Adaptive load adjustment method in charging pile test equipment and related equipment

Abstract

The invention relates to a self-adaptive load adjusting method in charging pile test equipment and related equipment, and the method comprises the following steps: carrying out the multi-dimensional electrical parameter collection and analysis of a target charging pile, and obtaining initial electrical data; performing output power fluctuation state calculation on the target charging pile based on the initial electrical data to obtain output power fluctuation state parameters; if the output power fluctuation state parameter exceeds a preset threshold value, carrying out load type identification on the to-be-charged equipment based on the output power fluctuation state parameter to obtain a load type; based on the load type, carrying out load adjustment strategy selection on a target charging pile to obtain a load adjustment instruction; through the IGBT driving circuit arranged in the target charging pile, power output control is carried out on the target charging pile based on the load adjusting instruction, the target output power is obtained, and the technical problems that how the charging pile can stably work under various conditions and how to intelligently adapt to different types of load requirements are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and particularly to an adaptive load regulation method and related devices in a charging pile testing device. Background Art

[0002] With the popularization of electric vehicles, charging piles, as an important bridge connecting the power grid and electric vehicles, their performance and reliability are directly related to the charging experience of users and the promotion effect of electric vehicles. However, in actual applications, charging piles face many challenges. For example, there are significant differences in the charging power requirements of electric vehicles of different brands and models, which leads to the need for charging piles to flexibly adjust the output power when charging different vehicles. In addition, due to factors such as grid voltage fluctuations and load changes, the actual output power of charging piles may be unstable, affecting the charging efficiency and even causing damage to the vehicle battery. Therefore, in order to ensure that charging piles can work stably under various conditions and can intelligently adapt to different types of load requirements, it is crucial to study an effective adaptive load regulation method.

[0003] Most of the existing charging pile testing devices on the market currently focus on basic function tests, such as maximum output power tests, charging interface compatibility tests, etc., but there is a lack of testing for the performance of charging piles under dynamic conditions, especially the response ability test when facing load mutations or changes in the grid environment. This makes it difficult to discover some potential problems. For example, when a charging pile is connected to a device to be charged with different characteristics, without an appropriate load regulation mechanism, it may lead to mismatched power output during the charging process, thereby reducing the charging efficiency or being unable to meet the requirements of fast charging. At the same time, the lack of effective load identification technology also limits the adaptability of charging piles to different types of loads and increases the difficulty of achieving efficient and safe charging.

[0004] In order to overcome the above problems, an adaptive load regulation method based on a charging pile testing device is proposed. The aim is to collect and analyze multi-dimensional electrical parameters of the target charging pile, monitor and evaluate the output power fluctuation state of the charging pile in real time, and then select an appropriate load regulation strategy according to the actual situation. By introducing advanced IGBT drive circuit control technology, this method not only improves the adaptability and response speed of charging piles to different load types, but also realizes more accurate and smooth power output control. The application of this method helps to improve the overall performance of charging piles, ensure stable charging services in complex and changing usage environments, and also helps to promote the development of charging pile technology and provide users with a better charging experience. Summary of the Invention

[0005] The main object of the present invention is to provide an adaptive load regulation method and related equipment in a charging pile testing device, which solves the technical problem of how a charging pile can operate stably under various conditions and can intelligently adapt to different types of load requirements.

[0006] To achieve the above object, the present invention provides an adaptive load regulation method in a charging pile testing device, including the following steps: Collect and analyze multi-dimensional electrical parameters of the target charging pile to obtain initial electrical data; Calculate the output power fluctuation state of the target charging pile based on the initial electrical data to obtain an output power fluctuation state parameter; If the output power fluctuation state parameter exceeds a preset threshold, identify the load type of the device to be charged based on the output power fluctuation state parameter to obtain the load type; wherein, the target charging pile is electrically connected to the device to be charged; Select a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction; Based on the load regulation instruction, control the power output of the target charging pile through the IGBT drive circuit provided in the target charging pile to obtain a target output power.

[0007] Further, the collecting and analyzing multi-dimensional electrical parameters of the target charging pile to obtain initial electrical data includes: Sample the voltage and current at the output port in the target charging pile through a high-speed sampling circuit to obtain original sampling data; wherein, the original sampling data includes an original voltage value, an original current value, and a sampling timestamp; Filter the noise from the original sampling data to obtain filtered sampling data; Filter the noise from and calibrate the original sampling data to obtain calibrated electrical sampling data; Perform multi-scale decomposition on the calibrated electrical sampling data through wavelet transform to extract the dynamic change characteristics of the original voltage value and the original current value to obtain time series characteristics; Separate the signals of the time series characteristics based on independent component analysis to obtain electrical characteristic components; Dynamically track the electrical characteristic components by using a state observer to obtain dynamic response parameters, and use the dynamic response parameters as the initial electrical data.

[0008] Further, the calculating the output power fluctuation state of the target charging pile based on the initial electrical data to obtain an output power fluctuation state parameter includes: Calculate the instantaneous power of the initial electrical data to obtain an instantaneous power sequence; Perform a sliding window process on the instantaneous power sequence to obtain the instantaneous power sequences within multiple sliding windows; Use the discrete Fourier transform algorithm to perform frequency domain analysis on the instantaneous power sequences within the multiple sliding windows to obtain the power spectral density; Calculate the output power fluctuation state of the target charging pile based on the power spectral density to obtain output power fluctuation characteristic parameters.

[0009] Further, the method for identifying the load type of the device to be charged based on the output power fluctuation state parameters to obtain the load type includes: Perform empirical mode decomposition on the output power fluctuation state parameters to obtain a set of intrinsic mode function components; wherein, the set of intrinsic mode function components is used to characterize the intrinsic oscillation modes of load power fluctuations at different time scales; Perform Hilbert-Huang transform on the set of intrinsic mode function components to obtain the marginal spectrum; wherein, the marginal spectrum is used to reflect the energy distribution of different frequency components over the entire time period; Calculate the multi-scale permutation entropy based on the marginal spectrum to obtain a multi-scale permutation entropy feature vector; Perform local sensitive hashing calculation on the multi-scale permutation entropy feature vector to obtain a hash feature fingerprint; Calculate the similarity degree of the load type features in the preset load type feature library based on the hash feature fingerprint to obtain a similarity score; Match the load type corresponding to the highest similarity score to the device to be charged to obtain the load type; wherein, the load type includes an electric vehicle power battery, an energy storage system, industrial equipment, and a general lighting load.

[0010] Further, the method for selecting a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction includes: Obtain the charging indicators corresponding to the load type, and during the charging process, perform non-linear power prediction and control planning on the target charging pile based on the charging indicators to obtain a power regulation trajectory; Perform parameter identification on the power regulation trajectory through a preset recursive least squares method to obtain a set of system characteristic parameters in the target charging pile; wherein, the set of system characteristic parameters includes the impedance characteristics during the charging process, power factor compensation parameters, and harmonic distortion rate; Construct the control constraint conditions of the target charging pile based on the set of system characteristic parameters; wherein, the control constraint conditions include power fluctuation limit conditions, voltage stability constraints, and current harmonic limits; Using a multi-objective particle swarm optimization algorithm, the system characteristic parameter set is optimized and solved based on the control constraint conditions to obtain a real-time control quantity; Based on the real-time control quantity, an IGBT drive signal instruction is synthesized to obtain a load regulation instruction.

[0011] Furthermore, the non-linear power prediction and control planning of the target charging pile based on the charging index to obtain a power regulation trajectory includes: Based on the charging index, a state space model of the target charging pile is constructed to obtain a state space equation; The state space equation is expanded by Taylor series to obtain a linearized state space equation; Based on the linearized state space equation, non-linear power prediction and control planning of the target charging pile are carried out to obtain a power prediction control law; Through a preset model predictive control algorithm, rolling optimization calculation is carried out on the power prediction control law to obtain an optimal control sequence; Based on the optimal control sequence, power output control of the target charging pile is carried out to obtain a predicted power output; Trajectory tracking and analysis are carried out on the predicted power output to obtain a power regulation trajectory.

[0012] Furthermore, the power output control of the target charging pile based on the load regulation instruction through the IGBT drive circuit set in the target charging pile to obtain a target output power includes: Based on the load regulation instruction, a vector control signal is generated for the IGBT drive circuit to obtain an SVPWM signal; Based on the SVPWM signal, the switching state of the IGBT drive circuit is switched to obtain an IGBT switching action sequence; Harmonic analysis is carried out on the IGBT drive circuit using the IGBT switching action sequence to obtain a current harmonic spectrum; Based on the current harmonic spectrum, harmonic optimization of the SVPWM signal is carried out to obtain an optimized SVPWM signal; Based on the optimized SVPWM signal, output current control of the target charging pile is carried out to obtain an output current waveform; Based on the preset threshold, instantaneous power calculation is carried out on the output current waveform to obtain a target output power.

