Battery car charging pile power automatic calibration detection device and detection method

Abstract

The invention discloses a battery car charging pile power automatic calibration detection device and detection method, and relates to the technical field of battery car charging pile power calibration, and the method specifically comprises the following steps: after a charging pile enters a low-power charging state, obtaining the charging process analysis information of the charging pile in real time, and carrying out the analysis after the obtaining; judging whether a power calibration mechanism of the charging pile needs to be adjusted or not; when it is judged that the power calibration mechanism of the charging pile needs to be adjusted, a low-power dynamic calibration adjustment mechanism is started, and the output power of the charging pile is adjusted in real time; output power fluctuation information of the charging pile is obtained in real time, analysis is carried out after obtaining, whether the low-power dynamic calibration adjustment mechanism can accurately detect tiny fluctuation of current and voltage or not is evaluated, and the low-power dynamic calibration adjustment mechanism is optimized according to the evaluation result. According to the invention, the problem of inaccurate calibration in a low-power charging state is solved, and the stability and efficiency of the charging process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power calibration of a battery vehicle charging pile, and in particular to an automatic calibration detection device and a detection method for a battery vehicle charging pile power. Background Art

[0002] Battery charging piles are devices designed to provide charging services for battery vehicles such as electric bicycles and electric motorcycles. They are usually installed in public or private places and can be connected to battery vehicles through cables and provide power supply. Since different battery vehicles have different battery capacities and charging requirements, and environmental conditions such as temperature and voltage fluctuations will also affect the output power of the charging pile, it is crucial to calibrate the power of the battery charging pile. The purpose of power calibration is to ensure that the charging pile can charge different battery vehicles with stable and accurate power output, avoiding problems such as low charging efficiency, overcharging or undercharging caused by unstable power, thereby extending battery life and improving user experience. At the same time, the power calibration of the charging pile is tested to verify whether the calibration is accurate and ensure that it can continue to remain within the preset power standard range. If the calibration effect is not good, the charging pile may not provide effective charging services, and may even cause equipment damage or safety hazards. Therefore, calibration testing is a key step that can detect problems in time and take corresponding measures to ensure the safety, reliability and compliance of the charging pile. Especially in the public charging network, power calibration and testing are essential to maintain the stability of the overall system.

[0003] The existing automatic calibration and detection technology for battery charging pile power is usually tested through the following process: First, the charging pile will collect key parameters such as current, voltage, and power in real time during the charging process, and record the data in combination with environmental conditions such as temperature. Then, these data will be analyzed by the software system to calculate the deviation between the current output power of the charging pile and the standard power. According to the deviation analysis results, the system will automatically adjust the output power of the charging pile to match the preset power standard. During the calibration process, the system will continuously monitor the power output changes of the charging pile to determine whether the calibration is effective. If the system detects that the power deviation exceeds the allowable range, it will trigger an alarm and record the abnormal situation for further processing. Finally, the system generates a calibration test report, compares the power output before and after calibration, and evaluates the calibration effect. This process is achieved through automated means without manual intervention, ensuring that the power output of the charging pile in different usage environments is always within a safe and stable range, thereby improving the reliability of the equipment and the user experience.

[0004] The prior art has the following deficiencies: When the battery car is close to full charge, the charging pile will automatically enter a low-power charging state to avoid overcharging the battery. However, when the battery is charged at low power for a long time, the power output demand of the charging pile is low, and the change range of current and voltage is also very small. Because this change is too subtle, the sensitivity of the existing battery car charging pile power automatic calibration detection technology is insufficient, and it is unable to accurately detect these subtle voltage and current fluctuations. The system still operates according to the calibration mechanism in the high-power state and fails to make dynamic adjustments for low-power charging scenarios. This situation will cause the output power to fail to match the actual demand, power fluctuations will occur, and the calibration response will lag, which will in turn affect the stability of the charging process. Specifically, too low power output will prolong the charging time and lead to a decrease in charging efficiency; and failure to stop power output in time may put the battery at risk of overcharging. The failure of the calibration mechanism of the existing technology in the low-power state not only affects the overall operating efficiency of the charging pile, but also has a negative impact on the user experience, especially during the long-term charging process, the system cannot accurately identify when the power output needs to be adjusted, which may eventually lead to an increase in the operating load of the equipment, overcharging of the battery, and even safety hazards.

[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 battery vehicle charging pile power automatic calibration detection device and detection method 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 automatic calibration and detection of battery vehicle charging pile power, which specifically comprises the following steps: When the battery vehicle is charging through the charging pile, the charging status identification information of the battery vehicle and the charging pile is collected in real time, and analyzed after collection to identify the time period when the charging pile enters the low-power charging state; After the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and analyzed after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted; When it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the low-power dynamic calibration adjustment mechanism is started to adjust the output power of the charging pile in real time to match the low-power charging requirements of the battery vehicle; After the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time, and analyzed after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the small fluctuations of current and voltage, and optimize the low-power dynamic calibration adjustment mechanism according to the evaluation results; After optimizing the low-power dynamic calibration adjustment mechanism, the power output data of the charging pile is continuously collected, and trend analysis is performed based on the long-term collected data to evaluate whether the calibration mechanism can continue to meet the low-power charging needs of electric vehicles, and the calibration mechanism is further optimized based on the analysis results.

[0008] Preferably, after the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and analyzed after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted, which specifically includes the following steps: After the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and pre-processed after acquisition; Extract the current dynamic fluctuation information and power efficiency adaptation information from the preprocessed charging process analysis information of the charging pile, and analyze them after extraction to generate the current stability control coefficient and the power adaptability deviation index respectively; A mechanism judgment model is constructed for the generated current stability control coefficient and power adaptability deviation index to generate a judgment coefficient. The generated judgment coefficient is compared with a pre-set judgment coefficient threshold, and it is determined whether the power calibration mechanism of the charging pile needs to be adjusted based on the comparison result.

[0009] Preferably, the acquisition logic of the current stability control coefficient and the power adaptability deviation index is as follows: Extract the current dynamic fluctuation information from the preprocessed charging process analysis information of the charging pile, specifically including the actual output current and actual output power of the charging pile at different times within a period of time in the low-power charging state, and calibrate the actual output current and actual output power of the charging pile at different times within a period of time in the low-power charging state as and , It is the number of the actual output current and actual output power of the charging pile at different times within a period of time when it is in a low-power charging state. , is a positive integer; The maximum and minimum values ​​of the actual output current of the charging pile at different times within a period of time in the low-power charging state are calibrated as and ; Calculate the average value of the actual output current and the average value of the actual output power of the charging pile in the low-power charging state over a period of time. The specific calculation formula is as follows: In the formula, is the average value of the actual output current of the charging pile in a low-power charging state over a period of time. It is the average value of the actual output power of the charging pile in a low-power charging state over a period of time; Calculate the current stability control coefficient. The specific calculation formula is as follows: In the formula, is the current stability control coefficient; The power efficiency adaptation information in the charging process analysis information of the preprocessed charging pile is extracted, specifically including the preset output power of the charging pile in the low-power charging state within a period of time and the actual power of the battery at different times during the process of charging the battery of the electric vehicle by the charging pile in the low-power charging state, and the preset output power of the charging pile in the low-power charging state within a period of time and the actual power of the battery at different times during the process of charging the battery of the electric vehicle by the charging pile in the low-power charging state are calibrated as and , It is the number of the actual power of the battery at different times during a period of time when the charging pile is in a low-power charging state. , is a positive integer; Calculate the power adaptability deviation index. The specific calculation formula is: In the formula, is the power adaptability deviation index.

