Method for measuring internal resistance of battery based on variable-frequency pulse type discharge method

Abstract

The invention relates to the technical field of internal resistance measurement, in particular to a method for measuring the internal resistance of a battery based on a variable-frequency pulse type discharge method. The method comprises the following steps: acquiring ripple voltage signals at two ends of a battery; extracting an alternating current component of the ripple voltage signal, and performing fast Fourier transform analysis on the alternating current component to obtain a fundamental frequency and a carrier frequency of the current ripple; respectively calculating time periods of the fundamental wave frequency and the carrier frequency to obtain a fundamental wave period and a carrier period; determining a reference period according to the fundamental wave period and the carrier wave period, and generating a dynamic discharge interval time range; acquiring an alternating current side voltage phase of the uninterruptible power supply; and performing zero value judgment on the alternating-current side voltage phase, and triggering a pulse discharge instruction when the alternating-current side voltage phase crosses zero. According to the invention, through dynamic adjustment of the discharge interval, frequency-variable pulse discharge and real-time internal resistance calculation, the measurement accuracy of the internal resistance of the battery is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of internal resistance measurement, and particularly to a method for measuring the internal resistance of a battery based on a pulse discharge method with variable frequency. Background Art

[0002] Traditional battery internal resistance test methods, such as the DC method or the AC method, although widely used, have certain limitations in terms of accuracy and adaptability. When measuring the internal resistance of a battery using the DC method, the influence of frequency response on the internal electrochemical process of the battery cannot be considered, and the test results are easily affected by factors such as temperature and aging. Although the AC method can overcome these problems to a certain extent, for complex battery systems, especially high-rate batteries or batteries with strong electrochemical characteristics, its frequency response characteristics are difficult to comprehensively reflect the change of the internal resistance of the battery. In order to improve the accuracy and sensitivity of the test, the pulse discharge method with variable frequency has emerged. This method can more comprehensively simulate the actual performance of the battery under different working conditions by applying a pulse signal with adjustable frequency, and capture the change of the internal resistance at different frequencies. However, the existing technology still uses slow internal resistance measurement for the measurement of the internal resistance, and cannot effectively filter the interference of external power supply fluctuations on the internal resistance measurement, resulting in low accuracy of the resistance measurement. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for measuring the internal resistance of a battery based on a pulse discharge method with variable frequency to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for measuring the internal resistance of a battery based on a pulse discharge method with variable frequency, the method includes the following steps:

[0005] Step S1: Obtain the ripple voltage signal at both ends of the battery; extract the AC component of the ripple voltage signal, and perform fast Fourier transform analysis on the AC component to obtain the fundamental frequency and carrier frequency of the current ripple;

[0006] Step S2: Calculate the time periods of the fundamental frequency and the carrier frequency respectively to obtain the fundamental period and the carrier period; confirm the reference period according to the fundamental period and the carrier period, and generate a dynamic discharge interval time range;

[0007] Step S3: Obtain the AC side voltage phase of the uninterruptible power supply; perform zero-crossing discrimination on the AC side voltage phase. When the AC side voltage phase passes through zero, trigger a pulse discharge command, and perform DC pulse discharge based on a preset time window to obtain pulse discharge control data;

[0008] Step S4: Collect the voltage transient drop value and the discharge current value at the battery terminal during the discharge process; calculate the real-time internal resistance based on the ratio of the voltage transient drop value to the discharge current value;

[0009] Step S5: Confirm the adjacent pulse discharge time intervals for the pulse discharge control data, dynamically adjust the pulse discharge time intervals according to the dynamic discharge interval time range, and generate variable pulse discharge data; use the variable pulse discharge data to calibrate the real-time internal resistance to generate the actual battery internal resistance.

[0010] By acquiring the ripple voltage signal and performing fast Fourier transform (FFT) analysis, the present invention can accurately separate the fundamental wave and carrier frequencies, thereby more accurately confirm the discharge timing and improve the accuracy of battery internal resistance measurement. Calculating the internal resistance using the ratio of the real-time voltage transient drop value to the discharge current value reduces the influence of external interference on the measurement accuracy. Combining with the voltage phase of the alternating current side of the uninterruptible power supply (UPS) to trigger the discharge at zero point reduces the interference to the power grid and ensures the stability of the discharge process. Dynamically adjust the pulse discharge time intervals according to the fundamental wave period and carrier period, optimize the discharge strategy, and improve the reliability of the measurement. Calibrate the real-time internal resistance through the variable pulse discharge data to eliminate the influence of factors such as ambient temperature and battery aging, and obtain the actual battery internal resistance closer to the true value. Since the measured actual battery internal resistance can dynamically reflect the health status of the battery, it can be used to predict the battery life and early warning of battery degradation, thereby improving the intelligent level of the battery management system (BMS). Adopting the zero-point trigger + pulse discharge method reduces the loss of the battery and avoids the overheating and safety hazards caused by long-term high-current discharge, ensuring the safety and sustainability of the battery measurement process. Therefore, the present invention improves the accuracy of battery internal resistance measurement through dynamic adjustment of the discharge interval, variable-frequency pulse discharge, and real-time internal resistance calculation.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Collect the voltage signal at both ends of the battery in real time to obtain the original battery voltage signal;

[0013] Step S12: Perform signal preprocessing on the original battery voltage signal to generate a standard battery voltage signal, where the signal preprocessing includes signal denoising, signal enhancement, and signal gain;

[0014] Step S13: Perform high-pass filtering on the standard battery voltage signal to remove the DC component, generate a ripple voltage signal, and extract the AC component of the ripple voltage signal;

[0015] Step S14: Perform fast Fourier transform analysis on the AC component to obtain the fundamental wave frequency and carrier frequency of the current ripple.

[0016] Through signal preprocessing (denoising, enhancement, gain), the present invention effectively reduces external noise and measurement errors, making the obtained standard battery voltage signal more stable and reliable. A high-pass filter is used to remove the DC component and only retain the useful ripple voltage signal, reducing low-frequency interference and improving the accuracy of analysis. Fast Fourier Transform (FFT) analysis is adopted to efficiently decompose the signal and accurately obtain the fundamental frequency and carrier frequency of the ripple signal, providing precise timing information for subsequent pulse discharge control. The ripple voltage signal can reflect the dynamic characteristics of the battery, and analyzing its spectral components can be used to evaluate the battery health status (such as internal impedance changes, aging conditions, etc.), improving the intelligence level of the Battery Management System (BMS). By adopting the method of real-time acquisition + signal processing, the measurement process can adapt to different working environments (such as temperature changes, current fluctuations, etc.), enhancing the measurement stability and reliability. The fundamental period and carrier period are accurately extracted, providing basic data for dynamically adjusting the discharge time interval in subsequent steps, making the discharge process more targeted and further improving the measurement effect.

[0017] Preferably, step S14 includes the following steps:

[0018] Step S141: Perform windowing processing on the ripple voltage signal according to the AC component to generate the windowed ripple signal;

[0019] Step S142: Perform fast Fourier transform on the windowed ripple signal to convert the signal from the time domain to the frequency domain, generating the ripple voltage spectrum data;

[0020] Step S143: Perform peak detection on the ripple voltage spectrum data, identify the frequency component corresponding to the highest amplitude, extract the fundamental frequency and its amplitude, and obtain the fundamental frequency;

[0021] Step S144: Perform secondary peak detection on the ripple voltage spectrum data, identify the main peaks in the high-frequency components, extract the carrier frequency and its amplitude, and obtain the carrier frequency.

[0022] Through windowing processing, the present invention reduces the spectral leakage phenomenon, enhances the frequency-domain characteristics of the signal, and makes the subsequent Fast Fourier Transform (FFT) analysis more accurate. Through FFT analysis, the time-domain signal is converted into a frequency-domain signal, and the fundamental wave and carrier components in the ripple voltage signal are accurately identified. Peak detection can accurately identify the most important fundamental wave frequency in the signal, ensuring the acquisition of the fundamental wave frequency and its amplitude, and providing an accurate time reference for battery discharge control. Sub-peak detection effectively identifies the main frequency components (i.e., carrier frequency) in the high-frequency part, which is crucial for the detailed regulation during the battery charge and discharge process and can optimize the discharge strategy and battery management. This method can provide more-dimensional frequency data for battery performance monitoring through accurate analysis of the ripple voltage spectrum data. By monitoring the change trends of the fundamental wave frequency and carrier frequency, it helps to discover the changes in the internal performance of the battery and improve the battery health assessment ability. The Fast Fourier Transform (FFT) and peak detection algorithms optimize the signal processing process and can meet the requirements of real-time monitoring of the battery state and optimization of the control strategy. Accurately obtaining the fundamental wave frequency and carrier frequency enables the pulse discharge control to be dynamically adjusted based on the specific battery frequency characteristics, improving the charge and discharge efficiency and lifespan of the battery.

[0023] Preferably, step S2 includes the following steps:

[0024] Step S21: Take the reciprocal of the fundamental wave frequency, calculate the time period corresponding to the fundamental wave frequency, and generate the fundamental wave period;

[0025] Step S22: Take the reciprocal of the carrier frequency, calculate the time period corresponding to the carrier frequency, and generate the carrier period;

[0026] Step S23: Calculate the least common multiple or greatest common divisor of the fundamental wave period and the carrier period to determine the period relationship between the two, and generate the ripple reference period;

[0027] Step S24: Conduct segmented analysis on the ripple reference period, and set the upper and lower limit ranges of the discharge interval according to the results of the segmented analysis, and generate the dynamic discharge interval time range.