[0013] The present invention also provides an adaptive load regulation device in a charging pile test equipment, including: An acquisition module 21 for collecting and analyzing multi-dimensional electrical parameters of a target charging pile to obtain initial electrical data; A calculation module 22, configured to calculate an output power fluctuation state of the target charging pile based on the initial electrical data, so as to obtain an output power fluctuation state parameter; An identification module 23, configured to, if the output power fluctuation state parameter exceeds a preset threshold, identify a load type of the device to be charged based on the output power fluctuation state parameter, so as to obtain a load type; wherein, the target charging pile is electrically connected to the device to be charged; A selection module 24, configured to select a load regulation strategy for the target charging pile based on the load type, so as to obtain a load regulation instruction; A control module 25, configured to control the power output of the target charging pile based on the load regulation instruction through an IGBT drive circuit arranged in the target charging pile, so as to obtain a target output power.

[0014] The present invention further provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0015] The present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0016] The adaptive load regulation method in the charging pile testing device provided by the present invention includes the following steps: collecting and analyzing multi-dimensional electrical parameters of a target charging pile to obtain initial electrical data; calculating an output power fluctuation state of the target charging pile based on the initial electrical data to obtain an output power fluctuation state parameter; if the output power fluctuation state parameter exceeds a preset threshold, identifying a load type of the device to be charged based on the output power fluctuation state parameter to obtain a load type; wherein, the target charging pile is electrically connected to the device to be charged; selecting a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction; controlling the power output of the target charging pile based on the load regulation instruction through an IGBT drive circuit arranged in the target charging pile to obtain a target output power. Through the above technical means, the technical problem of how the charging pile can work stably under various conditions and can intelligently adapt to different types of load requirements is solved, the ability to identify the load type based on the output power fluctuation state parameter is realized, so that the charging pile can intelligently judge the characteristics of the connected device to be charged, and accordingly select the most suitable load regulation strategy. This characteristic not only improves the compatibility of the charging pile with different types of electric vehicles, but also ensures the safety and reliability of the charging process, and reduces the risk caused by mismatched loads. Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the steps of an adaptive load regulation method in a charging pile testing device according to an embodiment of the present invention; Figure 2 It is a structural block diagram of an adaptive load regulation device in a charging pile testing device according to an embodiment of the present invention; Figure 3 It is a schematic structural block diagram of a computer device according to an embodiment of the present invention.

[0018] The realization, functional features and advantages of the object of the present invention will be further described with reference to the accompanying drawings in combination with the embodiments. Detailed implementation manners

[0019] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] As Figure 1 shown, Figure 1 It is a schematic diagram of the steps of an adaptive load regulation method in a charging pile testing device according to an embodiment of the present invention; An embodiment of the present invention provides an adaptive load regulation method in a charging pile testing device, including the following steps: Step S1, collect and analyze multi-dimensional electrical parameters of the target charging pile to obtain initial electrical data.

[0021] Specifically, in the process of implementing the multi-dimensional electrical parameter collection and analysis of the target charging pile to obtain the initial electrical data, this step aims to ensure that we can comprehensively understand the performance of the charging pile during actual operation. Specifically, this process involves using a series of precise sensors and technologies to monitor the key electrical parameters of the charging pile, such as voltage, current, power factor, etc. These parameters are crucial for evaluating the working state of the charging pile. To ensure the accuracy and reliability of the data, the measuring equipment used must have high precision and be able to work stably in various environments. For example, in a typical electric vehicle charging station scenario, technicians will deploy specially designed data collection modules, which are closely integrated with the internal circuit of the charging pile to obtain the above-mentioned multi-dimensional electrical parameters in real time. Once the collection starts, the system will automatically record and store these raw data, and then analyze and process them through advanced algorithms. This analysis not only includes simple numerical readings, but more importantly, it is necessary to understand the meaning behind these data, that is, how to reflect the current output characteristics of the charging pile and its potential change trends. For example, when an electric vehicle is connected to the charging pile, the system will immediately start the data collection program and start monitoring issues such as whether the voltage level is stable and whether the current fluctuates abnormally from the moment of connection. Subsequently, based on this immediately obtained information, the system can calculate the output power of the charging pile and whether there are any factors that may cause a decrease in efficiency or safety risks. If unusual patterns or indicators deviate from the normal range, it means that it may be necessary to further investigate the reasons or adjust the operating parameters to optimize the performance. In addition, to ensure the effectiveness of the analysis results, the influence of environmental factors also needs to be considered. For example, in outdoor public charging stations, weather conditions (such as temperature changes, increased humidity) may affect electrical components and thus change their behavioral characteristics. Therefore, in the analysis process, external environmental information should also be combined to correct possible deviations, so that the finally obtained initial electrical data is closer to the actual situation. In this way, we can provide a solid foundation for the subsequent steps, that is, calculating the output power fluctuation state of the target charging pile based on the said initial electrical data, so as to ensure the effective implementation of the entire adaptive load regulation method. Such a coherent data collection and analysis process ensures that the charging pile test equipment can accurately identify different types of load requirements and make appropriate responses to maintain the best charging efficiency and service quality.

[0022] Step S2: Calculate the output power fluctuation state of the target charging pile based on the initial electrical data to obtain the output power fluctuation state parameters.

[0023] Specifically, the process of calculating the output power fluctuation state of the target charging pile based on the initial electrical data to obtain the output power fluctuation state parameters is a key link in the entire adaptive load regulation method. In this process, first, it is necessary to rely on the multi-dimensional electrical parameter acquisition and analysis results obtained in the previous steps, namely the so-called initial electrical data. These data contain a series of key performance indicators of the charging pile in the actual working state, such as voltage, current, power factor, etc., which provide the basis for the subsequent calculations. When these initial electrical data are obtained, the system will use a specially designed algorithm to evaluate the stability of the charging pile's output power. Specifically, the system will identify the trend of power output changing over time and any abnormal fluctuations through means such as time series analysis based on the recorded data points. For example, in a typical electric vehicle charging station application scenario, when an electric vehicle is connected to the charging pile and starts charging, the system will not only monitor the immediate power output level but also continuously track the power change over a period of time. If irregular fluctuations or changes beyond the normal range are found in the power output during this period, it indicates that the charging pile may be in a non-ideal output power fluctuation state. To quantify this fluctuation degree, the system further calculates specific output power fluctuation state parameters. These parameters can include but are not limited to the maximum power deviation, average power volatility, transient response time, etc., which are used to characterize various aspects of the charging pile's output power stability. For example, in an outdoor public charging station, due to an instantaneous drop in the grid voltage or a load mutation, if the output power of the charging pile shows a short but significant drop and then quickly returns to the normal level, such an event will be recorded, and the impact degree will be measured by calculating the above-mentioned state parameters. By carefully analyzing these parameters, it can be determined whether the power fluctuation exceeds the preset threshold, thus judging whether further measures need to be taken. Once it is confirmed that the output power fluctuation state parameters indeed exceed the set safety or efficiency standards, the next step is to initiate the load type identification process to ensure that the most appropriate load regulation strategy can be selected for different devices to be charged. For example, for high-performance electric vehicles with high requirements for charging speed, the system may give priority to providing a more stable high-power output; while for ordinary household electric vehicles, the output power can be adjusted appropriately according to the actual situation to achieve the best charging effect and resource utilization efficiency. Therefore, calculating the output power fluctuation state of the target charging pile based on the initial electrical data is not only a prerequisite for achieving precise control but also a necessary step to ensure the safety and efficiency of the charging process.

[0024] Step S3, if the output power fluctuation state parameters exceed the preset threshold, then based on the output power fluctuation state parameters, identify the load type of the device to be charged to obtain the load type; wherein, the target charging pile is electrically connected to the device to be charged.

