High-precision PWM control method, device, equipment and storage medium

Abstract

The invention relates to the field of PWM control, in particular to a high-precision PWM control method, device and equipment and a storage medium. The method comprises the following steps: collecting real-time charging monitoring parameters of a vehicle battery, carrying out dynamic load trend evolution, and constructing a time sequence load trend evolution curve; identifying a real-time PWM signal of the charging gun; carrying out nonlinear compensation adjustment based on the time sequence load trend evolution curve to obtain a nonlinear frequency compensation strategy; performing transient load fluctuation situation mining and transient load response matching adjustment according to the time sequence load trend evolution curve, and constructing a transient response frequency adjustment curve; and calculating the maximum safe charging power according to the real-time charging monitoring parameters, performing intelligent PWM waveform fine tuning on a transient response frequency adjustment curve, and constructing an optimal duty ratio fine tuning strategy. According to the invention, through dynamic and high-precision PWM control, the charging efficiency, safety and stability of the new energy vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the field of PWM control, and in particular to a high-precision PWM control method, device, equipment and storage medium. Background Art

[0002] With the transformation of the global energy structure and the improvement of environmental awareness, new energy vehicles (NEVs) have been widely promoted and applied as an environmentally friendly and energy-saving means of transportation. The popularity of new energy vehicles has not only promoted the popularization of green travel, but also accelerated the integration of smart transportation and renewable energy. However, the construction of charging facilities has become an urgent problem to be solved in the promotion of new energy vehicles. As a key equipment in the charging process of new energy vehicles, the charging gun has a direct impact on the user experience and the operating efficiency of the charging infrastructure.

[0003] Traditional new energy vehicle charging guns mostly use simple switching power supplies or fixed frequency adjustment methods in the control system. Although these traditional control methods can meet basic charging needs, with the continuous increase in charging power, the diversification of charging needs and the continuous development of battery technology, the control accuracy and charging efficiency of traditional charging guns can no longer meet the requirements of modern new energy vehicles for charging speed, charging safety and stability. Especially in high-power fast charging scenarios, the challenges of the charging gun's electromagnetic compatibility, temperature control system and battery management system are more prominent, and power fluctuations, electromagnetic interference, overheating and other problems are prone to occur, affecting the stability and safety of the charging process.

[0004] However, there are still some challenges in the high-precision PWM control technology for new energy vehicle charging guns, such as the accuracy of real-time data acquisition, the complexity of the control algorithm, and the suppression of electromagnetic interference, which affect the overall performance and application effect of the charging gun. Therefore, how to design a high-precision and high-stability PWM control method to improve the safety, stability and efficiency of the charging gun in practical applications has become an important issue that needs to be solved in the field of new energy vehicle charging technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a high-precision PWM control method, device, equipment and storage medium to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a high-precision PWM control method, comprising the following steps: Step S1: collecting real-time charging monitoring parameters of the vehicle battery, performing dynamic load trend evolution, and constructing a time series load trend evolution curve; Step S2: Identify the real-time PWM signal of the charging gun; and perform non-linear compensation adjustment based on the time-series load trend evolution curve to obtain a non-linear frequency compensation strategy; Step S3: Perform transient load wave dynamic potential mining and transient load response matching adjustment according to the time-series load trend evolution curve, and construct a transient response frequency adjustment curve; Step S4: Calculate the maximum safe charging power according to the real-time charging monitoring parameters, and perform intelligent PWM waveform fine-tuning on the transient response frequency adjustment curve to construct an optimal duty cycle fine-tuning strategy; Step S5: Perform immediate charging control according to the non-linear frequency compensation strategy and the optimal duty cycle fine-tuning strategy, collect the electromagnetic parameters of the charging gun scenario, and fit the electromagnetic interference distribution field; Step S6: Predict the frequency control error according to the electromagnetic interference distribution field, and perform error compensation to generate an intelligent PWM control optimization engine.

[0007] In this specification, a high-precision PWM control device is also provided for executing the high-precision PWM control method as described above, including: A load trend module for collecting real-time charging monitoring parameters of the vehicle battery, and performing dynamic load trend evolution to construct a time-series load trend evolution curve; A non-linear compensation adjustment module for identifying the real-time PWM signal of the charging gun; and performing non-linear compensation adjustment based on the time-series load trend evolution curve to obtain a non-linear frequency compensation strategy; A frequency adjustment module for performing transient load wave dynamic potential mining and transient load response matching adjustment according to the time-series load trend evolution curve, and constructing a transient response frequency adjustment curve; A duty cycle fine-tuning module for calculating the maximum safe charging power according to the real-time charging monitoring parameters, and performing intelligent PWM waveform fine-tuning on the transient response frequency adjustment curve to construct an optimal duty cycle fine-tuning strategy; An immediate charging control module for performing immediate charging control according to the non-linear frequency compensation strategy and the optimal duty cycle fine-tuning strategy, collecting the electromagnetic parameters of the charging gun scenario, and fitting the electromagnetic interference distribution field; An error compensation module for predicting the frequency control error according to the electromagnetic interference distribution field, and performing error compensation to generate an intelligent PWM control optimization engine.

[0008] The present invention also 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 high-precision PWM control method described in any one of the above are implemented.

[0009] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the high-precision PWM control method described in any one of the above are implemented.

[0010] The beneficial effects of the present invention are specifically as follows: By collecting real-time monitoring parameters (such as voltage, current, etc.) during the battery charging process, the charging state of the battery can be dynamically understood. Constructing a timing load trend evolution curve based on these real-time parameters helps predict the load change trend of the battery, avoid sudden load fluctuations, and ensure that the battery is charged under stable conditions. Provide accurate data support for subsequent load compensation, frequency adjustment, and intelligent adjustment strategies to ensure that each adjustment step can make accurate decisions based on real data. The real-time identification of the charging gun PWM signal helps to understand the control signal status during the charging process in real time, ensuring that the control strategy can be adjusted in a timely manner. Through the non-linear compensation adjustment based on the load trend evolution curve, complex load changes can be adapted to ensure that the frequency during the charging process is not overly interfered, thereby improving the accuracy of PWM control. The non-linear frequency compensation strategy can accurately adjust the frequency response during the charging process, reduce electromagnetic interference generated in the system, and improve charging stability. By exploring the dynamic potential of transient load waves, instantaneous load fluctuations during the battery charging process can be captured and responded to in a timely manner. By constructing a transient response frequency adjustment curve, it can be ensured that transient load fluctuations during the charging process are quickly and accurately adjusted, avoiding abnormal fluctuations in current or voltage during the charging process. Effectively reduce the impact of transient fluctuations on the accuracy of PWM control, and improve the stability and efficiency of the overall charging process. Based on the timing load trend evolution curve, calculate the maximum safe charging power to avoid the risk of overload. By intelligently fine-tuning the duty cycle of the PWM waveform, the power output can be further optimized during the charging process, avoiding unnecessary losses and improving charging efficiency. The combination of the maximum charging power and the transient response curve ensures stability during the charging process, maximizes the charging speed while avoiding battery overheating or damage. According to the non-linear frequency compensation strategy and the duty cycle fine-tuning strategy, the PWM control can be optimized in real time to accurately control the power output during the battery charging process. Collecting electromagnetic parameters in the charging gun scenario and fitting the electromagnetic interference distribution field helps to understand the distribution of electromagnetic interference in advance and take measures to reduce interference and reduce unnecessary electromagnetic pollution. Through the fitting of electromagnetic interference, the system can be optimized specifically in different scenarios to minimize the interference impact to the greatest extent. Based on the modeling of the electromagnetic interference distribution field, the possible frequency control errors during the charging process can be predicted, and measures can be taken in advance for adjustment. Through real-time error compensation, improve the accuracy of frequency control to ensure that the PWM control is more stable and accurate. Through the generation of an intelligent PWM control optimization engine, automatic adjustment during the charging process can be achieved, improving the adaptive ability of the system and enhancing the safety and efficiency of the charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Schematic diagram of the step flow of a high-precision PWM control method of the present invention; Figure 2 Schematic diagram of the detailed implementation steps of step S1; Figure 3 Schematic diagram of the detailed implementation steps of step S2; Figure 4 Schematic diagram of the detailed implementation steps of step S3. Specific implementation manner

