Metallized film capacitor capacitance prediction method and system, medium and program product

Abstract

The invention discloses a metallized film capacitor capacitance prediction method and system, a medium and a program product, and the method comprises the steps: measuring the deformation of a metallized film sample under different tensile loads, and obtaining load deformation relation data; calculating stress distribution data of the metallized film sample under each tensile load; measuring the actual dielectric constant of the metallized film sample under each tensile load, and determining a stress dielectric constant mapping function; calculating the actual curvature radius of each layer of metallized film in the metallized film capacitor to be measured according to the structural parameters and a preset winding tension value; substituting the actual curvature radius into a bending stress calculation formula, and calculating to obtain an actual stress value of each layer of metallized film; substituting the actual stress value of each layer of metallized film into the stress dielectric constant mapping function to obtain a corrected dielectric constant of each layer of metallized film; and calculating a capacitance prediction value of the to-be-measured metallized film capacitor according to the corrected dielectric constant and the structural parameters. The accuracy of predicting the capacitance of the capacitor is improved.

Description

Technical Field

[0001] The present application belongs to the field of capacitance prediction, and in particular relates to a capacitance prediction method, system, medium and program product for metallized film capacitors. Background Art

[0002] Metallized film capacitors are important energy storage components in electronic devices. Accurately predicting their capacitance is crucial for product quality control and production efficiency. Traditionally, capacitance testing of metallized film capacitors relies on physical measurements, which is not only time-consuming but also requires post-production. Any substandard capacitance requires re-production, resulting in significant waste of manpower and resources.

[0003] In related technologies, capacitance can be predicted by establishing a mathematical model linking structural parameters such as the dielectric constant, plate area, and plate spacing with capacitance. This method allows capacitance to be predicted before production, avoiding the problem of discovering substandard capacitors after production is complete, thereby improving production efficiency.

[0004] However, the stress and deformation generated by the metallized film during the winding process will affect the capacitance. The winding stress will cause the thickness and dielectric constant of the metallized film to change, causing the predicted results to deviate from the actual capacitance, reducing the accuracy of the capacitor capacitance prediction. Summary of the Invention

[0005] The present application provides a method, system, medium and program product for predicting the capacitance of a metallized film capacitor, which are used to improve the accuracy of capacitor capacitance prediction.

[0006] In a first aspect, the present application provides a method for predicting the capacitance of a metallized film capacitor, which obtains material parameters of a preset number of metallized film samples, wherein the material parameters include initial dielectric constant, density, and elastic modulus; Measure the deformation of the metallized film sample under different tensile loads to obtain load-deformation relationship data; Calculate the stress distribution data of the metallized film sample under each tensile load based on the material parameters and load-deformation relationship data; The actual dielectric constant of the metallized film sample under each tensile load is measured, and the actual dielectric constant is fitted with the corresponding stress distribution data to obtain the stress dielectric constant mapping function; Obtaining structural parameters of the metallized film capacitor to be tested, the structural parameters including the number of winding layers of the metallized film, a preset winding tension value, and a core shaft diameter; Calculate the actual curvature radius of each layer of the metallized film in the metallized film capacitor to be tested according to the structural parameters and the preset winding tension value; Substituting the actual curvature radius of each metallized film layer into the bending stress calculation formula, the actual stress value of each metallized film layer is calculated; Substituting the actual stress value of each metallized film layer into the stress dielectric constant mapping function, the corrected dielectric constant of each metallized film layer is obtained; The capacitance prediction value of the metallized film capacitor to be tested is calculated based on the corrected dielectric constant and structural parameters.

[0007] By employing this technical solution, the material parameters and load-deformation relationship data of the metallized film samples were obtained, and a mapping relationship between stress and dielectric constant was established, accurately describing the changes in the dielectric constant of the metallized film under different stress states. The actual curvature radius and stress value of each layer of the metallized film were calculated based on the structural parameters of the capacitor to be tested. The corrected dielectric constant was obtained by combining the stress-dielectric constant mapping function, so that the capacitance prediction process takes into account the actual effect of stress on the dielectric constant during the winding process. By establishing a correlation mechanism between material properties, structural parameters, and capacitance, this prediction method overcomes the shortcomings of traditional methods that only calculate based on the initial dielectric constant and ignore the influence of stress, thereby improving the accuracy of capacitance prediction.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, calculating stress distribution data of the metallized film sample under each tensile load based on material parameters and load-deformation relationship data specifically includes: The load-deformation relationship data is divided into several strain intervals according to the degree of stretching, and the nominal stress value of each strain interval is calculated based on the elastic modulus; The mass change of the metallized film sample in each strain range is calculated in combination with the density; Calculate the actual cross-sectional area of ​​each strain interval based on the volume invariance principle and the law of conservation of mass; The nominal stress value is corrected using the actual cross-sectional area to obtain the true stress value; The strain-true stress correspondence is established according to the true stress value, and the stress distribution data under each tensile load is obtained.

[0009] By adopting the above technical solution, the load-deformation relationship data is divided into strain intervals according to the degree of stretching and the nominal stress value is calculated. The stress value is corrected by considering the change in mass and the change in actual cross-sectional area, and the corresponding relationship between strain and true stress is established. The volume invariance principle and the law of conservation of mass are introduced to correct the change in cross-sectional area. The method of correcting the nominal stress value by calculating the actual cross-sectional area to obtain the true stress value makes the stress distribution data more consistent with the actual stress state of the metallized film during the tensile deformation process, reducing the problem of deviation between the calculation results and the actual situation under large deformation conditions of traditional stress calculation methods, and providing more accurate stress data for the subsequent establishment of stress-dielectric constant mapping relationship.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, fitting the actual dielectric constant with the corresponding stress distribution data to obtain a stress dielectric constant mapping function specifically includes: The stress distribution data is divided into several stress intervals according to the size, and the range of each stress interval is an integer multiple of the preset stress threshold; Perform weighted averaging on the actual dielectric constant within each stress interval to obtain the corresponding characteristic dielectric constant value; The characteristic dielectric constant value of each stress interval is fitted with the stress midpoint value using a piecewise polynomial fitting method to obtain the stress dielectric constant mapping function.