[0010] Preferably, the generated current stability control coefficient and power adaptability deviation index Construct a mechanism judgment model and generate judgment coefficients through weighted summation , and the generated judgment coefficient The pre-set judgment coefficient threshold Compare and determine whether the power calibration mechanism of the charging pile needs to be adjusted based on the comparison results. The specific comparison analysis is as follows: like , there is no need to adjust the power calibration mechanism of the charging pile; like , the power calibration mechanism of the charging pile needs to be adjusted.

[0011] Preferably, when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the low-power dynamic calibration adjustment mechanism is started to adjust the output power of the charging pile in real time to match the low-power charging demand of the electric vehicle. Specifically, when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the dynamic data of current fluctuation and power output are obtained in real time according to the current low-power charging demand of the electric vehicle; the real-time obtained current fluctuation data and power output data are analyzed through the low-power dynamic calibration adjustment mechanism, and the current power output adjustment range of the charging pile is calculated; according to the calculated adjustment range, the output power of the charging pile is adjusted in real time to gradually approach the low-power charging demand of the electric vehicle.

[0012] Preferably, after the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time, and analyzed after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage, and optimize the low-power dynamic calibration adjustment mechanism according to the evaluation results, which specifically includes the following steps: After the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time and pre-processed after acquisition; Extracting the current dynamic fluctuation control information and the power deviation stability analysis information from the preprocessed output power fluctuation information of the charging pile, and analyzing them after extraction to generate the current dynamic response control coefficient and the power deviation stability index; Determine the pre-set current dynamic response control coefficient threshold and power deviation stability index threshold, and compare them with the generated current dynamic response control coefficient and power deviation stability index respectively after determination, evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage based on the comparison results, and optimize the low-power dynamic calibration adjustment mechanism based on the evaluation results.

[0013] Preferably, the acquisition logic of the current dynamic response control coefficient and the power deviation stability index is as follows: The current dynamic fluctuation control information is extracted from the pre-processed output power fluctuation information of the charging pile, specifically including the actual output current, actual output power and battery charge of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output current, actual output power and battery charge of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are calibrated as , and , It is the number of the actual output current, actual output power and battery capacity of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism. , is a positive integer; Calculate the average value of the actual output current of the charging pile within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism. The specific calculation formula is as follows: In the formula, It is the average value of the actual output current of the charging pile over a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism; The rate of change of the battery charge of the battery car at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism is calibrated as follows: , according to the formula: , and the maximum and minimum values ​​of the battery charge change rate of the electric vehicle at different times are calibrated as and ; Calculate the current dynamic response control coefficient. The specific calculation formula is as follows: In the formula, is the current dynamic response control coefficient; The power deviation stability analysis information is extracted from the preprocessed output power fluctuation information of the charging pile, specifically including the actual output voltage, preset output voltage, actual output power, expected output power and corresponding time points of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output voltage and preset output voltage of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are calibrated as and , is the number of the actual output voltage of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output power and expected output power of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are respectively expressed by function according to the time series and To express, is a time point, and the time period is defined as ; Calculate the average value of the actual output voltage of the charging pile within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism , according to the formula: ; Calculate the power deviation stability index. The specific calculation formula is as follows: In the formula, is the power deviation stability index.

[0014] Preferably, a preset current dynamic response control coefficient threshold is determined and power deviation stability index threshold , and after determination, respectively with the generated current dynamic response control coefficient and power deviation stability index A comparison is performed to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage based on the comparison results, and the low-power dynamic calibration adjustment mechanism is optimized based on the evaluation results. The specific comparison analysis is as follows: If satisfied and ,The low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations of current and voltage, and there is no need to optimize the low-power dynamic calibration adjustment mechanism; If not satisfied and ,The low-power dynamic calibration and adjustment mechanism cannot accurately detect small fluctuations of current and voltage, and the low-power dynamic calibration and adjustment mechanism needs to be optimized, including: increasing the data acquisition frequency, adjusting the weight parameters of the calibration mechanism, and recalibrating the threshold range of current and power fluctuations.

[0015] Preferably, a battery vehicle charging pile power automatic calibration detection device comprises a charging state recognition module, a charging process analysis module, a low power dynamic calibration start module, a power fluctuation assessment module and a long-term trend analysis module; The charging status identification module collects the charging status identification information of the battery vehicle and the charging pile in real time during the charging process of the battery vehicle through the charging pile, and analyzes the information after the collection to identify the time period when the charging pile enters the low-power charging state; The charging process analysis module obtains the charging process analysis information of the charging pile in real time after the charging pile enters the low-power charging state, and analyzes it after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted; The low-power dynamic calibration start module starts the low-power dynamic calibration adjustment mechanism when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, and adjusts the output power of the charging pile in real time to match the low-power charging demand of the battery vehicle; The power fluctuation assessment module obtains the output power fluctuation information of the charging pile in real time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and analyzes it after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the slight fluctuations of current and voltage, and optimizes the low-power dynamic calibration adjustment mechanism according to the evaluation results; The long-term trend analysis module, after optimizing the low-power dynamic calibration adjustment mechanism, continuously collects the power output data of the charging pile, and performs trend analysis based on the long-term collected data to evaluate whether the calibration mechanism can continue to meet the low-power charging needs of electric vehicles, and further optimizes the calibration mechanism based on the analysis results.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention collects the charging status identification information of the battery vehicle and the charging pile in real time, so that the system can promptly identify and respond when the charging pile enters the low-power state. This early identification capability solves the problem that the traditional system cannot detect in time due to small current and voltage fluctuations in the low-power charging state, thereby avoiding the power output lag phenomenon. By real-time analysis and judgment on whether calibration adjustment is needed, the stability and efficiency of the charging process are ensured. The technical effect is mainly reflected in the significant improvement of the system's sensitivity in low-power charging scenarios.

[0017] 2. The activation of the low-power dynamic calibration adjustment mechanism in the present invention and the current stability control coefficient and power deviation stability index generated by the advanced algorithm greatly improve the adaptability and response speed of the system. This mechanism can dynamically adjust the output power of the charging pile to match the low-power demand of the battery vehicle, eliminate the gap between power output and actual demand, and avoid the risk of overcharging or excessive power fluctuations. In particular, by analyzing the slight fluctuations in current and voltage, the system can accurately detect whether the calibration is accurate, further optimizing the charging efficiency and safety.

[0018] 3. The present invention provides the system with adaptive optimization capabilities through long-term data collection and trend analysis, ensuring that the calibration mechanism can be continuously updated and optimized based on historical data. During the long-term charging process, the system can predict future low-power charging needs by analyzing the trend of power output, and dynamically adjust the calibration mechanism based on the trend to ensure that it continues to meet the needs of electric vehicles. The overall solution enhances the flexibility and stability of the system, not only improves the charging efficiency, but also reduces the risk of battery damage caused by improper calibration, and improves the user experience and the long-term operational reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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.

[0020] Figure 1 The present invention is a flow chart of an automatic calibration detection device and detection method for a battery vehicle charging pile power.

[0021] Figure 2 The present invention is a schematic diagram of a module of an automatic calibration detection device and a detection method for a battery vehicle charging pile power. DETAILED DESCRIPTION

[0022] 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.