[0012] 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.

[0013] The embodiments of the present application provide a high-precision PWM control method, device, equipment and storage medium. The execution subjects of the high-precision PWM control method, device, equipment and storage medium include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc. that carry this system and can be regarded as general computing nodes of the present application. The data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0014] Please refer to Figures 1 to 4 , the present invention provides a high-precision PWM control method, and the high-precision PWM control method includes the following steps: Step S1: Collect real-time charging monitoring parameters of the vehicle battery, perform dynamic load trend evolution, and construct a time-series load trend evolution curve; Step S2: Identify the real-time PWM signal of the charging gun; and perform non-linear compensation adjustment based on the time-series load trend evolution curve to obtain a non-linear frequency compensation strategy; Step S3: Perform transient load wave dynamic potential mining and transient load response matching adjustment according to the time-series load trend evolution curve, and construct a transient response frequency adjustment curve; Step S4: Calculate the maximum safe charging power according to the real-time charging monitoring parameters, and perform intelligent PWM waveform fine-tuning on the transient response frequency adjustment curve to construct an optimal duty cycle fine-tuning strategy; Step S5: Perform instant charging control according to the non-linear frequency compensation strategy and the optimal duty cycle fine-tuning strategy, collect electromagnetic parameters of the charging gun scenario, and fit the electromagnetic interference distribution field; Step S6: Predict the frequency control error according to the electromagnetic interference distribution field, and perform error compensation to generate an intelligent PWM control optimization engine.

[0015] By collecting real-time monitoring parameters (such as voltage, current, etc.) during the battery charging process, the charging status of the battery can be dynamically understood. Constructing a time-series load trend evolution curve based on these real-time parameters helps predict the load change trend of the battery, avoid sudden load fluctuations, and ensure that the battery is charged under stable conditions. Provide accurate data support for subsequent load compensation, frequency regulation, and intelligent regulation strategies to ensure that each regulation step can make precise decisions based on real data. The real-time identification of the charging gun PWM signal helps to understand the status of the control signal during the charging process in real time, ensuring that the control strategy can be adjusted in a timely manner. Through non-linear compensation adjustment based on the load trend evolution curve, it can adapt to complex load changes, ensure that the frequency during the charging process is not overly interfered, and thus improve the accuracy of PWM control. The non-linear frequency compensation strategy can accurately adjust the frequency response during the charging process, reduce electromagnetic interference generated in the system, and improve charging stability. By exploring the dynamic potential of transient load waves, the instantaneous load fluctuations during the battery charging process can be captured and responded to in a timely manner. By constructing a transient response frequency regulation curve, it can ensure that the transient load fluctuations during the charging process are quickly and accurately regulated, avoiding abnormal fluctuations in current or voltage during the charging process. Effectively reduce the impact of transient fluctuations on the PWM control accuracy, and improve the stability and efficiency of the overall charging process. Based on the time-series load trend evolution curve, calculate the maximum safe charging power to avoid the risk of overload. By intelligently fine-tuning the duty cycle of the PWM waveform, the power output can be further optimized during the charging process, avoiding unnecessary losses and improving charging efficiency. The combination of the maximum charging power and the transient response curve ensures the stability during the charging process, maximizes the charging speed while avoiding battery overheating or damage. According to the non-linear frequency compensation strategy and the duty cycle fine-tuning strategy, the PWM control can be optimized in real time to accurately control the power output during the battery charging process. Collect the electromagnetic parameters in the charging gun scenario and fit the electromagnetic interference distribution field, which helps to understand the distribution of electromagnetic interference in advance and take measures to reduce interference and reduce unnecessary electromagnetic pollution. Through the fitting of electromagnetic interference, the system can be optimized specifically in different scenarios to minimize the interference impact to the greatest extent. Based on the modeling of the electromagnetic interference distribution field, the possible frequency control errors during the charging process can be predicted, and measures can be taken in advance for adjustment. Through real-time error compensation, improve the accuracy of frequency control and ensure that the PWM control is more stable and accurate. Through the generation of an intelligent PWM control optimization engine, automatic adjustment during the charging process can be achieved, improving the adaptive ability of the system and enhancing the safety and efficiency of the charging process.

[0016] In the embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of a high-precision PWM control method of the present invention. In this example, the steps of the high-precision PWM control method include: Step S1: Collect the real-time charging monitoring parameters of the vehicle battery, conduct dynamic load trend evolution, and construct a time-series load trend evolution curve; In this embodiment, configure a charging monitoring system to collect parameters such as the current, voltage, temperature, and charging time of the vehicle battery in real time. Ensure that the accuracy and sampling frequency of the sensors are high enough to accurately reflect the charging status. Set to collect data once per second, record the current as 10A, the voltage as 12V, and the temperature as 25°C, and continuously monitor the changes during the charging process. Store the collected real-time charging parameters in the database and ensure that the data is structured for subsequent analysis. The record should include timestamps, various parameters, and their changes. Generate a table containing information such as time, current, voltage, and temperature for load trend analysis. Use the collected current and voltage data to calculate the instantaneous power (P = VI) and analyze the load trend over time. The sliding window method can be used to calculate the average power and its change rate to capture the dynamic load characteristics. If the average power changes by 5W / s within a certain period of time, record this dynamic trend to form a time-series load trend evolution curve. Visualize the calculated dynamic load data to generate a time-series load trend evolution curve. The X-axis is time, and the Y-axis is power. The curve should show the overall trend of load changes. Draw a chart to show the change in power during the charging process, providing basic data support for subsequent steps.