[0028] By taking the reciprocals of the fundamental wave frequency and the carrier frequency respectively, the corresponding time periods are calculated, and the periodic characteristics of the frequency can be accurately obtained, providing an accurate time reference for the subsequent dynamic discharge process. Using the least common multiple or the greatest common divisor to calculate the relationship between the fundamental wave and carrier periods ensures time synchronization during the discharge process, avoiding period misalignment, and thus ensuring the stability of the battery discharge strategy. By performing segmented analysis on the ripple reference period, not only can the periodic changes between the fundamental wave and carrier frequencies be clearly understood, but also a reasonable discharge interval range can be set in combination with this information, making the battery discharge process more refined. The generated dynamic discharge interval time range can provide a basis for dynamically adjusting the discharge strategy, thereby improving the charge-discharge efficiency and stability of the battery. According to the period relationship and the results of segmented analysis, the discharge interval is dynamically adjusted, enabling the battery discharge control to be automatically optimized according to real-time frequency changes, enhancing the intelligent management ability of the battery. This method can dynamically adjust the discharge mode according to the working state of the battery, avoid unstable or unbalanced battery discharge states, and effectively extend the service life of the battery. By setting the upper and lower limit ranges of the discharge interval, premature battery wear caused by period disorders or over-discharge is avoided. The generation of the dynamic discharge interval time range helps to control the time accuracy of the discharge, thereby reducing the risk of damage to the battery caused by improper discharge.

[0029] Preferably, the segmented analysis of the ripple reference period includes:

[0030] Calculating the mean, standard deviation, and coefficient of variation of the ripple reference period; evaluating the change stability of the ripple reference period based on the mean, standard deviation, and coefficient of variation, and generating ripple reference period statistical data;

[0031] Segmenting the periodic change trend of the ripple reference period statistical data to generate segmented data of the ripple reference period;

[0032] Extracting the periodic characteristics from the segmented data of the ripple reference period to obtain the results of the segmented analysis.

[0033] By calculating the mean, standard deviation, and coefficient of variation of the ripple reference period, the present invention can quantitatively evaluate the stability and volatility of periodic data. The mean can provide a reference value for the discharge strategy, while the standard deviation and coefficient of variation reveal the fluctuation of the data. The introduction of the coefficient of variation helps to compare the relative change degrees of different periodic data, further evaluate the stability of the system, avoid extreme periodic fluctuations, and thus improve the reliability of the battery discharge process. By segmenting the periodic change trend of the statistical data of the ripple reference period, the change trend of the ripple period in different time periods can be observed more carefully, providing a clear basis for the subsequent adjustment of the dynamic discharge interval. The segmented data can help the battery management system make different response strategies at different times, thereby improving the response sensitivity and adaptability of the system. After segmented analysis, based on the results of periodic feature extraction, the change law of the ripple period can be captured more accurately. In this way, the system can flexibly adjust the discharge strategy according to different period segments, avoiding the decline of discharge efficiency or battery damage caused by unstable periods. The results of segmented analysis provide dynamic monitoring data of periodic changes, making the battery discharge process more refined and intelligent, and then optimizing the discharge performance and efficiency. By accurately evaluating the stability of periodic data and performing segmented analysis, the battery can be effectively prevented from being in an overly fluctuating periodic environment. Poor long-term periodic stability leads to rapid battery loss, while precise management can extend the service life of the battery. Regular periodic evaluation and adjustment help prevent overcharging and over-discharging of the battery caused by unstable periodic changes, thereby protecting the battery performance and reducing losses.

[0034] Preferably, step S3 includes the following steps:

[0035] Step S31: Collect the AC-side voltage of the uninterruptible power supply in real time, record the complete AC voltage waveform, and generate the UPS AC voltage time series data;

[0036] Step S32: Perform sinusoidal fitting on the UPS AC voltage time series data, extract the phase angle information, and generate the AC-side voltage phase of the uninterruptible power supply;

[0037] Step S33: Make a numerical determination of the AC-side voltage phase, detect the zero-crossing point of the voltage waveform from negative to positive or from positive to negative, and generate the UPS AC voltage zero-crossing discrimination data;

[0038] Step S34: Based on the UPS AC voltage zero-crossing discrimination data, send a pulse discharge trigger signal at the zero-crossing point of the voltage waveform to generate a pulse discharge instruction;

[0039] Step S35: Execute DC pulse discharge according to the pulse discharge instruction within a preset time window, and record the discharge time, amplitude, and duration to generate pulse discharge control data.

[0040] The present invention extracts the phase angle information of the AC voltage waveform through sine fitting, which can accurately obtain the phase information of the voltage waveform, providing reliable data support for the subsequent triggering of pulsed discharge. The accurate extraction of the phase angle makes the timing of pulsed discharge more accurate, avoiding discharge errors or instability caused by inaccurate timing determination. By numerically determining the phase of the AC voltage, the zero-crossing point of the voltage waveform can be accurately detected. The zero-crossing point is a crucial moment for triggering pulsed discharge, which can ensure that the timing of the pulsed discharge signal is synchronized with the AC voltage waveform, thereby achieving precise discharge control. The detection of the zero-crossing point ensures the correct triggering at the transition point of the voltage waveform from negative to positive or from positive to negative, reducing the discharge fluctuations caused by timing errors and improving the stability and reliability of the entire system. Through the discrimination and triggering of the voltage zero-crossing point, the timing control of pulsed discharge becomes more refined. Triggering the discharge signal at the voltage zero-crossing point can make the pulsed discharge more accurate, avoiding premature or late discharge triggering. Executing the DC pulsed discharge according to the preset time window can ensure that the discharge process occurs within the ideal time range, avoiding over-discharge or under-discharge situations and ensuring the stability and efficiency of the battery discharge process. Through the precise generation and execution of the pulsed discharge command, a high degree of control over the battery discharge process can be achieved, avoiding a decline in battery performance caused by unstable voltage changes. Recording the discharge time, amplitude, and duration further provides accurate monitoring data, which can be used to evaluate the battery discharge effect in real time, optimize the battery management strategy, and reduce unnecessary energy consumption. Precise pulsed discharge control can effectively ensure the stable operation of the uninterruptible power supply (UPS), providing a precise discharge response at critical moments and preventing the system from malfunctioning or interrupting due to power fluctuations. In addition, accurate zero-crossing determination and pulsed discharge strategies not only improve the battery efficiency but also enhance the response speed of the UPS system, enabling the system to respond promptly to voltage fluctuations and ensuring the continuous and stable operation of the system.

[0041] Preferably, step S4 includes the following steps:

[0042] Step S41: Synchronously collect the pulsed discharge control data, record the voltage change at the battery terminal at the moment of discharge, and generate the battery terminal voltage time series data;

[0043] Step S42: Analyze the characteristics of the battery terminal voltage time series data, identify the discharge trigger point, and calculate the voltage difference before and after discharge to extract the voltage transient drop value;

[0044] Step S43: Perform current measurement on the pulsed discharge control data to generate the discharge current value;

[0045] Step S44: Calculate the ratio of the voltage transient drop value to the discharge current value to obtain the real-time internal resistance of the battery. The formula for the ratio calculation is as follows:

[0046]

[0047] wherein, R battery is the real-time internal resistance of the battery, ΔV is the value of the transient voltage drop, and I is the discharge current.

[0048] Through the feature analysis of the battery terminal voltage time-series data, the present invention accurately identifies the discharge trigger point and calculates the value of the transient voltage drop, which provides high-precision voltage data for subsequent internal resistance calculation and ensures the accuracy of internal resistance measurement. By identifying the transient voltage drop and extracting the key voltage difference, the change of the battery voltage during the discharge process can be accurately captured, thereby reflecting the true state of the battery. Step S44 provides a ratio calculation formula based on the value of the transient voltage drop ΔV and the discharge current value I capable of calculating the internal resistance R of the battery in real time battery . This internal resistance value is an important parameter for evaluating the battery performance and health status. The calculation of the real-time internal resistance can timely reflect the electrical loss and health status inside the battery, which helps to timely detect the performance degradation or faults of the battery. The measurement of the discharge current value provides a reliable data basis for the calculation of the real-time internal resistance of the battery. The accurate measurement of the current value helps to improve the accuracy of the internal resistance calculation and ensures that the monitoring result reflects the true load condition of the battery. Through the accurately measured real-time internal resistance, the health status and remaining service life of the battery can be better evaluated, which provides key data for the battery management system (BMS), helps to dynamically adjust the charge and discharge strategies, avoid battery damage and extend its service life. The monitoring of the internal resistance value can help predict the trend of battery performance degradation, perform maintenance or replacement in time, and improve the operation reliability of the system. Through the precise synchronous acquisition of voltage and current and the measurement of internal resistance, the performance change of the battery during the discharge process can be responded to in real time. Such precise control not only helps to improve the battery use efficiency, but also can avoid the damage of the battery in the over-discharge or over-charge state. The precise internal resistance calculation helps to optimize the discharge process and reduce the energy loss caused by the too large internal resistance of the battery. By monitoring the resistance change in real time during the battery discharge process, the system can avoid inefficient energy conversion and improve the overall efficiency.

[0049] Preferably, the feature analysis of the battery terminal voltage time-series data in step S42 to identify the discharge trigger point includes:

[0050] Performing low-pass filtering on the battery terminal voltage time-series data to generate smooth voltage time-series data;

[0051] Performing steady-state analysis on the smooth voltage time-series data, extracting the average voltage value within a period of time before discharge, and generating pre-discharge steady-state voltage data;

[0052] Performing first-order difference operation on the smooth voltage time-series data to identify the mutation point at the moment of discharge and generate discharge trigger point data;

[0053] Perform validity verification on the discharge trigger point data to generate discharge trigger point validity verification data; screen the discharge trigger point data based on the discharge starting point validity verification data to obtain the discharge trigger point.

