Method and apparatus for modeling thin film capacitor capacitance degradation

Abstract

The application provides a thin film capacitor capacity degradation modeling method and device, the method comprising: obtaining aging experiment data of a thin film capacitor at a constant temperature, the aging data comprising the capacity of the thin film capacitor measured at intervals of a preset time period; obtaining a thin film capacitor capacity degradation curve based on the aging experiment data; dividing the capacity change into a first stage and a second stage based on the capacity degradation curve; modeling the first stage using a first-order linear function fitting and modeling the second stage using an exponential function fitting; iteratively estimating the parameters of the first-order linear function and the exponential function using an extended Kalman filter algorithm to minimize the fitting error of the two-stage function in the respective stages and achieve seamless splicing to obtain a spliced thin film capacitor capacity degradation model. The modeling form of the application is simple and has low computational complexity, and is suitable for health state monitoring and residual life prediction of thin film capacitors in power electronic systems.

Description

Technical Field

[0001] This application relates to the field of power electronic device health assessment technology, and in particular to a method and apparatus for modeling capacitance degradation of thin-film capacitors. Background Technology

[0002] Film capacitors are among the most critical energy storage and filtering components in train traction systems, widely used in core components such as traction converters, auxiliary power supplies, and high-speed circuit breakers. Their main functions include balancing instantaneous power differences between input and output, reducing DC voltage variations, and minimizing ripple. However, under high-temperature, high-density, and high-load operating environments, the capacitance of film capacitors gradually decreases, leading to performance degradation or even failure, affecting the reliable operation of the traction system and the entire train.

[0003] Currently, traditional modeling methods for studying the degradation of thin-film capacitors typically employ a single function and rely primarily on empirical statistical lifetime estimation models, such as the Weibull distribution model and the single exponential model. While these models characterize the aging process of thin-film capacitors to some extent, they have the following shortcomings: 1) Limited accuracy: Traditional models often rely on empirical functions or statistical distributions for fitting, and the modeling accuracy depends on fitting a large amount of experimental data.

[0004] 2) The process is not well described: Traditional degradation models often use the end of the life as the modeling target, which makes it difficult to accurately describe the change of capacitance at different stages; 3) Poor engineering adaptability: The model often requires repeated parameter tuning and experimental verification, and its generalization ability for capacitors of different capacities under different operating conditions is insufficient.

[0005] Therefore, establishing an accurate, clearly delineated, and universally applicable capacitance degradation model for thin-film capacitors is a key issue in improving the reliability of train traction systems. Summary of the Invention

[0006] In view of this, this application provides a method and apparatus for modeling capacitance degradation of thin-film capacitors to solve at least one of the aforementioned problems.

[0007] To achieve the above objectives, this application adopts the following approach: According to a first aspect of this application, a method for modeling capacitance degradation in thin-film capacitors is provided, comprising: Acquire aging test data of film capacitors at a constant temperature, the aging test data including the capacitance of film capacitors measured at preset time intervals; Based on the aging test data, a capacitance degradation curve of the thin-film capacitor was obtained. Based on the capacitance degradation curve, the capacitance change is divided into a first stage and a second stage. The first stage is modeled using a linear function fit, and the second stage is modeled using an exponential function fit. The parameters of the linear function and the exponential function are iteratively estimated using the extended Kalman filter algorithm to minimize the fitting error of the two-stage functions in their respective stages and achieve seamless splicing, resulting in the spliced ​​film capacitor capacitance degradation model.

[0008] In one embodiment of this application, the method described above for obtaining aging test data of film capacitors at a constant temperature includes: obtaining aging test data of film capacitors at a constant temperature through an accelerated life test platform.

[0009] In one embodiment of this application, the linear function in the above method is: y1 = a1 × x1; the exponential function is y2 = A × e Bx2 +C; where x1 and x2 are aging times, y1 and y2 are capacitance values ​​corresponding to x1 and x2, and a1, A, B, and C are model parameters.