[0017] Step S2: Identify the real-time PWM signal of the charging gun; and perform non-linear compensation adjustment based on the time-series load trend evolution curve to obtain a non-linear frequency compensation strategy; In this embodiment, install a high-precision oscilloscope or digital signal processor in the charging gun to monitor the waveform of the PWM signal in real time. Ensure that the device can capture the frequency and duty cycle of the PWM signal. Set the sampling frequency to 1kHz to ensure that the subtle changes in the PWM signal can be accurately captured. Collect the waveform of the PWM signal of the charging gun in real time and analyze its duty cycle and frequency. Use methods such as Fourier transform to extract frequency domain features to evaluate the stability of the PWM signal. If the duty cycle of the PWM signal is 40%, record this parameter for non-linear compensation adjustment. Based on the time-series load trend evolution curve, analyze the impact of the PWM signal on the load. By establishing a compensation model, dynamically adjust the PWM frequency to cope with the impact of load changes. If the load increases and causes the PWM frequency to be low, the PWM signal frequency needs to be adjusted to maintain the charging efficiency. Combine the adjusted PWM frequency with the dynamic load trend to form a non-linear frequency compensation strategy. This strategy should be able to adapt to load changes in real time to ensure the stability of the charging process. Record the new PWM frequency value and generate a compensation strategy document for subsequent reference.

[0018] Step S3: Conduct transient load wave dynamic potential mining and transient load response matching adjustment based on the time-series load trend evolution curve, and construct a transient response frequency adjustment curve; In this embodiment, based on the time-series load trend evolution curve, transient load characteristics are extracted, including instantaneous power fluctuations and their change rates. Statistical analysis methods (such as standard deviation, mean) are used to evaluate the fluctuation characteristics of the transient load. During a charging cycle, if the transient load fluctuation amplitude is 2A, this characteristic is recorded for subsequent analysis. Dynamic potential analysis is performed on the transient load fluctuations to identify the patterns of load changes. Machine learning models (such as clustering analysis) can be used to classify the load changes to identify potential load fluctuation trends. Through clustering analysis, the load fluctuations are divided into high, medium, and low fluctuation categories for targeted adjustment. According to the extracted transient load characteristics, the PWM control signal is adjusted in real time to match the load changes. The frequency and duty cycle of the PWM signal are adjusted to ensure timely load response during the charging process. If the PWM frequency needs to be quickly adjusted during a load mutation, the adjusted PWM signal parameters are recorded to ensure stable charging. Visualize the transient load response adjustment results to generate a transient response frequency adjustment curve. The X-axis represents time, the Y-axis is the PWM frequency, and the curve shows the relationship between load changes and the PWM signal.

[0019] Step S4: Calculate the maximum safe charging power based on the real-time charging monitoring parameters, and perform intelligent PWM waveform fine-tuning on the transient response frequency adjustment curve to construct an optimal duty cycle fine-tuning strategy; In this embodiment, based on the real-time charging monitoring parameters (voltage and current), the maximum safe charging power is calculated. The formula is P_max = V × I_max, where I_max is the safe maximum charging current. If the battery voltage is 12V and the maximum charging current is 15A, then the calculated maximum safe charging power is P_max = 12V × 15A = 180W. Evaluate the transient response frequency adjustment curve to determine the duty cycle and frequency of the PWM signal at the maximum safe charging power. Fine-tune according to the load change situation to ensure the efficiency of the charging process. If the current PWM frequency is 500Hz and the duty cycle is 40%, then at the maximum safe power, it may need to be adjusted to 45% to improve the charging efficiency. Adjust the frequency and duty cycle of the PWM waveform according to the real-time calculated maximum safe charging power. Ensure that the adjusted PWM signal can effectively respond to the load changes during the charging process. If the adjusted PWM signal frequency is 505Hz and the duty cycle is 45%, record these parameters for subsequent monitoring. Combine the fine-tuned PWM waveform with the dynamic safe charging power to form an optimal duty cycle fine-tuning strategy. Record this strategy for application in subsequent charging processes.

[0020] Step S5: Perform instant charging control according to the non - linear frequency compensation strategy and the optimal duty - cycle fine - tuning strategy, collect the electromagnetic parameters of the charging gun scenario, and fit the electromagnetic interference distribution field. In this embodiment, instant charging control is implemented according to the non - linear frequency compensation strategy and the optimal duty - cycle fine - tuning strategy. Ensure that the charging system can dynamically respond to load changes and adjust the PWM signal to optimize the charging process. Monitor the changes in current and voltage during the charging process and adjust the PWM signal in a timely manner to adapt to the load. Install electromagnetic field sensors around the charging gun to monitor electromagnetic parameters in real time, such as the electromagnetic field strength and frequency characteristics. Ensure that the sensors have high precision and fast response characteristics. Set to collect electromagnetic field strength data once per second and record the changes in electromagnetic field strength during the charging process. Analyze the collected electromagnetic parameters and fit the distribution field of electromagnetic interference. Use spatial interpolation methods (such as Kriging interpolation) to generate a three - dimensional distribution model of electromagnetic interference intensity. If the measured electromagnetic field strengths at different positions are 5μT, 6μT, and 4μT, then generate the electromagnetic interference distribution field of the entire area through the interpolation method.

[0021] Step S6: Predict the frequency control error based on the electromagnetic interference distribution field, perform error compensation, and generate an intelligent PWM control optimization engine.

[0022] In this embodiment, based on the electromagnetic interference distribution field, use a prediction model (such as the ARIMA model) to calculate the frequency control error. Input the real - time collected current, voltage, and electromagnetic interference parameters into the model to obtain the predicted value. If the model predicts that the frequency control error is - 0.03Hz, record this predicted value for subsequent compensation. Calculate the error feed - forward compensation value according to the predicted frequency control error. The formula is C_ff = K * E_pred, where K is the compensation coefficient and E_pred is the predicted frequency control error. If K is set to 3, then the compensation value C_ff = 3 * (-0.03)= - 0.09Hz, and record this compensation value for subsequent use. Adjust the PWM control parameters according to the calculated error feed - forward compensation value. Update the PWM frequency and duty - cycle in real time to ensure response to the frequency deviation caused by electromagnetic interference. If the current PWM frequency is 500Hz, then the adjusted PWM frequency is 500Hz - 0.09Hz = 499.91Hz, and record the updated parameters. Integrate the above optimization process into an intelligent PWM control optimization engine to achieve adaptive control of the charging process. Through real - time monitoring and adjustment, ensure the stable operation of the charging system under various working conditions.

[0023] In this embodiment, refer to Figure 2 , which is a schematic diagram of the detailed implementation steps of Step S1. In this embodiment, the detailed implementation steps of Step S1 include: Collect real - time charging monitoring parameters of the vehicle battery; Calculate the temperature change range of the real-time charging monitoring parameters; Perform time-series temperature fluctuation fitting on the temperature change range to construct a multi-point charging temperature fluctuation graph; Extract the real-time current-voltage index of the real-time charging monitoring parameters; Conduct real-time charging load analysis based on the real-time current-voltage index to obtain the real-time charging load characteristics of the vehicle battery; Perform dynamic load trend evolution on the real-time charging load characteristics of the vehicle battery according to the multi-point charging temperature fluctuation graph to construct a time-series load trend evolution curve.

