A method and system for monitoring the charge and discharge characteristics of an electric vehicle capacitor

Abstract

The application relates to the technical field of electric vehicle capacitor monitoring, and provides an electric vehicle capacitor charge-discharge characteristic monitoring method and system, which comprises the following steps: acquiring a current signal flowing through a capacitor; performing frequency domain analysis on the current signal to obtain a ripple characteristic parameter of a frequency ripple component in the current signal; continuously monitoring the ripple characteristic parameter to obtain a monitoring result, and determining a change rate of the ripple characteristic parameter according to the monitoring result; determining a change acceleration corresponding to the change rate according to the change rate; judging a local deterioration process inside the capacitor according to the change acceleration to obtain a judgment result of the local deterioration process; and adjusting a charging parameter or issuing a warning according to the judgment result of the local deterioration process. The application has the effect of improving the operation safety and reliability of the electric vehicle in a fast charging scene.

Description

Technical Field

[0001] This invention relates to the technical field of electric vehicle capacitor monitoring, and specifically to a method and system for monitoring the charging and discharging characteristics of electric vehicle capacitors. Background Technology

[0002] In fast-charging scenarios for electric vehicles, the vehicle's power management system utilizes large-capacity capacitors to smooth the high-pulse current input from the charging station and maintain the stability of the vehicle's high-voltage line voltage. However, existing monitoring methods primarily focus on the overall current, surface temperature, and macroscopic changes in resistance and capacitance of the capacitor, making it difficult to detect localized overheating and early damage within the capacitor caused by specific high-frequency fluctuations in the current. This subtle internal damage, once accumulated to a certain extent, may suddenly lead to capacitor failure without any external indication, posing a safety hazard to vehicle operation.

[0003] In the context of super-fast charging for electric vehicles, the DC power output from the charging station is not purely stable DC, but rather pulsating DC processed by a high-frequency switching converter. The vehicle's internal power management system also employs high-frequency switching technology for efficient power transmission, resulting in significant AC ripple current superimposed on the current flowing through the vehicle's large-capacity capacitors. These ripple currents have diverse frequency components, ranging from several kilohertz to tens or even hundreds of kilohertz, exhibiting complex waveforms.

[0004] Existing monitoring systems, when assessing capacitor current load, often focus on measuring the overall root-mean-square (RMS) current flowing through the capacitor, or simply obtaining its DC component through low-pass filtering, rarely conducting in-depth analysis of the detailed spectral characteristics of the ripple current. This leads to a situation where, even if the measured RMS current is within the capacitor's rated range, if the ripple current contains certain high-amplitude components at specific frequencies, these high-frequency components may induce localized, unexpected thermal effects within the capacitor. For example, due to the skin effect, proximity effect, or the characteristics of dielectric loss at specific frequencies, high-frequency currents may cause abnormally high current densities in localized areas, generating significantly more localized heat than other areas.

[0005] Because these localized hot spots are caused by ripple currents of a specific frequency, and the heat is concentrated in the capacitor's internal microstructure, externally mounted temperature sensors (usually attached to the capacitor's outer casing) are insensitive to these deep-seated hot spots. The heat from these hot spots is averaged and diluted by the surrounding normal area when conducted to the casing surface, resulting in surface temperature readings that remain within the normal range and cannot accurately reflect the true internal heat load distribution. Therefore, based on the lowered surface temperature and overall RMS current, the monitoring system incorrectly judges the capacitor's operating condition as healthy and fails to proactively implement protective measures such as current limiting or power reduction.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] This application discloses a method and system for monitoring the charging and discharging characteristics of electric vehicle capacitors, aiming to solve the technical problem that existing electric vehicle capacitor monitoring methods are unable to detect internal localized degradation, leading to safety hazards.

[0008] The technical solution of this application is as follows: In a first aspect, this application discloses a method for monitoring the charging and discharging characteristics of an electric vehicle capacitor, comprising the following steps: Obtain the current signal flowing through the capacitor; Frequency domain analysis is performed on the current signal to obtain the ripple characteristic parameters of the frequency ripple component in the current signal; Continuously monitor the ripple characteristic parameters, obtain the monitoring results, and determine the rate of change of the ripple characteristic parameters based on the monitoring results; Determine the acceleration corresponding to the rate of change based on the rate of change; Based on the changing acceleration, the local degradation process inside the capacitor is determined, and the result of the local degradation process is obtained. Based on the assessment of the local degradation process, adjust the charging parameters or issue a warning.

[0009] Through this technical solution, this application can obtain the ripple characteristic parameters of the frequency ripple component by frequency domain analysis of the capacitor current signal, and further monitor its rate of change and acceleration of change, thereby accurately judging the local degradation process inside the capacitor. This overcomes the shortcomings of the prior art, which only focuses on the overall current and surface temperature and cannot detect the internal local damage, and effectively improves the safety of the electric vehicle charging process.

[0010] Secondly, this application also discloses an electric vehicle capacitor charging and discharging characteristic monitoring system for performing electric vehicle capacitor charging and discharging characteristic monitoring, including: The current signal acquisition module is used to acquire the current signal flowing through the capacitor; The ripple feature acquisition module is used to perform frequency domain analysis on the current signal to obtain the ripple feature parameters of the frequency ripple component in the current signal. The rate of change determination module is used to continuously monitor the ripple characteristic parameters, obtain the monitoring results, and determine the rate of change of the ripple characteristic parameters based on the monitoring results. The variable acceleration determination module is used to determine the variable acceleration corresponding to the rate of change based on the rate of change. The degradation process judgment module is used to judge the local degradation process inside the capacitor based on the changing acceleration and obtain the judgment result of the local degradation process. The judgment result response module is used to adjust charging parameters or issue warnings based on the judgment result of the local degradation process.

[0011] This application provides a monitoring system for the charging and discharging characteristics of electric vehicle capacitors. Through modular design, it realizes the acquisition of capacitor current signals, ripple characteristic analysis, degradation process judgment and corresponding response measures, which can effectively monitor the local degradation of capacitors and improve the safety of electric vehicle charging.

[0012] Beneficial Effects: This application discloses a method for monitoring the charging and discharging characteristics of an electric vehicle capacitor. By acquiring the current signal flowing through the capacitor and performing frequency domain analysis, the ripple characteristic parameters of the frequency ripple component in the current signal can be accurately captured. Furthermore, by continuously monitoring the rate and acceleration of change of these ripple characteristic parameters, this application can gain in-depth insight into the local degradation process inside the capacitor. This method overcomes the limitations of existing technologies that only focus on the overall current, surface temperature, and macroscopic resistance and capacitance changes of the capacitor, failing to detect internal local overheating and early damage. By judging the local degradation process, this application can adjust charging parameters in a timely manner or issue early warnings, thereby effectively avoiding sudden capacitor failure caused by the accumulation of internal micro-damage, and significantly improving the operational safety and reliability of electric vehicles in fast charging scenarios. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for monitoring the charging and discharging characteristics of an electric vehicle capacitor in one embodiment of the present invention; Figure 2 This is a flowchart of a method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to another embodiment of the present invention; Figure 3 This is a system block diagram of an electric vehicle capacitor charging and discharging characteristic monitoring system according to another embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Electric vehicle capacitor charging and discharging characteristic monitoring system; 11. Current signal acquisition module; 12. Ripple characteristic acquisition module; 13. Change rate determination module; 14. Change acceleration determination module; 15. Degradation process judgment module; 16. Judgment result response module. Detailed Implementation

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] This application proposes a method for monitoring the charging and discharging characteristics of electric vehicle capacitors, combined with... Figure 1 As shown, it includes: S1, acquire the current signal flowing through the capacitor; S2, perform frequency domain analysis on the current signal to obtain the ripple characteristic parameters of the frequency ripple component in the current signal; S3, continuously monitor the ripple characteristic parameters, obtain the monitoring results, and determine the rate of change of the ripple characteristic parameters based on the monitoring results; S4. Determine the acceleration corresponding to the rate of change based on the rate of change. S5. Based on the changing acceleration, determine the local degradation process inside the capacitor and obtain the judgment result of the local degradation process. S6 adjusts charging parameters or issues a warning based on the judgment of the local degradation process.

