ratio of the amplitude and frequency of the sudden increase in the peak value in the current waveform, the rate of continuous increase of the current over a period of time and the cumulative amplitude are extracted from the current data set. After analyzing the extracted features, the peak quantization reference value and the continuous increase quantization reference value are generated respectively. The peak quantization reference value reflects the instantaneous abnormal increase degree and frequency of the charging current; the continuous increase quantization reference value reflects the long-term deviation of the current from the stable value.

5. A method for multi-stage power distribution of a DC charging pile according to claim 4, characterized in that: After obtaining the peak quantization reference value and the continuous rise quantization reference value obtained by abnormal analysis of the current data in the current data set, the peak quantization reference value and the continuous rise quantization reference value are input into a pre-trained neural network model, and a current potential abnormality index is generated through the model. The abnormal operating current situation of the electric vehicle battery during charging is predicted through the current potential abnormality index.

6. A method for multi-stage power distribution of a DC charging pile according to claim 5, characterized in that: The current potential abnormality index generated when predicting the current operation trend of the electric vehicle battery charging under the monitoring window through the neural network model is compared and analyzed with the preset current potential abnormality index reference threshold, and the current situation in the monitoring window is divided. The specific division steps are as follows: If the current potential abnormality index is greater than the current potential abnormality index reference threshold, the current situation in the monitoring window is divided into the potential abnormal operation stage; if the current potential abnormality index is less than the current potential abnormality index reference threshold, the current situation in the monitoring window is divided into the potential abnormal operation stage.

7. A method for multi-stage power distribution of a DC charging pile according to claim 4, characterized in that: The specific steps of extracting the ratio of the amplitude and frequency of the spike in the current waveform from the current data set and analyzing the extracted features within the monitoring window to generate the spike quantization reference value are as follows: The spike features in the current waveform are extracted from the current data set, including the spike increase amplitude and the spike increase frequency. The spike increase amplitude is defined as the maximum instantaneous change value of the current between two adjacent time points. The calculation expression is: ,in, For time The current value, For time The current value, j Indicates the time point index. is the spike increase magnitude, which is used to quantify the severity of a single spike change. is the sampling time interval; the spike frequency is defined as the ratio of the number of spike events in the monitoring window to the window length, and the calculation expression is: ,in, is the frequency of spikes, indicating the density of abnormal fluctuations. is the duration of the monitoring window, is the count of spike burst events; Introducing time weighting factor , which is used to enhance the weight of recent peak events. The calculation expression of the time weighting coefficient is: ,in, is the weight decay factor used to control time sensitivity; The peak quantization reference value is calculated by combining the time weighting coefficient through a nonlinear fusion formula. The calculation expression is: , where It is the peak quantification reference value, which quantifies the degree of sudden increase of charging current and its frequency. and are the weights of the normalized spike burst amplitude and the normalized spike burst frequency, respectively, to adjust the influence of the spike burst amplitude and the spike burst frequency. The calculation expressions of the normalized spike burst amplitude and the normalized spike burst frequency are: ， in, and They represent the normalized spike burst magnitude and the normalized spike burst frequency, respectively. and They represent the maximum values ​​of the spike increase magnitude and spike increase frequency within the monitoring window respectively.

8. A method for multi-stage power distribution of a DC charging pile according to claim 4, characterized in that: The specific steps of extracting the rate and cumulative amplitude of the continuous increase of current over a period of time from the current data set and analyzing the extracted features within the monitoring window to generate the continuous increase quantitative reference value are as follows: Within the monitoring window, the current data set Analyze in time series order to extract the continuous rising rate and cumulative amplitude of the current over a period of time. It is defined as the ratio of the cumulative value of the current change rate within the monitoring window to time, and the expression is: , where It indicates the continuous rate of increase of current over a period of time. and Indicates the number of consecutive k and k +1 current value at a time point, n Indicates the total number of time points in the monitoring window. Indicates that only the rising part of the current is considered, ignoring the falling or stable changes; the cumulative amplitude It is defined as the cumulative sum of all current rise changes within the monitoring window, and the expression is: , where Indicates the cumulative amplitude of the current over a period of time. The cumulative amplitude indicates the accumulation of all increments of the current rise, reflecting the cumulative amplitude of the current deviation from the initial value; By measuring the continuous increase rate of current over a period of time and cumulative amplitude Nonlinear combination analysis is performed to generate a continuously rising quantitative reference value, which reflects the trend of the current deviating from the stable value in the long term. The expression for generating the continuously rising quantitative reference value is: , where Indicates a continuously rising quantitative reference value, used to quantify the abnormal degree of long-term deviation of the current from the stable value. and The continuous rising rate and cumulative amplitude The weights of the continuous rising rate and cumulative amplitude are used to adjust the impact of the results.