[0011] By adopting the above technical solution, the stress distribution data is divided into fixed stress intervals. The actual dielectric constant within each interval is weighted averaged to obtain the characteristic dielectric constant value. A piecewise polynomial fitting method is then used to establish the mapping relationship between stress and dielectric constant. This data processing method based on stress interval division reduces the impact of the discreteness of the measured data on the fitting accuracy. The characteristic dielectric constant value of each stress interval is extracted through weighted averaging, allowing the mapping function to better reflect the inherent relationship between stress and dielectric constant. Compared with single function fitting, the piecewise polynomial fitting method has stronger local fitting capabilities and can more accurately describe the changing characteristics of dielectric constant under different stress levels, thereby improving the applicability and reliability of the stress-dielectric constant mapping function.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after calculating the capacitance prediction value of the metallized film capacitor to be tested based on the corrected dielectric constant and structural parameters, the method further includes: Calculate the relative error between the predicted capacitance value and the measured capacitance value; When it is determined that the relative error is greater than a preset allowable error threshold, the fitting coefficient in the stress dielectric constant mapping function is adjusted; Recalculate and correct the dielectric constant and capacitance prediction values ​​until the relative error is less than the preset allowable error threshold; The output relative error is less than the final capacitance prediction value corresponding to the preset allowable error threshold.

[0013] By adopting the above technical solution, when the relative error exceeds the preset allowable error threshold, the fitting coefficients of the stress-dielectric constant mapping function are adjusted and recalculated until the relative error meets the accuracy requirements. This iterative optimization prediction method reduces the systematic error that may exist in a single prediction and achieves continuous improvement in prediction accuracy through multiple calculations and parameter adjustments. This method uses the prediction error as the optimization target and achieves adaptive optimization of the prediction model through feedback regulation, improving the reliability and practicality of the capacitance prediction results, making the prediction results more closely match the performance characteristics of the capacitor under actual operating conditions.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, adjusting the fitting coefficient in the stress dielectric constant mapping function specifically includes: Construct an error loss function based on the relative error, and use the fitting coefficient as the variable to be optimized; The gradient descent algorithm is used to minimize the error loss function and iteratively update the fitting coefficients; When the value of the error loss function is less than the preset convergence threshold or the number of iterations reaches the preset upper limit, the optimized fitting coefficient is output.

[0015] By adopting the above technical solution, an error loss function is constructed and the fitting coefficients are used as the variables to be optimized, transforming the optimization problem of the stress-dielectric constant mapping function into a computable mathematical optimization problem. A gradient descent algorithm is used to minimize the error loss function, adaptively adjusting the fitting coefficients according to the direction and magnitude of the error change. During each iteration, the algorithm calculates the gradient of the error loss function with respect to each fitting coefficient and updates the coefficient values ​​along the gradient descent direction, allowing the stress-dielectric constant mapping function to gradually approach the true physical relationship. Setting a preset convergence threshold and an upper limit on the number of iterations as optimization termination conditions not only improves the convergence of the optimization results, but also reduces the waste of computational resources caused by over-optimization, thereby improving the fitting accuracy of the stress-dielectric constant mapping function.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the output relative error is less than the final capacitance prediction value corresponding to the preset allowable error threshold, the method further includes: Calculate the volumetric specific capacitance of the metallized film capacitor to be tested based on the final capacitance prediction value and structural parameters; Calculate the relative deviation of the volumetric capacitance from a preset volumetric capacitance target value; When it is determined that the relative deviation is greater than the preset deviation threshold, the recipe parameters of the metallized film material are adjusted, and the step of calculating the actual curvature radius of each layer of the metallized film in the metallized film capacitor to be tested according to the structural parameters and the preset winding tension value is re-executed.

[0017] By adopting the above technical solution, when the relative deviation of volumetric capacitance exceeds a preset threshold, product performance is improved by adjusting the metallized film material formulation parameters, achieving closed-loop control of product quality. This formulation parameter adjustment method based on volumetric capacitance deviation directly feeds product performance indicators back into the material formulation process, making material performance optimization clearly goal-oriented. By recalculating the actual curvature radius and subsequent parameters of each layer of the metallized film, the effect of the formulation adjustment can be accurately evaluated, improving the efficiency and accuracy of product performance optimization.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, adjusting the formulation parameters of the metallized film material specifically includes: Based on the relative deviation between the volumetric capacitance and the preset volumetric capacitance target value, determine the recipe parameters that need to be adjusted and the adjustment direction and adjustment range; Within the preset recipe parameter adjustment range, update the recipe parameter value according to the adjustment direction and adjustment range.

[0019] By adopting this technical solution, the formulation parameter adjustment scheme is determined based on the relative deviation of the volumetric capacitance from the preset target value, establishing a quantitative relationship between performance deviation and formulation adjustment. By clarifying the adjustment direction and adjustment range of the formulation parameters, the formulation optimization process is made more precise and controllable. Updating parameters within the preset formulation parameter adjustment range not only improves the effectiveness of the adjustment, but also reduces the quality risks caused by parameter deviations from the reasonable range. This improves product performance while ensuring the stability of the production process. Through a quantitative parameter adjustment mechanism, the formulation optimization process is less dependent on experience, and the scientific nature and efficiency of formulation optimization are improved.

[0020] In a second aspect, an embodiment of the present application provides a metallized film capacitor capacitance prediction system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a system, enables the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product is run on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The present application provides a method for predicting the capacitance of a metallized film capacitor, which obtains the material parameters and load-deformation relationship data of the metallized film sample, establishes a mapping relationship between stress and dielectric constant, and realizes an accurate description of the change in dielectric constant of the metallized film under different stress states. The actual curvature radius and stress value of each layer of the metallized film are calculated based on the structural parameters of the capacitor to be tested, and the corrected dielectric constant is obtained by combining the stress dielectric constant mapping function, so that the capacitance prediction process takes into account the actual effect of stress on the dielectric constant during the winding process. This prediction method overcomes the defect of the traditional method of only calculating based on the initial dielectric constant and ignoring the influence of stress by establishing a correlation mechanism between material properties, structural parameters and capacitance, thereby improving the accuracy of capacitance prediction.