[0054] In the present invention, by performing low-pass filtering on the battery terminal voltage time-series data, high-frequency noise is effectively removed, and smoother voltage time-series data is generated. This provides a cleaner and more accurate data source for subsequent analysis, reduces external interference, and improves the stability and reliability of the voltage data. Steady-state analysis extracts the average voltage value before discharge, ensuring that the steady-state voltage before battery discharge can be accurately extracted. This provides reference voltage data for subsequent analysis of voltage transient changes and helps accurately capture changes in the battery state. Through first-order difference operation, mutation points in the voltage signal, that is, the instant of battery discharge, can be accurately identified. This method can keenly capture sharp changes in voltage and ensure the accurate positioning of the discharge trigger point. The identified discharge trigger point provides key data for subsequent internal resistance calculation and pulsed discharge regulation. By performing validity verification on the discharge trigger point data, inaccurate trigger points are effectively excluded, ensuring that the finally obtained discharge trigger point is reliable. This process eliminates inaccurate data caused by noise or other interference sources through a correction and screening mechanism, ensuring precise control of the discharge process. Through the combination of steady-state analysis, difference operation, and validity verification, key signals during the battery discharge process can be extracted more precisely. This multi-level and refined analysis method can improve the adaptability and accuracy of the system under various battery states. Accurately identifying the discharge trigger point can provide the battery management system with the accurate discharge start time, enabling subsequent discharge control, internal resistance monitoring, etc. to be synchronized more precisely. In this way, the discharge process can be optimized and the battery management strategy can be adjusted in real time, reducing energy waste and improving the battery usage efficiency. By performing validity verification and screening on the discharge trigger point, misjudgment caused by noise or incorrect data is minimized, thereby reducing risks during system operation. It ensures that each discharge operation is carried out at the correct time, enhancing the reliability of the battery management system. The accurate discharge trigger point can not only help determine the working state of the battery but also provide real-time feedback for the battery health management. This helps monitor the long-term performance of the battery and adjust the management strategy according to real-time data, extending the battery service life.

[0055] Preferably, step S5 includes the following steps:

[0056] Step S51: Perform time-series analysis on the pulsed discharge control data, extract the trigger time points of adjacent pulsed discharges, and calculate their time intervals to generate pulsed discharge time interval data;

[0057] Step S52: Evaluate the volatility of the pulse discharge time intervals for the pulse discharge time interval data to generate pulse discharge time stability data;

[0058] Step S53: Dynamically adjust the pulse discharge time interval data for two adjacent times based on the pulse discharge time stability data to obtain pulse discharge variable data, where the dynamic adjustment specifically is: making the pulse discharge time interval data for two adjacent times fall within a preset multiple range of the dynamic discharge interval time range and not forming an integer multiple relationship with the fundamental wave period and the carrier wave period;

[0059] Step S54: Use the pulse discharge variable data to perform internal resistance calibration on the real-time internal resistance to generate the actual battery internal resistance so as to execute the real battery internal resistance measurement operation.

[0060] Through performing time series analysis on the pulse discharge control data, the present invention can accurately extract the trigger time points of adjacent pulse discharges and calculate the time intervals therebetween. This step ensures the accuracy of the pulse discharge time interval data, providing a reliable time data basis for subsequent discharge control and internal resistance calibration. By evaluating the volatility of the pulse discharge time interval data, pulse discharge time stability data is generated. This evaluation helps to discover unstable factors during the discharge process and provides a basis for subsequent optimization and adjustment, ensuring the stability of the battery discharge process. Based on the pulse discharge time stability data for dynamic adjustment, it can be ensured that the pulse discharge time intervals for two adjacent times are within the preset dynamic discharge interval time range and the formation of an integer multiple relationship between the fundamental wave period and the carrier wave period is avoided. Through this adjustment, periodic errors and resonance effects during the discharge process can be reduced, the accuracy of the discharge operation can be improved, and system instability caused by frequency matching can be prevented. By using the pulse discharge variable data to perform internal resistance calibration on the real-time internal resistance, the battery internal resistance can be measured more precisely dynamically. By adjusting the pulse discharge time interval data and the internal resistance measurement strategy in real time, the actual battery internal resistance can be obtained, improving the accuracy and reliability of the internal resistance measurement, which is of great significance for battery performance monitoring and health assessment. During the pulse discharge process, by adjusting the time interval, the discharge process can be precisely controlled, avoiding over-discharge or under-discharge of the battery caused by improper time intervals, thereby improving the working efficiency of the battery and extending its service life. By dynamically adjusting the pulse discharge time interval and performing internal resistance calibration, the charge and discharge strategy and health status monitoring of the battery can be continuously optimized. In this way, the battery management system can respond to changes in the battery state in real time and perform more reasonable regulation, thereby improving the overall efficiency of the system and the battery life. By dynamically adjusting the pulse discharge time interval and performing precise internal resistance calibration, errors caused by frequency mismatch or too short / too long time intervals can be effectively reduced, the risks during the discharge process can be lowered, and the safety and reliability of the battery operation can be improved.

[0061] Preferably, step S52 includes the following steps:

[0062] Step S521: Detect outliers in the pulse discharge time interval data to generate abnormal discharge time interval data;

[0063] Step S522: Perform differential calculation on the pulse discharge time interval data, analyze the change amplitude of adjacent intervals, and calculate the volatility to generate pulse discharge time interval volatility data;

[0064] Step S523: Perform weighted scoring based on the pulse discharge time interval data, abnormal discharge time interval data, and pulse discharge time interval volatility data to evaluate the stability of the discharge time interval and generate pulse discharge time stability data.

[0065] By detecting outliers in the pulse discharge time interval data, the present invention can effectively identify and eliminate abnormal discharge time interval data. This process helps to exclude data anomalies caused by external interference, equipment failures, or other abnormal factors, ensuring the accuracy and reliability of subsequent analysis results. By performing differential calculation on the pulse discharge time interval data and analyzing the change amplitude of adjacent intervals, the volatility of the pulse discharge time interval can be calculated. This process can reveal unstable factors during the discharge process, identify time periods with large fluctuations, and provide an important basis for subsequent stability evaluation and optimization. Combining the abnormal discharge time interval data, pulse discharge time interval data, and pulse discharge time interval volatility data for weighted scoring can more comprehensively and accurately evaluate the stability of the discharge time interval. This weighted scoring method considers multiple factors, making the stability evaluation of the discharge time interval more accurate and better reflecting the stability during the actual discharge process. Based on the pulse discharge time stability data, unstable segments during the discharge process can be effectively identified, providing a basis for subsequent dynamic adjustment of the pulse discharge time interval. By monitoring and analyzing the stability of the discharge time interval, the control strategy of pulse discharge can be optimized to ensure that the battery is charged and discharged in a relatively stable state, reducing the risks brought by unstable factors. Through systematic analysis and evaluation of the pulse discharge time interval data, precise control of the battery discharge process can be achieved, reducing discharge errors caused by unstable fluctuations, ensuring the stable operation of the battery system, and improving the reliability and efficiency of discharge. Through outlier detection and volatility analysis, potential discharge problems can be identified in advance. Specifically, if there are large fluctuations or abnormal interval data, the system can detect them in a timely manner and take corresponding corrective measures to avoid the expansion of problems, which is helpful for the long-term health and stability of the battery. By evaluating and analyzing the stability of the pulse discharge time interval, the battery management system can be further optimized to achieve more precise charge and discharge control, thereby improving the intelligence and adaptability of battery management. Description of the Drawings

[0066] Figure 1 It is a schematic diagram of the step flow of a method for measuring the internal resistance of a battery based on a pulse discharge method with variable frequency;

[0067] Figure 2 It is Figure 1 a detailed implementation step flow diagram of step S3 in

[0068] Figure 3 It is Figure 1 a detailed implementation step flow diagram of step S4 in

[0069] The realization, functional characteristics and advantages of the purpose of the present invention will be further described in conjunction with the embodiments with reference to the attached drawings. Specific embodiments

[0070] The technical method of the present invention will be clearly and completely described below with reference to the attached drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0071] In addition, the attached drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, so the repeated description of them will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0072] It should be understood that although the terms "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0073] To achieve the above object, please refer to Figures 1 to 3 , a method for measuring the internal resistance of a battery based on a pulse discharge method with variable frequency, the method includes the following steps:

[0074] Step S1: Obtain the ripple voltage signal across the battery; extract the AC component of the ripple voltage signal and perform fast Fourier transform analysis on the AC component to obtain the fundamental frequency and carrier frequency of the current ripple;

[0075] Step S2: Calculate the time periods of the fundamental frequency and carrier frequency respectively to obtain the fundamental period and carrier period; confirm the reference period based on the fundamental period and carrier period, and generate a dynamic discharge interval time range;

[0076] Step S3: Obtain the AC side voltage phase of the uninterruptible power supply; perform zero-crossing discrimination on the AC side voltage phase. When the AC side voltage phase crosses zero, trigger a pulse discharge command and perform DC pulse discharge based on a preset time window to obtain pulse discharge control data;

[0077] Step S4: Collect the voltage transient drop value and discharge current value at the battery terminal during the discharge process; calculate the real-time internal resistance based on the ratio of the voltage transient drop value to the discharge current value;

[0078] Step S5: Confirm the adjacent pulse discharge time intervals for the pulse discharge control data, and dynamically adjust the pulse discharge time intervals according to the dynamic discharge interval time range to generate pulse discharge variable data; use the pulse discharge variable data to calibrate the real-time internal resistance to generate the actual battery internal resistance.