[0010] In one embodiment of this application, the above method employs an extended Kalman filter algorithm to iteratively estimate the parameters of the linear function and the exponential function, minimizing the fitting error of the two-stage functions at their respective stages and achieving seamless stitching, including: Define a state vector, which includes the model parameters to be estimated [a1, A, B, C]; Establish state transition equations and observation equations. The state transition equations assume that the model parameters remain unchanged in adjacent time steps. The observation equations correlate the measured capacitance value with the predicted capacitance value calculated from the state vector. Initialize the state vector and error covariance matrix; The extended Kalman filter algorithm is used to iteratively update the state vector and error covariance matrix to satisfy the convergence condition, thus obtaining the final model parameter estimates.

[0011] In one embodiment of this application, the convergence conditions of the above method include the update amount of the state vector being less than a preset threshold, or the change in the error covariance matrix being less than a preset threshold, or reaching the maximum number of iterations.

[0012] In one embodiment of this application, the above method further includes: during the iterative update process of the extended Kalman filter, by introducing a forgetting factor or resetting the error covariance matrix, to improve the algorithm's adaptability to the time-varying characteristics of model parameters.

[0013] In one embodiment of this application, the linear function of the first stage and the exponential function of the second stage in the above method satisfy the following condition at the junction: y1(xt) = y2(xt); dy1(xt) / dx = dy2(xt) / dx; Here, xt is the connection point between the first stage and the second stage.

[0014] According to a second aspect of this application, a device for modeling capacitance degradation of a thin-film capacitor is provided, comprising: The experimental data acquisition unit is used to acquire aging experimental data of the film capacitor at a constant temperature. The aging experimental data includes the capacitance of the film capacitor measured at preset time intervals. The curve acquisition unit is used to obtain the capacitance degradation curve of the thin-film capacitor based on the aging test data. A stage division unit is used to divide the capacitance change into a first stage and a second stage based on the capacitance degradation curve. The phased modeling unit is used to model the first phase using a linear function fit and the second phase using an exponential function fit. The parameter estimation and model splicing unit is used to iteratively estimate the parameters of the linear function and the exponential function using the extended Kalman filter algorithm, so as to minimize the fitting error of the two-stage functions in their respective stages and achieve seamless splicing, thereby obtaining the spliced ​​film capacitor capacitance degradation model.

[0015] In one embodiment of this application, the experimental data acquisition unit is specifically used to acquire aging experimental data of film capacitors at a constant temperature through an accelerated life test platform.

[0016] In one embodiment of this application, the linear function in the above method is: y1 = a1 × x1; the exponential function is y2 = A × e Bx2 +C; where x1 and x2 are aging times, y1 and y2 are capacitance values ​​corresponding to x1 and x2, and a1, A, B, and C are model parameters.

[0017] In one embodiment of this application, the parameter estimation and model splicing unit includes: A state vector setting module is used to set a state vector, which includes the model parameters to be estimated [a1, A, B, C]; The equation establishment module is used to establish state transition equations and observation equations. The state transition equations assume that the model parameters remain unchanged in adjacent time steps, and the observation equations correlate the measured capacitance value with the predicted capacitance value calculated from the state vector. An initialization module is used to initialize the state vector and the error covariance matrix. The iterative update module is used to iteratively update the state vector and error covariance matrix based on the extended Kalman filter algorithm to meet the convergence condition and obtain the final model parameter estimates.

[0018] In one embodiment of this application, the convergence conditions include the update amount of the state vector being less than a preset threshold, or the change in the error covariance matrix being less than a preset threshold, or reaching the maximum number of iterations.

[0019] In one embodiment of this application, the parameter estimation and model splicing unit is further used to: improve the algorithm's adaptability to the time-varying characteristics of model parameters by introducing a forgetting factor or resetting the error covariance matrix during the iterative update process of the extended Kalman filter.

[0020] In one embodiment of this application, the linear function of the first stage and the exponential function of the second stage satisfy the following condition at the junction: y1(xt) = y2(xt); dy1(xt) / dx = dy2(xt) / dx; Here, xt is the connection point between the first stage and the second stage.

