A supercapacitor charging and discharging control method, system and storage medium

Abstract

The application relates to the technical field of data processing, and discloses a super capacitor charging and discharging control method, a system and a storage medium. The method comprises the following steps: collecting voltage and current signals through a sensor, and obtaining initial data by using an analog-to-digital conversion technology; processing the signals by using a filtering algorithm, and extracting a characteristic point distribution sequence; judging potential drift and adjusting voltage offset according to a voltage offset amplitude and a load change weight; determining an internal resistance growth rate and a capacity attenuation curve by fitting an aging trend, and estimating residual capacity; adjusting a voltage reference point and a current threshold by combining a charging efficiency deviation value, and optimizing a charging process; and ensuring long-term stable operation of the super capacitor by updating control parameters. The application improves the efficiency and stability of the super capacitor charging and discharging process.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a supercapacitor charging and discharging control method, system and storage medium. Background Technology

[0002] Supercapacitors, as highly efficient energy storage devices, are widely used in new energy, electric vehicles, and grid peak shaving. With their rapid charge-discharge capabilities and long cycle life, supercapacitors play a crucial role in driving energy transition. However, in practical applications, the performance of supercapacitors is affected by various complex operating conditions. Especially during long-term use, potential drift and aging-related capacity decay and efficiency reduction severely limit their stability and lifespan.

[0003] Currently, existing monitoring and control methods have significant shortcomings in capturing the dynamic operating status of supercapacitors. Traditional methods mostly rely on periodic testing or static calibration, making it difficult to track dynamic changes in voltage and current in real time. For example, in the high-frequency charging and discharging scenarios of electric vehicles, traditional technologies cannot detect voltage deviations in a timely manner, resulting in uncontrolled increases in internal resistance and capacity decay, thus affecting system efficiency and reliability. The lack of real-time adjustment leads to reduced energy utilization and may even cause premature equipment failure.

[0004] Potential drift is a core issue faced by supercapacitors during charging and discharging. As internal resistance increases and capacitor materials age, the voltage reference point shifts, affecting charging efficiency and the estimation of remaining capacity. Especially in grid peak-shaving applications, supercapacitors need to cope with frequent load fluctuations. If voltage drift cannot be monitored in real time and the current limit cannot be adjusted promptly, charging efficiency deviations will increase, accelerating capacitor aging. Traditional static monitoring methods struggle to address the dynamic and complex nature of potential drift; therefore, a technology capable of extracting voltage drift characteristics in real time and dynamically controlling it is urgently needed.

[0005] Therefore, the key issue of this study is how to monitor potential drift in real time under complex operating conditions and dynamically adjust operating parameters according to the offset amplitude in order to maintain the charging efficiency and long-term stability of the supercapacitor. Summary of the Invention

[0006] This application provides a method, system, and storage medium for controlling the charging and discharging of a supercapacitor, which can improve the efficiency and stability of the charging and discharging process of a supercapacitor.

[0007] In a first aspect, this application provides a method for controlling the charging and discharging of a supercapacitor, the method comprising:

[0008] Step S1: Collect analog signals during the charging and discharging process of the supercapacitor using sensors. The analog signals include voltage signals and current signals. Use analog-to-digital conversion technology to convert the analog signals into digital form to obtain initial time series data.

[0009] Step S2: Process the initial time series data using a filtering algorithm to obtain the signal feature point distribution sequence;

[0010] Step S3: Extract the voltage offset amplitude from the signal feature point distribution sequence, and determine whether potential drift has occurred based on the voltage offset amplitude and historical load change weights, and adjust the voltage offset amplitude accordingly;

[0011] Step S4: Based on the voltage offset amplitude, fit the aging trend, determine the internal resistance growth rate and capacity decay curve, and calculate the estimated remaining capacity of the supercapacitor.

[0012] Step S5: Determine the charging efficiency deviation value based on the internal resistance growth rate and the capacity decay curve, combined with the historical load change weight and signal sampling frequency;

[0013] Step S6: Adjust the signal feature point distribution sequence according to the charging efficiency deviation value to obtain the adjusted voltage reference point and current upper limit threshold;

[0014] Step S7: Based on the adjusted voltage reference point and the upper current threshold, and combined with the internal resistance growth rate, update the supercapacitor control parameters to obtain a long-term stable operating parameter configuration.

[0015] Secondly, this application provides a supercapacitor charging and discharging control system, the system comprising:

[0016] The signal acquisition and conversion module is used to acquire analog signals during the charging and discharging process of the supercapacitor through sensors. The analog signals include voltage signals and current signals. The analog signals are converted into digital form using analog-to-digital conversion technology to obtain initial time series data.

[0017] The filtering feature extraction module is used to process the initial time series data using a filtering algorithm to obtain the signal feature point distribution sequence.

[0018] The potential drift determination module is used to extract the voltage offset amplitude from the signal feature point distribution sequence, determine whether potential drift has occurred based on the voltage offset amplitude and historical load change weights, and adjust the voltage offset amplitude accordingly.

[0019] The aging trend fitting module is used to fit the aging trend based on the voltage offset amplitude, determine the internal resistance growth rate and capacity decay curve, and calculate the estimated remaining capacity of the supercapacitor.

[0020] The efficiency deviation calculation module is used to determine the charging efficiency deviation value based on the internal resistance growth rate and the capacity decay curve, combined with the historical load change weight and the signal sampling frequency.

[0021] The parameter threshold adjustment module is used to adjust the signal feature point distribution sequence according to the charging efficiency deviation value to obtain the adjusted voltage reference point and current upper limit threshold.