[0024] In this embodiment, real-time monitoring devices are installed in the vehicle battery system, including current sensors, voltage sensors, and temperature sensors. Ensure that these sensors can collect key parameters during the charging process in real time, such as current, voltage, and temperature. The devices should have high-precision and high-frequency acquisition capabilities. For example, the current sensor can be set to collect data once per second, and the temperature sensor should also have the same sampling frequency. Configure a data acquisition system to transmit sensor data to the data processing unit in real time. Use a data acquisition card or a real-time data processing module to ensure the accuracy and integrity of the data. Set the working mode of the data acquisition system to continuous acquisition and ensure that the data storage capacity is sufficient to record all parameters during the entire charging process. During the charging process, record the current, voltage, and temperature data of the battery in real time. The data should be stored in the form of timestamps so that the change relationships between different parameters can be correlated during subsequent analysis. Record the current value as 10 A, the voltage as 12 V, and the temperature as 25 °C per second to ensure the accuracy and traceability of the data. Extract the temperature value at each time point from the real-time monitoring data and record the initial and final values of the temperature during the charging process. The temperature change range can be defined as the difference between these two values. If the temperature is 25 °C at the beginning of charging and 35 °C at the end of charging, the temperature change range is 10 °C. Use a simple mathematical formula to calculate the temperature change range: ΔT = T(final) – T(initial). Record the temperature change range for each charging cycle for subsequent analysis. If the temperature change ranges are 10 °C, 8 °C, and 12 °C in different charging cycles, record these data for comparison. Organize the calculated temperature change range data into a table, indicating the corresponding time points and charging status for subsequent analysis and visualization. Generate a table containing information such as time points, initial temperature, final temperature, and change range to ensure the clarity and systematicness of the data. Use time series analysis methods to fit the temperature change range. Common methods include the moving average method, exponential smoothing method, or polynomial fitting, etc. Selecting an appropriate method can improve the fitting accuracy. If polynomial fitting is selected, the order of the polynomial to be fitted needs to be determined to effectively describe the trend of temperature change. Fit according to the organized temperature change range data and use mathematical software (such as MATLAB, Python, etc.) to implement data fitting. Optimize the fitting parameters by calculating the least squares method. Fit the temperature change range with the time points to obtain the fitting curve equation, record the fitting parameters and the R² value to evaluate the fitting effect. Extract the real-time current and voltage data of the current charging cycle from the real-time monitoring system and calculate the real-time current-voltage index. The current-voltage index can be expressed by the formula I / V (current / voltage), which reflects the power characteristics during the charging process.If the real-time current is 10 A and the voltage is 12 V, then the current-voltage index is 10 / 12 = 0.8. Record the real-time current and voltage data at each time point and the calculated current-voltage index to ensure the continuity and integrity of the data. Generate a table containing information such as time, real-time current, real-time voltage, and current-voltage index for subsequent analysis. Conduct statistical analysis on the extracted real-time current-voltage index to evaluate the impact of current and voltage changes during charging on battery performance. Statistical indicators such as the average value and standard deviation can be calculated. If the current-voltage index fluctuates between 0.8 - 1.2 during charging, analyze the impact of its change trend on battery charging efficiency. Define the charging load characteristics based on the real-time current-voltage index. The load characteristics can include average load, peak load, and load fluctuation, etc. Load analysis helps understand the performance and stability of the battery during charging. If the average current is 8 A and the peak current is 10 A during a certain period, the load characteristic value can be calculated. Calculate the real-time charging load using the real-time current and voltage data. The load can be calculated by the formula P = I × V (Power = Current × Voltage) to evaluate the power load situation during charging. If the current is 8 A and the voltage is 12 V at a certain time point, the real-time charging load is 8 × 12 = 96 W. Record the real-time charging load characteristics at each time point and conduct statistical analysis to observe the changes in load characteristics during charging. The performance of the battery can be evaluated by calculating the average value and change range of the load. If the recorded load characteristic range fluctuates between 80 W - 100 W, it indicates that the charging process is stable. Conduct correlation analysis between the real-time charging load characteristics and the multi-time point charging temperature fluctuation graph to evaluate the impact of temperature fluctuation on the charging load. Generally, an increase in temperature may lead to a decrease in battery performance, thereby affecting the load characteristics. Analyze whether there are abnormal fluctuations in the charging load when the temperature fluctuates greatly. Based on the temperature fluctuation graph and real-time load data, construct a dynamic load trend evolution model. A regression analysis method can be used to evaluate the impact of temperature changes on load characteristics and predict future load trends. Use a linear regression model with temperature change as the independent variable and load characteristics as the dependent variable for analysis. Visualize the results of the dynamic load trend evolution to generate a time-series load trend evolution curve. The X-axis is time, and the Y-axis is load characteristics. The curve should show the trend of load changing with time and temperature. Draw a time-series curve graph to show the changes in charging load characteristics under different temperature conditions to help identify potential charging risks.

[0025] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of the said step S2 include: Identify the real-time PWM signal of the charging gun; Calculate the output frequency of the real-time PWM signal of the charging gun; Perform timing frequency fitting based on the output frequency to generate the PWM frequency curve of the charging gun; Mine the non-linear dynamic changes according to the real-time current-voltage index to obtain the non-linear dynamic change characteristics of the battery's electrical properties; Conduct in-depth analysis of the correlated changes of current and voltage for the non-linear dynamic change characteristics of the battery's electrical properties to obtain the in-depth non-linear dynamic correlation law; Perform non-linear compensation adjustment on the PWM frequency curve of the charging gun based on the in-depth non-linear dynamic correlation law to obtain the non-linear frequency compensation strategy.