9. A method for multi-stage power distribution of a DC charging pile according to claim 4, characterized in that: For the current conditions classified as potential abnormal operation stages, the actual monitoring frequency of current monitoring is dynamically increased according to the prediction results of the neural network model. The specific steps are as follows: In the potential abnormal operation stage, the dynamic monitoring frequency adjustment factor is calculated according to the current potential abnormal index generated by the neural network model prediction and the preset current potential abnormal index reference threshold. The dynamic monitoring frequency adjustment factor is determined according to the magnitude of the current potential abnormal index exceeding the current potential abnormal index reference threshold. The calculation expression is: , where is the potential abnormality index of the current in the current monitoring window, which is predicted by the neural network and quantifies the degree of abnormal risk. is the reference threshold of the current potential abnormality index, which is used to distinguish between normal and potential abnormal operation stages. To adjust the coefficient, control the sensitivity of the dynamic monitoring frequency adjustment factor, is a hyperbolic tangent function, used to smoothly monitor the change of the frequency adjustment factor; After obtaining the dynamic monitoring frequency adjustment factor After that, the actual monitoring frequency is calculated according to the initial monitoring frequency. The calculation expression is: , where is the initial monitoring frequency, indicating the basic monitoring frequency of the current during normal operation. The actual monitoring frequency after dynamic adjustment is used for current monitoring at the current potential abnormal stage; In the potential abnormal stage, according to the actual monitoring frequency calculated , optimize the time interval of data collection, and adjust the data update strategy of the monitoring window in real time, so that the data points in the window are denser and the abnormal change trend is captured more finely. The expression of the optimized data collection time interval is: , where is the optimized data collection time interval; Based on the updated monitoring window data, the real-time collected data is further analyzed to optimize the power output of the charging pile. The actual charging power of the optimized charging pile is: , where The actual charging power after dynamic adjustment will be reduced as the abnormal risk increases to protect the battery safety. is the rated charging power, which indicates the charging power value under normal conditions. Sensitivity parameter for power adjustment.

10. A system for multi-stage power distribution of a DC charging pile, used to implement the method for multi-stage power distribution of a DC charging pile as described in any one of claims 1 to 9, characterized in that: It includes initial monitoring module, data updating module, abnormal prediction module, stage division module, normal monitoring module and dynamic adjustment module; The initial monitoring module sets the initial monitoring frequency in the first charging stage of the DC charging pile according to the historical charging data and monitoring requirements, and collects the current data of the electric vehicle battery during charging at the initial monitoring frequency; The data update module introduces a time sliding monitoring window based on data acquisition, and organizes the current data in the monitoring window into a current data set in chronological order, so that the current data set is continuously updated over time; The abnormality prediction module, in the monitoring window, performs abnormal analysis on the current data in the current data set, inputs the analyzed features into a pre-trained neural network model, and predicts the current operation trend of the electric vehicle battery when charging through the neural network model; The stage division module divides the current situation in the monitoring window into a normal operation stage and a potential abnormal operation stage based on the output results of the neural network model; The normal monitoring module, for the current situation classified as the normal operation stage, continues to monitor the current when the electric vehicle battery is charged in real time at the set initial monitoring frequency; The dynamic adjustment module dynamically increases the actual monitoring frequency of current monitoring for current conditions classified as potential abnormal operation stages based on the prediction results of the neural network model, achieves accurate monitoring of abnormal current conditions, and reduces charging power to reduce current load.

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