[0022] The operating parameter update module is used to update the supercapacitor control parameters based on the adjusted voltage reference point and the upper current threshold, combined with the internal resistance growth rate, to obtain a long-term stable operating parameter configuration.

[0023] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described supercapacitor charging and discharging control method.

[0024] This application discloses a dynamic control method for potential drift and aging trends during the charging and discharging process of supercapacitors. This method solves the problem of traditional technologies being unable to monitor and adjust capacitor operating parameters in real time, thus effectively preventing capacity decay and efficiency reduction. The method involves real-time acquisition of voltage and current signals using sensors, generating standardized time-series data using analog-to-digital conversion, smoothing the signals using a Kalman filter algorithm, extracting key feature points, accurately calculating the voltage offset amplitude, and determining the potential drift. Based on the voltage offset amplitude, the aging trend is fitted, and the internal resistance growth rate and capacity decay curve are calculated to estimate the remaining capacity. Simultaneously, the charging efficiency deviation is determined by combining historical load change weights and signal sampling frequency. By dynamically adjusting the voltage reference point and current upper limit threshold, the control parameters are updated in real time to ensure long-term stable operation of the supercapacitor under different operating conditions. This invention, through real-time signal processing and feedback adjustment, significantly improves the charging efficiency and system stability of supercapacitors, extends their service life, reduces the impact of aging, and provides a highly efficient and reliable energy storage solution. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart of the supercapacitor charging and discharging control method of this application;

[0027] Figure 2 This is a flowchart of the signal feature point extraction and fusion process in this application;

[0028] Figure 3 This is a flowchart illustrating the dynamic calibration and threshold control process of this application;

[0029] Figure 4 This is a schematic diagram of the entire process system architecture for supercapacitor charging and discharging control in this application.

[0030] Figure 5 This is a schematic diagram of the supercapacitor charging and discharging control system of this application. Detailed Implementation

[0031] This application provides a method, system, and storage medium for controlling the charging and discharging of a supercapacitor. The terms "first," "second," "third," "fourth," etc. (if present)," in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0032] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of a supercapacitor charging and discharging control method in this application includes:

[0033] Step S1: Collect analog signals during the charging and discharging process of the supercapacitor using sensors. The analog signals include voltage and current signals. Use analog-to-digital conversion technology to convert the analog signals into digital form to obtain initial time series data.

[0034] In one specific embodiment, the process of performing step S1 may specifically include the following steps:

[0035] The voltage signal of the supercapacitor during the charging and discharging process is collected in real time by a voltage sensor;

[0036] The current signal of the supercapacitor during the charging and discharging process is collected synchronously by a current sensor;

[0037] An analog-to-digital converter is used to sample voltage and current signals to generate discrete digital voltage and current signal sequences.

[0038] The digital voltage signal sequence and the digital current signal sequence are time-synchronized to generate initial time series data containing timestamps;

[0039] Based on the preset sampling frequency, the initial time series data is normalized to obtain standardized initial time series data.

[0040] Specifically, voltage signals from the supercapacitor are acquired in real time during the charging and discharging process using a voltage sensor. A high-precision voltage sensor is installed between the positive and negative terminals of the supercapacitor, ensuring a response time of less than 1 millisecond to capture rapid voltage changes. The sensor is activated at the start of a charge / discharge cycle, continuously monitoring voltage fluctuations from the initial value to the peak value, such as the voltage rise from 0V to 2.7V during the charging phase. The acquired voltage signal accurately reflects the real-time state of the supercapacitor, providing a reliable basis for subsequent data processing. Current signals are simultaneously acquired using a current sensor, ensuring synchronized acquisition with the voltage signal and avoiding time difference errors. During the discharging phase, the process of the current dropping from its peak to zero is monitored, ensuring consistent time-series data generation and improving the reliability of the analysis. An analog-to-digital converter (ADC) is used to convert the voltage and current signals into discrete digital signal sequences. A 12-bit ADC is used for sampling at a sampling rate of 1kHz to ensure an accurate transition from continuous to discrete signals.

[0041] The digital voltage and current signal sequences are time-synchronized to generate initial time-series data with timestamps. Specifically, this involves comparing the sampling time points of the two sequences; if discrepancies exist, adjusting the time points of the current sequence using linear interpolation to align it with the voltage sequence; adding a uniform timestamp, such as an absolute time stamp in milliseconds, to each aligned data point; and verifying the consistency of the synchronized sequences to ensure that the voltage and current values ​​at the same timestamp correspond to actual physical events.

[0042] Considering high-load scenarios, especially in electric vehicle applications, when supercapacitors experience rapid discharge, a buffer can be added to store unsynchronized data, followed by batch processing for synchronization, reducing the real-time computational burden. This extension improves robustness in dynamic environments and helps prevent data loss. In energy storage systems, synchronization ensures that the initial time-series data captures the voltage drop at peak current, effectively identifying potential signs of aging and thus improving the long-term stability of the supercapacitor. The initial time-series data is normalized according to a preset sampling frequency to obtain standardized initial time-series data. The sampling frequency is set to 500Hz, and the maximum and minimum values ​​of the sequence are calculated. For each data point, the formula (data point value - minimum value) / (maximum value - minimum value) is applied to map all values ​​to the range of 0 to 1. The timestamps are adjusted to match the normalized sequence, ensuring that the standardized data retains the original timing information. For applications under different temperature conditions, the sampling frequency can be increased to 2kHz in high-temperature environments, and a temperature compensation factor (e.g., 0.95) can be introduced to correct signal deviations caused by thermal effects. After generating standardized data, smoothing filtering is performed to remove minor noise introduced by normalization. This extension is applicable to industrial energy systems, ensuring the accuracy of data in variable temperature scenarios and facilitating precise estimation of remaining capacity.