[0017] To facilitate understanding of the electric vehicle capacitor charging and discharging characteristic monitoring method of this application, the key terms involved in the text will be explained in a unified manner first.

[0018] In this application, "capacitor" refers to a large-capacity capacitor used in the power management system of an electric vehicle to smooth current and stabilize voltage. During vehicle charging, discharging, and energy recovery, the capacitor is subjected to varying current surges and continuous high-frequency ripple current. The state of its internal dielectric, electrode connection points, and conductive path directly affects the operational stability of the power management system.

[0019] "Current signal" refers to the real-time current data flowing through the capacitor. The current signal can be acquired by a current sensor installed on the capacitor's charging and discharging path, and is used to reflect the current changes of the capacitor under its current operating conditions.

[0020] Frequency domain analysis refers to the signal processing procedure of converting a time-domain current signal into a frequency-domain signal, used to identify the distribution and intensity characteristics of different frequency components in the current signal. Frequency domain analysis can be implemented using Fourier transform, fast Fourier transform, or other spectral analysis methods.

[0021] "Frequency ripple component" refers to the AC component superimposed on the DC current or low-frequency current variation of a capacitor. This type of AC component usually manifests as high-frequency fluctuations within a specific frequency range and corresponds to the capacitor's internal equivalent series resistance, dielectric loss, local heating, and charging / discharging conditions.

[0022] "Ripple characteristic parameters" refer to quantitative parameters used to characterize the state of frequency ripple components. These parameters can include amplitude, phase, harmonic content, band energy, dominant ripple frequency, and ripple energy distribution at a specific frequency. Through ripple characteristic parameters, the internal response state of a capacitor during charging and discharging can be reflected from a frequency domain perspective.

[0023] "Local degradation process" refers to the gradual decline in performance of a capacitor in a localized area due to factors such as dielectric aging, localized overheating, partial discharge, loose connections, or increased losses. This degradation process may not initially manifest as a significant decrease in overall capacitance, a significant increase in casing temperature, or a significant change in macroscopic impedance, but it will gradually become apparent through subtle changes in the high-frequency ripple response.

[0024] Based on the above terminology definitions, this application provides a method for monitoring the charging and discharging characteristics of an electric vehicle capacitor. This method acquires the current signal flowing through the capacitor and performs frequency domain analysis on the frequency ripple component of the current signal. It further tracks the rate and acceleration of change of the ripple characteristic parameters to determine whether there is a localized degradation process within the capacitor, and adjusts the charging parameters or outputs warning information based on the judgment result.

[0025] During implementation, the first step is to acquire the current signal flowing through the capacitor. This current signal can be obtained using a current sensor installed along the capacitor's charging and discharging path in the electric vehicle's power management system. Specifically, a Hall effect current sensor, a shunt, or other high-precision current detection devices can be used to convert the real-time current flowing through the capacitor into a processable electrical signal and transmit it to the data acquisition unit. By continuously acquiring the current along the capacitor's charging and discharging path, a fundamental signal reflecting the capacitor's actual operating state can be obtained.

[0026] After acquiring the current signal, frequency domain analysis is performed to obtain the ripple characteristic parameters of the frequency ripple components in the current signal. During frequency domain analysis, the time-domain current signal can be sampled and preprocessed first, and then the current spectrum can be obtained through Fast Fourier Transform or other spectral analysis methods. Based on the current spectrum, frequency ripple components within the target frequency range are extracted, and the corresponding amplitude, phase, harmonic content, band energy, or dominant ripple frequency and other ripple characteristic parameters are further obtained. Preferably, high-frequency ripple components that are highly correlated with internal capacitor losses, local heating, or changes in dielectric state can be the focus of analysis, allowing subsequent monitoring to concentrate on frequency domain response information that reflects local degradation.

[0027] After obtaining the ripple characteristic parameters, the ripple characteristic parameters are continuously monitored to obtain monitoring results, and the rate of change of the ripple characteristic parameters is determined based on the monitoring results. Continuous monitoring can be achieved by periodically repeating the current signal acquisition and frequency domain analysis process, allowing the system to continuously obtain the value of the same ripple characteristic parameter at different time points. By comparing the changes in the ripple characteristic parameters at adjacent time points or within continuous time windows, the rate of change of the ripple characteristic parameters over time can be determined. The rate of change characterizes how quickly the ripple characteristic parameters deviate from the steady state, and compared to the ripple value at a single moment, it is more reflective of whether the internal state of the capacitor is undergoing continuous change.

[0028] Furthermore, based on the rate of change, the corresponding acceleration is determined. The acceleration characterizes the trend of the rate of change of the ripple characteristic parameters themselves, i.e., whether the degradation trend of the ripple characteristic parameters is accelerating or slowing down. In practice, difference analysis, moving regression analysis, or trend fitting analysis can be performed on the rate of change over a continuous time period to obtain the corresponding acceleration. By introducing the acceleration, early accelerated degradation states that are difficult to distinguish based solely on the rate of change can be identified, avoiding the missed detection of internal degradation processes before the ripple characteristic parameters reach obvious abnormal levels.

[0029] After obtaining the acceleration change, the local degradation process inside the capacitor is determined based on the acceleration change, resulting in a judgment of the local degradation process. The judgment process can be implemented based on preset thresholds, degradation mode rules, or historical sample correspondences. For example, when the acceleration change of the amplitude of a certain frequency ripple component continuously increases, and this change matches the increase in capacitor dielectric loss or local heating characteristics, it can be determined that there are early signs of local degradation inside the capacitor; when the acceleration change further increases and continues to exceed a preset range, it can be determined that the local degradation process has entered an accelerated stage; when the acceleration change occurs together with phase drift, increased harmonic content, or concentrated changes in frequency band energy of a specific frequency ripple component, different degradation types such as dielectric aging, partial discharge, and loose internal connections can be further distinguished. Therefore, the judgment of the local degradation process no longer relies on a single macroscopic parameter, but is based on the dynamic change law of ripple characteristic parameters.

[0030] After determining the progress of localized degradation, charging parameters are adjusted or warnings are issued based on the assessment. When the assessment indicates early signs of localized degradation in the capacitor, adjustments can be made to the charging current, charging voltage, charging power, charging slope, or pulse charging strategy to reduce the capacitor's continued exposure to high ripple stress. When the assessment indicates that the localized degradation process has entered a higher-risk stage, a warning message can be triggered and output to the vehicle control system, charging management system, or maintenance terminal. The warning message may include the degradation type, degradation level, corresponding ripple characteristic parameters, rate of change, acceleration of change, and recommended charging restrictions or maintenance measures.