[0023] The present invention provides Figure 1 The method for automatic calibration and detection of battery charging pile power shown in the figure specifically includes the following steps: When the battery vehicle is charging through the charging pile, the charging status identification information of the battery vehicle and the charging pile is collected in real time, and analyzed after collection to identify the time period when the charging pile enters the low-power charging state; When the battery car is charging through the charging pile, the current, voltage, charging time and other key data of the battery car can be collected in real time through various sensor devices installed in the charging pile. The sensor can monitor the charging progress of the battery car and transmit the data to the main control system of the charging pile through the communication module. The main control system of the charging pile interacts with the charging control module of the battery car to obtain the current power information of the battery car battery to ensure that the actual charging demand and power demand of the battery are accurately recorded. All of this data can be collected and integrated in real time through the software management system of the charging pile. In order to enhance the accuracy of the collection, the system can also combine the ambient temperature data to ensure that the charging status information of the battery under different temperature conditions is reasonably evaluated.

[0024] After collecting these charging status identification information, the software module of the charging pile main control system will analyze the data in real time, and use advanced algorithms to model and predict the changing trends of current, voltage and charging time. When the software system detects that the current and voltage begin to slow down significantly and tend to stabilize, it can be combined with the charging time to judge that the charging progress of the battery vehicle is close to full charge and the battery charging demand is reduced. At this time, the system can recognize that the charging pile has entered a low-power charging state. By setting a data-based threshold (such as the current or voltage is reduced to a certain set range), the system can accurately determine the time period entering the low-power state. This identification process can be automatically run through the charging pile management software without manual intervention.

[0025] The significance of real-time collection and analysis of charging status identification information is that it can identify in advance the time period when the charging pile enters the low-power charging state, which is crucial for calibration adjustments in the low-power state. The technical problem we face is that the existing battery vehicle charging piles cannot accurately detect small changes in current and voltage in the low-power state, causing the system to still calibrate according to the high-power mode, ultimately causing power output mismatch, reduced charging efficiency, and even overcharging risks. Therefore, accurate identification of the low-power state in advance can provide the system with enough time to adjust the acquisition sensitivity, ensure that the subsequent calibration process can better adapt to the needs of low-power scenarios, and solve the problems of calibration lag and insufficient sensitivity in the existing technology. This step is the basis of the entire technical solution, ensuring that the system can respond in a timely manner, avoiding problems such as unstable charging and equipment overload, and improving overall charging efficiency and safety.

[0026] After the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and analyzed after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted; In this embodiment, after the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and analyzed after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted, which specifically includes the following steps: After the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and pre-processed after acquisition; After the charging pile enters the low-power charging state, the system can obtain real-time analysis information about the charging process of the charging pile through the sensor network installed inside the charging pile. These sensors can monitor key parameters such as current, voltage, and power output. The specific implementation method is that the sensor transmits this information to the control system of the charging pile in real time through the data acquisition module. The communication module of the control system interacts in real time with the analysis software in the charging management system, extracts the required data from the charging pile hardware through the software, and updates the data at a fixed frequency to ensure that the data at each moment can be accurately captured and transmitted to the main control system. The analysis software can directly extract key data in the low-power charging state from the collected information flow of current, voltage, and power, and automatically manage the data flow, so that the entire process does not require human intervention.

[0027] Preprocessing is to optimize the collected charging process analysis information to ensure the accuracy and stability of the data in the subsequent analysis stage. First, the collected raw data may contain noise or short-term fluctuations. These abnormal data may interfere with subsequent analysis, so data smoothing technology is needed to eliminate short-term fluctuations. Secondly, for sudden abnormal data that may appear during the charging process, the system needs to apply abnormal detection and elimination algorithms, such as setting thresholds to eliminate current or voltage readings that are out of range. Third, the system can use data normalization to standardize current, voltage, and power data in different ranges to the same numerical range to ensure the comparability between different data dimensions in subsequent calculations. The implementation of preprocessing can be carried out through the algorithm module of the software, using automated cleaning, smoothing, and normalization algorithms to make the processed data smoother and free from noise interference, providing high-quality data input for the subsequent generation of control coefficients.

[0028] Extract the current dynamic fluctuation information and power efficiency adaptation information from the preprocessed charging process analysis information of the charging pile, and analyze them after extraction to generate the current stability control coefficient and the power adaptability deviation index respectively; A mechanism judgment model is constructed for the generated current stability control coefficient and power adaptability deviation index to generate a judgment coefficient. The generated judgment coefficient is compared with a pre-set judgment coefficient threshold, and it is determined whether the power calibration mechanism of the charging pile needs to be adjusted based on the comparison result.

[0029] The pre-set judgment coefficient threshold can be determined by analyzing and modeling the historical data of the charging pile in various charging scenarios. First, the system can collect a large amount of current, voltage, and power output data of the charging pile under different loads and different power states, as well as the corresponding current stability control coefficient and power adaptability deviation index through software. Then, based on these historical data, the software can use regression analysis, cluster analysis, or machine learning algorithms to statistically model these data and identify the range of judgment coefficients under normal working conditions during the charging process. By analyzing the stability and power efficiency performance of the charging pile under different conditions (such as low-power charging, high-power charging, etc.), the software can automatically calculate the optimal threshold of the judgment coefficient. Finally, the system dynamically sets a reasonable judgment coefficient threshold based on the results of these model outputs to ensure that in actual operation, when the judgment coefficient generated in real time exceeds this preset threshold, the system can determine whether the power calibration mechanism needs to be adjusted. This process can be fully achieved through the automated analysis module of the software and continuously optimized based on the continuously updated historical data.

[0030] In this embodiment, the logic for obtaining the current stability control coefficient and the power adaptability deviation index is as follows: Extract the current dynamic fluctuation information from the preprocessed charging process analysis information of the charging pile, specifically including the actual output current and actual output power of the charging pile at different times within a period of time in the low-power charging state, and calibrate the actual output current and actual output power of the charging pile at different times within a period of time in the low-power charging state as and , It is the number of the actual output current and actual output power of the charging pile at different times within a period of time when it is in a low-power charging state. , is a positive integer; Extracting the current dynamic fluctuation information from the charging process analysis information of the pre-processed charging pile can be achieved in the following ways: First, the sensors and data acquisition modules installed inside the charging pile will monitor the changes in current and power during the charging process in real time. These sensors can collect the actual output current and actual output power at each moment in the low-power charging state. The data collected by the sensor will be transmitted to the software analysis module through the hardware control system of the charging pile. The software will capture data at a predetermined time interval (such as every second or every minute) and summarize the current and power data at all times. In order to ensure the accuracy of the data, the software will use data filtering and denoising algorithms to eliminate short-term interference or noise data, so as to ensure that the collected current and power data reflect the actual fluctuation of current and power during the charging process. Quantitative data such as real-time output current and real-time output power can be directly obtained through the output of these sensors, and these data will be stored in the database in the form of time series for subsequent analysis and processing. The software analysis module will automatically extract data within a specific time period from the database, match the current and power at different time points, and obtain the dynamic fluctuation information of current and power over a period of time. This process relies entirely on automated data collection, transmission, preprocessing and storage to ensure the real-time and accuracy of data extraction.