[0043] In renewable energy storage, standardization transforms uneven initial time-series data into a uniform format, facilitating subsequent filtering algorithms, thereby reducing noise interference and improving the accuracy of voltage offset determination. The beneficial effects of this method include enhanced response speed of the supercapacitor control system and extended equipment lifespan.

[0044] Step S2: Use a filtering algorithm to process the initial time series data and obtain the distribution sequence of signal feature points.

[0045] In one specific embodiment, the process of performing step S2 may specifically include the following steps:

[0046] The Kalman filter algorithm is used to process the voltage and current signals in the initial time series data respectively, filter out noise interference, and obtain a smooth voltage signal sequence and a denoised current signal sequence.

[0047] Based on the smoothed voltage signal sequence, the peak points and valley points of the voltage signal are extracted to generate a voltage feature point distribution sequence;

[0048] Based on the denoised current signal sequence, the abrupt change points and stable points of the current signal are extracted to generate a current feature point distribution sequence.

[0049] The voltage feature point distribution sequence and the current feature point distribution sequence are fused to generate a comprehensive signal feature point distribution sequence.

[0050] Specifically, the Kalman filter algorithm is used to process the voltage signal in the initial time series data, filtering out noise interference to obtain a smooth voltage signal sequence. Based on the voltage signal in the initial time series stability data, the Kalman filter algorithm is applied to estimate the state variables, progressively updating the predicted values ​​and covariance matrix of the voltage signal, thereby filtering out random noise interference and generating a smooth voltage signal sequence. The Kalman filter algorithm is a recursive estimation method that processes time series data through prediction and update stages. During the charging and discharging process of supercapacitors, the voltage signal may be affected by environmental noise; using Kalman filtering can obtain a more stable voltage sequence, which significantly improves the accuracy of subsequent analysis.

[0051] In one embodiment, the Kalman filter algorithm is used to process the current signal in the initial time series data to generate a denoised current signal sequence. The Kalman filter algorithm is used to estimate the state of the current signal, calculate the measurement residual, and adjust the gain matrix to filter out noise, resulting in a denoised current signal sequence. Based on the smoothed voltage signal sequence, the peak and valley points of the voltage signal are extracted to generate a voltage feature point distribution sequence. By identifying local maxima as peak points and local minima as valley points in the smoothed voltage signal sequence, the temporal position and amplitude values ​​of these feature points are calculated to form the voltage feature point distribution sequence.

[0052] In one embodiment, by setting a peak detection window size (e.g., 10 sampling points), the maximum value within each window is calculated by traversing the sequence. If the maximum value exceeds 1.2 times the average value of adjacent windows, it is marked as a peak point. Similarly, valley points are processed, and points below the average value of adjacent windows are identified. This allows for the aggregation of the temporal distribution of all peak and valley points, generating a voltage feature point distribution sequence. In high-load charging and discharging scenarios of supercapacitors, this method helps capture voltage fluctuation patterns, and the generated sequence effectively reflects stability and is beneficial for judging potential drift. Based on the denoised current signal sequence, locations with abrupt slope changes are detected as abrupt change points, and intervals with slopes close to zero are identified as stable points, thereby extracting the current feature point distribution sequence. This process is further refined to calculate the difference value of the sequence. If the absolute value of the difference exceeds a preset threshold (e.g., 0.5 amperes per second), it is marked as an abrupt change point. Continuous low-difference intervals are scanned, and if they persist for more than 5 sampling points, they are marked as stable points. The distributions of abrupt change points and stable points are integrated to form the current feature point distribution sequence. This method effectively identifies response delays in current signals. Abrupt changes correspond to the instantaneous load changes, while stable points represent the equilibrium state, which is beneficial for calculating charging efficiency deviations. Next, the voltage and current characteristic point distribution sequences are fused, and the weighted average or correlation coefficient of corresponding points is calculated to generate a comprehensive signal characteristic point distribution sequence. This fusion process ensures that voltage and current signals are aligned at the same timestamp by selecting a timing alignment method (such as nearest neighbor interpolation). Weighted fusion (e.g., voltage point weight 0.6, current point weight 0.4) is applied to calculate the fused value, generating a comprehensive signal characteristic point distribution sequence containing the fused points. This method improves signal robustness; in noisy environments, the fused sequence more accurately reflects aging trends, which is beneficial for estimating remaining capacity. Furthermore, load change weights are introduced for different load scenarios. If the load is high, the current sequence weight is increased to 0.7, and the fused value is recalculated to generate an adaptive comprehensive sequence. This extension effectively supports variable load applications, especially in capacitors with high cycle counts. The fused sequence better supports the calculation of internal resistance growth rates, providing assurance for long-term stable operation parameter configuration. (Reference) Figure 2 The figure illustrates the signal feature point extraction and fusion process.

[0053] Step S3: Extract the voltage offset amplitude from the signal feature point distribution sequence, and determine whether potential drift has occurred based on the voltage offset amplitude and historical load change weights, and adjust the voltage offset amplitude accordingly.