[0021] According to a third aspect of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0022] According to a fourth aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0023] According to a fifth aspect of the present invention, a computer program product is provided, comprising a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0024] As can be seen from the above technical solutions, the film capacitor capacitance degradation modeling method and apparatus provided in this application can effectively characterize the degradation features of film capacitors at different service stages. The modeling method is simple and computationally complex, making it suitable for health status monitoring and remaining life prediction of film capacitors in power electronic systems. The modeling method of this application targets capacitance as a key indicator, possessing advantages such as strong targeting, detailed modeling, and strong engineering applicability. Furthermore, this application can describe the degradation law of film capacitors with a concise degradation model, while also considering the capacitance increase trend characteristics of film capacitors in the early stages of degradation, thus providing more accurate performance analysis. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart illustrating a method for modeling capacitance degradation in thin-film capacitors according to an embodiment of this application. Figure 2 This is a structural diagram of the accelerated life testing platform provided in the embodiments of this application; Figure 3 This is a capacitance degradation curve of a thin-film capacitor provided in this embodiment; Figure 4 This is a flowchart of iterative estimation of model parameters using the extended Kalman filter algorithm provided in an embodiment of this application; Figure 5 This is a fitting diagram of the capacitance degradation model using an exponential function model in the second stage provided in the embodiments of this application; Figure 6 This is a fitting diagram of the capacitance degradation model using an exponential polynomial model in the second stage provided in the embodiments of this application; Figure 7 This is a comparison chart of the output of the film capacitor capacitance degradation model provided in the embodiments of this application and the measured curves of the single capacitance of three sets of test capacitors; Figure 8 This is a comparison graph of the output of the film capacitor capacitance degradation model provided in the embodiments of this application and the measured curve 1; Figure 9 This is a comparison graph of the output of the film capacitor capacitance degradation model provided in the embodiments of this application and the measured curve 2; Figure 10 This is a comparison graph of the output of the thin-film capacitor capacitance degradation model provided in the embodiments of this application and the measured curve 3; Figure 11 This is a schematic diagram of the structure of a thin-film capacitor capacitance degradation modeling device provided in an embodiment of this application; Figure 12 This is a schematic block diagram of the system configuration of the electronic device provided in the embodiments of the invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of this application are used to explain this application, but are not intended to limit this application.

[0027] like Figure 1 The diagram shown is a flowchart of a method for modeling capacitance degradation of a thin-film capacitor according to an embodiment of this application. The method includes the following steps: Step S101: Obtain aging test data of the film capacitor at a constant temperature, wherein the aging data includes the capacitance of the film capacitor measured at preset time intervals.

[0028] In this embodiment, aging test data of thin-film capacitors at a constant temperature can be obtained using an accelerated life testing platform. Figure 2 As can be seen, this accelerated life test platform includes: a high-temperature and high-humidity test chamber, an LCR meter, and a high-voltage DC power supply. The high-temperature and high-humidity test chamber is equipped with test capacitors and filter capacitors, which constitute the simulated circuit environment in the train set. The test can refer to "GB / T25121-2010 Power Electronic Capacitors for Rail Transit Locomotives and Rolling Stock Equipment" and design the aging circuit according to the actual operating conditions of the capacitors in the traction converter, so that the test can simulate and collect capacitance data of the capacitors at different aging times.

[0029] An accelerated life testing platform was built to conduct aging tests on thin-film capacitors under a constant temperature and pressure environment. This embodiment selected three groups of thin-film capacitor samples, numbered Cap1, Cap2, and Cap3. Initial parameter values ​​for the three groups were measured first. Then, each group of capacitors was placed in a high-temperature and high-humidity test chamber to ensure a consistent testing environment. During degradation, the capacitance was measured using an LCR meter at the test temperature, with a test interval set between 52 and 72 hours and a sampling frequency of 50Hz. At the end of the system aging process, each capacitor was discharged, and the residual voltage was measured. Discharge was considered complete when the residual voltage of all capacitors was below 30V. The total cumulative aging time at the end of the test was 3598 hours.

[0030] Step S102: Obtain the capacitance degradation curve of the thin-film capacitor based on the aging test data.

[0031] The aging test data obtained in step S101 is processed to form an intuitive capacitance degradation curve, revealing the trend and stage characteristics of the degradation process.