[0031] The maximum and minimum values ​​of the actual output current of the charging pile at different times within a period of time in the low-power charging state are calibrated as and ; Calculate the average value of the actual output current and the average value of the actual output power of the charging pile in the low-power charging state over a period of time. The specific calculation formula is as follows: In the formula, is the average value of the actual output current of the charging pile in a low-power charging state over a period of time. It is the average value of the actual output power of the charging pile in a low-power charging state over a period of time; Calculate the current stability control coefficient. The specific calculation formula is as follows: In the formula, is the current stability control coefficient; The current stability control coefficient in this formula The calculation method aims to evaluate the stability of current by normalizing and analyzing the fluctuation of multi-dimensional data. First, the formula The calculation is the current output at each moment in a period of time The average current value during this period The absolute deviation reflects the fluctuation amplitude of the current. The larger the deviation, the more unstable the current fluctuation. Divided by the total time , which is used to calculate the average fluctuation amplitude during this period. This step ensures a detailed examination of the current fluctuation at each moment. Next, represents the average output value of power. By introducing it, we can reflect the impact of the power level of the charging pile on the current stability. The larger this value is, the higher the power output is, and the greater the impact of fluctuations on charging is, so it needs to be considered. Finally, in the formula It is the extreme difference of current fluctuations, which is used to normalize current fluctuations so that the degree of fluctuation in different current intervals can be reasonably compared. Normalization can ensure that current fluctuations under different powers can be standardized, so as to calculate relatively comparable stability control coefficients. In summary, this calculation method is to comprehensively evaluate the fluctuation characteristics of current under low-power charging state, taking into account both the fluctuation amplitude of current and the influence of power output on fluctuation. The normalization process further improves the applicability and accuracy of the formula.

[0032] Current stability control coefficient The size of directly reflects the degree of current fluctuation in the low-power charging state, and can be used to determine whether the power calibration mechanism of the charging pile needs to be adjusted. When the value is large, it means that the current fluctuation is large, that is, the current is unstable during the charging process, which means that the current power calibration mechanism fails to effectively deal with the current fluctuation, which may lead to a decrease in charging efficiency or an unstable charging process. When the preset judgment threshold is exceeded, the power calibration mechanism of the charging pile needs to be adjusted to ensure that the current is more stable and the charging process is more efficient and safe. When the value is small, it indicates that the current fluctuation is small and the charging process is relatively stable. At this time, the power calibration mechanism does not need to be adjusted, and the current charging parameters have adapted to the low-power charging state.

[0033] The power efficiency adaptation information in the charging process analysis information of the preprocessed charging pile is extracted, specifically including the preset output power of the charging pile in the low-power charging state within a period of time and the actual power of the battery at different times during the process of charging the battery of the electric vehicle by the charging pile in the low-power charging state, and the preset output power of the charging pile in the low-power charging state within a period of time and the actual power of the battery at different times during the process of charging the battery of the electric vehicle by the charging pile in the low-power charging state are calibrated as and , It is the number of the actual power of the battery at different times during a period of time when the charging pile is in a low-power charging state. , is a positive integer; Extracting the power efficiency adaptation information of the preprocessed charging pile can be achieved in the following ways: First, the control system of the charging pile automatically calculates the preset output power under low-power charging state according to the battery parameters of the battery vehicle (such as capacity, battery type and current power). This preset value is set according to the battery power demand and charging strategy (such as avoiding overcharging or maintaining charging stability), and is usually calculated by the charging management software by analyzing the battery charging curve and historical charging data. The actual power of the battery during the charging process can be directly obtained from the battery management system (BMS) through the communication protocol between the charging pile and the battery of the battery vehicle. The BMS monitors the charging status of the battery in real time, including data such as current power, charging current and voltage, and transmits these data to the main control system of the charging pile in the form of digital signals. The software system records the actual power change of the battery at a specific time interval (such as every second or every minute), and generates a battery power change curve combined with multiple time points during the charging period. At the same time, the software extracts the preset output power and actual power data at different times for comparative analysis to obtain power efficiency adaptation information. The entire extraction process is automatically handled by the charging pile's software management system. Through data acquisition, calculation and analysis modules, it tracks, records and compares the preset power and actual power changes to ensure the accuracy of power output and match it with battery requirements.

[0034] Calculate the power adaptability deviation index. The specific calculation formula is: In the formula, is the power adaptability deviation index.

[0035] The power adaptability deviation index The calculation formula is designed to evaluate the deviation between the charging pile power output and the actual battery demand. First, the formula Indicates the actual power of the charging pile at each moment With expected power This part reflects the degree of deviation between the power output of the charging pile and the expected demand. The larger the difference, the worse the power adaptability. Then, multiply it by the absolute difference of the battery power change. , which combines the change of battery power at each moment and takes into account the battery status feedback during the charging process. The greater the change in power, the greater the impact of the power output difference on the charging process. In the denominator, first pass Calculate the average value within the time period to ensure that the impact of power fluctuations during the entire period is reasonably distributed. The final normalization factor It is the difference between the maximum and minimum values ​​of the current fluctuation, which is used to balance the impact of different current fluctuation ranges and prevent power fluctuations from being amplified or reduced by current anomalies. This normalization step ensures that the impact of power deviation can be standardized under different current fluctuation environments, making the power deviations in different situations comparable. In summary, this formula combines the deviation of power output, the change in battery charge, and the range of current fluctuations to comprehensively measure the adaptability of power output to battery requirements, and can effectively determine whether the charging pile needs power calibration when charging at low power.

[0036] Power Adaptability Deviation Index The size of directly reflects the degree of deviation between the charging pile power output and the actual battery demand, and is used to determine whether the power calibration mechanism of the charging pile needs to be adjusted. When the value is large, it means that the deviation between the actual output power and the expected power is large. Combined with the influence of battery power change and current fluctuation, it shows that the current power output fails to meet the charging needs of the battery. At this time, the power calibration mechanism of the charging pile may not effectively cope with the low power state, resulting in reduced efficiency or instability in the charging process, so the power calibration mechanism needs to be adjusted. On the contrary, if A small value indicates that the power output is relatively stable and the deviation is within a reasonable range. The current power calibration mechanism of the charging pile can effectively adapt to the needs of low power state, and no adjustment is required at this time.

[0037] In this embodiment, the generated current stability control coefficient and power adaptability deviation index Construct a mechanism judgment model and generate judgment coefficients through weighted summation , and the generated judgment coefficient The pre-set judgment coefficient threshold Compare and determine whether the power calibration mechanism of the charging pile needs to be adjusted based on the comparison results. The specific comparison analysis is as follows: like , there is no need to adjust the power calibration mechanism of the charging pile; This indicates that the current power output of the charging pile is well matched to the low-power charging requirements of the battery vehicle. In other words, the current fluctuation is small, the deviation between the power output and the actual battery requirements is within a reasonable range, and the charging process is stable and efficient. In this case, the system does not need to adjust the power calibration mechanism and continues to maintain the current charging strategy, which can ensure the safety and efficiency of the charging process, while reducing unnecessary calibration adjustment operations and avoiding waste of system resources.

[0038] like , the power calibration mechanism of the charging pile needs to be adjusted.

[0039] This situation shows that the current power calibration mechanism fails to effectively adapt to the needs of low-power charging. Specifically, it is manifested in large current fluctuations or significant deviations between the power output and the actual battery needs, which may lead to reduced charging efficiency, longer charging time, and even battery overcharging or other safety risks. In this case, the system must adjust the power calibration mechanism to re-optimize the output power of the charging pile to better match the battery's charging needs, thereby improving the stability and safety of the charging process.