[0054] In one specific embodiment, the process of performing step S3 may specifically include the following steps:

[0055] Extract the feature point offset of the voltage signal from the signal feature point distribution sequence, and calculate the voltage offset amplitude;

[0056] Based on the specifications of the supercapacitor, obtain the preset voltage stability threshold;

[0057] If the voltage deviation deviates from the voltage stability threshold, then the weight of historical load changes is obtained;

[0058] The probability value of potential drift is calculated based on the weight of historical load changes and the magnitude of voltage deviation.

[0059] If the probability value of potential drift is greater than the preset drift threshold, potential drift is determined to have occurred, and the voltage offset amplitude is adjusted.

[0060] Specifically, the signal feature point distribution sequence is generated after processing the initial time series stability data using the Kalman filter algorithm. It contains multiple feature points of the voltage signal, such as peak and valley points. Extracting the feature point offset of the voltage signal involves identifying the voltage difference between adjacent feature points in the sequence and calculating the average of these differences as the offset. Subsequently, the voltage offset amplitude is obtained by subtracting the offset from the reference voltage value of the sequence, ensuring the accuracy of subsequent judgments. The voltage stability threshold is a fixed value preset according to the supercapacitor specifications, for example, set to 0.05V, used to compare whether the voltage offset amplitude is within the normal range. When the voltage offset amplitude deviates from the voltage stability threshold, the load change weight is retrieved from the historical database. This weight reflects the impact of load fluctuations on voltage during past charge-discharge cycles, and the weight value is calculated based on the ratio of historical load peak to average. This allows for judgment of the severity of the offset in conjunction with actual operating scenarios, improving the reliability of the judgment. If the absolute value of the difference between the voltage offset amplitude and the voltage stability threshold is greater than 10% of the threshold range, the historical load change weight is queried. This weight is a value obtained by statistically averaging historical load changes, used to quantify the impact of the load on voltage stability. The probability value of potential drift is calculated based on the weight of historical load changes and the magnitude of voltage deviation.

[0061] Furthermore, the probability value of potential drift is calculated using a Bayesian probability formula, where historical load changes are weighted as prior probabilities and voltage drift magnitude is used as likelihood evidence. Specifically, the Bayesian probability formula is:

[0062] ;

[0063] in, The value is directly assigned by the weight of historical load changes, representing the prior probability of potential drift under specific load conditions; The likelihood of observing a specific offset given a known drift is calculated using the normalized value of the offset magnitude. The overall probability of the offset amplitude is used for standardization. In high-frequency load scenarios, the weight of historical load changes can be adjusted to a dynamic value, such as calculated based on the most recent load data, thereby improving adaptability in fast charge and discharge applications. In low-temperature environments, the weight of historical load changes and the offset amplitude are adjusted in conjunction with temperature influence factors to ensure the comprehensiveness and accuracy of the calculation. The probability value calculated by Bayes' theorem reflects the likelihood of potential drift, which helps to identify the aging trend of supercapacitors early, thereby enabling precise charge and discharge control. If the calculated potential drift probability value is greater than the preset drift threshold, potential drift is determined to have occurred, and the current voltage offset amplitude is generated as the basis for fitting the aging trend, supporting the configuration of long-term stable operation parameters.

[0064] Step S4: Based on the voltage offset amplitude, fit the aging trend, determine the internal resistance growth rate and capacity decay curve, and calculate the estimated remaining capacity of the supercapacitor.

[0065] In one specific embodiment, the process of performing step S4 may specifically include the following steps:

[0066] The least squares algorithm is used to fit the voltage offset amplitude to generate the aging trend slope;

[0067] Calculate the rate of increase in internal resistance of the supercapacitor based on the slope of the aging trend.

[0068] Construct a capacity decay curve based on the internal resistance growth rate;

[0069] Extract the characteristic parameters of capacity decay based on the capacity decay curve;

[0070] The remaining capacity of the supercapacitor is estimated based on the characteristic parameters and the rate of increase in internal resistance.

[0071] Specifically, a least squares algorithm is used to fit the voltage offset amplitude to generate an aging trend slope. Specifically, multiple voltage offset amplitude values ​​are collected as data points, and the least squares method is used to solve the linear regression equation to obtain the slope value as the aging trend slope. Based on the aging trend slope, the internal resistance growth rate of the supercapacitor is calculated. By obtaining the aging trend slope and combining it with the initial internal resistance value of the supercapacitor, the rate of change of internal resistance over time is calculated using a formula. The internal resistance growth rate is obtained by multiplying the aging trend slope by a time factor, which is determined based on the number of charge-discharge cycles. The calculated internal resistance growth rate can be compared with historical data to verify its stability under different loads. For electric vehicle applications, if the aging trend slope is 0.05 volts per cycle, the internal resistance growth rate is 1.2 times the initial internal resistance per 100 cycles. This calculation helps to identify potential faults early, improve system reliability, and accurately predict equipment lifespan. In high-temperature environments, the internal resistance growth rate can be adjusted by multiplying by a temperature correction factor; for example, a factor of 1.1 increases the rate by 10%, ensuring accurate monitoring under extreme conditions. Based on the internal resistance growth rate, a capacity decay curve is constructed. Using the internal resistance growth rate as an input parameter, an exponential decay model is employed to construct the capacity decay curve, which is expressed as follows: , It is in time Remaining capacity at any given time It is the initial capacity. It is the rate of increase of internal resistance. It's about time. The curve points are fitted to generate a continuous decay path, and the fit between the curve and the actual test data is verified.