[0032] Specifically, the percentage change or relative decrease in capacitance relative to the initial capacity can be calculated based on aging test data, thus forming a two-dimensional curve of time-capacity change. For example... Figure 3The figure shown is a capacitance degradation curve of a thin film capacitor provided in this embodiment. It is obtained based on the relevant test in step S101. Curves 1-3 correspond to the above three groups of thin film capacitor samples Cap1, Cap2 and Cap3, respectively. The horizontal axis is the aging time and the vertical axis is the percentage change in capacitance.

[0033] Step S103: Based on the capacitance degradation curve, the capacitance change is divided into a first stage and a second stage.

[0034] Through analysis Figure 3 The capacitance degradation curve can be divided into two stages based on the dynamic characteristics of the degradation curve: the first stage is a slight increase, and the second stage is a slow aging stage.

[0035] The first stage involves a relatively steep increase in capacitance, primarily due to dimensional changes in the thin-film material under temperature influence. For example, after heat setting, air between the films is expelled, the core becomes more compact, the electrode spacing decreases, and the capacitance increases. Additionally, applying voltage to the capacitor also leads to an increase in capacitance of 0.01% to 0.05%. The capacitance change in this stage is very short and can be considered a linear increase.

[0036] The second stage: As the aging time increases, the capacitance gradually enters a slow aging stage. In this stage, the capacitance change is very gradual; even under accelerated aging tests conducted under extreme conditions for several months, the capacitance change only reaches tens of microfarads. The capacitance change in this stage exhibits an exponential decay characteristic, and its physical process involves the coupling of multiple factors such as metallized electrode corrosion, dielectric material aging, and self-healing capacitance loss.

[0037] Step S104: The first stage is modeled using a linear function fit, and the second stage is modeled using an exponential function fit.

[0038] Based on the analysis of the first and second stages described above, this embodiment uses linear function fitting to model the first stage and exponential function fitting to model the second stage.

[0039] Specifically, the above linear function is: y1 = a1 × x1; The exponential function is: y2=A×e Bx2 +C; Where x1 and x2 are aging times, y1 and y2 are capacitance values ​​corresponding to x1 and x2, and a1, A, B, and C are model parameters.

[0040] Step S105: The parameters of the linear function and the exponential function are iteratively estimated using the extended Kalman filter algorithm to minimize the fitting error of the two-stage functions in their respective stages and achieve seamless splicing, thereby obtaining the spliced ​​film capacitor capacitance degradation model.

[0041] This step aims to use the extended Kalman filter algorithm to iteratively optimize and estimate the parameters of the piecewise functions (linear and exponential functions) in the two stages mentioned above, and to achieve seamless splicing of the two-stage functions at the connection point, ultimately obtaining a model that can accurately describe the capacitance degradation process of the thin-film capacitor.

[0042] Preferred, such as Figure 4 As shown, this step may further include the following sub-steps: Step S1051: Set the state vector, which includes the model parameters to be estimated [a1, A, B, C].

[0043] To facilitate the determination of the state variables that need to be estimated in the extended Kalman filter algorithm, the undetermined parameters of the model can be organized into a vector for iterative optimization.

[0044] Specifically, based on the model form determined in the preceding steps, the state vector includes the parameter a1 of the linear function y1 = a1 × x1, and the exponential function y2 = A × e Bx2 The parameters A, B, and C of +C are given. Therefore, the state vector can be represented as X = [a1, A, B, C].

[0045] Step S1052: Establish the state transition equation and the observation equation. The state transition equation assumes that the model parameters remain unchanged in adjacent time steps. The observation equation correlates the measured capacitance value with the predicted capacitance value calculated from the state vector.

[0046] The state transition equation is a dynamic model of the evolution of state variables over time, while the observation equation is the relationship between the state variables and the actual observed values.

[0047] In this step, it is assumed that the model parameters remain constant within adjacent time steps, meaning the parameters change slowly over time. The state transition equation can be expressed as follows: a1(k+1) = a1(k) + w a1 (k); A(k+1) = A(k) + w A (k); B(k+1) = B(k) + w B (k); C(k+1) = C(k) + w C(k); where, w a1 (k), w A (k), w B (k), w C (k) is process noise, representing the small change of the parameter at time step k, and is usually assumed to be Gaussian noise with a mean of 0.