[0079] By obtaining the ripple voltage signal and performing fast Fourier transform (FFT) analysis, the present invention can accurately separate the fundamental frequency and carrier frequency, thereby more accurately confirm the discharge timing and improve the accuracy of battery internal resistance measurement. Calculating the internal resistance using the ratio of the real-time voltage transient drop value to the discharge current value reduces the influence of external interference on the measurement accuracy. Combining with the AC side voltage phase of the uninterruptible power supply (UPS) to trigger discharge at zero point reduces the interference to the power grid and ensures the stability of the discharge process at the same time. Dynamically adjust the pulse discharge time intervals according to the fundamental period and carrier period, optimize the discharge strategy, and improve the reliability of the measurement. Calibrate the real-time internal resistance through the pulse discharge variable data to eliminate the influence of factors such as ambient temperature and battery aging, and obtain the actual battery internal resistance closer to the true value. Since the measured actual battery internal resistance can dynamically reflect the health status of the battery, it can be used to predict the battery life and give early warning of battery degradation, thereby improving the intelligence level of the battery management system (BMS). Adopting the zero-point trigger + pulse discharge method reduces the loss of the battery, and at the same time avoids the overheating and safety hazards caused by long-time large-current discharge, ensuring the safety and sustainability of the battery measurement process. Therefore, the present invention improves the accuracy of battery internal resistance measurement by dynamically adjusting the discharge interval, frequency-variable pulse discharge, and real-time internal resistance calculation.

[0080] In the embodiment of the present invention, refer to Figure 1As shown in the figure, it is a schematic diagram of the step flow of a method for measuring the internal resistance of a battery based on a variable-frequency pulsed discharge method according to the present invention. In this embodiment, the method for measuring the internal resistance of a battery based on a variable-frequency pulsed discharge method includes the following steps:

[0081] Step S1: Obtain the ripple voltage signal across the battery; extract the AC component of the ripple voltage signal and perform a fast Fourier transform analysis on the AC component to obtain the fundamental frequency and carrier frequency of the current ripple;

[0082] In the embodiment of the present invention, the ripple voltage signal is measured across the battery by using a high-precision oscilloscope or a data acquisition system. A high sampling rate (such as ≥100 kHz) is adopted to ensure the capture of high-frequency ripple components. The environmental noise is suppressed by a filter to improve the signal quality. The DC bias of the collected original ripple voltage signal is removed to obtain a pure AC component: Among them, is the DC component of the signal, which can be removed by moving average filtering or high-pass filtering (such as a cut-off frequency of 1 Hz). Window function processing (such as a Hanning window) is adopted to reduce spectral leakage. Calculate the FFT transform: Among them is the Fourier transform operation. Analyze the spectral amplitude, extract the fundamental frequency f b and the carrier frequency f c , and determine the corresponding frequency component based on the maximum peak value of the amplitude: (high-frequency region), record the fundamental frequency and carrier frequency for subsequent battery state evaluation.

[0083] Step S2: Calculate the time periods of the fundamental frequency and the carrier frequency respectively to obtain the fundamental period and the carrier period; confirm the reference period according to the fundamental period and the carrier period, and generate a dynamic discharge interval time range;

[0084] In the embodiment of the present invention, through the fundamental frequency f b and the carrier frequency f c obtained in step S1, calculate their time periods: Ensure the accuracy of the calculation of the fundamental period and the carrier period to avoid the influence of noise. There are various methods to select the reference period, usually including: the least common multiple (LCM) method (suitable for discrete signal analysis): T ref = lcm(T b , T c ); the weighted average method (suitable for continuous signal analysis): T ref = αT b + βT c , where α and β are weighting factors, usually set as α + β = 1. Set the discharge interval time range [T min , Tmax : T min = γ·T ref , T max = δ·T ref , where γ and δ are dynamically adjustable coefficients (specifically, 0.8 ≤ γ ≤ 1.2, 1.2 ≤ δ ≤ 1.5), ensuring a reasonable discharge interval. This range can be used to optimize the battery discharge strategy, specifically for dynamic load regulation, pulse discharge optimization, etc.

[0085] Step S3: Obtain the AC side voltage phase of the uninterruptible power supply; perform zero-crossing discrimination on the AC side voltage phase. When the AC side voltage phase passes through zero, trigger a pulse discharge command and execute DC pulse discharge based on a preset time window to obtain pulse discharge control data;

[0086] In the embodiment of the present invention, a voltage signal is obtained from the AC side of the uninterruptible power supply (UPS), and the phase angle of this signal is calculated in real time. The AC voltage signal changes periodically and can generally be represented as a sine wave, containing amplitude, frequency, and phase information. The phase angle reflects the position of the AC voltage at a specific moment and can be used for subsequent synchronization control. Within one cycle of the AC voltage, the voltage passes through zero twice, namely positive zero-crossing (changing from negative to positive) and negative zero-crossing (changing from positive to negative). The zero-crossing discrimination method is used to detect the voltage zero-crossing point, which can be achieved through software algorithms (such as digital signal processing) or hardware circuits (such as optocoupler sensors). Once the zero-crossing point is detected, it can be used to trigger the pulse discharge operation. When the AC voltage passes through zero, the system immediately triggers a pulse discharge command, causing the battery on the DC side to release a certain amount of energy. The role of pulse discharge is to improve the discharge efficiency and reduce the impact on the power grid. To precisely control the discharge process, a time window is set, that is, the start time and end time of the discharge. The setting of the time window can adopt a fixed duration (specifically 2 milliseconds) or be dynamically adjusted (optimized according to the power grid load situation). Within this time window, the battery discharges at high power through the DC side control module of the UPS. After the discharge is completed, the system will record the relevant data of this pulse discharge, including the start time, end time, battery voltage, current, etc. These data can be used to optimize the discharge strategy of the UPS, improving the system stability and discharge efficiency.

[0087] Step S4: Collect the voltage transient drop value and discharge current value at the battery terminal during the discharge process; calculate the real-time internal resistance based on the ratio of the voltage transient drop value to the discharge current value;

[0088] In the embodiment of the present invention, during the discharge process of an uninterruptible power supply (UPS) system, the battery voltage will drop instantaneously, and this change is called voltage transient drop. To accurately measure this change, the system needs to collect the battery terminal voltage before and after discharge in real time. The specific method is as follows: Before discharge, record the static voltage of the battery (i.e., the voltage when not discharging). At the moment of discharge, record the voltage change of the battery and extract the voltage value at the initial moment of discharge. Calculate the voltage transient drop value, that is, the voltage difference before and after discharge, and this value can be used for subsequent calculation of the battery internal resistance. During the discharge process, the output current of the battery will change with the load. To accurately measure the discharge current, a current sensor (such as a Hall current sensor, a shunt resistor, or a current transformer) can be used to monitor the discharge current in real time and record the current value at the moment of discharge. The specific method is as follows: At the same time as triggering the discharge, collect the discharge current value output by the battery. Record the current change situation to ensure data accuracy for subsequent calculation of the real-time internal resistance of the battery. The internal resistance of the battery (i.e., the internal resistance) determines its discharge capacity and efficiency. Through the voltage transient drop value and the discharge current value, the real-time internal resistance of the battery can be calculated: Battery real-time internal resistance = Voltage transient drop value ÷ Discharge current value; this calculation method is based on Ohm's law and can accurately reflect the impedance characteristics inside the battery. The calculated real-time internal resistance data will be stored in the battery management system (BMS) for analyzing the health status of the battery. If the internal resistance of the battery increases over time, it indicates that the battery is aging and the internal chemical reaction efficiency is decreasing. If the internal resistance of the battery is too high, it will cause the battery voltage to drop too fast, affecting the power supply stability of the UPS.

[0089] Step S5: Confirm the time interval between adjacent pulse discharges for the pulse discharge control data, and dynamically adjust the pulse discharge time interval according to the dynamic discharge interval time range to generate pulse discharge variable data; use the pulse discharge variable data to calibrate the internal resistance of the real-time internal resistance to generate the actual battery internal resistance.

[0090] In the embodiment of the present invention, by confirming the time interval between adjacent pulse discharges, the system will record the timestamps of each pulse discharge, and these timestamps reflect the triggering moments of each discharge pulse. Calculate the time interval between adjacent pulse discharges: Record the timestamp of the current pulse discharge, denoted as t current , record the timestamp of the previous pulse discharge, denoted as t prev , calculate the time interval between adjacent pulse discharges: T pulse =t current -t prev , where T pulse is the time interval between two adjacent pulse discharges. Set the upper and lower limits of the time interval of pulse discharge, that is, the maximum time interval T max and the minimum time interval T min , and these values can be set according to system requirements and battery characteristics. Calculate the time interval T between adjacent pulse dischargespulse and dynamically adjust according to the preset maximum and minimum time intervals: If T pulse < T min , then increase the time interval and adjust T pulse = T min ; If T pulse > T min , then increase the time interval and adjust T pulse = T max ; If T pulse is between the maximum and minimum time ranges, it remains unchanged. The dynamically adjusted pulse discharge time interval will be used as variable pulse discharge data for internal resistance calibration in subsequent steps. Calculate the real-time internal resistance R measured : Set a calibration factor K, which is obtained based on historical data, experimental calibration, or previous measurement results. The role of the calibration factor is to correct the internal resistance measurement error caused by dynamically adjusting the pulse discharge time interval to ensure a more accurate internal resistance value. Based on the calibration factor, correct the measured internal resistance: R actual = R measured × K, where R actual is the actual internal resistance of the calibrated battery, with the unit of ohm (Ω). Store the actual battery internal resistance data in the battery management system (BMS) for subsequent analysis and monitoring. Continuously monitor the change trend of the actual battery internal resistance. If the battery internal resistance gradually increases, it means that the battery has aged or its performance has declined, and the system can issue an alarm in advance and trigger a maintenance strategy. Optimize the battery management strategy of the UPS system according to the actual battery internal resistance. Specifically, adjust the charge and discharge cycle, control the discharge depth, etc., to extend the service life of the battery. Feed back the internal resistance data to the battery management system and perform continuous system optimization based on real-time data. Improve the stability and performance of the entire UPS system by optimizing the discharge interval and internal resistance calibration.