[0031] By employing the above processing method, this application does not rely solely on a decrease in overall capacitor capacitance, an abnormal increase in surface temperature, or a significant change in macroscopic impedance as the sole criterion for judgment. Instead, it extracts the frequency ripple component from the current signal flowing through the capacitor and further analyzes the rate and acceleration of change of ripple characteristic parameters to identify early signs of localized degradation within the capacitor. Since localized overheating, increased dielectric loss, or abnormal internal connections typically alter the capacitor's response to high-frequency ripple before gradually reflecting in macroscopic performance parameters, monitoring methods based on dynamic changes in ripple characteristic parameters can detect internal degradation trends earlier.

[0032] Furthermore, adjusting charging parameters based on the assessment of local degradation processes allows the power management system to proactively reduce the electrical and thermal stresses of capacitors before significant failures occur, and to issue timely warnings when the risk of degradation increases. This reduces the risk of capacitors continuing to operate under high loads without obvious external signs of abnormality, improving the safety and reliability of the electric vehicle charging and discharging process.

[0033] Optional, combined Figure 2As shown, the steps to determine the acceleration corresponding to the rate of change include: A1, smooth the rate of change to obtain a smoothed rate of change; A2, based on the smoothed rate of change, calculate the acceleration corresponding to the rate of change.

[0034] Before determining the corresponding acceleration based on the rate of change of the ripple characteristic parameters, the rate of change can be smoothed. Smoothing reduces random fluctuations in the rate of change sequence introduced by sampling errors, current sensor noise, short-term load disturbances, or frequency domain analysis errors, making the subsequently calculated acceleration more reflective of the actual evolution trend of the ripple characteristic parameters. Smoothing can be implemented using moving average filtering, exponential smoothing, Savitzky-Golay filtering, or other smoothing algorithms suitable for time-series data processing. The specific smoothing method used can be determined based on the degree of data fluctuation in the rate of change of the ripple characteristic parameters, real-time requirements, and the requirement to preserve edge trends. By smoothing the rate of change, short-term noise can be avoided from being amplified by the second difference and affecting the judgment result of the local degradation process.

[0035] After obtaining the smoothed rate of change, the corresponding acceleration is calculated based on it. The acceleration characterizes the state of the rate of change as it continues to change over time. Specifically, the acceleration can be calculated using numerical differentiation, for example, by subtracting the smoothed rates of change from those at adjacent sampling times and dividing by the corresponding time interval to obtain the average acceleration within that time interval; alternatively, the smoothed rate of change can be fitted with a trend within a preset time window, and the corresponding acceleration can be determined using the first-order result of the fitted curve. This method allows for the detection of whether the ripple characteristic parameters are accelerating, decelerating, or exhibiting unstable fluctuations, even before significant absolute shifts occur.

[0036] Optionally, the local degradation process inside the capacitor is determined based on the changing acceleration, and the determination result of the local degradation process is obtained, including the following processing steps.

[0037] This document defines the acceleration response modes for the frequency ripple characteristic parameters under different local degradation modes of a capacitor. Each acceleration response mode is a template based on the characteristics of acceleration changes within a preset time window. These characteristics include at least the amplitude range, sign variation rules, and trend curvature features. The acceleration response modes can be established during capacitor R&D testing, life cycle testing, fault reproduction experiments, or historical operating data analysis to describe the typical dynamic performance of the acceleration changes in frequency ripple characteristic parameters under different local degradation modes. Local degradation modes can include dielectric partial discharge, dielectric aging, electrode corrosion, loose internal connections, poor local contact, or other degradation types that can cause dynamic changes in the frequency ripple characteristic parameter response.

[0038] The model template is not a single threshold, but rather composed of multiple dynamic characteristics of the changing acceleration within a preset time window. The amplitude range defines the numerical distribution of the changing acceleration within that time window, distinguishing the intensity differences corresponding to continuous slow degradation, sudden accelerated degradation, and intermittent degradation. The sign change rule describes the variation of the sign of the changing acceleration within the time window, such as consistently positive, consistently negative, periodically reversing, or irregularly switching. The trend curvature feature characterizes the curve shape of the changing acceleration over time, such as approximately linear growth, exponential enhancement, slow decay, step change, or fluctuating change. By incorporating these characteristics into the model template, the changing acceleration response model can simultaneously reflect the degradation intensity, degradation direction, and degradation evolution morphology.

[0039] During actual operation, the system reads the changing acceleration of the frequency ripple characteristic parameters calculated in real time. This changing acceleration is further calculated from the current signal during capacitor operation through frequency domain analysis, ripple characteristic parameter extraction, rate of change determination, and smoothing. The system continuously collects real-time changing acceleration data within a preset time window and maps the changing acceleration sequence within that window to features for comparison. The composition of these features is consistent with the pattern template in the changing acceleration response mode, including at least the real-time amplitude range, real-time sign change rules, and real-time trend curvature features. Thus, the dynamic performance of changing acceleration in real-time operation can be converted into a feature expression that can be compared with the preset pattern template.

[0040] Subsequently, the features to be compared are compared with the corresponding features of the changing acceleration response mode to identify local degradation patterns. Similarity comparison can be comprehensively calculated based on the degree of overlap in amplitude ranges, the consistency of sign change rules, and the matching degree of trend curvature features. It can also be achieved using methods such as Euclidean distance, Mahalanobis distance, correlation coefficient, dynamic time warping, or classification models. Through similarity comparison, it can be determined which preset changing acceleration response mode the current real-time changing acceleration feature is closest to, thereby identifying the possible local degradation mode corresponding to the capacitor. If multiple local degradation modes are similar, the dominant local degradation mode can be determined based on the highest similarity, the similarity difference, or the confidence threshold, or multiple candidate local degradation modes can be output.

[0041] After identifying local degradation patterns, the system determines the stage of the local degradation process based on these patterns, thus obtaining a judgment result for the local degradation process. Different local degradation patterns typically have different degradation evolution paths. For example, partial discharge of the dielectric may manifest as a continuously positive and gradually increasing acceleration, while loosening of internal connections may manifest as alternating positive and negative acceleration with intermittent fluctuations. The system can match the identified local degradation patterns with preset degradation evolution paths and, combined with the amplitude level, duration, sign change state, and trend curvature characteristics of the current acceleration, determine whether the local degradation process is in the early, middle, late, or other preset stages. The resulting judgment result for the local degradation process can not only characterize the existence of local degradation but also the type of degradation and its development stage.

[0042] In some preferred embodiments, two typical local degradation modes are pre-obtained during capacitor life cycle testing. Mode A corresponds to slow aging caused by partial discharge of the dielectric. Within a preset ten-second time window, the acceleration of the frequency ripple characteristic parameter change under this mode exhibits an amplitude range between 0.01 and 0.05, a consistently positive sign, and a slowly linearly increasing trend curvature. Based on the aforementioned amplitude range, sign change rules, and trend curvature characteristics, a change acceleration response mode corresponding to Mode A can be constructed. Mode B corresponds to intermittent heating caused by poor contact at internal connection points. Within the same ten-second time window, the acceleration of the change under this mode exhibits an amplitude range between -0.02 and 0.08, with the sign frequently switching between positive and negative, and a drastically fluctuating trend curvature. Based on these characteristics, a change acceleration response mode corresponding to Mode B can be constructed.