[0025] Specifically, if the output power fluctuation state parameter exceeds a preset threshold, the load type of the device to be charged is identified based on the output power fluctuation state parameter to obtain the load type; wherein, the target charging pile is electrically connected to the device to be charged. This process is a crucial link in the entire adaptive load regulation method, which directly relates to the selection and implementation effect of subsequent load regulation strategies. When the output power fluctuation state parameter calculated by the system through the foregoing steps shows abnormal fluctuations and these fluctuations exceed the pre-set safety or efficiency threshold, the system will activate the load type identification mechanism. At this stage, the system relies on the initial electrical data collected previously and the just-calculated output power fluctuation state parameter to analyze the characteristics of the device to be charged currently connected to the charging pile. Specifically, the system will comprehensively consider factors such as the amplitude, frequency, and duration of power fluctuations, and combine historical data and various pre-stored charging characteristic models of electric vehicles to determine the specific load type of the connected device. For example, in an application scenario of an electric vehicle charging station, if a high-performance electric vehicle is connected to the charging pile and the system detects that the power output has fluctuated beyond the normal range, this may be due to the special requirements of the vehicle's battery management system for the charging current. At this time, the system will use the multi-dimensional data analysis method mentioned above to identify that this electric vehicle belongs to a load type with high power requirements and sensitive to charging stability. Once the load type is confirmed, the system can provide a basis for the selection of subsequent load regulation strategies. For different types of loads, the charging pile needs to take different response measures to optimize the charging process. Continuing with the example of the electric vehicle charging station, assume that at an outdoor public charging station, the system identifies that a connected electric vehicle has fast charging capabilities and its battery is at a low charge level. Then, to meet the user's demand for charging as quickly as possible while ensuring charging safety, the system may preferentially select a regulation strategy that can provide a stable high-power output. On the contrary, if it is an ordinary household electric vehicle, considering that it may not have fast charging capabilities, the system can choose a more moderate power output mode to extend the battery life and save energy. Therefore, the process of identifying the load type of the device to be charged based on the output power fluctuation state parameter not only helps to improve the charging efficiency and user experience, but also ensures the safety and reliability of the charging process, enabling the charging pile to intelligently adapt to various different load requirements in a complex and changeable actual environment.

[0026] Step S4, based on the load type, select a load regulation strategy for the target charging pile to obtain a load regulation instruction.

[0027] Specifically, the process of selecting a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction is a key link in the entire adaptive load regulation method for achieving precise control. At this stage, the system utilizes the load type information identified in the previous steps and combines multiple pre-set load regulation strategies to determine the most suitable adjustment method for the current charging situation and generate the corresponding load regulation instruction. When the system successfully identifies the load type of the device to be charged, it is then necessary to decide how to adjust the output power of the charging pile according to the characteristics of this type. For example, in the application scenario of an electric vehicle charging station, assume that the system has identified that a high-performance electric vehicle is connected. Such vehicles usually come with a large-capacity battery and an efficient battery management system and can accept fast charging with a high power. In this case, the system will select a solution from the pre-set load regulation strategy library that can provide a stable high-power output. This solution not only needs to ensure that sufficient power is provided to the vehicle in a short time but also takes into account factors such as grid voltage fluctuations and the working status of the charging pile itself to ensure the safety and stability of the charging process. Therefore, the system will comprehensively consider these conditions and finally select a load regulation strategy that can meet the user's fast charging needs without having a negative impact on the charging pile or the grid. On the other hand, if the system identifies an ordinary household electric vehicle, this type of vehicle may not have the fast charging function, or its battery status does not allow high-intensity charging. For this situation, the system will choose a more gentle and lasting charging mode. The load regulation strategy in this mode aims to extend the charging time and reduce the instantaneous power output, thereby reducing the stress on the battery, protecting the battery health, and also being more conducive to the reasonable allocation of grid resources. For example, at an outdoor public charging station, when an ordinary household electric vehicle is connected to the charging pile, the system may choose to gradually increase the power output rather than immediately providing the maximum power, so that the charging task can be completed safely and effectively without affecting the use of other users. Once the appropriate load regulation strategy is selected, the system will generate specific load regulation instructions. These instructions contain detailed parameter settings, such as power output level, current intensity, voltage range, etc., which directly guide the operation of the internal circuit of the charging pile. By precisely controlling the IGBT drive circuit, the charging pile can adjust its own power output according to the established instructions to ensure the best match with the connected device to be charged. In this way, whether it is facing a high-performance electric vehicle or an ordinary household electric vehicle, the charging pile can intelligently adapt to different load requirements and provide stable, efficient, and safe charging services. In summary, the process of selecting a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction is not only an important means to achieve personalized charging services but also the key to improving the overall performance and service quality of the charging pile.

[0028] Step S5: Through the IGBT drive circuit provided in the target charging pile, perform power output control on the target charging pile based on the load regulation instruction to obtain the target output power.

[0029] Specifically, the process of performing power output control on the target charging pile based on the load regulation instruction through the IGBT drive circuit provided in the target charging pile to obtain the target output power is the key step in the entire adaptive load regulation method to achieve the final optimized charging effect. This process relies on the previously determined load regulation strategy and the generated load regulation instruction to ensure that the charging pile can provide the most suitable power supply according to the specific needs of the connected device.

[0030] When the system has completed the identification of the load type and selected the appropriate load regulation strategy, the next task is to convert these strategies into actual operation instructions, namely load regulation instructions. These instructions contain specific parameters regarding how to adjust the output power of the charging pile, such as power level, current intensity, voltage range, etc. To achieve precise power output control, the system utilizes the IGBT (Insulated Gate Bipolar Transistor) drive circuit installed inside the charging pile. As an efficient switching element, IGBT can switch the power state at high speed and high efficiency, making it very suitable for applications that require fast response and fine adjustment. For example, in the application scenario of an electric vehicle charging station, assume that the system has identified a high-performance electric vehicle and selected a load regulation strategy to provide stable high-power output. At this time, the system will send corresponding load regulation instructions to the IGBT drive circuit, instructing it to adjust the output power of the charging pile according to the preset parameters. For instance, if the vehicle battery management system requests charging at maximum power, then the IGBT drive circuit will quickly respond after receiving the instruction, and increase the output power of the charging pile by changing its own conduction time and current intensity until the target value is reached. At the same time, the IGBT drive circuit will also monitor the output status in real time to ensure that there will be no damage or safety hazards due to overload or other abnormal conditions. On the other hand, for ordinary household electric vehicles, the system may choose a relatively gentle charging mode, gradually increasing the power output instead of immediately providing the maximum power. In this case, the IGBT drive circuit will also operate according to the load regulation instructions, but its adjustment method is more gentle, aiming to protect the battery health while prolonging the charging time. For example, at an outdoor public charging station, when an ordinary household electric vehicle is connected to the charging pile, the IGBT drive circuit will start from a lower power under the guidance of the instruction and gradually increase to the optimal charging rate to ensure that the charging process is both safe and efficient. In summary, by means of the IGBT drive circuit set in the target charging pile, power output control of the target charging pile based on the load regulation instructions not only achieves precise adaptation to different load types, but also ensures the safety and stability of the entire charging process. As the core component of the execution layer, the IGBT drive circuit plays a bridging role. It converts the abstract load regulation strategy into specific power output behaviors, enabling the charging pile to provide the best charging experience for users under various conditions. Whether it is facing high-performance electric vehicles or ordinary household electric vehicles, this process ensures that the charging pile can intelligently adapt to different load requirements, provide stable and efficient power support, thus promoting the development of charging pile technology and improving user satisfaction.

[0031] In a specific embodiment, the multi-dimensional electrical parameter collection and analysis of the target charging pile to obtain initial electrical data includes: The output port in the target charging pile is sampled for voltage and current through a high-speed sampling circuit to obtain original sampling data; wherein, the original sampling data includes an original voltage value, an original current value, and a sampling timestamp; The original sampling data is subjected to noise filtering to obtain filtered sampling data; The original sampling data is subjected to noise filtering and calibration to obtain calibrated electrical sampling data; The calibrated electrical sampling data is subjected to multi-scale decomposition through wavelet transform to extract the dynamic change characteristics of the original voltage value and the original current value, obtaining time series characteristics; Based on independent component analysis, signal separation is performed on the time series characteristics to obtain electrical characteristic components; A state observer is used to dynamically track the electrical characteristic components to obtain dynamic response parameters, and the dynamic response parameters are used as initial electrical data.