[0091] Preferably, step S1 includes the following steps:

[0092] Step S11: Real-time collect the voltage signal across the battery to obtain the original battery voltage signal;

[0093] Step S12: Perform signal preprocessing on the original battery voltage signal to generate a standard battery voltage signal, where the signal preprocessing includes signal denoising, signal enhancement, and signal gain;

[0094] Step S13: Perform high-pass filtering on the standard battery voltage signal to remove the DC component, generate a ripple voltage signal, and extract the AC component of the ripple voltage signal;

[0095] Step S14: Perform fast Fourier transform analysis on the AC component to obtain the fundamental frequency and carrier frequency of the current ripple.

[0096] In the embodiments of the present invention, a high-precision voltage sensor (such as a voltage acquisition card or a high-precision analog-to-digital converter) is connected to both ends of the battery to collect the voltage signal of the battery in real time. Through a data acquisition system (specifically a DAQ card, an embedded system, etc.), the voltage values at both ends of the battery are recorded in real time at a high frequency to ensure the accuracy and real-time nature of the data. Set the sampling frequency, and select an appropriate sampling rate according to the working characteristics of the battery, specifically 10 kHz or higher, so as to capture the rapidly changing voltage signal. Use a digital filter (such as a low-pass filter or a median filter) to remove the noise from the external environment or the high-frequency interference generated by the battery system itself. By analyzing the signal source, identify the typical noise frequency range of the battery (such as electromagnetic interference, noise caused by temperature changes, etc.), and apply specific filtering techniques to remove this noise. Apply amplitude amplification technology to the battery voltage signal to enhance the signal's recognizability by amplifying the signal intensity, facilitating subsequent signal processing and analysis. Use methods based on wavelet transform, Fourier transform, or other frequency-domain analysis methods to enhance the signal, highlighting the effective information part and suppressing the invalid or interfering signals. Adjust the amplitude of the voltage signal through a gain control circuit to ensure that the signal intensity is within an appropriate range to avoid signal saturation or being too weak. Automatically adjust the gain parameter to ensure that the signal gain is consistent with the actual state of the battery, thereby ensuring the accuracy of signal processing. After completing the denoising, enhancement, and gain processing, generate a standardized battery voltage signal as the basic signal for subsequent analysis. Use a high-pass filter with an appropriate cut-off frequency (such as a Butterworth filter, a Chebyshev filter, etc.) to remove the DC component from the battery voltage signal. Usually, select a cut-off frequency lower than the ripple frequency of the battery to ensure that the signal that does not affect the normal operation of the battery. According to the working frequency and ripple characteristics of the battery, set an appropriate cut-off frequency, generally select a frequency range higher than the DC component but lower than the ripple voltage. Filter out the DC component in the signal through the high-pass filter to obtain the ripple voltage signal. This signal reflects the fluctuating components inside the battery due to load changes, charging, etc. Further extract the AC components from the ripple voltage signal, which are usually caused by battery current and load fluctuations. Use the fast Fourier transform (FFT) algorithm to convert the ripple voltage signal into the frequency domain. Through FFT analysis, the frequency components of the signal can be obtained, and characteristics such as its fundamental frequency, harmonic frequency, and carrier frequency can be clarified. According to the sampling frequency and length of the signal, reasonably set the window size and overlap step of the FFT to ensure the balance between the resolution of the spectrum analysis and the calculation efficiency. Extract the fundamental frequency (i.e., the sine wave with the lowest frequency) from the FFT analysis result, which is usually related to the normal working mode of the battery. Identify the carrier frequency (i.e., the high-frequency component), which is usually caused by the high-frequency oscillation generated during the charging and discharging process of the battery. The extraction of the carrier frequency helps to analyze the load response and internal health status of the battery.

[0097] Preferably, step S14 includes the following steps:

[0098] Step S141: Perform windowing processing on the ripple voltage signal according to the AC component to generate a windowed ripple signal;

[0099] Step S142: Perform a fast Fourier transform on the windowed ripple signal to convert the signal from the time domain to the frequency domain, generating ripple voltage spectrum data;

[0100] Step S143: Perform peak detection on the ripple voltage spectrum data, identify the frequency component corresponding to the highest amplitude, extract the fundamental frequency and its amplitude to obtain the fundamental frequency;

[0101] Step S144: Perform secondary peak detection on the ripple voltage spectrum data, identify the main peaks in the high-frequency components, extract the carrier frequency and its amplitude to obtain the carrier frequency.

[0102] In the embodiments of the present invention, a window function suitable for the characteristics of the ripple voltage signal is selected. Commonly used window functions include rectangular window, Hanning window, Hamming window, Blackman window, etc. The selection of the window function should be adjusted according to the length of the signal, spectral leakage, and time-frequency resolution requirements. Multiply the selected window function point by point with the ripple voltage signal. The window function reduces the waveform discontinuity at both ends of the signal, reduces the influence of spectral leakage, and makes the frequency-domain analysis more accurate. The windowing operation makes the time window of the signal smoother, thereby reducing unnecessary frequency components in the frequency-domain analysis. Store the windowed signal as the input signal for subsequent fast Fourier transform (FFT) processing. Ensure that no data is lost during the signal processing and maintain the integrity of the signal. Use the FFT algorithm to transform the windowed signal. The FFT converts the time-domain signal into a frequency-domain signal. The core principle of the FFT is to calculate the frequency components of the signal through an efficient algorithm to obtain the frequency-domain data. When applying the FFT, select an appropriate sampling frequency and the number of sampling points to ensure that the transformation result has sufficient frequency resolution. Generally, the sampling frequency should be at least twice the highest frequency of the signal. Select an appropriate number of points (such as 1024, 2048 points, etc.) to ensure the accuracy of the FFT analysis. The output of the FFT is a complex number array representing the amplitude and phase of different frequency components. Usually, the amplitude is of concern, so calculate the modulus length of the complex number to obtain the amplitude spectrum of each frequency component. After the spectrum data is generated, it can be displayed graphically to show the energy distribution of the signal at different frequencies, or directly store the spectrum data for further analysis. Identify the maximum amplitude frequency in the spectrum by calculating the amplitude values of each frequency point in the spectrum data. A simple peak detection algorithm can be used, such as finding the local maximum point of the amplitude value. The maximum amplitude frequency usually corresponds to the fundamental frequency, which is the main oscillation frequency of the signal. At this time, extract this frequency and its corresponding amplitude value from the spectrum data as the fundamental frequency. In addition to the frequency, the amplitude data of the fundamental wave should also be recorded to ensure that its intensity can be comprehensively considered in subsequent analysis. Usually, the amplitude of the fundamental wave directly affects the amplitude characteristics of the ripple signal. Mark the fundamental frequency and its amplitude in the spectrogram to ensure that the fundamental frequency can be quickly identified and used in subsequent processing. In the spectrum data, the fundamental frequency usually corresponds to the main frequency component of the ripple signal, and near this fundamental frequency or in a higher frequency region, sub-peaks usually appear. The sub-peaks can represent, for example, the carrier frequency or other frequency components. Identify these frequency components by detecting the maximum frequency value after the fundamental wave. The sub-peak values are usually related to the carrier frequency, so identify these frequency points in the spectrum and extract their values. The carrier frequency is not always the second largest peak in the spectrum, especially in multi-carrier signals, and multiple detections need to be combined with the specific spectrum shape. Similar to the fundamental wave, the amplitude of the carrier frequency also needs to be extracted. By detecting the amplitude of the sub-peak, ensure the accuracy of its amplitude characteristics, and these amplitude data are crucial for subsequent signal analysis.Similarly, the carrier frequency and its amplitude should be marked in the spectrogram and can be stored in the system database for subsequent applications and data analysis.

[0103] Preferably, step S2 includes the following steps:

[0104] Step S21: Take the reciprocal of the fundamental frequency, calculate the time period corresponding to the fundamental frequency, and generate the fundamental period;

[0105] Step S22: Take the reciprocal of the carrier frequency, calculate the time period corresponding to the carrier frequency, and generate the carrier period;

[0106] Step S23: Calculate the least common multiple or greatest common divisor of the fundamental period and the carrier period to determine their period relationship and generate the ripple reference period;

[0107] Step S24: Perform segmented analysis on the ripple reference period, and set the upper and lower limit ranges of the discharge interval according to the results of the segmented analysis to generate the dynamic discharge interval time range.