[0043] During the actual operation of the capacitor, the system calculates the acceleration of the frequency ripple characteristic parameters in real time and maps the collected acceleration sequence to a feature to be compared within a ten-second time window. When the amplitude range, sign change rule, and trend curvature characteristics of the feature to be compared have a high similarity to the pattern template of mode A, the system identifies that the current capacitor is in the local degradation mode corresponding to mode A. Further, combining the acceleration range and duration corresponding to each stage in the preset degradation evolution path of mode A, if the current acceleration falls within the range corresponding to the middle stage of mode A, the local degradation process is judged as the middle stage of mode A. When the feature to be compared has a high similarity to the pattern template of mode B, the system identifies that the current capacitor is in the local degradation mode corresponding to mode B and determines its local degradation process stage according to the stage division rules corresponding to mode B.

[0044] Through the above processing, the changing acceleration is no longer used as a single numerical value in threshold judgment, but is organized into a dynamic pattern feature that includes amplitude range, sign change rules, and trend curvature characteristics. This method can distinguish the differences in ripple response under different local degradation modes and further identify the development stage of the local degradation process, so that subsequent charging parameter adjustments or warning outputs can be matched with the specific degradation type and degree.

[0045] Optionally, after determining the stage of the local degradation process based on the local degradation pattern and obtaining the judgment result of the local degradation process, the method further includes: Obtain the operating parameters during the charging process; these parameters include charging power parameters, ambient temperature parameters, and capacitor surface temperature parameters. Based on operating parameters and according to preset risk correction rules, a risk correction amount is generated to correct the judgment result of the local degradation process. Apply the risk correction amount to the judgment result of the local degradation process, and obtain and output the corrected judgment result.

[0046] Specifically, "operating condition parameters" refer to the external or internal operating conditions that affect the capacitor's working state and degradation process during electric vehicle charging. Among these, "charging power parameters" reflect the magnitude of the charging current and voltage; high-power charging typically accelerates capacitor degradation. "Ambient temperature parameters" refer to the temperature of the external environment in which the capacitor is located; high temperatures exacerbate chemical reactions and physical stress within the capacitor. "Capacitor surface temperature parameters" directly reflect the capacitor's heat dissipation and the heat generated by internal losses, serving as a key indicator for assessing the capacitor's thermal stress. Real-time acquisition of these parameters aims to provide more comprehensive background information for judging the degradation process.

[0047] The "risk correction rule" is a pre-established set of logic or models used to quantify the impact of different combinations of operating parameters on the judgment result of the local degradation process of the capacitor. This rule can be constructed based on a large amount of experimental data, historical operating data, and expert experience. For example, it can define the magnitude by which the degradation process judgment result needs to be adjusted upwards or downwards under specific charging power, ambient temperature, and capacitor surface temperature. Its purpose is to transform the risk factors of real-time operating conditions into actionable correction amounts.

[0048] In practical applications, the "risk correction amount" is a value or index calculated based on the "operating condition parameters" and "risk correction rules," used to adjust the initial judgment of the local degradation process. For example, when the operating condition parameters show that the capacitor is in a high-risk operating state, the risk correction amount may be a positive value, indicating that the judgment of the degradation process needs to be revised upward to reflect a higher actual risk; conversely, if the operating condition parameters show a low-risk operating state, a negative value or a zero value may be generated.

[0049] Furthermore, "applying the risk correction amount to the judgment result of the local degradation process" refers to combining the calculated risk correction amount with the preliminary judgment result of the local degradation process to generate a more accurate and more consistent correct judgment result with the actual operating conditions. This application method can be a simple numerical superposition, weighted average, or more complex methods such as table lookup and model calculation.

[0050] Optionally, after applying the risk correction amount to the judgment result of the local degradation process, obtaining and outputting the corrected judgment result, the method further includes predicting the failure probability of the capacitor based on the corrected judgment result. The step of predicting the failure probability of the capacitor based on the corrected judgment result includes: Based on the local degradation mode corresponding to the local degradation process and the corrected judgment result, retrieve the degradation evolution path corresponding to the local degradation mode from the preset degradation evolution path library. Based on the operating parameters, the degradation evolution path is modified according to the operating conditions to obtain the modified degradation evolution path. Based on the modified degradation evolution path, the failure probability of the capacitor is predicted.

[0051] "Predicting the failure probability of a capacitor" refers to assessing the likelihood of functional failure within a specific timeframe by analyzing the capacitor's current health status, historical degradation trends, and future operating conditions. This provides a quantifiable risk indicator, helping decision-makers better understand the capacitor's reliability status.

[0052] The "deterioration evolution path library" can be understood as a set of pre-stored typical trajectories or models showing how the health status of capacitors changes over time or under different local degradation modes. These paths are built based on a large amount of experimental data, historical operating data, or simulation models, and each path describes the possible evolution process from a certain degradation mode to final failure.

[0053] "Degradation evolution path" refers to the specific trajectory of changes in key performance parameters (such as capacitance and internal resistance) of a capacitor over time or with the intensity of use, from the identification of degradation mode to final failure. It reflects the aging pattern of a capacitor under a specific degradation mode.

[0054] "Operating condition correction" refers to adjusting the retrieved degradation evolution path based on actual or predicted operating condition parameters such as charging power parameters, ambient temperature parameters, and capacitor surface temperature parameters. This is because the degradation rate and mode of capacitors are significantly affected by the actual operating environment, and operating condition correction can make the predicted results closer to the actual situation.

[0055] In some preferred embodiments, assuming that through the aforementioned steps, the capacitor of an electric vehicle is determined to be in the "early stage of dielectric loss degradation" mode, and after risk correction, the judgment result of its local degradation process indicates that the degradation process has entered the "moderate stage". At this time, the system will retrieve a typical path describing the development of the early stage of dielectric loss degradation from the moderate stage to final failure from a preset degradation evolution path library based on the judgment results of the "early stage of dielectric loss degradation" mode and the "moderate stage". For example, this path may show that under standard operating conditions, the dielectric loss tangent of the capacitor will rise from the current value to the failure threshold within the next 1000 hours.

[0056] Furthermore, assuming the current charging process parameters show high charging power, and that the ambient temperature and capacitor surface temperature are also higher than standard operating conditions, the system will use these parameters to correct the retrieved degradation evolution path. Specifically, since high temperature and high power charging accelerate dielectric loss, the corrected degradation evolution path might show that, under the current conditions, the capacitor's dielectric loss tangent may reach the failure threshold within 800 hours.

[0057] Ultimately, based on this degradation evolution path corrected for operating conditions, the system can predict that the capacitor has a high probability of failure within the next 800 hours. For example, it predicts a 15% probability of failure within the next 500 hours and a 50% probability of failure within the next 800 hours. This quantitative failure probability prediction provides a direct basis for subsequent charging management decisions. For example, the system can suggest reducing the charging power or reminding the user to check the capacitor.

[0058] Optionally, based on the assessment of the local degradation process, the steps to adjust charging parameters or issue a warning include: Read the corrected degradation evolution path and the remaining charging time for the current charging task; Based on the corrected degradation evolution path, predict the probability of capacitor failure during the remaining charging time. Based on the assessment results of the local degradation process, a corresponding degradation risk level is generated; Based on the degradation risk level, failure probability, preset charging safety threshold, and charging efficiency target, determine the charging power adjustment strategy; Based on the charging power adjustment strategy, adjust charging parameters or issue warnings.

[0059] Specifically, before adjusting charging parameters or issuing warnings, it is first necessary to read the corrected degradation evolution path. This path is based on the capacitor's local degradation mode and the corrected judgment results, and is obtained after operating condition correction. It depicts the predicted future degradation trend of the capacitor under specific operating conditions. Simultaneously, it is also necessary to obtain the remaining charging time for the current charging task, which provides a time dimension for assessing the capacitor's risk within the remaining charging cycle.