[0032] Specifically, the process of collecting and analyzing multi-dimensional electrical parameters of the target charging pile to obtain initial electrical data is a fundamental link in the adaptive load regulation method to ensure the stable operation of the charging pile. This process not only involves obtaining raw sampling data through a high-speed sampling circuit, but also includes a series of complex signal processing steps, such as noise filtering, calibration, multi-scale decomposition by wavelet transform, independent component analysis (ICA), and dynamic tracking, etc., to ensure that the obtained initial electrical data can truly reflect the performance of the charging pile in actual operation. First, in this process, the system uses a high-speed sampling circuit installed inside the charging pile to sample the voltage and current at the output port of the target charging pile to obtain raw sampling data. These high-speed sampling circuits have high-frequency response characteristics and can capture a large number of accurate data points in a short time, thus generating a detailed raw sampling data set. Specifically, each sampling records the raw voltage value, raw current value, and the corresponding sampling timestamp, and these information together constitute the basic data set describing the output characteristics of the charging pile. For example, in the application scenario of an electric vehicle charging station, when an electric vehicle is connected to the charging pile and starts charging, the high-speed sampling circuit will be immediately activated and continuously monitor the voltage and current changes at the output port of the charging pile at a very high frequency (such as thousands of times per second) to ensure that no subtle changes are missed. This step is crucial for subsequent data processing because only accurate raw data can provide a reliable basis for the following analysis. Next, in order to improve the data quality and remove possible interference factors, the system will perform noise filtering on the raw sampling data to obtain filtered sampling data. In the actual environment, due to electromagnetic interference, line noise, etc., the collected data often contains certain random noise components, and these noises may mask the true signal characteristics and affect the accuracy of subsequent analysis. Therefore, it is particularly important to use advanced digital filtering techniques to eliminate or reduce the influence of noise. By applying appropriate filtering algorithms, such as low-pass filters or adaptive filters, the system can effectively remove high-frequency noise from the raw sampling data and leave relatively pure filtered sampling data. In addition, the system will also perform noise filtering and calibration on the raw sampling data to obtain calibrated electrical sampling data. The calibration process aims to correct measurement errors caused by sensor biases or environmental factors to ensure the authenticity and consistency of the data. For example, at an outdoor public charging station, when the charging pile faces different temperature or humidity conditions, the system can adjust the sampling data through the built-in calibration mechanism to make it closer to the actual situation. With the completion of noise filtering and calibration, the system will enter the multi-scale decomposition stage, where the mathematical tool of wavelet transform is used. Wavelet transform is an effective method for processing non-stationary signals. It can decompose the signal at different scales to extract characteristic information in different frequency ranges.By applying wavelet transform to the calibrated electrical sampling data, the system can capture the dynamic change characteristics of the original voltage value and the original current value, forming time series characteristics. These characteristics not only reflect the change trends of voltage and current over time, but also reveal the relationship between them and potential periodic patterns. For example, in the face of the rapid charging demand of high-performance electric vehicles, if the output power of the charging pile shows periodic fluctuations, such fluctuations will be manifested as significant changes at a specific scale in the results of wavelet transform. Through this method, we can more clearly identify the regular components of power fluctuations, providing valuable information for further analysis. After obtaining the time series characteristics, the system needs to further separate the signals to identify independent electrical feature components. This is because the actual power signal is usually the superposition result of multiple signals from different sources, including fundamental waves, harmonics, and other non-periodic disturbances. To gain a deeper understanding of the behavior characteristics of each component, the system adopts the independent component analysis (ICA), a statistical method. ICA can decompose the mixed multi-source signals into several independent sub-signals, and each sub-signal represents a specific physical phenomenon or operation mode. In this way, we can extract pure electrical feature components, such as fundamental wave components, harmonic components, etc., from the complex time series characteristics, and these components each carry unique information about the working state of the charging pile. For example, in the face of the regular charging demand of ordinary household electric vehicles, the system can use ICA to separate the special electrical feature components related to the charging process to better understand and optimize this process. Finally, to ensure that the obtained electrical feature components can accurately reflect the dynamic behavior of the charging pile, the system also needs to dynamically track them. Here, the concept of a state observer is introduced, which is a technique used to estimate the internal state variables of a complex system. By constructing an appropriate state observation model, the system can track the change trends of electrical feature components in real time, predict future state developments, and calculate the corresponding dynamic response parameters. These dynamic response parameters comprehensively reflect how the charging pile adjusts its working mode under different load conditions to maintain stable and efficient power output. For example, at outdoor public charging stations, when the charging pile faces situations such as grid voltage fluctuations or load mutations, the state observer can help us detect these changes in a timely manner and make quick responses according to the dynamic response parameters, such as adjusting the control strategy of the IGBT drive circuit to ensure that the charging pile is always in the best working state. Finally, through this series of carefully designed data processing procedures, the system successfully obtains the dynamic response parameters as the initial electrical data, which not only comprehensively cover the static and dynamic characteristics of the charging pile, but also lay a solid foundation for the subsequent calculation of the output power fluctuation state. In summary, the process of collecting and analyzing multi-dimensional electrical parameters of the target charging pile to obtain the initial electrical data is a technically implemented path with closely linked and progressive steps.From the initial high-speed sampling circuit sampling to the final state observer's dynamic tracking, every step closely revolves around how to extract the most valuable information from a vast amount of raw data. Through this method, the charging pile testing equipment can intelligently sense and adapt to various complex charging environments, providing users with a more stable, efficient, and safe charging service. Whether it is for the rapid charging of high-performance electric vehicles or the regular charging of ordinary household electric vehicles, this process can ensure that the charging pile is always in the best working condition, providing stable and reliable power support. For example, in an electric vehicle charging station, when a high-performance electric vehicle is connected to the charging pile, the system will, according to its load type and charging indicators, go through a series of steps such as high-speed sampling, noise filtering and calibration, and wavelet transform multi-scale decomposition, and finally generate a set of time series features and dynamic response parameters that can accurately reflect the output characteristics of the charging pile, thus achieving an efficient and safe charging experience.

[0033] In a specific embodiment, calculating the output power fluctuation state of the target charging pile based on the initial electrical data to obtain an output power fluctuation state parameter includes: Calculating the instantaneous power of the initial electrical data to obtain an instantaneous power sequence; Performing a sliding window process on the instantaneous power sequence to obtain instantaneous power sequences within multiple sliding windows; Using the discrete Fourier transform algorithm to perform frequency domain analysis on the instantaneous power sequences within the multiple sliding windows to obtain the power spectral density; Calculating the output power fluctuation state of the target charging pile based on the power spectral density to obtain an output power fluctuation characteristic parameter.

[0034] Specifically, the process of calculating the output power fluctuation state of the target charging pile based on the initial electrical data to obtain the output power fluctuation state parameters is a key link in the adaptive load regulation method to ensure the stable operation of the charging pile. This process not only depends on the initial electrical data obtained from the multi-dimensional electrical parameter collection and analysis in the previous steps, but also involves a series of complex mathematical operations and technical means to ensure the accurate evaluation of the output power stability of the charging pile in actual operation. First, in this process, the system uses the initial electrical data obtained from steps such as high-speed sampling circuit, noise filtering, instantaneous phase extraction, signal separation, and dynamic tracking to perform instantaneous power calculation, resulting in an instantaneous power sequence. The instantaneous power calculation is achieved by multiplying the instantaneous values of voltage and current, thereby generating an instantaneous power sequence that describes the variation of the charging pile's output power over time. For example, in the application scenario of an electric vehicle charging station, when an electric vehicle is connected to the charging pile to start charging, the system will calculate the instantaneous power at each moment in real time based on the previously obtained instantaneous voltage value and instantaneous current value, and record it to form a continuous data sequence. These instantaneous power values not only reflect the actual output level of the current charging pile but also provide basic data for subsequent fluctuation state analysis. Through this method, the system can capture every subtle change in the charging pile's output power, ensuring that no important information is missed. Next, to better understand the fluctuation characteristics in the instantaneous power sequence, the system performs a sliding window process on this sequence to obtain the instantaneous power sequences within multiple sliding windows. The sliding window is a common data analysis technique that intercepts data segments within a fixed-length time period (i.e., the window) and gradually moves the window position forward, thereby dividing the entire instantaneous power sequence into multiple smaller subsequences. This method helps to capture the power change trend within a short period of time while reducing the influence of random factors. For example, at an outdoor public charging station, assuming we set a sliding window of 5 seconds in length, then the system will recalculate the average instantaneous power value within a new 5-second window every second. In this way, not only can we observe the power output situation at each moment, but also analyze the overall change pattern over a period of time. Through this approach, we can more clearly identify the periodic and regular components of the power fluctuation, laying a solid foundation for subsequent frequency domain analysis. With the completion of the sliding window process, the system further uses the discrete Fourier transform (DFT) algorithm to perform frequency domain analysis on the instantaneous power sequences within the multiple sliding windows to obtain the power spectral density. The discrete Fourier transform is a powerful mathematical tool that can convert a signal in the time domain into a frequency domain representation, revealing the hidden frequency components and their intensity distributions in the signal. By applying the DFT algorithm to the instantaneous power sequence within each sliding window, the system can calculate the corresponding power spectral density, which is a dataset describing the energy magnitudes of different frequency components.For example, when faced with the rapid charging demand of high-performance electric vehicles, if the output power of the charging pile shows periodic fluctuations, these fluctuations will manifest as peaks at specific frequencies in the frequency domain. By analyzing the positions and heights of these peaks, we can accurately determine the main sources of power fluctuations and their impact levels. In addition, DFT can also help us discover subtle changes that may exist in the low-frequency or high-frequency regions, which is crucial for comprehensively understanding the working state of the charging pile. Finally, based on the power spectral density, the output power fluctuation state of the target charging pile is calculated to obtain output power fluctuation characteristic parameters. The goal of this stage is to extract the key indicators that can best characterize the output power fluctuation characteristics of the charging pile from the results of frequency domain analysis. Specifically, the system will focus on the energy distribution in certain specific frequency ranges in the power spectral density, such as the energy ratio of the fundamental wave and harmonic components, the concentration of the main fluctuation frequencies, etc. These characteristic parameters comprehensively reflect the stability and consistency of the output power of the charging pile and are of great significance for evaluating its performance. For example, in an electric vehicle charging station, when the system detects an abnormal increase in energy at a certain frequency, this may be a power instability phenomenon caused by grid voltage fluctuations or load mutations. At this time, the system can calculate the output power fluctuation characteristic parameters to quantify the degree of this fluctuation and accordingly determine whether measures need to be taken for adjustment. Ultimately, these characteristic parameters will become an important basis for subsequent load type identification and load regulation strategy selection, ensuring that the charging pile can provide stable and efficient power support under various complex conditions. In summary, the process of calculating the output power fluctuation state of the target charging pile based on the initial electrical data to obtain the output power fluctuation state parameters is a rigorous and closely linked technical implementation path. From the initial instantaneous power calculation to the final frequency domain analysis and characteristic parameter extraction, each step closely revolves around how to mine valuable information from a large amount of data. Through this method, the charging pile test equipment can intelligently sense and evaluate its own output power fluctuation situation, laying a solid foundation for further optimizing the charging process and improving the user experience. Whether it is for the rapid charging of high-performance electric vehicles or the regular charging of ordinary household electric vehicles, this process can ensure that the charging pile is always in the best working state and provides stable and reliable power supply. For example, in an electric vehicle charging station, when a high-performance electric vehicle is connected to the charging pile, the system will, according to its load type and charging indicators, go through a series of steps such as instantaneous power calculation, sliding window processing, discrete Fourier transform, etc., and finally generate a set of state parameters that can accurately reflect the output power fluctuation characteristics of the charging pile, thus realizing an efficient and safe charging experience.