[0108] In the embodiments of the present invention, the reciprocal calculation is performed on the fundamental frequency to obtain the corresponding time period. The fundamental frequency is usually the signal frequency of the lower frequency in the system, and the result of its reciprocal calculation will obtain the time period at this frequency. Through this period, the periodic characteristics of the fundamental wave on the time axis can be further understood. At this time, the generated fundamental period is the period time of the fundamental wave signal, indicating the time required for the fundamental wave signal to start from one period to the end of the next period. Similarly, the carrier frequency is the signal frequency of the higher frequency in the system. Taking the reciprocal of the carrier frequency to obtain its corresponding time period, that is, the carrier period. The periodic characteristics of the carrier signal are determined by its frequency, and the obtained carrier period after taking the reciprocal indicates the periodic change of the carrier signal on the time axis. The relationship between the fundamental period and the carrier period is crucial, so it is necessary to calculate the relationship between these two periods through the least common multiple (LCM) or greatest common divisor (GCD). The least common multiple calculation can help determine the periodic points where the fundamental wave and the carrier are synchronized on the time axis, while the greatest common divisor can be used to find their common periodic characteristics. According to the calculation results, the ripple reference period is generated, which represents the common periodicity of the fundamental period and the carrier period. This reference period is used for subsequent discharge process control. After determining the ripple reference period, by performing segmented analysis on it, further study its change characteristics in different time periods. The segmented analysis will help identify the changes in the periodic characteristics in different time periods. Based on these analysis results, the upper and lower limit ranges of the discharge interval can be set, that is, the reasonable interval of the discharge time within a specific time window. The dynamic discharge interval time range is adjusted and optimized according to the actual system requirements and operating conditions based on the ripple reference period.

[0109] Preferably, the segmented analysis of the ripple reference period includes:

[0110] Calculating the mean value, standard deviation and coefficient of variation of the ripple reference period; evaluating the change stability of the ripple reference period according to the mean value, standard deviation and coefficient of variation, and generating ripple reference period statistical data;

[0111] Segmenting the period change trend of the ripple reference period statistical data to generate ripple reference period segmented data;

[0112] Extracting the period characteristics from the ripple reference period segmented data to obtain the result of the segmented analysis.

[0113] In the embodiment of the present invention, assuming that the measurement results of the ripple reference period within a period of time are: 20 milliseconds, 21 milliseconds, 19 milliseconds, 22 milliseconds, 18 milliseconds, 20 milliseconds, 19 milliseconds, 21 milliseconds, 20 milliseconds, 20 milliseconds. Calculate the mean value (average period) of these values to be 20 milliseconds, calculate the standard deviation (used to measure the degree of period change) to be 0.45 milliseconds, calculate the coefficient of variation (the ratio of the standard deviation to the mean value, reflecting the relative degree of change) to be 2.25%, and generate the ripple reference period statistical data: the mean value is 20 milliseconds, the standard deviation is 0.45 milliseconds, and the coefficient of variation is 2.25%. According to the mean value, standard deviation and coefficient of variation, the change trend of the ripple reference period can be segmented. Assuming that through analysis, the change trend of the ripple reference period can be divided into the following segments: The first segment (stable interval): The change amplitude of the ripple reference period is small, the change range is ±0.5 milliseconds, and the period is relatively stable. The second segment (fluctuation interval): The change amplitude of the ripple reference period is large, and the fluctuation range is ±1 millisecond. The third segment (unstable interval): The ripple reference period fluctuates violently, and the change amplitude exceeds ±1 millisecond. Extract the period characteristics within each segmented area. Specifically: for the stable interval, extract that the standard deviation and coefficient of variation of the period are small and the fluctuation is relatively stable. For the fluctuation interval, extract that the standard deviation and coefficient of variation of the period are large and the fluctuation begins to increase. For the unstable interval, extract the large fluctuation characteristics of the period, the fluctuation amplitude increases significantly, and the stability of the equipment is affected. According to the characteristics of the segmentation, generate the segmented analysis result of the ripple reference period. Specifically: Stable interval: The ripple reference period fluctuates little and is suitable for routine work. Fluctuation interval: The ripple reference period begins to fluctuate, and the discharge interval needs to be adjusted. Unstable interval: The ripple reference period fluctuates greatly, resulting in a decline in equipment performance, and immediate adjustment or shutdown for maintenance is required.

[0114] As an example of the present invention, referring to Figure 2 as shown, in this example, the step S3 includes:

[0115] Step S31: Real-time collect the AC side voltage of the uninterruptible power supply, record the complete AC voltage waveform, and generate UPS AC voltage time series data;

[0116] Step S32: Perform sine fitting on the UPS AC voltage timing data, extract the phase angle information, and generate the AC side voltage phase of the uninterruptible power supply.

[0117] Step S33: Numerically determine the AC side voltage phase, detect the zero-crossing points where the voltage waveform changes from negative to positive or from positive to negative, and generate the UPS AC voltage zero-crossing discrimination data.

[0118] Step S34: Based on the UPS AC voltage zero-crossing discrimination data, send a pulse discharge trigger signal at the zero-crossing points of the voltage waveform to generate a pulse discharge instruction.

[0119] Step S35: Execute DC pulse discharge according to the pulse discharge instruction within a preset time window, and record the discharge time, amplitude, and duration to generate pulse discharge control data.

[0120] In the embodiment of the present invention, in this step, a high-precision voltage sensor (specifically, a sensor with a sampling rate of 1 kHz) is used to collect the AC side voltage of the UPS (uninterruptible power supply) in real time. The device collects 1000 data points per second and records the complete AC voltage waveform. Assume that the collected voltage waveform within one cycle is: Voltage(t) = V peak ·sin(2πft + θ), where V peak$V_{peak}$ is the peak value of the voltage, $f$ is the frequency of the AC power supply, $t$ is the time, and $\theta$ is the phase angle. Based on these real-time acquired data, a UPS AC voltage time series dataset is generated, which contains the voltage value and timestamp at each moment. For the UPS AC voltage time series data collected in step S31, the sine fitting method (specifically, the least squares method) is used to fit the voltage waveform: $V(t)=A\cdot\sin(2\pi ft + \theta)$, where $A$ is the voltage amplitude, $f$ is the frequency, and $\theta$ is the phase angle. Through fitting, the best fitting parameters of the voltage waveform are obtained. The phase angle $\theta$ in the fitting result is extracted as the phase of the voltage on the AC side of the uninterruptible power supply. Numerical determination is performed on the obtained AC side voltage phase to detect the zero-crossing point of the waveform, that is, the intersection point where the voltage changes from negative to positive or from positive to negative. The basis for determining the zero-crossing point is: $V(t)=0$. By detecting the zero-crossing points of the voltage waveform within a period, the zero-crossing discrimination data of the UPS AC voltage can be generated, and these data include the position (timestamp) of the zero-crossing point and the corresponding voltage value. According to the zero-crossing discrimination data obtained in step S33, a pulse discharge trigger signal is sent at each zero-crossing point, which can be achieved by setting a control system to activate the pulse discharge device when the zero-crossing point is detected, generating a pulse discharge instruction that contains the time of the trigger signal (i.e., the position of the zero-crossing point). After receiving the pulse discharge instruction, according to the preset time window (specifically, the discharge duration is 5 ms), the DC pulse discharge operation is performed. Record the time, discharge amplitude, and duration during the discharge process. Specifically, assume that the voltage amplitude is 200 V and the duration is 5 milliseconds during the discharge process, and record this information and generate the pulse discharge control data. The finally generated pulse discharge control data includes parameters such as the time, amplitude, and duration of each discharge, providing a basis for subsequent discharge control and adjustment.

[0121] As an example of the present invention, referring to Figure 3 as shown, in this example, step S4 includes:

[0122] Step S41: Synchronously collect the pulse discharge control data, record the voltage change at the battery terminal at the moment of discharge, and generate the battery terminal voltage time series data;

[0123] Step S42: Perform feature analysis on the battery terminal voltage time series data, identify the discharge trigger point, and calculate the voltage difference before and after discharge to extract the voltage transient drop value;

[0124] Step S43: Perform current measurement on the pulse discharge control data to generate the discharge current value;

[0125] Step S44: Calculate the ratio of the voltage transient drop value to the discharge current value to obtain the real-time internal resistance of the battery, and the formula for the ratio calculation is as follows:

[0126]

[0127] Among them, R battery is the real-time internal resistance of the battery, ΔV is the voltage transient drop value, and I is the discharge current.

[0128] In the embodiment of the present invention, by using a high-speed data acquisition system to perform real-time synchronous acquisition on the pulse discharge control data, it is ensured that the change of the battery terminal voltage can be accurately recorded at the moment of discharge. The acquisition system has a sampling frequency of at least 1 kHz and is synchronized with the pulse discharge control system to ensure that the captured voltage waveform can accurately reflect the change of the battery terminal voltage during the discharge process. The system will sample the acquired voltage signal through a voltage sensor and record the voltage time series data at the battery terminal in real time. These data include timestamps and corresponding voltage values for subsequent analysis. Perform feature analysis on the voltage time series data of the battery terminal acquired in step S41, and first identify the discharge trigger point. The discharge trigger point is usually a mutation point of the voltage waveform and can be located by detecting the steep change of the voltage waveform. Once the discharge trigger point is identified, calculate the voltage difference ΔV before and after the discharge. The voltage transient drop value ΔV refers to the voltage change amount of the battery terminal voltage from the discharge trigger point to the end of the discharge during the discharge process. Specifically, if the voltage before discharge is 3.5 V and the voltage drops to 3.2 V after discharge, then the voltage transient drop value ΔV = 0.3 V. Extract the voltage transient drop value to provide necessary parameters for subsequent internal resistance calculation. Use a current sensor to perform current measurement on the pulse discharge control data. The current sensor will record the discharge current value at the battery terminal in real time during the discharge process and synchronously record this data with the battery terminal voltage data. Through accurate current measurement, the current value during the discharge process is generated. Assume that the discharge current at the battery terminal is 5 A during the discharge process. Calculate the real-time internal resistance R of the battery using the voltage transient drop value ΔV and the discharge current value I battery , and the following formula is used for calculation: where ΔV is the voltage transient drop value and I is the discharge current value. Specifically, if the transient drop value of the battery terminal voltage is 0.3 V and the discharge current is 5 A, then the real-time internal resistance of the battery is: Through this calculation, the internal resistance value of the battery can be obtained in real time, which can effectively evaluate the health status of the battery. A lower battery internal resistance indicates a better battery state, while a higher internal resistance means battery aging or performance degradation.