[0024] 2. The present application provides a method for predicting the capacitance of a metallized film capacitor. When the relative error exceeds the preset allowable error threshold, the fitting coefficient of the stress dielectric constant mapping function is adjusted and recalculated until the relative error meets the accuracy requirement. This iterative optimization prediction method reduces the possible systematic error in a single prediction and achieves continuous improvement in prediction accuracy through multiple calculations and parameter adjustments. This method takes the prediction error as the optimization target and realizes adaptive optimization of the prediction model through feedback regulation, thereby improving the reliability and practicality of the capacitance prediction results and making the prediction results closer to the performance characteristics of the capacitor under actual working conditions.

[0025] 3. This application provides a method for predicting the capacitance of metallized film capacitors. When the relative deviation of the volumetric capacitance exceeds a preset threshold, the product performance is improved by adjusting the formula parameters of the metallized film material, thereby achieving closed-loop control of product quality. This method of adjusting the formula parameters based on the volumetric capacitance deviation directly feeds back the product performance indicators to the material formulation link, making the optimization of material performance clearly goal-oriented. By recalculating the actual curvature radius and subsequent parameters of each layer of metallized film, the effect of the formula adjustment can be accurately evaluated, thereby improving the efficiency and accuracy of product performance optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method for predicting the capacitance of a metallized film capacitor in an embodiment of the present application.

[0027] Figure 2 This is another flow chart of a method for predicting the capacitance of a metallized film capacitor in an embodiment of the present application.

[0028] Figure 3 This is a schematic diagram of the physical device structure of a metallized film capacitor capacitance prediction system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0031] The following uses an embodiment and combines Figure 1 , a method for predicting the capacitance of a metallized film capacitor in an embodiment of the present application is described: See also Figure 1 , is a flow chart of a method for predicting the capacitance of a metallized film capacitor in an embodiment of the present application.

[0032] S101, obtaining material parameters of a preset number of metallized film samples, measuring deformations of the metallized film samples under tensile loads of different magnitudes, and obtaining load-deformation relationship data; The system obtains the material parameters of a preset number of metallized film samples, measures the deformation of the metallized film samples under tensile loads of different sizes, and obtains load-deformation relationship data, where the material parameters include initial dielectric constant, density, and elastic modulus.

[0033] The system first obtains a certain number of metallized film samples and measures their material parameters, including initial dielectric constant, density, and elastic modulus. These parameters reflect the basic physical properties of the metallized film material. Next, the system applies varying tensile loads to the metallized film samples and measures the sample's deformation under each load. The deformation can be changes in length, width, or thickness. Through multiple measurements, the system obtains data on the corresponding relationship between load and deformation, known as load-deformation relationship data.

[0034] The system can perform tensile tests on metallized film samples using an electronic universal testing machine, collecting load and deformation data via force and displacement sensors. To improve measurement accuracy, the system can perform multiple tests on the sample and then statistically analyze the test results, eliminating abnormal data and obtaining reliable load-deformation relationship data. Furthermore, the system can employ digital image correlation technology, recording the sample's deformation process with a high-speed camera and then analyzing the deformation field using image processing algorithms to determine the displacement and strain distribution at each point on the sample surface, thereby obtaining more detailed deformation data.

[0035] When measuring the deformation of metallized film samples, there may be measurement errors caused by non-uniform deformation or local defects in the sample. To solve this problem, the system can introduce full-field strain measurement technology, using methods such as digital speckle or grating to obtain the full-field displacement and strain distribution of the sample surface, thereby achieving accurate characterization of the sample deformation. At the same time, the system can also use multi-scale testing technology to obtain load-deformation relationship data at the macro scale while analyzing the microstructural changes of the metallized film at the micro scale, studying the intrinsic connection between microstructure and mechanical properties, establishing a multi-scale mechanical model, and improving the accuracy and reliability of material property analysis.

[0036] S102, calculating stress distribution data of the metallized film sample under each tensile load based on material parameters and load-deformation relationship data; The system calculates the stress distribution data of the metallized film sample under each tensile load based on the material parameters and load-deformation relationship data. Specifically: the load-deformation relationship data is divided into several strain intervals according to the degree of stretching, and the nominal stress value of each strain interval is calculated based on the elastic modulus; the mass change of the metallized film sample in each strain interval is calculated in combination with the density; the actual cross-sectional area of ​​each strain interval is calculated according to the volume invariance principle and the law of conservation of mass; the nominal stress value is corrected using the actual cross-sectional area to obtain the true stress value; the strain-true stress correspondence is established based on the true stress value to obtain the stress distribution data under each tensile load.

[0037] After obtaining the load-deformation relationship data, the system needs to further calculate the stress distribution of the metallized film sample under each tensile load. Stress is an important indicator for evaluating the stress state of a material and directly affects its deformation and failure behavior. The system first divides the load-deformation relationship data into several strain intervals according to the degree of stretching, with each interval representing a deformation stage. Then, based on the material's elastic modulus, the system calculates the nominal stress value for each strain interval. The nominal stress is the stress calculated based on the initial cross-sectional area and does not take into account the change in cross-sectional area caused by material deformation. To obtain a more accurate stress value, the system needs to consider the volume invariance and mass conservation during the material deformation process, calculate the actual cross-sectional area change of the material during deformation, and then use the actual cross-sectional area to correct the nominal stress to obtain the actual stress value. Finally, based on the actual stress values ​​in each strain interval, the system establishes a corresponding relationship between strain and true stress, and obtains the stress distribution data of the metallized film sample under each tensile load.

[0038] The system can use finite element analysis technology to establish a mechanical model of the metallized film sample based on load-deformation relationship data and material parameters, and then obtain the stress distribution inside the sample through numerical solution. In order to improve calculation efficiency and accuracy, the system can use adaptive meshing technology to automatically adjust the mesh density according to the stress gradient, using dense meshes in areas with drastic stress changes and sparse meshes in areas with gentle stress changes. In addition, the system can also introduce crystal plasticity theory, consider the anisotropy and heterogeneity of the microstructure of the metallized film, establish a constitutive model of polycrystalline materials, simulate the plastic deformation behavior of the material under load, and obtain more realistic and reliable stress distribution results.