[0060] Predicting the capacitor's failure probability within the remaining charging time, based on the corrected degradation path, refers to using a model or algorithm to calculate the likelihood of the capacitor failing before completing the current charging task, based on the known degradation trend and remaining charging time. This failure probability is a key quantitative indicator used to assess the safety of the charging process.

[0061] In practical applications, generating a corresponding degradation risk level based on the assessment of the local degradation process means mapping the current local degradation state of the capacitor (e.g., mild degradation, moderate degradation, severe degradation, etc.) to a preset risk level system, such as levels 1 to 5 from low to high. This risk level intuitively reflects the current health status of the capacitor.

[0062] Furthermore, a charging power adjustment strategy is determined based on the degradation risk level, failure probability, preset charging safety threshold, and charging efficiency target. The charging safety threshold is a preset failure probability threshold within an allowable range, used to limit the upper limit of capacitor failure risk during the remaining charging time, ensuring the safety of the charging process. The charging efficiency target represents the desired charging speed or energy transfer efficiency. By comprehensively considering these four factors, a charging power adjustment strategy that balances safety and efficiency can be formulated. For example, when the failure probability is close to or exceeds the safety threshold, it may be necessary to reduce the charging power; when the failure probability is far below the safety threshold and the charging efficiency target is high, the charging power can be appropriately increased.

[0063] Therefore, based on the charging power adjustment strategy, charging parameters can be adjusted or warnings can be issued. Adjustments to charging parameters may include charging current, charging voltage, or charging power. When the risk is high, in addition to adjusting charging parameters, warning information can also be issued, such as notifying users or maintenance personnel through the vehicle's onboard system or remote monitoring platform to alert them to potential risks.

[0064] In some preferred embodiments, assuming an electric vehicle is charging, the system has obtained the corrected degradation evolution path using the method described above and identified the capacitor as being in a "moderate degradation" mode, with the corresponding local degradation process judgment result being "degradation stage 2". The remaining charging time for the current charging task is 30 minutes. Based on the corrected degradation evolution path, the system predicts that the capacitor failure probability is 8% in the next 30 minutes. Simultaneously, the system generates a corresponding degradation risk level of "medium risk" based on "degradation stage 2". The preset charging safety threshold is 5%, and the charging efficiency target is "high".

[0065] At this point, the system will comprehensively consider these four factors: a failure probability of 8% (above the safety threshold of 5%), a medium risk level, and a high charging efficiency target. Since the failure probability has exceeded the safety threshold, the system will prioritize safety. Therefore, based on the preset charging power adjustment strategy, the system will determine a reduction in charging power, such as reducing the charging power from the current value by 15%. Simultaneously, the system may issue a "suggested check" warning message, prompting the user or maintenance personnel to pay attention to the health status of the capacitor. In this way, the solution proposed in this application can dynamically optimize the charging process while ensuring charging safety, avoiding potential risks and providing timely feedback to the user.

[0066] Optionally, the steps for determining the charging power adjustment strategy based on the degradation risk level, failure probability, preset charging safety threshold, and charging efficiency target include: The system reads the capacitor's failure probability, charging safety threshold, charging efficiency target, and degradation risk level. The charging safety threshold is a preset, permissible failure probability threshold used to limit the upper limit of capacitor failure risk within the remaining charging time.

[0067] When the difference between the failure probability and the charging safety threshold is within a preset fluctuation range and the charging efficiency target is higher than the preset target, the dynamic adjustment range of the charging power is determined based on the difference between the failure probability and the charging safety threshold, as well as the charging efficiency target. Specifically, the preset fluctuation range refers to an interval within which the failure probability is allowed to fluctuate slightly around the safety threshold. Within this interval, the system can perform more precise power adjustments to maximize charging efficiency while ensuring basic safety. The dynamic adjustment range means that the charging power can be increased or decreased slightly according to real-time conditions to achieve an optimal balance.

[0068] When the difference between the failure probability and the charging safety threshold exceeds a preset fluctuation range, and the failure probability exceeds the charging safety threshold, the reduction in charging power is determined based on the degree of excess and the charging efficiency target. This means that once the failure risk significantly exceeds the safety limit, the system will prioritize safety and correspondingly reduce the charging power significantly according to the degree of risk exceeding the threshold.

[0069] When the difference between the failure probability and the charging safety threshold exceeds a preset fluctuation range, and the failure probability is lower than the charging safety threshold, the increase in charging power is determined based on the degradation risk level and the charging efficiency target. This indicates that when the capacitor failure risk is lower than the charging safety threshold, the system can moderately increase the charging power while meeting the charging efficiency target; furthermore, the degradation risk level constrains the increase, making the increase more conservative as the degradation risk level increases.

[0070] The dynamic adjustment range, reduction range, or increase range are summarized into a charging power adjustment strategy and output.

[0071] In some preferred embodiments, it is assumed that the failure probability of a capacitor in an electric vehicle is monitored in real time during the charging process.

[0072] Specifically, when the capacitor failure probability is 0.05, the preset charging safety threshold is 0.06, and the preset fluctuation range is ±0.01, the difference between the failure probability and the safety threshold (-0.01) is within the preset fluctuation range. If the charging efficiency target is high at this time, the system will determine a dynamic adjustment range, such as an increase of 5%, based on this difference and the efficiency target, to slightly speed up the charging speed while ensuring safety.

[0073] For example, if the probability of capacitor failure rises to 0.08, significantly exceeding the safety threshold of 0.06 and exceeding the preset fluctuation range, the system will determine a reduction in charging power, for example, by 20%, based on the degree of exceedance of 0.02 and the current charging efficiency target, to quickly reduce the risk and ensure charging safety.

[0074] In one specific implementation, if the capacitor failure probability is 0.02, far below the safety threshold of 0.06, and exceeds the preset fluctuation range, while the charging efficiency target is high, the system will determine a charging power increase of, for example, 15%, based on the degradation risk level and the charging efficiency target to fully utilize the capacitor's good condition and accelerate the charging process. When the degradation risk level is high, the increase will be correspondingly reduced.

[0075] Through the different scenarios described above, the solution proposed in this application can intelligently adjust the charging power according to the real-time health status of the capacitor and the charging demand, thereby achieving the best balance between charging safety and efficiency.

[0076] Optionally, the step of determining the dynamic adjustment range of charging power based on the difference between the failure probability and the charging safety threshold, as well as the charging efficiency target, includes: A power adjustment response curve is set, which defines the initial value of the charging power adjustment range under different combinations of the degree of difference and the charging efficiency target; where different degrees of difference refer to the different degrees of difference between the failure probability and the charging safety threshold. Obtain the health status indicators of the capacitor, including the rate of change of internal impedance and the dielectric loss tangent. Based on the health status indicators, the power adjustment response curve is corrected to obtain the corrected power adjustment response curve; Based on the corrected power adjustment response curve, the dynamic adjustment range of the charging power is determined and output.