[0040] In order to stabilize the generated current and power adaptability deviation index Construct a mechanism judgment model and generate judgment coefficients through weighted summation First, we need to set two weight coefficients and , which are used to balance the impact of current fluctuation and power adaptability deviation on the overall judgment. The setting of these two weights can be adjusted according to the priority of system operation or historical data. For example, when current stability is more important, The larger the value is, the higher the weight will be given to the power adaptability deviation. The calculation formula is as follows: In this formula, and The value of usually satisfies This ensures that the weight ratio is reasonable and that the contribution of all factors to the final judgment coefficient can be balanced. For example, if the current fluctuation has a greater impact on the charging pile, you can set and , reflecting that current fluctuations have a higher weight in the entire judgment model. In actual operation, the system can automatically adjust the weight by analyzing the historical data of the charging process to ensure that the generated judgment coefficient More accurately reflects the current state of the system. Finally, the generated judgment coefficient Will be used with the preset judgment threshold The comparison is then made to determine whether the power calibration mechanism of the charging pile needs to be adjusted.

[0041] When it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the low-power dynamic calibration adjustment mechanism is started to adjust the output power of the charging pile in real time to match the low-power charging requirements of the battery vehicle; In this embodiment, when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the low-power dynamic calibration adjustment mechanism is started, and the output power of the charging pile is adjusted in real time to match the low-power charging demand of the electric vehicle. Specifically, when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the dynamic data of current fluctuation and power output are obtained in real time according to the current low-power charging demand of the electric vehicle; the real-time obtained current fluctuation data and power output data are analyzed through the low-power dynamic calibration adjustment mechanism, and the current power output adjustment range of the charging pile is calculated; according to the calculated adjustment range, the output power of the charging pile is adjusted in real time to gradually approach the low-power charging demand of the electric vehicle.

[0042] In order to obtain dynamic data of current fluctuations and power output in real time, this can be achieved through sensors and data acquisition modules inside the charging pile. These sensors can monitor key parameters such as current, voltage and power output, and transmit these data to the software module in the charging management system through the control system. The software module will collect and record this data at predetermined time intervals (such as every second or every minute). This process is completed through an automated monitoring mechanism to ensure that the system has real-time knowledge of current fluctuations and power output during the charging process. The importance of this step lies in that current fluctuations and power output are the basic data for determining whether the power calibration mechanism needs to be adjusted. Only through accurate real-time data acquisition can the accuracy of the subsequent calibration and adjustment process be ensured.

[0043] After the data collection is completed, the analysis module of the charging management system will analyze the current fluctuation data and power output data obtained in real time. The software will use a preset mathematical model (such as regression analysis or machine learning algorithm) to calculate the difference between the current power output of the charging pile and the low-power charging demand of the battery vehicle. By analyzing these data, the software can derive the adjustment range of the power output to ensure that the output power of the charging pile gradually matches the charging demand of the battery vehicle. The analysis process can be achieved through the established data model and the pre-set power demand standard. The model will determine whether power adjustment is required based on historical data and current dynamic data. The purpose of this step is to find the difference between the current power output of the charging pile and the actual demand of the battery vehicle through data analysis, so as to accurately calculate the power range that needs to be adjusted.

[0044] After calculating the power amplitude that needs to be adjusted, the system will adjust the output power of the charging pile in real time through the software control module. The software will gradually modify the power output parameters of the charging pile according to the calculated adjustment amplitude to ensure that the output power gradually approaches the low-power charging requirements of the battery vehicle. The adjustment can be achieved through the power control module of the charging pile, and the system dynamically adjusts the power output according to the adjustment instructions. During this process, the software will continuously monitor the power output and current fluctuations to ensure that the adjustment process is stable and efficient. The importance of this step is that real-time adjustment of the output power of the charging pile can ensure charging efficiency and prevent problems such as extended charging time or battery overcharging due to power output mismatch.

[0045] After the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time, and analyzed after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the small fluctuations of current and voltage, and optimize the low-power dynamic calibration adjustment mechanism according to the evaluation results; In this embodiment, after the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time, and analyzed after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the small fluctuations of current and voltage, and optimize the low-power dynamic calibration adjustment mechanism according to the evaluation results, which specifically includes the following steps: After the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time and pre-processed after acquisition; After the charging pile starts the low-power dynamic calibration adjustment mechanism, the real-time acquisition of output power fluctuation information can be achieved through the sensors and data acquisition modules installed inside the charging pile. Specifically, the power sensor monitors the changes in current and voltage, and calculates the power output in real time through the control system. At each time point, the system calculates the power output of the charging pile according to the formula P=I×VP, and records the real-time power data at predetermined time intervals (such as every second or every minute). These data are transmitted to the analysis module of the charging management system through the communication module. In order to ensure the real-time nature of the data, the data acquisition and transmission process is completely controlled by the automated software system without manual intervention. The software organizes these power fluctuation information according to the time series structure to ensure that the subsequent analysis process can obtain continuous and accurate dynamic data.

[0046] Preprocessing is a key step to ensure the accuracy of power fluctuation information and data quality. The main purpose is to remove noise, smooth fluctuations, and normalize. First, in the actual charging process, it may be affected by the external environment (such as instantaneous voltage fluctuations or sensor errors), and these noise data will interfere with the analysis results. Therefore, the software uses filtering algorithms, such as low-pass filters, to smooth short-term power fluctuations and remove possible noise. Secondly, considering that the range of data may vary greatly under different power states, the system needs to normalize the data so that different data dimensions can be compared with each other in subsequent analysis. Preprocessing also includes abnormal data detection, such as setting the upper and lower limits of the power fluctuation range and removing abnormal values ​​that exceed the normal range. The entire preprocessing process is implemented through the automated data cleaning module in the software, making the processed data more consistent and accurate, providing a reliable basis for subsequent coefficient calculation and evaluation.

[0047] Extracting the current dynamic fluctuation control information and the power deviation stability analysis information from the preprocessed output power fluctuation information of the charging pile, and analyzing them after extraction to generate the current dynamic response control coefficient and the power deviation stability index; Determine the pre-set current dynamic response control coefficient threshold and power deviation stability index threshold, and compare them with the generated current dynamic response control coefficient and power deviation stability index respectively after determination, evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage based on the comparison results, and optimize the low-power dynamic calibration adjustment mechanism based on the evaluation results.

[0048] Determining the preset current dynamic response control coefficient threshold and power deviation stability index threshold can be achieved by analyzing and modeling the historical data of the charging pile. First, the system needs to collect historical data of the charging pile in different charging scenarios through software, including current fluctuation data, power output data, voltage change data, etc. under low-power charging state. Then, the software processes these historical data by using statistical analysis and machine learning algorithms to generate distribution models of the current dynamic response control coefficient and power deviation stability index. By analyzing these models, the software can identify the reasonable range of these two parameters under normal charging conditions and set an optimal threshold range based on the performance of the charging pile and different charging requirements. Usually, the system calculates parameters such as peak value, average value, and standard deviation in historical data, and uses regression analysis or cluster analysis to determine the threshold range. Finally, the system automatically generates and sets the preset thresholds of the current dynamic response control coefficient and the power deviation stability index based on these analysis results, so as to be used in the real-time evaluation process to determine whether the calibration mechanism needs to be adjusted. This process is automatically completed by the software to ensure the accuracy and flexibility of threshold setting.