[0039] S103, measuring the actual dielectric constant of the metallized film sample under each tensile load, and fitting the actual dielectric constant with the corresponding stress distribution data to obtain a stress dielectric constant mapping function; The system measures the actual dielectric constant of the metallized film sample under each tensile load and establishes a quantitative relationship between stress and dielectric constant. The dielectric constant reflects the material's capacitance characteristics and is a key parameter affecting the capacitance of metallized film capacitors. By measuring the actual dielectric constant under different stress states and correlating it with the corresponding stress distribution data, the system can derive a mapping function between stress and dielectric constant, known as the stress-dielectric constant mapping function. This mapping function quantitatively describes the dependence of the dielectric properties of the metallized film material on stress, laying the foundation for subsequent capacitance prediction.

[0040] The system uses an LCR digital bridge to measure the dielectric constant of metallized film samples under different loads. By varying the test frequency and voltage, the system can obtain the dielectric spectrum and dielectric nonlinear characteristic curve of the sample under different conditions. To improve measurement accuracy, the system can perform temperature and cable compensation on the measurement results to eliminate the influence of environmental factors and parasitic parameters. After obtaining the actual dielectric constant data, the system uses data fitting techniques to establish a quantitative relationship model between stress and dielectric constant. Considering the possible nonlinear relationship between stress and dielectric constant, the system can use polynomial fitting, spline function fitting, or neural network fitting methods to obtain the optimal mapping function. Furthermore, the system can use methods such as cross-validation to evaluate the fitting accuracy and generalization ability of the mapping function to ensure model reliability.

[0041] S104, obtaining structural parameters of the metallized film capacitor to be tested, and calculating the actual curvature radius of each metallized film layer in the metallized film capacitor to be tested according to the structural parameters and a preset winding tension value; The system obtains the structural parameters of the metallized film capacitor to be tested, and calculates the actual curvature radius of each layer of the metallized film in the metallized film capacitor to be tested based on the structural parameters and preset winding tension values, wherein the structural parameters include the number of winding layers of the metallized film, the preset winding tension value and the core shaft diameter.

[0042] This step is to calculate the actual stress state of each layer of metallized film in the metallized film capacitor to be tested. The capacitance of the capacitor is not only related to the material properties, but also closely related to the structural parameters of the capacitor. The system first obtains the structural parameters of the capacitor to be tested, mainly including the number of winding layers of the metallized film, the winding tension and the core shaft diameter. The number of winding layers determines the total area of ​​the metallized film in the capacitor, the winding tension reflects the tensile stress exerted on the metallized film during the winding process, and the core shaft diameter affects the degree of bending of the metallized film. Based on these structural parameters, the system can calculate the actual curvature radius of each layer of metallized film in the capacitor. The curvature radius is an important geometric parameter for measuring the degree of bending deformation of the metallized film and directly affects the stress distribution of the metallized film.

[0043] The system can use a winding mechanics model to deduce the radial position and circumferential curvature radius of each layer of metallized film in the capacitor based on the winding tension, core shaft diameter and number of winding layers. Commonly used winding mechanics models include linear elastic models, viscoelastic models and plastic models. The system can select a suitable winding mechanics model based on the mechanical properties of the metallized film material to improve the calculation accuracy. In order to take into account the changes in tension during the winding process, the system can use a segmented winding model to divide the winding process into several stages, using different tension values ​​for calculation in each stage to obtain a more realistic curvature radius distribution. At the same time, the system can also introduce finite element analysis technology to establish a numerical simulation model of the winding process, and obtain the stress and strain evolution process of the metallized film through dynamic solution, and then obtain the actual curvature radius of each layer of the metallized film.

[0044] S105, substituting the actual curvature radius of each metallized film layer into the bending stress calculation formula to calculate the actual stress value of each metallized film layer; The system uses the actual curvature radius of each metallized film layer calculated in the previous step to further calculate the actual stress value of each metallized film layer. The metallized film undergoes bending deformation during the winding process, which generates bending stress within the metallized film. The magnitude of the bending stress is closely related to the curvature radius; the smaller the curvature radius, the greater the bending stress. By substituting the curvature radius into the bending stress calculation formula, the system can determine the actual stress value of each metallized film layer. These stress values ​​reflect the actual stress state of the metallized film in the capacitor and are a key factor affecting capacitance.

[0045] The calculation formula for bending stress can be derived based on the theory of material mechanics. Common calculation formulas include the Lame formula, the yield criterion formula, and the like. The system can select a suitable calculation formula based on the mechanical properties of the metallized film material to improve the accuracy of stress calculation. For capacitors with a multi-layer winding structure, the system needs to calculate the stress value of each layer of metallized film layer by layer, and consider the interaction and constraint relationship between adjacent layers. In order to improve calculation efficiency, the system can use a numerical integration method to discretize the continuous curvature radius distribution and convert it into a stress calculation problem with a finite number of discrete points. The stress distribution of each layer of metallized film is obtained through iterative solution. At the same time, the system can also introduce a stress correction coefficient to consider the influence of factors such as plastic deformation and residual stress during the winding process on the stress distribution, so as to obtain more realistic and reliable stress calculation results.

[0046] S106, substituting the actual stress value of each metallized film layer into the stress dielectric constant mapping function to obtain the corrected dielectric constant of each metallized film layer; The system uses the previously established stress-dielectric constant mapping function to convert the actual stress values ​​of each metallized film layer into corresponding corrected dielectric constant values. Because the metallized film is subjected to tensile and bending stresses during the winding process, its dielectric constant changes. The stress-dielectric constant mapping function quantitatively describes the effect of stress on the dielectric constant. By substituting the actual stress values ​​into this function, the system can obtain the corrected dielectric constant for each metallized film layer. This corrected dielectric constant accounts for the effects of stress and more accurately reflects the true capacitance characteristics of the metallized film in the capacitor.