[0077] Specifically, the power adjustment response curve refers to a pre-established mapping relationship used to guide the dynamic adjustment of charging power. This curve defines the initial value of the corresponding charging power adjustment range by combining different degrees of difference between the failure probability and the charging safety threshold, along with the charging efficiency target. For example, when the difference between the failure probability and the safety threshold is small and the charging efficiency target is high, the initial adjustment range may be set to a moderate increase; conversely, when the difference is large, the initial adjustment range may be set to a moderate decrease. Here, different degrees of difference can be understood as a quantification of the deviation between the failure probability and the charging safety threshold, such as being divided into intervals like "small difference," "medium difference," and "large difference."

[0078] Furthermore, to achieve more precise power adjustment, this application introduces capacitor health status indicators. These indicators specifically include the rate of change of internal impedance and the dielectric loss tangent. The rate of change of internal impedance reflects the change in the equivalent series resistance (ESR) inside the capacitor; an increase in ESR is usually an important indicator of capacitor aging. The dielectric loss tangent characterizes the loss characteristics of the capacitor's dielectric material; an increase in ESR usually indicates a decline in dielectric performance, potentially foreshadowing partial discharge or insulation degradation. These health status indicators directly reflect the degree of physical degradation of the capacitor, providing a deeper basis for the dynamic adjustment of charging power.

[0079] Based on this, the preset power adjustment response curve is corrected according to the acquired health status indicators. This means that even if the initial adjustment range under the combination of failure probability and charging efficiency targets is determined, if the rate of change of the capacitor's internal impedance or the dielectric loss tangent shows abnormalities, the initial adjustment range will still be further adjusted. For example, if the health status indicators show that the capacitor's degradation is higher than expected, even if the initial adjustment range is to increase, it may be corrected to decrease or remain unchanged to ensure charging safety. Through this correction, a revised power adjustment response curve that better reflects the capacitor's current actual health condition can be obtained.

[0080] Finally, based on this corrected power adjustment response curve, the dynamic adjustment range of the charging power is determined and output. This adjustment range will be directly used to guide the adjustment of charging parameters, thereby achieving fine-grained control of the charging process.

[0081] In some preferred embodiments, a specific example is given below. Assume that during a certain charging stage, based on the difference between the failure probability and the charging safety threshold, as well as the charging efficiency target, the dynamic adjustment range of the charging power is initially determined to be an increase of 5% using a preset power adjustment response curve. At this point, the system further acquires the health status indicators of the capacitor. For example, if the internal impedance change rate of the capacitor has reached 80% of the warning threshold, and the dielectric loss tangent also shows a significant upward trend, these indicators suggest that the actual degradation of the capacitor may be more severe than predicted by the failure probability model. Based on these health status indicators, the system will correct the original "5% increase" adjustment range according to preset correction rules. The corrected result may be an increase of 2%, or even, in some extreme cases, a decrease of 2% or no change. Finally, the system will output the corrected dynamic adjustment range of the charging power based on this corrected power adjustment response curve and adjust the charging parameters accordingly. In this way, even when the macroscopic prediction results allow for an increase in power, the strategy can be adjusted in a timely manner through feedback from microscopic health indicators to avoid potential risks and ensure the safety of the charging process and the long-term health of the capacitor.

[0082] Optionally, the steps for correcting the power adjustment response curve based on health status indicators to obtain the corrected power adjustment response curve include: Read the local degradation mode of the capacitor; Based on the local degradation mode, retrieve the internal impedance change rate correction factor and the dielectric loss tangent correction factor; Obtain the real-time rate of change of internal impedance and the tangent of dielectric loss angle; The real-time internal impedance change rate and the internal impedance change rate correction factor are calculated to obtain the internal impedance change rate correction amount; the dielectric loss tangent value and the dielectric loss tangent value correction factor are calculated to obtain the dielectric loss tangent value correction amount. The power adjustment response curve is adjusted based on the correction amount of the internal impedance change rate and the correction amount of the dielectric loss tangent to obtain the corrected power adjustment response curve.

[0083] Specifically, reading the local degradation mode of a capacitor refers to identifying the specific type of degradation the capacitor is currently experiencing, such as electrolyte drying, electrode passivation, or current collector corrosion. Different local degradation modes have different mechanisms and degrees of influence on the capacitor's health status indicators (such as the rate of change of internal impedance and the dielectric loss tangent). Therefore, based on the local degradation mode, correction factors for the rate of change of internal impedance and the dielectric loss tangent are retrieved. These correction factors are weights or adjustment parameters pre-set according to the response characteristics of health status indicators under different local degradation modes, aiming to differentiate the health status indicators for specific degradation modes. Real-time rates of change of internal impedance and dielectric loss tangents are obtained, reflecting the capacitor's current health status. Subsequently, the real-time rate of change of internal impedance is calculated using the correction factor to obtain the correction amount for the rate of change of internal impedance; simultaneously, the dielectric loss tangent is calculated using the correction factor to obtain the correction amount for the dielectric loss tangent. These corrections are quantitative adjustments to the original health status indicators, making them more accurately reflect the true impact under specific degradation modes. Finally, based on the correction amounts for the rate of change of internal impedance and the dielectric loss tangent, the power adjustment response curve is adjusted to obtain the corrected power adjustment response curve. This adjustment can be a global shift or scaling of the curve, or a local deformation of a specific range, to make the power adjustment response curve more accurately adapt to the current local degradation state of the capacitor.

[0084] In some preferred embodiments, assuming that the electric vehicle capacitor is identified as having a "drying electrolyte" local degradation mode during operation using the aforementioned method, the system will retrieve the corresponding internal impedance change rate correction factor (e.g., set to 1.2) and dielectric loss tangent correction factor (e.g., set to 0.8) from a preset database based on the "drying electrolyte" mode. Assuming the currently acquired real-time internal impedance change rate is 5% and the dielectric loss tangent is 0.03, the internal impedance change rate correction will be calculated as 5% multiplied by 1.2, resulting in 6%; the dielectric loss tangent correction will be calculated as 0.03 multiplied by 0.8, resulting in 0.024. Subsequently, based on these two corrections, the preset power adjustment response curve is adjusted. For example, if the "drying electrolyte" mode typically causes a rapid increase in capacitor internal resistance, the corrected curve may set a more aggressive reduction in charging power within a higher failure probability range to prioritize safety. Conversely, if the identified local degradation mode is "electrode passivation," the correction factor may be different, resulting in a smoother adjustment range for charging power in the corrected curve under the same health status indicators, thus balancing charging efficiency. In this way, this application can dynamically and intelligently adjust the charging power according to the specific degradation mode of the capacitor, thereby achieving more refined battery management.

[0085] This application also discloses a monitoring system for the charging and discharging characteristics of an electric vehicle capacitor, used to perform monitoring of the charging and discharging characteristics of an electric vehicle capacitor, combined with... Figure 3 As shown, the electric vehicle capacitor charging and discharging characteristic monitoring system 1 includes: The current signal acquisition module 11 is used to acquire the current signal flowing through the capacitor; The ripple feature acquisition module 12 is used to perform frequency domain analysis on the current signal to obtain the ripple feature parameters of the frequency ripple component in the current signal. The rate of change determination module 13 is used to continuously monitor the ripple characteristic parameters, obtain the monitoring results, and determine the rate of change of the ripple characteristic parameters based on the monitoring results. The variable acceleration determination module 14 is used to determine the variable acceleration corresponding to the rate of change based on the rate of change. The degradation process judgment module 15 is used to judge the local degradation process inside the capacitor based on the changing acceleration and obtain the judgment result of the local degradation process. The judgment result response module 16 is used to adjust the charging parameters or issue an early warning based on the judgment result of the local degradation process.