[0049] In this embodiment, the logic for obtaining the current dynamic response control coefficient and the power deviation stability index is as follows: The current dynamic fluctuation control information is extracted from the pre-processed output power fluctuation information of the charging pile, specifically including the actual output current, actual output power and battery charge of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output current, actual output power and battery charge of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are calibrated as , and , It is the number of the actual output current, actual output power and battery capacity of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism. , is a positive integer; After the charging pile starts the low-power dynamic calibration adjustment mechanism, the extraction of current dynamic fluctuation control information can be achieved through the sensor module and data acquisition system inside the charging pile. First, the charging pile is equipped with current sensors and power sensors, which can monitor the current and power output of the charging pile in real time. These sensors will transmit the collected raw data to the control and management system of the charging pile. Specifically, the current sensor records the actual output current at each time point (for example, every second), and the power sensor monitors the real-time output power. At the same time, the battery power data of the battery car is obtained through the battery management system (BMS), which is connected to the charging pile control system and can send information about battery power changes in real time. The control and management system interacts with these sensor modules through the internal communication interface, organizes the collected real-time data in a time series, and ensures that the actual output power, current and battery power of the charging pile are recorded at different time points. Through the automated data acquisition software module, these real-time data can be pre-processed and stored in the system database for subsequent analysis. The extraction of these quantitative data is fully automated, and high-frequency and accurate real-time monitoring is achieved through the software control process, thereby providing reliable data support for subsequent dynamic calibration.

[0050] Calculate the average value of the actual output current of the charging pile within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism. The specific calculation formula is as follows: In the formula, It is the average value of the actual output current of the charging pile over a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism; The rate of change of the battery charge of the battery car at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism is calibrated as follows: , according to the formula: , and the maximum and minimum values ​​of the battery charge change rate of the electric vehicle at different times are calibrated as and ; Calculate the current dynamic response control coefficient. The specific calculation formula is as follows: In the formula, is the current dynamic response control coefficient; Current dynamic response control coefficient The calculation formula of evaluates the response of the charging pile to current fluctuations in low power state through a series of operations. First, the formula The deviation between the actual output current and the average current at each time point is calculated to capture the magnitude of the current fluctuation. As a power weight, it is used to adjust the impact of current fluctuations on power output, because at high power output, the impact of current fluctuations on the overall system is more significant. The amplitude of the normalized current fluctuation reflects the relative size of the current fluctuation in the entire time period. Finally, divide the above result by the total time , and obtain the time average value to ensure that the coefficient can accurately reflect the current fluctuations during the entire charging process. This calculation method can combine the comprehensive information of current deviation, power output and fluctuation amplitude, providing a strong quantitative basis for regulating the current response under low power state, which helps to improve the overall operating efficiency and stability of the charging pile.

[0051] Current dynamic response control coefficient The size of directly reflects the system's sensitivity to current fluctuations. threshold, and the actual generated By comparing with this threshold, the ability of the calibration mechanism to detect small fluctuations in current can be evaluated. If the current fluctuation is higher than the preset threshold, it means that the current fluctuation is large, the system's response to the current is delayed or insufficient, and the calibration mechanism needs to be optimized to more accurately capture the current changes; on the contrary, if Below the threshold, it indicates that the system is able to respond and adjust to current fluctuations in a timely and accurate manner, and the calibration mechanism performs well and does not require further adjustment.

[0052] The power deviation stability analysis information is extracted from the preprocessed output power fluctuation information of the charging pile, specifically including the actual output voltage, preset output voltage, actual output power, expected output power and corresponding time points of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output voltage and preset output voltage of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are calibrated as and , is the number of the actual output voltage of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output power and expected output power of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are respectively expressed by function according to the time series and To express, is a time point, and the time period is defined as ; After the charging pile starts the low-power dynamic calibration adjustment mechanism, the extraction of power deviation stability analysis information depends on the voltage sensor, power sensor, built-in control system and preset parameter model of the charging pile. First, the voltage sensor of the charging pile can monitor the actual output voltage at each time point in real time, while the power sensor monitors the real-time actual output power. The preset output voltage and expected output power are generated by the control management software of the charging pile based on the current charging state of the battery of the electric vehicle and the system parameters in the low-power charging mode. These preset parameters are set based on the standard charging model or historical charging data in the system, and are automatically calculated and updated within the system. The corresponding time points are automatically recorded by the control system to synchronize the actual measured voltage and power with the preset values. The entire data extraction process is realized through the automatic data acquisition module of the charging pile control system. The system matches and stores the actual output voltage and power at each time point with the preset expected voltage and power. The raw data (actual voltage and power) collected by the sensor is transmitted to the data processing unit through the internal communication interface of the charging pile. The software timestamps these data and stores them in the system database in the form of time series to ensure that the voltage and power data at different times can be accurately matched. The preset output voltage and power are automatically generated by the system's internal algorithm and compared with real-time data. These data extraction and matching are completely done by software, and the system can monitor and adjust in real time with millisecond-level accuracy to ensure that the extracted power deviation stability analysis information can accurately reflect the voltage and power deviation during the charging process.

[0053] Calculate the average value of the actual output voltage of the charging pile within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism , according to the formula: ; Calculate the power deviation stability index. The specific calculation formula is as follows: In the formula, is the power deviation stability index.

[0054] Power Deviation Stability Index The calculation formula of measures the power output stability of the charging pile through the following steps. First, the formula is calculated by definite integral Calculate the actual output power over a period of time With expected power This captures the deviation in power output throughout the charging process. Divide this cumulative difference by the time interval , we can get the time-averaged power deviation, ensuring that the deviations in different charging periods are comparable. Next, the formula introduces the average value of the actual output voltage and the expected output voltage The ratio of is used to adjust the power deviation to reflect the impact of voltage fluctuation on power deviation. By combining the calculations of these two parts, The formula can comprehensively evaluate the degree of match between actual power output and expected power, reflecting whether the system can maintain output stability under low power conditions, thereby ensuring charging efficiency and safety.

[0055] Power Deviation Stability Index The size of reflects the system's sensitivity to the deviation between power output and expected power, and indirectly reflects the ability to detect voltage fluctuations. threshold, and the generated By comparing with the threshold, the accuracy of the calibration mechanism in regulating power and voltage fluctuations can be evaluated. If the voltage is higher than the threshold, it means that the system is not stable enough to the voltage change and power output in the low power state, and the power deviation is large. The calibration mechanism needs to be adjusted to ensure that it can accurately detect and regulate small voltage fluctuations. If it is below the threshold, it means that the system can effectively stabilize the power output, the voltage fluctuation is small, and the calibration mechanism is accurate enough without further optimization.

[0056] In this embodiment, the preset current dynamic response control coefficient threshold is determined and power deviation stability index threshold , and after determination, respectively with the generated current dynamic response control coefficient and power deviation stability index A comparison is performed to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage based on the comparison results, and the low-power dynamic calibration adjustment mechanism is optimized based on the evaluation results. The specific comparison analysis is as follows: If satisfied and ,The low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations of current and voltage, and there is no need to optimize the low-power dynamic calibration adjustment mechanism; This situation means that the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage, and the system performs well without further optimization. This shows that the output power and current of the charging pile are relatively stable under low-power charging conditions, and the system can capture and adjust these fluctuations in real time, so there will be no excessive deviations in power or current. In this case, the charging pile can operate efficiently and safely to ensure healthy charging of the battery.