[0129] Preferably, the feature analysis of the voltage time series data of the battery terminal in step S42 to identify the discharge trigger point includes:

[0130] Perform low-pass filtering on the voltage time series data of the battery terminal to generate smooth voltage time series data;

[0131] Perform steady-state analysis on the smooth voltage time series data, extract the average voltage value within a period of time before the discharge, and generate steady-state voltage data before the discharge;

[0132] Perform a first-order difference operation on the smoothed voltage time-series data, identify the mutation points at the moment of discharge, and generate discharge trigger point data;

[0133] Perform validity verification on the discharge trigger point data to generate discharge trigger point validity verification data; screen the effective discharge trigger points from the discharge trigger point data according to the discharge starting point validity verification data to obtain the discharge trigger points.

[0134] In the embodiments of the present invention, the voltage time-series data collected from the battery terminal is subjected to low-pass filtering to remove high-frequency noise and fluctuations, making the voltage waveform smoother. The cut-off frequency of the low-pass filter should be set according to the operating characteristics of the battery system. Specifically, 10 Hz is selected as the cut-off frequency of the filter to ensure the stability of the signal while retaining the low-frequency change characteristics of the battery voltage. The standard low-pass filter algorithm (such as FIR filtering or IIR filtering) is used to process the voltage time-series data, thereby obtaining smooth voltage time-series data. These data help to more clearly identify the voltage trend of the battery and effectively reduce misjudgments caused by noise. In the smoothed voltage time-series data, a stable time period before discharge is selected (specifically, the first 10 seconds). The battery voltage changes little during this time period, which can be used as the basis for steady-state voltage analysis. The average value of the voltage values during this time period is calculated to generate the steady-state voltage data before discharge. Through statistical methods (such as mean calculation), the average voltage before discharge is obtained. Specifically, within 10 seconds, the average value of the battery voltage data is 3.7 V, which is used as the steady-state voltage data. The first-order difference operation is used to perform difference calculation on the smoothed voltage time-series data to identify the mutation points with rapid changes in the voltage signal. This step can help accurately identify the voltage mutations during the discharge process, such as the mutation points when the voltage rapidly drops from the stable state. The difference calculation formula is: difference value = V(t + 1) - V(t), where V(t + 1) and V(t) are the voltage values at the current moment and the previous moment, respectively. By calculating the difference value, if the absolute value of the difference value is greater than the set threshold (such as 0.2 V / s), it is determined as the discharge trigger point. Through this process, the discharge trigger point data can be accurately extracted. Specifically, it is identified that the voltage rapidly drops from 3.7 V to 3.5 V at a certain moment, and the change rate is greater than 0.2 V / s. The validity of the identified discharge trigger points is verified. The verification methods include but are not limited to the following ways: Ensure that the time interval between the discharge trigger points meets the predetermined standard to avoid misidentification due to accidental signal fluctuations. Specifically, the time interval between two consecutive discharge trigger points should be greater than 100 milliseconds. Confirm whether the voltage amplitude change of the trigger point meets the expectation. If the voltage change amplitude is too small or there is no obvious change, the trigger point is considered invalid. Ensure that the voltage change before the discharge trigger point meets the steady-state voltage requirement to prevent incorrect triggering due to system instability. The discharge trigger points that are verified to be valid generate the valid verification data of the discharge trigger points for subsequent processing. According to the results of the validity verification, the valid discharge trigger points that meet all conditions are selected. The screening criteria include: The trigger points must meet the steady-state voltage conditions, the amplitude change of the trigger points meets the discharge law, and the time interval and change rate both meet the requirements. Finally, the screened valid discharge trigger point data is obtained, and these data will be used for subsequent discharge control and internal resistance calculation analysis.

[0135] Preferably, step S5 includes the following steps:

[0136] Step S51: Perform time series analysis on the pulse discharge control data, extract the trigger time points of adjacent pulse discharges, calculate their time intervals, and generate pulse discharge time interval data;

[0137] Step S52: Evaluate the volatility of the discharge intervals for the pulse discharge time interval data to generate pulse discharge time stability data;

[0138] Step S53: Dynamically adjust the pulse discharge time interval data for two adjacent times based on the pulse discharge time stability data to obtain pulse discharge variable data, where the dynamic adjustment specifically is: making the pulse discharge time interval data for two adjacent times fall within a preset multiple range of the dynamic discharge interval time range and not forming an integer multiple relationship with both the fundamental wave period and the carrier wave period;

[0139] Step S54: Use the pulse discharge variable data to perform internal resistance calibration on the real-time internal resistance to generate the actual battery internal resistance for performing the real battery internal resistance measurement operation.

[0140] In the embodiments of the present invention, through time series analysis of pulse discharge control data, the trigger time points of each pulse discharge are extracted. These time points are usually the change moments of the signals generated according to the pulse discharge instructions, and the specific trigger moments of each pulse are recorded. By calculating the time difference between adjacent pulse discharge time points, the time interval of each pulse discharge can be obtained. Specifically, assuming that the trigger times of two adjacent pulse discharges are 12:00:00 and 12:00:10 respectively, the time interval is 10 seconds. The same operation is performed on all adjacent pulse discharge events, and finally pulse discharge time interval data is generated, which represents the time interval between every two pulse discharges. Statistical analysis is performed on the pulse discharge time interval data to evaluate its volatility. Specifically, the mean and standard deviation of the time interval can be calculated to analyze the stability of the pulse discharge interval. If the standard deviation of the time interval is large, it indicates that the pulse discharge interval fluctuates greatly; otherwise, it means that the discharge interval is relatively stable. In addition, the coefficient of variation (the ratio of the standard deviation to the mean) can be calculated to further quantify the volatility and classify it as "high stability" or "low stability". Based on this analysis, pulse discharge time stability data is generated, indicating the fluctuation degree of the pulse discharge interval. According to the pulse discharge time stability data, the time interval between two adjacent pulse discharges is dynamically adjusted. The adjustment strategy includes the following aspects: Ensure that the time interval between two adjacent pulse discharges is within the preset dynamic discharge interval time range. Specifically, assuming that the dynamic time range is from 8 seconds to 12 seconds, the adjusted time interval should always be within this range. Ensure that the time interval between two adjacent pulse discharges does not form an integer multiple relationship with the fundamental wave period or the carrier wave period. Specifically, if the fundamental wave period is 10 seconds and the carrier wave period is 5 seconds, the adjusted pulse discharge time interval should avoid being an integer multiple value such as 10 seconds, 5 seconds, 2.5 seconds, etc., so as to avoid frequency interference and ensure the stability of the discharge process. Through the above adjustment, the generated pulse discharge variable data will have dynamic adaptability, ensuring that the time interval of each pulse discharge meets the system requirements and reducing potential interference. Calibrate the real-time internal resistance in combination with the pulse discharge variable data. According to the time interval, discharge current, and battery voltage change of each pulse discharge, the actual internal resistance of the battery is calculated. The specific calculation method is as follows: Use the internal resistance calculation formula where ΔV is the voltage transient drop value and I is the discharge current value. During the pulse discharge process, according to the adjusted time interval and the actual discharge current record, the voltage change and current change of each discharge are calculated, and then the internal resistance data of the battery is obtained. By measuring the internal resistance through multiple pulse discharges, the actual internal resistance value of the battery can be obtained, providing accurate data support for battery health assessment.

[0141] Preferably, step S52 includes the following steps:

[0142] Step S521: Detect outliers in the pulse discharge time interval data to generate abnormal discharge time interval data;

[0143] Step S522: Perform difference calculation on the pulse discharge time interval data, analyze the change amplitude of adjacent intervals, and calculate the volatility to generate pulse discharge time interval volatility data;

[0144] Step S523: Perform weighted scoring based on the pulse discharge time interval data, abnormal discharge time interval data, and pulse discharge time interval volatility data to evaluate the stability of the discharge time interval and generate pulse discharge time stability data.