[0048] The observation equation can associate the measured capacitance value with the predicted capacitance value calculated from the state vector. Since the model in this application is a piecewise function, it is necessary to select the corresponding function according to which stage the time t belongs to. If t < xt, then y (t) = a1 × t; if t >= xt, then y (t) = A × exp(B × (t - xt)) + C. The observation equation can be expressed as z(k) = h(X(k)) + v(k), where z(k) is the actual observation value, X(k) is the state vector, h(X(k)) is the observation function (i.e., the above piecewise function), and v(k) is the measurement noise, which is usually assumed to be Gaussian noise with a mean of 0.

[0049] Step S1053: Initialize the state vector and the error covariance matrix.

[0050] This step provides the initial state estimate value and the confidence level of the initial estimate for the extended Kalman filter algorithm. For the initialization of the state vector, according to the test data and prior knowledge, an initial value can be set for the state vector X = [a1, A, B, C]. For example, a1 can be estimated from the slope of the first stage, and A, B, and C can be estimated from the initial decay amplitude and offset of the second stage. The error covariance matrix P0 reflects the confidence level of the initial state estimate. If the confidence in the initial value is high, a smaller P0 value is set; otherwise, a larger P0 value is set. P0 can be set as a diagonal matrix, and the elements on the diagonal are the variances of the initial estimates of each state variable.

[0051] The initial estimate value X0 of the state vector and the initial value P0 of the error covariance matrix provide a starting point for the iteration of the extended Kalman filter algorithm.

[0052] Step S1054: Based on the extended Kalman filter algorithm, iteratively update the state vector and the error covariance matrix until the convergence condition is met, and obtain the final model parameter estimate value.

[0053] The iterative update process of this step is as follows: First, based on the state transition equation, predict the state vector X(k+1|k) and the error covariance matrix P(k+1|k) at the next time step.

[0054] Then, the Jacobian matrix H(k) of the observation equation at the current state estimate is calculated, and the Kalman gain K(k) is calculated based on this Jacobian matrix H(k). Next, the state vector X(k+1) and the error covariance matrix P(k+1) are updated using the Kalman gain K(k) and the observation residuals.

[0055] In this embodiment, convergence criteria can be set, such as the update amount of the state vector being less than a preset threshold, the change in the error covariance matrix being less than a preset threshold, or reaching the maximum number of iterations.

[0056] In order to ensure a smooth transition of the two-phase function at the connection point during the iteration process, this embodiment can further apply constraints to ensure that: y1(xt) = y2(xt); dy1(xt) / dx = dy2(xt) / dx; Here, xt is the connection point between the first stage and the second stage.

[0057] When the convergence condition is met, the iteration stops and the final model parameter estimates are output.

[0058] Furthermore, in the iterative update process of the extended Kalman filter described above, this embodiment can also improve the algorithm's adaptability to the time-varying characteristics of model parameters by introducing a forgetting factor or resetting the error covariance matrix.

[0059] In the standard extended Kalman filter algorithm, the state transition equation assumes that the model parameters remain constant within adjacent time steps, which to some extent limits the algorithm's adaptability to the time-varying characteristics of the model parameters. To improve the algorithm's adaptability to these characteristics, this embodiment employs two methods: introducing a forgetting factor or resetting the error covariance matrix. These are explained below: 1. Introduce a forgetting factor.

[0060] The forgetting factor is a parameter used to adjust the error covariance matrix. By increasing the error covariance matrix, it reduces the influence of old state estimates on the current state estimate, thus enabling the filter to track parameter changes more quickly. Specifically, after the prediction step of the extended Kalman filter algorithm, when updating the error covariance matrix, a forgetting factor λ (0 < λ ≤ 1) is introduced: P(k+1|k) = P(k+1|k) / λ.

[0061] When λ < 1, it is equivalent to increasing the prediction error covariance matrix P(k+1|k), reducing the influence of previous data on the current state estimation, making the filter pay more attention to the most recent data, thereby improving its adaptability to time-varying characteristics.

[0062] 2. Reset the error covariance matrix.