[0057] If not satisfied and ,The low-power dynamic calibration and adjustment mechanism cannot accurately detect small fluctuations of current and voltage, and the low-power dynamic calibration and adjustment mechanism needs to be optimized, including: increasing the data acquisition frequency, adjusting the weight parameters of the calibration mechanism, and recalibrating the threshold range of current and power fluctuations.

[0058] This situation means that the low-power dynamic calibration adjustment mechanism fails to effectively capture the small fluctuations in current and voltage, and the system exhibits detection lag or insufficient accuracy. To solve this problem, optimization measures can be implemented through software. First, the system can adjust the data acquisition frequency module to increase the sampling frequency of the sensor, so as to more accurately capture the real-time current and voltage fluctuations. Secondly, adjust the weight parameters in the algorithm, and optimize the impact of current and voltage fluctuations on power output regulation through software, so that the system can more accurately balance power and current fluctuations. Finally, recalibrate the threshold range of CDRRC and PDSI, and the system automatically adjusts the threshold based on real-time data or historical charging data analysis to ensure that small fluctuations can be sensitively detected. These optimization steps are implemented through software automation to ensure that the system can accurately respond and adjust the output in a low-power charging state, ensuring the stability and safety of the charging process.

[0059] After optimizing the low-power dynamic calibration adjustment mechanism, the power output data of the charging pile is continuously collected, and trend analysis is performed based on the long-term collected data to evaluate whether the calibration mechanism can continue to meet the low-power charging needs of electric vehicles, and the calibration mechanism is further optimized based on the analysis results.

[0060] After optimizing the low-power dynamic calibration adjustment mechanism, the way to continuously collect the power output data of the charging pile and perform trend analysis can be achieved through the data acquisition, storage, and analysis modules in the software system. Specifically, first, the software system will obtain the power output data of the charging pile in real time through the built-in sensors and monitoring equipment of the charging pile, and the acquisition frequency will be adjusted to the optimal level according to the previous optimization results. The sensor transmits the actual power output, current, voltage and other data collected to the data storage module in the system. These data are organized and saved in a time series manner for subsequent analysis.

[0061] Next, the software will perform trend analysis on the data collected over a long period of time. This can be achieved by introducing big data analysis algorithms or machine learning models. The system can set up automated analysis rules to regularly analyze the fluctuation trend of power output data, long-term current and voltage changes, and compare them with the ideal output in the charging model. For example, the system can evaluate whether the calibration mechanism can still accurately respond to demand under long-term low-power charging conditions by analyzing statistical indicators such as the mean and variance of power output. If the trend analysis shows that the power output is gradually deviating from the actual charging needs of the battery vehicle, it means that the adjustment effect of the calibration mechanism is gradually weakening.

[0062] The reason for doing this is that through trend analysis, the system can predict whether the future calibration mechanism can continue to meet the accuracy requirements of low-power charging. Based on the analysis results of long-term data, the system can dynamically optimize the calibration mechanism. For example, the system can dynamically adjust key parameters in the calibration mechanism, such as the calibration threshold range, response control weight, etc., based on the analysis results to ensure that the calibration mechanism can continue to be stable and adapt to different charging conditions and environmental changes during long-term operation. Through this data-driven trend analysis, the system can ensure that the charging pile always maintains efficient and stable output in a low-power state, meet the charging needs of electric vehicles, avoid low power or instability, and improve the safety and efficiency of the charging process.

[0063] like Figure 2 The battery vehicle charging pile power automatic calibration detection device shown includes a charging state recognition module, a charging process analysis module, a low-power dynamic calibration start module, a power fluctuation assessment module and a long-term trend analysis module; The charging status identification module collects the charging status identification information of the battery vehicle and the charging pile in real time during the charging process of the battery vehicle through the charging pile, and analyzes the information after the collection to identify the time period when the charging pile enters the low-power charging state; The charging process analysis module obtains the charging process analysis information of the charging pile in real time after the charging pile enters the low-power charging state, and analyzes it after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted; The low-power dynamic calibration start module starts the low-power dynamic calibration adjustment mechanism when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, and adjusts the output power of the charging pile in real time to match the low-power charging demand of the battery vehicle; The power fluctuation assessment module obtains the output power fluctuation information of the charging pile in real time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and analyzes it after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the slight fluctuations of current and voltage, and optimizes the low-power dynamic calibration adjustment mechanism according to the evaluation results; The long-term trend analysis module, after optimizing the low-power dynamic calibration adjustment mechanism, continuously collects the power output data of the charging pile, and performs trend analysis based on the long-term collected data to evaluate whether the calibration mechanism can continue to meet the low-power charging needs of electric vehicles, and further optimizes the calibration mechanism based on the analysis results.

[0064] 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.

[0065] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0066] 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.

[0067] 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.

[0068] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0069] 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.

[0070] 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.

[0071] 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 battery vehicle charging pile power automatic calibration detection method, characterized in that: The specific steps include: When the battery vehicle is charging through the charging pile, the charging status identification information of the battery vehicle and the charging pile is collected in real time, and analyzed after collection to identify the time period when the charging pile enters the low-power charging state; After the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and analyzed after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted; When it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the low-power dynamic calibration adjustment mechanism is started to adjust the output power of the charging pile in real time to match the low-power charging requirements of the battery vehicle; After the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time, and analyzed after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the small fluctuations of current and voltage, and optimize the low-power dynamic calibration adjustment mechanism according to the evaluation results; After optimizing the low-power dynamic calibration adjustment mechanism, the power output data of the charging pile is continuously collected, and trend analysis is performed based on the long-term collected data to evaluate whether the calibration mechanism can continue to meet the low-power charging needs of electric vehicles, and the calibration mechanism is further optimized based on the analysis results.

2. The method for automatic calibration and detection of battery vehicle charging pile power according to claim 1 is characterized in that: After the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and analyzed after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted, which specifically includes the following steps: After the charging pile enters the low-power charging state, the charging process analysis information of the charging pile is obtained in real time, and pre-processed after acquisition; Extract the current dynamic fluctuation information and power efficiency adaptation information from the preprocessed charging process analysis information of the charging pile, and analyze them after extraction to generate the current stability control coefficient and the power adaptability deviation index respectively; A mechanism judgment model is constructed for the generated current stability control coefficient and power adaptability deviation index to generate a judgment coefficient. The generated judgment coefficient is compared with a pre-set judgment coefficient threshold, and it is determined whether the power calibration mechanism of the charging pile needs to be adjusted based on the comparison result.