[0047] The system can use methods such as piecewise functions, polynomial functions, or neural networks to construct the stress-dielectric constant mapping function. For piecewise functions, the system divides the mapping function into several linear or nonlinear sub-functions according to the stress range. Each sub-function corresponds to a stress interval, and the parameters of the sub-function are determined by fitting the experimental data. For polynomial functions, the system selects the appropriate polynomial order and uses optimization algorithms such as least squares or gradient descent to fit the stress-dielectric constant experimental data to obtain the polynomial coefficients. For neural networks, the system constructs a network structure consisting of an input layer, a hidden layer, and an output layer, and trains the network parameters through a back-propagation algorithm to achieve a nonlinear mapping from stress to dielectric constant. When substituting the stress value to calculate the corrected dielectric constant, the system needs to check the monotonicity, continuity, and boundary conditions of the mapping function to ensure the rationality and stability of the calculation results.

[0048] S107 , calculating a predicted capacitance value of the metallized film capacitor to be tested based on the corrected dielectric constant and structural parameters.

[0049] Using the previously obtained corrected dielectric constants for each layer of metallized film, combined with the capacitor's structural parameters, the system can calculate a predicted capacitance value for the capacitor under test. Capacitance is a key parameter for measuring a capacitor's energy storage and charge transfer capabilities. It is directly proportional to the dielectric constant of the dielectric material and the electrode area, and inversely proportional to the thickness of the dielectric layer. By substituting the corrected dielectric constant into the capacitance calculation formula and taking into account structural parameters such as the number of winding layers, electrode area, and dielectric layer thickness of the capacitor, the system can obtain a predicted theoretical capacitance value for the capacitor. This predicted value comprehensively considers the influence of factors such as the metallized film material properties, winding process parameters, and capacitor structural design, and can provide an important basis for capacitor performance evaluation and optimized design.

[0050] The capacitance calculation formula can be derived based on the parallel plate capacitor model or the cylindrical capacitor model. For metallized film capacitors, the system usually adopts the cylindrical capacitor model, taking into account the geometric parameters such as the axial length, radial thickness and circumferential area of ​​the capacitor, and equates the multi-layer winding structure to a parallel combination of several single-layer cylindrical capacitors. During the calculation process, the system needs to calculate the capacitance of the single-layer capacitor based on the corrected dielectric constant and thickness of each layer of metallized film, and then add up the capacitance of all single-layer capacitors to obtain the total capacitance prediction value. In order to improve the calculation accuracy, the system can use a numerical integration method to consider the continuous change of dielectric constant and geometric parameters along the radial direction to obtain a more accurate capacitance calculation result. At the same time, the system can also introduce a capacitor equivalent circuit model, consider the influence of parasitic parameters such as electrode resistance and dielectric loss on capacitance, and establish a more comprehensive and refined capacitance prediction model.

[0051] In the above-described embodiment, material parameters and load-deformation relationship data for the metallized film sample are obtained, and a mapping relationship between stress and dielectric constant is established, enabling an accurate description of the changes in the dielectric constant of the metallized film under different stress states. The actual radius of curvature and stress values ​​of each layer of the metallized film are calculated based on the structural parameters of the capacitor to be tested. A modified dielectric constant is then derived by combining this with the stress-dielectric constant mapping function, allowing the capacitance prediction process to take into account the actual effect of stress on the dielectric constant during the winding process. By establishing a correlation between material properties, structural parameters, and capacitance, this prediction method overcomes the drawback of traditional methods that rely solely on initial dielectric constant calculations while ignoring the effects of stress, thereby improving the accuracy of capacitance prediction.

[0052] In order to make the capacitance prediction of metallized film capacitors more accurate and reliable, this application provides two specific implementation methods. Among them, the first implementation method focuses on the complete technical solution from obtaining material parameters to the final prediction of capacitance, and constructs a prediction method based on the stress-dielectric constant mapping relationship. On this basis, the second implementation method introduces the error correction and material parameter optimization mechanism of the prediction results, forming a continuous improvement plan for the prediction accuracy. Figure 2 , another method for predicting the capacitance of a metallized film capacitor in an embodiment of the present application is described: See also Figure 2 , is another flow chart of a method for predicting the capacitance of a metallized film capacitor in an embodiment of the present application.

[0053] S201, calculating the relative error between the predicted capacitance value and the measured capacitance value; In this step, the system evaluates the accuracy of the capacitance prediction model by measuring the degree of deviation between the predicted value and the actual value. To do this, the system calculates the relative error between the predicted capacitance and the measured capacitance. The relative error represents the percentage by which the predicted value deviates from the measured value and is an important indicator of prediction accuracy.

[0054] Specifically, the system first needs to obtain the measured capacitance of the metallized film capacitor, which can be directly measured using a device such as a capacitance tester. The measured value is then compared with the predicted capacitance value obtained in the previous step, and the difference between the two is calculated. This difference is then divided by the measured value to obtain a percentage of relative error. A positive relative error indicates that the predicted value is higher than the measured value, while a negative relative error indicates that the predicted value is lower than the measured value.

[0055] To improve the accuracy of relative error calculations, the system can perform statistical analysis on the predicted and measured values ​​of multiple samples, calculating comprehensive evaluation metrics such as average relative error and root mean square error. These metrics reflect the overall accuracy of the prediction model from different perspectives. Furthermore, the system can analyze the distribution characteristics of relative error, plotting error distribution histograms and boxplots to intuitively demonstrate the concentration and dispersion of prediction errors.

[0056] S202: If it is determined that the relative error is greater than a preset allowable error threshold, adjusting the fitting coefficient in the stress dielectric constant mapping function; When the system determines that the relative error is greater than the preset allowable error threshold, it adjusts the fitting coefficient in the stress dielectric constant mapping function. Specifically: an error loss function is constructed based on the relative error, and the fitting coefficient is used as the variable to be optimized; the gradient descent algorithm is used to minimize the error loss function, and the fitting coefficient is iteratively updated; when the value of the error loss function is less than the preset convergence threshold or the number of iterations reaches the preset upper limit, the optimized fitting coefficient is output.

[0057] In this step, the system determines whether the capacitance prediction model has achieved the desired accuracy based on the relative error calculated in the previous step. If the relative error exceeds the pre-set allowable range, it means that the current prediction model has significant deviations and the model parameters need to be adjusted and optimized.