[0063] When the system experiences sudden changes or the model parameters change significantly, the error covariance matrix may become too small, causing the filter to fail to effectively track the parameter changes. In this case, resetting the error covariance matrix can increase the uncertainty of the state estimation, allowing the filter to adapt to the new state more quickly. Specifically, after the update step of the extended Kalman filter algorithm, it is determined whether the error covariance matrix needs to be reset. The determination can be based on factors such as the magnitude of the observation residuals and the amount of state vector update. If a reset is needed, the error covariance matrix P(k+1) is reset to a larger diagonal matrix, or reset to its initial value P0. This effectively addresses sudden system changes or significant changes in model parameters.

[0064] Introducing a forgetting factor or resetting the error covariance matrix can improve the EKF algorithm's adaptability to time-varying model parameters. In practical applications, a suitable approach or a combination of both can be chosen based on the specific system characteristics and noise level. For example, a forgetting factor can be introduced first, enabling the filter to track slow parameter changes. Then, when the system experiences a sudden change, the error covariance matrix can be reset, allowing the filter to quickly adapt to the new state.

[0065] The method described above in this application will be further explained below based on the test conditions and the three sets of test capacitors Cap1, Cap2, and Cap3 in step S101: After the above steps S101-S105, the first-stage linear function is obtained as follows: The exponential function for the second stage is: .

[0066] Therefore, the capacitance degradation model for thin-film capacitors is as follows: .

[0067] It should be noted that the fitting effect of the capacitance degradation model in the second stage is better than that of the polynomial model, with an RMSE value of approximately 0.0040, while the RMSE of the polynomial model is greater than 0.0091. Their model fitting plots are shown below. Figure 5 , Figure 6 As shown, it can be seen that Figure 5 The fitting effect is significantly better than Figure 6 .

[0068] A comparison of the output of the above-mentioned film capacitor capacitance degradation model with the measured individual capacitance curves of the three sets of test capacitors is shown below. Figure 7As shown. Taking the second capacitance degradation curve as an example, a two-stage mathematical model is used to output the percentage change curve of capacitance during the aging period of 0~3508h, and compared with the measured curve. Figure 9 As shown, the two are highly consistent in key indicators such as overall trend, trend change points, and percentage change in maximum capacity.

[0069] In addition, comparisons were made with other capacitance degradation curves, such as... Figure 8 , Figure 10 As shown, the average prediction error is controlled within 4.28e-04, and the maximum error does not exceed 0.0089, thus verifying the accuracy and stability of the above-mentioned film capacitor capacitance degradation model.

[0070] The film capacitor capacitance degradation model obtained in this application can be directly applied to the following scenarios: 1. Capacitor condition assessment: Real-time calculation of capacitance change percentage to determine the health status of the capacitor. 2. Intelligent fault early warning: As a condition monitoring input, providing fault risk early warning for power electronic systems. 3. Maintenance strategy optimization: Optimizing capacitor maintenance cycles based on model prediction results to reduce fault risk. 4. Technical teaching and training: Can be integrated into traction converters for research and analysis of degradation mechanisms and modeling methods.

[0071] As can be seen from the above technical solution, the capacitance degradation modeling method for film capacitors provided in this application can effectively characterize the degradation features of film capacitors at different service stages. The modeling method is simple and computationally inexpensive, making it suitable for health status monitoring and remaining life prediction of film capacitors in power electronic systems. This application's modeling method targets capacitance as a key indicator, offering advantages such as strong specificity, detailed modeling, and strong engineering applicability. Furthermore, this application can describe the degradation law of film capacitors with a concise degradation model, while also considering the capacitance increase trend in the early stages of degradation, thus providing more accurate performance analysis.

[0072] like Figure 11 The diagram shows a structural schematic of a thin-film capacitor capacitance degradation modeling device provided in an embodiment of this application. The device includes: an experimental data acquisition unit 1110, a curve acquisition unit 1120, a stage division unit 1130, a staged modeling unit 1140, and a parameter estimation and model splicing unit 1150, which are connected sequentially. The experimental data acquisition unit 1110 is used to acquire aging experimental data of film capacitors at a constant temperature. The aging data includes the capacitance of the film capacitors measured at preset time intervals.

[0073] The curve acquisition unit 1120 is used to obtain the capacitance degradation curve of the thin film capacitor based on the aging test data.