[0145] In the embodiment of the present invention, outliers in the pulse discharge time interval data are detected by using a statistical analysis method. Common outlier detection methods include the box plot method, the Z-Score method, or distribution-based tests (such as the Grubbs test). Box plot method: Calculate the quartiles (Q1, Q3) of the data and set the outlier determination rule according to the interquartile range (IQR). Outliers are defined as data points less than Q1 - 1.5×IQR or greater than Q3 + 1.5×IQR. Z-Score method: For each time interval, calculate its Z-Score, that is Where X is the data point, μ is the mean of the data, and σ is the standard deviation of the data. If the Z-Score exceeds the preset threshold (such as 3), it is determined to be an outlier. Through the above method, the discharge time interval that does not conform to the normal distribution is identified, and the abnormal discharge time interval data is generated, that is, all data marked as abnormal. The pulse discharge time interval data is differentially operated to obtain the variation range of adjacent discharge time intervals. Specifically, if two adjacent time intervals are 10 seconds and 15 seconds, the difference is 5 seconds. Calculate the standard deviation of all adjacent interval differences, that is, volatility. The volatility reflects the degree of change in the pulse discharge time interval. The larger the volatility, the more drastic the time interval fluctuation. By calculating the difference between each adjacent time interval and counting its standard deviation, the pulse discharge time interval volatility data can be generated, which can be used to analyze the stability and consistency of pulse discharge. In order to comprehensively evaluate the stability of the pulse discharge time interval, it is necessary to weight the three data (pulse discharge time interval data, abnormal discharge time interval data, and pulse discharge time interval volatility data) for scoring. Based on these data, the following weighted scoring formula can be used for comprehensive scoring: stability score = w1 × time interval data - w2 × outlier data - w3 × volatility data; where w1, w2, and w3 are weight coefficients for each data item. The weights can be adjusted according to specific needs, specifically: w1 = 0.4, w2 = 0.3, and w3 = 0.3. Through weighted scoring, the pulse discharge time stability data is obtained, which can quantify the stability of the pulse discharge time interval. If the score is high, it means that the discharge time interval is relatively stable and the system operation is relatively reliable; if the score is low, it means that the time interval is unstable and the discharge strategy needs to be adjusted or the system needs to be optimized.

[0146] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0147] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for measuring the internal resistance of a battery based on a pulse discharge method with variable frequency, characterized in that, It includes the following steps: Step S1: Obtain the ripple voltage signal across the battery terminals; extract the AC component of the ripple voltage signal and perform fast Fourier transform analysis on the AC component to obtain the fundamental frequency and carrier frequency of the current ripple; Step S2: Calculate the time periods of the fundamental frequency and carrier frequency respectively to obtain the fundamental period and carrier period; confirm the reference period based on the fundamental period and carrier period, and generate a dynamic discharge interval time range; Step S3: Obtain the AC side voltage phase of the uninterruptible power supply; perform zero-crossing discrimination on the AC side voltage phase. When the AC side voltage phase crosses zero, trigger a pulse discharge command and perform DC pulse discharge based on a preset time window to obtain pulse discharge control data; Step S4: Collect the voltage transient drop value and discharge current value at the battery terminal during the discharge process; Calculate the real-time internal resistance based on the ratio of the voltage transient drop value to the discharge current value; Step S5: Confirm the adjacent pulse discharge time interval for the pulse discharge control data, and dynamically adjust the pulse discharge time interval according to the dynamic discharge interval time range to generate pulse discharge variable data; use the pulse discharge variable data to calibrate the real-time internal resistance to generate the actual battery internal resistance.

2. The method for measuring the internal resistance of a battery by a frequency-variable pulse discharge method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Collect the voltage signal across the battery terminals in real time to obtain the original battery voltage signal; Step S12: Perform signal preprocessing on the original battery voltage signal to generate a standard battery voltage signal, where the signal preprocessing includes signal denoising, signal enhancement, and signal gain; Step S13: Perform high-pass filtering on the standard battery voltage signal to remove the DC component, generate a ripple voltage signal and extract the AC component of the ripple voltage signal; Step S14: Perform fast Fourier transform analysis on the AC component to obtain the fundamental frequency and carrier frequency of the current ripple.

3. The method for measuring the internal resistance of a battery by the frequency-variable pulse discharge method according to claim 2, wherein Step S14 includes the following steps: Step S141: Perform windowing processing on the ripple voltage signal according to the AC component to generate a windowed ripple signal; Step S142: Perform fast Fourier transform on the windowed ripple signal to convert the signal from the time domain to the frequency domain and generate ripple voltage spectrum data; Step S143: Perform peak detection on the ripple voltage spectrum data, identify the frequency component corresponding to the highest amplitude, extract the fundamental frequency and its amplitude to obtain the fundamental frequency; Step S144: Perform secondary peak detection on the ripple voltage spectrum data, identify the main peaks in the high-frequency components, extract the carrier frequency and its amplitude to obtain the carrier frequency.

4. The method for measuring the internal resistance of a battery by a frequency-variable pulse discharge method according to claim 1, wherein, Step S2 includes the following steps: Step S21: Take the reciprocal of the fundamental frequency and calculate the time period corresponding to the fundamental frequency to generate the fundamental period; Step S22: Take the reciprocal of the carrier frequency and calculate the time period corresponding to the carrier frequency to generate the carrier period; Step S23: Calculate the least common multiple or greatest common divisor of the fundamental period and carrier period to determine the period relationship between the two and generate a ripple reference period; Step S24: Perform segmented analysis on the ripple reference period and set the upper and lower limit ranges of the discharge interval according to the results of the segmented analysis to generate a dynamic discharge interval time range.

5. The method for measuring the internal resistance of a battery by a frequency-variable pulse discharge method according to claim 4, characterized in that, The segmented analysis of the ripple reference period includes: Calculating the mean, standard deviation, and coefficient of variation of the ripple reference period; evaluating the change stability of the ripple reference period based on the mean, standard deviation, and coefficient of variation, and generating ripple reference period statistical data; Segmenting the periodic change trend of the ripple reference period statistical data to generate segmented data of the ripple reference period; Extracting periodic characteristics from the segmented data of the ripple reference period to obtain the result of the segmented analysis.

6. The method for measuring the internal resistance of a battery based on a frequency-variable pulsed discharge method according to claim 1, characterized in that Step S3 includes the following steps: Step S31: Collect the AC-side voltage of the uninterruptible power supply in real time, record the complete AC voltage waveform, and generate UPS AC voltage time-series data; Step S32: Perform sine fitting on the UPS AC voltage time-series data, extract the phase angle information, and generate the AC-side voltage phase of the uninterruptible power supply; Step S33: Make a numerical determination of the AC-side voltage phase, detect the zero-crossing point where the voltage waveform changes from negative to positive or from positive to negative, and generate UPS AC voltage zero-crossing discrimination data; Step S34: Based on the UPS AC voltage zero-crossing discrimination data, send a pulse discharge trigger signal at the zero-crossing point of the voltage waveform to generate a pulse discharge command; Step S35: Perform DC pulse discharge according to the pulse discharge command within a preset time window, and record the discharge time, amplitude, and duration to generate pulse discharge control data.

7. The method for measuring the internal resistance of a battery based on a frequency-variable pulse discharge method according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Synchronously collect the pulse discharge control data, record the voltage change at the battery terminal at the moment of discharge, and generate battery terminal voltage time-series data; Step S42: Perform feature analysis on the battery terminal voltage time-series data, identify the discharge trigger point, and calculate the voltage difference before and after discharge to extract the voltage transient drop value; Step S43: Perform current measurement on the pulse discharge control data to generate a discharge current value; Step S44: Calculate the ratio of the voltage transient drop value to the discharge current value to obtain the real-time internal resistance of the battery. The formula for the ratio calculation is as follows: Among them, R battery is the real-time internal resistance of the battery, ΔV is the value of the transient voltage drop, and I is the discharge current.

8. The method for measuring the internal resistance of a battery by a frequency-variable pulse discharge method according to claim 7, wherein In step S42, the feature analysis of the battery terminal voltage time-series data to identify the discharge trigger point includes: Perform low-pass filtering on the battery terminal voltage time-series data to generate smooth voltage time-series data; Perform steady-state analysis on the smooth voltage time-series data, extract the average voltage value within a period of time before discharge, and generate pre-discharge steady-state voltage data; Perform first-order difference operation on the smooth voltage time-series data to identify the mutation point at the moment of discharge and generate discharge trigger point data; Perform validity verification on the discharge trigger point data to generate discharge trigger point valid verification data; screen the effective discharge trigger points from the discharge trigger point data according to the discharge starting point valid verification data to obtain the discharge trigger point.

9. The method for measuring the internal resistance of a battery based on a frequency-variable pulsed discharge method according to claim 1, wherein Step S5 includes the following steps: Step S51: Perform time series analysis on the pulse discharge control data, extract the trigger time points of adjacent pulse discharges, and calculate their time intervals to generate pulse discharge time interval data; Step S52: Evaluate the volatility of the discharge interval for the pulse discharge time interval data to generate pulse discharge time stability data; Step S53: Dynamically adjust the pulse discharge time interval data between two adjacent times based on the pulse discharge time stability data to obtain variable pulse discharge data, where the dynamic adjustment specifically is: making the pulse discharge time interval data between two adjacent times fall within a preset multiple range of the dynamic discharge interval time range and not forming an integer multiple relationship with both the fundamental wave period and the carrier wave period; Step S54: Use the variable pulse discharge data to calibrate the real-time internal resistance and generate the actual battery internal resistance to perform the real battery internal resistance measurement operation.

10. The method for measuring the internal resistance of a battery by a frequency-variable pulse discharge method according to claim 9, wherein, Step S52 includes the following steps: Step S521: Detect outliers in the pulse discharge time interval data to generate abnormal discharge time interval data; Step S522: Perform differential calculation on the pulse discharge time interval data, analyze the change amplitude of adjacent intervals, and calculate the volatility to generate pulse discharge time interval volatility data; Step S523: Perform weighted scoring based on the pulse discharge time interval data, the abnormal discharge time interval data, and the pulse discharge time interval volatility data to evaluate the stability of the discharge time interval and generate pulse discharge time stability data.

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