[0086] This application also provides a capacitor charging and discharging characteristic monitoring system for electric vehicles, used to dynamically monitor the charging and discharging state of capacitors in the electric vehicle power management system, and to perform charging parameter adjustments or early warning outputs based on the judgment results of the local degradation process inside the capacitor. The system forms a continuous processing link around current signal acquisition, ripple feature extraction, rate of change analysis, acceleration analysis, judgment of local degradation process, and judgment result response, so that capacitor state monitoring is no longer limited to external parameters such as overall root mean square current, surface temperature, or macroscopic impedance, but can identify the early trend of local degradation inside the capacitor based on the frequency ripple component in the current signal flowing through the capacitor.

[0087] The electric vehicle capacitor charging and discharging characteristic monitoring system includes a current signal acquisition module. This module acquires the current signal flowing through the capacitor. It may include one or more current sensors installed along the capacitor charging and discharging path in the electric vehicle power management system, and a data acquisition circuit connected to the current sensors. The current sensors may be Hall effect current sensors, shunts, or other current detection devices capable of meeting the capacitor ripple current acquisition requirements. The current signal acquisition module converts the real-time current flowing through the capacitor into an electrical signal suitable for subsequent processing and outputs it to the ripple characteristic acquisition module.

[0088] The system also includes a ripple feature acquisition module. This module, connected to the current signal acquisition module, performs frequency domain analysis on the current signal to obtain ripple feature parameters of the frequency ripple components. The ripple feature acquisition module can be implemented using a digital signal processor, microcontroller, onboard computing unit, or application-specific integrated circuit (ASIC), and is internally configured with a Fast Fourier Transform (FFT) algorithm, spectrum analysis algorithm, or other frequency domain processing algorithms. After receiving the current signal output from the current signal acquisition module, the ripple feature acquisition module samples, preprocesses, and performs frequency domain conversion on the current signal, extracting the ripple amplitude, ripple frequency, phase, harmonic content, bandgap energy, or other ripple feature parameters within the target frequency range. The target frequency range can be determined based on the ripple response range corresponding to the capacitor's internal losses, localized heating, dielectric state changes, or charging / discharging conditions, ensuring that the ripple feature parameters reflect changes in the capacitor's internal local state.

[0089] The system also includes a rate of change determination module. This module, connected to the ripple feature acquisition module, continuously monitors the ripple feature parameters and determines their rate of change based on the parameters obtained at consecutive time points. The rate of change determination module receives the ripple feature parameters output by the ripple feature acquisition module according to a preset sampling period and calculates their rate of change after performing differential analysis, sliding window analysis, least squares fitting, or smoothing on the parameters at different time points. Through this module, the ripple feature parameters can be transformed from single-point values ​​into a trend representation that changes over time, thereby reflecting whether a continuous shift is occurring in the internal state of the capacitor.

[0090] The system also includes a variable acceleration determination module. This module, connected to the variable rate determination module, determines the corresponding variable acceleration based on the variable rate. The variable acceleration determination module can perform further differencing, regression fitting, or trend curvature analysis on the variable rate sequence to obtain the degree to which the variable rate changes over time. Variable acceleration is used to characterize whether the degradation trend of the ripple characteristic parameters accelerates, slows down, or fluctuates intermittently. Compared to judging solely based on the ripple characteristic parameters themselves or their rate of change, variable acceleration can reflect signs of accelerated local degradation earlier, providing a dynamic basis for subsequent identification of local degradation processes.

[0091] The system also includes a degradation process judgment module. This module, connected to the variable acceleration determination module, is used to determine the local degradation process within the capacitor based on the variable acceleration, and obtain the judgment result for the local degradation process. The degradation process judgment module can be implemented using an embedded controller, a software module in an onboard computing platform, or a dedicated decision unit. This module can preset variable acceleration response modes, threshold ranges, or mode templates corresponding to different local degradation modes, such as the ripple dynamic response characteristics corresponding to dielectric aging, partial discharge, loose internal connections, and poor local contact. The degradation process judgment module compares the real-time obtained variable acceleration with the preset variable acceleration response modes to identify whether the capacitor currently exhibits local degradation, the corresponding mode type of local degradation, and the stage of the local degradation process, and outputs the judgment result for the local degradation process.

[0092] The system also includes a judgment result response module. This module is connected to the degradation process judgment module and is used to adjust charging parameters or issue warnings based on the judgment results of local degradation processes. The judgment result response module can connect to the power management system, charging control unit, vehicle controller, or human-machine interface. When the judgment result of a local degradation process indicates early signs of local degradation in the capacitor, the judgment result response module can output charging parameter adjustment commands to the power management system to reduce charging current, limit charging voltage, reduce charging power, adjust charging slope, or change the pulse charging strategy. When the judgment result of a local degradation process indicates that the degradation risk reaches a preset warning level, the judgment result response module can output warning information through the vehicle's human-machine interface, audible and visual alarm devices, remote maintenance platform, or onboard communication unit. The warning information may include the degradation mode, degradation stage, corresponding ripple characteristic parameters, rate of change, acceleration of change, and recommended inspection or limiting measures.

[0093] In the above system, the current signal acquisition module provides real-time current data during the capacitor's charging and discharging process; the ripple feature acquisition module performs frequency domain analysis on the real-time current data to extract ripple feature parameters that reflect the local state inside the capacitor; the rate of change determination module tracks the time changes of the ripple feature parameters to obtain the rate of change; the acceleration change determination module further analyzes the changing state of the rate of change to obtain the acceleration change; the degradation process judgment module identifies the local degradation process inside the capacitor based on the acceleration change; and the judgment result response module adjusts charging parameters or outputs a warning based on the judgment result of the local degradation process. Thus, a closed-loop monitoring link from signal acquisition to risk response is formed among the modules.

[0094] In some implementations, the current signal acquisition module continuously collects the current signal flowing through the capacitor and sends it to the ripple feature acquisition module. The ripple feature acquisition module performs frequency domain analysis on the current signal and extracts the ripple amplitude, phase, and harmonic content within a specific high-frequency range as ripple feature parameters. The rate of change determination module tracks the ripple feature parameters over multiple consecutive monitoring cycles and calculates the rate of change of the ripple feature parameters. The acceleration determination module further analyzes the rate of change to determine the acceleration corresponding to the rate of change. The degradation process judgment module reads the acceleration and compares it with the acceleration response mode corresponding to a preset local degradation mode to identify whether the capacitor currently exhibits local degradation modes such as dielectric aging, partial discharge, or loose internal connections, and determines its current degradation stage. The judgment result response module, based on the judgment result output by the degradation process judgment module, outputs charging parameter adjustment commands to the power management system or outputs warning prompts to the human-machine interface.

[0095] Through the aforementioned system structure, capacitor health monitoring can be extended from macroscopic condition monitoring to dynamic analysis of frequency ripple response. Traditional monitoring systems typically focus on parameters such as the overall root-mean-square current, casing temperature, or macroscopic impedance of the capacitor. These parameters often show little change in the early stages of localized degradation, making it difficult to promptly reflect increased internal dielectric losses, localized overheating, or abnormal internal connections. This application extracts ripple characteristic parameters of the frequency ripple component through a ripple feature acquisition module, and continuously analyzes their dynamic changes through a rate of change determination module and an acceleration of change determination module, enabling the localized degradation process to be identified before it manifests as a significant external temperature rise or overall performance decline.