[0074] The stage division unit 1130 is used to divide the capacitance change into a first stage and a second stage based on the capacitance degradation curve.

[0075] The phased modeling unit 1140 is used to model the first stage using linear function fitting and the second stage using exponential function fitting.

[0076] The parameter estimation and model splicing unit 1150 is used to iteratively estimate the parameters of the linear function and the exponential function using the extended Kalman filter algorithm, so as to minimize the fitting error of the two-stage functions in their respective stages and achieve seamless splicing, thereby obtaining the spliced ​​film capacitor capacitance degradation model.

[0077] In one embodiment of this application, the experimental data acquisition unit 1110 is specifically used to acquire aging experimental data of film capacitors at a constant temperature through an accelerated life test platform.

[0078] In one embodiment of this application, the linear function in the above method is: y1 = a1 × x1; the exponential function is y2 = A × e Bx2 +C; where x1 and x2 are aging times, y1 and y2 are capacitance values ​​corresponding to x1 and x2, and a1, A, B, and C are model parameters.

[0079] In one embodiment of this application, the parameter estimation and model splicing unit 1150 includes: A state vector setting module is used to set a state vector, which includes the model parameters to be estimated [a1, A, B, C]; The equation establishment module is used to establish state transition equations and observation equations. The state transition equations assume that the model parameters remain unchanged in adjacent time steps, and the observation equations correlate the measured capacitance value with the predicted capacitance value calculated from the state vector. An initialization module is used to initialize the state vector and the error covariance matrix. The iterative update module is used to iteratively update the state vector and error covariance matrix based on the extended Kalman filter algorithm to meet the convergence condition and obtain the final model parameter estimates.

[0080] In one embodiment of this application, the convergence conditions include the update amount of the state vector being less than a preset threshold, or the change in the error covariance matrix being less than a preset threshold, or reaching the maximum number of iterations.

[0081] In one embodiment of this application, the parameter estimation and model splicing unit 1150 is further configured to: improve the algorithm's adaptability to the time-varying characteristics of model parameters by introducing a forgetting factor or resetting the error covariance matrix during the iterative update process of the extended Kalman filter.

[0082] In one embodiment of this application, the linear function of the first stage and the exponential function of the second stage satisfy the following condition at the junction: y1(xt) = y2(xt); dy1(xt) / dx = dy2(xt) / dx; Here, xt is the connection point between the first stage and the second stage.

[0083] As can be seen from the above technical solution, the film capacitor capacitance degradation modeling device provided in this application can effectively characterize the degradation features of film capacitors at different service stages. The modeling method is simple and computationally complex, making it suitable for health status monitoring and remaining life prediction of film capacitors in power electronic systems. The modeling method of this application targets capacitance as a key indicator, offering advantages such as strong specificity, detailed modeling, and strong engineering applicability. Furthermore, this application can describe the degradation law of film capacitors with a concise degradation model, while also considering the capacitance increase trend in the early stages of degradation, thus providing more accurate performance analysis.

[0084] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0085] This application also provides a computer-readable storage medium storing a computer program that performs the above-described methods.

[0086] This application also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described method.

[0087] It should be noted that the electronic device, computer-readable storage medium, and computer program product provided in the embodiments of this application specifically implement the above steps S502-S505.

[0088] like Figure 12 The electronic device 600 may also include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily need to include these components. Figure 12 All components shown; in addition, electronic device 600 may also include Figure 12The components shown can be referenced in the prior art.

[0089] like Figure 12 The central processing unit 100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device. The central processing unit 100 receives inputs and controls the operation of various components of the electronic device 600.

[0090] The memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 100 may execute the program stored in the memory 140 to perform information storage or processing, etc.

[0091] Input unit 120 provides input to central processing unit 100. Input unit 120 may be, for example, a keypad or touch input device. Power supply 170 provides power to electronic device 600. Display 160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0092] The memory 140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 140 can also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142 for storing application and function programs or processes for executing the operation of the electronic device 600 via the central processing unit 100.