[0026] In this embodiment, an appropriate sensor is installed in the charging gun to capture the Pulse Width Modulation (PWM) signal of the charging gun in real time. Usually, an oscilloscope or a Digital Signal Processor (DSP) is used to monitor the changes of the PWM signal in real time. The sampling frequency of the sensor should be set higher than the frequency of the PWM signal to ensure that the width and frequency of each pulse can be accurately captured. For example, it is set to sample 1000 times per second. The collected PWM signal is analyzed through signal processing algorithms, and filters are used to remove noise to improve the clarity and accuracy of the signal. Commonly used filtering methods include low-pass filtering and band-pass filtering. High-frequency noise is eliminated through a low-pass filter to ensure the validity of the collected signal and obtain a clear PWM waveform. Feature extraction is performed on the processed PWM signal to identify parameters such as its pulse width and frequency. The characteristic data of the PWM signal collected each time is recorded for subsequent analysis. If the pulse width of the PWM signal is recorded as 2 ms and the frequency is 500 Hz in a certain collection, these data are stored in the database to ensure the traceability of the data. The frequency can be calculated by measuring the period (T) of the PWM signal. The frequency (f) can be obtained from the formula f = 1 / T. By measuring the time interval of the collected signal, the period of the PWM signal is obtained. If, after measurement, the period of a PWM signal is 2 ms, then the frequency is f = 1 / 0.002 = 500 Hz. The calculated frequency data is recorded in the database to ensure the integrity of the frequency information at each time point. The record should include information such as timestamp, pulse width, period, and frequency. A table is generated, including time, pulse width, period, and frequency, for easy subsequent data analysis. During the charging process, the frequency may fluctuate with changes in current and charging status, so the frequency changes need to be monitored regularly. Thresholds can be set to trigger an alarm or record when the frequency change exceeds the set range. The PWM frequency data over a period of time is collected for time series fitting analysis. Ensure the integrity and continuity of the data to avoid missing data affecting the analysis results. Record the PWM frequency data per second within 10 minutes to form a frequency time series. Select an appropriate fitting model according to the characteristics of the frequency data. Commonly used fitting methods include linear fitting and polynomial fitting, etc. Selecting an appropriate model can improve the fitting accuracy. If the frequency data shows non-linear changes, a quadratic or cubic polynomial can be selected for fitting. Use mathematical software (such as MATLAB or Python) to fit the frequency data, calculate the fitting parameters, and generate a fitting curve. Optimize the fitting model through the least squares method to obtain the best fitting effect. If a quadratic polynomial fitting is adopted, the obtained fitting equation is f(t) = at² + bt + c, where a, b, and c are fitting parameters. Visualize the fitting results to generate the PWM frequency curve of the charging gun. The X-axis is time, and the Y-axis is the frequency change. The fitting curve should clearly show the overall trend of the frequency. Draw a chart to show the relationship between the actual frequency data points and the fitting curve for an intuitive observation of the frequency change.Extract the current and voltage data of the battery from the real-time monitoring system and calculate the real-time current-voltage index. The current-voltage index can be expressed by the formula I / V (current / voltage) to reflect the performance of the battery. If the real-time current is 10 A and the voltage is 12 V, then the current-voltage index is 10 / 12 = 0.83. Conduct dynamic change mining on the current-voltage index and calculate the changes of current and voltage over time. Statistical analysis methods (such as standard deviation, volatility, etc.) can be used to evaluate the non-linear dynamic characteristics of the battery. If the current-voltage index fluctuates between 0.7 and 1.2 during the charging process, the impact of its change on the battery charging efficiency can be analyzed. Record the dynamic change characteristics of the current-voltage index obtained by mining in the database to ensure the integrity and traceability of the data. The record should include information such as time, real-time current, voltage, and current-voltage index. Generate a table containing time, real-time current, voltage, and the corresponding current-voltage index for subsequent analysis. Select suitable statistical analysis methods (such as regression analysis, correlation analysis, etc.) to analyze the deep associations between current-voltage indices. By analyzing the change relationship between current and voltage, potential influencing factors can be identified. Use the Pearson correlation coefficient to evaluate the linear relationship between current and voltage and determine the degree of their correlation. Organize the current-voltage data and apply the selected analysis method to calculate the association relationship between current and voltage, and identify the deep dynamic association rules. If the correlation coefficient between current and voltage is found to be 0.85, it indicates a strong positive correlation between the two. Record the analysis results in the database to ensure the integrity and traceability of the data. The record includes the correlation coefficient between current and voltage and its statistical significance. Record the correlation analysis results of current and voltage, indicating whether it is significant (p<0.05) for subsequent use. According to the deep non-linear dynamic association rules, formulate a non-linear compensation strategy for the PWM frequency. The compensation strategy should consider the impact of current and voltage changes on the PWM frequency to optimize the charging efficiency. If it is found that an increase in current leads to a decrease in the PWM frequency, the duty cycle of the PWM signal can be adjusted to maintain a stable output frequency. According to the formulated compensation strategy, adjust the PWM signal of the charging gun in real time. By adjusting the duty cycle or frequency of the PWM signal, ensure the optimal performance of the battery during charging. If the PWM frequency needs to be adjusted to 550 Hz, modify the control signal in real time to achieve this goal. Evaluate the effect of the adjusted PWM frequency and monitor whether the battery charging performance has been improved. Record the performance data before and after the adjustment for comparative analysis. Record the charging efficiency, charging time, and battery temperature before and after the adjustment to evaluate the effectiveness of the compensation strategy.

[0027] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of the said step S3 include: Calculate the transient load change amplitude at multiple time points according to the evolution curve of the time-series load trend; Mine the transient load wave dynamic potential according to the transient load change amplitude, and extract the transient load fluctuation characteristics; Predict the future load fluctuation of the transient load fluctuation characteristics to obtain the transient load fluctuation prediction value; Perform transient load response matching adjustment on the non-linear compensation PWM frequency curve according to the transient load fluctuation prediction value, and construct a transient response frequency adjustment curve.

[0028] In this embodiment, load data at multiple time points are extracted from the recorded temporal load trend evolution curve. These data should be the load values monitored in real time, usually expressed in amperes (A), ensuring the integrity of the data at each time point. The charging currents recorded at each time point are 8A, 10A, 12A, 9A, etc., forming a set of load data sequences. The transient load change amplitude can be achieved by calculating the difference in load values at adjacent time points. The formula is ΔL = L_t - L_(t-1), where L_t is the load value at the current moment and L_(t-1) is the load value at the previous moment. If the load is 10A at time point T1 and 12A at time point T2, then the transient load change amplitude is ΔL = 12A - 10A = 2A. The calculated transient load change amplitudes are organized into a table, recording the load change amplitudes at each time point for subsequent analysis and visualization. A table containing time, current load, previous load, and transient load change amplitude is generated to ensure the clarity and systematicness of the data. Determine the analysis method for transient load fluctuation characteristics. Statistical analysis methods, such as mean, standard deviation, and peak factor, can be used to describe the characteristics of load fluctuations. The standard deviation of load changes can reflect the fluctuation amplitude. If the standard deviation is large, it indicates that the load changes violently. Based on the extracted transient load change amplitude data, calculate the fluctuation characteristics within each time period. This includes calculating the mean, maximum value, minimum value, and amplitude of load changes, etc. Within a time period, if the load change amplitudes are 2A, -1A, and 3A respectively, then the calculated mean is (2 - 1 + 3) / 3 = 1.33A. Record the extracted transient load fluctuation characteristics in the database to ensure the integrity and traceability of the data. The records include information such as time period, mean, standard deviation, and maximum load fluctuation. Select a suitable prediction model for future load fluctuation prediction. Commonly used models include time series analysis (such as ARIMA, SARIMA) or machine learning models (such as support vector machine, random forest, etc.). If the load fluctuation data has seasonality, a seasonal ARIMA model can be selected for prediction. Use the historical load fluctuation data to train the selected prediction model, which is divided into a training set and a test set. Evaluate the accuracy of the model through cross-validation to ensure that the model can accurately capture the trend of load fluctuations. Use the load fluctuation data of the past few weeks for training and judge its performance by evaluating the root mean square error (RMSE) of the model through the test set. Use the trained model to predict future load fluctuations and generate transient load fluctuation prediction values. These prediction values will be used as the basis for subsequent adjustment of the PWM frequency. Predict that the load fluctuation values at the next 3 time points are 2A, 1.5A, and 3A, and record these prediction results. According to the transient load fluctuation prediction values obtained from the previous analysis, adjust the non-linear compensation PWM frequency curve to maintain the stability of the load during the charging process. If the load fluctuation prediction value is 2A, the PWM frequency needs to be adjusted to 550Hz to maintain the load stability.According to the predicted value of transient load fluctuation, adjust the frequency and duty cycle of the PWM signal in real time to achieve load response matching. Ensure the coordination between the load and frequency during battery charging by adjusting the control signal. Adjust the PWM frequency from 500 Hz to 550 Hz to match the predicted load fluctuation. Visualize the adjusted PWM frequency and the predicted value of transient load fluctuation to form a transient response frequency adjustment curve. The X-axis represents time, and the Y-axis represents the PWM frequency. The curve should show the relationship between load fluctuation and PWM frequency. Draw a chart to show the change of the adjusted PWM frequency under different loads for easy observation of the effectiveness of frequency adjustment.