[0096] Furthermore, the degradation process judgment module and the judgment result response module enable the monitoring results to directly participate in power management control. When the system identifies that the local degradation process inside the capacitor is in its early stage, it can reduce the electrical and thermal stress on the capacitor by adjusting the charging parameters; when the degradation process enters a higher-risk stage, it can issue a timely warning to prompt users or maintenance personnel to inspect and handle the issue. Through this approach, the risk of sudden capacitor failure during charging can be reduced, and the safety and reliability of the electric vehicle power management system can be improved.

[0097] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring the charging and discharging characteristics of an electric vehicle capacitor, characterized in that, include: Obtain the current signal flowing through the capacitor; Frequency domain analysis is performed on the current signal to obtain the ripple characteristic parameters of the frequency ripple component in the current signal; The ripple characteristic parameters are continuously monitored to obtain monitoring results, and the rate of change of the ripple characteristic parameters is determined based on the monitoring results. Based on the rate of change, determine the acceleration corresponding to the rate of change; Based on the changing acceleration, the local degradation process inside the capacitor is determined, and the determination result of the local degradation process is obtained. Based on the assessment of the local degradation process, adjust the charging parameters or issue a warning.

2. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 1, characterized in that, The step of determining the acceleration corresponding to the rate of change based on the rate of change includes: The rate of change is smoothed to obtain a smoothed rate of change; Based on the smoothed rate of change, the acceleration corresponding to the rate of change is calculated.

3. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 1, characterized in that, The step of determining the local degradation process inside the capacitor based on the changing acceleration and obtaining the determination result of the local degradation process includes: The acceleration response mode of frequency ripple characteristic parameters under different local degradation modes of a preset capacitor is defined as a mode template based on the characteristics of acceleration variation within a preset time window. The characteristics of acceleration variation within the preset time window include at least amplitude range, sign variation rules, and trend curvature characteristics. Read the acceleration of the change in the frequency ripple characteristic parameters obtained in real time; The change acceleration of the frequency ripple characteristic parameters obtained in real time is mapped as a feature to be compared, and the feature to be compared is compared with the corresponding feature of the change acceleration response mode to identify local degradation mode. Based on the local degradation pattern, the stage of the local degradation process is determined, and the judgment result of the local degradation process is obtained.

4. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 3, characterized in that, After the step of determining the stage of the local degradation process based on the local degradation mode and obtaining the judgment result of the local degradation process, the method further includes: The operating parameters during the charging process are obtained; the operating parameters include charging power parameters, ambient temperature parameters, and capacitor surface temperature parameters. Based on the operating parameters, and in accordance with the preset risk correction rules, a risk correction amount is generated to correct the judgment result of the local degradation process. The risk correction amount is applied to the judgment result of the local degradation process to obtain and output the corrected judgment result.

5. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 4, characterized in that, After the step of applying the risk correction amount to the judgment result of the local degradation process, obtaining and outputting the corrected judgment result, the method further includes: Based on the corrected judgment result, the failure probability of the capacitor is predicted; the step of predicting the failure probability of the capacitor based on the corrected judgment result includes: Based on the local degradation mode corresponding to the local degradation process and the corrected judgment result, the degradation evolution path corresponding to the local degradation mode is retrieved from the preset degradation evolution path library. Based on the operating parameters, the degradation evolution path is modified to obtain the modified degradation evolution path. Based on the modified degradation evolution path, the failure probability of the capacitor is predicted.

6. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 5, characterized in that, The steps of adjusting charging parameters or issuing warnings based on the judgment of local degradation process include: Read the corrected degradation evolution path and the remaining charging time for the current charging task; Based on the corrected degradation evolution path, the probability of capacitor failure is predicted within the remaining charging time. Based on the assessment results of the local degradation process, a corresponding degradation risk level is generated; Based on the degradation risk level, the failure probability, the preset charging safety threshold, and the charging efficiency target, a charging power adjustment strategy is determined. Based on the aforementioned charging power adjustment strategy, adjust the charging parameters or issue a warning.

7. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 6, characterized in that, The step of determining the charging power adjustment strategy based on the degradation risk level, the failure probability, the preset charging safety threshold, and the charging efficiency target includes: Read the capacitor's failure probability, charging safety threshold, charging efficiency target, and degradation risk level; When the difference between the failure probability and the charging safety threshold is within a preset fluctuation range and the charging efficiency target is higher than the preset target, the dynamic adjustment range of the charging power is determined based on the difference between the failure probability and the charging safety threshold and the charging efficiency target; wherein, the charging safety threshold is a preset failure probability threshold within the allowable range, used to limit the upper limit of the capacitor failure risk within the remaining charging time; When the difference between the failure probability and the charging safety threshold exceeds a preset fluctuation range, and the failure probability exceeds the charging safety threshold, the reduction in charging power is determined based on the degree of excess and the charging efficiency target. When the difference between the failure probability and the charging safety threshold exceeds a preset fluctuation range, and the failure probability is lower than the charging safety threshold, the increase in charging power is determined based on the degradation risk level and the charging efficiency target. The dynamic adjustment range, reduction range, or increase range are summarized into a charging power adjustment strategy and output.

8. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 7, characterized in that, The step of determining the dynamic adjustment range of charging power based on the difference between the failure probability and the charging safety threshold, and the charging efficiency target, includes: A power adjustment response curve is set, which defines the initial value of the charging power adjustment range under different combinations of the degree of difference and the charging efficiency target; wherein, different degree of difference refers to the different degree of difference between the failure probability and the charging safety threshold; Obtain the health status indicators of the capacitor, including the rate of change of internal impedance and the dielectric loss tangent. Based on the health status indicators, the power adjustment response curve is corrected to obtain the corrected power adjustment response curve; Based on the corrected power adjustment response curve, the dynamic adjustment range of the charging power is determined and output.

9. The method for monitoring the charging and discharging characteristics of an electric vehicle capacitor according to claim 8, characterized in that, The step of correcting the power adjustment response curve based on the health status index to obtain the corrected power adjustment response curve includes: Read the local degradation mode of the capacitor; Based on the local degradation mode, retrieve the internal impedance change rate correction factor and the dielectric loss tangent correction factor; Obtain the real-time rate of change of internal impedance and the tangent of dielectric loss angle; The real-time internal impedance change rate and the internal impedance change rate correction factor are calculated to obtain the internal impedance change rate correction amount; the dielectric loss tangent value and the dielectric loss tangent value correction factor are calculated to obtain the dielectric loss tangent value correction amount. The power adjustment response curve is adjusted based on the internal impedance change rate correction and the dielectric loss tangent correction to obtain the corrected power adjustment response curve.

10. A system for monitoring the charging and discharging characteristics of an electric vehicle capacitor, used to monitor the charging and discharging characteristics of an electric vehicle capacitor, characterized in that, include: The current signal acquisition module is used to acquire the current signal flowing through the capacitor; The ripple feature acquisition module is used to perform frequency domain analysis on the current signal to obtain the ripple feature parameters of the frequency ripple component in the current signal. The rate of change determination module is used to continuously monitor the ripple characteristic parameters, obtain monitoring results, and determine the rate of change of the ripple characteristic parameters based on the monitoring results. A variable acceleration determination module is used to determine the variable acceleration corresponding to the variable rate based on the variable rate. The degradation process judgment module is used to judge the local degradation process inside the capacitor based on the changing acceleration, and obtain the judgment result of the local degradation process. The judgment result response module is used to adjust charging parameters or issue warnings based on the judgment result of the local degradation process.

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