[0093] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for the electronic device for communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0094] The communication module 110 is a transmitter / receiver that transmits and receives signals via the antenna 111. The communication module (transmitter / receiver) is coupled to the central processing unit 100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0095] Based on different communication technologies, multiple communication modules 110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication modules (transmitters / receivers) are also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby enabling typical telecommunications functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 130 is coupled to a central processing unit 100, enabling on-device recording via the microphone 132 and on-device playback of stored audio via the speaker 131.

[0096] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] This application uses specific embodiments to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for modeling capacitance degradation in thin-film capacitors, characterized in that, The method includes: Acquire aging test data of film capacitors at a constant temperature, the aging test data including the capacitance of film capacitors measured at preset time intervals; Based on the aging test data, a capacitance degradation curve of the thin-film capacitor was obtained. Based on the aforementioned capacitance degradation curve, the capacitance change is divided into a first stage and a second stage. The first stage is modeled using a linear function fit, and the second stage is modeled using an exponential function fit. The parameters of the linear function and the exponential function are iteratively estimated using the extended Kalman filter algorithm to minimize the fitting error of the two-stage functions in their respective stages and achieve seamless splicing, resulting in the spliced ​​film capacitor capacitance degradation model.

2. The method for modeling capacitance degradation of thin-film capacitors as described in claim 1, characterized in that, The acquisition of aging test data of film capacitors at constant temperature includes: acquiring aging test data of film capacitors at constant temperature through an accelerated life test platform.

3. The method for modeling capacitance degradation of thin-film capacitors as described in claim 1, characterized in that, The linear function is: y1 = a1 × x1; the exponential function is: y2 = A × e Bx2 +C; where x1 and x2 are aging times, y1 and y2 are capacitance values ​​corresponding to x1 and x2, and a1, A, B, and C are model parameters.

4. The method for modeling capacitance degradation of thin-film capacitors as described in claim 3, characterized in that, The step of iteratively estimating the parameters of the linear function and the exponential function using the extended Kalman filter algorithm to minimize the fitting error of the two-stage function in their respective stages and achieve seamless stitching includes: Define a state vector, which includes the model parameters to be estimated [a1, A, B, C]; Establish state transition equations and observation equations. The state transition equations assume that the model parameters remain unchanged in adjacent time steps. The observation equations correlate the measured capacitance value with the predicted capacitance value calculated from the state vector. Initialize the state vector and error covariance matrix; The extended Kalman filter algorithm is used to iteratively update the state vector and error covariance matrix to satisfy the convergence condition, thus obtaining the final model parameter estimates.

5. The method for modeling capacitance degradation of thin-film capacitors as described in claim 4, characterized in that, The convergence conditions include the update amount of the state vector being less than a preset threshold, or the change in the error covariance matrix being less than a preset threshold, or reaching the maximum number of iterations.

6. The method for modeling capacitance degradation of thin-film capacitors as described in claim 4, characterized in that, The method further includes: during the iterative update process of the extended Kalman filter, by introducing a forgetting factor or resetting the error covariance matrix, to improve the algorithm's adaptability to the time-varying characteristics of model parameters.

7. The method for modeling capacitance degradation of thin-film capacitors as described in any one of claims 4 to 6, characterized in that, The linear function of the first stage and the exponential function of the second stage satisfy the following condition at the junction: y1(xt) = y2(xt); dy1(xt) / dx = dy2(xt) / dx; Here, xt is the connection point between the first stage and the second stage.

8. A device for modeling capacitance degradation in thin-film capacitors, characterized in that, include: The experimental data acquisition unit is used to acquire aging experimental data of the film capacitor at a constant temperature. The aging experimental data includes the capacitance of the film capacitor measured at preset time intervals. The curve acquisition unit is used to obtain the capacitance degradation curve of the thin-film capacitor based on the aging test data. A stage division unit is used to divide the capacitance change into a first stage and a second stage based on the capacitance degradation curve. The phased modeling unit is used to model the first phase using a linear function fit and the second phase using an exponential function fit. The parameter estimation and model splicing unit is used to iteratively estimate the parameters of the linear function and the exponential function using the extended Kalman filter algorithm, so as to minimize the fitting error of the two-stage functions in their respective stages and achieve seamless splicing, thereby obtaining the spliced ​​film capacitor capacitance degradation model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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

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