[0058] The system first sets a preset allowable error threshold as a criterion for predictive accuracy. This threshold must take into account factors such as the capacitor's design precision and the level of control required for the production process. It must meet application requirements while not being too restrictive, making optimization difficult. When the relative error exceeds this threshold, the system activates a parameter adjustment mechanism to adaptively correct the prediction model.

[0059] The target parameter to adjust is the fitting coefficient in the stress-dielectric constant mapping function. This function describes the quantitative relationship between the stress state of the metallized film material and its dielectric constant and is a core component of the prediction model. The fitting coefficient actually reflects the sensitivity of stress to dielectric properties, and the fitting coefficient can vary significantly for different material systems. Therefore, by adjusting the fitting coefficient, systematic errors caused by deviations in material parameters can be effectively corrected.

[0060] Specifically, the system constructs an error loss function based on relative error to quantify the impact of prediction bias on model performance. Common loss functions include mean squared error, mean absolute error, and cross-entropy loss, each with different penalty levels. The system uses the fitting coefficients as the independent variables of the loss function and uses an optimization algorithm to find the optimal parameter values ​​that minimize the loss function.

[0061] During the optimization process, the system uses a gradient descent algorithm to iteratively optimize the error loss function. This algorithm calculates the gradient of the loss function with respect to each parameter and continuously adjusts the parameter values, updating them in the direction of fastest loss function descent, ultimately converging to the optimal solution with the smallest error. To improve optimization efficiency and robustness, the system selects appropriate hyperparameters such as the learning rate and regularization term to control the speed and direction of gradient descent.

[0062] Iterative optimization continues until the preset convergence criteria are met. Convergence criteria can be when the loss function value is less than a preset convergence threshold, indicating that the error has been reduced to an acceptable level; or when the number of iterations reaches a preset upper limit, indicating that the marginal effect of further parameter adjustment is minimal. When the convergence criteria are met, the system outputs the optimized fitting coefficients, which are used to update the stress-dielectric constant mapping function.

[0063] S203, recalculating and correcting the dielectric constant and capacitance prediction values ​​until the relative error is less than a preset allowable error threshold; In this step, the system uses the new fitting coefficients obtained in the previous optimization step to recalculate the corrected dielectric constant of the metallized film and update the predicted capacitance. This is an iterative correction process, and the system will repeat the calculation and evaluation until the relative error of the predicted value meets the accuracy requirements.

[0064] Specifically, the system first substitutes the optimized fitting coefficients into the stress-dielectric constant mapping function and calculates the corrected dielectric constant value based on the stress distribution of each layer of the metallized film. Due to the updated fitting coefficients, the corrected dielectric constant will differ from the initial calculation result. It comprehensively considers the influence of material properties and error correction, and can more accurately reflect the dielectric properties of the metallized film.

[0065] The system then substitutes the corrected dielectric constant value into the capacitance prediction formula and, combined with the capacitor's structural parameters, such as electrode area and dielectric layer thickness, recalculates a corrected capacitance prediction. This new prediction reflects the latest output of the prediction model after parameter tuning and should theoretically be more accurate than the previous prediction.

[0066] To verify the improved accuracy of the new predicted value, the system continues to calculate the relative error between it and the measured capacitance value and compares the calculated result with a preset tolerance threshold. If the relative error is less than or equal to the threshold, the current prediction model has achieved the desired accuracy target and the current predicted value can be used as the final output to guide subsequent capacitor design and production.

[0067] However, if the relative error is still greater than the preset threshold, it means that the accuracy of the current prediction model can be further improved. In this case, the system will trigger the parameter adjustment mechanism again, repeating the optimization process from the previous step. Based on the adjusted fitting coefficients, it will continue to search for new parameter values ​​that minimize the error loss function. The corrected dielectric constant and capacitance prediction values ​​will also be updated again, entering a new round of iterative calculations.

[0068] This iterative correction process continues until the relative error meets the required accuracy. Throughout this process, the accuracy of the predicted value will continue to improve with the number of iterations, gradually approaching the measured value. By setting appropriate iterative stopping conditions, such as the maximum number of iterations and the degree of error convergence, the algorithm's convergence speed and computational cost can be controlled, achieving a balance between prediction accuracy and computational efficiency.

[0069] S204, outputting a final capacitance prediction value corresponding to a relative error less than a preset allowable error threshold; In this step, the system outputs a final capacitance prediction that meets accuracy requirements, serving as an important reference for capacitor design and production. This prediction, which integrates the influence of multiple factors, including material properties, structural parameters, and process conditions, represents the optimal level of prediction currently achievable by the prediction model.

[0070] Specifically, after the iterative correction process is complete, the system identifies the predicted value with a relative error less than the preset tolerance threshold and marks it as the final capacitance prediction result. This result not only reflects the theoretical capacitance of the metallized film capacitor under the current design and process conditions, but also demonstrates the predictive power and accuracy of the prediction model after multiple rounds of optimization.

[0071] To present the forecast results more intuitively and comprehensively, the system can generate a forecast report and output the forecast values ​​and related information in the following ways: List the key data such as capacitance prediction value, measured value, relative error in the form of a table, and indicate the final prediction result; Draw a trend curve of the capacitance prediction value and the measured value as the number of iterations changes, and intuitively display the convergence process of the prediction value and the optimization effect; Calculate the confidence interval of the predicted value and give the possible fluctuation range of the capacitance, providing a safety margin reference for capacitor design; Compare the prediction results under different material formulations and structural parameters, analyze the influence and sensitivity of each factor on capacitance, and guide the optimization direction of capacitor design.

[0072] The output prediction results can help engineers and designers quickly and accurately evaluate the electrical performance of capacitors, verify the feasibility of design solutions, and make targeted optimization adjustments to design parameters. Furthermore, the prediction results can be used to guide quality control and product consistency management during the capacitor production process. By comparing predicted values ​​with measured values, abnormal deviations in the production process can be promptly identified and diagnosed, leading to continuous improvement in product quality and stability.

[0073] It's important to note that while the output capacitance prediction value has undergone multiple rounds of calibration and optimization, it's still a theoretical estimate and may deviate from the actual device capacitance. This deviation can be caused by fluctuations in material properties, changes in the production process, and environmental interference. Therefore, when using the prediction results to guide actual production, necessary verification and validation are required to ensure the reliability and robustness of the design and process parameters.