[0072] In energy storage system applications, if the internal resistance growth rate is 0.02 ohms per cycle, the constructed capacity decay curve shows that the capacity drops to 80% after 500 cycles. This method is particularly effective in high-load scenarios, such as grid peak regulation. The curve can be extended to account for the impact of current surges, generating a variation curve to support multi-scenario adaptation. In fast charge / discharge applications, a delay factor is added during construction, and the curve shows accelerated capacity decay, which is positively correlated with the internal resistance growth rate, providing comprehensive aging insights. Characteristic parameters of capacity decay are extracted from the capacity decay curve. Key points such as inflection points and decay rates are identified from the curve as characteristic parameters. Based on the characteristic parameters and the internal resistance growth rate, the remaining capacity estimate of the supercapacitor is calculated. By multiplying the characteristic parameters, such as the decay rate, by the internal resistance growth rate, a capacity loss factor is obtained. The remaining capacity estimate is the initial capacity minus the capacity loss factor multiplied by the number of cycles. The remaining capacity estimate is further corrected to incorporate temperature effects, ensuring the accuracy of the estimate. In renewable energy storage, if the characteristic parameter is 0.01 per cycle and the internal resistance growth rate is 0.015, the remaining capacity is estimated to be 70% of the initial value after 300 cycles. This facilitates real-time health assessment and supports preventative maintenance. This method supports consistent predictions under different voltage offset scenarios; for example, the estimation is more optimistic at low offsets and more conservative at high offsets, forming a complementary prediction framework. In multi-module supercapacitor bank applications, the remaining capacity is calculated through a weighted average of the characteristic parameters, enhancing the overall system robustness and supporting large-scale applications.

[0073] Step S5: Determine the charging efficiency deviation value based on the internal resistance growth rate and capacity decay curve, combined with the historical load change weight and signal sampling frequency.

[0074] In one specific embodiment, the process of performing step S5 may specifically include the following steps:

[0075] Calculate the current response delay based on the rate of increase of internal resistance;

[0076] Based on the capacity decay curve, extract the temperature influence factor;

[0077] Adjust the weighting coefficient of the temperature influence factor by taking into account the weighting of historical load changes;

[0078] Calculate the dynamic rate of change of the current response based on the signal sampling frequency;

[0079] The charging efficiency deviation value is generated based on the current response delay, temperature influence factor, and dynamic change rate.

[0080] Specifically, determining the charging efficiency deviation involves multiple calculation steps, based on the internal resistance growth rate and capacity decay curve, combined with historical load change weights and signal sampling frequency. First, the current response delay is calculated based on the internal resistance growth rate. The internal resistance growth rate is fitted to the aging trend slope using the least squares method and then multiplied by a preset delay coefficient, determined based on the supercapacitor's material properties. For example, for carbon-based capacitors, the coefficient is 0.05 seconds per ohm increase, used to calculate the current response delay. Next, a temperature influence factor is extracted based on the capacity decay curve. The capacity decay curve is generated using the least squares method, representing the capacity decay path with the number of cycles. By extracting the temperature-related component of the curve slope, a temperature influence factor is calculated. This factor reflects the amplification effect of each degree Celsius on the decay, aiding in weight adjustment. The weighting coefficient of the temperature influence factor is adjusted in conjunction with historical load change weights to ensure adaptability under different load conditions. The historical load change weights are calculated based on the voltage offset amplitude value and are used to reflect the impact of load fluctuations on the current capacitor state. The temperature influence factor is updated using a weighted average method to ensure that the cumulative effect of load and temperature is reflected.

[0081] Furthermore, the dynamic rate of change of the current response is calculated based on the signal sampling frequency. This dynamic rate of change is calculated by combining the sampling frequency with the standard deviation of the current fluctuation, ensuring that the response speed is quantified into a suitable indicator. Based on the current response delay, temperature influence factor, and dynamic rate of change, a charging efficiency deviation value is generated using a weighted summation formula. This value is directly used for subsequent charging efficiency evaluation and system control optimization. This method allows for the quantification of capacitor performance under different environmental and load conditions, enabling early identification of potential faults and adjustment of system parameters, thereby improving charging efficiency and the long-term stability of the equipment.

[0082] Step S6: Adjust the signal feature point distribution sequence according to the charging efficiency deviation value to obtain the adjusted voltage reference point and current upper limit threshold.

[0083] In one specific embodiment, the process of performing step S6 may specifically include the following steps:

[0084] Read the preset charging efficiency threshold from the storage unit of the supercapacitor control system;

[0085] If the charging efficiency deviation exceeds the charging efficiency threshold, a dynamic calibration algorithm is used to adjust the smoothness of the voltage signal in the signal feature point distribution sequence. At the same time, a dynamic calibration algorithm is used to adjust the current denoising signal in the signal feature point distribution sequence.

[0086] Adjust the voltage reference point based on the estimated remaining capacity, the correlation between the temperature influence factor and the number of cycles;

[0087] Based on the adjusted voltage reference point and the current denoising signal, a current upper limit threshold is generated.

[0088] Specifically, a preset charging efficiency threshold, such as 5%, is read from the storage unit of the supercapacitor control system. If the charging efficiency deviation exceeds this threshold, a dynamic calibration algorithm is used to adjust the smoothness of the voltage signal in the signal feature point distribution sequence, while simultaneously adjusting the noise reduction value of the current signal. The dynamic calibration algorithm is a feedback-based iterative adjustment process that gradually optimizes parameters to reduce deviation. In this process, initial time series stability data is obtained through analog-to-digital conversion, and noise is filtered out using a Kalman filter algorithm to generate a signal feature point distribution sequence. The charging efficiency deviation is calculated based on the signal feature point distribution sequence. If it exceeds the threshold, dynamic calibration is initiated to optimize the smoothness of the voltage signal. The trend is fitted using a least squares algorithm to reduce fluctuations in the voltage signal, ensuring a reduction in potential drift and improving system stability. Under high load scenarios, if the initial deviation is 8%, after three algorithm iterations, the smoothness improves from 0.7 to 0.9, significantly reducing potential drift.