[0035] In a specific embodiment, the load type of the device to be charged is identified based on the output power fluctuation state parameters to obtain the load type, including: Perform empirical mode decomposition on the output power fluctuation state parameters to obtain a set of intrinsic mode function components; wherein, the set of intrinsic mode function components is used to characterize the intrinsic oscillation modes of the load power fluctuation at different time scales; Perform Hilbert-Huang transform on the set of intrinsic mode function components to obtain a marginal spectrum; wherein, the marginal spectrum is used to reflect the energy distribution of different frequency components over the entire time period; Calculate the multi-scale permutation entropy based on the marginal spectrum to obtain a multi-scale permutation entropy feature vector; Perform local sensitive hashing calculation on the multi-scale permutation entropy feature vector to obtain a hashed feature fingerprint; Calculate the similarity degree of the load type features in the preset load type feature library based on the hashed feature fingerprint to obtain a similarity score; Match the load type corresponding to the highest similarity score to the load type of the device to be charged to obtain the load type; wherein, the load type includes electric vehicle power batteries, energy storage systems, industrial equipment, and general lighting loads.

[0036] Specifically, the process of identifying the load type of the device to be charged based on the output power fluctuation state parameter is a key step in the adaptive load regulation method to achieve precise matching and optimized control. This process not only depends on the previous detailed analysis of the output power fluctuation state of the charging pile, but also involves a series of advanced signal processing techniques, such as empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), multi-scale permutation entropy calculation, locality-sensitive hashing (LSH), etc., to ensure that the specific type of the device to be charged connected to the charging pile can be accurately identified. First, in this process, the system uses the state parameter obtained from the calculation of the output power fluctuation state and performs empirical mode decomposition (EMD) on it. EMD is a time-frequency analysis method suitable for non-linear and non-stationary signals. It decomposes the complex original signal into several intrinsic mode function components (IMFs), and each IMF represents an intrinsic oscillation mode at different time scales. For example, in the application scenario of an electric vehicle charging station, when an electric vehicle is connected to the charging pile and starts charging, the system will decompose it into multiple IMF components according to the previously calculated output power fluctuation state parameter through EMD. These components not only contain the power fluctuation information in different frequency ranges, but also can reveal the potential time structure characteristics, providing rich details for further analysis. Next, in order to understand the information carried by these IMF components more deeply, the system performs Hilbert-Huang transform (HHT) on the set of intrinsic mode function components. HHT combines the advantages of the Hilbert transform and EMD, and can effectively convert each IMF into the representation of instantaneous frequency and instantaneous amplitude, and extract the marginal spectrum from it. The marginal spectrum reflects the energy distribution of different frequency components over the entire time period, which is very useful for identifying the characteristics of the load type. For example, at an outdoor public charging station, assuming that we have obtained a set of IMF components, by applying HHT to them, we can generate a detailed marginal spectrum diagram, which shows the energy change trend of each frequency component during the charging process. The visualization of this frequency-domain energy distribution helps to discover the significant features that may be related to a specific load type, such as the energy peak in the high-frequency band or the continuous energy accumulation in the low-frequency band. With the acquisition of the marginal spectrum, the system further calculates the multi-scale permutation entropy based on the marginal spectrum to obtain a multi-scale permutation entropy feature vector. Permutation entropy is an index used to measure the complexity of a time series, which can capture the randomness and regularity inside the signal. By introducing the concept of multi-scale, the system can evaluate the permutation entropy value of the marginal spectrum at different time scales, so as to construct a multi-scale permutation entropy feature vector. This feature vector not only comprehensively reflects the global characteristics of the marginal spectrum, but also retains local details, which is of great significance for distinguishing different types of loads.For example, when faced with the rapid charging demand of high-performance electric vehicles, if there is an obvious periodic energy distribution in the marginal spectrum, the corresponding multi-scale permutation entropy feature vector may exhibit a lower entropy value, indicating that its power fluctuation has strong regularity; on the contrary, for ordinary household electric vehicles, since the charging process is relatively stable, its feature vector may show a higher entropy value, indicating relatively random power changes. Then, in order to improve the efficiency and accuracy of load type identification, the system performs local sensitive hashing (LSH) calculation on the multi-scale permutation entropy feature vector to obtain a hashed feature fingerprint. LSH is an approximate nearest neighbor search algorithm that accelerates similarity queries by mapping a high-dimensional feature space to a low-dimensional hashing space. In this process, each multi-scale permutation entropy feature vector is converted into a compact hashed feature fingerprint, which not only maintains the similarity relationship between the original features but also greatly reduces data storage and computational costs. For example, at an electric vehicle charging station, when the system needs to quickly determine the load type of a newly connected electric vehicle, it can quickly find the closest match by comparing the newly generated hashed feature fingerprint with the fingerprints in the pre-stored typical load type feature library. This method not only speeds up the identification speed but also improves the system's response ability. Finally, based on the hashed feature fingerprint, a similarity degree calculation is performed on the load type features in the preset load type feature library to obtain a similarity score. The load type feature library is a database containing various common load types (such as electric vehicle power batteries, energy storage systems, industrial equipment, and general lighting loads) and their corresponding feature fingerprints. The system determines the best match by calculating the similarity scores between the hashed feature fingerprint of the newly connected device and the load type features in the library. Specifically, the system selects the load type feature corresponding to the highest similarity score as the final identification result and accordingly performs a load type match on the device to be charged to obtain the load type. For example, at an outdoor public charging station, when a high-performance electric vehicle is connected to a charging pile, the system will confirm that the vehicle is a high-performance electric vehicle based on the comparison result of its hashed feature fingerprint with various load types in the library, and then select the most suitable charging strategy for it. This process not only ensures the accuracy of identification but also provides a solid foundation for the subsequent selection of load regulation strategies. In summary, the process of identifying the load type of the device to be charged based on the output power fluctuation state parameter to obtain the load type is a multi-level and multi-technology fusion technical implementation path. From the initial EMD decomposition to the final similarity degree calculation, each step closely focuses on how to extract the most discriminative features from complex power fluctuation data. Through this method, the charging pile test equipment can intelligently sense and identify the load type of the connected device, providing users with more personalized and efficient charging services.Whether it is for fast charging of high-performance electric vehicles or regular charging of ordinary household electric vehicles, this process can ensure that the charging pile is always in the best working condition and provide stable and reliable power support.

[0037] In a specific embodiment, the selecting a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction includes: Obtaining a charging index corresponding to the load type, and during the charging process, performing non-linear power prediction and control planning on the target charging pile based on the charging index to obtain a power regulation trajectory; Performing parameter identification on the power regulation trajectory through a preset recursive least squares method to obtain a system characteristic parameter set in the target charging pile; wherein, the system characteristic parameter set includes impedance characteristics during the charging process, power factor compensation parameters, and harmonic distortion rate; Constructing control constraint conditions for the target charging pile based on the system characteristic parameter set; wherein, the control constraint conditions include power fluctuation limit conditions, voltage stability constraints, and current harmonic limits; Through a multi-objective particle swarm optimization algorithm, optimizing and solving the system characteristic parameter set based on the control constraint conditions to obtain a real-time control quantity; Based on the real-time control quantity, synthesizing an IGBT drive signal instruction to obtain a load regulation instruction.