[0074] S205, calculating the volumetric specific capacitance of the metallized film capacitor to be tested based on the final capacitance prediction value and the structural parameters; In this step, the system uses the final capacitance prediction output from the previous step, combined with the capacitor's structural parameters, to calculate the volumetric capacitance of the capacitor under test. Volumetric capacitance is an important indicator of capacitor miniaturization and permittivity, and is of great significance in fields such as mobile communications and portable electronics.

[0075] Specifically, the formula for calculating volumetric capacitance is: Volumetric capacitance = Capacitance / Capacitor volume. Capacitance is the final capacitance value predicted in the previous step; the capacitance volume is calculated based on the capacitor's structural dimensions. For cylindrical metallized film capacitors, the capacitance volume can be estimated based on the capacitor's diameter and length. For capacitors of other shapes, such as square or oval, the volume calculation requires using the corresponding geometric relationship.

[0076] After determining the capacitance and capacitor volume, the system uses the ratio of the two as the calculated volumetric capacitance. This result directly reflects the capacitor's permittivity, or the amount of capacitance it can provide per unit volume. A higher volumetric capacitance means a greater capacitance density. While maintaining the same capacitance, the device can be made smaller and thinner, facilitating miniaturized integration of circuit systems.

[0077] S206, calculating the relative deviation between the volume specific capacitance and a preset volume specific capacitance target value; In this step, the system compares the volumetric capacitance calculated in the previous step with the preset target value, quantitatively assessing the difference between the two. This difference can be expressed as a relative deviation, which reflects the degree of closeness between the current capacitor design and the expected performance target.

[0078] The formula for calculating relative deviation is: Relative Deviation = (Volume Capacitance - Preset Target Value) / Preset Target Value multiplied by 100%. The preset target value is a volumetric capacitance indicator set by designers or project managers based on the capacitor's application requirements and market positioning, combined with industry standards and technological trends. It represents the comprehensive requirements and expectations for capacitor product performance, cost, and reliability.

[0079] Substituting the actual volumetric capacitance into the formula, subtracting the preset target value, and then dividing by the preset target value, we can get the relative deviation percentage between the two. A positive relative deviation indicates that the actual volumetric capacitance is higher than the expected target, exceeding the design expectations; a negative relative deviation indicates that the actual volumetric capacitance is lower than the expected target and fails to meet the design requirements. The larger the absolute value of the relative deviation, the further the current design deviates from the expected target, and the greater the degree of optimization adjustment required.

[0080] S207. When it is determined that the relative deviation is greater than the preset deviation threshold, adjust the recipe parameters of the metallized film material, and re-execute the step of calculating the actual curvature radius of each layer of the metallized film in the metallized film capacitor to be tested according to the structural parameters and the preset winding tension value.

[0081] If the system determines that the relative deviation is greater than a preset deviation threshold, it adjusts the recipe parameters of the metallized film material and re-executes the step of calculating the actual curvature radius of each metallized film layer in the metallized film capacitor to be tested based on the structural parameters and the preset winding tension value. Adjusting the recipe parameters of the metallized film material specifically includes: determining the recipe parameters that need to be adjusted, the direction of adjustment, and the adjustment range based on the relative deviation between the volumetric capacitance and the preset volumetric capacitance target value; and updating the recipe parameter values ​​according to the adjustment direction and adjustment range within the preset recipe parameter adjustment range.

[0082] In this step, the system determines whether the relative deviation calculated in the previous step exceeds a preset acceptable range. If the relative deviation is too large, exceeding a pre-set deviation threshold, it means that the current capacitor design cannot meet the expected performance requirements and further optimization and adjustment of the design parameters are required.

[0083] The preset deviation threshold should be determined based on factors such as the capacitor's performance requirements and production process capabilities. Generally, for capacitors requiring high precision and reliability, the deviation threshold should be set relatively low to ensure stable and consistent performance. For conventional capacitors with relatively loose performance requirements, the deviation threshold can be appropriately relaxed to balance performance and cost. Furthermore, the deviation threshold setting must account for uncertainties such as process and measurement errors, leaving a margin for performance deviation.

[0084] When the system detects a relative deviation exceeding a preset threshold, it activates a design optimization mechanism to adjust key capacitor design parameters in an effort to improve the capacitor's volumetric capacitance performance. In metallized film capacitors, one of the key factors influencing volumetric capacitance is the metallized film material formulation parameters, such as the size, distribution, and volume fraction of the metal particles. Adjusting these material parameters can significantly alter the metallized film's microstructure and dielectric properties, thereby affecting the overall capacitor's permittivity.

[0085] The system will determine the material formula parameters that need to be adjusted and the adjustment range based on the size and direction of the relative deviation between the volumetric capacitance and the preset target value. For example, when the relative deviation is negative, it indicates that the current volumetric capacitance is lower than the expected target and the dielectric constant of the metallized film needs to be increased. This can be achieved by increasing the volume fraction of metal particles, reducing the particle spacing, etc. When the relative deviation is positive, it indicates that the current volumetric capacitance has exceeded the expected target and the amount of metal particles added can be appropriately reduced to balance dielectric performance and cost.

[0086] After determining the adjustment plan for the material formulation parameters, the system will re-enter the previous calculation process to update the structural model and stress distribution of the metallized film capacitor. Due to the change in material formulation, the mechanical and electrical properties of the metallized film will also change accordingly, resulting in differences in the stress state and dielectric properties within the capacitor. Therefore, the system needs to recalculate key parameters such as the actual curvature radius, stress distribution, and local dielectric constant of each layer of the metallized film, and substitute them into the capacitance prediction model to obtain the revised volumetric capacitance value.

[0087] This is a closed-loop, iterative optimization process. The system will continuously repeat the steps of "deviation assessment - parameter adjustment - model correction - performance prediction" until the relative deviation of volumetric capacitance meets the preset requirements. Through this iterative optimization strategy, the optimal balance between material formulation, process parameters, and product performance can be found, achieving improved overall capacitor performance.