[0089] Furthermore, an adjusted voltage reference point is generated based on the correlation between the temperature influence factor and the number of cycles. The temperature influence factor is calculated by comparing the weights of historical load changes and the rate of internal resistance growth, quantifying the impact of temperature on capacity. By analyzing the influence of the number of cycles and fitting the aging trend slope, the correlation of capacity decay with increasing cycle count is determined. These factors are then used to adjust the voltage reference point. For example, in high-temperature environments, with a temperature influence factor of 1.5 and 1000 cycles, the reference point is adjusted to 2.3V, which is beneficial for extending the supercapacitor's lifespan. In low-temperature environments, with a factor of 0.8 and 500 cycles, the reference point is adjusted to 2.6V to compensate for current response delay. Additionally, for fast charge / discharge applications, the reference point is lowered from 2.5V to 2.2V based on the capacity decay curve, optimizing charging efficiency. Based on the adjusted voltage reference point and the denoised current signal value, a current upper limit threshold is generated through simple multiplication or weighted calculation to ensure that the supercapacitor maintains efficient and stable operation under dynamic load conditions. Figure 3 The figure illustrates the dynamic calibration and threshold control process.

[0090] Step S7: Based on the adjusted voltage reference point and current upper limit threshold, and combined with the internal resistance growth rate, update the supercapacitor control parameters to obtain a long-term stable operating parameter configuration.

[0091] In one specific embodiment, the process of performing step S7 may specifically include the following steps:

[0092] Update the voltage control parameters of the supercapacitor control system based on the adjusted voltage reference point;

[0093] Update the current limiting parameters of the supercapacitor control system based on the upper current threshold.

[0094] Calculate the drift change compensation value based on the updated current limit parameters and internal resistance growth rate;

[0095] The drift change compensation value and voltage control parameters are weighted and fused to obtain the final voltage reference point. Based on the final voltage reference point and the residual value of the signal characteristic point distribution, the control parameter configuration is generated.

[0096] Based on the configuration of the control parameters, the operating parameters of the supercapacitor control system are optimized to obtain a long-term stable operating parameter configuration.

[0097] Specifically, based on the adjusted voltage reference point, the supercapacitor control system updates the voltage control parameters and the current limiting parameters according to the upper current threshold. Based on the updated current limiting parameters and the internal resistance growth rate, a drift compensation value is calculated. Then, the drift compensation value is weighted and fused with the voltage control parameters to obtain the final voltage reference point. Based on the residual values ​​of the final voltage reference point and the signal characteristic point distribution, a control parameter configuration is generated, and the operating parameters of the supercapacitor control system are optimized to obtain a long-term stable operating parameter configuration.

[0098] Specifically, when generating the dynamic adjustment parameters for the voltage control curve, an adjusted voltage reference point is obtained. This reference point is obtained by adjusting the smoothness of the voltage signal and the denoising value of the current signal using a dynamic calibration algorithm. The dynamic calibration algorithm employs an iterative optimization method to smooth the signal and reduce the impact of noise. The voltage reference point is mapped onto the voltage control curve, and a linear interpolation method is used to calculate the adjustment coefficient on the curve, ensuring that the curve can dynamically respond to voltage changes. This allows the voltage control curve to adapt to real-time voltage deviations, achieving a more stable voltage output. For example, in a high-load scenario, assuming an initial deviation of 8%, after exceeding the 5% threshold, the algorithm iterates three times, improving the smoothness from 0.7 to 0.9. This significantly reduces potential drift and improves system stability.

[0099] Simultaneously, dynamic adjustment parameters for the current limiting curve are generated based on the upper current threshold. By reading the upper current threshold and combining it with the correlation between temperature influence factors and cycle count, the current limiting curve can dynamically adapt to threshold changes, reducing the risks caused by current response delay. The response delay compensation value of the control system is calculated by combining the internal resistance growth rate. The internal resistance growth rate reflects the rate at which the internal resistance of the supercapacitor increases with time and cycle count. The response delay compensation value is calculated by multiplying the internal resistance growth rate by a preset weighting factor, which helps to effectively offset the delay caused by aging and improve the system response speed. For example, in electric vehicle applications, if the internal resistance growth rate is 0.15 ohms per cycle, the calculated delay is 0.0075 seconds, revealing the reason for the slow response under high load and improving the system's early warning capability. Based on the response delay compensation value and the residual value of the signal feature point distribution sequence, an optimized configuration of the control parameters is generated. The filtered residual value obtained after processing the initial time series stability data using the Kalman filter algorithm is combined with the response delay compensation value, and an optimization algorithm is used to generate the configuration, ensuring that the generated parameter configuration minimizes errors and meets the system stability requirements. For example, in testing, if the residual value is 0.05 volts and the compensation value is 1.5 milliseconds, the optimized parameters are calculated using a weighted average to ensure that the configuration minimizes errors. Ultimately, the optimized configuration is used to update the long-term operating parameters of the supercapacitor control system, ensuring that the system can achieve sustained and stable operation under different loads and environments. Through this series of adjustments and optimizations, the charging and discharging efficiency, stability, and long-term reliability of the supercapacitor can be effectively improved, adapting to dynamic changes under high loads and complex operating conditions, ensuring normal operation of the equipment and extending its service life. For example, in an industrial energy storage system, if the internal resistance growth rate is 0.07 ohms per cycle, combined with a temperature influence factor increase of 1.1, the calculated compensation value is 3 milliseconds, which can optimize the response time and improve charging efficiency.