3. The method for automatic calibration and detection of battery vehicle charging pile power according to claim 2 is characterized in that: The acquisition logic of the current stability control coefficient and the power adaptability deviation index is as follows: Extract the current dynamic fluctuation information from the preprocessed charging process analysis information of the charging pile, specifically including the actual output current and actual output power of the charging pile at different times within a period of time in the low-power charging state, and calibrate the actual output current and actual output power of the charging pile at different times within a period of time in the low-power charging state as and , It is the number of the actual output current and actual output power of the charging pile at different times within a period of time when it is in a low-power charging state. , is a positive integer; The maximum and minimum values ​​of the actual output current of the charging pile at different times within a period of time in the low-power charging state are calibrated as and ; Calculate the average value of the actual output current and the average value of the actual output power of the charging pile in the low-power charging state over a period of time. The specific calculation formula is as follows: In the formula, is the average value of the actual output current of the charging pile in a low-power charging state over a period of time. It is the average value of the actual output power of the charging pile in a low-power charging state over a period of time; Calculate the current stability control coefficient. The specific calculation formula is as follows: In the formula, is the current stability control coefficient; The power efficiency adaptation information in the charging process analysis information of the preprocessed charging pile is extracted, specifically including the preset output power of the charging pile in the low-power charging state within a period of time and the actual power of the battery at different times during the process of charging the battery of the electric vehicle by the charging pile in the low-power charging state, and the preset output power of the charging pile in the low-power charging state within a period of time and the actual power of the battery at different times during the process of charging the battery of the electric vehicle by the charging pile in the low-power charging state are calibrated as and , It is the number of the actual power of the battery at different times during a period of time when the charging pile is in a low-power charging state. , is a positive integer; Calculate the power adaptability deviation index. The specific calculation formula is: In the formula, is the power adaptability deviation index.

4. The method for automatic calibration and detection of battery vehicle charging pile power according to claim 3 is characterized in that: The stability control coefficient of the generated current and power adaptability deviation index Construct a mechanism judgment model and generate judgment coefficients through weighted summation , and the generated judgment coefficient The pre-set judgment coefficient threshold Compare and determine whether the power calibration mechanism of the charging pile needs to be adjusted based on the comparison results. The specific comparison analysis is as follows: like , there is no need to adjust the power calibration mechanism of the charging pile; like , the power calibration mechanism of the charging pile needs to be adjusted.

5. A battery vehicle charging pile power automatic calibration detection method according to claim 4, characterized in that: When it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the low-power dynamic calibration adjustment mechanism is started to adjust the output power of the charging pile in real time to match the low-power charging demand of the battery vehicle. Specifically, when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, the dynamic data of current fluctuation and power output are obtained in real time according to the current low-power charging demand of the battery vehicle; The real-time acquired current fluctuation data and power output data are analyzed through the low-power dynamic calibration adjustment mechanism to calculate the current power output adjustment range of the charging pile. Based on the calculated adjustment range, the output power of the charging pile is adjusted in real time to gradually approach the low-power charging requirements of the electric vehicle.

6. A battery vehicle charging pile power automatic calibration detection method according to claim 5, characterized in that: After the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time, and analyzed after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the small fluctuations of current and voltage, and optimize the low-power dynamic calibration adjustment mechanism according to the evaluation results, which specifically includes the following steps: After the charging pile starts the low-power dynamic calibration adjustment mechanism, the output power fluctuation information of the charging pile is obtained in real time and pre-processed after acquisition; Extracting the current dynamic fluctuation control information and the power deviation stability analysis information from the preprocessed output power fluctuation information of the charging pile, and analyzing them after extraction to generate the current dynamic response control coefficient and the power deviation stability index; Determine the pre-set current dynamic response control coefficient threshold and power deviation stability index threshold, and compare them with the generated current dynamic response control coefficient and power deviation stability index respectively after determination, evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage based on the comparison results, and optimize the low-power dynamic calibration adjustment mechanism based on the evaluation results.

7. A battery vehicle charging pile power automatic calibration detection method according to claim 6, characterized in that: The acquisition logic of the current dynamic response control coefficient and the power deviation stability index is as follows: Extract the current dynamic fluctuation control information from the preprocessed output power fluctuation information of the charging pile, specifically including the actual output current, actual output power and battery charge of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and calibrate the actual output current, actual output power and battery charge of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism as , and , It is the number of the actual output current, actual output power and battery capacity of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism. , is a positive integer; Calculate the average value of the actual output current of the charging pile within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism. The specific calculation formula is as follows: In the formula, It is the average value of the actual output current of the charging pile over a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism; The rate of change of the battery charge of the battery car at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism is calibrated as follows: , according to the formula: , and the maximum and minimum values ​​of the battery charge change rate of the electric vehicle at different times are calibrated as and ; Calculate the current dynamic response control coefficient. The specific calculation formula is as follows: In the formula, is the current dynamic response control coefficient; The power deviation stability analysis information is extracted from the preprocessed output power fluctuation information of the charging pile, specifically including the actual output voltage, preset output voltage, actual output power, expected output power and corresponding time points of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output voltage and preset output voltage of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are calibrated as and , is the number of the actual output voltage of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and the actual output power and expected output power of the charging pile at different times within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism are respectively expressed by function according to the time series and To express, is a time point, and the time period is defined as ; Calculate the average value of the actual output voltage of the charging pile within a period of time after the charging pile starts the low-power dynamic calibration adjustment mechanism , according to the formula: ; Calculate the power deviation stability index. The specific calculation formula is as follows: In the formula, is the power deviation stability index.

8. The method for automatic calibration and detection of battery vehicle charging pile power according to claim 7, characterized in that: Determine the preset current dynamic response control coefficient threshold and power deviation stability index threshold , and after determination, respectively with the generated current dynamic response control coefficient and power deviation stability index A comparison is performed to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations in current and voltage based on the comparison results, and the low-power dynamic calibration adjustment mechanism is optimized based on the evaluation results. The specific comparison analysis is as follows: If satisfied and ,The low-power dynamic calibration adjustment mechanism can accurately detect small fluctuations of current and voltage, and there is no need to optimize the low-power dynamic calibration adjustment mechanism; If not satisfied and ,The low-power dynamic calibration and adjustment mechanism cannot accurately detect small fluctuations of current and voltage, and the low-power dynamic calibration and adjustment mechanism needs to be optimized, including: increasing the data acquisition frequency, adjusting the weight parameters of the calibration mechanism, and recalibrating the threshold range of current and power fluctuations.

9. An automatic calibration and detection device for battery vehicle charging pile power, used to implement an automatic calibration and detection method for battery vehicle charging pile power as described in any one of claims 1 to 8, characterized in that: It includes charging status identification module, charging process analysis module, low power dynamic calibration start module, power fluctuation assessment module and long-term trend analysis module; The charging status identification module collects the charging status identification information of the battery vehicle and the charging pile in real time during the charging process of the battery vehicle through the charging pile, and analyzes the information after the collection to identify the time period when the charging pile enters the low-power charging state; The charging process analysis module obtains the charging process analysis information of the charging pile in real time after the charging pile enters the low-power charging state, and analyzes it after acquisition to determine whether the power calibration mechanism of the charging pile needs to be adjusted; The low-power dynamic calibration start module starts the low-power dynamic calibration adjustment mechanism when it is determined that the power calibration mechanism of the charging pile needs to be adjusted, and adjusts the output power of the charging pile in real time to match the low-power charging demand of the battery vehicle; The power fluctuation assessment module obtains the output power fluctuation information of the charging pile in real time after the charging pile starts the low-power dynamic calibration adjustment mechanism, and analyzes it after acquisition to evaluate whether the low-power dynamic calibration adjustment mechanism can accurately detect the slight fluctuations of current and voltage, and optimizes the low-power dynamic calibration adjustment mechanism according to the evaluation results; The long-term trend analysis module, after optimizing the low-power dynamic calibration adjustment mechanism, continuously collects the power output data of the charging pile, and performs trend analysis based on the long-term collected data to evaluate whether the calibration mechanism can continue to meet the low-power charging needs of electric vehicles, and further optimizes the calibration mechanism based on the analysis results.

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