[0038] Specifically, the process of selecting a load regulation strategy for the target charging pile based on the load type and obtaining a load regulation instruction is the core link in the adaptive load regulation method to achieve efficient and stable charging services. This process not only depends on the load type information identified in the previous steps but also involves a series of complex control theories and technical means, including non-linear power prediction and control planning, recursive least squares parameter identification, construction of control constraint conditions, and multi-objective particle swarm optimization solution, etc., to ensure that the most suitable power support can be provided for different types of devices to be charged. First of all, in this process, the system will obtain the charging indicators corresponding to the load type and perform non-linear power prediction and control planning on the target charging pile based on these charging indicators during the charging process to obtain a power regulation trajectory. The charging indicators include various performance requirements related to a specific load type, such as the maximum allowable charging power, charging rate limit, battery temperature range, etc. For example, in the application scenario of an electric vehicle charging station, when the system identifies a high-performance electric vehicle, it will formulate a charging plan that can charge the vehicle as quickly as possible while ensuring safety according to the charging indicators provided by the vehicle's battery management system (BMS). To achieve this, the system uses a non-linear power prediction model to estimate the changing trend of power demand during the entire charging process and plans a reasonable power regulation trajectory accordingly. This trajectory not only reflects the ideal adjustment path of the output power from the start to the end of the charging process but also takes into account the influence of factors such as grid fluctuations and environmental temperature, ensuring the safety and efficiency of the charging process. Next, in order to more accurately understand and control the behavior of the charging pile, the system performs parameter identification on the power regulation trajectory through a preset recursive least squares method (RLS) to obtain a set of system characteristic parameters inside the target charging pile. RLS is an online parameter estimation method that can update model parameters in real time in a dynamic environment, thereby improving prediction accuracy. By applying the RLS algorithm to the power regulation trajectory, the system can accurately identify the key characteristics inside the charging pile, such as the impedance characteristics during the charging process, power factor compensation parameters, and harmonic distortion rate, etc. These sets of system characteristic parameters are crucial for evaluating the working state of the charging pile and its impact on the power grid. For example, at an outdoor public charging station, when the charging pile charges a vehicle according to a predetermined power regulation trajectory, the RLS algorithm can help us timely detect and quantify problems such as impedance changes and power factor drops caused by load changes or grid conditions, and then take measures to correct them. This method not only enhances the adaptive ability of the system but also provides a scientific basis for subsequent control strategy optimization. With the acquisition of the set of system characteristic parameters, the system further constructs the control constraint conditions of the target charging pile based on the set of system characteristic parameters. The control constraint conditions are a series of rules set to ensure that the charging pile will not cause damage to itself or other electrical equipment while meeting the load demand.Specifically, these constraint conditions include power fluctuation limit conditions, voltage stability constraints, current harmonic limits, etc. For example, in the face of the fast charging demand of high-performance electric vehicles, the system must ensure that the change in its output power remains within a reasonable range to avoid equipment failures caused by drastic fluctuations. At the same time, it is also necessary to maintain a stable output voltage level to prevent overvoltage or undervoltage phenomena. In addition, it is required to control the current harmonic content to reduce the impact on the power grid quality. By constructing these strict control constraint conditions, the system can always maintain the optimal working state of the charging pile in a complex and changing actual environment, ensuring the safety and reliability of the charging process. Finally, in order to find the optimal load regulation scheme, the system optimizes and solves the system characteristic parameter set based on the control constraint conditions through the multi-objective particle swarm optimization (MOPSO) algorithm to obtain the real-time control quantity. MOPSO is a heuristic search algorithm that can seek the best balance point among multiple conflicting objectives. In this process, the system uses the control constraint conditions as the boundary conditions of the optimization problem and tries to find a set of parameter settings that can satisfy all constraints and maximize the charging efficiency and user experience. For example, in an electric vehicle charging station, when the system needs to make a trade-off between fast charging and protecting the battery health, the MOPSO algorithm can simulate a large number of possible solutions and finally determine a set of optimal real-time control quantities, such as appropriate power output levels, current intensities, and voltage ranges. These real-time control quantities directly guide the operation of the IGBT drive circuit inside the charging pile, enabling it to accurately adjust its power output according to the established instructions to ensure the best match with the connected device to be charged. To sum up, the process of selecting the load regulation strategy for the target charging pile based on the load type and obtaining the load regulation instruction is a technically implemented path with closely linked and progressive steps. From the initial acquisition of charging indicators to the final synthesis of IGBT drive signal instructions, each step is closely centered around how to extract the optimal control strategy from complex system characteristics. Through this method, the charging pile test equipment can intelligently sense the load demand, flexibly respond to various practical challenges, and provide more personalized and efficient charging services. Whether it is for the fast charging of high-performance electric vehicles or the regular charging of ordinary household electric vehicles, this process can ensure that the charging pile is always in the best working state and provide stable and reliable power support. For example, at an outdoor public charging station, when a high-performance electric vehicle is connected to the charging pile, the system will generate the most suitable load regulation instruction for the vehicle through a series of precise analyses and optimizations based on its load type and charging indicators, realizing a fast and safe charging experience.

[0039] In a specific embodiment, the non-linear power prediction and control planning of the target charging pile based on the charging indicators to obtain the power adjustment trajectory includes: Construct a state space model for the target charging pile based on the charging metrics to obtain a state space equation; Perform Taylor series expansion on the state space equation to obtain a linearized state space equation; Based on the linearized state space equation, perform non-linear power prediction and control planning for the target charging pile to obtain a power prediction control law; Perform rolling optimization calculation on the power prediction control law through a preset model predictive control algorithm to obtain an optimal control sequence; Based on the optimal control sequence, perform power output control on the target charging pile to obtain a predicted power output; Perform trajectory tracking and analysis on the predicted power output to obtain a power regulation trajectory.

[0040] Specifically, the process of performing non - linear power prediction and control planning on the target charging pile based on the charging metrics to obtain a power adjustment trajectory is a key link in the adaptive load regulation method to ensure efficient and stable charging services. This process not only depends on the load type information identified in the previous steps and its corresponding charging metrics, but also involves a series of complex mathematical modeling and optimization calculation techniques, including state - space model construction, Taylor series expansion, non - linear power prediction and control planning, model predictive control algorithms, etc., to ensure that the most suitable power support can be provided for different types of devices to be charged. First, in this process, the system constructs a state - space model for the target charging pile based on the charging metrics to obtain a state - space equation. The state - space model is a mathematical framework used to describe dynamic systems. It can abstract the behavior of the charging pile during the charging process into a set of differential equations or difference equations, which reflect the relationship between the internal state of the system and external inputs. For example, in the application scenario of an electric vehicle charging station, when the system identifies a high - performance electric vehicle, it will construct an accurate state - space model according to the charging metrics provided by the vehicle's battery management system (BMS), such as the maximum allowable charging power, charging rate limit, battery temperature range, etc. This model not only covers electrical parameters such as voltage and current at the output port of the charging pile, but also includes factors such as the state of charge (SOC) of the battery and temperature changes, thus comprehensively describing the dynamic characteristics of the entire charging process. By establishing such a detailed state - space equation, the system provides a solid theoretical basis for subsequent prediction and control. Next, in order to simplify the complex state - space equation and make it more suitable for dealing with non - linear problems, the system performs a Taylor series expansion on the state - space equation to obtain a linearized state - space equation. The Taylor series expansion is a common mathematical tool that can approximately represent the behavior of a function near a specific point. By performing a Taylor expansion on the state - space equation at the current operating point, the system can convert the originally complex non - linear equation into a set of relatively simple linear equations, which not only improves the calculation efficiency but also makes subsequent prediction and control easier to implement. For example, at an outdoor public charging station, when the charging pile charges a vehicle according to a predetermined charging plan, through the Taylor series expansion, the system can quickly estimate the trend of power demand changes in the linearized state in the next period of time, providing the possibility for timely adjustment of the output power. This method not only enhances the real - time response ability of the system but also lays a foundation for precise control. With the acquisition of the linearized state - space equation, the system further performs non - linear power prediction and control planning on the target charging pile based on the linearized state - space equation to obtain a power prediction control law. Non - linear power prediction aims to use the above - mentioned linearized model to predict the power demand during the future charging process, and the control planning is to formulate a reasonable control strategy based on this.Specifically, the system will derive a set of power prediction control laws based on the linearized state-space equations. This set of control laws not only takes into account the dynamic characteristics of the charging pile itself, but also comprehensively considers various influencing factors such as grid conditions, environmental factors, and user demands. For example, when faced with the fast charging demand of high-performance electric vehicles, the system will use the power prediction control law to plan the optimal power adjustment path from the start to the end of charging, ensuring that it not only meets the fast charging demand of the vehicle, but also does not exceed the safety limit or have a negative impact on the power grid. This non-linear prediction and control planning based on the linearized model enables the system to always maintain the best working state of the charging pile in a complex and changing actual environment. Then, in order to find the optimal power adjustment scheme, the system performs rolling optimization calculations on the power prediction control law through a preset Model Predictive Control (MPC) algorithm to obtain the optimal control sequence. MPC is an advanced control strategy that maximizes long-term performance by predicting future system behavior and optimizing current control actions. In this process, the system continuously calculates the optimal control sequence for the next time period, that is, the best control quantity to be taken at each moment, to minimize the prediction error and meet all constraint conditions. For example, in an electric vehicle charging station, when the system needs to make a trade-off between fast charging and protecting the battery health, the MPC algorithm can determine an optimal control sequence, such as appropriate power output levels, current intensities, and voltage ranges, by simulating a large number of possible future scenarios. These control sequences directly guide the operation of the IGBT drive circuit inside the charging pile, enabling it to accurately adjust its power output according to the established instructions to ensure the best match with the connected device to be charged. Finally, based on the optimal control sequence, power output control is performed on the target charging pile to obtain the predicted power output, and trajectory tracking and analysis are performed on the predicted power output to obtain the power adjustment trajectory. The goal of this stage is to verify and adjust the prediction results to ensure that the actual output matches the expectation. The system will gradually adjust the output power of the charging pile according to the optimal control sequence obtained by the MPC algorithm and monitor the actual output situation in real time. If a deviation is found between the prediction and the actual situation, the system will immediately make corrections and recalculate the new control sequence until the ideal power output is achieved. For example, at an outdoor public charging station, when a high-performance electric vehicle is connected to the charging pile, the system will generate the most suitable power adjustment trajectory for the vehicle after a series of precise analyses and optimizations based on its load type and charging indicators. This trajectory not only reflects the ideal adjustment path of the output power from the start to the end of charging, but also takes into account the influence of factors such as grid fluctuations and environmental temperature, ensuring the safety and efficiency of the charging process. In summary, the process of non-linear power prediction and control planning for the target charging pile based on the charging indicators to obtain the power adjustment trajectory is a technically implemented path with closely linked and progressive steps.From the construction of the initial state - space model to the final trajectory tracking and analysis, every step closely revolves around how to extract the optimal control strategy from complex system dynamics. Through this method, the charging pile test equipment can intelligently sense the load demand, flexibly respond to various practical challenges, and provide more personalized and efficient charging services. Whether it is for the fast charging of high - performance electric vehicles or the regular charging of ordinary household electric vehicles, this process can ensure that the charging pile is always in the best working state and provides stable and reliable power support. For example, at an electric vehicle charging station, when a high - performance electric vehicle is connected to the charging pile, the system will, according to its load type and charging indicators, go through a series of steps such as state - space model construction, Taylor series expansion, non - linear power prediction, and control planning, and finally generate a power adjustment trajectory most suitable for the vehicle to achieve a fast and safe charging experience.