[0100] refer to Figure 4 The figure shows a schematic diagram of the entire process system architecture for supercapacitor charging and discharging control.

[0101] It is understood that the executing entity of this application can be a supercapacitor charging and discharging control system, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiments use a server as an example for illustration.

[0102] The supercapacitor charging and discharging control method in the embodiments of this application has been described above. The supercapacitor charging and discharging control system in the embodiments of this application is described below. Please refer to [link / reference]. Figure 5 One embodiment of the supercapacitor charging and discharging control system in this application includes:

[0103] The signal acquisition and conversion module is used to acquire analog signals during the charging and discharging process of the supercapacitor through sensors. The analog signals include voltage signals and current signals. The analog signals are converted into digital form using analog-to-digital conversion technology to obtain initial time series data.

[0104] The filter feature extraction module is used to process the initial time series data using a filtering algorithm to obtain the distribution sequence of signal feature points.

[0105] The potential drift detection module is used to extract the voltage offset amplitude from the signal feature point distribution sequence, and determine whether potential drift has occurred based on the voltage offset amplitude and historical load change weights, and adjust the voltage offset amplitude accordingly.

[0106] The aging trend fitting module is used to fit the aging trend based on the voltage offset amplitude, determine the internal resistance growth rate and capacity decay curve, and calculate the remaining capacity estimate of the supercapacitor.

[0107] The efficiency deviation calculation module is used to determine the charging efficiency deviation value based on the internal resistance growth rate and capacity decay curve, combined with the historical load change weight and signal sampling frequency.

[0108] The parameter threshold adjustment module is used to adjust the signal feature point distribution sequence according to the charging efficiency deviation value to obtain the adjusted voltage reference point and current upper limit threshold.

[0109] The operating parameter update module is used to update the supercapacitor control parameters based on the adjusted voltage reference point and current upper limit threshold, combined with the internal resistance growth rate, to obtain a long-term stable operating parameter configuration.

[0110] Through the collaborative operation of the aforementioned components, the signal acquisition and conversion module first acquires analog signals, including voltage and current signals, during the charging and discharging process of the supercapacitor using sensors. It then uses analog-to-digital conversion (ADC) technology to convert the analog signals into digital form, thus obtaining initial time-series data. The filtering and feature extraction module filters the initial time-series data to extract the signal feature point distribution sequence. The potential drift judgment module extracts the voltage offset amplitude from these feature point distribution sequences and, combined with historical load change weights, determines whether potential drift has occurred. If drift occurs, the voltage offset amplitude is adjusted to ensure more accurate potential drift judgment. The aging trend fitting module fits the aging trend based on the voltage offset amplitude, determines the internal resistance growth rate and capacity decay curve, and further calculates the estimated remaining capacity of the supercapacitor. The efficiency deviation calculation module calculates the charging efficiency deviation value based on the internal resistance growth rate and capacity decay curve, combined with historical load change weights and the signal sampling frequency. The parameter threshold adjustment module adjusts the signal feature point distribution sequence based on the charging efficiency deviation value to obtain the adjusted voltage reference point and current upper limit threshold. The operating parameter update module updates the supercapacitor's control parameters based on the adjusted voltage reference point and current upper limit threshold, combined with the internal resistance growth rate, ensuring a long-term stable operating parameter configuration. Through the coordinated work of these modules, the system can dynamically adjust the supercapacitor's operating parameters, optimize the charging and discharging process, thereby improving the system's charging efficiency, stability, and equipment lifespan.

[0111] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the supercapacitor charging and discharging control method.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling the charging and discharging of a supercapacitor, characterized in that, The method includes: Step S1: Collect analog signals during the charging and discharging process of the supercapacitor using sensors. The analog signals include voltage signals and current signals. Use analog-to-digital conversion technology to convert the analog signals into digital form to obtain initial time series data. Step S2: Process the initial time series data using a filtering algorithm to obtain the signal feature point distribution sequence; Step S3: Extract the voltage offset amplitude from the signal feature point distribution sequence. Based on the voltage offset amplitude and the historical load change weight, determine whether potential drift has occurred and adjust the voltage offset amplitude accordingly. The historical load change weight reflects the impact of load fluctuations on voltage during past charge and discharge cycles. The weight value is calculated based on the ratio of historical load peak value to average value. Step S4: Based on the voltage offset amplitude, fit the aging trend, determine the internal resistance growth rate and capacity decay curve, and calculate the estimated remaining capacity of the supercapacitor. Step S5: Determine the charging efficiency deviation value based on the internal resistance growth rate and the capacity decay curve, combined with the historical load change weight and signal sampling frequency; Step S6: Adjust the signal feature point distribution sequence according to the charging efficiency deviation value to obtain the adjusted voltage reference point and current upper limit threshold; Step S7: Based on the adjusted voltage reference point and the current upper limit threshold, and combined with the internal resistance growth rate, update the supercapacitor control parameters to obtain a long-term stable operating parameter configuration; the supercapacitor control parameters include: voltage control parameters, current limit parameters, and drift change compensation values; the operating parameter configuration includes: voltage reference point and control parameter configuration; Step S3 includes: extracting the feature point offset of the voltage signal from the signal feature point distribution sequence and calculating the voltage offset amplitude; obtaining a preset voltage stability threshold according to the specifications of the supercapacitor; if the voltage offset amplitude deviates from the voltage stability threshold, obtaining the historical load change weight; calculating the probability value of potential drift according to the historical load change weight and the voltage offset amplitude; if the probability value of potential drift is greater than the preset drift threshold, determining that potential drift has occurred and adjusting the voltage offset amplitude. Step S6 includes: reading a preset charging efficiency threshold from the storage unit of the supercapacitor control system; if the charging efficiency deviation exceeds the charging efficiency threshold, adjusting the smoothness of the voltage signal in the signal feature point distribution sequence using a dynamic calibration algorithm, and simultaneously adjusting the current denoising signal in the signal feature point distribution sequence using the dynamic calibration algorithm; adjusting the voltage reference point based on the correlation between the remaining capacity estimate, the temperature influence factor, and the number of cycles; and generating a current upper limit threshold based on the adjusted voltage reference point and the current denoising signal. The dynamic calibration algorithm is an iterative adjustment process based on feedback.