[0029] In this embodiment, step S4 includes the following steps: Calculate the real-time remaining capacity value of the battery according to the real-time charging monitoring parameters; Calculate the maximum safe charging power for the real-time remaining capacity value of the battery and extract the dynamic maximum safe charging power; Calculate the waveform duty cycle of the transient response frequency adjustment curve; Perform intelligent PWM waveform fine-tuning according to the dynamic maximum safe charging power and the waveform duty cycle to construct an optimal duty cycle fine-tuning strategy.

[0030] In this embodiment, the current, voltage and charging time data of the battery are extracted from the real-time charging monitoring system. These parameters are crucial for calculating the remaining capacity of the battery. Make sure that the data acquisition frequency is high enough to capture any rapid changes. Record the current current as 10A, the voltage as 12V, and the charging time as 1 hour. Based on these data, the battery's state of charge can be calculated. The remaining capacity of the battery can usually be calculated by the following formula: C (remaining) = C (total) - ∫I (t) dt, where C (total) is the total capacity of the battery, and ∫I (t) dt represents the total current integral during the charging time. If the rated capacity of the battery is 60Ah, after 1 hour of charging, the current is 10A, then the calculated charging amount is 10Ah, so the remaining capacity is C (remaining) = 60Ah - 10Ah = 50Ah. The calculated real-time remaining capacity value is recorded in the database to ensure that the capacity data at each time point is complete. The record should include information such as timestamp, current current, voltage, and remaining capacity. Generate a table containing time, current, voltage, and real-time remaining capacity for subsequent analysis and visualization. The maximum safe charging power refers to the maximum power that can be input to the battery without compromising battery performance and safety. Usually, the maximum power can be calculated by the formula P(max) = V × I(max), where V is the battery voltage and I(max) is the safe maximum charging current. If the battery voltage is 12V and the maximum safe charging current is set to 15A, the maximum safe charging power is P(max) = 12V × 15A = 180W. In the actual charging process, the battery voltage and safe maximum charging current may be affected by temperature, state, and health. Therefore, these parameters need to be dynamically monitored to calculate the dynamic maximum safe charging power in real time. If the battery temperature rises during the charging process, causing the maximum safe charging current to drop to 10A, the dynamic maximum safe charging power needs to be recalculated as P_max = 12V × 10A = 120W. Record the data of the dynamic maximum safe charging power in the database to ensure data integrity and traceability. The record should include information such as time, current voltage, maximum charging current and dynamic maximum power. Generate a table containing time, current voltage, maximum charging current and dynamic maximum safe charging power for subsequent analysis. Duty cycle refers to the ratio of the duration of the "high level" of the PWM signal in one cycle to the total time of the cycle. The duty cycle is usually expressed as a percentage. For a PWM waveform, a higher duty cycle means a greater average power output. If the period of the PWM waveform is 10ms, and the high level lasts for 4ms, the duty cycle is 40%. Analyze the transient response frequency adjustment curve, extract the high level time and period of its PWM waveform, and calculate the duty cycle by the formula.If the high-level duration is 3 ms and the period is 8 ms, the duty cycle is calculated as duty cycle = (3 ms / 8 ms) × 100% = 37.5%. Record the calculated duty cycle in the database to ensure the integrity of the duty cycle data at each time point. The record should include information such as time, PWM period, and high-level time. Generate a table containing time, PWM period, high-level time, and duty cycle for subsequent analysis and visualization. Based on the dynamic maximum safe charging power and the calculated waveform duty cycle, formulate a PWM waveform fine-tuning strategy. The goal is to optimize the charging efficiency and protect the battery by adjusting the duty cycle. If the dynamic maximum safe charging power is 120 W and the current PWM duty cycle is 40%, the duty cycle can be considered to be adjusted to 45% to increase the charging power. According to the formulated fine-tuning strategy, adjust the duty cycle or frequency of the PWM signal in real time to optimize the charging process. Ensure the stability of the battery during charging by adjusting the control signal. Adjust the duty cycle of the PWM signal from 40% to 45% to better adapt to the current charging demand and battery state. Visualize the fine-tuned PWM waveform and the dynamic maximum safe charging power to form an optimal duty cycle fine-tuning strategy curve. The X-axis is time, and the Y-axis is the PWM duty cycle. The curve should show the adjusted waveform and its impact on the charging performance. Draw a chart showing the change in the adjusted PWM duty cycle under different charging conditions to observe the effectiveness of the fine-tuning strategy.

[0031] In this embodiment, the specific steps of step S5 are as follows: Perform instant charging control according to the non-linear frequency compensation strategy and the optimal duty cycle fine-tuning strategy, and collect the electromagnetic parameters of the charging gun scenario; Calculate the current scenario magnetic flux density of the electromagnetic parameters of the charging gun scenario; Quantify the electromagnetic interference intensity of the electromagnetic parameters of the charging gun scenario and perform intensity distribution analysis to obtain the electromagnetic interference intensity distribution characteristics; Based on the current scenario magnetic flux density and the electromagnetic interference intensity distribution characteristics, perform spatial interference field distribution fitting to construct an electromagnetic interference distribution field.