[0088] In the above embodiment, when the relative error exceeds the preset allowable error threshold, the fitting coefficients of the stress-dielectric constant mapping function are adjusted and recalculated until the relative error meets the accuracy requirement. This iterative optimization prediction method reduces the systematic error that may exist in a single prediction and achieves continuous improvement in prediction accuracy through multiple calculations and parameter adjustments. This method uses the prediction error as the optimization target and achieves adaptive optimization of the prediction model through feedback regulation, improving the reliability and practicality of the capacitance prediction results and making the prediction results more closely match the performance characteristics of the capacitor under actual operating conditions.

[0089] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a metallized film capacitor capacitance prediction system provided in an embodiment of the present application.

[0090] It should be noted that Figure 3 The structure of the system shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0091] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes, such as the methods described in the above embodiments, based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage unit 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0092] The following components are connected to the I / O interface 305: an input section 306 including a camera, infrared sensor, and the like; an output section 307 including a liquid crystal display (LCD) and speakers; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the media can be installed in the storage section 308 as needed.

[0093] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from removable media 311. When executed by the central processing unit (CPU) 301, the computer program performs the various functions defined in the present invention.

[0094] It should be noted that the computer-readable medium described in the embodiments of the present invention may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take any of a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0096] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiments, or may exist independently and not incorporated into the system. The storage medium carries one or more computer programs, and when executed by a processor of a system, the system implements the methods provided in the above embodiments.

[0097] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0098] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0099] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0100] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for predicting the capacitance of a metallized film capacitor, characterized in that: include: Obtaining material parameters of a preset number of metallized film samples, wherein the material parameters include initial dielectric constant, density, and elastic modulus; measuring the deformation of the metallized film sample under different tensile loads to obtain load-deformation relationship data; Calculating stress distribution data of the metallized film sample under each tensile load according to the material parameters and the load-deformation relationship data; Measuring the actual dielectric constant of the metallized film sample under each tensile load, and fitting the actual dielectric constant with corresponding stress distribution data to obtain a stress dielectric constant mapping function; Obtaining structural parameters of the metallized film capacitor to be tested, wherein the structural parameters include the number of winding layers of the metallized film, a preset winding tension value, and a core shaft diameter; Calculating the actual curvature radius of each layer of the metallized film in the metallized film capacitor to be tested according to the structural parameters and the preset winding tension value; Substituting the actual curvature radius of each layer of the metallized film into the bending stress calculation formula to calculate the actual stress value of each layer of the metallized film; Substituting the actual stress value of each metallized film layer into the stress dielectric constant mapping function to obtain the corrected dielectric constant of each metallized film layer; A predicted capacitance value of the metallized film capacitor to be tested is calculated according to the corrected dielectric constant and the structural parameters.

2. The method according to claim 1, characterized in that Calculating the stress distribution data of the metallized film sample under each tensile load according to the material parameters and the load-deformation relationship data specifically includes: Dividing the load-deformation relationship data into a plurality of strain intervals according to the degree of stretching, and calculating the nominal stress value of each strain interval based on the elastic modulus; Calculating the mass change of the metallized film sample in each of the strain intervals based on the density; Calculate the actual cross-sectional area of ​​each strain interval according to the volume invariance principle and the law of conservation of mass; Correcting the nominal stress value using the actual cross-sectional area to obtain a true stress value; A strain-to-real stress correspondence is established based on the true stress value to obtain stress distribution data under each tensile load.

3. The method according to claim 1, characterized in that The fitting of the actual dielectric constant with the corresponding stress distribution data to obtain a stress dielectric constant mapping function specifically includes: Dividing the stress distribution data into a plurality of stress intervals according to their magnitude, wherein the range of each stress interval is an integer multiple of a preset stress threshold; Performing weighted averaging processing on the actual dielectric constant within each stress interval to obtain a corresponding characteristic dielectric constant value; The characteristic dielectric constant value of each stress interval is fitted with the stress midpoint value by using a piecewise polynomial fitting method to obtain a stress dielectric constant mapping function.

4. The method according to claim 1, wherein After calculating the capacitance prediction value of the metallized film capacitor to be tested according to the corrected dielectric constant and the structural parameters, the method further includes: Calculating the relative error between the predicted capacitance value and the measured capacitance value; When it is determined that the relative error is greater than a preset allowable error threshold, adjusting the fitting coefficient in the stress dielectric constant mapping function; Recalculating the corrected dielectric constant and the capacitance prediction value until the relative error is less than the preset allowable error threshold; Outputting a final capacitance prediction value corresponding to the relative error being less than the preset allowable error threshold.

5. The method according to claim 4, characterized in that The adjusting the fitting coefficient in the stress dielectric constant mapping function specifically includes: Constructing an error loss function based on the relative error, and using the fitting coefficient as a variable to be optimized; Using a gradient descent algorithm to minimize the error loss function and iteratively update the fitting coefficient; When the value of the error loss function is less than a preset convergence threshold or the number of iterations reaches a preset upper limit, the optimized fitting coefficient is output.

6. The method according to claim 4, characterized in that After outputting the final capacitance prediction value corresponding to the relative error being less than the preset allowable error threshold, the method further includes: Calculating the volumetric specific capacitance of the metallized film capacitor to be tested according to the final capacitance prediction value and the structural parameters; Calculating a relative deviation between the volumetric capacitance and a preset volumetric capacitance target value; When it is determined that the relative deviation is greater than the preset deviation threshold, the recipe parameters of the metallized film material are adjusted, and the step of calculating the actual curvature radius of each layer of the metallized film in the metallized film capacitor to be tested based on the structural parameters and the preset winding tension value is re-executed.

7. The method according to claim 6, characterized in that The adjusting of the formula parameters of the metallized film material specifically includes: Determining the recipe parameters that need to be adjusted, and the direction and magnitude of the adjustment based on the relative deviation between the volumetric capacitance and the preset volumetric capacitance target value; Within the preset recipe parameter adjustment range, the recipe parameter value is updated according to the adjustment direction and the adjustment amplitude.

8. A metallized film capacitor capacitance prediction system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to perform the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to perform the method according to any one of claims 1 to 7.

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