2. The supercapacitor charging and discharging control method according to claim 1, characterized in that, Step S1 includes: The voltage signal of the supercapacitor during the charging and discharging process is collected in real time by a voltage sensor; The current signal of the supercapacitor during the charging and discharging process is collected synchronously by a current sensor; The voltage signal and the current signal are sampled using an analog-to-digital converter to generate discrete digital voltage signal sequences and digital current signal sequences; The digital voltage signal sequence and the digital current signal sequence are time-synchronized to generate initial time-series data containing timestamps; The initial time series data is normalized according to a preset sampling frequency to obtain standardized initial time series data.

3. The supercapacitor charging and discharging control method according to claim 1, characterized in that, Step S2 includes: The voltage and current signals in the initial time series data are processed by the Kalman filter algorithm to filter out noise interference and obtain a smooth voltage signal sequence and a denoised current signal sequence. Based on the smoothed voltage signal sequence, the peak points and valley points of the voltage signal are extracted to generate a voltage feature point distribution sequence; Based on the denoised current signal sequence, the abrupt change points and stable points of the current signal are extracted to generate a current feature point distribution sequence. The voltage feature point distribution sequence and the current feature point distribution sequence are fused to generate a comprehensive signal feature point distribution sequence.

4. The supercapacitor charging and discharging control method according to claim 1, characterized in that, Step S4 includes: The voltage offset amplitude is fitted using the least squares algorithm to generate the aging trend slope; Calculate the rate of increase in internal resistance of the supercapacitor based on the slope of the aging trend. Based on the internal resistance growth rate, a capacity decay curve is constructed; Based on the capacity decay curve, extract the characteristic parameters of capacity decay; The remaining capacity estimate of the supercapacitor is calculated based on the characteristic parameters and the internal resistance growth rate.

5. The supercapacitor charging and discharging control method according to claim 4, characterized in that, Step S5 includes: Calculate the current response delay based on the internal resistance growth rate; Based on the capacity decay curve, extract the temperature influence factor; The weighting coefficient of the temperature influence factor is adjusted based on the weighting of the historical load changes. Calculate the dynamic rate of change of the current response based on the signal sampling frequency; A charging efficiency deviation value is generated based on the current response delay, the temperature influence factor, and the dynamic change rate.

6. The supercapacitor charging and discharging control method according to claim 1, characterized in that, Step S7 includes: Update the voltage control parameters of the supercapacitor control system based on the adjusted voltage reference point. Update the current limiting parameters of the supercapacitor control system according to the aforementioned upper current threshold. Calculate the drift change compensation value based on the updated current limit parameters and the internal resistance growth rate; The drift change compensation value and the voltage control parameters are weighted and fused to obtain the final voltage reference point. Based on the final voltage reference point and the residual value of the signal feature point distribution, the control parameter configuration is generated. Based on the aforementioned control parameter configuration, the operating parameters of the supercapacitor control system are optimized to obtain a long-term stable operating parameter configuration.

7. A supercapacitor charging and discharging control system, used to implement the supercapacitor charging and discharging control method as described in any one of claims 1 to 6, characterized in that, The supercapacitor charging and discharging control system includes: The signal acquisition and conversion module is used to acquire analog signals during the charging and discharging process of the supercapacitor through sensors. The analog signals include voltage signals and current signals. The analog signals are converted into digital form using analog-to-digital conversion technology to obtain initial time series data. The filtering feature extraction module is used to process the initial time series data using a filtering algorithm to obtain the signal feature point distribution sequence. The potential drift determination module is used to extract the voltage offset amplitude from the signal feature point distribution sequence, determine whether potential drift has occurred based on the voltage offset amplitude and historical load change weights, and adjust the voltage offset amplitude accordingly. The aging trend fitting module is used to fit the aging trend based on the voltage offset amplitude, determine the internal resistance growth rate and capacity decay curve, and calculate the estimated remaining capacity of the supercapacitor. The efficiency deviation calculation module is used to determine the charging efficiency deviation value based on the internal resistance growth rate and the capacity decay curve, combined with the historical load change weight and the signal sampling frequency. The parameter threshold adjustment module is used to adjust the signal feature point distribution sequence according to the charging efficiency deviation value to obtain the adjusted voltage reference point and current upper limit threshold. The operating parameter update module is used to update the supercapacitor control parameters based on the adjusted voltage reference point and the upper current threshold, combined with the internal resistance growth rate, to obtain a long-term stable operating parameter configuration.

8. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements a supercapacitor charging and discharging control method as described in any one of claims 1 to 6.

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