[0032] In this embodiment, according to the non-linear frequency compensation strategy and the optimal duty cycle fine-tuning strategy, the charging control system is configured to achieve real-time monitoring and adjustment of the charging process. Ensure that the control system can respond to various current and voltage changes of the charging gun. Set up a control algorithm so that it can dynamically adjust the PWM frequency and duty cycle according to the real-time monitoring data to optimize the charging efficiency and safety. Install electromagnetic field sensors in the surrounding area of the charging gun, and these sensors can monitor the intensity and frequency characteristics of the electromagnetic field in real time. Commonly used sensors include Hall effect sensors and electromagnetic field probes. Ensure that the sensors have high sensitivity and fast response characteristics, and can instantaneously capture electromagnetic parameter data during the charging process, such as magnetic flux density and electric field strength. During the charging process, collect the electromagnetic parameter data around the charging gun in real time, including current, voltage, and electromagnetic field intensity. The data should be stored in the form of timestamps for subsequent analysis. Record that during the charging process, the current is 10 A, the voltage is 12 V, and the electromagnetic field intensity is 5 μT to ensure the integrity and traceability of the data. Magnetic flux density (B) is an important parameter describing the intensity of the electromagnetic field, usually calculated by the formula B = μH, where μ is the magnetic permeability and H is the magnetic field strength. The magnetic field strength can be directly measured by an electromagnetic field sensor. Determine the environmental conditions when the charging gun is working, such as temperature and humidity, in order to select an appropriate magnetic permeability value for calculation. Extract the magnetic field strength (H) from the real-time collected electromagnetic parameters, and apply the above formula to calculate the magnetic flux density of the current scenario. Ensure that the influence of environmental factors on the magnetic permeability is considered. If H is measured as 100 A / m during the charging process and μ is 4π × 10^-7 H / m at room temperature, then the magnetic flux density is calculated as B = (4π × 10^-7) × 100 = 1.26 × 10^-4 T. Analyze the electromagnetic parameters to quantify the electromagnetic interference intensity. The interference intensity can usually be evaluated by methods such as signal-to-noise ratio (SNR) and power spectral density (PSD). Use a spectrum analyzer to perform Fourier transform on the electromagnetic signal to obtain the frequency domain characteristics and analyze the interference intensity at different frequencies. Organize the interference intensity data into a distribution map, analyze the electromagnetic interference intensity at different positions and time points, and identify possible interference sources. If the interference intensity in a certain frequency band is significantly higher than other frequency bands, it may indicate the existence of a strong interference source. Record the interference intensity data at each position for spatial analysis. Record the quantization results and distribution characteristics of the interference intensity in the database to ensure the integrity of the data. Generate an interference intensity distribution map to visually display the change of the interference intensity. Create a heat map to show the distribution of electromagnetic interference intensity at different positions to facilitate the identification of strong interference areas. Select a suitable mathematical model for the distribution fitting of the spatial interference field. Commonly used models include Gaussian model, exponential decay model, etc. Selecting a suitable model can improve the fitting accuracy. If the interference field shows an obvious central radiation characteristic, the Gaussian model can be selected for fitting. Based on the recorded electromagnetic interference intensity distribution data, use optimization algorithms such as the least squares method to fit the selected model.By calculating the fitting parameters, the model can accurately describe the spatial distribution characteristics of the interference field. If the Gaussian model is used, the parameters obtained after fitting can be used to describe the central position and the extent of expansion of the interference field. Record the fitting results in the database to ensure the integrity and traceability of the data. Generate a spatial interference field distribution map to visually display the fitted interference field area. Draw a three-dimensional graph to show the distribution of the fitted electromagnetic interference field, which is convenient for observing the intensity and distribution characteristics of the interference field.

[0033] In this embodiment, the specific steps of step S6 are as follows: Predict the frequency control error based on the electromagnetic interference distribution field to generate a real-time frequency control error prediction value; Perform error feedforward compensation calculation based on the real-time frequency control error prediction value to obtain a frequency error feedforward compensation value; Adjust the PWM control parameters according to the frequency error feedforward compensation value to generate error compensation control parameters; Perform adaptive filtering of the control signal based on the error compensation control parameters to generate an intelligent PWM control optimization engine.

[0034] In this embodiment, based on the analysis of the electromagnetic interference distribution field, a prediction model for frequency control error is established. The error prediction model is generally established based on historical frequency control data and current electromagnetic interference characteristics. Common methods include linear regression, time series prediction, and machine learning models. Using a historical data set, considering factors such as current, charging time, and electromagnetic interference intensity, a linear regression model is trained to predict the frequency control error. During the charging process, relevant parameters such as current, voltage, and electromagnetic interference intensity are collected in real time and these data are input into the prediction model. Ensure that the data collection frequency is high enough to update the error prediction in real time. Record that at a certain moment, the current is 10A, the voltage is 12V, and the electromagnetic interference intensity is 5μT, and input these data into the model to calculate the frequency control error. Calculate the real-time frequency control error prediction value through the established error prediction model. This value reflects the frequency deviation caused by electromagnetic interference and helps with subsequent compensation adjustments. If the prediction value output by the model is -0.05Hz, it means that the current frequency is 0.05Hz lower than the target frequency. Feedforward compensation is a method of adjusting the control signal in advance by predicting the error. Based on the real-time frequency control error prediction value, calculate the error feedforward compensation value to ensure that the system can quickly respond to the frequency deviation. Usually, the compensation value can be calculated by the formula C_ff = K * E_pred, where C_ff is the compensation value, E_pred is the predicted frequency control error, and K is the compensation coefficient. Set an appropriate compensation coefficient K to balance the response speed and stability of the system. Usually, during the system debugging stage, the optimal K value is determined through experiments. If K is set to 2, then according to the prediction error E_pred = -0.05Hz, the calculated feedforward compensation value is C_ff = 2 * (-0.05) = -0.1Hz. Record the calculated frequency error feedforward compensation value in the database to ensure the integrity of the compensation data at each time point. The record should include information such as timestamp, prediction error, and feedforward compensation value. Generate a table containing time, frequency prediction error, and feedforward compensation value for subsequent analysis and visualization. Adjust the PWM control parameters according to the feedforward compensation value. The PWM control parameters mainly include duty cycle and frequency, and precise control of the charging process can be achieved through the adjustment of these parameters. If the current PWM frequency is 500Hz and the duty cycle is 40%, corresponding adjustments can be made according to the feedforward compensation value. Adjust the PWM frequency and duty cycle to compensate for the predicted frequency error. Set the new PWM frequency as F_new = F_current + C_ff, where F_current is the current frequency and C_ff is the feedforward compensation value. If the current frequency is 500Hz and the feedforward compensation value is -0.1Hz, then the new PWM frequency is F_new = 500Hz - 0.1Hz = 499.9Hz.Adaptive filtering is a method of dynamically adjusting the filter parameters, which can optimize the filtering effect in real time according to the changes of the input signal. Through adaptive filtering, the fluctuations caused by electromagnetic interference can be effectively suppressed, and the stability of the PWM control signal can be improved. Commonly used adaptive filtering algorithms include the least mean square error (LMS) algorithm and the recursive least squares (RLS) algorithm. According to the current PWM control parameters and the real-time control signal, the initial parameters of the adaptive filter are set. Usually, the learning rate and the filter order need to be set to achieve effective adaptive adjustment. Set the learning rate to 0.1 and the filter order to 4 to ensure that the filter can quickly respond to the changes of the signal. Input the real-time PWM control signal into the adaptive filter for filtering. The signal output by the filter is used as the final PWM control signal to achieve the optimized control of the charging process. If the output of the PWM signal processed by the filter is 499.8Hz, then this signal is sent as the control signal to the charging system.

[0035] In the present invention, there is also provided a high-precision PWM control device for implementing the high-precision PWM control method as described above, including: A load trend module for collecting real-time charging monitoring parameters of the vehicle battery and performing dynamic load trend evolution to construct a time-series load trend evolution curve; A non-linear compensation adjustment module for identifying the real-time PWM signal of the charging gun; and performing non-linear compensation adjustment based on the time-series load trend evolution curve to obtain a non-linear frequency compensation strategy; A frequency adjustment module for performing transient load wave dynamic potential mining and transient load response matching adjustment according to the time-series load trend evolution curve to construct a transient response frequency adjustment curve; A duty cycle fine-tuning module for calculating the maximum safe charging power according to the real-time charging monitoring parameters and performing intelligent PWM waveform fine-tuning on the transient response frequency adjustment curve to construct an optimal duty cycle fine-tuning strategy; An immediate charging control module for performing immediate charging control according to the non-linear frequency compensation strategy and the optimal duty cycle fine-tuning strategy, collecting the electromagnetic parameters of the charging gun scenario and fitting the electromagnetic interference distribution field; An error compensation module for predicting the frequency control error according to the electromagnetic interference distribution field and performing error compensation to generate an intelligent PWM control optimization engine.