[0041] In a specific embodiment, the power output control of the target charging pile based on the load regulation instruction by the IGBT drive circuit provided in the target charging pile to obtain the target output power includes: Generating a vector control signal for the IGBT drive circuit based on the load regulation instruction to obtain an SVPWM signal; Switching the switching state of the IGBT drive circuit based on the SVPWM signal to obtain an IGBT switching action sequence; Performing harmonic analysis on the IGBT drive circuit with the IGBT switching action sequence to obtain a current harmonic spectrum; Optimizing the SVPWM signal based on the current harmonic spectrum to obtain an optimized SVPWM signal; Controlling the output current of the target charging pile based on the optimized SVPWM signal to obtain an output current waveform; Calculating the instantaneous power based on the output current waveform with the preset threshold to obtain the target output power.

[0042] Specifically, the process of controlling the power output of the target charging pile based on the load regulation instruction by the IGBT drive circuit set in the target charging pile to obtain the target output power is the core link in the adaptive load regulation method to achieve precise power supply. This process not only depends on the load regulation instruction generated in the previous steps but also involves a series of complex control technologies and optimization algorithms, including vector control signal generation, switching state switching, harmonic analysis and optimization, etc., to ensure that the most suitable power support can be provided for different types of devices to be charged. First, in this process, the system generates a vector control signal for the IGBT drive circuit based on the load regulation instruction to obtain an SVPWM (Space Vector Pulse Width Modulation) signal. Vector control is an advanced motor control technology that can decompose the stator current of an AC motor into a magnetic field component and a torque component for independent control respectively, thereby improving the dynamic response speed and steady-state accuracy of the system. In the application of charging piles, vector control is used to generate the SVPWM signal, which is an efficient PWM (Pulse Width Modulation) technology that can more effectively utilize the DC bus voltage to generate the required AC voltage waveform. For example, in the application scenario of an electric vehicle charging station, when the system generates specific load regulation instructions according to the load type recognition result, these instructions will be converted into specific vector control parameters, and then the SVPWM signal is generated. This signal contains detailed power output requirements, such as power level, current intensity, voltage range, etc., which directly guide the operation of the IGBT drive circuit. The SVPWM signal can not only achieve high-efficiency power conversion but also reduce harmonic distortion and improve the quality of the output electric energy. Next, to achieve the actual adjustment of power output, the system switches the switching state of the IGBT drive circuit based on the SVPWM signal to obtain the IGBT switching action sequence. The IGBT (Insulated Gate Bipolar Transistor) is an efficient switching element that can switch the power state at high speed and high efficiency, and is very suitable for application scenarios that require fast response and fine adjustment. By decoding the SVPWM signal, the system can determine whether the IGBT should be in the on or off state at each moment and generate the corresponding IGBT switching action sequence accordingly. For example, at an outdoor public charging station, when a high-performance electric vehicle is connected to the charging pile, the system will accurately control the switching action of the IGBT according to the pre-calculated SVPWM signal to achieve a smooth transition from a lower power to a higher power. This not only ensures the safety and stability of the charging process but also improves the charging efficiency and meets the user's demand for fast charging. With the generation of the IGBT switching action sequence, the system performs harmonic analysis on the IGBT switching action sequence for the IGBT drive circuit to obtain the current harmonic spectrum. Harmonic analysis is to evaluate and improve the power quality in the power system because non-sinusoidal waveforms will cause additional energy losses and potential equipment damage.By performing Fourier transform or other appropriate mathematical processing on the switching action sequence of the IGBT drive circuit, the system can extract the harmonic components in the current to form a current harmonic spectrum. For example, when facing the demand for fast charging of high-performance electric vehicles, since high-frequency switching operations may introduce a certain amount of harmonic distortion, the system needs to monitor and record these harmonic components in real time so that subsequent measures can be taken to optimize them. This method not only helps to improve the quality of output power, but also reduces the impact on the power grid and other electrical equipment. Then, in order to further improve the power quality and system performance, the system performs harmonic optimization on the SVPWM signal based on the current harmonic spectrum to obtain an optimized SVPWM signal. Harmonic optimization aims to minimize the harmonic content in the current while maintaining or improving the overall efficiency of the system. By adjusting the generation algorithm of the SVPWM signal, the system can effectively suppress the main harmonic components without significantly reducing the power output capacity. For example, at an electric vehicle charging station, when it is detected that the current harmonic spectrum contains a large number of low-order harmonics, the system can reduce the impact of these harmonics by modifying the modulation method of the SVPWM signal, such as increasing the zero vector time or changing the modulation ratio. The optimized SVPWM signal can not only provide a purer output current, but also extend the battery life and protect other sensitive electronic devices connected to the same power grid. Finally, the output current of the target charging pile is controlled based on the optimized SVPWM signal to obtain the output current waveform, and the instantaneous power calculation of the output current waveform is performed based on the preset threshold to obtain the target output power. The goal of this stage is to ensure that the final output power meets the established requirements and is stable and reliable. The system will accurately control the output current of the IGBT drive circuit according to the optimized SVPWM signal so that its waveform is as close to the ideal sine waveform as possible. Then, by calculating the instantaneous power of the output current waveform, the system can monitor the actual output power of the charging pile in real time and compare it with the preset threshold. If it is found that the power fluctuation exceeds the allowable range, the system will immediately make adjustments to ensure that the output power is always kept in the best state. For example, at an outdoor public charging station, when an ordinary household electric vehicle is connected to the charging pile, the system will control the output current through the optimized SVPWM signal according to its load characteristics to ensure that the output power is stable and efficient during the entire charging process, without overload or waste of resources. In summary, the process of obtaining the target output power by controlling the power output of the target charging pile based on the load regulation instruction through the IGBT drive circuit set in the target charging pile is an interlocking and progressive technical implementation path. From the initial vector control signal generation to the final instantaneous power calculation, each step is closely centered on how to extract the optimal control strategy from the complex system dynamics.Through this method, the charging pile testing equipment can intelligently sense the load demand, flexibly respond to various practical challenges, and provide more personalized and efficient charging services for users. Whether it is for the fast charging of high-performance electric vehicles or the regular charging of ordinary household electric vehicles, this process can ensure that the charging pile is always in the best working condition and provide stable and reliable power support. For example, at an electric vehicle charging station, when a high-performance electric vehicle is connected to the charging pile, the system will generate a power output control scheme that is most suitable for the vehicle through a series of steps such as vector control signal generation, switch state switching, harmonic analysis and optimization based on its load type and charging indicators, so as to achieve a fast and safe charging experience.

[0043] The above describes the adaptive load regulation method in the charging pile testing equipment in the embodiments of the present invention. Next, the adaptive load regulation system in the charging pile testing equipment in the embodiments of the present invention will be described. Please refer to Figure 2 , an embodiment of the adaptive load regulation system in the charging pile testing equipment in the embodiments of the present invention includes: A collection module 21, configured to collect and analyze multi-dimensional electrical parameters of a target charging pile to obtain initial electrical data; A calculation module 22, configured to calculate the output power fluctuation state of the target charging pile based on the initial electrical data to obtain an output power fluctuation state parameter; An identification module 23, configured to, if the output power fluctuation state parameter exceeds a preset threshold, identify the load type of the device to be charged based on the output power fluctuation state parameter to obtain the load type; wherein, the target charging pile is electrically connected to the device to be charged; A selection module 24, configured to select a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction; A control module 25, configured to control the power output of the target charging pile based on the load regulation instruction through an IGBT drive circuit provided in the target charging pile to obtain a target output power.

[0044] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to that described in the above method embodiment, and details will not be repeated here.

[0045] Refer to Figure 3 , the embodiments of the present invention also provide a computer device, and its internal structure can be as Figure 3As shown in the figure. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0046] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0047] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0048] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to memory, storage, database, or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0049] It should be noted that in this document, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article or method comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, apparatus, article or method. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, apparatus, article or method comprising such element.

[0050] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are similarly included in the patent protection scope of the present invention.

Claims

1. An adaptive load regulation method in a charging pile testing device, characterized in that Including the following steps: Collect and analyze multi-dimensional electrical parameters of the target charging pile to obtain initial electrical data; Calculate the output power fluctuation state of the target charging pile based on the initial electrical data to obtain output power fluctuation state parameters; If the output power fluctuation state parameters exceed the preset threshold, identify the load type of the device to be charged based on the output power fluctuation state parameters to obtain the load type; wherein, the target charging pile is electrically connected to the device to be charged; Select a load regulation strategy for the target charging pile based on the load type to obtain a load regulation command; Based on the load regulation command, control the power output of the target charging pile through the IGBT drive circuit provided in the target charging pile to obtain the target output power.

2. The adaptive load regulation method in the charging pile testing device according to claim 1, characterized in that, The collecting and analyzing multi-dimensional electrical parameters of the target charging pile to obtain initial electrical data includes: Sample the voltage and current of the output port in the target charging pile through a high-speed sampling circuit to obtain original sampling data; wherein, the original sampling data includes an original voltage value, an original current value, and a sampling timestamp; Filter the noise of the original sampling data to obtain filtered sampling data; Filter the noise of the original sampling data and calibrate it to obtain calibrated electrical sampling data; Perform multi-scale decomposition on the calibrated electrical sampling data through wavelet transform to extract the dynamic change characteristics of the original voltage value and the original current value to obtain time series characteristics; Separate signals from the time series characteristics based on independent component analysis to obtain electrical characteristic components; Dynamically track the electrical characteristic components using a state observer to obtain dynamic response parameters, and use the dynamic response parameters as initial electrical data.

3. The adaptive load regulation method in the charging pile testing equipment according to claim 1, wherein The calculating the output power fluctuation state of the target charging pile based on the initial electrical data to obtain output power fluctuation state parameters includes: Calculate the instantaneous power of the initial electrical data to obtain an instantaneous power sequence; Perform a sliding window process on the instantaneous power sequence to obtain instantaneous power sequences within multiple sliding windows; Perform frequency domain analysis on the instantaneous power sequences within the multiple sliding windows using the discrete Fourier transform algorithm to obtain the power spectral density; Calculate the output power fluctuation state of the target charging pile based on the power spectral density to obtain output power fluctuation characteristic parameters.

4. The adaptive load regulation method in the charging pile testing equipment according to claim 1, wherein, The identifying the load type of the device to be charged based on the output power fluctuation state parameters to obtain the load type includes: Perform empirical mode decomposition on the output power fluctuation state parameters to obtain a set of intrinsic mode function components; wherein, the set of intrinsic mode function components is used to characterize the intrinsic oscillation mode of load power fluctuation at different time scales; Perform Hilbert-Huang transform on the set of intrinsic mode function components to obtain the marginal spectrum; wherein, the marginal spectrum is used to reflect the energy distribution of different frequency components over the entire time period; Calculate the multi-scale permutation entropy based on the marginal spectrum to obtain a multi-scale permutation entropy feature vector; Perform local sensitive hashing calculation on the multi-scale permutation entropy feature vector to obtain a hashed feature fingerprint; Calculate the similarity degree of the load type features in the preset load type feature library based on the hashed feature fingerprint to obtain a similarity score; Match the load type of the device to be charged with the load type feature corresponding to the highest similarity score to obtain the load type; wherein, the load type includes an electric vehicle power battery, an energy storage system, an industrial device, and a general lighting load.

5. The adaptive load regulation method in the charging pile testing equipment according to claim 1, characterized in that The selecting a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction includes: Obtain the charging index corresponding to the load type, and during the charging process, perform non-linear power prediction and control planning on the target charging pile based on the charging index to obtain a power regulation trajectory; Perform parameter identification on the power regulation trajectory by using a preset recursive least squares method to obtain a system feature parameter set in the target charging pile; wherein, the system feature parameter set includes impedance characteristics during the charging process, power factor compensation parameters, and harmonic distortion rate; Construct control constraint conditions for the target charging pile based on the system feature parameter set; wherein, the control constraint conditions include power fluctuation limit conditions, voltage stability constraints, and current harmonic limits; Optimize and solve the system feature parameter set based on the control constraint conditions by using a multi-objective particle swarm optimization algorithm to obtain a real-time control quantity; Synthesize an IGBT drive signal instruction based on the real-time control quantity to obtain a load regulation instruction.

6. The adaptive load regulation method in the charging pile testing equipment according to claim 5, characterized in that The performing non-linear power prediction and control planning on the target charging pile based on the charging index to obtain a power regulation trajectory includes: Construct a state space model for the target charging pile based on the charging index to obtain a state space equation; Perform Taylor series expansion on the state space equation to obtain a linearized state space equation; Perform non-linear power prediction and control planning on the target charging pile based on the linearized state space equation to obtain a power prediction control law; Perform rolling optimization calculation on the power prediction control law by using a preset model predictive control algorithm to obtain an optimal control sequence; Perform power output control on the target charging pile based on the optimal control sequence to obtain a predicted power output; Perform trajectory tracking and analysis on the predicted power output to obtain a power regulation trajectory.

7. The adaptive load regulation method in the charging pile testing equipment according to claim 1, characterized in that The performing power output control on the target charging pile based on the load regulation instruction by using an IGBT drive circuit arranged in the target charging pile to obtain a target output power includes: Generate a vector control signal for the IGBT drive circuit based on the load regulation instruction to obtain an SVPWM signal; Perform switching state switching on the IGBT drive circuit based on the SVPWM signal to obtain an IGBT switching action sequence; Perform harmonic analysis on the IGBT drive circuit based on the IGBT switching action sequence to obtain a current harmonic spectrum; Optimize the SVPWM signal based on the current harmonic spectrum to obtain an optimized SVPWM signal; Based on the optimized SVPWM signal, perform output current control on the target charging pile to obtain an output current waveform; Based on the preset threshold, perform instantaneous power calculation on the output current waveform to obtain the target output power.

8. An adaptive load regulating device in a charging pile testing device, characterized in that, It includes: A collection module, configured to collect and analyze multi-dimensional electrical parameters of the target charging pile to obtain initial electrical data; A calculation module, configured to calculate the output power fluctuation state of the target charging pile based on the initial electrical data to obtain an output power fluctuation state parameter; An identification module, configured to, if the output power fluctuation state parameter exceeds the preset threshold, identify the load type of the device to be charged based on the output power fluctuation state parameter to obtain the load type; wherein, the target charging pile is electrically connected to the device to be charged; A selection module, configured to select a load regulation strategy for the target charging pile based on the load type to obtain a load regulation instruction; A control module, configured to perform power output control on the target charging pile based on the load regulation instruction through the IGBT drive circuit provided in the target charging pile to obtain the target output power.

9. A computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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