[0036] The present invention also 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 high-precision PWM control method as described in any one of the above are implemented.

[0037] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the high-precision PWM control method described in any one of the above are implemented.

[0038] Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units are referred to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0039] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it is stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application essentially or the part that contributes to the prior art or all or part of the technical solution is embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that store program codes. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application documents within the present invention.

[0040] As described above, these are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A high-precision PWM control method, characterized in that: The following steps are involved: Step S1: collecting real-time charging monitoring parameters of the vehicle battery, performing dynamic load trend evolution, and constructing a time series load trend evolution curve; Step S2: Identify the real-time PWM signal of the charging gun; and perform nonlinear compensation adjustment based on the time-series load trend evolution curve to obtain a nonlinear frequency compensation strategy; Step S3: According to the time series load trend evolution curve, the transient load fluctuation situation is mined and the transient load response matching adjustment is performed to construct a transient response frequency adjustment curve; Step S4: Calculate the maximum safe charging power according to the real-time charging monitoring parameters, and perform intelligent PWM waveform fine-tuning on the transient response frequency adjustment curve to construct an optimal duty cycle fine-tuning strategy; Step S5: Perform instant charging control according to the nonlinear frequency compensation strategy and the optimal duty cycle fine-tuning strategy, collect the electromagnetic parameters of the charging gun scene and fit the electromagnetic interference distribution field; Step S6: Predict the frequency control error according to the electromagnetic interference distribution field, perform error compensation, and generate an intelligent PWM control optimization engine.

2. The high-precision PWM control method according to claim 1, characterized in that: The specific steps of step S1 are: Collect real-time charging monitoring parameters of vehicle batteries; Calculating the temperature variation of the real-time charging monitoring parameter; Performing time series temperature fluctuation fitting on the temperature variation amplitude to construct a multi-time point charging temperature fluctuation diagram; Extracting the real-time current and voltage index of the real-time charging monitoring parameter; Perform real-time charging load analysis based on real-time current and voltage index to obtain the real-time charging load characteristics of the vehicle battery; According to the multi-time point charging temperature fluctuation diagram, the real-time charging load characteristics of the vehicle battery are dynamically evolved to construct a time series load trend evolution curve.

3. The high-precision PWM control method according to claim 1, characterized in that: The specific steps of step S2 are: Identify the real-time PWM signal of the charging gun; Calculate the output frequency of the real-time PWM signal of the charging gun; Perform timing frequency fitting based on the output frequency to generate the charging gun PWM frequency curve; According to the real-time current and voltage index, nonlinear dynamic change mining is carried out to obtain the nonlinear dynamic change characteristics of battery electrical properties; Conduct deep current-voltage correlation analysis on the nonlinear dynamic change characteristics of battery electrical properties to obtain deep nonlinear dynamic correlation rules; Based on the deep nonlinear dynamic correlation law, the nonlinear compensation adjustment of the charging gun PWM frequency curve is performed to obtain a nonlinear frequency compensation strategy.

4. The high-precision PWM control method according to claim 1, characterized in that: The specific steps of step S3 are: Calculate the transient load variation amplitude at multiple time points according to the time series load trend evolution curve; Mining transient load fluctuation trends according to the transient load change amplitude to extract transient load fluctuation characteristics; Predicting future load fluctuations based on transient load fluctuation characteristics to obtain a transient load fluctuation prediction value; According to the transient load fluctuation prediction value, the transient load response matching adjustment is performed on the nonlinear compensation PWM frequency curve to construct a transient response frequency adjustment curve.

5. The high-precision PWM control method according to claim 1, characterized in that: The specific steps of step S4 are: Calculate the real-time remaining capacity value of the battery according to the real-time charging monitoring parameters; Calculate the maximum safe charging power based on the real-time remaining capacity of the battery and extract the dynamic maximum safe charging power; Calculating the waveform duty cycle of the transient response frequency adjustment curve; Intelligent PWM waveform fine-tuning is performed according to the dynamic maximum safe charging power and the waveform duty cycle, and an optimal duty cycle fine-tuning strategy is constructed.

6. The high-precision PWM control method according to claim 1, characterized in that: The specific steps of step S5 are: Perform instant charging control based on nonlinear frequency compensation strategy and optimal duty cycle fine-tuning strategy, and collect electromagnetic parameters of charging gun scenarios; Calculate the current scene magnetic flux density of the charging gun scene electromagnetic parameters; Quantify the electromagnetic interference intensity of the electromagnetic parameters of the charging gun scene and perform intensity distribution analysis to obtain the electromagnetic interference intensity distribution characteristics; Based on the current scene magnetic flux density and electromagnetic interference intensity distribution characteristics, the spatial interference field distribution is fitted to construct the electromagnetic interference distribution field.

7. The high-precision PWM control method according to claim 1, characterized in that: The specific steps of step S6 are: Perform frequency control error prediction according to the electromagnetic interference distribution field to generate a real-time frequency control error prediction value; Performing error feedforward compensation calculation according to the real-time frequency control error prediction value to obtain a frequency error feedforward compensation value; Adjust PWM control parameters according to the frequency error feedforward compensation value to generate error compensation control parameters; Based on the error compensation control parameters, the control signal is adaptively filtered to generate an intelligent PWM control optimization engine.

8. A high-precision PWM control device, characterized in that: The method for executing the high-precision PWM control method according to claim 1 comprises: The load trend module is used to collect the real-time charging monitoring parameters of the vehicle battery, perform dynamic load trend evolution, and construct a time-series load trend evolution curve; The nonlinear compensation adjustment module is used to identify the real-time PWM signal of the charging gun; and to make nonlinear compensation adjustments based on the time-series load trend evolution curve to obtain a nonlinear frequency compensation strategy; The frequency regulation module is used to mine the transient load fluctuation situation and adjust the transient load response according to the time series load trend evolution curve, and construct the transient response frequency regulation curve; A duty cycle fine-tuning module is used to calculate the maximum safe charging power according to the real-time charging monitoring parameters, and to perform intelligent PWM waveform fine-tuning on the transient response frequency adjustment curve to construct an optimal duty cycle fine-tuning strategy; Instant charging control module, used to perform instant charging control based on nonlinear frequency compensation strategy and optimal duty cycle fine-tuning strategy, collect electromagnetic parameters of charging gun scene and fit electromagnetic interference distribution field; The error compensation module is used to predict the frequency control error according to the electromagnetic interference distribution field, perform error compensation, and generate an intelligent PWM control optimization engine.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the high-precision PWM control 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 a processor, the steps of the high-precision PWM control method according to any one of claims 1 to 7 are implemented.

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